def __init__(self, *args, **kwargs):
# pull non-standard keyword arguments
refresh = kwargs.pop('auto_refresh', False)
# dynamically set the subplot positions based on the figure size
# -- only if the user hasn't customised the subplot params
figsize = kwargs.get('figsize', rcParams['figure.figsize'])
subplotpars = get_subplot_params(figsize)
use_subplotpars = 'subplotpars' not in kwargs and all([
rcParams['figure.subplot.%s' % pos] ==
MPL_RCPARAMS['figure.subplot.%s' % pos] for
pos in ('left', 'bottom', 'right', 'top')])
if use_subplotpars:
kwargs['subplotpars'] = subplotpars
# generated figure, with associated interactivity from pyplot
super(Plot, self).__init__(*args, **kwargs)
backend_mod, _, draw_if_interactive, _ = backends.pylab_setup()
try:
manager = backend_mod.new_figure_manager_given_figure(1, self)
except AttributeError:
canvas = backend_mod.FigureCanvas(self)
manager = FigureManagerBase(canvas, 1)
cid = manager.canvas.mpl_connect(
'button_press_event',
lambda ev: _pylab_helpers.Gcf.set_active(manager))
manager._cidgcf = cid
_pylab_helpers.Gcf.set_active(manager)
draw_if_interactive()
# finalise
self.set_auto_refresh(refresh)
self.colorbars = []
self._coloraxes = []
def __init__(self, *args, **kwargs):
# pull non-standard keyword arguments
auto_refresh = kwargs.pop('auto_refresh', False)
# dynamically set the subplot positions based on the figure size
# -- only if the user hasn't customised the subplot params
figsize = kwargs.get('figsize', rcParams['figure.figsize'])
subplotpars = get_subplot_params(figsize)
use_subplotpars = 'subplotpars' not in kwargs and all([
rcParams['figure.subplot.%s' % pos]
== MPL_RCPARAMS['figure.subplot.%s' % pos]
for pos in ('left', 'bottom', 'right', 'top')
])
if use_subplotpars:
kwargs['subplotpars'] = subplotpars
# generated figure, with associated interactivity from pyplot
super(Plot, self).__init__(*args, **kwargs)
backend_mod, _, draw_if_interactive, _ = backends.pylab_setup()
try:
manager = backend_mod.new_figure_manager_given_figure(1, self)
except AttributeError:
canvas = backend_mod.FigureCanvas(self)
manager = FigureManagerBase(canvas, 1)
cid = manager.canvas.mpl_connect(
'button_press_event',
lambda ev: _pylab_helpers.Gcf.set_active(manager))
manager._cidgcf = cid
_pylab_helpers.Gcf.set_active(manager)
draw_if_interactive()
# finalise
self.set_auto_refresh(auto_refresh)
self.colorbars = []
self._coloraxes = []
def render(self, fig):
if self._render_fig_manager is None:
# Chooses the backend_mod based on matplotlib configuration
mplib.interactive(True)
self._render_backend_mod = pylab_setup()[0]
self._render_fig_manager = \
self._render_backend_mod.new_figure_manager_given_figure(1, fig)
self._render_fig_manager.canvas.figure = fig
self._render_fig_manager.canvas.draw()
self._render_backend_mod.show(block=False)
return self._render_fig_manager
def __init__(self, *args, **kwargs):
# pull non-standard keyword arguments
auto_refresh = kwargs.pop('auto_refresh', False)
# generated figure, with associated interactivity from pyplot
super(Plot, self).__init__(*args, **kwargs)
backend_mod, _, draw_if_interactive, _ = backends.pylab_setup()
manager = backend_mod.new_figure_manager_given_figure(1, self)
cid = manager.canvas.mpl_connect(
'button_press_event',
lambda ev: _pylab_helpers.Gcf.set_active(manager))
manager._cidgcf = cid
_pylab_helpers.Gcf.set_active(manager)
draw_if_interactive()
# finalise
self.set_auto_refresh(auto_refresh)
self.colorbars = []
self._coloraxes = []
def mollview(m, rot=None, coord=None, unit='',
xsize=1000, nest=False,
min=None, max=None, flip='astro',
format='%g',
cbar=True, cmap=None,
norm=None,
graticule=False, graticule_labels=False,
**kwargs):
"""Plot an healpix map (given as an array) in Mollweide projection.
Parameters
----------
map : float, array-like or None
An array containing the map, supports masked maps, see the `ma` function.
If None, will display a blank map, useful for overplotting.
rot : scalar or sequence, optional
Describe the rotation to apply.
In the form (lon, lat, psi) (unit: degrees) : the point at
longitude *lon* and latitude *lat* will be at the center. An additional rotation
of angle *psi* around this direction is applied.
coord : sequence of character, optional
Either one of 'G', 'E' or 'C' to describe the coordinate
system of the map, or a sequence of 2 of these to rotate
the map from the first to the second coordinate system.
unit : str, optional
A text describing the unit of the data. Default: ''
xsize : int, optional
The size of the image. Default: 800
nest : bool, optional
If True, ordering scheme is NESTED. Default: False (RING)
min : float, optional
The minimum range value
max : float, optional
The maximum range value
flip : {'astro', 'geo'}, optional
Defines the convention of projection : 'astro' (default, east towards left, west towards right)
or 'geo' (east towards roght, west towards left)
format : str, optional
The format of the scale label. Default: '%g'
cbar : bool, optional
Display the colorbar. Default: True
norm : {'hist', 'log', None}
Color normalization, hist= histogram equalized color mapping,
log= logarithmic color mapping, default: None (linear color mapping)
kwargs : keywords
any additional keyword is passed to pcolormesh
graticule : bool
add graticule
graticule_labels : bool
longitude and latitude labels
"""
# not implemented features
if not (norm is None):
raise exceptions.NotImplementedError()
# Create the figure
import matplotlib.pyplot as plt
from matplotlib.backends import pylab_setup
_backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
width = 8.5
fig = plt.figure(figsize=(width,width*.63))
ax = fig.add_subplot(111, projection="mollweide")
# FIXME: make a more general axes creation that works also with subplots
#ax = plt.gcf().add_axes((.125, .1, .9, .9), projection="mollweide")
# remove white space around the image
plt.subplots_adjust(left=0.02, right=0.98, top=0.95, bottom=0.05)
if graticule and graticule_labels:
plt.subplots_adjust(left=0.04, right=0.98, top=0.95, bottom=0.05)
# auto min and max
if min is None:
min = m.min()
if max is None:
max = m.max()
# allow callers to override the hold state by passing hold=True|False
washold = ax.ishold()
hold = kwargs.pop('hold', None)
if hold is not None:
ax.hold(hold)
try:
ysize = xsize/2
theta = np.linspace(np.pi, 0, ysize)
phi = np.linspace(-np.pi, np.pi, xsize)
longitude = np.radians(np.linspace(-180, 180, xsize))
if flip == "astro":
longitude = longitude[::-1]
latitude = np.radians(np.linspace(-90, 90, ysize))
# project the map to a rectangular matrix xsize x ysize
PHI, THETA = np.meshgrid(phi, theta)
# coord or rotation
if coord or rot:
r = Rotator(coord=coord, rot=rot, inv=True)
THETA, PHI = r(THETA.flatten(), PHI.flatten())
THETA = THETA.reshape(ysize, xsize)
PHI = PHI.reshape(ysize, xsize)
nside = npix2nside(len(m))
grid_pix = ang2pix(nside, THETA, PHI, nest=nest)
grid_map = m[grid_pix]
# plot
ret = plt.pcolormesh(longitude, latitude, grid_map, vmin=min, vmax=max, rasterized=True, **kwargs)
# graticule
plt.grid(graticule)
if graticule:
longitude_grid_spacing = 60 # deg
ax.set_longitude_grid(longitude_grid_spacing)
if width < 10:
ax.set_latitude_grid(45)
ax.set_longitude_grid_ends(90)
if graticule_labels:
ax.xaxis.set_major_formatter(ThetaFormatterShiftPi(longitude_grid_spacing))
else:
# remove longitude and latitude labels
ax.xaxis.set_ticklabels([])
ax.yaxis.set_ticklabels([])
# colorbar
if cbar:
cb = fig.colorbar(ret, orientation='horizontal', shrink=.4, pad=0.05, ticks=[min, max])
cb.ax.xaxis.set_label_text(unit)
cb.ax.xaxis.labelpad = -8
# workaround for issue with viewers, see colorbar docstring
cb.solids.set_edgecolor("face")
draw_if_interactive()
finally:
ax.hold(washold)
return ret
def main():
parser = ArgumentParser(
formatter_class=type(
"", (ArgumentDefaultsHelpFormatter, RawTextHelpFormatter), {}),
epilog="""\
Example usage:
$ python %(prog)s plot \\
'{{"backend": "agg"}}' \\
'{{"backend": "agg", "agg.path.chunksize": 1000}}' \\
'{{"backend": "module://mplcairo.base", \\
"lines.antialiased": __import__("mplcairo").antialias_t.FAST}}' \\
'{{"backend": "module://mplcairo.base", \\
"lines.antialiased": __import__("mplcairo").antialias_t.BEST}}'
""".format(sys.argv[0]))
parser.add_argument(
"-n", "--n-elements", type=lambda s: [int(n) for n in s.split(",")],
default=[10, 30, 100, 300, 1000, 3000, 10000],
help="comma-separated list of number of elements")
parser.add_argument(
"method", choices=["plot", "fill", "scatter"],
help="Axes method")
parser.add_argument(
"rcs", type=eval, nargs="+", metavar="rc",
help="rc parameters to test (will be eval'd)")
args = parser.parse_args()
n_elems = args.n_elements
method = args.method
rcs = args.rcs
results = []
for rc in rcs:
# Emulate rc_context, but without the validation (to support mplcairo's
# antialiasing enum).
try:
orig_rc = dict.copy(plt.rcParams)
dict.update(plt.rcParams, rc)
fig, ax = plt.subplots()
backend_mod, *_ = backends.pylab_setup(plt.rcParams["backend"])
ax.figure.canvas = backend_mod.FigureCanvas(ax.figure)
results.append(get_times(ax, method, n_elems))
plt.close(fig)
finally:
dict.update(plt.rcParams, orig_rc)
_, main_ax = plt.subplots()
main_ax.set(xlabel="number of elements", ylabel="time per edge (s)",
xscale="log", yscale="log")
for i, (rc, all_times) in enumerate(zip(rcs, results)):
normalized = [
np.array(times) / n for n, times in zip(n_elems, all_times)]
for norm in normalized:
norm.sort()
# Use the minimum time as aggregate: the cdfs show that the
# distributions are very long tailed.
main_ax.plot(n_elems, [norm[0] for norm in normalized],
"o", label=pprint.pformat(rc))
fig, detail_ax = plt.subplots()
fig.suptitle(pprint.pformat(rc))
for n, norm in zip(n_elems, normalized):
detail_ax.plot(norm, np.linspace(0, 1, len(norm))[::-1],
drawstyle="steps-pre",
label="{} elements (N={})".format(n, len(norm)))
detail_ax.set(xlabel="time per element (s)", xscale="log",
ylabel="CCDF")
detail_ax.legend(loc="upper right")
main_ax.legend(loc="upper center")
plt.show()
"""
plot_radar.py
Class instance used to make Display.
"""
# Load the needed packages
import numpy as np
import scipy
import os
import pyart
from matplotlib.backends import pylab_setup
backend = pylab_setup()[0]
FigureCanvasQTAgg = backend.FigureCanvasQTAgg
NavigationToolbar = backend.NavigationToolbar2QT
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
#from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as \
# NavigationToolbar
from matplotlib.figure import Figure
from matplotlib.colors import Normalize as mlabNormalize
from matplotlib.colorbar import ColorbarBase as mlabColorbarBase
from matplotlib.pyplot import cm
from ..core import (Variable, Component, common, VariableChoose, QtCore,
QtGui, QtWidgets)
from ..core.points import Points
# Save image file type and DPI (resolution)
IMAGE_EXT = 'png'
DPI = 200
# ========================================================================
#!/usr/bin/env python
import numpy as np
import matplotlib.axes
import matplotlib.patches
from matplotlib.quiver import Quiver as _Quiver
from matplotlib.collections import EllipseCollection
from matplotlib.backends import pylab_setup
from matplotlib.pyplot import sci
from matplotlib.pyplot import gca
import warnings
_backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
def compute_abphi(sigma_x, sigma_y, rho):
n = len(sigma_x)
a = []
b = []
phi = []
for i in range(n):
if np.ma.is_masked(sigma_x[i]) | np.ma.is_masked(sigma_y[i]) | np.ma.is_masked(rho[i]):
a += [0.0]
b += [0.0]
phi += [0.0]
continue
sigma_xy = rho[i] * sigma_x[i] * sigma_y[i]
cov_mat = np.array([[sigma_x[i] ** 2, sigma_xy], [sigma_xy, sigma_y[i] ** 2]])
val, vec = np.linalg.eig(cov_mat)
maxidx = np.argmax(val)
minidx = np.argmin(val)
#!/usr/bin/env python
import numpy as np
import matplotlib.axes
import matplotlib.patches
from matplotlib.quiver import Quiver as _Quiver
from matplotlib.collections import EllipseCollection
from matplotlib.backends import pylab_setup
from matplotlib.pyplot import sci
from matplotlib.pyplot import gca
_backend_mod, new_figure_manager, draw_if_interactive, _show = pylab_setup()
def compute_abphi(sigma_x,sigma_y,rho):
n = len(sigma_x)
a = []
b = []
phi = []
for i in range(n):
if (np.ma.is_masked(sigma_x[i]) |
np.ma.is_masked(sigma_y[i]) |
np.ma.is_masked(rho[i])):
a += [0.0]
b += [0.0]
phi += [0.0]
continue
sigma_xy = rho[i]*sigma_x[i]*sigma_y[i]
cov_mat = np.array([[sigma_x[i]**2,sigma_xy],
[sigma_xy,sigma_y[i]**2]])
val,vec = np.linalg.eig(cov_mat)
maxidx = np.argmax(val)
"""
plot_radar.py
Class instance used to make Display.
"""
# Load the needed packages
import numpy as np
import scipy
import os
import pyart
from matplotlib.backends import pylab_setup
backend = pylab_setup()[0]
FigureCanvasQTAgg = backend.FigureCanvasQTAgg
NavigationToolbar = backend.NavigationToolbar2QT
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
#from matplotlib.backends.backend_qt4agg import NavigationToolbar2QT as \
# NavigationToolbar
from matplotlib.figure import Figure
from matplotlib.colors import Normalize as mlabNormalize
from matplotlib.colorbar import ColorbarBase as mlabColorbarBase
from matplotlib.pyplot import cm
from ..core import (Variable, Component, common, VariableChoose, QtCore,
QtGui, QtWidgets)
from ..core.points import Points
# Save image file type and DPI (resolution)
IMAGE_EXT = 'png'
DPI = 200
# ========================================================================
import numpy
# from numpy import isscalar, iterable
from matplotlib.transforms import Bbox
from matplotlib.backends import pylab_setup
new_figure_manager, draw_if_interactive, show = pylab_setup()
from matplotlib.pyplot import figure
import re
import xml.dom.minidom
from xml.sax.handler import ContentHandler
from xml.sax import make_parser
def get_type(typestr):
pass
class XMLFigure(ContentHandler):
"""
The XML parser. Creates an expat parser, and then handles the defined
tags specified in 'start_tags' and 'end_tags'. Currently the allowed
tags are: figure, subplot, plot
"""
def create_parser(self):
parser = make_parser()
"""
cmap_value.py
"""
# Load the needed packages
import numpy as np
from ..core import QtWidgets, QtGui, QtCore
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.backends import pylab_setup
FigureCanvasQTAgg = pylab_setup()[0].FigureCanvasQTAgg
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
from matplotlib import colors
import pyart
from .. import core, components
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
class ColormapEdit(core.Component):
@classmethod
def guiStart(self, parent=None):
kwargs, independent = core.common._SimplePluginStart(
"ColormapEdit").startDisplay()
kwargs['parent'] = parent
return self(**kwargs), independent
"""
cmap_value.py
"""
# Load the needed packages
import numpy as np
from ..core import QtWidgets, QtGui, QtCore
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from matplotlib.backends import pylab_setup
FigureCanvasQTAgg = pylab_setup()[0].FigureCanvasQTAgg
#from matplotlib.backends.backend_qt4agg import FigureCanvasQTAgg
from matplotlib import colors
import pyart
from .. import core
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
class ColormapEdit(core.Component):
@classmethod
def guiStart(self, parent=None):
kwargs, independent = core.common._SimplePluginStart(
"ColormapEdit").startDisplay()
kwargs['parent'] = parent
return self(**kwargs), independent
import numpy
#from numpy import isscalar, iterable
from matplotlib.transforms import Bbox
from matplotlib.backends import pylab_setup
new_figure_manager, draw_if_interactive, show = pylab_setup()
from matplotlib.pyplot import figure
import re
import xml.dom.minidom
from xml.sax.handler import ContentHandler
from xml.sax import make_parser
def get_type(typestr):
pass
class XMLFigure(ContentHandler):
"""
The XML parser. Creates an expat parser, and then handles the defined
tags specified in 'start_tags' and 'end_tags'. Currently the allowed
tags are: figure, subplot, plot
"""
def create_parser(self):
parser = make_parser()
parser.setContentHandler(self)
return parser