예제 #1
0
파일: core.py 프로젝트: stefco/gwpy
    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 = []
예제 #2
0
파일: core.py 프로젝트: Phatom/gwpy
    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 = []
예제 #3
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 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
예제 #4
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    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 = []
예제 #5
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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
예제 #6
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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()
예제 #7
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"""
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
# ========================================================================
예제 #8
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#!/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)
예제 #9
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파일: quiver.py 프로젝트: watpet/SlipPy
#!/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)
예제 #10
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"""
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
# ========================================================================
예제 #11
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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()
예제 #12
0
파일: cmap.py 프로젝트: bmacnamara/artview
"""
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
예제 #13
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"""
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
예제 #14
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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