Source code for metpy.plots.skewt

# Copyright (c) 2014,2015,2016,2017,2019 MetPy Developers.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Make Skew-T Log-P based plots.

Contain tools for making Skew-T Log-P plots, including the base plotting class,
`SkewT`, as well as a class for making a `Hodograph`.
"""

from contextlib import ExitStack
import warnings

import matplotlib
from matplotlib.axes import Axes
import matplotlib.axis as maxis
from matplotlib.collections import LineCollection
import matplotlib.colors as mcolors
from matplotlib.patches import Circle
from matplotlib.projections import register_projection
import matplotlib.spines as mspines
from matplotlib.ticker import MultipleLocator, NullFormatter, ScalarFormatter
import matplotlib.transforms as transforms
import numpy as np

from ._util import colored_line
from ..calc import dewpoint, dry_lapse, el, lcl, moist_lapse, vapor_pressure
from ..calc.tools import _delete_masked_points
from ..interpolate import interpolate_1d
from ..package_tools import Exporter
from ..units import concatenate, units

exporter = Exporter(globals())


class SkewTTransform(transforms.Affine2D):
    """Perform Skew transform for Skew-T plotting.

    This works in pixel space, so is designed to be applied after the normal plotting
    transformations.
    """

    def __init__(self, bbox, rot):
        """Initialize skew transform.

        This needs a reference to the parent bounding box to do the appropriate math and
        to register it as a child so that the transform is invalidated and regenerated if
        the bounding box changes.
        """
        super().__init__()
        self._bbox = bbox
        self.set_children(bbox)
        self.invalidate()

        # We're not trying to support changing the rotation, so go ahead and convert to
        # the right factor for skewing here and just save that.
        self._rot_factor = np.tan(np.deg2rad(rot))

    def get_matrix(self):
        """Return transformation matrix."""
        if self._invalid:
            # The following matrix is equivalent to the following:
            # x0, y0 = self._bbox.xmin, self._bbox.ymin
            # self.translate(-x0, -y0).skew_deg(self._rot, 0).translate(x0, y0)
            # Setting it this way is just more efficient.
            self._mtx = np.array([[1.0, self._rot_factor, -self._rot_factor * self._bbox.ymin],
                                  [0.0, 1.0, 0.0],
                                  [0.0, 0.0, 1.0]])

            # Need to clear both the invalid flag *and* reset the inverse, which is cached
            # by the parent class.
            self._invalid = 0
            self._inverted = None
        return self._mtx


class SkewXTick(maxis.XTick):
    r"""Make x-axis ticks for Skew-T plots.

    This class adds to the standard :class:`matplotlib.axis.XTick` dynamic checking
    for whether a top or bottom tick is actually within the data limits at that part
    and draw as appropriate. It also performs similar checking for gridlines.
    """

    # Taken from matplotlib's SkewT example to update for matplotlib 3.1's changes to
    # state management for ticks. See matplotlib/matplotlib#10088
    def draw(self, renderer):
        """Draw the tick."""
        # When adding the callbacks with `stack.callback`, we fetch the current
        # visibility state of the artist with `get_visible`; the ExitStack will
        # restore these states (`set_visible`) at the end of the block (after
        # the draw).
        with ExitStack() as stack:
            for artist in [self.gridline, self.tick1line, self.tick2line,
                           self.label1, self.label2]:
                stack.callback(artist.set_visible, artist.get_visible())

            self.tick1line.set_visible(self.tick1line.get_visible() and self.lower_in_bounds)
            self.label1.set_visible(self.label1.get_visible() and self.lower_in_bounds)
            self.tick2line.set_visible(self.tick2line.get_visible() and self.upper_in_bounds)
            self.label2.set_visible(self.label2.get_visible() and self.upper_in_bounds)
            self.gridline.set_visible(self.gridline.get_visible() and self.grid_in_bounds)
            super().draw(renderer)

    @property
    def lower_in_bounds(self):
        """Whether the lower part of the tick is in bounds."""
        return transforms.interval_contains(self.axes.lower_xlim, self.get_loc())

    @property
    def upper_in_bounds(self):
        """Whether the upper part of the tick is in bounds."""
        return transforms.interval_contains(self.axes.upper_xlim, self.get_loc())

    @property
    def grid_in_bounds(self):
        """Whether any of the tick grid line is in bounds."""
        return transforms.interval_contains(self.axes.xaxis.get_view_interval(),
                                            self.get_loc())


class SkewXAxis(maxis.XAxis):
    r"""Make an x-axis that works properly for Skew-T plots.

    This class exists to force the use of our custom :class:`SkewXTick` as well
    as provide a custom value for interval that combines the extents of the
    upper and lower x-limits from the axes.
    """

    def _get_tick(self, major):
        # Warning stuff can go away when we only support Matplotlib >=3.3
        with warnings.catch_warnings():
            warnings.simplefilter('ignore', getattr(
                matplotlib, 'MatplotlibDeprecationWarning', DeprecationWarning))
            return SkewXTick(self.axes, None, label=None, major=major)

    # Needed to properly handle tight bbox
    def _get_tick_bboxes(self, ticks, renderer):
        """Return lists of bboxes for ticks' label1's and label2's."""
        return ([tick.label1.get_window_extent(renderer)
                 for tick in ticks if tick.label1.get_visible() and tick.lower_in_bounds],
                [tick.label2.get_window_extent(renderer)
                 for tick in ticks if tick.label2.get_visible() and tick.upper_in_bounds])

    def get_view_interval(self):
        """Get the view interval."""
        return self.axes.upper_xlim[0], self.axes.lower_xlim[1]


class SkewSpine(mspines.Spine):
    r"""Make an x-axis spine that works properly for Skew-T plots.

    This class exists to use the separate x-limits from the axes to properly
    locate the spine.
    """

    def _adjust_location(self):
        pts = self._path.vertices
        if self.spine_type == 'top':
            pts[:, 0] = self.axes.upper_xlim
        else:
            pts[:, 0] = self.axes.lower_xlim


class SkewXAxes(Axes):
    r"""Make a set of axes for Skew-T plots.

    This class handles registration of the skew-xaxes as a projection as well as setting up
    the appropriate transformations. It also makes sure we use our instances for spines
    and x-axis: :class:`SkewSpine` and :class:`SkewXAxis`. It provides properties to
    facilitate finding the x-limits for the bottom and top of the plot as well.
    """

    # The projection must specify a name.  This will be used be the
    # user to select the projection, i.e. ``subplot(111,
    # projection='skewx')``.
    name = 'skewx'

    def __init__(self, *args, **kwargs):
        r"""Initialize `SkewXAxes`.

        Parameters
        ----------
        args : Arbitrary positional arguments
            Passed to :class:`matplotlib.axes.Axes`

        position: int, optional
            The rotation of the x-axis against the y-axis, in degrees.

        kwargs : Arbitrary keyword arguments
            Passed to :class:`matplotlib.axes.Axes`

        """
        # This needs to be popped and set before moving on
        self.rot = kwargs.pop('rotation', 30)
        super().__init__(*args, **kwargs)

    def _init_axis(self):
        # Taken from Axes and modified to use our modified X-axis
        self.xaxis = SkewXAxis(self)
        self.spines['top'].register_axis(self.xaxis)
        self.spines['bottom'].register_axis(self.xaxis)
        self.yaxis = maxis.YAxis(self)
        self.spines['left'].register_axis(self.yaxis)
        self.spines['right'].register_axis(self.yaxis)

    def _gen_axes_spines(self, locations=None, offset=0.0, units='inches'):
        # pylint: disable=unused-argument
        spines = {'top': SkewSpine.linear_spine(self, 'top'),
                  'bottom': mspines.Spine.linear_spine(self, 'bottom'),
                  'left': mspines.Spine.linear_spine(self, 'left'),
                  'right': mspines.Spine.linear_spine(self, 'right')}
        return spines

    def _set_lim_and_transforms(self):
        """Set limits and transforms.

        This is called once when the plot is created to set up all the
        transforms for the data, text and grids.

        """
        # Get the standard transform setup from the Axes base class
        super()._set_lim_and_transforms()

        # This transformation handles the skewing
        skew_trans = SkewTTransform(self.bbox, self.rot)

        # Create the full transform from Data to Pixels
        self.transData += skew_trans

        # Blended transforms like this need to have the skewing applied using
        # both axes, in axes coords like before.
        self._xaxis_transform += skew_trans

    @property
    def lower_xlim(self):
        """Get the data limits for the x-axis along the bottom of the axes."""
        return self.axes.viewLim.intervalx

    @property
    def upper_xlim(self):
        """Get the data limits for the x-axis along the top of the axes."""
        return self.transData.inverted().transform([[self.bbox.xmin, self.bbox.ymax],
                                                    self.bbox.max])[:, 0]


# Now register the projection with matplotlib so the user can select it.
register_projection(SkewXAxes)


[docs]@exporter.export class SkewT: r"""Make Skew-T log-P plots of data. This class simplifies the process of creating Skew-T log-P plots in using matplotlib. It handles requesting the appropriate skewed projection, and provides simplified wrappers to make it easy to plot data, add wind barbs, and add other lines to the plots (e.g. dry adiabats) Attributes ---------- ax : `matplotlib.axes.Axes` The underlying Axes instance, which can be used for calling additional plot functions (e.g. `axvline`) """
[docs] def __init__(self, fig=None, rotation=30, subplot=None, rect=None, aspect=80.5): r"""Create SkewT - logP plots. Parameters ---------- fig : matplotlib.figure.Figure, optional Source figure to use for plotting. If none is given, a new :class:`matplotlib.figure.Figure` instance will be created. rotation : float or int, optional Controls the rotation of temperature relative to horizontal. Given in degrees counterclockwise from x-axis. Defaults to 30 degrees. subplot : tuple[int, int, int] or `matplotlib.gridspec.SubplotSpec` instance, optional Controls the size/position of the created subplot. This allows creating the skewT as part of a collection of subplots. If subplot is a tuple, it should conform to the specification used for :meth:`matplotlib.figure.Figure.add_subplot`. The :class:`matplotlib.gridspec.SubplotSpec` can be created by using :class:`matplotlib.gridspec.GridSpec`. rect : tuple[float, float, float, float], optional Rectangle (left, bottom, width, height) in which to place the axes. This allows the user to place the axes at an arbitrary point on the figure. aspect : float, int, or 'auto', optional Aspect ratio (i.e. ratio of y-scale to x-scale) to maintain in the plot. Defaults to 80.5. Passing the string ``'auto'`` tells matplotlib to handle the aspect ratio automatically (this is not recommended for SkewT). """ if fig is None: import matplotlib.pyplot as plt figsize = plt.rcParams.get('figure.figsize', (7, 7)) fig = plt.figure(figsize=figsize) self._fig = fig if rect and subplot: raise ValueError("Specify only one of `rect' and `subplot', but not both") elif rect: self.ax = fig.add_axes(rect, projection='skewx', rotation=rotation) else: if subplot is not None: # Handle being passed a tuple for the subplot, or a GridSpec instance try: len(subplot) except TypeError: subplot = (subplot,) else: subplot = (1, 1, 1) self.ax = fig.add_subplot(*subplot, projection='skewx', rotation=rotation) # Set the yaxis as inverted with log scaling self.ax.set_yscale('log') # Override default ticking for log scaling self.ax.yaxis.set_major_formatter(ScalarFormatter()) self.ax.yaxis.set_major_locator(MultipleLocator(100)) self.ax.yaxis.set_minor_formatter(NullFormatter()) # Needed to make sure matplotlib doesn't freak out and create a bunch of ticks # Also takes care of inverting the y-axis self.ax.set_ylim(1050, 100) self.ax.yaxis.set_units(units.hPa) # Try to make sane default temperature plotting ticks self.ax.xaxis.set_major_locator(MultipleLocator(10)) self.ax.xaxis.set_units(units.degC) self.ax.set_xlim(-40, 50) self.ax.grid(True) self.mixing_lines = None self.dry_adiabats = None self.moist_adiabats = None # Maintain a reasonable ratio of data limits. Only works on Matplotlib >= 3.2 if matplotlib.__version__[:3] > '3.1': self.ax.set_aspect(aspect, adjustable='box')
[docs] def plot(self, pressure, t, *args, **kwargs): r"""Plot data. Simple wrapper around plot so that pressure is the first (independent) input. This is essentially a wrapper around `plot`. Parameters ---------- pressure : array_like pressure values t : array_like temperature values, can also be used for things like dew point args Other positional arguments to pass to :func:`~matplotlib.pyplot.plot` kwargs Other keyword arguments to pass to :func:`~matplotlib.pyplot.plot` Returns ------- list[matplotlib.lines.Line2D] lines plotted See Also -------- :func:`matplotlib.pyplot.plot` """ # Skew-T logP plotting t, pressure = _delete_masked_points(t, pressure) return self.ax.plot(t, pressure, *args, **kwargs)
[docs] def plot_barbs(self, pressure, u, v, c=None, xloc=1.0, x_clip_radius=0.1, y_clip_radius=0.08, **kwargs): r"""Plot wind barbs. Adds wind barbs to the skew-T plot. This is a wrapper around the `barbs` command that adds to appropriate transform to place the barbs in a vertical line, located as a function of pressure. Parameters ---------- pressure : array_like pressure values u : array_like U (East-West) component of wind v : array_like V (North-South) component of wind c: An optional array used to map colors to the barbs xloc : float, optional Position for the barbs, in normalized axes coordinates, where 0.0 denotes far left and 1.0 denotes far right. Defaults to far right. x_clip_radius : float, optional Space, in normalized axes coordinates, to leave before clipping wind barbs in the x-direction. Defaults to 0.1. y_clip_radius : float, optional Space, in normalized axes coordinates, to leave above/below plot before clipping wind barbs in the y-direction. Defaults to 0.08. plot_units: `pint.unit` Units to plot in (performing conversion if necessary). Defaults to given units. kwargs Other keyword arguments to pass to :func:`~matplotlib.pyplot.barbs` Returns ------- matplotlib.quiver.Barbs instance created See Also -------- :func:`matplotlib.pyplot.barbs` """ # If plot_units specified, convert the data to those units plotting_units = kwargs.pop('plot_units', None) if plotting_units: if hasattr(u, 'units') and hasattr(v, 'units'): u = u.to(plotting_units) v = v.to(plotting_units) else: raise ValueError('To convert to plotting units, units must be attached to ' 'u and v wind components.') # Assemble array of x-locations in axes space x = np.empty_like(pressure) x.fill(xloc) # Do barbs plot at this location if c is not None: b = self.ax.barbs(x, pressure, u, v, c, transform=self.ax.get_yaxis_transform(which='tick2'), clip_on=True, zorder=2, **kwargs) else: b = self.ax.barbs(x, pressure, u, v, transform=self.ax.get_yaxis_transform(which='tick2'), clip_on=True, zorder=2, **kwargs) # Override the default clip box, which is the axes rectangle, so we can have # barbs that extend outside. ax_bbox = transforms.Bbox([[xloc - x_clip_radius, -y_clip_radius], [xloc + x_clip_radius, 1.0 + y_clip_radius]]) b.set_clip_box(transforms.TransformedBbox(ax_bbox, self.ax.transAxes)) return b
[docs] def plot_dry_adiabats(self, t0=None, pressure=None, **kwargs): r"""Plot dry adiabats. Adds dry adiabats (lines of constant potential temperature) to the plot. The default style of these lines is dashed red lines with an alpha value of 0.5. These can be overridden using keyword arguments. Parameters ---------- t0 : array_like, optional Starting temperature values in Kelvin. If none are given, they will be generated using the current temperature range at the bottom of the plot. pressure : array_like, optional Pressure values to be included in the dry adiabats. If not specified, they will be linearly distributed across the current plotted pressure range. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :func:`~metpy.calc.thermo.dry_lapse` :meth:`plot_moist_adiabats` :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.dry_adiabats: self.dry_adiabats.remove() # Determine set of starting temps if necessary if t0 is None: xmin, xmax = self.ax.get_xlim() t0 = units.Quantity(np.arange(xmin, xmax + 1, 10), 'degC') # Get pressure levels based on ylims if necessary if pressure is None: pressure = units.Quantity(np.linspace(*self.ax.get_ylim()), 'mbar') # Assemble into data for plotting t = dry_lapse(pressure, t0[:, np.newaxis], units.Quantity(1000., 'mbar')).to(units.degC) linedata = [np.vstack((ti.m, pressure.m)).T for ti in t] # Add to plot kwargs.setdefault('colors', 'r') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.5) self.dry_adiabats = self.ax.add_collection(LineCollection(linedata, **kwargs)) return self.dry_adiabats
[docs] def plot_moist_adiabats(self, t0=None, pressure=None, **kwargs): r"""Plot moist adiabats. Adds saturated pseudo-adiabats (lines of constant equivalent potential temperature) to the plot. The default style of these lines is dashed blue lines with an alpha value of 0.5. These can be overridden using keyword arguments. Parameters ---------- t0 : array_like, optional Starting temperature values in Kelvin. If none are given, they will be generated using the current temperature range at the bottom of the plot. pressure : array_like, optional Pressure values to be included in the moist adiabats. If not specified, they will be linearly distributed across the current plotted pressure range. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :func:`~metpy.calc.thermo.moist_lapse` :meth:`plot_dry_adiabats` :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.moist_adiabats: self.moist_adiabats.remove() # Determine set of starting temps if necessary if t0 is None: xmin, xmax = self.ax.get_xlim() t0 = units.Quantity(np.concatenate((np.arange(xmin, 0, 10), np.arange(0, xmax + 1, 5))), 'degC') # Get pressure levels based on ylims if necessary if pressure is None: pressure = units.Quantity(np.linspace(*self.ax.get_ylim()), 'mbar') # Assemble into data for plotting t = moist_lapse(pressure, t0[:, np.newaxis], units.Quantity(1000., 'mbar')).to(units.degC) linedata = [np.vstack((ti.m, pressure.m)).T for ti in t] # Add to plot kwargs.setdefault('colors', 'b') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.5) self.moist_adiabats = self.ax.add_collection(LineCollection(linedata, **kwargs)) return self.moist_adiabats
[docs] def plot_mixing_lines(self, mixing_ratio=None, pressure=None, **kwargs): r"""Plot lines of constant mixing ratio. Adds lines of constant mixing ratio (isohumes) to the plot. The default style of these lines is dashed green lines with an alpha value of 0.8. These can be overridden using keyword arguments. Parameters ---------- mixing_ratio : array_like, optional Unitless mixing ratio values to plot. If none are given, default values are used. pressure : array_like, optional Pressure values to be included in the isohumes. If not specified, they will be linearly distributed across the current plotted pressure range up to 600 mb. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :class:`matplotlib.collections.LineCollection` """ # Remove old lines if self.mixing_lines: self.mixing_lines.remove() # Default mixing level values if necessary if mixing_ratio is None: mixing_ratio = np.array([0.0004, 0.001, 0.002, 0.004, 0.007, 0.01, 0.016, 0.024, 0.032]).reshape(-1, 1) # Set pressure range if necessary if pressure is None: pressure = units.Quantity(np.linspace(600, max(self.ax.get_ylim())), 'mbar') # Assemble data for plotting td = dewpoint(vapor_pressure(pressure, mixing_ratio)) linedata = [np.vstack((t.m, pressure.m)).T for t in td] # Add to plot kwargs.setdefault('colors', 'g') kwargs.setdefault('linestyles', 'dashed') kwargs.setdefault('alpha', 0.8) self.mixing_lines = self.ax.add_collection(LineCollection(linedata, **kwargs)) return self.mixing_lines
[docs] def shade_area(self, y, x1, x2=0, which='both', **kwargs): r"""Shade area between two curves. Shades areas between curves. Area can be where one is greater or less than the other or all areas shaded. Parameters ---------- y : array_like 1-dimensional array of numeric y-values x1 : array_like 1-dimensional array of numeric x-values x2 : array_like 1-dimensional array of numeric x-values which : string Specifies if `positive`, `negative`, or `both` areas are being shaded. Will be overridden by where. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ fill_properties = {'positive': {'facecolor': 'tab:red', 'alpha': 0.4, 'where': x1 > x2}, 'negative': {'facecolor': 'tab:blue', 'alpha': 0.4, 'where': x1 < x2}, 'both': {'facecolor': 'tab:green', 'alpha': 0.4, 'where': None}} try: fill_args = fill_properties[which] fill_args.update(kwargs) except KeyError: raise ValueError(f'Unknown option for which: {which}') from None arrs = y, x1, x2 if fill_args['where'] is not None: arrs = arrs + (fill_args['where'],) fill_args.pop('where', None) fill_args['interpolate'] = True arrs = _delete_masked_points(*arrs) return self.ax.fill_betweenx(*arrs, **fill_args)
[docs] def shade_cape(self, pressure, t, t_parcel, **kwargs): r"""Shade areas of Convective Available Potential Energy (CAPE). Shades areas where the parcel is warmer than the environment (areas of positive buoyancy. Parameters ---------- pressure : array_like Pressure values t : array_like Temperature values dewpoint : array_like Dewpoint values t_parcel : array_like Parcel path temperature values limit_shading : bool Eliminate shading below the LCL or above the EL, default is True kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ return self.shade_area(pressure, t_parcel, t, which='positive', **kwargs)
[docs] def shade_cin(self, pressure, t, t_parcel, dewpoint=None, **kwargs): r"""Shade areas of Convective INhibition (CIN). Shades areas where the parcel is cooler than the environment (areas of negative buoyancy). If `dewpoint` is passed in, negative area below the lifting condensation level or above the equilibrium level is not shaded. Parameters ---------- pressure : array_like Pressure values t : array_like Temperature values t_parcel : array_like Parcel path temperature values dewpoint : array_like Dew point values, optional kwargs Other keyword arguments to pass to :class:`matplotlib.collections.PolyCollection` Returns ------- :class:`matplotlib.collections.PolyCollection` See Also -------- :class:`matplotlib.collections.PolyCollection` :func:`matplotlib.axes.Axes.fill_betweenx` """ if dewpoint is not None: lcl_p, _ = lcl(pressure[0], t[0], dewpoint[0]) el_p, _ = el(pressure, t, dewpoint, t_parcel) idx = np.logical_and(pressure > el_p, pressure < lcl_p) else: idx = np.arange(0, len(pressure)) return self.shade_area(pressure[idx], t_parcel[idx], t[idx], which='negative', **kwargs)
[docs]@exporter.export class Hodograph: r"""Make a hodograph of wind data. Plots the u and v components of the wind along the x and y axes, respectively. This class simplifies the process of creating a hodograph using matplotlib. It provides helpers for creating a circular grid and for plotting the wind as a line colored by another value (such as wind speed). Attributes ---------- ax : `matplotlib.axes.Axes` The underlying Axes instance used for all plotting """
[docs] def __init__(self, ax=None, component_range=80): r"""Create a Hodograph instance. Parameters ---------- ax : `matplotlib.axes.Axes`, optional The `Axes` instance used for plotting component_range : value The maximum range of the plot. Used to set plot bounds and control the maximum number of grid rings needed. """ if ax is None: import matplotlib.pyplot as plt self.ax = plt.figure().add_subplot(1, 1, 1) else: self.ax = ax self.ax.set_aspect('equal', 'box') self.ax.set_xlim(-component_range, component_range) self.ax.set_ylim(-component_range, component_range) # == sqrt(2) * max_range, which is the distance at the corner self.max_range = 1.4142135 * component_range
[docs] def add_grid(self, increment=10., **kwargs): r"""Add grid lines to hodograph. Creates lines for the x- and y-axes, as well as circles denoting wind speed values. Parameters ---------- increment : value, optional The value increment between rings kwargs Other kwargs to control appearance of lines See Also -------- :class:`matplotlib.patches.Circle` :meth:`matplotlib.axes.Axes.axhline` :meth:`matplotlib.axes.Axes.axvline` """ # Some default arguments. Take those, and update with any # arguments passed in grid_args = {'color': 'grey', 'linestyle': 'dashed'} if kwargs: grid_args.update(kwargs) # Take those args and make appropriate for a Circle circle_args = grid_args.copy() color = circle_args.pop('color', None) circle_args['edgecolor'] = color circle_args['fill'] = False self.rings = [] for r in np.arange(increment, self.max_range, increment): c = Circle((0, 0), radius=r, **circle_args) self.ax.add_patch(c) self.rings.append(c) # Add lines for x=0 and y=0 self.yaxis = self.ax.axvline(0, **grid_args) self.xaxis = self.ax.axhline(0, **grid_args)
@staticmethod def _form_line_args(kwargs): """Simplify taking the default line style and extending with kwargs.""" def_args = {'linewidth': 3} def_args.update(kwargs) return def_args
[docs] def plot(self, u, v, **kwargs): r"""Plot u, v data. Plots the wind data on the hodograph. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind kwargs Other keyword arguments to pass to :meth:`matplotlib.axes.Axes.plot` Returns ------- list[matplotlib.lines.Line2D] lines plotted See Also -------- :meth:`Hodograph.plot_colormapped` """ line_args = self._form_line_args(kwargs) u, v = _delete_masked_points(u, v) return self.ax.plot(u, v, **line_args)
[docs] def wind_vectors(self, u, v, **kwargs): r"""Plot u, v data as wind vectors. Plot the wind data as vectors for each level, beginning at the origin. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind kwargs Other keyword arguments to pass to :meth:`matplotlib.axes.Axes.quiver` Returns ------- matplotlib.quiver.Quiver arrows plotted """ quiver_args = {'units': 'xy', 'scale': 1} quiver_args.update(**kwargs) center_position = np.zeros_like(u) return self.ax.quiver(center_position, center_position, u, v, **quiver_args)
[docs] def plot_colormapped(self, u, v, c, intervals=None, colors=None, **kwargs): r"""Plot u, v data, with line colored based on a third set of data. Plots the wind data on the hodograph, but with a colormapped line. Takes a third variable besides the winds (e.g. heights or pressure levels) and either a colormap to color it with or a series of contour intervals and colors to create a colormap and norm to control colormapping. The intervals must always be in increasing order. For using custom contour intervals with height data, the function will automatically interpolate to the contour intervals from the height and wind data, as well as convert the input contour intervals from height AGL to MSL to work with the provided heights. Parameters ---------- u : array_like u-component of wind v : array_like v-component of wind c : array_like data to use for colormapping (e.g. heights, pressure, wind speed) intervals: array-like, optional Array of intervals for c to use in coloring the hodograph. colors: list, optional Array of strings representing colors for the hodograph segments. kwargs Other keyword arguments to pass to :class:`matplotlib.collections.LineCollection` Returns ------- matplotlib.collections.LineCollection instance created See Also -------- :meth:`Hodograph.plot` """ u, v, c = _delete_masked_points(u, v, c) # Plotting a color segmented hodograph if colors: cmap = mcolors.ListedColormap(colors) # If we are segmenting by height (a length), interpolate the contour intervals if intervals.dimensionality == {'[length]': 1.0}: # Find any intervals not in the data and interpolate them interpolation_heights = np.array([bound.m for bound in intervals if bound not in c]) interpolation_heights = units.Quantity(np.sort(interpolation_heights), intervals.units) (interpolated_heights, interpolated_u, interpolated_v) = interpolate_1d(interpolation_heights, c, c, u, v) # Combine the interpolated data with the actual data c = concatenate([c, interpolated_heights]) u = concatenate([u, interpolated_u]) v = concatenate([v, interpolated_v]) sort_inds = np.argsort(c) c = c[sort_inds] u = u[sort_inds] v = v[sort_inds] # Unit conversion required for coloring of bounds/data in dissimilar units # to work properly. c = c.to_base_units() # TODO: This shouldn't be required! intervals = intervals.to_base_units() # If segmenting by anything else, do not interpolate, just use the data else: intervals = units.Quantity(np.asarray(intervals), intervals.units) norm = mcolors.BoundaryNorm(intervals.magnitude, cmap.N) cmap.set_over('none') cmap.set_under('none') kwargs['cmap'] = cmap kwargs['norm'] = norm line_args = self._form_line_args(kwargs) # Plotting a continuously colored line else: line_args = self._form_line_args(kwargs) # Do the plotting lc = colored_line(u, v, c, **line_args) self.ax.add_collection(lc) return lc