Source code for spacepy.plot

#!/usr/bin/env python
# -*- coding: utf-8 -*-

plot: SpacePy plotting routines

This package aims to make getting publication ready plots easier.
It provides classes and functions for different types of plot 
(e.g. Spectrogram, levelPlot), for helping make plots more cleanly 
(e.g. set_target, dual_half_circle), and for making plots convey 
information more cleanly, with less effort 
(e.g. applySmartTimeTicks, style).

This plot module now provides style sheets. For most standard plotting 
we recommend the *default* style sheet (aka *spacepy*). To auto-apply the
default plot style the following should be added to your spacepy.rc file::

    apply_plot_styles: True

Different plot types may not work well with this style, so we have provided
alternatives. For polar plots, spectrograms, or anything with larger blocks 
of color, it may be better to use one of the alternatives::

    import spacepy.plot as splot'altgrid') # inverts background from default so it's white'polar') # designed for filled polar plots
    splot.revert_style() # put the style back to matplotlib defaults

Authors: Brian Larsen and Steve Morley
Institution: Los Alamos National Laboratory

Copyright 2011-2016 Los Alamos National Security, LLC.

.. autosummary::
   :template: clean_function.rst
   :toctree: autosummary


Most of the functionality in the plot module is made available directly 
through the *plot* namespace. However, the plot module does contain
several submodules listed below

.. autosummary::
    :toctree: autosummary
    :template: clean_module.rst


    from import Mapping
except ImportError:
    from collections import Mapping
import os
import warnings
import numpy as np
import matplotlib as mpl
from matplotlib.patches import Wedge
from matplotlib import __version__ as mplv
import matplotlib.pyplot as plt
from spacepy import __path__ as basepath
from spacepy.datamodel import dmcopy
import spacepy.datamanager as dman
from .. import config
from .spectrogram import *
from .utils import *
from .carrington import *

[docs]def plot(*args, **kwargs): '''Convenience wrapper for matplotlib's plot function As with matplotlib's plot function, *args* is a variable length argument, allowing for multiple *x*, *y* pairs, each with optional format string. For full details, see matplotlib.pyplot.plot Other Parameters ---------------- smartTimeTicks : boolean If True then use applySmartTimeTicks to set x-axis labeling figsize : array-like, 2 elements Set figure size directly on call to plot, (width, height) **kwargs : other keywords Other keywords to pass to matplotlib.pyplot.plot ''' if 'smartTimeTicks' in kwargs: sTT = kwargs['smartTimeTicks'] del kwargs['smartTimeTicks'] else: sTT = False if 'figsize' not in kwargs: kwargs['figsize'] = (10,6) fig = plt.figure(figsize=kwargs['figsize']) del kwargs['figsize'] pobj = plt.plot(*args, **kwargs) if sTT is True: ax = plt.gca() applySmartTimeTicks(ax, args[0], dolimit=True, dolabel=False) return pobj
[docs]def available(returnvals=False): '''List the available plot styles provided by spacepy.plot Note that some of the available styles have multiple aliases. To apply an available style, use ``. ''' spacepystyle = os.path.join('{0}'.format(basepath[0]), 'data', 'spacepy.mplstyle') spacepyaltstyle = os.path.join('{0}'.format(basepath[0]), 'data', 'spacepy_altgrid.mplstyle') polarstyle = os.path.join('{0}'.format(basepath[0]), 'data', 'spacepy_polar.mplstyle') lookdict = {'default': spacepystyle, 'spacepy': spacepystyle, 'spacepy_altgrid': spacepyaltstyle, 'altgrid': spacepyaltstyle, 'spacepy_polar': polarstyle, 'polar': polarstyle } if returnvals: return lookdict else: return list(lookdict.keys())
[docs]def style(look=None, cmap='plasma'): ''' Apply SpacePy's matplotlib style settings from a known style sheet. Parameters ---------- look : str Name of style. For a list of available style names, see `spacepy.plot.available`. ''' lookdict = available(returnvals=True) try: usestyle = lookdict[look] except KeyError: usestyle = lookdict['default'] try: except AttributeError: not available, old matplotlib? with warnings.catch_warnings(): warnings.simplefilter("ignore") dum = mpl.rc_params_from_file(usestyle) styapply = dict() #remove None values as these seem to cause issues... for key in dum: if dum[key] is not None: styapply[key] = dum[key] for key in styapply: mpl.rcParams[key] = styapply[key] mpl.rcParams['image.cmap'] = cmap
#save current rcParams before applying spacepy style oldParams = dict() for key, val in mpl.rcParams.items(): oldParams[key] = dmcopy(val) if config['apply_plot_styles']: style()
[docs]def revert_style(): '''Revert plot style settings to those in use prior to importing spacepy.plot ''' with warnings.catch_warnings(): warnings.simplefilter("ignore") for key in oldParams: try: mpl.rcParams[key] = oldParams[key] except ValueError: pass
[docs]def dual_half_circle(center=(0,0), radius=1.0, sun_direction='right', ax=None, colors=('w','k'), **kwargs): """ Plot two half circles to a plot with the specified face colors and rotation. This is normal to use to denote the sun direction in magnetospheric science plots. Other Parameters ---------------- center : array-like, 2 elements Center in data coordinates of the circles, default (0,0) radius : float Radius of the circles, defualt 1.0 sun_direction : string or float The rotation direction of the first (white) circle. Options are ['down', 'down right', 'right', 'up left', 'up right', 'up', 'down left', 'left'] or an angle in degrees counter-clockwise from up. Default right. ax : matplotlib.axes Axis to plot the circles on. colors : array-like, 2 elements The two colors for the circle fill. The First number is the light and second is the dark. **kwargs : other keywords Other keywords to pass to matplotlib.patches.Wedge Returns ------- out : tuple Tuple of the two wedge objects Examples -------- >>> import spacepy.plot >>> spacepy.plot.dual_half_circle() """ sun_dict = {'left':90, 'right':-90, 'up':0, 'down':180, 'up right':-45, 'up left':45, 'down right':45+180, 'down left':-45+180, } try: angle = sun_dict[sun_direction] except KeyError: try: angle = float(sun_direction) except ValueError: raise(ValueError("Sun_direction was not understood, must be a float or {0}".format(sun_dict.keys()))) theta1, theta2 = angle, angle + 180 if ax is None: ax = plt.gca() w1 = Wedge(center, radius, theta1, theta2, fc=colors[0],transform=ax.transData._b, **kwargs) w2 = Wedge(center, radius, theta2, theta1, fc=colors[1], transform=ax.transData._b,**kwargs) for wedge in [w1, w2]: ax.add_artist(wedge) return (w1, w2)
[docs]def levelPlot(data, var=None, time=None, levels=(3, 5), target=None, colors=None, **kwargs): """ Draw a step-plot with up to 5 levels following a color cycle (e.g. Kp index "stoplight") Parameters ---------- data : array-like, or dict-like Data for plotting. If dict-like, the key providing an array-like to plot must be given to var keyword argument. Other Parameters ---------------- var : string Name of key in dict-like input that contains data time : array-like or string Name of key in dict-like that contains time, or arraylike of datetimes levels : array-like, up to 5 levels Breaks between levels in data that should be shown as distinct colors target : figure or axes Target axes or figure window colors : array-like Colors to use for the color sequence (if insufficient colors, will use as a cycle) **kwargs : other keywords Other keywords to pass to spacepy.toolbox.binHisto Returns ------- binned : tuple Tuple of the binned data and bins Examples -------- >>> import spacepy.plot as splot >>> import spacepy.time as spt >>> import spacepy.omni as om >>> tt = spt.tickrange('2012/09/28','2012/10/2', 3/24.) >>> omni = om.get_omni(tt) >>> splot.levelPlot(omni, var='Kp', time='UTC', colors=['seagreen', 'orange', 'crimson']) """ #assume dict-like/key-access, before moving to array-like if var is not None: try: usearr = data[var] except KeyError: raise KeyError('Key "{1}" not present in data'.format(var)) else: #var is None, so make sure we don't have a dict-like if not isinstance(data, Mapping): usearr = np.asarray(data) else: raise TypeError('Data appears to be dict-like without a key being given') tflag = False if time is not None: from scipy.stats import mode try: times = data[time] except (KeyError, ValueError, IndexError): times = time try: times = matplotlib.dates.date2num(times) tflag = True except AttributeError: #the x-data are a non-datetime times = np.asarray(time) #now add the end-point stepsize, dum = mode(np.diff(times), axis=None) times = np.hstack([times, times[-1]+stepsize]) else: times = np.asarray(range(0, len(usearr)+1)) if not colors: if len(levels)<=3: #traffic light colours that are distinct to protanopes and deuteranopes colors = ['lime', 'yellow', 'crimson', 'saddlebrown'] else: colors = matplotlib.rcParams['axes.color_cycle'] else: try: assert len(colors) > len(levels) except AssertionError: #cycle the given colors, if not enough are given colors = list(colors)*int(1+len(levels)/len(colors)) if 'alpha' not in kwargs: kwargs['alpha']=0.75 if 'legend' not in kwargs: legend = False else: legend = kwargs['legend'] del kwargs['legend'] fig, ax = set_target(target) subset = np.asarray(dmcopy(usearr)) def fill_between_steps(ax, x, y1, **kwargs): y2 = np.zeros_like(y1) stepsxx = x.repeat(2)[1:-1] stepsyy = y1.repeat(2) y2 = np.zeros_like(stepsyy) ax.fill_between(stepsxx, stepsyy, y2, **kwargs) if mpl.__version__<'1.5.0': #pre-v1.5.0, need to manually add an artist for the legend p = plt.Rectangle((0, 0), 0, 0, **kwargs) ax.add_patch(p) #below threshold 1 idx = 0 inds = usearr>levels[0] subset[inds] = np.nan kwargs['label'] = u'≤{0}'.format(levels[idx]) fill_between_steps(ax, times, subset, color=colors[0], zorder=30, **kwargs) #for each of the "between" thresholds for idx in range(1,len(levels)): subset = np.asarray(dmcopy(usearr)) inds = np.bitwise_or(usearr<=levels[idx-1], usearr>levels[idx]) subset[inds] = np.nan kwargs['label'] = u'>{0},≤{1}'.format(levels[idx-1], levels[idx]) fill_between_steps(ax, times, subset, color=colors[idx], zorder=30-(idx*2), **kwargs) #last idx += 1 try: inds = usearr<=levels[idx-1] subset = np.asarray(dmcopy(usearr)) subset[inds] = np.nan kwargs['label'] = '>{0}'.format(levels[-1]) fill_between_steps(ax, times, subset, color=colors[idx], zorder=30-(idx*2), **kwargs) except: pass #if required, set x axis to times if tflag: try: applySmartTimeTicks(ax, data[time]) except (IndexError, KeyError): #using data array to index, so should just use time applySmartTimeTicks(ax, time) ax.grid(False, which='minor') #minor grid usually looks bad on these... if legend: ncols = len(levels)+1 if ncols > 3: ncols = ncols//2 ax.legend(loc='upper left', ncol=ncols) return ax