# Note: The first part of this file can be modified in place, but the latter part
# is autogenerated by the boilerplate.py script.
"""
Provides a MATLAB-like plotting framework.
:mod:`~matplotlib.pylab` combines pyplot with numpy into a single namespace.
This is convenient for interactive work, but for programming it
is recommended that the namespaces be kept separate, e.g.::
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1);
y = np.sin(x)
plt.plot(x, y)
"""
from __future__ import print_function
import sys, warnings
import matplotlib
from matplotlib import _pylab_helpers, interactive
from matplotlib.cbook import dedent, silent_list, is_string_like, is_numlike
from matplotlib import docstring
from matplotlib.figure import Figure, figaspect
from matplotlib.backend_bases import FigureCanvasBase
from matplotlib.image import imread as _imread
from matplotlib.image import imsave as _imsave
from matplotlib import rcParams, rcParamsDefault, get_backend
from matplotlib.rcsetup import interactive_bk as _interactive_bk
from matplotlib.artist import getp, get, Artist
from matplotlib.artist import setp as _setp
from matplotlib.axes import Axes, Subplot, _string_to_bool
from matplotlib.projections import PolarAxes
from matplotlib import mlab # for csv2rec, detrend_none, window_hanning
from matplotlib.scale import get_scale_docs, get_scale_names
from matplotlib import cm
from matplotlib.cm import get_cmap, register_cmap
import numpy as np
# We may not need the following imports here:
from matplotlib.colors import Normalize, normalize # latter for backwards compat.
from matplotlib.lines import Line2D
from matplotlib.text import Text, Annotation
from matplotlib.patches import Polygon, Rectangle, Circle, Arrow
from matplotlib.widgets import SubplotTool, Button, Slider, Widget
from ticker import TickHelper, Formatter, FixedFormatter, NullFormatter,\
FuncFormatter, FormatStrFormatter, ScalarFormatter,\
LogFormatter, LogFormatterExponent, LogFormatterMathtext,\
Locator, IndexLocator, FixedLocator, NullLocator,\
LinearLocator, LogLocator, AutoLocator, MultipleLocator,\
MaxNLocator
try:
reload
except NameError:
# Python 3
from imp import reload
## Backend detection ##
def _backend_selection():
""" If rcParams['backend_fallback'] is true, check to see if the
current backend is compatible with the current running event
loop, and if not switches to a compatible one.
"""
backend = rcParams['backend']
if not rcParams['backend_fallback'] or \
backend not in _interactive_bk:
return
is_agg_backend = rcParams['backend'].endswith('Agg')
if 'wx' in sys.modules and not backend in ('WX', 'WXAgg'):
import wx
if wx.App.IsMainLoopRunning():
rcParams['backend'] = 'wx' + 'Agg' * is_agg_backend
elif 'qt' in sys.modules and not backend == 'QtAgg':
import qt
if not qt.qApp.startingUp():
# The mainloop is running.
rcParams['backend'] = 'qtAgg'
elif 'PyQt4.QtCore' in sys.modules and not backend == 'Qt4Agg':
import PyQt4.QtGui
if not PyQt4.QtGui.qApp.startingUp():
# The mainloop is running.
rcParams['backend'] = 'qt4Agg'
elif 'gtk' in sys.modules and not backend in ('GTK', 'GTKAgg',
'GTKCairo'):
import gobject
if gobject.MainLoop().is_running():
rcParams['backend'] = 'gtk' + 'Agg' * is_agg_backend
elif 'Tkinter' in sys.modules and not backend == 'TkAgg':
#import Tkinter
pass #what if anything do we need to do for tkinter?
_backend_selection()
## Global ##
from matplotlib.backends import pylab_setup
new_figure_manager, draw_if_interactive, _show = pylab_setup()
@docstring.copy_dedent(Artist.findobj)
def findobj(o=None, match=None):
if o is None:
o = gcf()
return o.findobj(match)
def switch_backend(newbackend):
"""
Switch the default backend to newbackend. This feature is
**experimental**, and is only expected to work switching to an
image backend. Eg, if you have a bunch of PostScript scripts that
you want to run from an interactive ipython session, you may want
to switch to the PS backend before running them to avoid having a
bunch of GUI windows popup. If you try to interactively switch
from one GUI backend to another, you will explode.
Calling this command will close all open windows.
"""
close('all')
global new_figure_manager, draw_if_interactive, _show
matplotlib.use(newbackend, warn=False, force=True)
from matplotlib.backends import pylab_setup
new_figure_manager, draw_if_interactive, _show = pylab_setup()
def show(*args, **kw):
"""
When running in ipython with its pylab mode, display all
figures and return to the ipython prompt.
In non-interactive mode, display all figures and block until
the figures have been closed; in interactive mode it has no
effect unless figures were created prior to a change from
non-interactive to interactive mode (not recommended). In
that case it displays the figures but does not block.
A single experimental keyword argument, *block*, may be
set to True or False to override the blocking behavior
described above.
"""
global _show
_show(*args, **kw)
def isinteractive():
"""
Return *True* if matplotlib is in interactive mode, *False* otherwise.
"""
return matplotlib.is_interactive()
def ioff():
'Turn interactive mode off.'
matplotlib.interactive(False)
def ion():
'Turn interactive mode on.'
matplotlib.interactive(True)
def pause(interval):
"""
Pause for *interval* seconds.
If there is an active figure it will be updated and displayed,
and the gui event loop will run during the pause.
If there is no active figure, or if a non-interactive backend
is in use, this executes time.sleep(interval).
This can be used for crude animation. For more complex
animation, see :mod:`matplotlib.animation`.
This function is experimental; its behavior may be changed
or extended in a future release.
"""
backend = rcParams['backend']
if backend in _interactive_bk:
figManager = _pylab_helpers.Gcf.get_active()
if figManager is not None:
canvas = figManager.canvas
canvas.draw()
show(block=False)
canvas.start_event_loop(interval)
return
# No on-screen figure is active, so sleep() is all we need.
import time
time.sleep(interval)
@docstring.copy_dedent(matplotlib.rc)
def rc(*args, **kwargs):
matplotlib.rc(*args, **kwargs)
@docstring.copy_dedent(matplotlib.rcdefaults)
def rcdefaults():
matplotlib.rcdefaults()
draw_if_interactive()
# The current "image" (ScalarMappable) is retrieved or set
# only via the pyplot interface using the following two
# functions:
def gci():
"""
Get the current :class:`~matplotlib.cm.ScalarMappable` instance
(image or patch collection), or *None* if no images or patch
collections have been defined. The commands
:func:`~matplotlib.pyplot.imshow` and
:func:`~matplotlib.pyplot.figimage` create
:class:`~matplotlib.image.Image` instances, and the commands
:func:`~matplotlib.pyplot.pcolor` and
:func:`~matplotlib.pyplot.scatter` create
:class:`~matplotlib.collections.Collection` instances.
The current image is an attribute of the current axes, or the
nearest earlier axes in the current figure that contains an
image.
"""
return gcf()._gci()
def sci(im):
"""
Set the current image (target of colormap commands like
:func:`~matplotlib.pyplot.jet`, :func:`~matplotlib.pyplot.hot` or
:func:`~matplotlib.pyplot.clim`). The current image is an
attribute of the current axes.
"""
gca()._sci(im)
## Any Artist ##
# (getp is simply imported)
@docstring.copy(_setp)
def setp(*args, **kwargs):
ret = _setp(*args, **kwargs)
draw_if_interactive()
return ret
## Figures ##
def figure(num=None, # autoincrement if None, else integer from 1-N
figsize = None, # defaults to rc figure.figsize
dpi = None, # defaults to rc figure.dpi
facecolor = None, # defaults to rc figure.facecolor
edgecolor = None, # defaults to rc figure.edgecolor
frameon = True,
FigureClass = Figure,
**kwargs
):
"""
call signature::
figure(num=None, figsize=(8, 6), dpi=80, facecolor='w', edgecolor='k')
Create a new figure and return a :class:`matplotlib.figure.Figure`
instance. If *num* = *None*, the figure number will be incremented and
a new figure will be created. The returned figure objects have a
*number* attribute holding this number.
If *num* is an integer, and ``figure(num)`` already exists, make it
active and return a reference to it. If ``figure(num)`` does not exist
it will be created. Numbering starts at 1, MATLAB style::
figure(1)
The same applies if *num* is a string. In this case *num* will be used
as an explicit figure label::
figure("today")
and in windowed backends, the window title will be set to this figure
label.
If you are creating many figures, make sure you explicitly call "close"
on the figures you are not using, because this will enable pylab
to properly clean up the memory.
Optional keyword arguments:
========= =======================================================
Keyword Description
========= =======================================================
figsize width x height in inches; defaults to rc figure.figsize
dpi resolution; defaults to rc figure.dpi
facecolor the background color; defaults to rc figure.facecolor
edgecolor the border color; defaults to rc figure.edgecolor
========= =======================================================
rcParams defines the default values, which can be modified in the
matplotlibrc file
*FigureClass* is a :class:`~matplotlib.figure.Figure` or derived
class that will be passed on to :meth:`new_figure_manager` in the
backends which allows you to hook custom Figure classes into the
pylab interface. Additional kwargs will be passed on to your
figure init function.
"""
if figsize is None : figsize = rcParams['figure.figsize']
if dpi is None : dpi = rcParams['figure.dpi']
if facecolor is None : facecolor = rcParams['figure.facecolor']
if edgecolor is None : edgecolor = rcParams['figure.edgecolor']
allnums = get_fignums()
figLabel = ''
if num is None:
if allnums:
num = max(allnums) + 1
else:
num = 1
elif is_string_like(num):
figLabel = num
allLabels = get_figlabels()
if figLabel not in allLabels:
if figLabel == 'all':
warnings.warn("close('all') closes all existing figures")
if len(allLabels):
num = max(allnums) + 1
else:
num = 1
else:
inum = allLabels.index(figLabel)
num = allnums[inum]
else:
num = int(num) # crude validation of num argument
figManager = _pylab_helpers.Gcf.get_fig_manager(num)
if figManager is None:
if get_backend().lower() == 'ps': dpi = 72
figManager = new_figure_manager(num, figsize=figsize,
dpi=dpi,
facecolor=facecolor,
edgecolor=edgecolor,
frameon=frameon,
FigureClass=FigureClass,
**kwargs)
if figLabel:
figManager.set_window_title(figLabel)
figManager.canvas.figure.set_label(figLabel)
# make this figure current on button press event
def make_active(event):
_pylab_helpers.Gcf.set_active(figManager)
cid = figManager.canvas.mpl_connect('button_press_event', make_active)
figManager._cidgcf = cid
_pylab_helpers.Gcf.set_active(figManager)
figManager.canvas.figure.number = num
draw_if_interactive()
return figManager.canvas.figure
def gcf():
"Return a reference to the current figure."
figManager = _pylab_helpers.Gcf.get_active()
if figManager is not None:
return figManager.canvas.figure
else:
return figure()
fignum_exists = _pylab_helpers.Gcf.has_fignum
def get_fignums():
"Return a list of existing figure numbers."
fignums = _pylab_helpers.Gcf.figs.keys()
fignums.sort()
return fignums
def get_figlabels():
"Return a list of existing figure labels."
figManagers = _pylab_helpers.Gcf.get_all_fig_managers()
figManagers.sort(key=lambda m: m.num)
return [m.canvas.figure.get_label() for m in figManagers]
def get_current_fig_manager():
figManager = _pylab_helpers.Gcf.get_active()
if figManager is None:
gcf() # creates an active figure as a side effect
figManager = _pylab_helpers.Gcf.get_active()
return figManager
@docstring.copy_dedent(FigureCanvasBase.mpl_connect)
def connect(s, func):
return get_current_fig_manager().canvas.mpl_connect(s, func)
@docstring.copy_dedent(FigureCanvasBase.mpl_disconnect)
def disconnect(cid):
return get_current_fig_manager().canvas.mpl_disconnect(cid)
def close(*args):
"""
Close a figure window
``close()`` by itself closes the current figure
``close(h)`` where *h* is a :class:`Figure` instance, closes that figure
``close(num)`` closes figure number *num*
``close(name)`` where *name* is a string, closes figure with that label
``close('all')`` closes all the figure windows
"""
if len(args)==0:
figManager = _pylab_helpers.Gcf.get_active()
if figManager is None: return
else:
_pylab_helpers.Gcf.destroy(figManager.num)
elif len(args)==1:
arg = args[0]
if arg=='all':
_pylab_helpers.Gcf.destroy_all()
elif isinstance(arg, int):
_pylab_helpers.Gcf.destroy(arg)
elif is_string_like(arg):
allLabels = get_figlabels()
if arg in allLabels:
num = get_fignums()[allLabels.index(arg)]
_pylab_helpers.Gcf.destroy(num)
elif isinstance(arg, Figure):
_pylab_helpers.Gcf.destroy_fig(arg)
else:
raise TypeError('Unrecognized argument type %s to close'%type(arg))
else:
raise TypeError('close takes 0 or 1 arguments')
def clf():
"""
Clear the current figure
"""
gcf().clf()
draw_if_interactive()
def draw():
"""
Redraw the current figure.
This is used in interactive mode to update a figure that
has been altered using one or more plot object method calls;
it is not needed if figure modification is done entirely
with pyplot functions, if a sequence of modifications ends
with a pyplot function, or if matplotlib is in non-interactive
mode and the sequence of modifications ends with :func:`show` or
:func:`savefig`.
A more object-oriented alternative, given any
:class:`~matplotlib.figure.Figure` instance, :attr:`fig`, that
was created using a :mod:`~matplotlib.pyplot` function, is::
fig.canvas.draw()
"""
get_current_fig_manager().canvas.draw()
@docstring.copy_dedent(Figure.savefig)
def savefig(*args, **kwargs):
fig = gcf()
return fig.savefig(*args, **kwargs)
@docstring.copy_dedent(Figure.ginput)
def ginput(*args, **kwargs):
"""
Blocking call to interact with the figure.
This will wait for *n* clicks from the user and return a list of the
coordinates of each click.
If *timeout* is negative, does not timeout.
"""
return gcf().ginput(*args, **kwargs)
@docstring.copy_dedent(Figure.waitforbuttonpress)
def waitforbuttonpress(*args, **kwargs):
"""
Blocking call to interact with the figure.
This will wait for *n* key or mouse clicks from the user and
return a list containing True's for keyboard clicks and False's
for mouse clicks.
If *timeout* is negative, does not timeout.
"""
return gcf().waitforbuttonpress(*args, **kwargs)
# Putting things in figures
@docstring.copy_dedent(Figure.text)
def figtext(*args, **kwargs):
ret = gcf().text(*args, **kwargs)
draw_if_interactive()
return ret
@docstring.copy_dedent(Figure.suptitle)
def suptitle(*args, **kwargs):
ret = gcf().suptitle(*args, **kwargs)
draw_if_interactive()
return ret
@docstring.Appender("Addition kwargs: hold = [True|False] overrides default hold state", "\n")
@docstring.copy_dedent(Figure.figimage)
def figimage(*args, **kwargs):
# allow callers to override the hold state by passing hold=True|False
ret = gcf().figimage(*args, **kwargs)
draw_if_interactive()
#sci(ret) # JDH figimage should not set current image -- it is not mappable, etc
return ret
def figlegend(handles, labels, loc, **kwargs):
"""
Place a legend in the figure.
*labels*
a sequence of strings
*handles*
a sequence of :class:`~matplotlib.lines.Line2D` or
:class:`~matplotlib.patches.Patch` instances
*loc*
can be a string or an integer specifying the legend
location
A :class:`matplotlib.legend.Legend` instance is returned.
Example::
figlegend( (line1, line2, line3),
('label1', 'label2', 'label3'),
'upper right' )
.. seealso::
:func:`~matplotlib.pyplot.legend`
"""
l = gcf().legend(handles, labels, loc, **kwargs)
draw_if_interactive()
return l
## Figure and Axes hybrid ##
def hold(b=None):
"""
Set the hold state. If *b* is None (default), toggle the
hold state, else set the hold state to boolean value *b*::
hold() # toggle hold
hold(True) # hold is on
hold(False) # hold is off
When *hold* is *True*, subsequent plot commands will be added to
the current axes. When *hold* is *False*, the current axes and
figure will be cleared on the next plot command.
"""
fig = gcf()
ax = fig.gca()
fig.hold(b)
ax.hold(b)
# b=None toggles the hold state, so let's get get the current hold
# state; but should pyplot hold toggle the rc setting - me thinks
# not
b = ax.ishold()
rc('axes', hold=b)
def ishold():
"""
Return the hold status of the current axes
"""
return gca().ishold()
def over(func, *args, **kwargs):
"""
over calls::
func(*args, **kwargs)
with ``hold(True)`` and then restores the hold state.
"""
h = ishold()
hold(True)
func(*args, **kwargs)
hold(h)
## Axes ##
def axes(*args, **kwargs):
"""
Add an axes at position rect specified by:
- ``axes()`` by itself creates a default full ``subplot(111)`` window axis.
- ``axes(rect, axisbg='w')`` where *rect* = [left, bottom, width,
height] in normalized (0, 1) units. *axisbg* is the background
color for the axis, default white.
- ``axes(h)`` where *h* is an axes instance makes *h* the current
axis. An :class:`~matplotlib.axes.Axes` instance is returned.
======= ============ ================================================
kwarg Accepts Desctiption
======= ============ ================================================
axisbg color the axes background color
frameon [True|False] display the frame?
sharex otherax current axes shares xaxis attribute with otherax
sharey otherax current axes shares yaxis attribute with otherax
polar [True|False] use a polar axes?
======= ============ ================================================
Examples:
* :file:`examples/pylab_examples/axes_demo.py` places custom axes.
* :file:`examples/pylab_examples/shared_axis_demo.py` uses
*sharex* and *sharey*.
"""
nargs = len(args)
if len(args)==0: return subplot(111, **kwargs)
if nargs>1:
raise TypeError('Only one non keyword arg to axes allowed')
arg = args[0]
if isinstance(arg, Axes):
a = gcf().sca(arg)
else:
rect = arg
a = gcf().add_axes(rect, **kwargs)
draw_if_interactive()
return a
def delaxes(*args):
"""
``delaxes(ax)``: remove *ax* from the current figure. If *ax*
doesn't exist, an error will be raised.
``delaxes()``: delete the current axes
"""
if not len(args):
ax = gca()
else:
ax = args[0]
ret = gcf().delaxes(ax)
draw_if_interactive()
return ret
def sca(ax):
"""
Set the current Axes instance to *ax*. The current Figure
is updated to the parent of *ax*.
"""
managers = _pylab_helpers.Gcf.get_all_fig_managers()
for m in managers:
if ax in m.canvas.figure.axes:
_pylab_helpers.Gcf.set_active(m)
m.canvas.figure.sca(ax)
return
raise ValueError("Axes instance argument was not found in a figure.")
def gca(**kwargs):
"""
Return the current axis instance. This can be used to control
axis properties either using set or the
:class:`~matplotlib.axes.Axes` methods, for example, setting the
xaxis range::
plot(t,s)
set(gca(), 'xlim', [0,10])
or::
plot(t,s)
a = gca()
a.set_xlim([0,10])
"""
ax = gcf().gca(**kwargs)
return ax
# More ways of creating axes:
def subplot(*args, **kwargs):
"""
Create a subplot command, creating axes with::
subplot(numRows, numCols, plotNum)
where *plotNum* = 1 is the first plot number and increasing *plotNums*
fill rows first. max(*plotNum*) == *numRows* * *numCols*
You can leave out the commas if *numRows* 1 and M>1 are returned as a 2d
array.
If *False*, no squeezing at all is done: the returned axis
object is always a 2-d array contaning Axis instances, even if it
ends up being 1x1.
*subplot_kw* : dict
Dict with keywords passed to the
:meth:`~matplotlib.figure.Figure.add_subplot` call used to
create each subplots.
*fig_kw* : dict
Dict with keywords passed to the :func:`figure` call. Note that all
keywords not recognized above will be automatically included here.
Returns:
fig, ax : tuple
- *fig* is the :class:`matplotlib.figure.Figure` object
- *ax* can be either a single axis object or an array of axis
objects if more than one subplot was created. The dimensions
of the resulting array can be controlled with the squeeze
keyword, see above.
Examples::
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
# Just a figure and one subplot
f, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')
# Two subplots, unpack the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)
# Four polar axes
plt.subplots(2, 2, subplot_kw=dict(polar=True))
# Share a X axis with each column of subplots
plt.subplots(2, 2, sharex='col')
# Share a Y axis with each row of subplots
plt.subplots(2, 2, sharey='row')
# Share a X and Y axis with all subplots
plt.subplots(2, 2, sharex='all', sharey='all')
# same as
plt.subplots(2, 2, sharex=True, sharey=True)
"""
# for backwards compatability
if isinstance(sharex, bool):
if sharex:
sharex = "all"
else:
sharex = "none"
if isinstance(sharey, bool):
if sharey:
sharey = "all"
else:
sharey = "none"
share_values = ["all", "row", "col", "none"]
if sharex not in share_values:
# This check was added because it is very easy to type subplots(1, 2, 1)
# when subplot(1, 2, 1) was intended. In most cases, no error will
# ever occur, but mysterious behavior will result because what was
# intended to be the subplot index is instead treated as a bool for
# sharex.
if isinstance(sharex, int):
warnings.warn("sharex argument to subplots() was an integer."
" Did you intend to use subplot() (without 's')?")
raise ValueError("sharex [%s] must be one of %s" % \
(sharex, share_values))
if sharey not in share_values:
raise ValueError("sharey [%s] must be one of %s" % \
(sharey, share_values))
if subplot_kw is None:
subplot_kw = {}
fig = figure(**fig_kw)
# Create empty object array to hold all axes. It's easiest to make it 1-d
# so we can just append subplots upon creation, and then
nplots = nrows*ncols
axarr = np.empty(nplots, dtype=object)
# Create first subplot separately, so we can share it if requested
ax0 = fig.add_subplot(nrows, ncols, 1, **subplot_kw)
#if sharex:
# subplot_kw['sharex'] = ax0
#if sharey:
# subplot_kw['sharey'] = ax0
axarr[0] = ax0
r, c = np.mgrid[:nrows, :ncols]
r = r.flatten() * ncols
c = c.flatten()
lookup = {
"none": np.arange(nplots),
"all": np.zeros(nplots, dtype=int),
"row": r,
"col": c,
}
sxs = lookup[sharex]
sys = lookup[sharey]
# Note off-by-one counting because add_subplot uses the MATLAB 1-based
# convention.
for i in range(1, nplots):
if sxs[i] == i:
subplot_kw['sharex'] = None
else:
subplot_kw['sharex'] = axarr[sxs[i]]
if sys[i] == i:
subplot_kw['sharey'] = None
else:
subplot_kw['sharey'] = axarr[sys[i]]
axarr[i] = fig.add_subplot(nrows, ncols, i + 1, **subplot_kw)
# returned axis array will be always 2-d, even if nrows=ncols=1
axarr = axarr.reshape(nrows, ncols)
# turn off redundant tick labeling
if sharex in ["col", "all"] and nrows > 1:
#if sharex and nrows>1:
# turn off all but the bottom row
for ax in axarr[:-1, :].flat:
for label in ax.get_xticklabels():
label.set_visible(False)
if sharey in ["row", "all"] and ncols > 1:
#if sharey and ncols>1:
# turn off all but the first column
for ax in axarr[:, 1:].flat:
for label in ax.get_yticklabels():
label.set_visible(False)
if squeeze:
# Reshape the array to have the final desired dimension (nrow,ncol),
# though discarding unneeded dimensions that equal 1. If we only have
# one subplot, just return it instead of a 1-element array.
if nplots==1:
ret = fig, axarr[0,0]
else:
ret = fig, axarr.squeeze()
else:
# returned axis array will be always 2-d, even if nrows=ncols=1
ret = fig, axarr.reshape(nrows, ncols)
return ret
from gridspec import GridSpec
def subplot2grid(shape, loc, rowspan=1, colspan=1, **kwargs):
"""
It creates a subplot in a grid of *shape*, at location of *loc*,
spanning *rowspan*, *colspan* cells in each direction.
The index for loc is 0-based. ::
subplot2grid(shape, loc, rowspan=1, colspan=1)
is identical to ::
gridspec=GridSpec(shape[0], shape[2])
subplotspec=gridspec.new_subplotspec(loc, rowspan, colspan)
subplot(subplotspec)
"""
fig = gcf()
s1, s2 = shape
subplotspec = GridSpec(s1, s2).new_subplotspec(loc,
rowspan=rowspan,
colspan=colspan)
a = fig.add_subplot(subplotspec, **kwargs)
bbox = a.bbox
byebye = []
for other in fig.axes:
if other==a: continue
if bbox.fully_overlaps(other.bbox):
byebye.append(other)
for ax in byebye: delaxes(ax)
draw_if_interactive()
return a
def twinx(ax=None):
"""
Make a second axes overlay *ax* (or the current axes if *ax* is
*None*) sharing the xaxis. The ticks for *ax2* will be placed on
the right, and the *ax2* instance is returned.
.. seealso::
:file:`examples/api_examples/two_scales.py`
For an example
"""
if ax is None:
ax=gca()
ax1 = ax.twinx()
draw_if_interactive()
return ax1
def twiny(ax=None):
"""
Make a second axes overlay *ax* (or the current axes if *ax* is
*None*) sharing the yaxis. The ticks for *ax2* will be placed on
the top, and the *ax2* instance is returned.
"""
if ax is None:
ax=gca()
ax1 = ax.twiny()
draw_if_interactive()
return ax1
def subplots_adjust(*args, **kwargs):
"""
call signature::
subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=None, hspace=None)
Tune the subplot layout via the
:class:`matplotlib.figure.SubplotParams` mechanism. The parameter
meanings (and suggested defaults) are::
left = 0.125 # the left side of the subplots of the figure
right = 0.9 # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.9 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.2 # the amount of height reserved for white space between subplots
The actual defaults are controlled by the rc file
"""
fig = gcf()
fig.subplots_adjust(*args, **kwargs)
draw_if_interactive()
def subplot_tool(targetfig=None):
"""
Launch a subplot tool window for *targetfig* (default gcf).
A :class:`matplotlib.widgets.SubplotTool` instance is returned.
"""
tbar = rcParams['toolbar'] # turn off the navigation toolbar for the toolfig
rcParams['toolbar'] = 'None'
if targetfig is None:
manager = get_current_fig_manager()
targetfig = manager.canvas.figure
else:
# find the manager for this figure
for manager in _pylab_helpers.Gcf._activeQue:
if manager.canvas.figure==targetfig: break
else: raise RuntimeError('Could not find manager for targetfig')
toolfig = figure(figsize=(6,3))
toolfig.subplots_adjust(top=0.9)
ret = SubplotTool(targetfig, toolfig)
rcParams['toolbar'] = tbar
_pylab_helpers.Gcf.set_active(manager) # restore the current figure
return ret
def tight_layout(pad=1.2, h_pad=None, w_pad=None):
"""
Adjust subplot parameters to give specified padding.
Parameters:
pad : float
padding between the figure edge and the edges of subplots, as a fraction of the font-size.
h_pad, w_pad : float
padding (height/width) between edges of adjacent subplots.
Defaults to `pad_inches`.
"""
fig = gcf()
fig.tight_layout(pad=pad, h_pad=h_pad, w_pad=w_pad)
draw_if_interactive()
def box(on=None):
"""
Turn the axes box on or off according to *on*.
*on* may be a boolean or a string, 'on' or 'off'.
If *on* is *None*, toggle state.
"""
ax = gca()
on = _string_to_bool(on)
if on is None:
on = not ax.get_frame_on()
ax.set_frame_on(on)
draw_if_interactive()
def title(s, *args, **kwargs):
"""
Set the title of the current axis to *s*.
Default font override is::
override = {'fontsize': 'medium',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'}
.. seealso::
:func:`~matplotlib.pyplot.text`
for information on how override and the optional args work.
"""
l = gca().set_title(s, *args, **kwargs)
draw_if_interactive()
return l
## Axis ##
def axis(*v, **kwargs):
"""
Set/Get the axis properties:
>>> axis()
returns the current axes limits ``[xmin, xmax, ymin, ymax]``.
>>> axis(v)
sets the min and max of the x and y axes, with
``v = [xmin, xmax, ymin, ymax]``.
>>> axis('off')
turns off the axis lines and labels.
>>> axis('equal')
changes limits of *x* or *y* axis so that equal increments of *x*
and *y* have the same length; a circle is circular.
>>> axis('scaled')
achieves the same result by changing the dimensions of the plot box instead
of the axis data limits.
>>> axis('tight')
changes *x* and *y* axis limits such that all data is shown. If
all data is already shown, it will move it to the center of the
figure without modifying (*xmax* - *xmin*) or (*ymax* -
*ymin*). Note this is slightly different than in MATLAB.
>>> axis('image')
is 'scaled' with the axis limits equal to the data limits.
>>> axis('auto')
and
>>> axis('normal')
are deprecated. They restore default behavior; axis limits are automatically
scaled to make the data fit comfortably within the plot box.
if ``len(*v)==0``, you can pass in *xmin*, *xmax*, *ymin*, *ymax*
as kwargs selectively to alter just those limits without changing
the others.
The xmin, xmax, ymin, ymax tuple is returned
.. seealso::
:func:`xlim`, :func:`ylim`
For setting the x- and y-limits individually.
"""
ax = gca()
v = ax.axis(*v, **kwargs)
draw_if_interactive()
return v
def xlabel(s, *args, **kwargs):
"""
Set the *x* axis label of the current axis to *s*
Default override is::
override = {
'fontsize' : 'small',
'verticalalignment' : 'top',
'horizontalalignment' : 'center'
}
.. seealso::
:func:`~matplotlib.pyplot.text`
For information on how override and the optional args work
"""
l = gca().set_xlabel(s, *args, **kwargs)
draw_if_interactive()
return l
def ylabel(s, *args, **kwargs):
"""
Set the *y* axis label of the current axis to *s*.
Defaults override is::
override = {
'fontsize' : 'small',
'verticalalignment' : 'center',
'horizontalalignment' : 'right',
'rotation'='vertical' : }
.. seealso::
:func:`~matplotlib.pyplot.text`
For information on how override and the optional args
work.
"""
l = gca().set_ylabel(s, *args, **kwargs)
draw_if_interactive()
return l
def xlim(*args, **kwargs):
"""
Set/Get the xlimits of the current axes::
xmin, xmax = xlim() # return the current xlim
xlim( (xmin, xmax) ) # set the xlim to xmin, xmax
xlim( xmin, xmax ) # set the xlim to xmin, xmax
If you do not specify args, you can pass the xmin and xmax as
kwargs, eg.::
xlim(xmax=3) # adjust the max leaving min unchanged
xlim(xmin=1) # adjust the min leaving max unchanged
Setting limits turns autoscaling off for the x-axis.
The new axis limits are returned as a length 2 tuple.
"""
ax = gca()
if not args and not kwargs:
return ax.get_xlim()
ret = ax.set_xlim(*args, **kwargs)
draw_if_interactive()
return ret
def ylim(*args, **kwargs):
"""
Set/Get the ylimits of the current axes::
ymin, ymax = ylim() # return the current ylim
ylim( (ymin, ymax) ) # set the ylim to ymin, ymax
ylim( ymin, ymax ) # set the ylim to ymin, ymax
If you do not specify args, you can pass the *ymin* and *ymax* as
kwargs, eg.::
ylim(ymax=3) # adjust the max leaving min unchanged
ylim(ymin=1) # adjust the min leaving max unchanged
Setting limits turns autoscaling off for the y-axis.
The new axis limits are returned as a length 2 tuple.
"""
ax = gca()
if not args and not kwargs:
return ax.get_ylim()
ret = ax.set_ylim(*args, **kwargs)
draw_if_interactive()
return ret
@docstring.dedent_interpd
def xscale(*args, **kwargs):
"""
call signature::
xscale(scale, **kwargs)
Set the scaling for the x-axis: %(scale)s
Different keywords may be accepted, depending on the scale:
%(scale_docs)s
"""
ax = gca()
ax.set_xscale(*args, **kwargs)
draw_if_interactive()
@docstring.dedent_interpd
def yscale(*args, **kwargs):
"""
call signature::
yscale(scale, **kwargs)
Set the scaling for the y-axis: %(scale)s
Different keywords may be accepted, depending on the scale:
%(scale_docs)s
"""
ax = gca()
ax.set_yscale(*args, **kwargs)
draw_if_interactive()
def xticks(*args, **kwargs):
"""
Set/Get the xlimits of the current ticklocs and labels::
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = xticks()
# set the locations of the xticks
xticks( arange(6) )
# set the locations and labels of the xticks
xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are :class:`~matplotlib.text.Text`
properties. For example, to rotate long labels::
xticks( arange(12), calendar.month_name[1:13], rotation=17 )
"""
ax = gca()
if len(args)==0:
locs = ax.get_xticks()
labels = ax.get_xticklabels()
elif len(args)==1:
locs = ax.set_xticks(args[0])
labels = ax.get_xticklabels()
elif len(args)==2:
locs = ax.set_xticks(args[0])
labels = ax.set_xticklabels(args[1], **kwargs)
else: raise TypeError('Illegal number of arguments to xticks')
if len(kwargs):
for l in labels:
l.update(kwargs)
draw_if_interactive()
return locs, silent_list('Text xticklabel', labels)
def yticks(*args, **kwargs):
"""
Set/Get the ylimits of the current ticklocs and labels::
# return locs, labels where locs is an array of tick locations and
# labels is an array of tick labels.
locs, labels = yticks()
# set the locations of the yticks
yticks( arange(6) )
# set the locations and labels of the yticks
yticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )
The keyword args, if any, are :class:`~matplotlib.text.Text`
properties. For example, to rotate long labels::
yticks( arange(12), calendar.month_name[1:13], rotation=45 )
"""
ax = gca()
if len(args)==0:
locs = ax.get_yticks()
labels = ax.get_yticklabels()
elif len(args)==1:
locs = ax.set_yticks(args[0])
labels = ax.get_yticklabels()
elif len(args)==2:
locs = ax.set_yticks(args[0])
labels = ax.set_yticklabels(args[1], **kwargs)
else: raise TypeError('Illegal number of arguments to yticks')
if len(kwargs):
for l in labels:
l.update(kwargs)
draw_if_interactive()
return ( locs,
silent_list('Text yticklabel', labels)
)
def minorticks_on():
"""
Display minor ticks on the current plot.
Displaying minor ticks reduces performance; turn them off using
minorticks_off() if drawing speed is a problem.
"""
gca().minorticks_on()
draw_if_interactive()
def minorticks_off():
"""
Remove minor ticks from the current plot.
"""
gca().minorticks_off()
draw_if_interactive()
def rgrids(*args, **kwargs):
"""
Set/Get the radial locations of the gridlines and ticklabels on a
polar plot.
call signatures::
lines, labels = rgrids()
lines, labels = rgrids(radii, labels=None, angle=22.5, **kwargs)
When called with no arguments, :func:`rgrid` simply returns the
tuple (*lines*, *labels*), where *lines* is an array of radial
gridlines (:class:`~matplotlib.lines.Line2D` instances) and
*labels* is an array of tick labels
(:class:`~matplotlib.text.Text` instances). When called with
arguments, the labels will appear at the specified radial
distances and angles.
*labels*, if not *None*, is a len(*radii*) list of strings of the
labels to use at each angle.
If *labels* is None, the rformatter will be used
Examples::
# set the locations of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0) )
# set the locations and labels of the radial gridlines and labels
lines, labels = rgrids( (0.25, 0.5, 1.0), ('Tom', 'Dick', 'Harry' )
"""
ax = gca()
if not isinstance(ax, PolarAxes):
raise RuntimeError('rgrids only defined for polar axes')
if len(args)==0:
lines = ax.yaxis.get_gridlines()
labels = ax.yaxis.get_ticklabels()
else:
lines, labels = ax.set_rgrids(*args, **kwargs)
draw_if_interactive()
return ( silent_list('Line2D rgridline', lines),
silent_list('Text rgridlabel', labels) )
def thetagrids(*args, **kwargs):
"""
Set/Get the theta locations of the gridlines and ticklabels.
If no arguments are passed, return a tuple (*lines*, *labels*)
where *lines* is an array of radial gridlines
(:class:`~matplotlib.lines.Line2D` instances) and *labels* is an
array of tick labels (:class:`~matplotlib.text.Text` instances)::
lines, labels = thetagrids()
Otherwise the syntax is::
lines, labels = thetagrids(angles, labels=None, fmt='%d', frac = 1.1)
set the angles at which to place the theta grids (these gridlines
are equal along the theta dimension).
*angles* is in degrees.
*labels*, if not *None*, is a len(angles) list of strings of the
labels to use at each angle.
If *labels* is *None*, the labels will be ``fmt%angle``.
*frac* is the fraction of the polar axes radius at which to place
the label (1 is the edge). Eg. 1.05 is outside the axes and 0.95
is inside the axes.
Return value is a list of tuples (*lines*, *labels*):
- *lines* are :class:`~matplotlib.lines.Line2D` instances
- *labels* are :class:`~matplotlib.text.Text` instances.
Note that on input, the *labels* argument is a list of strings,
and on output it is a list of :class:`~matplotlib.text.Text`
instances.
Examples::
# set the locations of the radial gridlines and labels
lines, labels = thetagrids( range(45,360,90) )
# set the locations and labels of the radial gridlines and labels
lines, labels = thetagrids( range(45,360,90), ('NE', 'NW', 'SW','SE') )
"""
ax = gca()
if not isinstance(ax, PolarAxes):
raise RuntimeError('rgrids only defined for polar axes')
if len(args)==0:
lines = ax.xaxis.get_ticklines()
labels = ax.xaxis.get_ticklabels()
else:
lines, labels = ax.set_thetagrids(*args, **kwargs)
draw_if_interactive()
return (silent_list('Line2D thetagridline', lines),
silent_list('Text thetagridlabel', labels)
)
## Plotting Info ##
def plotting():
"""
Plotting commands
=============== =========================================================
Command Description
=============== =========================================================
axes Create a new axes
axis Set or return the current axis limits
bar make a bar chart
boxplot make a box and whiskers chart
cla clear current axes
clabel label a contour plot
clf clear a figure window
close close a figure window
colorbar add a colorbar to the current figure
cohere make a plot of coherence
contour make a contour plot
contourf make a filled contour plot
csd make a plot of cross spectral density
draw force a redraw of the current figure
errorbar make an errorbar graph
figlegend add a legend to the figure
figimage add an image to the figure, w/o resampling
figtext add text in figure coords
figure create or change active figure
fill make filled polygons
fill_between make filled polygons between two sets of y-values
fill_betweenx make filled polygons between two sets of x-values
gca return the current axes
gcf return the current figure
gci get the current image, or None
getp get a graphics property
hist make a histogram
hist2d make a 2d histogram
hold set the hold state on current axes
legend add a legend to the axes
loglog a log log plot
imread load image file into array
imsave save array as an image file
imshow plot image data
matshow display a matrix in a new figure preserving aspect
pcolor make a pseudocolor plot
plot make a line plot
plotfile plot data from a flat file
psd make a plot of power spectral density
quiver make a direction field (arrows) plot
rc control the default params
savefig save the current figure
scatter make a scatter plot
setp set a graphics property
semilogx log x axis
semilogy log y axis
show in non-interactive mode, display all figures and block
until they have been closed; in interactive mode,
show generally has no effect.
specgram a spectrogram plot
stackplot make a stacked plot
stem make a stem plot
subplot make a subplot (numrows, numcols, axesnum)
table add a table to the axes
text add some text at location x,y to the current axes
title add a title to the current axes
xlabel add an xlabel to the current axes
ylabel add a ylabel to the current axes
=============== =========================================================
The following commands will set the default colormap accordingly:
* autumn
* bone
* cool
* copper
* flag
* gray
* hot
* hsv
* jet
* pink
* prism
* spring
* summer
* winter
* spectral
"""
pass
def get_plot_commands(): return ( 'axes', 'axis', 'bar', 'boxplot', 'cla', 'clf',
'close', 'colorbar', 'cohere', 'csd', 'draw', 'errorbar',
'figlegend', 'figtext', 'figimage', 'figure', 'fill', 'gca',
'gcf', 'gci', 'get', 'gray', 'barh', 'jet', 'hist', 'hist2d', 'hold', 'imread', 'imsave',
'imshow', 'legend', 'loglog', 'quiver', 'rc', 'pcolor', 'pcolormesh', 'plot', 'psd',
'savefig', 'scatter', 'set', 'semilogx', 'semilogy', 'show',
'specgram', 'stem', 'subplot', 'table', 'text', 'title', 'xlabel',
'ylabel', 'pie', 'polar')
def colors():
"""
This is a do-nothing function to provide you with help on how
matplotlib handles colors.
Commands which take color arguments can use several formats to
specify the colors. For the basic builtin colors, you can use a
single letter
===== =======
Alias Color
===== =======
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white
===== =======
For a greater range of colors, you have two options. You can
specify the color using an html hex string, as in::
color = '#eeefff'
or you can pass an R,G,B tuple, where each of R,G,B are in the
range [0,1].
You can also use any legal html name for a color, for example::
color = 'red'
color = 'burlywood'
color = 'chartreuse'
The example below creates a subplot with a dark
slate gray background::
subplot(111, axisbg=(0.1843, 0.3098, 0.3098))
Here is an example that creates a pale turqoise title::
title('Is this the best color?', color='#afeeee')
"""
pass
def colormaps():
"""
Matplotlib provides a number of colormaps, and others can be added using
:func:`register_cmap`. This function documents the built-in colormaps,
and will also return a list of all registered colormaps if called.
You can set the colormap for an image, pcolor, scatter, etc,
using a keyword argument::
imshow(X, cmap=cm.hot)
or using the :func:`set_cmap` function::
imshow(X)
pyplot.set_cmap('hot')
pyplot.set_cmap('jet')
In interactive mode, :func:`set_cmap` will update the colormap post-hoc,
allowing you to see which one works best for your data.
All built-in colormaps can be reversed by appending ``_r``: For instance,
``gray_r`` is the reverse of ``gray``.
There are several common color schemes used in visualization:
Sequential schemes
for unipolar data that progresses from low to high
Diverging schemes
for bipolar data that emphasizes positive or negative deviations from a
central value
Cyclic schemes
meant for plotting values that wrap around at the
endpoints, such as phase angle, wind direction, or time of day
Qualitative schemes
for nominal data that has no inherent ordering, where color is used
only to distinguish categories
The base colormaps are (with the exception of `spectral`) derived from
those of the same name provided with Matlab:
========= =======================================================
Colormap Description
========= =======================================================
autumn sequential linearly-increasing shades of red-orange-yellow
bone sequential increasing black-white color map with
a tinge of blue, to emulate X-ray film
cool linearly-decreasing shades of cyan-magenta
copper sequential increasing shades of black-copper
flag repetitive red-white-blue-black pattern (not cyclic at
endpoints)
gray sequential linearly-increasing black-to-white
grayscale
hot sequential black-red-yellow-white, to emulate blackbody
radiation from an object at increasing temperatures
hsv cyclic red-yellow-green-cyan-blue-magenta-red, formed
by changing the hue component in the HSV color space
jet a spectral map with dark endpoints, blue-cyan-yellow-red;
based on a fluid-jet simulation by NCSA [#]_
pink sequential increasing pastel black-pink-white, meant
for sepia tone colorization of photographs
prism repetitive red-yellow-green-blue-purple-...-green pattern
(not cyclic at endpoints)
spring linearly-increasing shades of magenta-yellow
summer sequential linearly-increasing shades of green-yellow
winter linearly-increasing shades of blue-green
spectral black-purple-blue-green-yellow-red-white spectrum
========= =======================================================
For the above list only, you can also set the colormap using the
corresponding pylab shortcut interface function, similar to Matlab::
imshow(X)
hot()
jet()
The next set of palettes are from the `Yorick scientific visualisation
package `_, an evolution of
the GIST package, both by David H. Munro:
============ =======================================================
Colormap Description
============ =======================================================
gist_earth mapmaker's colors from dark blue deep ocean to green
lowlands to brown highlands to white mountains
gist_heat sequential increasing black-red-orange-white, to emulate
blackbody radiation from an iron bar as it grows hotter
gist_ncar pseudo-spectral black-blue-green-yellow-red-purple-white
colormap from National Center for Atmospheric
Research [#]_
gist_rainbow runs through the colors in spectral order from red to
violet at full saturation (like *hsv* but not cyclic)
gist_stern "Stern special" color table from Interactive Data
Language software
============ =======================================================
The following colormaps are based on the `ColorBrewer
`_ color specifications and designs developed by
Cynthia Brewer:
ColorBrewer Diverging (luminance is highest at the midpoint, and
decreases towards differently-colored endpoints):
======== ===================================
Colormap Description
======== ===================================
BrBG brown, white, blue-green
PiYG pink, white, yellow-green
PRGn purple, white, green
PuOr orange, white, purple
RdBu red, white, blue
RdGy red, white, gray
RdYlBu red, yellow, blue
RdYlGn red, yellow, green
Spectral red, orange, yellow, green, blue
======== ===================================
ColorBrewer Sequential (luminance decreases monotonically):
======== ====================================
Colormap Description
======== ====================================
Blues white to dark blue
BuGn white, light blue, dark green
BuPu white, light blue, dark purple
GnBu white, light green, dark blue
Greens white to dark green
Greys white to black (not linear)
Oranges white, orange, dark brown
OrRd white, orange, dark red
PuBu white, light purple, dark blue
PuBuGn white, light purple, dark green
PuRd white, light purple, dark red
Purples white to dark purple
RdPu white, pink, dark purple
Reds white to dark red
YlGn light yellow, dark green
YlGnBu light yellow, light green, dark blue
YlOrBr light yellow, orange, dark brown
YlOrRd light yellow, orange, dark red
======== ====================================
ColorBrewer Qualitative:
(For plotting nominal data, :class:`ListedColormap` should be used,
not :class:`LinearSegmentedColormap`. Different sets of colors are
recommended for different numbers of categories. These continuous
versions of the qualitative schemes may be removed or converted in the
future.)
* Accent
* Dark2
* Paired
* Pastel1
* Pastel2
* Set1
* Set2
* Set3
Other miscellaneous schemes:
========= =======================================================
Colormap Description
========= =======================================================
afmhot sequential black-orange-yellow-white blackbody
spectrum, commonly used in atomic force microscopy
brg blue-red-green
bwr diverging blue-white-red
coolwarm diverging blue-gray-red, meant to avoid issues with 3D
shading, color blindness, and ordering of colors [#]_
CMRmap "Default colormaps on color images often reproduce to
confusing grayscale images. The proposed colormap
maintains an aesthetically pleasing color image that
automatically reproduces to a monotonic grayscale with
discrete, quantifiable saturation levels." [#]_
cubehelix Unlike most other color schemes cubehelix was designed
by D.A. Green to be monotonically increasing in terms
of perceived brightness. Also, when printed on a black
and white postscript printer, the scheme results in a
greyscale with monotonically increasing brightness.
This color scheme is named cubehelix because the r,g,b
values produced can be visualised as a squashed helix
around the diagonal in the r,g,b color cube.
gnuplot gnuplot's traditional pm3d scheme
(black-blue-red-yellow)
gnuplot2 sequential color printable as gray
(black-blue-violet-yellow-white)
ocean green-blue-white
rainbow spectral purple-blue-green-yellow-orange-red colormap
with diverging luminance
seismic diverging blue-white-red
terrain mapmaker's colors, blue-green-yellow-brown-white,
originally from IGOR Pro
========= =======================================================
The following colormaps are redundant and may be removed in future
versions. It's recommended to use *gray* or *gray_r* instead, which
produce identical output:
========= =======================================================
Colormap Description
========= =======================================================
gist_gray identical to *gray*
gist_yarg identical to *gray_r*
binary identical to *gray_r*
========= =======================================================
.. rubric:: Footnotes
.. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor
choice for scientific visualization by many researchers: `Rainbow Color
Map (Still) Considered Harmful
`_
.. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command
Language. See `Color Table Gallery
`_
.. [#] See `Diverging Color Maps for Scientific Visualization
`_ by Kenneth
Moreland.
.. [#] See `A Color Map for Effective Black-and-White Rendering of
Color-Scale Images
`_
by Carey Rappaport
"""
return sorted(cm.cmap_d.keys())
## Plotting part 1: manually generated functions and wrappers ##
import matplotlib.colorbar
def colorbar(mappable=None, cax=None, ax=None, **kw):
if mappable is None:
mappable = gci()
if mappable is None:
raise RuntimeError('No mappable was found to use for colorbar '
'creation. First define a mappable such as '
'an image (with imshow) or a contour set ('
'with contourf).')
if ax is None:
ax = gca()
ret = gcf().colorbar(mappable, cax = cax, ax=ax, **kw)
draw_if_interactive()
return ret
colorbar.__doc__ = matplotlib.colorbar.colorbar_doc
def clim(vmin=None, vmax=None):
"""
Set the color limits of the current image
To apply clim to all axes images do::
clim(0, 0.5)
If either *vmin* or *vmax* is None, the image min/max respectively
will be used for color scaling.
If you want to set the clim of multiple images,
use, for example::
for im in gca().get_images():
im.set_clim(0, 0.05)
"""
im = gci()
if im is None:
raise RuntimeError('You must first define an image, eg with imshow')
im.set_clim(vmin, vmax)
draw_if_interactive()
def set_cmap(cmap):
'''
set the default colormap to *cmap* and apply to current image if any.
See help(colormaps) for more information.
*cmap* must be a :class:`colors.Colormap` instance, or
the name of a registered colormap.
See :func:`register_cmap` and :func:`get_cmap`.
'''
cmap = cm.get_cmap(cmap)
rc('image', cmap=cmap.name)
im = gci()
if im is not None:
im.set_cmap(cmap)
else:
raise RuntimeError('You must first define an image, eg with imshow')
draw_if_interactive()
@docstring.copy_dedent(_imread)
def imread(*args, **kwargs):
return _imread(*args, **kwargs)
@docstring.copy_dedent(_imsave)
def imsave(*args, **kwargs):
return _imsave(*args, **kwargs)
def matshow(A, fignum=None, **kw):
"""
Display an array as a matrix in a new figure window.
The origin is set at the upper left hand corner and rows (first
dimension of the array) are displayed horizontally. The aspect
ratio of the figure window is that of the array, unless this would
make an excessively short or narrow figure.
Tick labels for the xaxis are placed on top.
With the exception of *fignum*, keyword arguments are passed to
:func:`~matplotlib.pyplot.imshow`. You may set the *origin*
kwarg to "lower" if you want the first row in the array to be
at the bottom instead of the top.
*fignum*: [ None | integer | False ]
By default, :func:`matshow` creates a new figure window with
automatic numbering. If *fignum* is given as an integer, the
created figure will use this figure number. Because of how
:func:`matshow` tries to set the figure aspect ratio to be the
one of the array, if you provide the number of an already
existing figure, strange things may happen.
If *fignum* is *False* or 0, a new figure window will **NOT** be created.
"""
A = np.asanyarray(A)
if fignum is False or fignum is 0:
ax = gca()
else:
# Extract actual aspect ratio of array and make appropriately sized figure
fig = figure(fignum, figsize=figaspect(A))
ax = fig.add_axes([0.15, 0.09, 0.775, 0.775])
im = ax.matshow(A, **kw)
sci(im)
draw_if_interactive()
return im
def polar(*args, **kwargs):
"""
call signature::
polar(theta, r, **kwargs)
Make a polar plot. Multiple *theta*, *r* arguments are supported,
with format strings, as in :func:`~matplotlib.pyplot.plot`.
An optional kwarg *resolution* sets the number of vertices to
interpolate between each pair of points. The default is 1,
which disables interpolation.
"""
resolution = kwargs.pop('resolution', 1)
ax = gca(polar=True, resolution=resolution)
ret = ax.plot(*args, **kwargs)
draw_if_interactive()
return ret
def plotfile(fname, cols=(0,), plotfuncs=None,
comments='#', skiprows=0, checkrows=5, delimiter=',', names=None,
subplots=True, newfig=True,
**kwargs):
"""
Plot the data in *fname*
*cols* is a sequence of column identifiers to plot. An identifier
is either an int or a string. If it is an int, it indicates the
column number. If it is a string, it indicates the column header.
matplotlib will make column headers lower case, replace spaces with
underscores, and remove all illegal characters; so ``'Adj Close*'``
will have name ``'adj_close'``.
- If len(*cols*) == 1, only that column will be plotted on the *y* axis.
- If len(*cols*) > 1, the first element will be an identifier for
data for the *x* axis and the remaining elements will be the
column indexes for multiple subplots if *subplots* is *True*
(the default), or for lines in a single subplot if *subplots*
is *False*.
*plotfuncs*, if not *None*, is a dictionary mapping identifier to
an :class:`~matplotlib.axes.Axes` plotting function as a string.
Default is 'plot', other choices are 'semilogy', 'fill', 'bar',
etc. You must use the same type of identifier in the *cols*
vector as you use in the *plotfuncs* dictionary, eg., integer
column numbers in both or column names in both. If *subplots*
is *False*, then including any function such as 'semilogy'
that changes the axis scaling will set the scaling for all
columns.
*comments*, *skiprows*, *checkrows*, *delimiter*, and *names*
are all passed on to :func:`matplotlib.pylab.csv2rec` to
load the data into a record array.
If *newfig* is *True*, the plot always will be made in a new figure;
if *False*, it will be made in the current figure if one exists,
else in a new figure.
kwargs are passed on to plotting functions.
Example usage::
# plot the 2nd and 4th column against the 1st in two subplots
plotfile(fname, (0,1,3))
# plot using column names; specify an alternate plot type for volume
plotfile(fname, ('date', 'volume', 'adj_close'),
plotfuncs={'volume': 'semilogy'})
Note: plotfile is intended as a convenience for quickly plotting
data from flat files; it is not intended as an alternative
interface to general plotting with pyplot or matplotlib.
"""
if newfig:
fig = figure()
else:
fig = gcf()
if len(cols)