/usr/lib64/python2.6/site-packages/matplotlib
Edit: /usr/lib64/python2.6/site-packages/matplotlib/axes.pyo (248284B)
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c C s t d d S( Ns. Axes.frame was removed in favor of Axes.spines( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_frame~ s c C s | i d S( s clear the axesN( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt clear s c C s | i i | d S( s
Set the color cycle for any future plot commands on this Axes.
clist is a list of mpl color specifiers.
N( R R0 ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR0 s c C s | i S( s" return the HOLD status of the axes( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt ishold s c C s+ | d j o | i | _ n
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call signature::
hold(b=None)
Set the hold state. If *hold* is *None* (default), toggle the
*hold* state. Else set the *hold* state to boolean value *b*.
Examples:
* toggle hold:
>>> hold()
* turn hold on:
>>> hold(True)
* turn hold off
>>> hold(False)
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
N( R R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt hold s
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*aspect*
======== ================================================
value description
======== ================================================
'auto' automatic; fill position rectangle with data
'normal' same as 'auto'; deprecated
'equal' same scaling from data to plot units for x and y
num a circle will be stretched such that the height
is num times the width. aspect=1 is the same as
aspect='equal'.
======== ================================================
*adjustable*
========= ============================
value description
========= ============================
'box' change physical size of axes
'datalim' change xlim or ylim
========= ============================
*anchor*
===== =====================
value description
===== =====================
'C' centered
'SW' lower left corner
'S' middle of bottom edge
'SE' lower right corner
etc.
===== =====================
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ACCEPTS: [ 'box' | 'datalim' ]
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*anchor*
===== ============
value description
===== ============
'C' Center
'SW' bottom left
'S' bottom
'SE' bottom right
'E' right
'NE' top right
'N' top
'NW' top left
'W' left
===== ============
i s argument must be among %ss , N( R R t coefst keysR, RK R
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Returns the aspect ratio of the raw data.
This method is intended to be overridden by new projection
types.
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Returns the aspect ratio of the raw data in log scale.
Will be used when both axis scales are in log.
gKH9( RP RQ Rl RR RS t log10( R( RT RU RV RW RX RY ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_data_ratio_log s
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Use :meth:`_aspect` and :meth:`_adjustable` to modify the
axes box or the view limits.
R R t logR s: aspect is not supported for Axes with xscale=%s, yscale=%sR R NRE i R R s7 shared axes: "adjustable" is being changed to "datalim"gKH9g ?g{Gzt?g?i s9 adjustable='datalim' cannot work with shared x and y axesg ?g @g $@(& R R Rc RC t
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y_expandert dLt dL_widtht dL_heightt xrt yrt xmargt ymargt Ysizet Xsizet Xmargt Ymargt xmt ymt changext changeyt adjust_yt adjyt ycR; R< t xcR9 R: ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt apply_aspect. s
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#
""
c O s t | d j ot | d o| d i } | d j o | i n1| d j o | i n| d j o | i t | i d | i | d j o | i d d d q| d j o* | i d d d
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Convenience method for manipulating the x and y view limits
and the aspect ratio of the plot.
*kwargs* are passed on to :meth:`set_xlim` and
:meth:`set_ylim`
i i t ont offRE t tightt scaledRD R t imageRH R R RI R s- Unrecognized string %s to axis; try on or offR RT RU RV RW i s$ v must contain [xmin xmax ymin ymax]i i ( s equals tightR s normals autoR N( R, Rf t lowert set_axis_ont set_axis_offt set_autoscale_onRc R t autoscale_viewR R
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Return a list of artists the axes contains.
.. deprecated:: 0.98
s Use get_children insteadN( t DeprecationWarning( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_child_artists s c C s t i d t | i S( s Return the axes Rectangle frames use ax.patch instead( R` Ra R R/ ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR> s c C s | i S( sB Return the legend.Legend instance, or None if no legend is defined( R& ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_legend s c C s t i d | i S( s2 return a list of Axes images contained by the Axest AxesImage( R t silent_listR% ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_images
s c C s t i d | i S( s, Return a list of lines contained by the AxesRX ( R R R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_lines s c C s | i S( s Return the XAxis instance( R5 ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_xaxis s c C s t i d | i i S( s2 Get the x grid lines as a list of Line2D instancess Line2D xgridline( R R R5 t
get_gridlines( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_xgridlines s c C s t i d | i i S( s1 Get the xtick lines as a list of Line2D instancess Text xtickline( R R R5 t
get_ticklines( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_xticklines s c C s | i S( s Return the YAxis instance( R6 ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_yaxis s c C s t i d | i i S( s2 Get the y grid lines as a list of Line2D instancess Line2D ygridline( R R R6 R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_ygridlines$ s c C s t i d | i i S( s1 Get the ytick lines as a list of Line2D instancess Line2D ytickline( R R R6 R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_yticklines( s c C s: t | i t | i t | i t | i d j S( s Return *True* if any artists have been added to axes.
This should not be used to determine whether the *dataLim*
need to be updated, and may not actually be useful for
anything.
i ( R, R' R% R R! ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt has_data. s
&c sP | i i i | i | | i i f d | _ | S( sf
Add any :class:`~matplotlib.artist.Artist` to the axes.
Returns the artist.
c s i i | S( ( R$ t remove( Rv ( R( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt E s ( R R$ Rm R R5 R/ t _remove_method( R( R ( ( R( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
add_artist; s
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Add a :class:`~matplotlib.collections.Collection` instance
to the axes.
Returns the collection.
s collection%dc s i i | S( ( R' R ( Rv ( R( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR [ s N( t get_labelR R, R' Rm R t
get_clip_pathR R5 R/ t _pathst update_datalimt get_datalimR R ( R( t
collectiont autolimR ( ( R( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt add_collectionH s
!c s i | | i d j o | i i n i | | i p | i d t i n i i
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Add a :class:`~matplotlib.lines.Line2D` to the list of plot
lines
Returns the line.
s _line%dc s i i | S( ( R R ( Rv ( R( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR m s N( R R R R5 R/ t _update_line_limitsR R R, R Rm R ( R( RC ( ( R( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt add_line^ s
c C sX | i } | i i d j o5 | i i | | i d | i d | i t | _ n d S( Ni t updatext updatey( t get_patht verticesR
R t update_from_pathR t x_isdatat y_isdataR ( R( RC t p( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR p s
c sg i | | i d j o | i i n i | i i | f d | _ | S( s
Add a :class:`~matplotlib.patches.Patch` *p* to the list of
axes patches; the clipbox will be set to the Axes clipping
box. If the transform is not set, it will be set to
:attr:`transData`.
Returns the patch.
c s i i | S( ( R! R ( Rv ( R( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s N( R R R R5 R/ t _update_patch_limitsR! Rm R ( R( R ( ( R( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt add_patchx s
c C s t | t i o+ | i d j p | i d j o d S| i i } | i d j oz | i i | } | i
| i j o, | i
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| d | i d | i n d S( s$ update the data limits for patch *p*i NR R ( R R` R t get_widtht
get_heightR R R
t get_patch_transformt transformt get_data_transformR t invertedR R R ( R( R/ R t xysR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s & c sC i | i i | | i i f d | _ | S( s
Add a :class:`~matplotlib.tables.Table` instance to the
list of axes tables
Returns the table.
c s i i | S( ( R# R ( Rv ( R( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s ( R R# Rm R5 R/ R ( R( t tab( ( R( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt add_table s
c C s_ | i i t t | _ x | i D] } | i | q# Wx | i D] } | i | qD Wd S( s2 recompute the data limits based on current artistsN( R t ignoreRc R R R R! R ( R( RC R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s
c C sr t | o t | o d St i | p t i | } n | i i | | i d | d | t | _ d S( s@ Update the data lim bbox with seq of xy tups or equiv. 2-D arrayNR R (
t iterableR, R t
isMaskedArrayRN t asarrayR t update_from_data_xyR R ( R( R R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s
c C sF t | o t | o d S| i i | | | i t | _ d S( s, Update the data lim bbox with seq of xy tupsN( R R, R t update_from_dataR R ( R( RS R" ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt update_datalim_numerix s c C s) | i i t i i | i | g d S( so
Update the datalim to include the given
:class:`~matplotlib.transforms.Bbox` *bounds*
N( R R R R t union( R( R} ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt update_datalim_bounds s c C sd | i d j p | i d j o d S| d j o( | i i p | i i | qZ n | d j o( | i i p | i i | q n | d j o | i d | i i } | | i i j o5 | i i | | d j o | i i | q n | i d | i i } | | i i j o5 | i i | | d j o | i i | q\q`n d S( sA look for unit *kwargs* and update the axis instances as necessaryNR3 R4 ( R5 R R6 t
have_unitsRK R7 R8 R9 ( R( t xdatat ydataR< R3 R4 ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt _process_unit_info s&
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return *True* if the given *mouseevent* (in display coords)
is in the Axes
i ( R/ t contains( R( t
mouseevent( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt in_axes s c C s | i o | i S( sS
Get whether autoscaling is applied for both axes on plot commands
( R R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_autoscale_on s c C s | i S( sT
Get whether autoscaling for the x-axis is applied on plot commands
( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_autoscalex_on
s c C s | i S( sT
Get whether autoscaling for the y-axis is applied on plot commands
( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_autoscaley_on s c C s | | _ | | _ d S( sl
Set whether autoscaling is applied on plot commands
accepts: [ *True* | *False* ]
N( R R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s c C s
| | _ d S( s{
Set whether autoscaling for the x-axis is applied on plot commands
accepts: [ *True* | *False* ]
N( R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_autoscalex_on s c C s
| | _ d S( s{
Set whether autoscaling for the y-axis is applied on plot commands
accepts: [ *True* | *False* ]
N( R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_autoscaley_on' s c C s
| | _ d S( sI
Set zorder value below which artists will be rasterized
N( R ( R( t z( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_rasterization_zorder/ s c C s | i S( sI
Get zorder value below which artists will be rasterized
( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_rasterization_zorder5 s c C s | oe | i o[ | i i | } g } | D] } | | i q. ~ } t i i | } | i \ } }
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autoscale the view limits using the data limits. You can
selectively autoscale only a single axis, eg, the xaxis by
setting *scaley* to *False*. The autoscaling preserves any
axis direction reversal that has already been done.
i N( R R t get_siblingsR R R R t intervalxR R t intervalyR, R% R R! Rj Ri R5 t get_major_locatort view_limitsR6 ( R( R t scalext scaleyt xsharedR t axt dlt bbR9 R: t ysharedt _[2]R; R< t XLt YL( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR < s0 $$ c C s | d j o
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D] \ } } } | i! | qW| i1 n x$ | D] \ } } } | i! | qW| i2 d | | _ d S( s* Draw everything (plot lines, axes, labels)s No renderer definedNR% g ?g @i i (3 R R t RuntimeErrort get_visiblet
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draw_imageR R t stop_rasterizingt close_group( R( t renderert inframeR R R$ R t iR t dsut rasterization_zorderR Rt t dsu_rasterizedt _[3]t imt magt _[4]t imsR R t tRg Rh Ru Rv R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR ` s
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This method can only be used after an initial draw which
caches the renderer. It is used to efficiently update Axes
data (axis ticks, labels, etc are not updated)
N( R R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt draw_artist s c C s | i | i d t d S( s
This method can only be used after an initial draw which
caches the renderer. It is used to efficiently update Axes
data (axis ticks, labels, etc are not updated)
R
N( R R Rc ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt redraw_in_frame s c C s | i S( N( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_renderer_cache s c C s | i d j o t d n g } | i i D] } | | i | f q4 ~ } | i | i } | i x! | D] \ } } | i | qw Wd S( Ns You must first call ax.draw()( t
_lastRendererR R t animatedRN R R t blitR ( R( R R R R t tmp( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt __draw_animate s 3
c C s | i S( s?
Get whether the axes rectangle patch is drawn
( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_frame_on s c C s
| | _ d S( sf
Set whether the axes rectangle patch is drawn
ACCEPTS: [ *True* | *False* ]
N( R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_frame_on s c C s | i S( s7
Get whether axis below is true or not
( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_axisbelow s c C s
| | _ d S( s
Set whether the axis ticks and gridlines are above or below most artists
ACCEPTS: [ *True* | *False* ]
N( R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
set_axisbelow s c K sA t | o
t } n | i i | | | i i | | d S( s
call signature::
grid(self, b=None, **kwargs)
Set the axes grids on or off; *b* is a boolean
If *b* is *None* and ``len(kwargs)==0``, toggle the grid state. If
*kwargs* are supplied, it is assumed that you want a grid and *b*
is thus set to *True*
*kawrgs* are used to set the grid line properties, eg::
ax.grid(color='r', linestyle='-', linewidth=2)
Valid :class:`~matplotlib.lines.Line2D` kwargs are
%(Line2D)s
N( R, Rc R5 R R6 ( R( R R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR
s
c K s+ | i d d i } | i d d } | d j oH y | \ } } | | d Wq t t f j
o t d q Xn | i d d i } | d d j o
t } n^ | d j o0 t } | d
j o
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Convenience method for manipulating the ScalarFormatter
used by default for linear axes.
Optional keyword arguments:
============ =====================================
Keyword Description
============ =====================================
*style* [ 'sci' (or 'scientific') | 'plain' ]
plain turns off scientific notation
*scilimits* (m, n), pair of integers; if *style*
is 'sci', scientific notation will
be used for numbers outside the range
10`-m`:sup: to 10`n`:sup:.
Use (0,0) to include all numbers.
*axis* [ 'x' | 'y' | 'both' ]
============ =====================================
Only the major ticks are affected.
If the method is called when the
:class:`~matplotlib.ticker.ScalarFormatter` is not the
:class:`~matplotlib.ticker.Formatter` being used, an
:exc:`AttributeError` will be raised.
t styleR t scilimitsi s* scilimits must be a sequence of 2 integersR R i t scit plaint commas comma style remains to be addeds %s is not a valid style valueRS R" s0 This method only works with the ScalarFormatter.N( R$ R% ( R7 R R R
RA Rc R t NotImplementedErrorR5 R t formattert set_scientificR6 t set_powerlimitsR ( R( R< R! R" R! t nR t sbt cb( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt ticklabel_format# sD
"c C s
t | _ d S( s turn off the axisN( R R4 ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR c s c C s
t | _ d S( s turn on the axisN( Rc R4 ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR g s c C s | i S( s Return the axis background color( R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_axis_bgcolork s c C s | | _ | i i | d S( s
set the axes background color
ACCEPTS: any matplotlib color - see
:func:`~matplotlib.pyplot.colors`
N( R R/ R1 ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_axis_bgcoloro s c C s& | i \ } } | i | | d S( s Invert the x-axis.N( R R ( R( R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt invert_xaxis| s c C s | i \ } } | | j S( s' Returns True if the x-axis is inverted.( R ( R( R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt xaxis_inverted s c C s8 | i \ } } | | j o | | f S| | f Sd S( sa
Returns the x-axis numerical bounds where::
lowerBound < upperBound
N( R ( R( R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyRP s
c C s | d j o t | o | \ } } n | i \ } } | d j o
| } n | d j o
| } n | i o5 | | j o | i | | q | i | | n2 | | j o | i | | n | i | | d S( s
Set the lower and upper numerical bounds of the x-axis.
This method will honor axes inversion regardless of parameter order.
N( R R RP R1 R ( R( R t uppert old_lowert old_upper( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyRj s
c C s t | i i S( s7
Get the x-axis range [*xmin*, *xmax*]
( R{ R R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s c K s | d j o t | o | \ } } n | i d | | f | d j o | i | } n | d j o | i | } n | i \ } } | d j o
| } n | d j o
| } n t i | | d t \ } } | i i | | \ } } | | f | i
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d | x | i i | D]j } | | j oW | i | i
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call signature::
set_xlim(self, *args, **kwargs)
Set the limits for the xaxis
Returns the current xlimits as a length 2 tuple: [*xmin*, *xmax*]
Examples::
set_xlim((valmin, valmax))
set_xlim(valmin, valmax)
set_xlim(xmin=1) # xmax unchanged
set_xlim(xmax=1) # xmin unchanged
Keyword arguments:
*ymin*: scalar
the min of the ylim
*ymax*: scalar
the max of the ylim
*emit*: [ True | False ]
notify observers of lim change
ACCEPTS: len(2) sequence of floats
R t
increasingR R N( R R R RL R R t nonsingularR R5 t limit_range_for_scaleR R R t processR R R R t canvast draw_idle( R( RT RU R R< t old_xmint old_xmaxt other( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s2
c C s$ d d i t i | i i S( Ns! return the xaxis scale string: %ss , ( R R R R5 R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR^ s c K s+ | i i | | | i | i d S( s
call signature::
set_xscale(value)
Set the scaling of the x-axis: %(scale)s
ACCEPTS: [%(scale)s]
Different kwargs are accepted, depending on the scale:
%(scale_docs)s
N( R5 R R R ( R( t valueR< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s
s | R t
scale_docsc C s | i i d | S( s) Return the x ticks as a list of locationsR ( R5 t get_ticklocs( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_xticks s c C s | i i | d | S( s[
Set the x ticks with list of *ticks*
ACCEPTS: sequence of floats
R ( R5 t set_ticks( R( t ticksR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
set_xticks
s c C s t i d | i i S( s0 Get the xtick labels as a list of Text instancess Text xticklabel( R R R5 t get_majorticklabels( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_xmajorticklabels s c C s t i d | i i S( s0 Get the xtick labels as a list of Text instancess Text xticklabel( R R R5 t get_minorticklabels( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_xminorticklabels s c C s t i d | i i d | S( s0 Get the xtick labels as a list of Text instancess Text xticklabelR ( R R R5 t get_ticklabels( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_xticklabels s c K s | i i | | d | | S( so
call signature::
set_xticklabels(labels, fontdict=None, minor=False, **kwargs)
Set the xtick labels with list of strings *labels*. Return a
list of axis text instances.
*kwargs* set the :class:`~matplotlib.text.Text` properties.
Valid properties are
%(Text)s
ACCEPTS: sequence of strings
R ( R5 t set_ticklabels( R( t labelst fontdictR R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_xticklabels! s c C s& | i \ } } | i | | d S( s Invert the y-axis.N( R R ( R( R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt invert_yaxis5 s c C s | i \ } } | | j S( s' Returns True if the y-axis is inverted.( R ( R( R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt yaxis_inverted: s c C s8 | i \ } } | | j o | | f S| | f Sd S( sE Return y-axis numerical bounds in the form of lowerBound < upperBoundN( R ( R( R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyRQ ? s
c C s | d j o t | o | \ } } n | i \ } } | d j o
| } n | d j o
| } n | i o5 | | j o | i | | q | i | | n2 | | j o | i | | n | i | | d S( s Set the lower and upper numerical bounds of the y-axis.
This method will honor axes inversion regardless of parameter order.
N( R R RQ RP R ( R( R R2 R3 R4 ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyRi G s
c C s t | i i S( s7
Get the y-axis range [*ymin*, *ymax*]
( R{ R R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR ^ s c K s | d j o t | o | \ } } n | d j o | i | } n | d j o | i | } n | i \ } } | d j o
| } n | d j o
| } n t i | | d t \ } } | i i | | \ } } | | f | i _
| o | i i d | x | i
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d t | i | i j o' | i i d j o | i i i qq"q"Wn | | f S( sk
call signature::
set_ylim(self, *args, **kwargs):
Set the limits for the yaxis; v = [ymin, ymax]::
set_ylim((valmin, valmax))
set_ylim(valmin, valmax)
set_ylim(ymin=1) # ymax unchanged
set_ylim(ymax=1) # ymin unchanged
Keyword arguments:
*ymin*: scalar
the min of the ylim
*ymax*: scalar
the max of the ylim
*emit*: [ True | False ]
notify observers of lim change
Returns the current ylimits as a length 2 tuple
ACCEPTS: len(2) sequence of floats
R5 R R N( R R RM R R R6 R R6 R7 R R R R8 R R R R R9 R: ( R( RV RW R R< t old_ymint old_ymaxR= ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR d s0
c C s$ d d i t i | i i S( Ns! return the xaxis scale string: %ss , ( R R R R6 R ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR_ s c K s+ | i i | | | i | i d S( s
call signature::
set_yscale(value)
Set the scaling of the y-axis: %(scale)s
ACCEPTS: [%(scale)s]
Different kwargs are accepted, depending on the scale:
%(scale_docs)s
N( R6 R R R ( R( R> R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s
c C s | i i d | S( s) Return the y ticks as a list of locationsR ( R6 R@ ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_yticks s c C s | i i | d | S( s
Set the y ticks with list of *ticks*
ACCEPTS: sequence of floats
Keyword arguments:
*minor*: [ False | True ]
Sets the minor ticks if True
R ( R6 RB ( R( RC R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
set_yticks s c C s t i d | i i S( s0 Get the xtick labels as a list of Text instancess Text yticklabel( R R R6 RE ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_ymajorticklabels s c C s t i d | i i S( s0 Get the xtick labels as a list of Text instancess Text yticklabel( R R R6 RG ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_yminorticklabels s c C s t i d | i i d | S( s0 Get the xtick labels as a list of Text instancess Text yticklabelR ( R R R6 RI ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_yticklabels s c K s | i i | | d | | S( s
call signature::
set_yticklabels(labels, fontdict=None, minor=False, **kwargs)
Set the ytick labels with list of strings *labels*. Return a list of
:class:`~matplotlib.text.Text` instances.
*kwargs* set :class:`~matplotlib.text.Text` properties for the labels.
Valid properties are
%(Text)s
ACCEPTS: sequence of strings
R ( R6 RK ( R( RL RM R R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_yticklabels s c C so | i i \ } } | d j o t i i } } | t i d d } | i d | | f | i | | g \ } } | | f | i _ | | f | i _ n | i i
} t | t i
p# t i | } | i i | n | i i d d j o t | i i | i _ n | i | i i } t | t i p& t i | | } | i i | n d S( s Sets up x-axis ticks and labels that treat the x data as dates.
*tz* is the time zone to use in labeling dates. Defaults to rc value.
g t daysi
R i N( R R t datetimet datet todayt timedeltaR RL R R5 R R t mdatest DateLocatort AutoDateLocatort set_major_locatorR{ t refresht get_major_formattert
DateFormattert AutoDateFormattert set_major_formatter( R( t tzRT RU t dmaxR\ t dminR R' ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
xaxis_date s&
c C so | i i \ } } | d j o t i i } } | t i d d } | i d | | f | i | | g \ } } | | f | i _ | | f | i _ n | i i
} t | t i
p# t i | } | i i | n | i i d d j o t | i i | i _ n | i | i i } t | t i p& t i | | } | i i | n d S( s Sets up y-axis ticks and labels that treat the y data as dates.
*tz* is the time zone to use in labeling dates. Defaults to rc value.
g RY i
R i N( R R RZ R[ R\ R] R RM R R6 R R R^ R_ R` Ra R{ Rb R5 Rc Rd Re Rf ( R( Rg RV RW Rh R\ Ri R R' ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
yaxis_date s&
c C sJ y | i | SWn2 t j
o& | i i i } | | } | SXd S( s
Return *x* string formatted. This function will use the attribute
self.fmt_xdata if it is callable, else will fall back on the xaxis
major formatter
N( R RA R5 Rc t format_data_short( R( RS RG RE ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt format_xdata1 s c C sJ y | i | SWn2 t j
o& | i i i } | | } | SXd S( s
Return y string formatted. This function will use the
:attr:`fmt_ydata` attribute if it is callable, else will fall
back on the yaxis major formatter
N( R RA R6 Rc Rl ( R( R" RG RE ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt format_ydata= s c C sZ | d j o
d } n | i | } | d j o
d } n | i | } d | | f S( s4 return a format string formatting the *x*, *y* coords ???s x=%s y=%sN( R Rm Rn ( R( RS R" t xst ys( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt format_coordI s
c C s t S( sA
Return *True* if this axes support the zoom box
( Rc ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt can_zoomW s c C s | i S( sF
Get whether the axes responds to navigation commands
( t _navigate( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_navigate] s c C s
| | _ d S( sq
Set whether the axes responds to navigation toolbar commands
ACCEPTS: [ True | False ]
N( Rs ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR c s c C s | i S( sR
Get the navigation toolbar button status: 'PAN', 'ZOOM', or None
( t _navigate_mode( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_navigate_modek s c C s
| | _ d S( s
Set the navigation toolbar button status;
.. warning::
this is not a user-API function.
N( Ru ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR q s c
C sa t i d | i i d | i i d | i i i d | i i d | d | | _ d S( s0
Called when a pan operation has started.
*x*, *y* are the mouse coordinates in display coords.
button is the mouse button number:
* 1: LEFT
* 2: MIDDLE
* 3: RIGHT
.. note::
Intended to be overridden by new projection types.
t limt transt
trans_inverseR RS R" N( R t BunchR R R R R t
_pan_start( R( RS R" t button( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt start_pan{ s c C s
| ` d S( s
Called when a pan operation completes (when the mouse button
is up.)
.. note::
Intended to be overridden by new projection types.
N( R{ ( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt end_pan s c C s d } | i } | | i } | | i } | d j o | d j o d S| d j o? | | | | \ } } | i i | | i | i } n | d j oy | t | i i } | t | i i } | | | | \ } } | i
d j o d | | } | } n t i d | | f }
t i
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s^
Called when the mouse moves during a pan operation.
*button* is the mouse button number:
* 1: LEFT
* 2: MIDDLE
* 3: RIGHT
*key* is a "shift" key
*x*, *y* are the mouse coordinates in display coords.
.. note::
Intended to be overridden by new projection types.
c S s" | d j o- t | t | j o
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i Ni i R g ?g $@s Overflow while panning( R{ RS R" R t
translatedt transformedRy Ri Rg Rh RC RN t powert arrayRw Rx R R t
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c C s | i S( s
return the cursor propertiess as a (*linewidth*, *color*)
tuple, where *linewidth* is a float and *color* is an RGBA
tuple
( t _cursorProps( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_cursor_props s c G s{ t | d j o | d \ } } n0 t | d j o | \ } } n
t d t i i | } | | f | _ d S( s
Set the cursor property as::
ax.set_cursor_props(linewidth, color)
or::
ax.set_cursor_props((linewidth, color))
ACCEPTS: a (*float*, *color*) tuple
i i i s' args must be a (linewidth, color) tupleN( R, R
R R t to_rgbaR ( R( R; t lwR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s c C s t d d S( s
Register observers to be notified when certain events occur. Register
with callback functions with the following signatures. The function
has the following signature::
func(ax) # where ax is the instance making the callback.
The following events can be connected to:
'xlim_changed','ylim_changed'
The connection id is is returned - you can use this with
disconnect to disconnect from the axes event
s3 use the callbacks CallbackRegistry instance insteadN( R ( R( R RG ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s c C s t d d S( s disconnect from the Axes event.s3 use the callbacks CallbackRegistry instance insteadN( R ( R( t cid( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
disconnect
s c C s g } | i | i | i | i | i | i | i | i | i | i | i | i | i | i | i | i | i
d j o | i | i
n | i | i | i | i
| i | i | i | i i | S( s return a list of child artistsN( Rm R5 R6 R R R! R" R# R$ R% R& R R' R- R/ R R ( R( t children( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_children
s c C s1 t | i o | i | | S| i i | S( sS Test whether the mouse event occured in the axes.
Returns T/F, {}
( t callablet _containsR/ R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR )
s c C s | i i | S( s
Returns True if the point (tuple of x,y) is inside the axes
(the area defined by the its patch). A pixel coordinate is
required.
( R/ t contains_point( R( t point( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR 2
s c G s> t | d j o t d n t i i | | d d S( s
call signature::
pick(mouseevent)
each child artist will fire a pick event if mouseevent is over
the artist and the artist has picker set
i sC New pick API implemented -- see API_CHANGES in the src distributioni N( R, R R R t pick( R( R; ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR ;
s c
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| | f d } d f d } | i | i | i } t | o t t | } n t | oc t g } | D] } | | d f q ~ }
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Return the artist under point that is closest to the *x*, *y*.
If *trans* is *None*, *x*, and *y* are in window coords,
(0,0 = lower left). Otherwise, *trans* is a
:class:`~matplotlib.transforms.Transform` that specifies the
coordinate system of *x*, *y*.
The selection of artists from amongst which the pick function
finds an artist can be narrowed using the optional keyword
argument *among*. If provided, this should be either a sequence
of permitted artists or a function taking an artist as its
argument and returning a true value if and only if that artist
can be selected.
Note this algorithm calculates distance to the vertices of the
polygon, so if you want to pick a patch, click on the edge!
c S s9 | \ } } | \ } } t i | | d | | d S( s& return the distance between two pointsi ( RR t sqrt( t p1t p2R: R< t x2t y2( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt dist_pointsa
s c S s3 | \ } } t t i | | d | | d S( s@ *x* and *y* are arrays; return the distance to the closest pointi ( t minRN R ( R RS R" R: R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt dist_x_yg
s c
sC t | t oq | i } | i \ } } } } | | f | | | f | | | | f | | | f f } t | \ } } n t | t o7 | i } | i i | }
t |
\ } } nZ t | t i
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d t } | i i | | \ } } n t i | t i | S( Nt orig( R R, R R} Rg t PatchR R t transform_pathR
RX t get_xdataR t get_ydatat numerix_x_yRN R (
R R Rt R Ru Rv t vertst xtt ytt patht tvertsR R ( t xywinR ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt distl
s : "i s" among must be callable or iterablei N(
R t transform_pointR R! R" R t filtert testR t dictR
R, R ( R( RS R" Rx t amongR R R$ R R# t amongdR R R t ds( ( R R s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt __pickI
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-
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Get the title text string.
( R- t get_text( R( ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt get_title
s c K sw h t d d 6d d 6d d 6} | i i | | i i | | d j o | i i | n | i i | | i S( sD
call signature::
set_title(label, fontdict=None, **kwargs):
Set the title for the axes.
kwargs are Text properties:
%(Text)s
ACCEPTS: str
.. seealso::
:meth:`text`
for information on how override and the optional args work
s axes.titlesizet fontsizeR R
R R N( R R- t set_textt updateR ( R( R RM R< t default( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt set_title
s
c C s | i i } | i S( s-
Get the xlabel text string.
( R5 R R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_xlabel
s c K s3 | d j o | | i _ n | i i | | | S( s
call signature::
set_xlabel(xlabel, fontdict=None, labelpad=None, **kwargs)
Set the label for the xaxis.
*labelpad* is the spacing in points between the label and the x-axis
Valid kwargs are Text properties:
%(Text)s
ACCEPTS: str
.. seealso::
:meth:`text`
for information on how override and the optional args work
N( R R5 t labelpadt set_label_text( R( t xlabelRM R R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
set_xlabel
s
c C s | i i } | i S( s-
Get the ylabel text string.
( R6 R R ( R( R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
get_ylabel
s c K s3 | d j o | | i _ n | i i | | | S( s
call signature::
set_ylabel(ylabel, fontdict=None, labelpad=None, **kwargs)
Set the label for the yaxis
*labelpad* is the spacing in points between the label and the y-axis
Valid kwargs are Text properties:
%(Text)s
ACCEPTS: str
.. seealso::
:meth:`text`
for information on how override and the optional args work
N( R R6 R R ( R( t ylabelRM R R< ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt
set_ylabel
s
c s h d d 6d d 6 i d 6} | o" t i d | d | d | } n t i d | d | d | } i | | i | | d j o | i | n | i | i i | f d | _ d
| j o | i
i n | S( s
call signature::
text(x, y, s, fontdict=None, **kwargs)
Add text in string *s* to axis at location *x*, *y*, data
coordinates.
Keyword arguments:
*fontdict*:
A dictionary to override the default text properties.
If *fontdict* is *None*, the defaults are determined by your rc
parameters.
*withdash*: [ False | True ]
Creates a :class:`~matplotlib.text.TextWithDash` instance
instead of a :class:`~matplotlib.text.Text` instance.
Individual keyword arguments can be used to override any given
parameter::
text(x, y, s, fontsize=12)
The default transform specifies that text is in data coords,
alternatively, you can specify text in axis coords (0,0 is
lower-left and 1,1 is upper-right). The example below places
text in the center of the axes::
text(0.5, 0.5,'matplotlib',
horizontalalignment='center',
verticalalignment='center',
transform = ax.transAxes)
You can put a rectangular box around the text instance (eg. to
set a background color) by using the keyword *bbox*. *bbox* is
a dictionary of :class:`matplotlib.patches.Rectangle`
properties. For example::
text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))
Valid kwargs are :class:`matplotlib.text.Text` properties:
%(Text)s
R R
R R R RS R" R c s i i | S( ( R" R ( Rv ( R( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR 9 s t clip_onN( R R+ t TextWithDashR, R R R R" Rm R R. R ( R( RS R" R RM t withdashR< R R ( ( R( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR
s& /
c O sj t i | | } | i t i | i | | i d o | i | i n | i i
| | S( s
call signature::
annotate(s, xy, xytext=None, xycoords='data',
textcoords='data', arrowprops=None, **kwargs)
Keyword arguments:
%(Annotation)s
.. plot:: mpl_examples/pylab_examples/annotation_demo2.py
R ( R+ t
AnnotationR R R R t has_keyR5 R/ R" Rm ( R( R; R< R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt annotateA s
i i c K s | i \ } } | i d | d | | i | } | | j p
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call signature::
axhline(y=0, xmin=0, xmax=1, **kwargs)
Axis Horizontal Line
Draw a horizontal line at *y* from *xmin* to *xmax*. With the
default values of *xmin* = 0 and *xmax* = 1, this line will
always span the horizontal extent of the axes, regardless of
the xlim settings, even if you change them, eg. with the
:meth:`set_xlim` command. That is, the horizontal extent is
in axes coords: 0=left, 0.5=middle, 1.0=right but the *y*
location is in data coordinates.
Return value is the :class:`~matplotlib.lines.Line2D`
instance. kwargs are the same as kwargs to plot, and can be
used to control the line properties. Eg.,
* draw a thick red hline at *y* = 0 that spans the xrange
>>> axhline(linewidth=4, color='r')
* draw a default hline at *y* = 1 that spans the xrange
>>> axhline(y=1)
* draw a default hline at *y* = .5 that spans the the middle half of
the xrange
>>> axhline(y=.5, xmin=0.25, xmax=0.75)
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:meth:`axhspan`
for example plot and source code
R R< R R R (
RQ R RM R R R R R
RX R R R R ( R( R" RT RU R< RV RW t yyR Rx Rt ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt axhlineX s +'
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call signature::
axvline(x=0, ymin=0, ymax=1, **kwargs)
Axis Vertical Line
Draw a vertical line at *x* from *ymin* to *ymax*. With the
default values of *ymin* = 0 and *ymax* = 1, this line will
always span the vertical extent of the axes, regardless of the
ylim settings, even if you change them, eg. with the
:meth:`set_ylim` command. That is, the vertical extent is in
axes coords: 0=bottom, 0.5=middle, 1.0=top but the *x* location
is in data coordinates.
Return value is the :class:`~matplotlib.lines.Line2D`
instance. kwargs are the same as kwargs to plot, and can be
used to control the line properties. Eg.,
* draw a thick red vline at *x* = 0 that spans the yrange
>>> axvline(linewidth=4, color='r')
* draw a default vline at *x* = 1 that spans the yrange
>>> axvline(x=1)
* draw a default vline at *x* = .5 that spans the the middle half of
the yrange
>>> axvline(x=.5, ymin=0.25, ymax=0.75)
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:meth:`axhspan`
for example plot and source code
R R< R R R (
RP R RL R R R R R
RX R R R R ( R( RS RV RW R< RT RU t xxR Rx Rt ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt axvline s +'
c K s t i | i | i } | i | | g | | g d | | i | | g \ } } | i | | g \ } } | | f | | f | | f | | f f } t i | | } | i | t
| _ | i | | i
d t
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call signature::
axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs)
Axis Horizontal Span.
*y* coords are in data units and *x* coords are in axes (relative
0-1) units.
Draw a horizontal span (rectangle) from *ymin* to *ymax*.
With the default values of *xmin* = 0 and *xmax* = 1, this
always spans the xrange, regardless of the xlim settings, even
if you change them, eg. with the :meth:`set_xlim` command.
That is, the horizontal extent is in axes coords: 0=left,
0.5=middle, 1.0=right but the *y* location is in data
coordinates.
Return value is a :class:`matplotlib.patches.Polygon`
instance.
Examples:
* draw a gray rectangle from *y* = 0.25-0.75 that spans the
horizontal extent of the axes
>>> axhspan(0.25, 0.75, facecolor='0.5', alpha=0.5)
Valid kwargs are :class:`~matplotlib.patches.Polygon` properties:
%(Polygon)s
**Example:**
.. plot:: mpl_examples/pylab_examples/axhspan_demo.py
R< R ( R R R R R RL RM R` Ra R R R R R ( R( RV RW RT RU R< Rx R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt axhspan s &"*
c K s t i | i | i } | i | | g | | g d | | i | | g \ } } | i | | g \ } } | | f | | f | | f | | f g } t i | | } | i | t
| _ | i | | i
d t
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call signature::
axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs)
Axis Vertical Span.
*x* coords are in data units and *y* coords are in axes (relative
0-1) units.
Draw a vertical span (rectangle) from *xmin* to *xmax*. With
the default values of *ymin* = 0 and *ymax* = 1, this always
spans the yrange, regardless of the ylim settings, even if you
change them, eg. with the :meth:`set_ylim` command. That is,
the vertical extent is in axes coords: 0=bottom, 0.5=middle,
1.0=top but the *y* location is in data coordinates.
Return value is the :class:`matplotlib.patches.Polygon`
instance.
Examples:
* draw a vertical green translucent rectangle from x=1.25 to 1.55 that
spans the yrange of the axes
>>> axvspan(1.25, 1.55, facecolor='g', alpha=0.5)
Valid kwargs are :class:`~matplotlib.patches.Polygon`
properties:
%(Polygon)s
.. seealso::
:meth:`axhspan`
for example plot and source code
R< R ( R R R R R RL RM R` Ra R R R R R ( R( RT RU RV RW R< Rx R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt axvspan s &"*
R# t solidc
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j o t d n | i | | g | d | | i | } | i | } | i | } t | p
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call signature::
hlines(y, xmin, xmax, colors='k', linestyles='solid', **kwargs)
Plot horizontal lines at each *y* from *xmin* to *xmax*.
Returns the :class:`~matplotlib.collections.LineCollection`
that was added.
Required arguments:
*y*:
a 1-D numpy array or iterable.
*xmin* and *xmax*:
can be scalars or ``len(x)`` numpy arrays. If they are
scalars, then the respective values are constant, else the
widths of the lines are determined by *xmin* and *xmax*.
Optional keyword arguments:
*colors*:
a line collections color argument, either a single color
or a ``len(y)`` list of colors
*linestyles*:
[ 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
**Example:**
.. plot:: mpl_examples/pylab_examples/hline_demo.py
R s^ hlines now uses a collections.LineCollection and not a list of Line2D to draw; see API_CHANGESR< i s& xmin and y are unequal sized sequencess& xmax and y are unequal sized sequencesR t
linestylesR N( Rj R R R RM RL R RN R R, t resizeRP R
Rg t mcollt LineCollectionR R R Rl R R ( R( R" RT RU R R R R< R t thisxmint thisxmaxt thisyR t collt minxt maxxt minyt maxyt corners( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt hlinesE sJ #
A
c
K s | i d d j o t d n | i d | d | | g d | | i | } | i | } | i | } t | p
| g } n t | p
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| g } n t i | } t i | } t i | } t | d j o t i
| | i } n t | d j o t i
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\ } } | |
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d | d
| d | } | i | | i | t | } t | } t t | t | } t t | t | } | | f | | f f } | i | | i | S(
s
call signature::
vlines(x, ymin, ymax, color='k', linestyles='solid')
Plot vertical lines at each *x* from *ymin* to *ymax*. *ymin*
or *ymax* can be scalars or len(*x*) numpy arrays. If they are
scalars, then the respective values are constant, else the
heights of the lines are determined by *ymin* and *ymax*.
*colors*
a line collections color args, either a single color
or a len(*x*) list of colors
*linestyles*
one of [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ]
Returns the :class:`matplotlib.collections.LineCollection`
that was added.
kwargs are :class:`~matplotlib.collections.LineCollection` properties:
%(LineCollection)s
R s^ vlines now uses a collections.LineCollection and not a list of Line2D to draw; see API_CHANGESR R R< i s& ymin and x are unequal sized sequencess& ymax and x are unequal sized sequencesR R R N( Rj R R R RL RM R RN R R, R RP R
R t TRg R R R R R Rl R R ( R( RS RV RW R R R R< t YR t thisxt thisymint thisymaxR R R R R R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt vlines sL "
A
c O s | i d t } | i d t } | i p | i n g } x4 | i | | D] } | i | | i | qU W| i d | d | | S( s
Plot lines and/or markers to the
:class:`~matplotlib.axes.Axes`. *args* is a variable length
argument, allowing for multiple *x*, *y* pairs with an
optional format string. For example, each of the following is
legal::
plot(x, y) # plot x and y using default line style and color
plot(x, y, 'bo') # plot x and y using blue circle markers
plot(y) # plot y using x as index array 0..N-1
plot(y, 'r+') # ditto, but with red plusses
If *x* and/or *y* is 2-dimensional, then the corresponding columns
will be plotted.
An arbitrary number of *x*, *y*, *fmt* groups can be
specified, as in::
a.plot(x1, y1, 'g^', x2, y2, 'g-')
Return value is a list of lines that were added.
The following format string characters are accepted to control
the line style or marker:
================ ===============================
character description
================ ===============================
``'-'`` solid line style
``'--'`` dashed line style
``'-.'`` dash-dot line style
``':'`` dotted line style
``'.'`` point marker
``','`` pixel marker
``'o'`` circle marker
``'v'`` triangle_down marker
``'^'`` triangle_up marker
``'<'`` triangle_left marker
``'>'`` triangle_right marker
``'1'`` tri_down marker
``'2'`` tri_up marker
``'3'`` tri_left marker
``'4'`` tri_right marker
``'s'`` square marker
``'p'`` pentagon marker
``'*'`` star marker
``'h'`` hexagon1 marker
``'H'`` hexagon2 marker
``'+'`` plus marker
``'x'`` x marker
``'D'`` diamond marker
``'d'`` thin_diamond marker
``'|'`` vline marker
``'_'`` hline marker
================ ===============================
The following color abbreviations are supported:
========== ========
character color
========== ========
'b' blue
'g' green
'r' red
'c' cyan
'm' magenta
'y' yellow
'k' black
'w' white
========== ========
In addition, you can specify colors in many weird and
wonderful ways, including full names (``'green'``), hex
strings (``'#008000'``), RGB or RGBA tuples (``(0,1,0,1)``) or
grayscale intensities as a string (``'0.8'``). Of these, the
string specifications can be used in place of a ``fmt`` group,
but the tuple forms can be used only as ``kwargs``.
Line styles and colors are combined in a single format string, as in
``'bo'`` for blue circles.
The *kwargs* can be used to set line properties (any property that has
a ``set_*`` method). You can use this to set a line label (for auto
legends), linewidth, anitialising, marker face color, etc. Here is an
example::
plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
plot([1,2,3], [1,4,9], 'rs', label='line 2')
axis([0, 4, 0, 10])
legend()
If you make multiple lines with one plot command, the kwargs
apply to all those lines, e.g.::
plot(x1, y1, x2, y2, antialised=False)
Neither line will be antialiased.
You do not need to use format strings, which are just
abbreviations. All of the line properties can be controlled
by keyword arguments. For example, you can set the color,
marker, linestyle, and markercolor with::
plot(x, y, color='green', linestyle='dashed', marker='o',
markerfacecolor='blue', markersize=12). See
:class:`~matplotlib.lines.Line2D` for details.
The kwargs are :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
kwargs *scalex* and *scaley*, if defined, are passed on to
:meth:`~matplotlib.axes.Axes.autoscale_view` to determine
whether the *x* and *y* axes are autoscaled; the default is
*True*.
R R ( R7 Rc R R R R Rm R ( R( R; R< R R R RC ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR$ s v
t boc K sn | i p | i n | i | | | | } | o | i | n | o | i | n | i | S( sH
call signature::
plot_date(x, y, fmt='bo', tz=None, xdate=True, ydate=False, **kwargs)
Similar to the :func:`~matplotlib.pyplot.plot` command, except
the *x* or *y* (or both) data is considered to be dates, and the
axis is labeled accordingly.
*x* and/or *y* can be a sequence of dates represented as float
days since 0001-01-01 UTC.
Keyword arguments:
*fmt*: string
The plot format string.
*tz*: [ None | timezone string ]
The time zone to use in labeling dates. If *None*, defaults to rc
value.
*xdate*: [ True | False ]
If *True*, the *x*-axis will be labeled with dates.
*ydate*: [ False | True ]
If *True*, the *y*-axis will be labeled with dates.
Note if you are using custom date tickers and formatters, it
may be necessary to set the formatters/locators after the call
to :meth:`plot_date` since :meth:`plot_date` will set the
default tick locator to
:class:`matplotlib.dates.AutoDateLocator` (if the tick
locator is not already set to a
:class:`matplotlib.dates.DateLocator` instance) and the
default tick formatter to
:class:`matplotlib.dates.AutoDateFormatter` (if the tick
formatter is not already set to a
:class:`matplotlib.dates.DateFormatter` instance).
Valid kwargs are :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:mod:`~matplotlib.dates`
for helper functions
:func:`~matplotlib.dates.date2num`,
:func:`~matplotlib.dates.num2date` and
:func:`~matplotlib.dates.drange`
for help on creating the required floating point
dates.
( R R R$ Rj Rk R ( R( RS R" R Rg t xdatet ydateR< R= ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt plot_dater
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call signature::
loglog(*args, **kwargs)
Make a plot with log scaling on the *x* and *y* axis.
:func:`~matplotlib.pyplot.loglog` supports all the keyword
arguments of :func:`~matplotlib.pyplot.plot` and
:meth:`matplotlib.axes.Axes.set_xscale` /
:meth:`matplotlib.axes.Axes.set_yscale`.
Notable keyword arguments:
*basex*/*basey*: scalar > 1
base of the *x*/*y* logarithm
*subsx*/*subsy*: [ None | sequence ]
the location of the minor *x*/*y* ticks; *None* defaults
to autosubs, which depend on the number of decades in the
plot; see :meth:`matplotlib.axes.Axes.set_xscale` /
:meth:`matplotlib.axes.Axes.set_yscale` for details
*nonposx*/*nonposy*: ['mask' | 'clip' ]
non-positive values in *x* or *y* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/log_demo.py
t basexi
t subsxt nonposxt maskt baseyt subsyt nonposyR] N( R R R7 R R R Rc R$ ( R( R; R< R R R Rt ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt loglog
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call signature::
semilogx(*args, **kwargs)
Make a plot with log scaling on the *x* axis.
:func:`semilogx` supports all the keyword arguments of
:func:`~matplotlib.pyplot.plot` and
:meth:`matplotlib.axes.Axes.set_xscale`.
Notable keyword arguments:
*basex*: scalar > 1
base of the *x* logarithm
*subsx*: [ None | sequence ]
The location of the minor xticks; *None* defaults to
autosubs, which depend on the number of decades in the
plot; see :meth:`~matplotlib.axes.Axes.set_xscale` for
details.
*nonposx*: ['mask' | 'clip' ]
non-positive values in *x* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:meth:`loglog`
For example code and figure
R i
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call signature::
semilogy(*args, **kwargs)
Make a plot with log scaling on the *y* axis.
:func:`semilogy` supports all the keyword arguments of
:func:`~matplotlib.pylab.plot` and
:meth:`matplotlib.axes.Axes.set_yscale`.
Notable keyword arguments:
*basey*: scalar > 1
Base of the *y* logarithm
*subsy*: [ None | sequence ]
The location of the minor yticks; *None* defaults to
autosubs, which depend on the number of decades in the
plot; see :meth:`~matplotlib.axes.Axes.set_yscale` for
details.
*nonposy*: ['mask' | 'clip' ]
non-positive values in *y* can be masked as
invalid, or clipped to a very small positive number
The remaining valid kwargs are
:class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
.. seealso::
:meth:`loglog`
For example code and figure
R i
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call signature::
acorr(x, normed=True, detrend=mlab.detrend_none, usevlines=True,
maxlags=10, **kwargs)
Plot the autocorrelation of *x*. If *normed* = *True*,
normalize the data by the autocorrelation at 0-th lag. *x* is
detrended by the *detrend* callable (default no normalization).
Data are plotted as ``plot(lags, c, **kwargs)``
Return value is a tuple (*lags*, *c*, *line*) where:
- *lags* are a length 2*maxlags+1 lag vector
- *c* is the 2*maxlags+1 auto correlation vector
- *line* is a :class:`~matplotlib.lines.Line2D` instance
returned by :meth:`plot`
The default *linestyle* is None and the default *marker* is
``'o'``, though these can be overridden with keyword args.
The cross correlation is performed with
:func:`numpy.correlate` with *mode* = 2.
If *usevlines* is *True*, :meth:`~matplotlib.axes.Axes.vlines`
rather than :meth:`~matplotlib.axes.Axes.plot` is used to draw
vertical lines from the origin to the acorr. Otherwise, the
plot style is determined by the kwargs, which are
:class:`~matplotlib.lines.Line2D` properties.
*maxlags* is a positive integer detailing the number of lags
to show. The default value of *None* will return all
:math:`2 imes \mathrm{len}(x) - 1` lags.
The return value is a tuple (*lags*, *c*, *linecol*, *b*)
where
- *linecol* is the
:class:`~matplotlib.collections.LineCollection`
- *b* is the *x*-axis.
.. seealso::
:meth:`~matplotlib.axes.Axes.plot` or
:meth:`~matplotlib.axes.Axes.vlines`
For documentation on valid kwargs.
**Example:**
:func:`~matplotlib.pyplot.xcorr` above, and
:func:`~matplotlib.pyplot.acorr` below.
**Example:**
.. plot:: mpl_examples/pylab_examples/xcorr_demo.py
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call signatures::
scatter(x, y, s=20, c='b', marker='o', cmap=None, norm=None,
vmin=None, vmax=None, alpha=1.0, linewidths=None,
verts=None, **kwargs)
Make a scatter plot of *x* versus *y*, where *x*, *y* are
converted to 1-D sequences which must be of the same length, *N*.
Keyword arguments:
*s*:
size in points^2. It is a scalar or an array of the same
length as *x* and *y*.
*c*:
a color. *c* can be a single color format string, or a
sequence of color specifications of length *N*, or a
sequence of *N* numbers to be mapped to colors using the
*cmap* and *norm* specified via kwargs (see below). Note
that *c* should not be a single numeric RGB or RGBA
sequence because that is indistinguishable from an array
of values to be colormapped. *c* can be a 2-D array in
which the rows are RGB or RGBA, however.
*marker*:
can be one of:
===== ==============
Value Description
===== ==============
's' square
'o' circle
'^' triangle up
'>' triangle right
'v' triangle down
'<' triangle left
'd' diamond
'p' pentagram
'h' hexagon
'8' octagon
'+' plus
'x' cross
===== ==============
The marker can also be a tuple (*numsides*, *style*,
*angle*), which will create a custom, regular symbol.
*numsides*:
the number of sides
*style*:
the style of the regular symbol:
===== =============================================
Value Description
===== =============================================
0 a regular polygon
1 a star-like symbol
2 an asterisk
3 a circle (*numsides* and *angle* is ignored)
===== =============================================
*angle*:
the angle of rotation of the symbol
Finally, *marker* can be (*verts*, 0): *verts* is a
sequence of (*x*, *y*) vertices for a custom scatter
symbol. Alternatively, use the kwarg combination
*marker* = *None*, *verts* = *verts*.
Any or all of *x*, *y*, *s*, and *c* may be masked arrays, in
which case all masks will be combined and only unmasked points
will be plotted.
Other keyword arguments: the color mapping and normalization
arguments will be used only if *c* is an array of floats.
*cmap*: [ None | Colormap ]
A :class:`matplotlib.colors.Colormap` instance or registered
name. If *None*, defaults to rc ``image.cmap``. *cmap* is
only used if *c* is an array of floats.
*norm*: [ None | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0, 1. If *None*, use the default
:func:`normalize`. *norm* is only used if *c* is an array
of floats.
*vmin*/*vmax*:
*vmin* and *vmax* are used in conjunction with norm to
normalize luminance data. If either are None, the min and
max of the color array *C* is used. Note if you pass a
*norm* instance, your settings for *vmin* and *vmax* will
be ignored.
*alpha*: 0 <= scalar <= 1
The alpha value for the patches
*linewidths*: [ None | scalar | sequence ]
If *None*, defaults to (lines.linewidth,). Note that this
is a tuple, and if you set the linewidths argument you
must set it as a sequence of floats, as required by
:class:`~matplotlib.collections.RegularPolyCollection`.
Optional kwargs control the
:class:`~matplotlib.collections.Collection` properties; in
particular:
*edgecolors*:
'none' to plot faces with no outlines
*facecolors*:
'none' to plot unfilled outlines
Here are the standard descriptions of all the
:class:`~matplotlib.collections.Collection` kwargs:
%(Collection)s
A :class:`~matplotlib.collections.Collection` instance is
returned.
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call signature::
hexbin(x, y, C = None, gridsize = 100, bins = None,
xscale = 'linear', yscale = 'linear',
cmap=None, norm=None, vmin=None, vmax=None,
alpha=1.0, linewidths=None, edgecolors='none'
reduce_C_function = np.mean, mincnt=None, marginals=True
**kwargs)
Make a hexagonal binning plot of *x* versus *y*, where *x*,
*y* are 1-D sequences of the same length, *N*. If *C* is None
(the default), this is a histogram of the number of occurences
of the observations at (x[i],y[i]).
If *C* is specified, it specifies values at the coordinate
(x[i],y[i]). These values are accumulated for each hexagonal
bin and then reduced according to *reduce_C_function*, which
defaults to numpy's mean function (np.mean). (If *C* is
specified, it must also be a 1-D sequence of the same length
as *x* and *y*.)
*x*, *y* and/or *C* may be masked arrays, in which case only
unmasked points will be plotted.
Optional keyword arguments:
*gridsize*: [ 100 | integer ]
The number of hexagons in the *x*-direction, default is
100. The corresponding number of hexagons in the
*y*-direction is chosen such that the hexagons are
approximately regular. Alternatively, gridsize can be a
tuple with two elements specifying the number of hexagons
in the *x*-direction and the *y*-direction.
*bins*: [ None | 'log' | integer | sequence ]
If *None*, no binning is applied; the color of each hexagon
directly corresponds to its count value.
If 'log', use a logarithmic scale for the color
map. Internally, :math:`log_{10}(i+1)` is used to
determine the hexagon color.
If an integer, divide the counts in the specified number
of bins, and color the hexagons accordingly.
If a sequence of values, the values of the lower bound of
the bins to be used.
*xscale*: [ 'linear' | 'log' ]
Use a linear or log10 scale on the horizontal axis.
*scale*: [ 'linear' | 'log' ]
Use a linear or log10 scale on the vertical axis.
*mincnt*: None | a positive integer
If not None, only display cells with more than *mincnt*
number of points in the cell
*marginals*: True|False
if marginals is True, plot the marginal density as
colormapped rectagles along the bottom of the x-axis and
left of the y-axis
*extent*: [ None | scalars (left, right, bottom, top) ]
The limits of the bins. The default assigns the limits
based on gridsize, x, y, xscale and yscale.
Other keyword arguments controlling color mapping and normalization
arguments:
*cmap*: [ None | Colormap ]
a :class:`matplotlib.cm.Colormap` instance. If *None*,
defaults to rc ``image.cmap``.
*norm*: [ None | Normalize ]
:class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0,1.
*vmin*/*vmax*: scalar
*vmin* and *vmax* are used in conjunction with *norm* to normalize
luminance data. If either are *None*, the min and max of the color
array *C* is used. Note if you pass a norm instance, your settings
for *vmin* and *vmax* will be ignored.
*alpha*: scalar
the alpha value for the patches
*linewidths*: [ None | scalar ]
If *None*, defaults to rc lines.linewidth. Note that this
is a tuple, and if you set the linewidths argument you
must set it as a sequence of floats, as required by
:class:`~matplotlib.collections.RegularPolyCollection`.
Other keyword arguments controlling the Collection properties:
*edgecolors*: [ None | mpl color | color sequence ]
If 'none', draws the edges in the same color as the fill color.
This is the default, as it avoids unsightly unpainted pixels
between the hexagons.
If *None*, draws the outlines in the default color.
If a matplotlib color arg or sequence of rgba tuples, draws the
outlines in the specified color.
Here are the standard descriptions of all the
:class:`~matplotlib.collections.Collection` kwargs:
%(Collection)s
The return value is a
:class:`~matplotlib.collections.PolyCollection` instance; use
:meth:`~matplotlib.collection.PolyCollection.get_array` on
this :class:`~matplotlib.collections.PolyCollection` to get
the counts in each hexagon.. If marginals is True, horizontal
bar and vertical bar (both PolyCollections) will be attached
to the return collection as attributes *hbar* and *vbar*
**Example:**
.. plot:: mpl_examples/pylab_examples/hexbin_demo.py
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*norm* is only used for an MxN float array.
*vmin*/*vmax*: [ None | scalar ]
Used to scale a luminance image to 0-1. If either is
*None*, the min and max of the luminance values will be
used. Note if *norm* is not *None*, the settings for
*vmin* and *vmax* will be ignored.
*alpha*: scalar
The alpha blending value, between 0 (transparent) and 1 (opaque)
*origin*: [ None | 'upper' | 'lower' ]
Place the [0,0] index of the array in the upper left or lower left
corner of the axes. If *None*, default to rc ``image.origin``.
*extent*: [ None | scalars (left, right, bottom, top) ]
Data limits for the axes. The default assigns zero-based row,
column indices to the *x*, *y* centers of the pixels.
*shape*: [ None | scalars (columns, rows) ]
For raw buffer images
*filternorm*:
A parameter for the antigrain image resize filter. From the
antigrain documentation, if *filternorm* = 1, the filter normalizes
integer values and corrects the rounding errors. It doesn't do
anything with the source floating point values, it corrects only
integers according to the rule of 1.0 which means that any sum of
pixel weights must be equal to 1.0. So, the filter function must
produce a graph of the proper shape.
*filterrad*:
The filter radius for filters that have a radius
parameter, i.e. when interpolation is one of: 'sinc',
'lanczos' or 'blackman'
Additional kwargs are :class:`~matplotlib.artist.Artist` properties:
%(Artist)s
**Example:**
.. plot:: mpl_examples/pylab_examples/image_demo.py
s image.aspectt
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call signatures::
pcolor(C, **kwargs)
pcolor(X, Y, C, **kwargs)
Create a pseudocolor plot of a 2-D array.
*C* is the array of color values.
*X* and *Y*, if given, specify the (*x*, *y*) coordinates of
the colored quadrilaterals; the quadrilateral for C[i,j] has
corners at::
(X[i, j], Y[i, j]),
(X[i, j+1], Y[i, j+1]),
(X[i+1, j], Y[i+1, j]),
(X[i+1, j+1], Y[i+1, j+1]).
Ideally the dimensions of *X* and *Y* should be one greater
than those of *C*; if the dimensions are the same, then the
last row and column of *C* will be ignored.
Note that the the column index corresponds to the
*x*-coordinate, and the row index corresponds to *y*; for
details, see the :ref:`Grid Orientation
` section below.
If either or both of *X* and *Y* are 1-D arrays or column vectors,
they will be expanded as needed into the appropriate 2-D arrays,
making a rectangular grid.
*X*, *Y* and *C* may be masked arrays. If either C[i, j], or one
of the vertices surrounding C[i,j] (*X* or *Y* at [i, j], [i+1, j],
[i, j+1],[i+1, j+1]) is masked, nothing is plotted.
Keyword arguments:
*cmap*: [ None | Colormap ]
A :class:`matplotlib.cm.Colormap` instance. If *None*, use
rc settings.
norm: [ None | Normalize ]
An :class:`matplotlib.colors.Normalize` instance is used
to scale luminance data to 0,1. If *None*, defaults to
:func:`normalize`.
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If either are *None*, the min
and max of the color array *C* is used. If you pass a
*norm* instance, *vmin* and *vmax* will be ignored.
*shading*: [ 'flat' | 'faceted' ]
If 'faceted', a black grid is drawn around each rectangle; if
'flat', edges are not drawn. Default is 'flat', contrary to
Matlab(TM).
This kwarg is deprecated; please use 'edgecolors' instead:
* shading='flat' -- edgecolors='None'
* shading='faceted -- edgecolors='k'
*edgecolors*: [ None | 'None' | color | color sequence]
If *None*, the rc setting is used by default.
If 'None', edges will not be visible.
An mpl color or sequence of colors will set the edge color
*alpha*: 0 <= scalar <= 1
the alpha blending value
Return value is a :class:`matplotlib.collection.Collection`
instance.
.. _axes-pcolor-grid-orientation:
The grid orientation follows the Matlab(TM) convention: an
array *C* with shape (*nrows*, *ncolumns*) is plotted with
the column number as *X* and the row number as *Y*, increasing
up; hence it is plotted the way the array would be printed,
except that the *Y* axis is reversed. That is, *C* is taken
as *C*(*y*, *x*).
Similarly for :func:`~matplotlib.pyplot.meshgrid`::
x = np.arange(5)
y = np.arange(3)
X, Y = meshgrid(x,y)
is equivalent to:
X = array([[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]])
Y = array([[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2]])
so if you have::
C = rand( len(x), len(y))
then you need::
pcolor(X, Y, C.T)
or::
pcolor(C.T)
Matlab :func:`pcolor` always discards the last row and column
of *C*, but matplotlib displays the last row and column if *X* and
*Y* are not specified, or if *X* and *Y* have one more row and
column than *C*.
kwargs can be used to control the
:class:`~matplotlib.collection.PolyCollection` properties:
%(PolyCollection)s
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compressedR' R( R R R (( R( R; R< R R R R R R\ R@ R R RZ R R t xymaskRR R t ravelmaskt X1t Y1t X2t Y2t X3t Y3t X4t Y4t npolyR R R R R RS R" R R R R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR^ s |
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call signatures::
pcolormesh(C)
pcolormesh(X, Y, C)
pcolormesh(C, **kwargs)
*C* may be a masked array, but *X* and *Y* may not. Masked
array support is implemented via *cmap* and *norm*; in
contrast, :func:`~matplotlib.pyplot.pcolor` simply does not
draw quadrilaterals with masked colors or vertices.
Keyword arguments:
*cmap*: [ None | Colormap ]
A :class:`matplotlib.cm.Colormap` instance. If None, use
rc settings.
*norm*: [ None | Normalize ]
A :class:`matplotlib.colors.Normalize` instance is used to
scale luminance data to 0,1. If None, defaults to
:func:`normalize`.
*vmin*/*vmax*: [ None | scalar ]
*vmin* and *vmax* are used in conjunction with *norm* to
normalize luminance data. If either are *None*, the min
and max of the color array *C* is used. If you pass a
*norm* instance, *vmin* and *vmax* will be ignored.
*shading*: [ 'flat' | 'faceted' ]
If 'faceted', a black grid is drawn around each rectangle; if
'flat', edges are not drawn. Default is 'flat', contrary to
Matlab(TM).
This kwarg is deprecated; please use 'edgecolors' instead:
* shading='flat' -- edgecolors='None'
* shading='faceted -- edgecolors='k'
*edgecolors*: [ None | 'None' | color | color sequence]
If None, the rc setting is used by default.
If 'None', edges will not be visible.
An mpl color or sequence of colors will set the edge color
*alpha*: 0 <= scalar <= 1
the alpha blending value
Return value is a :class:`matplotlib.collection.QuadMesh`
object.
kwargs can be used to control the
:class:`matplotlib.collections.QuadMesh`
properties:
%(QuadMesh)s
.. seealso::
:func:`~matplotlib.pyplot.pcolor`
For an explanation of the grid orientation and the
expansion of 1-D *X* and/or *Y* to 2-D arrays.
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call signature::
hist(x, bins=10, range=None, normed=False, cumulative=False,
bottom=None, histtype='bar', align='mid',
orientation='vertical', rwidth=None, log=False, **kwargs)
Compute and draw the histogram of *x*. The return value is a
tuple (*n*, *bins*, *patches*) or ([*n0*, *n1*, ...], *bins*,
[*patches0*, *patches1*,...]) if the input contains multiple
data.
Keyword arguments:
*bins*:
Either an integer number of bins or a sequence giving the
bins. *x* are the data to be binned. *x* can be an array,
a 2D array with multiple data in its columns, or a list of
arrays with data of different length. Note, if *bins*
is an integer input argument=numbins, *bins* + 1 bin edges
will be returned, compatible with the semantics of
:func:`numpy.histogram` with the *new* = True argument.
Unequally spaced bins are supported if *bins* is a sequence.
*range*:
The lower and upper range of the bins. Lower and upper outliers
are ignored. If not provided, *range* is (x.min(), x.max()).
Range has no effect if *bins* is a sequence.
If *bins* is a sequence or *range* is specified, autoscaling is
set off (*autoscale_on* is set to *False*) and the xaxis limits
are set to encompass the full specified bin range.
*normed*:
If *True*, the first element of the return tuple will
be the counts normalized to form a probability density, i.e.,
``n/(len(x)*dbin)``. In a probability density, the integral of
the histogram should be 1; you can verify that with a
trapezoidal integration of the probability density function::
pdf, bins, patches = ax.hist(...)
print np.sum(pdf * np.diff(bins))
*weights*
An array of weights, of the same shape as *x*. Each value in
*x* only contributes its associated weight towards the bin
count (instead of 1). If *normed* is True, the weights are
normalized, so that the integral of the density over the range
remains 1.
*cumulative*:
If *True*, then a histogram is computed where each bin
gives the counts in that bin plus all bins for smaller values.
The last bin gives the total number of datapoints. If *normed*
is also *True* then the histogram is normalized such that the
last bin equals 1. If *cumulative* evaluates to less than 0
(e.g. -1), the direction of accumulation is reversed. In this
case, if *normed* is also *True*, then the histogram is normalized
such that the first bin equals 1.
*histtype*: [ 'bar' | 'barstacked' | 'step' | 'stepfilled' ]
The type of histogram to draw.
- 'bar' is a traditional bar-type histogram. If multiple data
are given the bars are aranged side by side.
- 'barstacked' is a bar-type histogram where multiple
data are stacked on top of each other.
- 'step' generates a lineplot that is by default
unfilled.
- 'stepfilled' generates a lineplot that is by default
filled.
*align*: ['left' | 'mid' | 'right' ]
Controls how the histogram is plotted.
- 'left': bars are centered on the left bin edges.
- 'mid': bars are centered between the bin edges.
- 'right': bars are centered on the right bin edges.
*orientation*: [ 'horizontal' | 'vertical' ]
If 'horizontal', :func:`~matplotlib.pyplot.barh` will be
used for bar-type histograms and the *bottom* kwarg will be
the left edges.
*rwidth*:
The relative width of the bars as a fraction of the bin
width. If *None*, automatically compute the width. Ignored
if *histtype* = 'step' or 'stepfilled'.
*log*:
If *True*, the histogram axis will be set to a log scale.
If *log* is *True* and *x* is a 1D array, empty bins will
be filtered out and only the non-empty (*n*, *bins*,
*patches*) will be returned.
kwargs are used to update the properties of the hist
:class:`~matplotlib.patches.Rectangle` instances:
%(Rectangle)s
You can use labels for your histogram, and only the first
:class:`~matplotlib.patches.Rectangle` gets the label (the
others get the magic string '_nolegend_'. This will make the
histograms work in the intuitive way for bar charts::
ax.hist(10+2*np.random.randn(1000), label='men')
ax.hist(12+3*np.random.randn(1000), label='women', alpha=0.5)
ax.legend()
label can also be a sequence of strings. If multiple data is
provided in *x*, the labels are asigned sequentially to the
histograms.
**Example:**
.. plot:: mpl_examples/pylab_examples/histogram_demo.py
Rg sF hist now uses the rwidth to give relative width and not absolute widthi i i s> 2D hist should be nsamples x nvariables; this looks transposeds/ Can not use provided data to create a histograms- Can not use provided weights to create a hists' weights should have the same shape as xs 1.3R R t newt weightsiR6 g ?g g?g t
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R R Rl R R2 R7 R6 R Ri R R R] R R R' R R R7 R R# Rg R R R R5 R R Rj R6 Ri R (. R( RS R R R R t
cumulativeR t histtypeR) R* t rwidthR] R< t txR Ru t twt binsgivent hist_kwargsR* R! t slcR R R! t totwidtht stackedt drRg t dwt boffsetR R/ R" R] RT RU RV RW R RL t lblR R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt hist sr ~
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call signature::
psd(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs)
The power spectral density by Welch's average periodogram
method. The vector *x* is divided into *NFFT* length
segments. Each segment is detrended by function *detrend* and
windowed by function *window*. *noverlap* gives the length of
the overlap between segments. The :math:`|\mathrm{fft}(i)|^2`
of each segment :math:`i` are averaged to compute *Pxx*, with a
scaling to correct for power loss due to windowing. *Fs* is the
sampling frequency.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
Returns the tuple (*Pxx*, *freqs*).
For plotting, the power is plotted as
:math:`10\log_{10}(P_{xx})` for decibels, though *Pxx* itself
is returned.
References:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the :class:`~matplotlib.lines.Line2D` properties:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/psd_demo.py
s dB/Hzt dBi
t Frequencys Power Spectral Density (%s)i g?i N( R R R t psdR, RP R Rc R$ RN R[ R R R R R R1 Rh RR R t ceilRT ( R( RS t NFFTt Fst FcR t windowt noverlapt pad_tot sidest
scale_by_freqR< t pxxt freqst psd_unitsR R t intvt logiR RC ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR n s, -
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call signature::
csd(x, y, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs)
The cross spectral density :math:`P_{xy}` by Welch's average
periodogram method. The vectors *x* and *y* are divided into
*NFFT* length segments. Each segment is detrended by function
*detrend* and windowed by function *window*. The product of
the direct FFTs of *x* and *y* are averaged over each segment
to compute :math:`P_{xy}`, with a scaling to correct for power
loss due to windowing.
Returns the tuple (*Pxy*, *freqs*). *P* is the cross spectrum
(complex valued), and :math:`10\log_{10}|P_{xy}|` is
plotted.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
References:
Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the Line2D properties:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/csd_demo.py
.. seealso:
:meth:`psd`
For a description of the optional parameters.
i
R s Cross Spectrum Magnitude (dB)i ( R R R t csdR, RP R$ RN R[ t absoluteR R R Rc R R R1 Rh RR R R RT ( R( RS R" R R R R R R R R R R< t pxyR R R R R RC ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s /
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call signature::
cohere(x, y, NFFT=256, Fs=2, Fc=0, detrend = mlab.detrend_none,
window = mlab.window_hanning, noverlap=0, pad_to=None,
sides='default', scale_by_freq=None, **kwargs)
:meth:`cohere` the coherence between *x* and *y*. Coherence
is the normalized cross spectral density:
.. math::
C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}}
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the x extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
The return value is a tuple (*Cxy*, *f*), where *f* are the
frequencies of the coherence vector.
kwargs are applied to the lines.
References:
* Bendat & Piersol -- Random Data: Analysis and Measurement
Procedures, John Wiley & Sons (1986)
kwargs control the :class:`~matplotlib.lines.Line2D`
properties of the coherence plot:
%(Line2D)s
**Example:**
.. plot:: mpl_examples/pylab_examples/cohere_demo.py
R t Coherence( R R R t cohereR$ R R R Rc ( R( RS R" R R R R R R R R R R< t cxyR ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR s ,
i c
K s | i p | i n t i | | | | | | |
| | \ } } } d t i | } t i | } | d j o d t i | f } n | \ } } | | 7} | | | d | d f } | i | | d | |
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call signature::
specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, **kwargs)
Compute a spectrogram of data in *x*. Data are split into
*NFFT* length segments and the PSD of each section is
computed. The windowing function *window* is applied to each
segment, and the amount of overlap of each segment is
specified with *noverlap*.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the y extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
*cmap*:
A :class:`matplotlib.cm.Colormap` instance; if *None* use
default determined by rc
*xextent*:
The image extent along the x-axis. xextent = (xmin,xmax)
The default is (0,max(bins)), where bins is the return
value from :func:`mlab.specgram`
*kwargs*:
Additional kwargs are passed on to imshow which makes the
specgram image
Return value is (*Pxx*, *freqs*, *bins*, *im*):
- *bins* are the time points the spectrogram is calculated over
- *freqs* is an array of frequencies
- *Pxx* is a len(times) x len(freqs) array of power
- *im* is a :class:`matplotlib.image.AxesImage` instance
Note: If *x* is real (i.e. non-complex), only the positive
spectrum is shown. If *x* is complex, both positive and
negative parts of the spectrum are shown. This can be
overridden using the *sides* keyword argument.
**Example:**
.. plot:: mpl_examples/pylab_examples/specgram_demo.py
g $@i iR R N( R R R t specgramRN R[ t flipudR R( RT R ( R( RS R R R R R R R t xextentR R R R< t PxxR R t ZRT RU R R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyR 9 s 8
RE c K s | d% j o d } t i d n | d% j o' | d% j o t | d o
d } n | d% j o | d% j o t i | } t i | | j } d | j o# t i d d g d d | d precision` will be plotted.
For :class:`scipy.sparse.spmatrix` instances, there is a
special case: if *precision* is 'present', any value present in
the array will be plotted, even if it is identically zero.
The array will be plotted as it would be printed, with
the first index (row) increasing down and the second
index (column) increasing to the right.
By default aspect is 'equal', so that each array element
occupies a square space; set the aspect kwarg to 'auto'
to allow the plot to fill the plot box, or to any scalar
number to specify the aspect ratio of an array element
directly.
Two plotting styles are available: image or marker. Both
are available for full arrays, but only the marker style
works for :class:`scipy.sparse.spmatrix` instances.
If *marker* and *markersize* are *None*, an image will be
returned and any remaining kwargs are passed to
:func:`~matplotlib.pyplot.imshow`; else, a
:class:`~matplotlib.lines.Line2D` object will be returned with
the value of marker determining the marker type, and any
remaining kwargs passed to the
:meth:`~matplotlib.axes.Axes.plot` method.
If *marker* and *markersize* are *None*, useful kwargs include:
* *cmap*
* *alpha*
.. seealso::
:func:`~matplotlib.pyplot.imshow`
For image options.
For controlling colors, e.g. cyan background and red marks,
use::
cmap = mcolors.ListedColormap(['c','r'])
If *marker* or *markersize* is not *None*, useful kwargs include:
* *marker*
* *markersize*
* *color*
Useful values for *marker* include:
* 's' square (default)
* 'o' circle
* '.' point
* ',' pixel
.. seealso::
:func:`~matplotlib.pyplot.plot`
For plotting options
i s Use precision=0 instead of Nonet tocooR R Ru R# R7 t binaryg g ?RP Rv RG R RQ R2 t presenti
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c K s t i | } | i \ } } d | d | d d g } h | d 6d d 6d d 6d d 6} | i | | i | | } | i i d
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Plot a matrix or array as an image.
The matrix will be shown the way it would be printed,
with the first row at the top. Row and column numbering
is zero-based.
Argument:
*Z* anything that can be interpreted as a 2-D array
kwargs all are passed to :meth:`~matplotlib.axes.Axes.imshow`.
:meth:`matshow` sets defaults for *extent*, *origin*,
*interpolation*, and *aspect*; use care in overriding the
*extent* and *origin* kwargs, because they interact. (Also,
if you want to change them, you probably should be using
imshow directly in your own version of matshow.)
Returns: an :class:`matplotlib.image.AxesImage` instance.
g g ?R R2 RQ Rv RP RE RG g?R R i R i i i i
R ( RN R RP R RT R- R R5 R R Ra R R Rc R6 ( R( R R< R R R RY R ( ( s5 /usr/lib64/python2.6/site-packages/matplotlib/axes.pyt matshow s&
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C sg g } g } | i | | i i o | i | i n | i i oB | i | i i | i i | \ } } | i | | g n | i i oB | i | i i | i i | \ } } | i | | g n | i g } | D] } | | i | q ~ t i
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return the tight bounding box of the axes.
The dimension of the Bbox in canvas coordinate.
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Base class for subplots, which are :class:`Axes` instances with
additional methods to facilitate generating and manipulating a set
of :class:`Axes` within a figure.
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*fig* is a :class:`matplotlib.figure.Figure` instance.
*args* is the tuple (*numRows*, *numCols*, *plotNum*), where
the array of subplots in the figure has dimensions *numRows*,
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If *numRows* <= *numCols* <= *plotNum* < 10, *args* can be the
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set the visible property on ticklabels so xticklabels are
visible only if the subplot is in the last row and yticklabels
are visible only if the subplot is in the first column
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