matplot_plt

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Annotation

A :class:`~matplotlib.text.Text` class to make annotating things in the figure, such as :class:`~matplotlib.figure.Figure`, :class:`~matplotlib.axes.Axes`, :class:`~matplotlib.patches.Rectangle`, etc., easier. Annotate the *x*, *y* point *xy* with text *s* at *x*, *y* location *xytext*. (If *xytext* = *None*, defaults to *xy*, and if *textcoords* = *None*, defaults to *xycoords*).

SubplotTool

A tool to adjust to subplot params of a :class:`matplotlib.figure.Figure`

Widget

Abstract base class for GUI neutral widgets

Artist

Abstract base class for someone who renders into a :class:`FigureCanvas`.

colorbar

Add a colorbar to a plot. Function signatures for the :mod:`~matplotlib.pyplot` interface; all but the first are also method signatures for the :meth:`~matplotlib.figure.Figure.colorbar` method:: colorbar(**kwargs) colorbar(mappable, **kwargs) colorbar(mappable, cax=cax, **kwargs) colorbar(mappable, ax=ax, **kwargs) arguments: *mappable* the :class:`~matplotlib.image.Image`, :class:`~matplotlib.contour.ContourSet`, etc. to which the colorbar applies; this argument is mandatory for the :meth:`~matplotlib.figure.Figure.colorbar` method but optional for the :func:`~matplotlib.pyplot.colorbar` function, which sets the default to the current image. keyword arguments: *cax* None | axes object into which the colorbar will be drawn *ax* None | parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes. *use_gridspec* False | If *cax* is None, a new *cax* is created as an instance of Axes. If *ax* is an instance of Subplot and *use_gridspec* is True, *cax* is created as an instance of Subplot using the grid_spec module. Additional keyword arguments are of two kinds: axes properties: ============= ==================================================== Property Description ============= ==================================================== *orientation* vertical or horizontal *fraction* 0.15; fraction of original axes to use for colorbar *pad* 0.05 if vertical, 0.15 if horizontal; fraction of original axes between colorbar and new image axes *shrink* 1.0; fraction by which to shrink the colorbar *aspect* 20; ratio of long to short dimensions *anchor* (0.0, 0.5) if vertical; (0.5, 1.0) if horizontal; the anchor point of the colorbar axes *panchor* (1.0, 0.5) if vertical; (0.5, 0.0) if horizontal; the anchor point of the colorbar parent axes. If False, the parent axes' anchor will be unchanged ============= ==================================================== colorbar properties: ============ ==================================================== Property Description ============ ==================================================== *extend* [ 'neither' | 'both' | 'min' | 'max' ] If not 'neither', make pointed end(s) for out-of- range values. These are set for a given colormap using the colormap set_under and set_over methods. *extendfrac* [ *None* | 'auto' | length | lengths ] If set to *None*, both the minimum and maximum triangular colorbar extensions with have a length of 5% of the interior colorbar length (this is the default setting). If set to 'auto', makes the triangular colorbar extensions the same lengths as the interior boxes (when *spacing* is set to 'uniform') or the same lengths as the respective adjacent interior boxes (when *spacing* is set to 'proportional'). If a scalar, indicates the length of both the minimum and maximum triangular colorbar extensions as a fraction of the interior colorbar length. A two-element sequence of fractions may also be given, indicating the lengths of the minimum and maximum colorbar extensions respectively as a fraction of the interior colorbar length. *extendrect* [ *False* | *True* ] If *False* the minimum and maximum colorbar extensions will be triangular (the default). If *True* the extensions will be rectangular. *spacing* [ 'uniform' | 'proportional' ] Uniform spacing gives each discrete color the same space; proportional makes the space proportional to the data interval. *ticks* [ None | list of ticks | Locator object ] If None, ticks are determined automatically from the input. *format* [ None | format string | Formatter object ] If None, the :class:`~matplotlib.ticker.ScalarFormatter` is used. If a format string is given, e.g., '%.3f', that is used. An alternative :class:`~matplotlib.ticker.Formatter` object may be given instead. *drawedges* [ False | True ] If true, draw lines at color boundaries. ============ ==================================================== The following will probably be useful only in the context of indexed colors (that is, when the mappable has norm=NoNorm()), or other unusual circumstances. ============ =================================================== Property Description ============ =================================================== *boundaries* None or a sequence *values* None or a sequence which must be of length 1 less than the sequence of *boundaries*. For each region delimited by adjacent entries in *boundaries*, the color mapped to the corresponding value in values will be used. ============ =================================================== If *mappable* is a :class:`~matplotlib.contours.ContourSet`, its *extend* kwarg is included automatically. Note that the *shrink* kwarg provides a simple way to keep a vertical colorbar, for example, from being taller than the axes of the mappable to which the colorbar is attached; but it is a manual method requiring some trial and error. If the colorbar is too tall (or a horizontal colorbar is too wide) use a smaller value of *shrink*. For more precise control, you can manually specify the positions of the axes objects in which the mappable and the colorbar are drawn. In this case, do not use any of the axes properties kwargs. It is known that some vector graphics viewer (svg and pdf) renders white gaps between segments of the colorbar. This is due to bugs in the viewers not matplotlib. As a workaround the colorbar can be rendered with overlapping segments:: cbar = colorbar() cbar.solids.set_edgecolor("face") draw() However this has negative consequences in other circumstances. Particularly with semi transparent images (alpha < 1) and colorbar extensions and is not enabled by default see (issue #1188). returns: :class:`~matplotlib.colorbar.Colorbar` instance; see also its base class, :class:`~matplotlib.colorbar.ColorbarBase`. Call the :meth:`~matplotlib.colorbar.ColorbarBase.set_label` method to label the colorbar.

NullFormatter

Always return the empty string

Arrow

An arrow patch.

LogLocator

Determine the tick locations for log axes

disconnect

Disconnect callback id cid Example usage:: cid = canvas.mpl_connect('button_press_event', on_press) #...later canvas.mpl_disconnect(cid)

Rectangle

Draw a rectangle with lower left at *xy* = (*x*, *y*) with specified *width* and *height*.

LogFormatter

Format values for log axis;

LogFormatterExponent

Format values for log axis; using ``exponent = log_base(value)``

boxplot

Make a box and whisker plot. Call signature:: boxplot(self, x, notch=False, sym='b+', vert=True, whis=1.5, positions=None, widths=None, patch_artist=False, bootstrap=None, usermedians=None, conf_intervals=None, meanline=False, showmeans=False, showcaps=True, showbox=True, showfliers=True, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True): Make a box and whisker plot for each column of *x* or each vector in sequence *x*. The box extends from the lower to upper quartile values of the data, with a line at the median. The whiskers extend from the box to show the range of the data. Flier points are those past the end of the whiskers. Parameters ---------- x : Array or a sequence of vectors. The input data. notch : bool, default = False If False, produces a rectangular box plot. If True, will produce a notched box plot sym : str or None, default = None The default symbol for flier points. Enter an empty string ('') if you don't want to show fliers. If `None`, then the fliers default to 'b+' If you want more control use the flierprops kwarg. vert : bool, default = True If True (default), makes the boxes vertical. If False, makes horizontal boxes. whis : float, sequence (default = 1.5) or string As a float, determines the reach of the whiskers past the first and third quartiles (e.g., Q3 + whis*IQR, IQR = interquartile range, Q3-Q1). Beyond the whiskers, data are considered outliers and are plotted as individual points. Set this to an unreasonably high value to force the whiskers to show the min and max values. Alternatively, set this to an ascending sequence of percentile (e.g., [5, 95]) to set the whiskers at specific percentiles of the data. Finally, *whis* can be the string 'range' to force the whiskers to the min and max of the data. In the edge case that the 25th and 75th percentiles are equivalent, *whis* will be automatically set to 'range'. bootstrap : None (default) or integer Specifies whether to bootstrap the confidence intervals around the median for notched boxplots. If bootstrap==None, no bootstrapping is performed, and notches are calculated using a Gaussian-based asymptotic approximation (see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and Kendall and Stuart, 1967). Otherwise, bootstrap specifies the number of times to bootstrap the median to determine it's 95% confidence intervals. Values between 1000 and 10000 are recommended. usermedians : array-like or None (default) An array or sequence whose first dimension (or length) is compatible with *x*. This overrides the medians computed by matplotlib for each element of *usermedians* that is not None. When an element of *usermedians* == None, the median will be computed by matplotlib as normal. conf_intervals : array-like or None (default) Array or sequence whose first dimension (or length) is compatible with *x* and whose second dimension is 2. When the current element of *conf_intervals* is not None, the notch locations computed by matplotlib are overridden (assuming notch is True). When an element of *conf_intervals* is None, boxplot compute notches the method specified by the other kwargs (e.g., *bootstrap*). positions : array-like, default = [1, 2, ..., n] Sets the positions of the boxes. The ticks and limits are automatically set to match the positions. widths : array-like, default = 0.5 Either a scalar or a vector and sets the width of each box. The default is 0.5, or ``0.15*(distance between extreme positions)`` if that is smaller. labels : sequence or None (default) Labels for each dataset. Length must be compatible with dimensions of *x* patch_artist : bool, default = False If False produces boxes with the Line2D artist If True produces boxes with the Patch artist showmeans : bool, default = False If True, will toggle one the rendering of the means showcaps : bool, default = True If True, will toggle one the rendering of the caps showbox : bool, default = True If True, will toggle one the rendering of box showfliers : bool, default = True If True, will toggle one the rendering of the fliers boxprops : dict or None (default) If provided, will set the plotting style of the boxes whiskerprops : dict or None (default) If provided, will set the plotting style of the whiskers capprops : dict or None (default) If provided, will set the plotting style of the caps flierprops : dict or None (default) If provided, will set the plotting style of the fliers medianprops : dict or None (default) If provided, will set the plotting style of the medians meanprops : dict or None (default) If provided, will set the plotting style of the means meanline : bool, default = False If True (and *showmeans* is True), will try to render the mean as a line spanning the full width of the box according to *meanprops*. Not recommended if *shownotches* is also True. Otherwise, means will be shown as points. Returns ------- result : dict A dictionary mapping each component of the boxplot to a list of the :class:`matplotlib.lines.Line2D` instances created. That dictionary has the following keys (assuming vertical boxplots): - boxes: the main body of the boxplot showing the quartiles and the median's confidence intervals if enabled. - medians: horizonal lines at the median of each box. - whiskers: the vertical lines extending to the most extreme, n-outlier data points. - caps: the horizontal lines at the ends of the whiskers. - fliers: points representing data that extend beyond the whiskers (outliers). - means: points or lines representing the means. Examples -------- .. plot:: mpl_examples/statistics/boxplot_demo.py Additional kwargs: hold = [True|False] overrides default hold state

_pylab_helpers

Manage figures for pyplot interface.

NullLocator

No ticks

IndexLocator

Place a tick on every multiple of some base number of points plotted, e.g., on every 5th point. It is assumed that you are doing index plotting; i.e., the axis is 0, len(data). This is mainly useful for x ticks.

colormaps

Matplotlib provides a number of colormaps, and others can be added using :func:`~matplotlib.cm.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 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 ========= ======================================================= 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 <http://yorick.sourceforge.net/index.php>`_, 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 <http://colorbrewer.org>`_ 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 nipy_spectral black-purple-blue-green-yellow-red-white spectrum, originally from the Neuroimaging in Python project 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 the names in the descriptions instead, which produce identical output: ========= ======================================================= Colormap Description ========= ======================================================= gist_gray identical to *gray* gist_yarg identical to *gray_r* binary identical to *gray_r* spectral identical to *nipy_spectral* [#]_ ========= ======================================================= .. rubric:: Footnotes .. [#] Rainbow colormaps, ``jet`` in particular, are considered a poor choice for scientific visualization by many researchers: `Rainbow Color Map (Still) Considered Harmful <http://www.jwave.vt.edu/%7Erkriz/Projects/create_color_table/color_07.pdf>`_ .. [#] Resembles "BkBlAqGrYeOrReViWh200" from NCAR Command Language. See `Color Table Gallery <http://www.ncl.ucar.edu/Document/Graphics/color_table_gallery.shtml>`_ .. [#] See `Diverging Color Maps for Scientific Visualization <http://www.cs.unm.edu/~kmorel/documents/ColorMaps/>`_ by Kenneth Moreland. .. [#] See `A Color Map for Effective Black-and-White Rendering of Color-Scale Images <http://www.mathworks.com/matlabcentral/fileexchange/2662-cmrmap-m>`_ by Carey Rappaport .. [#] Changed to distinguish from ColorBrewer's *Spectral* map. :func:`spectral` still works, but ``set_cmap('nipy_spectral')`` is recommended for clarity.

barbs

Plot a 2-D field of barbs. Call signatures:: barb(U, V, **kw) barb(U, V, C, **kw) barb(X, Y, U, V, **kw) barb(X, Y, U, V, C, **kw) Arguments: *X*, *Y*: The x and y coordinates of the barb locations (default is head of barb; see *pivot* kwarg) *U*, *V*: Give the x and y components of the barb shaft *C*: An optional array used to map colors to the barbs All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y* are absent, they will be generated as a uniform grid. If *U* and *V* are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)`` match the column and row dimensions of *U*, then *X* and *Y* will be expanded with :func:`numpy.meshgrid`. *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not supported at present. Keyword arguments: *length*: Length of the barb in points; the other parts of the barb are scaled against this. Default is 9 *pivot*: [ 'tip' | 'middle' ] The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name *pivot*. Default is 'tip' *barbcolor*: [ color | color sequence ] Specifies the color all parts of the barb except any flags. This parameter is analagous to the *edgecolor* parameter for polygons, which can be used instead. However this parameter will override facecolor. *flagcolor*: [ color | color sequence ] Specifies the color of any flags on the barb. This parameter is analagous to the *facecolor* parameter for polygons, which can be used instead. However this parameter will override facecolor. If this is not set (and *C* has not either) then *flagcolor* will be set to match *barbcolor* so that the barb has a uniform color. If *C* has been set, *flagcolor* has no effect. *sizes*: A dictionary of coefficients specifying the ratio of a given feature to the length of the barb. Only those values one wishes to override need to be included. These features include: - 'spacing' - space between features (flags, full/half barbs) - 'height' - height (distance from shaft to top) of a flag or full barb - 'width' - width of a flag, twice the width of a full barb - 'emptybarb' - radius of the circle used for low magnitudes *fill_empty*: A flag on whether the empty barbs (circles) that are drawn should be filled with the flag color. If they are not filled, they will be drawn such that no color is applied to the center. Default is False *rounding*: A flag to indicate whether the vector magnitude should be rounded when allocating barb components. If True, the magnitude is rounded to the nearest multiple of the half-barb increment. If False, the magnitude is simply truncated to the next lowest multiple. Default is True *barb_increments*: A dictionary of increments specifying values to associate with different parts of the barb. Only those values one wishes to override need to be included. - 'half' - half barbs (Default is 5) - 'full' - full barbs (Default is 10) - 'flag' - flags (default is 50) *flip_barb*: Either a single boolean flag or an array of booleans. Single boolean indicates whether the lines and flags should point opposite to normal for all barbs. An array (which should be the same size as the other data arrays) indicates whether to flip for each individual barb. Normal behavior is for the barbs and lines to point right (comes from wind barbs having these features point towards low pressure in the Northern Hemisphere.) Default is False Barbs are traditionally used in meteorology as a way to plot the speed and direction of wind observations, but can technically be used to plot any two dimensional vector quantity. As opposed to arrows, which give vector magnitude by the length of the arrow, the barbs give more quantitative information about the vector magnitude by putting slanted lines or a triangle for various increments in magnitude, as show schematically below:: : /\ \ : / \ \ : / \ \ \ : / \ \ \ : ------------------------------ .. note the double \ at the end of each line to make the figure .. render correctly The largest increment is given by a triangle (or "flag"). After those come full lines (barbs). The smallest increment is a half line. There is only, of course, ever at most 1 half line. If the magnitude is small and only needs a single half-line and no full lines or triangles, the half-line is offset from the end of the barb so that it can be easily distinguished from barbs with a single full line. The magnitude for the barb shown above would nominally be 65, using the standard increments of 50, 10, and 5. linewidths and edgecolors can be used to customize the barb. Additional :class:`~matplotlib.collections.PolyCollection` keyword arguments: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/barb_demo.py Additional kwargs: hold = [True|False] overrides default hold state

contour

Plot contours. :func:`~matplotlib.pyplot.contour` and :func:`~matplotlib.pyplot.contourf` draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions. :func:`~matplotlib.pyplot.contourf` differs from the MATLAB version in that it does not draw the polygon edges. To draw edges, add line contours with calls to :func:`~matplotlib.pyplot.contour`. Call signatures:: contour(Z) make a contour plot of an array *Z*. The level values are chosen automatically. :: contour(X,Y,Z) *X*, *Y* specify the (x, y) coordinates of the surface :: contour(Z,N) contour(X,Y,Z,N) contour *N* automatically-chosen levels. :: contour(Z,V) contour(X,Y,Z,V) draw contour lines at the values specified in sequence *V* :: contourf(..., V) fill the ``len(V)-1`` regions between the values in *V* :: contour(Z, **kwargs) Use keyword args to control colors, linewidth, origin, cmap ... see below for more details. *X* and *Y* must both be 2-D with the same shape as *Z*, or they must both be 1-D such that ``len(X)`` is the number of columns in *Z* and ``len(Y)`` is the number of rows in *Z*. ``C = contour(...)`` returns a :class:`~matplotlib.contour.QuadContourSet` object. Optional keyword arguments: *colors*: [ *None* | string | (mpl_colors) ] If *None*, the colormap specified by cmap will be used. If a string, like 'r' or 'red', all levels will be plotted in this color. If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified. *alpha*: float The alpha blending value *cmap*: [ *None* | Colormap ] A cm :class:`~matplotlib.colors.Colormap` instance or *None*. If *cmap* is *None* and *colors* is *None*, a default Colormap is used. *norm*: [ *None* | Normalize ] A :class:`matplotlib.colors.Normalize` instance for scaling data values to colors. If *norm* is *None* and *colors* is *None*, the default linear scaling is used. *vmin*, *vmax*: [ *None* | scalar ] If not *None*, either or both of these values will be supplied to the :class:`matplotlib.colors.Normalize` instance, overriding the default color scaling based on *levels*. *levels*: [level0, level1, ..., leveln] A list of floating point numbers indicating the level curves to draw; e.g., to draw just the zero contour pass ``levels=[0]`` *origin*: [ *None* | 'upper' | 'lower' | 'image' ] If *None*, the first value of *Z* will correspond to the lower left corner, location (0,0). If 'image', the rc value for ``image.origin`` will be used. This keyword is not active if *X* and *Y* are specified in the call to contour. *extent*: [ *None* | (x0,x1,y0,y1) ] If *origin* is not *None*, then *extent* is interpreted as in :func:`matplotlib.pyplot.imshow`: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If *origin* is *None*, then (*x0*, *y0*) is the position of Z[0,0], and (*x1*, *y1*) is the position of Z[-1,-1]. This keyword is not active if *X* and *Y* are specified in the call to contour. *locator*: [ *None* | ticker.Locator subclass ] If *locator* is *None*, the default :class:`~matplotlib.ticker.MaxNLocator` is used. The locator is used to determine the contour levels if they are not given explicitly via the *V* argument. *extend*: [ 'neither' | 'both' | 'min' | 'max' ] Unless this is 'neither', contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via :meth:`matplotlib.colors.Colormap.set_under` and :meth:`matplotlib.colors.Colormap.set_over` methods. *xunits*, *yunits*: [ *None* | registered units ] Override axis units by specifying an instance of a :class:`matplotlib.units.ConversionInterface`. *antialiased*: [ *True* | *False* ] enable antialiasing, overriding the defaults. For filled contours, the default is *True*. For line contours, it is taken from rcParams['lines.antialiased']. contour-only keyword arguments: *linewidths*: [ *None* | number | tuple of numbers ] If *linewidths* is *None*, the default width in ``lines.linewidth`` in ``matplotlibrc`` is used. If a number, all levels will be plotted with this linewidth. If a tuple, different levels will be plotted with different linewidths in the order specified. *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ] If *linestyles* is *None*, the default is 'solid' unless the lines are monochrome. In that case, negative contours will take their linestyle from the ``matplotlibrc`` ``contour.negative_linestyle`` setting. *linestyles* can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary. contourf-only keyword arguments: *nchunk*: [ 0 | integer ] If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly *nchunk* by *nchunk* points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless *antialiased* is *False*. *hatches*: A list of cross hatch patterns to use on the filled areas. If None, no hatching will be added to the contour. Hatching is supported in the PostScript, PDF, SVG and Agg backends only. Note: contourf fills intervals that are closed at the top; that is, for boundaries *z1* and *z2*, the filled region is:: z1 < z <= z2 There is one exception: if the lowest boundary coincides with the minimum value of the *z* array, then that minimum value will be included in the lowest interval. **Examples:** .. plot:: mpl_examples/pylab_examples/contour_demo.py .. plot:: mpl_examples/pylab_examples/contourf_demo.py Additional kwargs: hold = [True|False] overrides default hold state

contourf

angle_spectrum

Plot the angle spectrum. Call signature:: angle_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning, pad_to=None, sides='default', **kwargs) Compute the angle spectrum (wrapped phase spectrum) of *x*. Data is padded to a length of *pad_to* and the windowing function *window* is applied to the signal. *x*: 1-D array or sequence Array or sequence containing the data Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to the length of the input signal (i.e. no padding). *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 (*spectrum*, *freqs*, *line*): *spectrum*: 1-D array The values for the angle spectrum in radians (real valued) *freqs*: 1-D array The frequencies corresponding to the elements in *spectrum* *line*: a :class:`~matplotlib.lines.Line2D` instance The line created by this function kwargs control the :class:`~matplotlib.lines.Line2D` properties: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/spectrum_demo.py .. seealso:: :func:`magnitude_spectrum` :func:`angle_spectrum` plots the magnitudes of the corresponding frequencies. :func:`phase_spectrum` :func:`phase_spectrum` plots the unwrapped version of this function. :func:`specgram` :func:`specgram` can plot the angle spectrum of segments within the signal in a colormap. Additional kwargs: hold = [True|False] overrides default hold state

cohere

Plot the coherence between *x* and *y*. 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) Plot the coherence between *x* and *y*. Coherence is the normalized cross spectral density: .. math:: C_{xy} = \frac{|P_{xy}|^2}{P_{xx}P_{yy}} Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. This can be different from *NFFT*, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to *NFFT* *NFFT*: integer The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should *NOT* be used to get zero padding, or the scaling of the result will be incorrect. Use *pad_to* for this instead. *detrend*: [ 'default' | 'constant' | 'mean' | 'linear' | 'none'] or callable The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the *detrend* parameter is a vector, in matplotlib is it a function. The :mod:`~matplotlib.pylab` module defines :func:`~matplotlib.pylab.detrend_none`, :func:`~matplotlib.pylab.detrend_mean`, and :func:`~matplotlib.pylab.detrend_linear`, but you can use a custom function as well. You can also use a string to choose one of the functions. 'default', 'constant', and 'mean' call :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls :func:`~matplotlib.pylab.detrend_linear`. 'none' calls :func:`~matplotlib.pylab.detrend_none`. *scale_by_freq*: boolean Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. *noverlap*: integer The number of points of overlap between blocks. The default value is 0 (no overlap). *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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/cohere_demo.py Additional kwargs: hold = [True|False] overrides default hold state

FixedFormatter

Return fixed strings for tick labels

MaxNLocator

Select no more than N intervals at nice locations.

__name__

str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str Create a new string object from the given object. If encoding or errors is specified, then the object must expose a data buffer that will be decoded using the given encoding and error handler. Otherwise, returns the result of object.__str__() (if defined) or repr(object). encoding defaults to sys.getdefaultencoding(). errors defaults to 'strict'.

rcParamsDefault

A dictionary object including validation validating functions are defined and associated with rc parameters in :mod:`matplotlib.rcsetup`

findobj

Find artist objects. Recursively find all :class:`~matplotlib.artist.Artist` instances contained in self. *match* can be - None: return all objects contained in artist. - function with signature ``boolean = match(artist)`` used to filter matches - class instance: e.g., Line2D. Only return artists of class type. If *include_self* is True (default), include self in the list to be checked for a match.

Axes

The :class:`Axes` contains most of the figure elements: :class:`~matplotlib.axis.Axis`, :class:`~matplotlib.axis.Tick`, :class:`~matplotlib.lines.Line2D`, :class:`~matplotlib.text.Text`, :class:`~matplotlib.patches.Polygon`, etc., and sets the coordinate system. The :class:`Axes` instance supports callbacks through a callbacks attribute which is a :class:`~matplotlib.cbook.CallbackRegistry` instance. The events you can connect to are 'xlim_changed' and 'ylim_changed' and the callback will be called with func(*ax*) where *ax* is the :class:`Axes` instance.

ScalarFormatter

Tick location is a plain old number. If useOffset==True and the data range is much smaller than the data average, then an offset will be determined such that the tick labels are meaningful. Scientific notation is used for data < 10^-n or data >= 10^m, where n and m are the power limits set using set_powerlimits((n,m)). The defaults for these are controlled by the axes.formatter.limits rc parameter.

FixedLocator

Tick locations are fixed. If nbins is not None, the array of possible positions will be subsampled to keep the number of ticks <= nbins +1. The subsampling will be done so as to include the smallest absolute value; for example, if zero is included in the array of possibilities, then it is guaranteed to be one of the chosen ticks.

__builtins__

dict() -> new empty dictionary dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable: d[k] = v dict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)

Normalize

A class which, when called, can normalize data into the ``[0.0, 1.0]`` interval.

normalize

.. deprecated:: 1.3 The normalize class alias was deprecated in version 1.3. Use Normalize instead. A class which, when called, can normalize data into the ``[0.0, 1.0]`` interval.

plotting

==================== ================================================================================================================================= Function Description ==================== ================================================================================================================================= `acorr` Plot the autocorrelation of `x`. `angle_spectrum` Plot the angle spectrum. `annotate` Create an annotation: a piece of text referring to a data point. `arrow` Add an arrow to the axes. `autoscale` Autoscale the axis view to the data (toggle). `axes` Add an axes to the figure. `axhline` Add a horizontal line across the axis. `axhspan` Add a horizontal span (rectangle) across the axis. `axis` Convenience method to get or set axis properties. `axvline` Add a vertical line across the axes. `axvspan` Add a vertical span (rectangle) across the axes. `bar` Make a bar plot. `barbs` Plot a 2-D field of barbs. `barh` Make a horizontal bar plot. `box` Turn the axes box on or off. `boxplot` Make a box and whisker plot. `broken_barh` Plot horizontal bars. `cla` Clear the current axes. `clabel` Label a contour plot. `clf` Clear the current figure. `clim` Set the color limits of the current image. `close` Close a figure window. `cohere` Plot the coherence between *x* and *y*. `colorbar` Add a colorbar to a plot. `contour` Plot contours. `contourf` Plot contours. `csd` Plot the cross-spectral density. `delaxes` Remove an axes from the current figure. `draw` Redraw the current figure. `errorbar` Plot an errorbar graph. `eventplot` Plot identical parallel lines at specific positions. `figimage` Adds a non-resampled image to the figure. `figlegend` Place a legend in the figure. `figtext` Add text to figure. `figure` Creates a new figure. `fill` Plot filled polygons. `fill_between` Make filled polygons between two curves. `fill_betweenx` Make filled polygons between two horizontal curves. `findobj` Find artist objects. `gca` Get the current :class:`~matplotlib.axes.Axes` instance on the current figure matching the given keyword args, or create one. `gcf` Get a reference to the current figure. `gci` Get the current colorable artist. `get_figlabels` Return a list of existing figure labels. `get_fignums` Return a list of existing figure numbers. `grid` Turn the axes grids on or off. `hexbin` Make a hexagonal binning plot. `hist` Plot a histogram. `hist2d` Make a 2D histogram plot. `hlines` Plot horizontal lines at each `y` from `xmin` to `xmax`. `hold` Set the hold state. `imread` Read an image from a file into an array. `imsave` Save an array as in image file. `imshow` Display an image on the axes. `ioff` Turn interactive mode off. `ion` Turn interactive mode on. `ishold` Return the hold status of the current axes. `isinteractive` Return status of interactive mode. `legend` Places a legend on the axes. `locator_params` Control behavior of tick locators. `loglog` Make a plot with log scaling on both the *x* and *y* axis. `magnitude_spectrum` Plot the magnitude spectrum. `margins` Set or retrieve autoscaling margins. `matshow` Display an array as a matrix in a new figure window. `minorticks_off` Remove minor ticks from the current plot. `minorticks_on` Display minor ticks on the current plot. `over` Call a function with hold(True). `pause` Pause for *interval* seconds. `pcolor` Create a pseudocolor plot of a 2-D array. `pcolormesh` Plot a quadrilateral mesh. `phase_spectrum` Plot the phase spectrum. `pie` Plot a pie chart. `plot` Plot lines and/or markers to the :class:`~matplotlib.axes.Axes`. `plot_date` Plot with data with dates. `plotfile` Plot the data in in a file. `polar` Make a polar plot. `psd` Plot the power spectral density. `quiver` Plot a 2-D field of arrows. `quiverkey` Add a key to a quiver plot. `rc` Set the current rc params. `rc_context` Return a context manager for managing rc settings. `rcdefaults` Restore the default rc params. `rgrids` Get or set the radial gridlines on a polar plot. `savefig` Save the current figure. `sca` Set the current Axes instance to *ax*. `scatter` Make a scatter plot of x vs y, where x and y are sequence like objects of the same lengths. `sci` Set the current image. `semilogx` Make a plot with log scaling on the *x* axis. `semilogy` Make a plot with log scaling on the *y* axis. `set_cmap` Set the default colormap. `setp` Set a property on an artist object. `show` Display a figure. `specgram` Plot a spectrogram. `spy` Plot the sparsity pattern on a 2-D array. `stackplot` Draws a stacked area plot. `stem` Create a stem plot. `step` Make a step plot. `streamplot` Draws streamlines of a vector flow. `subplot` Return a subplot axes positioned by the given grid definition. `subplot2grid` Create a subplot in a grid. `subplot_tool` Launch a subplot tool window for a figure. `subplots` Create a figure with a set of subplots already made. `subplots_adjust` Tune the subplot layout. `suptitle` Add a centered title to the figure. `switch_backend` Switch the default backend. `table` Add a table to the current axes. `text` Add text to the axes. `thetagrids` Get or set the theta locations of the gridlines in a polar plot. `tick_params` Change the appearance of ticks and tick labels. `ticklabel_format` Change the `~matplotlib.ticker.ScalarFormatter` used by default for linear axes. `tight_layout` Automatically adjust subplot parameters to give specified padding. `title` Set a title of the current axes. `tricontour` Draw contours on an unstructured triangular grid. `tricontourf` Draw contours on an unstructured triangular grid. `tripcolor` Create a pseudocolor plot of an unstructured triangular grid. `triplot` Draw a unstructured triangular grid as lines and/or markers. `twinx` Make a second axes that shares the *x*-axis. `twiny` Make a second axes that shares the *y*-axis. `violinplot` Make a violin plot. `vlines` Plot vertical lines. `xcorr` Plot the cross correlation between *x* and *y*. `xkcd` Turns on `xkcd <http://xkcd.com/>`_ sketch-style drawing mode. `xlabel` Set the *x* axis label of the current axis. `xlim` Get or set the *x* limits of the current axes. `xscale` Set the scaling of the *x*-axis. `xticks` Get or set the *x*-limits of the current tick locations and labels. `ylabel` Set the *y* axis label of the current axis. `ylim` Get or set the *y*-limits of the current axes. `yscale` Set the scaling of the *y*-axis. `yticks` Get or set the *y*-limits of the current tick locations and labels. ==================== =================================================================================================================================

Button

A GUI neutral button. For the button to remain responsive you must keep a reference to it. The following attributes are accessible *ax* The :class:`matplotlib.axes.Axes` the button renders into. *label* A :class:`matplotlib.text.Text` instance. *color* The color of the button when not hovering. *hovercolor* The color of the button when hovering. Call :meth:`on_clicked` to connect to the button

Circle

A circle patch.

GridSpec

A class that specifies the geometry of the grid that a subplot will be placed. The location of grid is determined by similar way as the SubplotParams.

rcParams

A dictionary object including validation validating functions are defined and associated with rc parameters in :mod:`matplotlib.rcsetup`

Polygon

A general polygon patch.

Line2D

A line - the line can have both a solid linestyle connecting all the vertices, and a marker at each vertex. Additionally, the drawing of the solid line is influenced by the drawstyle, e.g., one can create "stepped" lines in various styles.

_backend_mod

A matplotlib backend for publishing figures via display_data

PolarAxes

A polar graph projection, where the input dimensions are *theta*, *r*. Theta starts pointing east and goes anti-clockwise.

Slider

A slider representing a floating point range. For the slider to remain responsive you must maintain a reference to it. The following attributes are defined *ax* : the slider :class:`matplotlib.axes.Axes` instance *val* : the current slider value *vline* : a :class:`matplotlib.lines.Line2D` instance representing the initial value of the slider *poly* : A :class:`matplotlib.patches.Polygon` instance which is the slider knob *valfmt* : the format string for formatting the slider text *label* : a :class:`matplotlib.text.Text` instance for the slider label *closedmin* : whether the slider is closed on the minimum *closedmax* : whether the slider is closed on the maximum *slidermin* : another slider - if not *None*, this slider must be greater than *slidermin* *slidermax* : another slider - if not *None*, this slider must be less than *slidermax* *dragging* : allow for mouse dragging on slider Call :meth:`on_changed` to connect to the slider event

suptitle

Add a centered title to the figure. kwargs are :class:`matplotlib.text.Text` properties. Using figure coordinates, the defaults are: *x* : 0.5 The x location of the text in figure coords *y* : 0.98 The y location of the text in figure coords *horizontalalignment* : 'center' The horizontal alignment of the text *verticalalignment* : 'top' The vertical alignment of the text A :class:`matplotlib.text.Text` instance is returned. Example:: fig.suptitle('this is the figure title', fontsize=12)

register_cmap

Add a colormap to the set recognized by :func:`get_cmap`. It can be used in two ways:: register_cmap(name='swirly', cmap=swirly_cmap) register_cmap(name='choppy', data=choppydata, lut=128) In the first case, *cmap* must be a :class:`matplotlib.colors.Colormap` instance. The *name* is optional; if absent, the name will be the :attr:`~matplotlib.colors.Colormap.name` attribute of the *cmap*. In the second case, the three arguments are passed to the :class:`~matplotlib.colors.LinearSegmentedColormap` initializer, and the resulting colormap is registered.

axhline

Add a horizontal line across the axis. Parameters ---------- y : scalar, optional, default: 0 y position in data coordinates of the horizontal line. xmin : scalar, optional, default: 0 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. xmax : scalar, optional, default: 1 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. Returns ------- `~matplotlib.lines.Line2D` Notes ----- kwargs are the same as kwargs to plot, and can be used to control the line properties. e.g., Examples -------- * 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, with the exception of 'transform': agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number See also -------- axhspan : for example plot and source code Additional kwargs: hold = [True|False] overrides default hold state

axhspan

Add a horizontal span (rectangle) across the axis. Call signature:: axhspan(ymin, ymax, xmin=0, xmax=1, **kwargs) *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, e.g., 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: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance capstyle: ['butt' | 'round' | 'projecting'] clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] joinstyle: ['miter' | 'round' | 'bevel'] label: string or anything printable with '%s' conversion. linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/axhspan_demo.py Additional kwargs: hold = [True|False] overrides default hold state

quiverkey

Add a key to a quiver plot. Call signature:: quiverkey(Q, X, Y, U, label, **kw) Arguments: *Q*: The Quiver instance returned by a call to quiver. *X*, *Y*: The location of the key; additional explanation follows. *U*: The length of the key *label*: A string with the length and units of the key Keyword arguments: *coordinates* = [ 'axes' | 'figure' | 'data' | 'inches' ] Coordinate system and units for *X*, *Y*: 'axes' and 'figure' are normalized coordinate systems with 0,0 in the lower left and 1,1 in the upper right; 'data' are the axes data coordinates (used for the locations of the vectors in the quiver plot itself); 'inches' is position in the figure in inches, with 0,0 at the lower left corner. *color*: overrides face and edge colors from *Q*. *labelpos* = [ 'N' | 'S' | 'E' | 'W' ] Position the label above, below, to the right, to the left of the arrow, respectively. *labelsep*: Distance in inches between the arrow and the label. Default is 0.1 *labelcolor*: defaults to default :class:`~matplotlib.text.Text` color. *fontproperties*: A dictionary with keyword arguments accepted by the :class:`~matplotlib.font_manager.FontProperties` initializer: *family*, *style*, *variant*, *size*, *weight* Any additional keyword arguments are used to override vector properties taken from *Q*. The positioning of the key depends on *X*, *Y*, *coordinates*, and *labelpos*. If *labelpos* is 'N' or 'S', *X*, *Y* give the position of the middle of the key arrow. If *labelpos* is 'E', *X*, *Y* positions the head, and if *labelpos* is 'W', *X*, *Y* positions the tail; in either of these two cases, *X*, *Y* is somewhere in the middle of the arrow+label key object. Additional kwargs: hold = [True|False] overrides default hold state

table

Add a table to the current axes. Call signature:: table(cellText=None, cellColours=None, cellLoc='right', colWidths=None, rowLabels=None, rowColours=None, rowLoc='left', colLabels=None, colColours=None, colLoc='center', loc='bottom', bbox=None): Returns a :class:`matplotlib.table.Table` instance. For finer grained control over tables, use the :class:`~matplotlib.table.Table` class and add it to the axes with :meth:`~matplotlib.axes.Axes.add_table`. Thanks to John Gill for providing the class and table. kwargs control the :class:`~matplotlib.table.Table` properties: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] contains: a callable function figure: a :class:`matplotlib.figure.Figure` instance fontsize: a float in points gid: an id string label: string or anything printable with '%s' conversion. lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number

axvline

Add a vertical line across the axes. Parameters ---------- x : scalar, optional, default: 0 x position in data coordinates of the vertical line. ymin : scalar, optional, default: 0 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. ymax : scalar, optional, default: 1 Should be between 0 and 1, 0 being the far left of the plot, 1 the far right of the plot. Returns ------- `~matplotlib.lines.Line2D` Examples --------- * 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, with the exception of 'transform': agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number See also -------- axhspan : for example plot and source code Additional kwargs: hold = [True|False] overrides default hold state

axvspan

Add a vertical span (rectangle) across the axes. Call signature:: axvspan(xmin, xmax, ymin=0, ymax=1, **kwargs) *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, e.g., 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: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance capstyle: ['butt' | 'round' | 'projecting'] clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] joinstyle: ['miter' | 'round' | 'bevel'] label: string or anything printable with '%s' conversion. linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number .. seealso:: :meth:`axhspan` for example plot and source code Additional kwargs: hold = [True|False] overrides default hold state

arrow

Add an arrow to the axes. Call signature:: arrow(x, y, dx, dy, **kwargs) Draws arrow on specified axis from (*x*, *y*) to (*x* + *dx*, *y* + *dy*). Uses FancyArrow patch to construct the arrow. The resulting arrow is affected by the axes aspect ratio and limits. This may produce an arrow whose head is not square with its stem. To create an arrow whose head is square with its stem, use :meth:`annotate` for example:: ax.annotate("", xy=(0.5, 0.5), xytext=(0, 0), arrowprops=dict(arrowstyle="->")) Optional kwargs control the arrow construction and properties: Constructor arguments *width*: float (default: 0.001) width of full arrow tail *length_includes_head*: [True | False] (default: False) True if head is to be counted in calculating the length. *head_width*: float or None (default: 3*width) total width of the full arrow head *head_length*: float or None (default: 1.5 * head_width) length of arrow head *shape*: ['full', 'left', 'right'] (default: 'full') draw the left-half, right-half, or full arrow *overhang*: float (default: 0) fraction that the arrow is swept back (0 overhang means triangular shape). Can be negative or greater than one. *head_starts_at_zero*: [True | False] (default: False) if True, the head starts being drawn at coordinate 0 instead of ending at coordinate 0. Other valid kwargs (inherited from :class:`Patch`) are: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance capstyle: ['butt' | 'round' | 'projecting'] clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] joinstyle: ['miter' | 'round' | 'bevel'] label: string or anything printable with '%s' conversion. linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/arrow_demo.py Additional kwargs: hold = [True|False] overrides default hold state

axes

Add an axes to the figure. The axes is added 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 Description ======= ============== ============================================== 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? aspect [str | num] ['equal', 'auto'] or a number. If a number the ratio of x-unit/y-unit in screen-space. Also see :meth:`~matplotlib.axes.Axes.set_aspect`. ======= ============== ============================================== Examples: * :file:`examples/pylab_examples/axes_demo.py` places custom axes. * :file:`examples/pylab_examples/shared_axis_demo.py` uses *sharex* and *sharey*.

figtext

text

Add text to the axes. Add text in string `s` to axis at location `x`, `y`, data coordinates. Parameters ---------- x, y : scalars data coordinates s : string text fontdict : dictionary, optional, default: None A dictionary to override the default text properties. If fontdict is None, the defaults are determined by your rc parameters. withdash : boolean, optional, default: False Creates a `~matplotlib.text.TextWithDash` instance instead of a `~matplotlib.text.Text` instance. Other parameters ---------------- kwargs : `~matplotlib.text.Text` properties. Other miscellaneous text parameters. Examples -------- 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 (e.g., to set a background color) by using the keyword `bbox`. `bbox` is a dictionary of `~matplotlib.patches.Rectangle` properties. For example:: >>> text(x, y, s, bbox=dict(facecolor='red', alpha=0.5))

figimage

Adds a non-resampled image to the figure. call signatures:: figimage(X, **kwargs) adds a non-resampled array *X* to the figure. :: figimage(X, xo, yo) with pixel offsets *xo*, *yo*, *X* must be a float array: * If *X* is MxN, assume luminance (grayscale) * If *X* is MxNx3, assume RGB * If *X* is MxNx4, assume RGBA Optional keyword arguments: ========= ========================================================= Keyword Description ========= ========================================================= xo or yo An integer, the *x* and *y* image offset in pixels cmap a :class:`matplotlib.colors.Colormap` instance, e.g., cm.jet. If *None*, default to the rc ``image.cmap`` value norm a :class:`matplotlib.colors.Normalize` instance. The default is normalization(). This scales luminance -> 0-1 vmin|vmax are 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 you pass a norm instance, the settings for *vmin* and *vmax* will be ignored. alpha the alpha blending value, default is *None* origin [ 'upper' | 'lower' ] Indicates where the [0,0] index of the array is in the upper left or lower left corner of the axes. Defaults to the rc image.origin value ========= ========================================================= figimage complements the axes image (:meth:`~matplotlib.axes.Axes.imshow`) which will be resampled to fit the current axes. If you want a resampled image to fill the entire figure, you can define an :class:`~matplotlib.axes.Axes` with size [0,1,0,1]. An :class:`matplotlib.image.FigureImage` instance is returned. .. plot:: mpl_examples/pylab_examples/figimage_demo.py Additional kwargs are Artist kwargs passed on to :class:`~matplotlib.image.FigureImage` Addition kwargs: hold = [True|False] overrides default hold state

_autogen_docstring

Autogenerated wrappers will get their docstring from a base function with an addendum.

tight_layout

Automatically 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`. rect : if rect is given, it is interpreted as a rectangle (left, bottom, right, top) in the normalized figure coordinate that the whole subplots area (including labels) will fit into. Default is (0, 0, 1, 1).

autoscale

Autoscale the axis view to the data (toggle). Convenience method for simple axis view autoscaling. It turns autoscaling on or off, and then, if autoscaling for either axis is on, it performs the autoscaling on the specified axis or axes. *enable*: [True | False | None] True (default) turns autoscaling on, False turns it off. None leaves the autoscaling state unchanged. *axis*: ['x' | 'y' | 'both'] which axis to operate on; default is 'both' *tight*: [True | False | None] If True, set view limits to data limits; if False, let the locator and margins expand the view limits; if None, use tight scaling if the only artist is an image, otherwise treat *tight* as False. The *tight* setting is retained for future autoscaling until it is explicitly changed. Returns None.

over

Call a function with hold(True). Calls:: func(*args, **kwargs) with ``hold(True)`` and then restores the hold state.

ginput

Call signature:: ginput(self, n=1, timeout=30, show_clicks=True, mouse_add=1, mouse_pop=3, mouse_stop=2) 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 zero or negative, does not timeout. If *n* is zero or negative, accumulate clicks until a middle click (or potentially both mouse buttons at once) terminates the input. Right clicking cancels last input. The buttons used for the various actions (adding points, removing points, terminating the inputs) can be overriden via the arguments *mouse_add*, *mouse_pop* and *mouse_stop*, that give the associated mouse button: 1 for left, 2 for middle, 3 for right. The keyboard can also be used to select points in case your mouse does not have one or more of the buttons. The delete and backspace keys act like right clicking (i.e., remove last point), the enter key terminates input and any other key (not already used by the window manager) selects a point.

waitforbuttonpress

Call signature:: waitforbuttonpress(self, timeout=-1) Blocking call to interact with the figure. This will return True is a key was pressed, False if a mouse button was pressed and None if *timeout* was reached without either being pressed. If *timeout* is negative, does not timeout.

ticklabel_format

Change the `~matplotlib.ticker.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. *useOffset* [True | False | offset]; if True, the offset will be calculated as needed; if False, no offset will be used; if a numeric offset is specified, it will be used. *axis* [ 'x' | 'y' | 'both' ] *useLocale* If True, format the number according to the current locale. This affects things such as the character used for the decimal separator. If False, use C-style (English) formatting. The default setting is controlled by the axes.formatter.use_locale rcparam. ============ ========================================= 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.

tick_params

Change the appearance of ticks and tick labels. Keyword arguments: *axis* : ['x' | 'y' | 'both'] Axis on which to operate; default is 'both'. *reset* : [True | False] If *True*, set all parameters to defaults before processing other keyword arguments. Default is *False*. *which* : ['major' | 'minor' | 'both'] Default is 'major'; apply arguments to *which* ticks. *direction* : ['in' | 'out' | 'inout'] Puts ticks inside the axes, outside the axes, or both. *length* Tick length in points. *width* Tick width in points. *color* Tick color; accepts any mpl color spec. *pad* Distance in points between tick and label. *labelsize* Tick label font size in points or as a string (e.g., 'large'). *labelcolor* Tick label color; mpl color spec. *colors* Changes the tick color and the label color to the same value: mpl color spec. *zorder* Tick and label zorder. *bottom*, *top*, *left*, *right* : [bool | 'on' | 'off'] controls whether to draw the respective ticks. *labelbottom*, *labeltop*, *labelleft*, *labelright* Boolean or ['on' | 'off'], controls whether to draw the respective tick labels. Example:: ax.tick_params(direction='out', length=6, width=2, colors='r') This will make all major ticks be red, pointing out of the box, and with dimensions 6 points by 2 points. Tick labels will also be red.

cla

Clear the current axes.

clf

Clear the current figure.

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

__loader__

Concrete implementation of SourceLoader using the file system.

connect

Connect event with string *s* to *func*. The signature of *func* is:: def func(event) where event is a :class:`matplotlib.backend_bases.Event`. The following events are recognized - 'button_press_event' - 'button_release_event' - 'draw_event' - 'key_press_event' - 'key_release_event' - 'motion_notify_event' - 'pick_event' - 'resize_event' - 'scroll_event' - 'figure_enter_event', - 'figure_leave_event', - 'axes_enter_event', - 'axes_leave_event' - 'close_event' For the location events (button and key press/release), if the mouse is over the axes, the variable ``event.inaxes`` will be set to the :class:`~matplotlib.axes.Axes` the event occurs is over, and additionally, the variables ``event.xdata`` and ``event.ydata`` will be defined. This is the mouse location in data coords. See :class:`~matplotlib.backend_bases.KeyEvent` and :class:`~matplotlib.backend_bases.MouseEvent` for more info. Return value is a connection id that can be used with :meth:`~matplotlib.backend_bases.Event.mpl_disconnect`. Example usage:: def on_press(event): print('you pressed', event.button, event.xdata, event.ydata) cid = canvas.mpl_connect('button_press_event', on_press)

locator_params

Control behavior of tick locators. Keyword arguments: *axis* ['x' | 'y' | 'both'] Axis on which to operate; default is 'both'. *tight* [True | False | None] Parameter passed to :meth:`autoscale_view`. Default is None, for no change. Remaining keyword arguments are passed to directly to the :meth:`~matplotlib.ticker.MaxNLocator.set_params` method. Typically one might want to reduce the maximum number of ticks and use tight bounds when plotting small subplots, for example:: ax.locator_params(tight=True, nbins=4) Because the locator is involved in autoscaling, :meth:`autoscale_view` is called automatically after the parameters are changed. This presently works only for the :class:`~matplotlib.ticker.MaxNLocator` used by default on linear axes, but it may be generalized.

axis

Convenience method to get or set axis properties. Calling with no arguments:: >>> 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.

Formatter

Convert the tick location to a string

subplots

Create a figure with a set of subplots already made. This utility wrapper makes it convenient to create common layouts of subplots, including the enclosing figure object, in a single call. Keyword arguments: *nrows* : int Number of rows of the subplot grid. Defaults to 1. *ncols* : int Number of columns of the subplot grid. Defaults to 1. *sharex* : string or bool If *True*, the X axis will be shared amongst all subplots. If *True* and you have multiple rows, the x tick labels on all but the last row of plots will have visible set to *False* If a string must be one of "row", "col", "all", or "none". "all" has the same effect as *True*, "none" has the same effect as *False*. If "row", each subplot row will share a X axis. If "col", each subplot column will share a X axis and the x tick labels on all but the last row will have visible set to *False*. *sharey* : string or bool If *True*, the Y axis will be shared amongst all subplots. If *True* and you have multiple columns, the y tick labels on all but the first column of plots will have visible set to *False* If a string must be one of "row", "col", "all", or "none". "all" has the same effect as *True*, "none" has the same effect as *False*. If "row", each subplot row will share a Y axis and the y tick labels on all but the first column will have visible set to *False*. If "col", each subplot column will share a Y axis. *squeeze* : bool If *True*, extra dimensions are squeezed out from the returned axis object: - if only one subplot is constructed (nrows=ncols=1), the resulting single Axis object is returned as a scalar. - for Nx1 or 1xN subplots, the returned object is a 1-d numpy object array of Axis objects are returned as numpy 1-d arrays. - for NxM subplots with N>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 containing 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. *gridspec_kw* : dict Dict with keywords passed to the :class:`~matplotlib.gridspec.GridSpec` constructor used to create the grid the subplots are placed on. *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)

figaspect

Create a figure with specified aspect ratio. If *arg* is a number, use that aspect ratio. If *arg* is an array, figaspect will determine the width and height for a figure that would fit array preserving aspect ratio. The figure width, height in inches are returned. Be sure to create an axes with equal with and height, e.g., Example usage:: # make a figure twice as tall as it is wide w, h = figaspect(2.) fig = Figure(figsize=(w,h)) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.imshow(A, **kwargs) # make a figure with the proper aspect for an array A = rand(5,3) w, h = figaspect(A) fig = Figure(figsize=(w,h)) ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) ax.imshow(A, **kwargs) Thanks to Fernando Perez for this function

new_figure_manager

Create a new figure manager instance

pcolor

Create a pseudocolor plot of a 2-D array. .. note:: pcolor can be very slow for large arrays; consider using the similar but much faster :func:`~matplotlib.pyplot.pcolormesh` instead. Call signatures:: pcolor(C, **kwargs) pcolor(X, Y, C, **kwargs) *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 <axes-pcolor-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.colors.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 is *None*, it is autoscaled to the respective min or max of the color array *C*. If not *None*, *vmin* or *vmax* passed in here override any pre-existing values supplied in the *norm* instance. *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. 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`` or *None* the alpha blending value *snap*: bool Whether to snap the mesh to pixel boundaries. Return value is a :class:`matplotlib.collections.Collection` instance. .. _axes-pcolor-grid-orientation: The grid orientation follows the MATLAB 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:`meshgrid`:: x = np.arange(5) y = np.arange(3) X, Y = np.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 to transpose C:: 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.collections.PolyCollection` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number .. note:: The default *antialiaseds* is False if the default *edgecolors*="none" is used. This eliminates artificial lines at patch boundaries, and works regardless of the value of alpha. If *edgecolors* is not "none", then the default *antialiaseds* is taken from rcParams['patch.antialiased'], which defaults to *True*. Stroking the edges may be preferred if *alpha* is 1, but will cause artifacts otherwise. .. seealso:: :func:`~matplotlib.pyplot.pcolormesh` For an explanation of the differences between pcolor and pcolormesh. Additional kwargs: hold = [True|False] overrides default hold state

tripcolor

Create a pseudocolor plot of an unstructured triangular grid. The triangulation can be specified in one of two ways; either:: tripcolor(triangulation, ...) where triangulation is a :class:`matplotlib.tri.Triangulation` object, or :: tripcolor(x, y, ...) tripcolor(x, y, triangles, ...) tripcolor(x, y, triangles=triangles, ...) tripcolor(x, y, mask=mask, ...) tripcolor(x, y, triangles, mask=mask, ...) in which case a Triangulation object will be created. See :class:`~matplotlib.tri.Triangulation` for a explanation of these possibilities. The next argument must be *C*, the array of color values, either one per point in the triangulation if color values are defined at points, or one per triangle in the triangulation if color values are defined at triangles. If there are the same number of points and triangles in the triangulation it is assumed that color values are defined at points; to force the use of color values at triangles use the kwarg *facecolors*=C instead of just *C*. *shading* may be 'flat' (the default) or 'gouraud'. If *shading* is 'flat' and C values are defined at points, the color values used for each triangle are from the mean C of the triangle's three points. If *shading* is 'gouraud' then color values must be defined at points. *shading* of 'faceted' is deprecated; please use *edgecolors* instead. The remaining kwargs are the same as for :meth:`~matplotlib.axes.Axes.pcolor`. **Example:** .. plot:: mpl_examples/pylab_examples/tripcolor_demo.py Additional kwargs: hold = [True|False] overrides default hold state

stem

Create a stem plot. Call signatures:: stem(y, linefmt='b-', markerfmt='bo', basefmt='r-') stem(x, y, linefmt='b-', markerfmt='bo', basefmt='r-') A stem plot plots vertical lines (using *linefmt*) at each *x* location from the baseline to *y*, and places a marker there using *markerfmt*. A horizontal line at 0 is is plotted using *basefmt*. If no *x* values are provided, the default is (0, 1, ..., len(y) - 1) Return value is a tuple (*markerline*, *stemlines*, *baseline*). .. seealso:: This `document <http://www.mathworks.com/help/techdoc/ref/stem.html>`_ for details. **Example:** .. plot:: mpl_examples/pylab_examples/stem_plot.py Additional kwargs: hold = [True|False] overrides default hold state

subplot2grid

Create a subplot in a grid. The grid is specified by *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)

annotate

Create an annotation: a piece of text referring to a data point. Parameters ---------- s : string label xy : (x, y) position of element to annotate xytext : (x, y) , optional, default: None position of the label `s` xycoords : string, optional, default: "data" string that indicates what type of coordinates `xy` is. Examples: "figure points", "figure pixels", "figure fraction", "axes points", .... See `matplotlib.text.Annotation` for more details. textcoords : string, optional string that indicates what type of coordinates `text` is. Examples: "figure points", "figure pixels", "figure fraction", "axes points", .... See `matplotlib.text.Annotation` for more details. Default is None. arrowprops : `matplotlib.lines.Line2D` properties, optional Dictionary of line properties for the arrow that connects the annotation to the point. If the dictionnary has a key `arrowstyle`, a `~matplotlib.patches.FancyArrowPatch` instance is created and drawn. See `matplotlib.text.Annotation` for more details on valid options. Default is None. Returns ------- a : `~matplotlib.text.Annotation` Notes ----- *arrowprops*, if not *None*, is a dictionary of line properties (see :class:`matplotlib.lines.Line2D`) for the arrow that connects annotation to the point. If the dictionary has a key *arrowstyle*, a `~matplotlib.patches.FancyArrowPatch` instance is created with the given dictionary and is drawn. Otherwise, a `~matplotlib.patches.YAArrow` patch instance is created and drawn. Valid keys for `~matplotlib.patches.YAArrow` are: ========= =========================================================== Key Description ========= =========================================================== width the width of the arrow in points frac the fraction of the arrow length occupied by the head headwidth the width of the base of the arrow head in points shrink oftentimes it is convenient to have the arrowtip and base a bit away from the text and point being annotated. If *d* is the distance between the text and annotated point, shrink will shorten the arrow so the tip and base are shink percent of the distance *d* away from the endpoints. i.e., ``shrink=0.05 is 5%`` ? any key for :class:`matplotlib.patches.polygon` ========= =========================================================== Valid keys for `~matplotlib.patches.FancyArrowPatch` are: =============== ====================================================== Key Description =============== ====================================================== arrowstyle the arrow style connectionstyle the connection style relpos default is (0.5, 0.5) patchA default is bounding box of the text patchB default is None shrinkA default is 2 points shrinkB default is 2 points mutation_scale default is text size (in points) mutation_aspect default is 1. ? any key for :class:`matplotlib.patches.PathPatch` =============== ====================================================== *xycoords* and *textcoords* are strings that indicate the coordinates of *xy* and *xytext*, and may be one of the following values: ================= =================================================== Property Description ================= =================================================== 'figure points' points from the lower left corner of the figure 'figure pixels' pixels from the lower left corner of the figure 'figure fraction' 0,0 is lower left of figure and 1,1 is upper right 'axes points' points from lower left corner of axes 'axes pixels' pixels from lower left corner of axes 'axes fraction' 0,0 is lower left of axes and 1,1 is upper right 'data' use the coordinate system of the object being annotated (default) 'offset points' Specify an offset (in points) from the *xy* value 'polar' you can specify *theta*, *r* for the annotation, even in cartesian plots. Note that if you are using a polar axes, you do not need to specify polar for the coordinate system since that is the native "data" coordinate system. ================= =================================================== If a 'points' or 'pixels' option is specified, values will be added to the bottom-left and if negative, values will be subtracted from the top-right. e.g.:: # 10 points to the right of the left border of the axes and # 5 points below the top border xy=(10,-5), xycoords='axes points' You may use an instance of :class:`~matplotlib.transforms.Transform` or :class:`~matplotlib.artist.Artist`. See :ref:`plotting-guide-annotation` for more details. The *annotation_clip* attribute controls the visibility of the annotation when it goes outside the axes area. If `True`, the annotation will only be drawn when the *xy* is inside the axes. If `False`, the annotation will always be drawn regardless of its position. The default is `None`, which behave as `True` only if *xycoords* is "data". Additional kwargs are `~matplotlib.text.Text` properties: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance backgroundcolor: any matplotlib color bbox: rectangle prop dict clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: any matplotlib color contains: a callable function family or fontname or fontfamily or name: [FONTNAME | 'serif' | 'sans-serif' | 'cursive' | 'fantasy' | 'monospace' ] figure: a :class:`matplotlib.figure.Figure` instance fontproperties or font_properties: a :class:`matplotlib.font_manager.FontProperties` instance gid: an id string horizontalalignment or ha: [ 'center' | 'right' | 'left' ] label: string or anything printable with '%s' conversion. linespacing: float (multiple of font size) lod: [True | False] multialignment: ['left' | 'right' | 'center' ] path_effects: unknown picker: [None|float|boolean|callable] position: (x,y) rasterized: [True | False | None] rotation: [ angle in degrees | 'vertical' | 'horizontal' ] rotation_mode: unknown size or fontsize: [size in points | 'xx-small' | 'x-small' | 'small' | 'medium' | 'large' | 'x-large' | 'xx-large' ] sketch_params: unknown snap: unknown stretch or fontstretch: [a numeric value in range 0-1000 | 'ultra-condensed' | 'extra-condensed' | 'condensed' | 'semi-condensed' | 'normal' | 'semi-expanded' | 'expanded' | 'extra-expanded' | 'ultra-expanded' ] style or fontstyle: [ 'normal' | 'italic' | 'oblique'] text: string or anything printable with '%s' conversion. transform: :class:`~matplotlib.transforms.Transform` instance url: a url string variant or fontvariant: [ 'normal' | 'small-caps' ] verticalalignment or ma or va: [ 'center' | 'top' | 'bottom' | 'baseline' ] visible: [True | False] weight or fontweight: [a numeric value in range 0-1000 | 'ultralight' | 'light' | 'normal' | 'regular' | 'book' | 'medium' | 'roman' | 'semibold' | 'demibold' | 'demi' | 'bold' | 'heavy' | 'extra bold' | 'black' ] x: float y: float zorder: any number Examples -------- .. plot:: mpl_examples/pylab_examples/annotation_demo2.py

figure

Creates a new figure. Parameters ---------- num : integer or string, optional, default: none If not provided, a new figure will be created, and the figure number will be incremented. The figure objects holds this number in a `number` attribute. If num is provided, and a figure with this id already exists, make it active, and returns a reference to it. If this figure does not exists, create it and returns it. If num is a string, the window title will be set to this figure's `num`. figsize : tuple of integers, optional, default: None width, height in inches. If not provided, defaults to rc figure.figsize. dpi : integer, optional, default: None resolution of the figure. If not provided, defaults to rc figure.dpi. facecolor : the background color. If not provided, defaults to rc figure.facecolor edgecolor : the border color. If not provided, defaults to rc figure.edgecolor Returns ------- figure : Figure The Figure instance returned will also be passed to new_figure_manager in the backends, which allows to hook custom Figure classes into the pylab interface. Additional kwargs will be passed to the figure init function. Notes ----- 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. rcParams defines the default values, which can be modified in the matplotlibrc file

LinearLocator

Determine the tick locations The first time this function is called it will try to set the number of ticks to make a nice tick partitioning. Thereafter the number of ticks will be fixed so that interactive navigation will be nice

Locator

Determine the tick locations; Note, you should not use the same locator between different :class:`~matplotlib.axis.Axis` because the locator stores references to the Axis data and view limits

show

Display a figure. 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.

matshow

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.

imshow

Display an image on the axes. Parameters ----------- X : array_like, shape (n, m) or (n, m, 3) or (n, m, 4) Display the image in `X` to current axes. `X` may be a float array, a uint8 array or a PIL image. If `X` is an array, it can have the following shapes: - MxN -- luminance (grayscale, float array only) - MxNx3 -- RGB (float or uint8 array) - MxNx4 -- RGBA (float or uint8 array) The value for each component of MxNx3 and MxNx4 float arrays should be in the range 0.0 to 1.0; MxN float arrays may be normalised. cmap : `~matplotlib.colors.Colormap`, optional, default: None If None, default to rc `image.cmap` value. `cmap` is ignored when `X` has RGB(A) information aspect : ['auto' | 'equal' | scalar], optional, default: None If 'auto', changes the image aspect ratio to match that of the axes. If 'equal', and `extent` is None, changes the axes aspect ratio to match that of the image. If `extent` is not `None`, the axes aspect ratio is changed to match that of the extent. If None, default to rc ``image.aspect`` value. interpolation : string, optional, default: None Acceptable values are 'none', 'nearest', 'bilinear', 'bicubic', 'spline16', 'spline36', 'hanning', 'hamming', 'hermite', 'kaiser', 'quadric', 'catrom', 'gaussian', 'bessel', 'mitchell', 'sinc', 'lanczos' If `interpolation` is None, default to rc `image.interpolation`. See also the `filternorm` and `filterrad` parameters. If `interpolation` is 'none', then no interpolation is performed on the Agg, ps and pdf backends. Other backends will fall back to 'nearest'. norm : `~matplotlib.colors.Normalize`, optional, default: None A `~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 `X` is an array of floats. vmin, vmax : scalar, optional, default: None `vmin` and `vmax` are used in conjunction with norm to normalize luminance data. Note if you pass a `norm` instance, your settings for `vmin` and `vmax` will be ignored. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque) origin : ['upper' | 'lower'], optional, default: None 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 : scalars (left, right, bottom, top), optional, default: None The location, in data-coordinates, of the lower-left and upper-right corners. If `None`, the image is positioned such that the pixel centers fall on zero-based (row, column) indices. shape : scalars (columns, rows), optional, default: None For raw buffer images filternorm : scalar, optional, default: 1 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 : scalar, optional, default: 4.0 The filter radius for filters that have a radius parameter, i.e. when interpolation is one of: 'sinc', 'lanczos' or 'blackman' Returns -------- image : `~matplotlib.image.AxesImage` Other parameters ---------------- kwargs : `~matplotlib.artist.Artist` properties. See also -------- matshow : Plot a matrix or an array as an image. Examples -------- .. plot:: mpl_examples/pylab_examples/image_demo.py Additional kwargs: hold = [True|False] overrides default hold state

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.

triplot

Draw a unstructured triangular grid as lines and/or markers. The triangulation to plot can be specified in one of two ways; either:: triplot(triangulation, ...) where triangulation is a :class:`matplotlib.tri.Triangulation` object, or :: triplot(x, y, ...) triplot(x, y, triangles, ...) triplot(x, y, triangles=triangles, ...) triplot(x, y, mask=mask, ...) triplot(x, y, triangles, mask=mask, ...) in which case a Triangulation object will be created. See :class:`~matplotlib.tri.Triangulation` for a explanation of these possibilities. The remaining args and kwargs are the same as for :meth:`~matplotlib.axes.Axes.plot`. Return a list of 2 :class:`~matplotlib.lines.Line2D` containing respectively: - the lines plotted for triangles edges - the markers plotted for triangles nodes **Example:** .. plot:: mpl_examples/pylab_examples/triplot_demo.py Additional kwargs: hold = [True|False] overrides default hold state

tricontour

Draw contours on an unstructured triangular grid. :func:`~matplotlib.pyplot.tricontour` and :func:`~matplotlib.pyplot.tricontourf` draw contour lines and filled contours, respectively. Except as noted, function signatures and return values are the same for both versions. The triangulation can be specified in one of two ways; either:: tricontour(triangulation, ...) where triangulation is a :class:`matplotlib.tri.Triangulation` object, or :: tricontour(x, y, ...) tricontour(x, y, triangles, ...) tricontour(x, y, triangles=triangles, ...) tricontour(x, y, mask=mask, ...) tricontour(x, y, triangles, mask=mask, ...) in which case a Triangulation object will be created. See :class:`~matplotlib.tri.Triangulation` for a explanation of these possibilities. The remaining arguments may be:: tricontour(..., Z) where *Z* is the array of values to contour, one per point in the triangulation. The level values are chosen automatically. :: tricontour(..., Z, N) contour *N* automatically-chosen levels. :: tricontour(..., Z, V) draw contour lines at the values specified in sequence *V* :: tricontourf(..., Z, V) fill the (len(*V*)-1) regions between the values in *V* :: tricontour(Z, **kwargs) Use keyword args to control colors, linewidth, origin, cmap ... see below for more details. ``C = tricontour(...)`` returns a :class:`~matplotlib.contour.TriContourSet` object. Optional keyword arguments: *colors*: [ *None* | string | (mpl_colors) ] If *None*, the colormap specified by cmap will be used. If a string, like 'r' or 'red', all levels will be plotted in this color. If a tuple of matplotlib color args (string, float, rgb, etc), different levels will be plotted in different colors in the order specified. *alpha*: float The alpha blending value *cmap*: [ *None* | Colormap ] A cm :class:`~matplotlib.colors.Colormap` instance or *None*. If *cmap* is *None* and *colors* is *None*, a default Colormap is used. *norm*: [ *None* | Normalize ] A :class:`matplotlib.colors.Normalize` instance for scaling data values to colors. If *norm* is *None* and *colors* is *None*, the default linear scaling is used. *levels* [level0, level1, ..., leveln] A list of floating point numbers indicating the level curves to draw; e.g., to draw just the zero contour pass ``levels=[0]`` *origin*: [ *None* | 'upper' | 'lower' | 'image' ] If *None*, the first value of *Z* will correspond to the lower left corner, location (0,0). If 'image', the rc value for ``image.origin`` will be used. This keyword is not active if *X* and *Y* are specified in the call to contour. *extent*: [ *None* | (x0,x1,y0,y1) ] If *origin* is not *None*, then *extent* is interpreted as in :func:`matplotlib.pyplot.imshow`: it gives the outer pixel boundaries. In this case, the position of Z[0,0] is the center of the pixel, not a corner. If *origin* is *None*, then (*x0*, *y0*) is the position of Z[0,0], and (*x1*, *y1*) is the position of Z[-1,-1]. This keyword is not active if *X* and *Y* are specified in the call to contour. *locator*: [ *None* | ticker.Locator subclass ] If *locator* is None, the default :class:`~matplotlib.ticker.MaxNLocator` is used. The locator is used to determine the contour levels if they are not given explicitly via the *V* argument. *extend*: [ 'neither' | 'both' | 'min' | 'max' ] Unless this is 'neither', contour levels are automatically added to one or both ends of the range so that all data are included. These added ranges are then mapped to the special colormap values which default to the ends of the colormap range, but can be set via :meth:`matplotlib.colors.Colormap.set_under` and :meth:`matplotlib.colors.Colormap.set_over` methods. *xunits*, *yunits*: [ *None* | registered units ] Override axis units by specifying an instance of a :class:`matplotlib.units.ConversionInterface`. tricontour-only keyword arguments: *linewidths*: [ *None* | number | tuple of numbers ] If *linewidths* is *None*, the default width in ``lines.linewidth`` in ``matplotlibrc`` is used. If a number, all levels will be plotted with this linewidth. If a tuple, different levels will be plotted with different linewidths in the order specified *linestyles*: [ *None* | 'solid' | 'dashed' | 'dashdot' | 'dotted' ] If *linestyles* is *None*, the 'solid' is used. *linestyles* can also be an iterable of the above strings specifying a set of linestyles to be used. If this iterable is shorter than the number of contour levels it will be repeated as necessary. If contour is using a monochrome colormap and the contour level is less than 0, then the linestyle specified in ``contour.negative_linestyle`` in ``matplotlibrc`` will be used. tricontourf-only keyword arguments: *antialiased*: [ *True* | *False* ] enable antialiasing *nchunk*: [ 0 | integer ] If 0, no subdivision of the domain. Specify a positive integer to divide the domain into subdomains of roughly *nchunk* by *nchunk* points. This may never actually be advantageous, so this option may be removed. Chunking introduces artifacts at the chunk boundaries unless *antialiased* is *False*. Note: tricontourf fills intervals that are closed at the top; that is, for boundaries *z1* and *z2*, the filled region is:: z1 < z <= z2 There is one exception: if the lowest boundary coincides with the minimum value of the *z* array, then that minimum value will be included in the lowest interval. **Examples:** .. plot:: mpl_examples/pylab_examples/tricontour_demo.py Additional kwargs: hold = [True|False] overrides default hold state

tricontourf

stackplot

Draws a stacked area plot. *x* : 1d array of dimension N *y* : 2d array of dimension MxN, OR any number 1d arrays each of dimension 1xN. The data is assumed to be unstacked. Each of the following calls is legal:: stackplot(x, y) # where y is MxN stackplot(x, y1, y2, y3, y4) # where y1, y2, y3, y4, are all 1xNm Keyword arguments: *baseline* : ['zero', 'sym', 'wiggle', 'weighted_wiggle'] Method used to calculate the baseline. 'zero' is just a simple stacked plot. 'sym' is symmetric around zero and is sometimes called `ThemeRiver`. 'wiggle' minimizes the sum of the squared slopes. 'weighted_wiggle' does the same but weights to account for size of each layer. It is also called `Streamgraph`-layout. More details can be found at http://www.leebyron.com/else/streamgraph/. *colors* : A list or tuple of colors. These will be cycled through and used to colour the stacked areas. All other keyword arguments are passed to :func:`~matplotlib.Axes.fill_between` Returns *r* : A list of :class:`~matplotlib.collections.PolyCollection`, one for each element in the stacked area plot. Note that :class:`~matplotlib.legend.Legend` does not support :class:`~matplotlib.collections.PolyCollection` objects. To create a legend on a stackplot, use a proxy artist: http://matplotlib.org/users/legend_guide.html#using-proxy-artist Additional kwargs: hold = [True|False] overrides default hold state

streamplot

Draws streamlines of a vector flow. *x*, *y* : 1d arrays an *evenly spaced* grid. *u*, *v* : 2d arrays x and y-velocities. Number of rows should match length of y, and the number of columns should match x. *density* : float or 2-tuple Controls the closeness of streamlines. When `density = 1`, the domain is divided into a 30x30 grid---*density* linearly scales this grid. Each cell in the grid can have, at most, one traversing streamline. For different densities in each direction, use [density_x, density_y]. *linewidth* : numeric or 2d array vary linewidth when given a 2d array with the same shape as velocities. *color* : matplotlib color code, or 2d array Streamline color. When given an array with the same shape as velocities, *color* values are converted to colors using *cmap*. *cmap* : :class:`~matplotlib.colors.Colormap` Colormap used to plot streamlines and arrows. Only necessary when using an array input for *color*. *norm* : :class:`~matplotlib.colors.Normalize` Normalize object used to scale luminance data to 0, 1. If None, stretch (min, max) to (0, 1). Only necessary when *color* is an array. *arrowsize* : float Factor scale arrow size. *arrowstyle* : str Arrow style specification. See :class:`~matplotlib.patches.FancyArrowPatch`. *minlength* : float Minimum length of streamline in axes coordinates. *zorder* : int any number Returns: *stream_container* : StreamplotSet Container object with attributes - lines: `matplotlib.collections.LineCollection` of streamlines - arrows: collection of `matplotlib.patches.FancyArrowPatch` objects representing arrows half-way along stream lines. This container will probably change in the future to allow changes to the colormap, alpha, etc. for both lines and arrows, but these changes should be backward compatible. Additional kwargs: hold = [True|False] overrides default hold state

LogFormatterMathtext

Format values for log axis; using ``exponent = log_base(value)``

_setup_pyplot_info_docstrings

Generates the plotting and docstring. These must be done after the entire module is imported, so it is called from the end of this module, which is generated by boilerplate.py.

get_cmap

Get a colormap instance, defaulting to rc values if *name* is None. Colormaps added with :func:`register_cmap` take precedence over built-in colormaps. If *name* is a :class:`matplotlib.colors.Colormap` instance, it will be returned. If *lut* is not None it must be an integer giving the number of entries desired in the lookup table, and *name* must be a standard mpl colormap name with a corresponding data dictionary in *datad*.

gcf

Get a reference to the current figure.

get_plot_commands

Get a sorted list of all of the plotting commands.

xlim

Get or set the *x* limits 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, e.g.:: 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.

xticks

Get or set the *x*-limits of the current tick locations 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 )

ylim

Get or set the *y*-limits 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, e.g.:: 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.

yticks

Get or set the *y*-limits of the current tick locations 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 )

rgrids

Get or set the radial gridlines 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' )

thetagrids

Get or set the theta locations of the gridlines in a polar plot. 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). e.g., 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') )

gca

Get the current :class:`~matplotlib.axes.Axes` instance on the current figure matching the given keyword args, or create one. Examples --------- To get the current polar axes on the current figure:: plt.gca(projection='polar') If the current axes doesn't exist, or isn't a polar one, the appropriate axes will be created and then returned. See Also -------- matplotlib.figure.Figure.gca : The figure's gca method.

gci

Get the current colorable artist. Specifically, returns 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.

Text

Handle storing and drawing of text in window or data coordinates.

get_scale_docs

Helper function for generating docstrings related to scales.

_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.

draw_if_interactive

Is called after every pylab drawing command

clabel

Label a contour plot. Call signature:: clabel(cs, **kwargs) Adds labels to line contours in *cs*, where *cs* is a :class:`~matplotlib.contour.ContourSet` object returned by contour. :: clabel(cs, v, **kwargs) only labels contours listed in *v*. Optional keyword arguments: *fontsize*: size in points or relative size e.g., 'smaller', 'x-large' *colors*: - if *None*, the color of each label matches the color of the corresponding contour - if one string color, e.g., *colors* = 'r' or *colors* = 'red', all labels will be plotted in this color - if a tuple of matplotlib color args (string, float, rgb, etc), different labels will be plotted in different colors in the order specified *inline*: controls whether the underlying contour is removed or not. Default is *True*. *inline_spacing*: space in pixels to leave on each side of label when placing inline. Defaults to 5. This spacing will be exact for labels at locations where the contour is straight, less so for labels on curved contours. *fmt*: a format string for the label. Default is '%1.3f' Alternatively, this can be a dictionary matching contour levels with arbitrary strings to use for each contour level (i.e., fmt[level]=string), or it can be any callable, such as a :class:`~matplotlib.ticker.Formatter` instance, that returns a string when called with a numeric contour level. *manual*: if *True*, contour labels will be placed manually using mouse clicks. Click the first button near a contour to add a label, click the second button (or potentially both mouse buttons at once) to finish adding labels. The third button can be used to remove the last label added, but only if labels are not inline. Alternatively, the keyboard can be used to select label locations (enter to end label placement, delete or backspace act like the third mouse button, and any other key will select a label location). *manual* can be an iterable object of x,y tuples. Contour labels will be created as if mouse is clicked at each x,y positions. *rightside_up*: if *True* (default), label rotations will always be plus or minus 90 degrees from level. *use_clabeltext*: if *True* (default is False), ClabelText class (instead of matplotlib.Text) is used to create labels. ClabelText recalculates rotation angles of texts during the drawing time, therefore this can be used if aspect of the axes changes. .. plot:: mpl_examples/pylab_examples/contour_demo.py Additional kwargs: hold = [True|False] overrides default hold state

subplot_tool

Launch a subplot tool window for a figure. A :class:`matplotlib.widgets.SubplotTool` instance is returned.

hist2d

Make a 2D histogram plot. Parameters ---------- x, y: array_like, shape (n, ) Input values bins: [None | int | [int, int] | array_like | [array, array]] The bin specification: - If int, the number of bins for the two dimensions (nx=ny=bins). - If [int, int], the number of bins in each dimension (nx, ny = bins). - If array_like, the bin edges for the two dimensions (x_edges=y_edges=bins). - If [array, array], the bin edges in each dimension (x_edges, y_edges = bins). The default value is 10. range : array_like shape(2, 2), optional, default: None The leftmost and rightmost edges of the bins along each dimension (if not specified explicitly in the bins parameters): [[xmin, xmax], [ymin, ymax]]. All values outside of this range will be considered outliers and not tallied in the histogram. normed : boolean, optional, default: False Normalize histogram. weights : array_like, shape (n, ), optional, default: None An array of values w_i weighing each sample (x_i, y_i). cmin : scalar, optional, default: None All bins that has count less than cmin will not be displayed and these count values in the return value count histogram will also be set to nan upon return cmax : scalar, optional, default: None All bins that has count more than cmax will not be displayed (set to none before passing to imshow) and these count values in the return value count histogram will also be set to nan upon return Returns ------- The return value is ``(counts, xedges, yedges, Image)``. Other parameters ----------------- kwargs : :meth:`pcolorfast` properties. See also -------- hist : 1D histogram Notes ----- Rendering the histogram with a logarithmic color scale is accomplished by passing a :class:`colors.LogNorm` instance to the *norm* keyword argument. Likewise, power-law normalization (similar in effect to gamma correction) can be accomplished with :class:`colors.PowerNorm`. Examples -------- .. plot:: mpl_examples/pylab_examples/hist2d_demo.py Additional kwargs: hold = [True|False] overrides default hold state

bar

Make a bar plot. Make a bar plot with rectangles bounded by: `left`, `left` + `width`, `bottom`, `bottom` + `height` (left, right, bottom and top edges) Parameters ---------- left : sequence of scalars the x coordinates of the left sides of the bars height : sequence of scalars the heights of the bars width : scalar or array-like, optional, default: 0.8 the width(s) of the bars bottom : scalar or array-like, optional, default: None the y coordinate(s) of the bars color : scalar or array-like, optional the colors of the bar faces edgecolor : scalar or array-like, optional the colors of the bar edges linewidth : scalar or array-like, optional, default: None width of bar edge(s). If None, use default linewidth; If 0, don't draw edges. xerr : scalar or array-like, optional, default: None if not None, will be used to generate errorbar(s) on the bar chart yerr : scalar or array-like, optional, default: None if not None, will be used to generate errorbar(s) on the bar chart ecolor : scalar or array-like, optional, default: None specifies the color of errorbar(s) capsize : integer, optional, default: 3 determines the length in points of the error bar caps error_kw : dictionary of kwargs to be passed to errorbar method. *ecolor* and *capsize* may be specified here rather than as independent kwargs. align : ['edge' | 'center'], optional, default: 'edge' If `edge`, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). If `center`, interpret the `left` argument as the coordinates of the centers of the bars. orientation : 'vertical' | 'horizontal', optional, default: 'vertical' The orientation of the bars. log : boolean, optional, default: False If true, sets the axis to be log scale Returns ------- `matplotlib.patches.Rectangle` instances. Notes ----- The optional arguments `color`, `edgecolor`, `linewidth`, `xerr`, and `yerr` can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Detail: `xerr` and `yerr` are passed directly to :meth:`errorbar`, so they can also have shape 2xN for independent specification of lower and upper errors. Other optional kwargs: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance capstyle: ['butt' | 'round' | 'projecting'] clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] joinstyle: ['miter' | 'round' | 'bevel'] label: string or anything printable with '%s' conversion. linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number See also -------- barh: Plot a horizontal bar plot. Examples -------- **Example:** A stacked bar chart. .. plot:: mpl_examples/pylab_examples/bar_stacked.py Additional kwargs: hold = [True|False] overrides default hold state

hexbin

Make a hexagonal binning plot. Call signature:: hexbin(x, y, C = None, gridsize = 100, bins = None, xscale = 'linear', yscale = 'linear', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, 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.colors.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 between 0 and 1, or *None* 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* | ``'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: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number The return value is a :class:`~matplotlib.collections.PolyCollection` instance; use :meth:`~matplotlib.collections.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 Additional kwargs: hold = [True|False] overrides default hold state

barh

Make a horizontal bar plot. Make a horizontal bar plot with rectangles bounded by: `left`, `left` + `width`, `bottom`, `bottom` + `height` (left, right, bottom and top edges) `bottom`, `width`, `height`, and `left` can be either scalars or sequences Parameters ---------- bottom : scalar or array-like the y coordinate(s) of the bars width : scalar or array-like the width(s) of the bars height : sequence of scalars, optional, default: 0.8 the heights of the bars left : sequence of scalars the x coordinates of the left sides of the bars Returns -------- `matplotlib.patches.Rectangle` instances. Other parameters ---------------- color : scalar or array-like, optional the colors of the bars edgecolor : scalar or array-like, optional the colors of the bar edges linewidth : scalar or array-like, optional, default: None width of bar edge(s). If None, use default linewidth; If 0, don't draw edges. xerr : scalar or array-like, optional, default: None if not None, will be used to generate errorbar(s) on the bar chart yerr : scalar or array-like, optional, default: None if not None, will be used to generate errorbar(s) on the bar chart ecolor : scalar or array-like, optional, default: None specifies the color of errorbar(s) capsize : integer, optional, default: 3 determines the length in points of the error bar caps error_kw : dictionary of kwargs to be passed to errorbar method. `ecolor` and `capsize` may be specified here rather than as independent kwargs. align : ['edge' | 'center'], optional, default: 'edge' If `edge`, aligns bars by their left edges (for vertical bars) and by their bottom edges (for horizontal bars). If `center`, interpret the `left` argument as the coordinates of the centers of the bars. orientation : 'vertical' | 'horizontal', optional, default: 'vertical' The orientation of the bars. log : boolean, optional, default: False If true, sets the axis to be log scale Notes ----- The optional arguments `color`, `edgecolor`, `linewidth`, `xerr`, and `yerr` can be either scalars or sequences of length equal to the number of bars. This enables you to use bar as the basis for stacked bar charts, or candlestick plots. Detail: `xerr` and `yerr` are passed directly to :meth:`errorbar`, so they can also have shape 2xN for independent specification of lower and upper errors. Other optional kwargs: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance capstyle: ['butt' | 'round' | 'projecting'] clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] joinstyle: ['miter' | 'round' | 'bevel'] label: string or anything printable with '%s' conversion. linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number See also -------- bar: Plot a vertical bar plot. Additional kwargs: hold = [True|False] overrides default hold state

step

Make a step plot. Call signature:: step(x, y, *args, **kwargs) Additional keyword args to :func:`step` are the same as those for :func:`~matplotlib.pyplot.plot`. *x* and *y* must be 1-D sequences, and it is assumed, but not checked, that *x* is uniformly increasing. Keyword arguments: *where*: [ 'pre' | 'post' | 'mid' ] If 'pre', the interval from x[i] to x[i+1] has level y[i+1] If 'post', that interval has level y[i] If 'mid', the jumps in *y* occur half-way between the *x*-values. Additional kwargs: hold = [True|False] overrides default hold state

loglog

Make a plot with log scaling on both the *x* and *y* axis. Call signature:: loglog(*args, **kwargs) :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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/log_demo.py Additional kwargs: hold = [True|False] overrides default hold state

semilogx

Make a plot with log scaling on the *x* axis. Call signature:: semilogx(*args, **kwargs) :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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number .. seealso:: :meth:`loglog` For example code and figure Additional kwargs: hold = [True|False] overrides default hold state

semilogy

Make a plot with log scaling on the *y* axis. call signature:: semilogy(*args, **kwargs) :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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number .. seealso:: :meth:`loglog` For example code and figure Additional kwargs: hold = [True|False] overrides default hold state

polar

Make a polar plot. call signature:: polar(theta, r, **kwargs) Multiple *theta*, *r* arguments are supported, with format strings, as in :func:`~matplotlib.pyplot.plot`.

scatter

Make a scatter plot of x vs y, where x and y are sequence like objects of the same lengths. Parameters ---------- x, y : array_like, shape (n, ) Input data s : scalar or array_like, shape (n, ), optional, default: 20 size in points^2. c : color or sequence of color, optional, default : 'b' `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 : `~matplotlib.markers.MarkerStyle`, optional, default: 'o' See `~matplotlib.markers` for more information on the different styles of markers scatter supports. cmap : `~matplotlib.colors.Colormap`, optional, default: None A `~matplotlib.colors.Colormap` instance or registered name. `cmap` is only used if `c` is an array of floats. If None, defaults to rc `image.cmap`. norm : `~matplotlib.colors.Normalize`, optional, default: None A `~matplotlib.colors.Normalize` instance is used to scale luminance data to 0, 1. `norm` is only used if `c` is an array of floats. If `None`, use the default :func:`normalize`. vmin, vmax : scalar, optional, default: None `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 is used. Note if you pass a `norm` instance, your settings for `vmin` and `vmax` will be ignored. alpha : scalar, optional, default: None The alpha blending value, between 0 (transparent) and 1 (opaque) linewidths : scalar or array_like, optional, default: None 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 `~matplotlib.collections.RegularPolyCollection`. Returns ------- paths : `~matplotlib.collections.PathCollection` Other parameters ---------------- kwargs : `~matplotlib.collections.Collection` properties Notes ------ 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. Examples -------- .. plot:: mpl_examples/shapes_and_collections/scatter_demo.py Additional kwargs: hold = [True|False] overrides default hold state

twinx

Make a second axes that shares the *x*-axis. The new axes will overlay *ax* (or the current axes if *ax* is *None*). 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

twiny

Make a second axes that shares the *y*-axis. The new axis will overlay *ax* (or the current axes if *ax* is *None*). The ticks for *ax2* will be placed on the top, and the *ax2* instance is returned.

violinplot

Make a violin plot. Call signature:: violinplot(dataset, positions=None, vert=True, widths=0.5, showmeans=False, showextrema=True, showmedians=False, points=100, bw_method=None): Make a violin plot for each column of *dataset* or each vector in sequence *dataset*. Each filled area extends to represent the entire data range, with optional lines at the mean, the median, the minimum, and the maximum. Parameters ---------- dataset : Array or a sequence of vectors. The input data. positions : array-like, default = [1, 2, ..., n] Sets the positions of the violins. The ticks and limits are automatically set to match the positions. vert : bool, default = True. If true, creates a vertical violin plot. Otherwise, creates a horizontal violin plot. widths : array-like, default = 0.5 Either a scalar or a vector that sets the maximal width of each violin. The default is 0.5, which uses about half of the available horizontal space. showmeans : bool, default = False If `True`, will toggle rendering of the means. showextrema : bool, default = True If `True`, will toggle rendering of the extrema. showmedians : bool, default = False If `True`, will toggle rendering of the medians. points : scalar, default = 100 Defines the number of points to evaluate each of the gaussian kernel density estimations at. bw_method : str, scalar or callable, optional The method used to calculate the estimator bandwidth. This can be 'scott', 'silverman', a scalar constant or a callable. If a scalar, this will be used directly as `kde.factor`. If a callable, it should take a `GaussianKDE` instance as its only parameter and return a scalar. If None (default), 'scott' is used. Returns ------- result : dict A dictionary mapping each component of the violinplot to a list of the corresponding collection instances created. The dictionary has the following keys: - ``bodies``: A list of the :class:`matplotlib.collections.PolyCollection` instances containing the filled area of each violin. - ``means``: A :class:`matplotlib.collections.LineCollection` instance created to identify the mean values of each of the violin's distribution. - ``mins``: A :class:`matplotlib.collections.LineCollection` instance created to identify the bottom of each violin's distribution. - ``maxes``: A :class:`matplotlib.collections.LineCollection` instance created to identify the top of each violin's distribution. - ``bars``: A :class:`matplotlib.collections.LineCollection` instance created to identify the centers of each violin's distribution. - ``medians``: A :class:`matplotlib.collections.LineCollection` instance created to identify the median values of each of the violin's distribution. Additional kwargs: hold = [True|False] overrides default hold state

fill_between

Make filled polygons between two curves. Call signature:: fill_between(x, y1, y2=0, where=None, **kwargs) Create a :class:`~matplotlib.collections.PolyCollection` filling the regions between *y1* and *y2* where ``where==True`` *x* : An N-length array of the x data *y1* : An N-length array (or scalar) of the y data *y2* : An N-length array (or scalar) of the y data *where* : If *None*, default to fill between everywhere. If not *None*, it is an N-length numpy boolean array and the fill will only happen over the regions where ``where==True``. *interpolate* : If *True*, interpolate between the two lines to find the precise point of intersection. Otherwise, the start and end points of the filled region will only occur on explicit values in the *x* array. *kwargs* : Keyword args passed on to the :class:`~matplotlib.collections.PolyCollection`. kwargs control the :class:`~matplotlib.patches.Polygon` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number .. plot:: mpl_examples/pylab_examples/fill_between_demo.py .. seealso:: :meth:`fill_betweenx` for filling between two sets of x-values Additional kwargs: hold = [True|False] overrides default hold state

fill_betweenx

Make filled polygons between two horizontal curves. Call signature:: fill_betweenx(y, x1, x2=0, where=None, **kwargs) Create a :class:`~matplotlib.collections.PolyCollection` filling the regions between *x1* and *x2* where ``where==True`` *y* : An N-length array of the y data *x1* : An N-length array (or scalar) of the x data *x2* : An N-length array (or scalar) of the x data *where* : If *None*, default to fill between everywhere. If not *None*, it is a N length numpy boolean array and the fill will only happen over the regions where ``where==True`` *kwargs* : keyword args passed on to the :class:`~matplotlib.collections.PolyCollection` kwargs control the :class:`~matplotlib.patches.Polygon` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number .. plot:: mpl_examples/pylab_examples/fill_betweenx_demo.py .. seealso:: :meth:`fill_between` for filling between two sets of y-values Additional kwargs: hold = [True|False] overrides default hold state

NumPy ===== Provides 1. An array object of arbitrary homogeneous items 2. Fast mathematical operations over arrays 3. Linear Algebra, Fourier Transforms, Random Number Generation How to use the documentation ---------------------------- Documentation is available in two forms: docstrings provided with the code, and a loose standing reference guide, available from `the NumPy homepage <http://www.scipy.org>`_. We recommend exploring the docstrings using `IPython <http://ipython.scipy.org>`_, an advanced Python shell with TAB-completion and introspection capabilities. See below for further instructions. The docstring examples assume that `numpy` has been imported as `np`:: >>> import numpy as np Code snippets are indicated by three greater-than signs:: >>> x = 42 >>> x = x + 1 Use the built-in ``help`` function to view a function's docstring:: >>> help(np.sort) ... # doctest: +SKIP For some objects, ``np.info(obj)`` may provide additional help. This is particularly true if you see the line "Help on ufunc object:" at the top of the help() page. Ufuncs are implemented in C, not Python, for speed. The native Python help() does not know how to view their help, but our np.info() function does. To search for documents containing a keyword, do:: >>> np.lookfor('keyword') ... # doctest: +SKIP General-purpose documents like a glossary and help on the basic concepts of numpy are available under the ``doc`` sub-module:: >>> from numpy import doc >>> help(doc) ... # doctest: +SKIP Available subpackages --------------------- doc Topical documentation on broadcasting, indexing, etc. lib Basic functions used by several sub-packages. random Core Random Tools linalg Core Linear Algebra Tools fft Core FFT routines polynomial Polynomial tools testing Numpy testing tools f2py Fortran to Python Interface Generator. distutils Enhancements to distutils with support for Fortran compilers support and more. Utilities --------- test Run numpy unittests show_config Show numpy build configuration dual Overwrite certain functions with high-performance Scipy tools matlib Make everything matrices. __version__ Numpy version string Viewing documentation using IPython ----------------------------------- Start IPython with the NumPy profile (``ipython -p numpy``), which will import `numpy` under the alias `np`. Then, use the ``cpaste`` command to paste examples into the shell. To see which functions are available in `numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use ``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow down the list. To view the docstring for a function, use ``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view the source code). Copies vs. in-place operation ----------------------------- Most of the functions in `numpy` return a copy of the array argument (e.g., `np.sort`). In-place versions of these functions are often available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``. Exceptions to this rule are documented.

mlab

Numerical python functions written for compatability with MATLAB commands with the same names. MATLAB compatible functions ------------------------------- :func:`cohere` Coherence (normalized cross spectral density) :func:`csd` Cross spectral density uing Welch's average periodogram :func:`detrend` Remove the mean or best fit line from an array :func:`find` Return the indices where some condition is true; numpy.nonzero is similar but more general. :func:`griddata` Interpolate irregularly distributed data to a regular grid. :func:`prctile` Find the percentiles of a sequence :func:`prepca` Principal Component Analysis :func:`psd` Power spectral density uing Welch's average periodogram :func:`rk4` A 4th order runge kutta integrator for 1D or ND systems :func:`specgram` Spectrogram (spectrum over segments of time) Miscellaneous functions ------------------------- Functions that don't exist in MATLAB, but are useful anyway: :func:`cohere_pairs` Coherence over all pairs. This is not a MATLAB function, but we compute coherence a lot in my lab, and we compute it for a lot of pairs. This function is optimized to do this efficiently by caching the direct FFTs. :func:`rk4` A 4th order Runge-Kutta ODE integrator in case you ever find yourself stranded without scipy (and the far superior scipy.integrate tools) :func:`contiguous_regions` Return the indices of the regions spanned by some logical mask :func:`cross_from_below` Return the indices where a 1D array crosses a threshold from below :func:`cross_from_above` Return the indices where a 1D array crosses a threshold from above :func:`complex_spectrum` Return the complex-valued frequency spectrum of a signal :func:`magnitude_spectrum` Return the magnitude of the frequency spectrum of a signal :func:`angle_spectrum` Return the angle (wrapped phase) of the frequency spectrum of a signal :func:`phase_spectrum` Return the phase (unwrapped angle) of the frequency spectrum of a signal :func:`detrend_mean` Remove the mean from a line. :func:`demean` Remove the mean from a line. This function is the same as as :func:`detrend_mean` except for the default *axis*. :func:`detrend_linear` Remove the best fit line from a line. :func:`detrend_none` Return the original line. :func:`stride_windows` Get all windows in an array in a memory-efficient manner :func:`stride_repeat` Repeat an array in a memory-efficient manner :func:`apply_window` Apply a window along a given axis record array helper functions ------------------------------- A collection of helper methods for numpyrecord arrays .. _htmlonly: See :ref:`misc-examples-index` :func:`rec2txt` Pretty print a record array :func:`rec2csv` Store record array in CSV file :func:`csv2rec` Import record array from CSV file with type inspection :func:`rec_append_fields` Adds field(s)/array(s) to record array :func:`rec_drop_fields` Drop fields from record array :func:`rec_join` Join two record arrays on sequence of fields :func:`recs_join` A simple join of multiple recarrays using a single column as a key :func:`rec_groupby` Summarize data by groups (similar to SQL GROUP BY) :func:`rec_summarize` Helper code to filter rec array fields into new fields For the rec viewer functions(e rec2csv), there are a bunch of Format objects you can pass into the functions that will do things like color negative values red, set percent formatting and scaling, etc. Example usage:: r = csv2rec('somefile.csv', checkrows=0) formatd = dict( weight = FormatFloat(2), change = FormatPercent(2), cost = FormatThousands(2), ) rec2excel(r, 'test.xls', formatd=formatd) rec2csv(r, 'test.csv', formatd=formatd) scroll = rec2gtk(r, formatd=formatd) win = gtk.Window() win.set_size_request(600,800) win.add(scroll) win.show_all() gtk.main() Deprecated functions --------------------- The following are deprecated; please import directly from numpy (with care--function signatures may differ): :func:`load` Load ASCII file - use numpy.loadtxt :func:`save` Save ASCII file - use numpy.savetxt

pause

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.

figlegend

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`

legend

Places a legend on the axes. To make a legend for lines which already exist on the axes (via plot for instance), simply call this function with an iterable of strings, one for each legend item. For example:: ax.plot([1, 2, 3]) ax.legend(['A simple line']) However, in order to keep the "label" and the legend element instance together, it is preferable to specify the label either at artist creation, or by calling the :meth:`~matplotlib.artist.Artist.set_label` method on the artist:: line, = ax.plot([1, 2, 3], label='Inline label') # Overwrite the label by calling the method. line.set_label('Label via method') ax.legend() Specific lines can be excluded from the automatic legend element selection by defining a label starting with an underscore. This is default for all artists, so calling :meth:`legend` without any arguments and without setting the labels manually will result in no legend being drawn. For full control of which artists have a legend entry, it is possible to pass an iterable of legend artists followed by an iterable of legend labels respectively:: legend((line1, line2, line3), ('label1', 'label2', 'label3')) Parameters ---------- loc : int or string or pair of floats, default: 0 The location of the legend. Possible codes are: =============== ============= Location String Location Code =============== ============= 'best' 0 'upper right' 1 'upper left' 2 'lower left' 3 'lower right' 4 'right' 5 'center left' 6 'center right' 7 'lower center' 8 'upper center' 9 'center' 10 =============== ============= Alternatively can be a 2-tuple giving ``x, y`` of the lower-left corner of the legend in axes coordinates (in which case ``bbox_to_anchor`` will be ignored). bbox_to_anchor : :class:`matplotlib.transforms.BboxBase` instance or tuple of floats Specify any arbitrary location for the legend in `bbox_transform` coordinates (default Axes coordinates). For example, to put the legend's upper right hand corner in the center of the axes the following keywords can be used:: loc='upper right', bbox_to_anchor=(0.5, 0.5) ncol : integer The number of columns that the legend has. Default is 1. prop : None or :class:`matplotlib.font_manager.FontProperties` or dict The font properties of the legend. If None (default), the current :data:`matplotlib.rcParams` will be used. fontsize : int or float or {'xx-small', 'x-small', 'small', 'medium', 'large', 'x-large', 'xx-large'} Controls the font size of the legend. If the value is numeric the size will be the absolute font size in points. String values are relative to the current default font size. This argument is only used if `prop` is not specified. numpoints : None or int The number of marker points in the legend when creating a legend entry for a line/:class:`matplotlib.lines.Line2D`. Default is ``None`` which will take the value from the ``legend.numpoints`` :data:`rcParam<matplotlib.rcParams>`. scatterpoints : None or int The number of marker points in the legend when creating a legend entry for a scatter plot/ :class:`matplotlib.collections.PathCollection`. Default is ``None`` which will take the value from the ``legend.scatterpoints`` :data:`rcParam<matplotlib.rcParams>`. scatteryoffsets : iterable of floats The vertical offset (relative to the font size) for the markers created for a scatter plot legend entry. 0.0 is at the base the legend text, and 1.0 is at the top. To draw all markers at the same height, set to ``[0.5]``. Default ``[0.375, 0.5, 0.3125]``. markerscale : None or int or float The relative size of legend markers compared with the originally drawn ones. Default is ``None`` which will take the value from the ``legend.markerscale`` :data:`rcParam <matplotlib.rcParams>`. frameon : None or bool Control whether a frame should be drawn around the legend. Default is ``None`` which will take the value from the ``legend.frameon`` :data:`rcParam<matplotlib.rcParams>`. fancybox : None or bool Control whether round edges should be enabled around the :class:`~matplotlib.patches.FancyBboxPatch` which makes up the legend's background. Default is ``None`` which will take the value from the ``legend.fancybox`` :data:`rcParam<matplotlib.rcParams>`. shadow : None or bool Control whether to draw a shadow behind the legend. Default is ``None`` which will take the value from the ``legend.shadow`` :data:`rcParam<matplotlib.rcParams>`. framealpha : None or float Control the alpha transparency of the legend's frame. Default is ``None`` which will take the value from the ``legend.framealpha`` :data:`rcParam<matplotlib.rcParams>`. mode : {"expand", None} If `mode` is set to ``"expand"`` the legend will be horizontally expanded to fill the axes area (or `bbox_to_anchor` if defines the legend's size). bbox_transform : None or :class:`matplotlib.transforms.Transform` The transform for the bounding box (`bbox_to_anchor`). For a value of ``None`` (default) the Axes' :data:`~matplotlib.axes.Axes.transAxes` transform will be used. title : str or None The legend's title. Default is no title (``None``). borderpad : float or None The fractional whitespace inside the legend border. Measured in font-size units. Default is ``None`` which will take the value from the ``legend.borderpad`` :data:`rcParam<matplotlib.rcParams>`. labelspacing : float or None The vertical space between the legend entries. Measured in font-size units. Default is ``None`` which will take the value from the ``legend.labelspacing`` :data:`rcParam<matplotlib.rcParams>`. handlelength : float or None The length of the legend handles. Measured in font-size units. Default is ``None`` which will take the value from the ``legend.handlelength`` :data:`rcParam<matplotlib.rcParams>`. handletextpad : float or None The pad between the legend handle and text. Measured in font-size units. Default is ``None`` which will take the value from the ``legend.handletextpad`` :data:`rcParam<matplotlib.rcParams>`. borderaxespad : float or None The pad between the axes and legend border. Measured in font-size units. Default is ``None`` which will take the value from the ``legend.borderaxespad`` :data:`rcParam<matplotlib.rcParams>`. columnspacing : float or None The spacing between columns. Measured in font-size units. Default is ``None`` which will take the value from the ``legend.columnspacing`` :data:`rcParam<matplotlib.rcParams>`. handler_map : dict or None The custom dictionary mapping instances or types to a legend handler. This `handler_map` updates the default handler map found at :func:`matplotlib.legend.Legend.get_legend_handler_map`. Notes ----- Not all kinds of artist are supported by the legend command. See :ref:`plotting-guide-legend` for details. Examples -------- .. plot:: mpl_examples/api/legend_demo.py

quiver

Plot a 2-D field of arrows. call signatures:: quiver(U, V, **kw) quiver(U, V, C, **kw) quiver(X, Y, U, V, **kw) quiver(X, Y, U, V, C, **kw) Arguments: *X*, *Y*: The x and y coordinates of the arrow locations (default is tail of arrow; see *pivot* kwarg) *U*, *V*: Give the x and y components of the arrow vectors *C*: An optional array used to map colors to the arrows All arguments may be 1-D or 2-D arrays or sequences. If *X* and *Y* are absent, they will be generated as a uniform grid. If *U* and *V* are 2-D arrays but *X* and *Y* are 1-D, and if ``len(X)`` and ``len(Y)`` match the column and row dimensions of *U*, then *X* and *Y* will be expanded with :func:`numpy.meshgrid`. *U*, *V*, *C* may be masked arrays, but masked *X*, *Y* are not supported at present. Keyword arguments: *units*: [ 'width' | 'height' | 'dots' | 'inches' | 'x' | 'y' | 'xy' ] Arrow units; the arrow dimensions *except for length* are in multiples of this unit. * 'width' or 'height': the width or height of the axes * 'dots' or 'inches': pixels or inches, based on the figure dpi * 'x', 'y', or 'xy': *X*, *Y*, or sqrt(X^2+Y^2) data units The arrows scale differently depending on the units. For 'x' or 'y', the arrows get larger as one zooms in; for other units, the arrow size is independent of the zoom state. For 'width or 'height', the arrow size increases with the width and height of the axes, respectively, when the the window is resized; for 'dots' or 'inches', resizing does not change the arrows. *angles*: [ 'uv' | 'xy' | array ] With the default 'uv', the arrow axis aspect ratio is 1, so that if *U*==*V* the orientation of the arrow on the plot is 45 degrees CCW from the horizontal axis (positive to the right). With 'xy', the arrow points from (x,y) to (x+u, y+v). Use this for plotting a gradient field, for example. Alternatively, arbitrary angles may be specified as an array of values in degrees, CCW from the horizontal axis. Note: inverting a data axis will correspondingly invert the arrows *only* with `angles='xy'`. *scale*: [ *None* | float ] Data units per arrow length unit, e.g., m/s per plot width; a smaller scale parameter makes the arrow longer. If *None*, a simple autoscaling algorithm is used, based on the average vector length and the number of vectors. The arrow length unit is given by the *scale_units* parameter *scale_units*: *None*, or any of the *units* options. For example, if *scale_units* is 'inches', *scale* is 2.0, and ``(u,v) = (1,0)``, then the vector will be 0.5 inches long. If *scale_units* is 'width', then the vector will be half the width of the axes. If *scale_units* is 'x' then the vector will be 0.5 x-axis units. To plot vectors in the x-y plane, with u and v having the same units as x and y, use "angles='xy', scale_units='xy', scale=1". *width*: Shaft width in arrow units; default depends on choice of units, above, and number of vectors; a typical starting value is about 0.005 times the width of the plot. *headwidth*: scalar Head width as multiple of shaft width, default is 3 *headlength*: scalar Head length as multiple of shaft width, default is 5 *headaxislength*: scalar Head length at shaft intersection, default is 4.5 *minshaft*: scalar Length below which arrow scales, in units of head length. Do not set this to less than 1, or small arrows will look terrible! Default is 1 *minlength*: scalar Minimum length as a multiple of shaft width; if an arrow length is less than this, plot a dot (hexagon) of this diameter instead. Default is 1. *pivot*: [ 'tail' | 'middle' | 'tip' ] The part of the arrow that is at the grid point; the arrow rotates about this point, hence the name *pivot*. *color*: [ color | color sequence ] This is a synonym for the :class:`~matplotlib.collections.PolyCollection` facecolor kwarg. If *C* has been set, *color* has no effect. The defaults give a slightly swept-back arrow; to make the head a triangle, make *headaxislength* the same as *headlength*. To make the arrow more pointed, reduce *headwidth* or increase *headlength* and *headaxislength*. To make the head smaller relative to the shaft, scale down all the head parameters. You will probably do best to leave minshaft alone. linewidths and edgecolors can be used to customize the arrow outlines. Additional :class:`~matplotlib.collections.PolyCollection` keyword arguments: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number Additional kwargs: hold = [True|False] overrides default hold state

hist

Plot a histogram. 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. Multiple data can be provided via *x* as a list of datasets of potentially different length ([*x0*, *x1*, ...]), or as a 2-D ndarray in which each column is a dataset. Note that the ndarray form is transposed relative to the list form. Masked arrays are not supported at present. Parameters ---------- x : (n,) array or sequence of (n,) arrays Input values, this takes either a single array or a sequency of arrays which are not required to be of the same length bins : integer or array_like, optional If an integer is given, `bins + 1` bin edges are returned, consistently with :func:`numpy.histogram` for numpy version >= 1.3. Unequally spaced bins are supported if `bins` is a sequence. default is 10 range : tuple or None, optional 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 based on the specified bin range instead of the range of x. Default is ``None`` normed : boolean, optional 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)``, i.e., the integral of the histogram will sum to 1. If *stacked* is also *True*, the sum of the histograms is normalized to 1. Default is ``False`` weights : (n, ) array_like or None, optional 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. Default is ``None`` cumulative : boolean, optional 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. Default is ``False`` bottom : array_like, scalar, or None Location of the bottom baseline of each bin. If a scalar, the base line for each bin is shifted by the same amount. If an array, each bin is shifted independently and the length of bottom must match the number of bins. If None, defaults to 0. Default is ``None`` histtype : {'bar', 'barstacked', 'step', 'stepfilled'}, optional 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. Default is 'bar' align : {'left', 'mid', 'right'}, optional 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. Default is 'mid' orientation : {'horizontal', 'vertical'}, optional If 'horizontal', `~matplotlib.pyplot.barh` will be used for bar-type histograms and the *bottom* kwarg will be the left edges. rwidth : scalar or None, optional The relative width of the bars as a fraction of the bin width. If `None`, automatically compute the width. Ignored if `histtype` is 'step' or 'stepfilled'. Default is ``None`` log : boolean, optional 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. Default is ``False`` color : color or array_like of colors or None, optional Color spec or sequence of color specs, one per dataset. Default (`None`) uses the standard line color sequence. Default is ``None`` label : string or None, optional String, or sequence of strings to match multiple datasets. Bar charts yield multiple patches per dataset, but only the first gets the label, so that the legend command will work as expected. default is ``None`` stacked : boolean, optional If `True`, multiple data are stacked on top of each other If `False` multiple data are aranged side by side if histtype is 'bar' or on top of each other if histtype is 'step' Default is ``False`` Returns ------- n : array or list of arrays The values of the histogram bins. See **normed** and **weights** for a description of the possible semantics. If input **x** is an array, then this is an array of length **nbins**. If input is a sequence arrays ``[data1, data2,..]``, then this is a list of arrays with the values of the histograms for each of the arrays in the same order. bins : array The edges of the bins. Length nbins + 1 (nbins left edges and right edge of last bin). Always a single array even when multiple data sets are passed in. patches : list or list of lists Silent list of individual patches used to create the histogram or list of such list if multiple input datasets. Other Parameters ---------------- kwargs : `~matplotlib.patches.Patch` properties See also -------- hist2d : 2D histograms Notes ----- Until numpy release 1.5, the underlying numpy histogram function was incorrect with `normed`=`True` if bin sizes were unequal. MPL inherited that error. It is now corrected within MPL when using earlier numpy versions. Examples -------- .. plot:: mpl_examples/statistics/histogram_demo_features.py Additional kwargs: hold = [True|False] overrides default hold state

pie

Plot a pie chart. Call signature:: pie(x, explode=None, labels=None, colors=('b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'), autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=None, radius=None, counterclock=True, wedgeprops=None, textprops=None) Make a pie chart of array *x*. The fractional area of each wedge is given by x/sum(x). If sum(x) <= 1, then the values of x give the fractional area directly and the array will not be normalized. The wedges are plotted counterclockwise, by default starting from the x-axis. Keyword arguments: *explode*: [ *None* | len(x) sequence ] If not *None*, is a ``len(x)`` array which specifies the fraction of the radius with which to offset each wedge. *colors*: [ *None* | color sequence ] A sequence of matplotlib color args through which the pie chart will cycle. *labels*: [ *None* | len(x) sequence of strings ] A sequence of strings providing the labels for each wedge *autopct*: [ *None* | format string | format function ] If not *None*, is a string or function used to label the wedges with their numeric value. The label will be placed inside the wedge. If it is a format string, the label will be ``fmt%pct``. If it is a function, it will be called. *pctdistance*: scalar The ratio between the center of each pie slice and the start of the text generated by *autopct*. Ignored if *autopct* is *None*; default is 0.6. *labeldistance*: scalar The radial distance at which the pie labels are drawn *shadow*: [ *False* | *True* ] Draw a shadow beneath the pie. *startangle*: [ *None* | Offset angle ] If not *None*, rotates the start of the pie chart by *angle* degrees counterclockwise from the x-axis. *radius*: [ *None* | scalar ] The radius of the pie, if *radius* is *None* it will be set to 1. *counterclock*: [ *False* | *True* ] Specify fractions direction, clockwise or counterclockwise. *wedgeprops*: [ *None* | dict of key value pairs ] Dict of arguments passed to the wedge objects making the pie. For example, you can pass in wedgeprops = { 'linewidth' : 3 } to set the width of the wedge border lines equal to 3. For more details, look at the doc/arguments of the wedge object. By default `clip_on=False`. *textprops*: [ *None* | dict of key value pairs ] Dict of arguments to pass to the text objects. The pie chart will probably look best if the figure and axes are square, or the Axes aspect is equal. e.g.:: figure(figsize=(8,8)) ax = axes([0.1, 0.1, 0.8, 0.8]) or:: axes(aspect=1) Return value: If *autopct* is *None*, return the tuple (*patches*, *texts*): - *patches* is a sequence of :class:`matplotlib.patches.Wedge` instances - *texts* is a list of the label :class:`matplotlib.text.Text` instances. If *autopct* is not *None*, return the tuple (*patches*, *texts*, *autotexts*), where *patches* and *texts* are as above, and *autotexts* is a list of :class:`~matplotlib.text.Text` instances for the numeric labels. Additional kwargs: hold = [True|False] overrides default hold state

pcolormesh

Plot a quadrilateral mesh. Call signatures:: pcolormesh(C) pcolormesh(X, Y, C) pcolormesh(C, **kwargs) Create a pseudocolor plot of a 2-D array. pcolormesh is similar to :func:`~matplotlib.pyplot.pcolor`, but uses a different mechanism and returns a different object; pcolor returns a :class:`~matplotlib.collections.PolyCollection` but pcolormesh returns a :class:`~matplotlib.collections.QuadMesh`. It is much faster, so it is almost always preferred for large arrays. *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.colors.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 is *None*, it is autoscaled to the respective min or max of the color array *C*. If not *None*, *vmin* or *vmax* passed in here override any pre-existing values supplied in the *norm* instance. *shading*: [ 'flat' | 'gouraud' ] 'flat' indicates a solid color for each quad. When 'gouraud', each quad will be Gouraud shaded. When gouraud shading, edgecolors is ignored. *edgecolors*: [*None* | ``'None'`` | ``'face'`` | color | color sequence] If *None*, the rc setting is used by default. If ``'None'``, edges will not be visible. If ``'face'``, edges will have the same color as the faces. An mpl color or sequence of colors will set the edge color *alpha*: ``0 <= scalar <= 1`` or *None* the alpha blending value Return value is a :class:`matplotlib.collections.QuadMesh` object. kwargs can be used to control the :class:`matplotlib.collections.QuadMesh` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number .. 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. Additional kwargs: hold = [True|False] overrides default hold state

specgram

Plot a spectrogram. 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, mode='default', scale='default', **kwargs) Compute and plot a spectrogram of data in *x*. Data are split into *NFFT* length segments and the spectrum 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*. The spectrogram is plotted as a colormap (using imshow). *x*: 1-D array or sequence Array or sequence containing the data Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. This can be different from *NFFT*, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to *NFFT* *NFFT*: integer The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should *NOT* be used to get zero padding, or the scaling of the result will be incorrect. Use *pad_to* for this instead. *detrend*: [ 'default' | 'constant' | 'mean' | 'linear' | 'none'] or callable The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the *detrend* parameter is a vector, in matplotlib is it a function. The :mod:`~matplotlib.pylab` module defines :func:`~matplotlib.pylab.detrend_none`, :func:`~matplotlib.pylab.detrend_mean`, and :func:`~matplotlib.pylab.detrend_linear`, but you can use a custom function as well. You can also use a string to choose one of the functions. 'default', 'constant', and 'mean' call :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls :func:`~matplotlib.pylab.detrend_linear`. 'none' calls :func:`~matplotlib.pylab.detrend_none`. *scale_by_freq*: boolean Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. *mode*: [ 'default' | 'psd' | 'magnitude' | 'angle' | 'phase' ] What sort of spectrum to use. Default is 'psd'. which takes the power spectral density. 'complex' returns the complex-valued frequency spectrum. 'magnitude' returns the magnitude spectrum. 'angle' returns the phase spectrum without unwrapping. 'phase' returns the phase spectrum with unwrapping. *noverlap*: integer The number of points of overlap between blocks. The default value is 128. *scale*: [ 'default' | 'linear' | 'dB' ] The scaling of the values in the *spec*. 'linear' is no scaling. 'dB' returns the values in dB scale. When *mode* is 'psd', this is dB power (10 * log10). Otherwise this is dB amplitude (20 * log10). 'default' is 'dB' if *mode* is 'psd' or 'magnitude' and 'linear' otherwise. This must be 'linear' if *mode* is 'angle' or 'phase'. *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. *cmap*: A :class:`matplotlib.colors.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:`~matplotlib.mlab.specgram` *kwargs*: Additional kwargs are passed on to imshow which makes the specgram image .. note:: *detrend* and *scale_by_freq* only apply when *mode* is set to 'psd' Returns the tuple (*spectrum*, *freqs*, *t*, *im*): *spectrum*: 2-D array columns are the periodograms of successive segments *freqs*: 1-D array The frequencies corresponding to the rows in *spectrum* *t*: 1-D array The times corresponding to midpoints of segments (i.e the columns in *spectrum*) *im*: instance of class :class:`~matplotlib.image.AxesImage` The image created by imshow containing the spectrogram **Example:** .. plot:: mpl_examples/pylab_examples/specgram_demo.py .. seealso:: :func:`psd` :func:`psd` differs in the default overlap; in returning the mean of the segment periodograms; in not returning times; and in generating a line plot instead of colormap. :func:`magnitude_spectrum` A single spectrum, similar to having a single segment when *mode* is 'magnitude'. Plots a line instead of a colormap. :func:`angle_spectrum` A single spectrum, similar to having a single segment when *mode* is 'angle'. Plots a line instead of a colormap. :func:`phase_spectrum` A single spectrum, similar to having a single segment when *mode* is 'phase'. Plots a line instead of a colormap. Additional kwargs: hold = [True|False] overrides default hold state

errorbar

Plot an errorbar graph. Call signature:: errorbar(x, y, yerr=None, xerr=None, fmt='', ecolor=None, elinewidth=None, capsize=3, barsabove=False, lolims=False, uplims=False, xlolims=False, xuplims=False, errorevery=1, capthick=None) Plot *x* versus *y* with error deltas in *yerr* and *xerr*. Vertical errorbars are plotted if *yerr* is not *None*. Horizontal errorbars are plotted if *xerr* is not *None*. *x*, *y*, *xerr*, and *yerr* can all be scalars, which plots a single error bar at *x*, *y*. Optional keyword arguments: *xerr*/*yerr*: [ scalar | N, Nx1, or 2xN array-like ] If a scalar number, len(N) array-like object, or an Nx1 array-like object, errorbars are drawn at +/-value relative to the data. If a sequence of shape 2xN, errorbars are drawn at -row1 and +row2 relative to the data. *fmt*: [ '' | 'none' | plot format string ] The plot format symbol. If *fmt* is 'none' (case-insensitive), only the errorbars are plotted. This is used for adding errorbars to a bar plot, for example. Default is '', an empty plot format string; properties are then identical to the defaults for :meth:`plot`. *ecolor*: [ *None* | mpl color ] A matplotlib color arg which gives the color the errorbar lines; if *None*, use the color of the line connecting the markers. *elinewidth*: scalar The linewidth of the errorbar lines. If *None*, use the linewidth. *capsize*: scalar The length of the error bar caps in points *capthick*: scalar An alias kwarg to *markeredgewidth* (a.k.a. - *mew*). This setting is a more sensible name for the property that controls the thickness of the error bar cap in points. For backwards compatibility, if *mew* or *markeredgewidth* are given, then they will over-ride *capthick*. This may change in future releases. *barsabove*: [ *True* | *False* ] if *True*, will plot the errorbars above the plot symbols. Default is below. *lolims* / *uplims* / *xlolims* / *xuplims*: [ *False* | *True* ] These arguments can be used to indicate that a value gives only upper/lower limits. In that case a caret symbol is used to indicate this. lims-arguments may be of the same type as *xerr* and *yerr*. To use limits with inverted axes, :meth:`set_xlim` or :meth:`set_ylim` must be called before :meth:`errorbar`. *errorevery*: positive integer subsamples the errorbars. e.g., if everyerror=5, errorbars for every 5-th datapoint will be plotted. The data plot itself still shows all data points. All other keyword arguments are passed on to the plot command for the markers. For example, this code makes big red squares with thick green edges:: x,y,yerr = rand(3,10) errorbar(x, y, yerr, marker='s', mfc='red', mec='green', ms=20, mew=4) where *mfc*, *mec*, *ms* and *mew* are aliases for the longer property names, *markerfacecolor*, *markeredgecolor*, *markersize* and *markeredgewith*. valid kwargs for the marker properties are agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number Returns (*plotline*, *caplines*, *barlinecols*): *plotline*: :class:`~matplotlib.lines.Line2D` instance *x*, *y* plot markers and/or line *caplines*: list of error bar cap :class:`~matplotlib.lines.Line2D` instances *barlinecols*: list of :class:`~matplotlib.collections.LineCollection` instances for the horizontal and vertical error ranges. **Example:** .. plot:: mpl_examples/statistics/errorbar_demo.py Additional kwargs: hold = [True|False] overrides default hold state

fill

Plot filled polygons. Call signature:: fill(*args, **kwargs) *args* is a variable length argument, allowing for multiple *x*, *y* pairs with an optional color format string; see :func:`~matplotlib.pyplot.plot` for details on the argument parsing. For example, to plot a polygon with vertices at *x*, *y* in blue.:: ax.fill(x,y, 'b' ) An arbitrary number of *x*, *y*, *color* groups can be specified:: ax.fill(x1, y1, 'g', x2, y2, 'r') Return value is a list of :class:`~matplotlib.patches.Patch` instances that were added. The same color strings that :func:`~matplotlib.pyplot.plot` supports are supported by the fill format string. If you would like to fill below a curve, e.g., shade a region between 0 and *y* along *x*, use :meth:`fill_between` The *closed* kwarg will close the polygon when *True* (default). kwargs control the :class:`~matplotlib.patches.Polygon` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or aa: [True | False] or None for default axes: an :class:`~matplotlib.axes.Axes` instance capstyle: ['butt' | 'round' | 'projecting'] clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color: matplotlib color spec contains: a callable function edgecolor or ec: mpl color spec, or None for default, or 'none' for no color facecolor or fc: mpl color spec, or None for default, or 'none' for no color figure: a :class:`matplotlib.figure.Figure` instance fill: [True | False] gid: an id string hatch: ['/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*'] joinstyle: ['miter' | 'round' | 'bevel'] label: string or anything printable with '%s' conversion. linestyle or ls: ['solid' | 'dashed' | 'dashdot' | 'dotted'] linewidth or lw: float or None for default lod: [True | False] path_effects: unknown picker: [None|float|boolean|callable] rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string visible: [True | False] zorder: any number **Example:** .. plot:: mpl_examples/lines_bars_and_markers/fill_demo.py Additional kwargs: hold = [True|False] overrides default hold state

broken_barh

Plot horizontal bars. Call signature:: broken_barh(self, xranges, yrange, **kwargs) A collection of horizontal bars spanning *yrange* with a sequence of *xranges*. Required arguments: ========= ============================== Argument Description ========= ============================== *xranges* sequence of (*xmin*, *xwidth*) *yrange* sequence of (*ymin*, *ywidth*) ========= ============================== kwargs are :class:`matplotlib.collections.BrokenBarHCollection` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown visible: [True | False] zorder: any number these can either be a single argument, i.e.,:: facecolors = 'black' or a sequence of arguments for the various bars, i.e.,:: facecolors = ('black', 'red', 'green') **Example:** .. plot:: mpl_examples/pylab_examples/broken_barh.py Additional kwargs: hold = [True|False] overrides default hold state

hlines

Plot horizontal lines at each `y` from `xmin` to `xmax`. Parameters ---------- y : scalar or sequence of scalar y-indexes where to plot the lines. xmin, xmax : scalar or 1D array_like Respective beginning and end of each line. If scalars are provided, all lines will have same length. colors : array_like of colors, optional, default: 'k' linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional label : string, optional, default: '' Returns ------- lines : `~matplotlib.collections.LineCollection` Other parameters ---------------- kwargs : `~matplotlib.collections.LineCollection` properties. See also -------- vlines : vertical lines Examples -------- .. plot:: mpl_examples/pylab_examples/vline_hline_demo.py Additional kwargs: hold = [True|False] overrides default hold state

eventplot

Plot identical parallel lines at specific positions. Call signature:: eventplot(positions, orientation='horizontal', lineoffsets=0, linelengths=1, linewidths=None, color =None, linestyles='solid' Plot parallel lines at the given positions. positions should be a 1D or 2D array-like object, with each row corresponding to a row or column of lines. This type of plot is commonly used in neuroscience for representing neural events, where it is commonly called a spike raster, dot raster, or raster plot. However, it is useful in any situation where you wish to show the timing or position of multiple sets of discrete events, such as the arrival times of people to a business on each day of the month or the date of hurricanes each year of the last century. *orientation* : [ 'horizonal' | 'vertical' ] 'horizonal' : the lines will be vertical and arranged in rows "vertical' : lines will be horizontal and arranged in columns *lineoffsets* : A float or array-like containing floats. *linelengths* : A float or array-like containing floats. *linewidths* : A float or array-like containing floats. *colors* must be a sequence of RGBA tuples (e.g., arbitrary color strings, etc, not allowed) or a list of such sequences *linestyles* : [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ] or an array of these values For linelengths, linewidths, colors, and linestyles, if only a single value is given, that value is applied to all lines. If an array-like is given, it must have the same length as positions, and each value will be applied to the corresponding row or column in positions. Returns a list of :class:`matplotlib.collections.EventCollection` objects that were added. kwargs are :class:`~matplotlib.collections.LineCollection` properties: agg_filter: unknown alpha: float or None animated: [True | False] antialiased or antialiaseds: Boolean or sequence of booleans array: unknown axes: an :class:`~matplotlib.axes.Axes` instance clim: a length 2 sequence of floats clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] cmap: a colormap or registered colormap name color: matplotlib color arg or sequence of rgba tuples contains: a callable function edgecolor or edgecolors: matplotlib color arg or sequence of rgba tuples facecolor or facecolors: matplotlib color arg or sequence of rgba tuples figure: a :class:`matplotlib.figure.Figure` instance gid: an id string hatch: [ '/' | '\\' | '|' | '-' | '+' | 'x' | 'o' | 'O' | '.' | '*' ] label: string or anything printable with '%s' conversion. linestyle or dashes or linestyles: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) ] linewidth or linewidths or lw: float or sequence of floats lod: [True | False] norm: unknown offset_position: unknown offsets: float or sequence of floats path_effects: unknown paths: unknown picker: [None|float|boolean|callable] pickradius: unknown rasterized: [True | False | None] segments: unknown sketch_params: unknown snap: unknown transform: :class:`~matplotlib.transforms.Transform` instance url: a url string urls: unknown verts: unknown visible: [True | False] zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/eventplot_demo.py Additional kwargs: hold = [True|False] overrides default hold state

plot

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. By default, each line is assigned a different color specified by a 'color cycle'. To change this behavior, you can edit the axes.color_cycle rcParam. 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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number 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*. Additional kwargs: hold = [True|False] overrides default hold state

acorr

Plot the autocorrelation of `x`. Parameters ---------- x : sequence of scalar hold : boolean, optional, default: True detrend : callable, optional, default: `mlab.detrend_none` x is detrended by the `detrend` callable. Default is no normalization. normed : boolean, optional, default: True if True, normalize the data by the autocorrelation at the 0-th lag. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x) - 1 lags. Returns ------- (lags, c, line, b) : where: - `lags` are a length 2`maxlags+1 lag vector. - `c` is the 2`maxlags+1 auto correlation vectorI - `line` is a `~matplotlib.lines.Line2D` instance returned by `plot`. - `b` is the x-axis. Other parameters ----------------- linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None Only used if usevlines is False. marker : string, optional, default: 'o' Notes ----- The cross correlation is performed with :func:`numpy.correlate` with `mode` = 2. Examples -------- `~matplotlib.pyplot.xcorr` is top graph, and `~matplotlib.pyplot.acorr` is bottom graph. .. plot:: mpl_examples/pylab_examples/xcorr_demo.py Additional kwargs: hold = [True|False] overrides default hold state

xcorr

Plot the cross correlation between *x* and *y*. Parameters ---------- x : sequence of scalars of length n y : sequence of scalars of length n hold : boolean, optional, default: True detrend : callable, optional, default: `mlab.detrend_none` x is detrended by the `detrend` callable. Default is no normalization. normed : boolean, optional, default: True if True, normalize the data by the autocorrelation at the 0-th lag. usevlines : boolean, optional, default: True if True, Axes.vlines is used to plot the vertical lines from the origin to the acorr. Otherwise, Axes.plot is used. maxlags : integer, optional, default: 10 number of lags to show. If None, will return all 2 * len(x) - 1 lags. Returns ------- (lags, c, line, b) : where: - `lags` are a length 2`maxlags+1 lag vector. - `c` is the 2`maxlags+1 auto correlation vectorI - `line` is a `~matplotlib.lines.Line2D` instance returned by `plot`. - `b` is the x-axis (none, if plot is used). Other parameters ----------------- linestyle : `~matplotlib.lines.Line2D` prop, optional, default: None Only used if usevlines is False. marker : string, optional, default: 'o' Notes ----- The cross correlation is performed with :func:`numpy.correlate` with `mode` = 2. Additional kwargs: hold = [True|False] overrides default hold state

csd

Plot the cross-spectral density. 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, return_line=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*. *noverlap* gives the length of the overlap between segments. 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. If len(*x*) < *NFFT* or len(*y*) < *NFFT*, they will be zero padded to *NFFT*. *x*, *y*: 1-D arrays or sequences Arrays or sequences containing the data Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. This can be different from *NFFT*, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to *NFFT* *NFFT*: integer The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should *NOT* be used to get zero padding, or the scaling of the result will be incorrect. Use *pad_to* for this instead. *detrend*: [ 'default' | 'constant' | 'mean' | 'linear' | 'none'] or callable The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the *detrend* parameter is a vector, in matplotlib is it a function. The :mod:`~matplotlib.pylab` module defines :func:`~matplotlib.pylab.detrend_none`, :func:`~matplotlib.pylab.detrend_mean`, and :func:`~matplotlib.pylab.detrend_linear`, but you can use a custom function as well. You can also use a string to choose one of the functions. 'default', 'constant', and 'mean' call :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls :func:`~matplotlib.pylab.detrend_linear`. 'none' calls :func:`~matplotlib.pylab.detrend_none`. *scale_by_freq*: boolean Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. *noverlap*: integer The number of points of overlap between segments. The default value is 0 (no overlap). *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. *return_line*: bool Whether to include the line object plotted in the returned values. Default is False. If *return_line* is False, returns the tuple (*Pxy*, *freqs*). If *return_line* is True, returns the tuple (*Pxy*, *freqs*. *line*): *Pxy*: 1-D array The values for the cross spectrum `P_{xy}` before scaling (complex valued) *freqs*: 1-D array The frequencies corresponding to the elements in *Pxy* *line*: a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Only returend if *return_line* is True. For plotting, the power is plotted as :math:`10\log_{10}(P_{xy})` for decibels, though `P_{xy}` itself is returned. References: Bendat & Piersol -- Random Data: Analysis and Measurement Procedures, John Wiley & Sons (1986) kwargs control the Line2D properties: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/csd_demo.py .. seealso:: :func:`psd` :func:`psd` is the equivalent to setting y=x. Additional kwargs: hold = [True|False] overrides default hold state

plotfile

Plot the data in in a file. *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, e.g., 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.

magnitude_spectrum

Plot the magnitude spectrum. Call signature:: magnitude_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning, pad_to=None, sides='default', **kwargs) Compute the magnitude spectrum of *x*. Data is padded to a length of *pad_to* and the windowing function *window* is applied to the signal. *x*: 1-D array or sequence Array or sequence containing the data Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to the length of the input signal (i.e. no padding). *scale*: [ 'default' | 'linear' | 'dB' ] The scaling of the values in the *spec*. 'linear' is no scaling. 'dB' returns the values in dB scale. When *mode* is 'density', this is dB power (10 * log10). Otherwise this is dB amplitude (20 * log10). 'default' is 'linear'. *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 (*spectrum*, *freqs*, *line*): *spectrum*: 1-D array The values for the magnitude spectrum before scaling (real valued) *freqs*: 1-D array The frequencies corresponding to the elements in *spectrum* *line*: a :class:`~matplotlib.lines.Line2D` instance The line created by this function kwargs control the :class:`~matplotlib.lines.Line2D` properties: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/spectrum_demo.py .. seealso:: :func:`psd` :func:`psd` plots the power spectral density.`. :func:`angle_spectrum` :func:`angle_spectrum` plots the angles of the corresponding frequencies. :func:`phase_spectrum` :func:`phase_spectrum` plots the phase (unwrapped angle) of the corresponding frequencies. :func:`specgram` :func:`specgram` can plot the magnitude spectrum of segments within the signal in a colormap. Additional kwargs: hold = [True|False] overrides default hold state

phase_spectrum

Plot the phase spectrum. Call signature:: phase_spectrum(x, Fs=2, Fc=0, window=mlab.window_hanning, pad_to=None, sides='default', **kwargs) Compute the phase spectrum (unwrapped angle spectrum) of *x*. Data is padded to a length of *pad_to* and the windowing function *window* is applied to the signal. *x*: 1-D array or sequence Array or sequence containing the data Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to the length of the input signal (i.e. no padding). *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 (*spectrum*, *freqs*, *line*): *spectrum*: 1-D array The values for the phase spectrum in radians (real valued) *freqs*: 1-D array The frequencies corresponding to the elements in *spectrum* *line*: a :class:`~matplotlib.lines.Line2D` instance The line created by this function kwargs control the :class:`~matplotlib.lines.Line2D` properties: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/spectrum_demo.py .. seealso:: :func:`magnitude_spectrum` :func:`magnitude_spectrum` plots the magnitudes of the corresponding frequencies. :func:`angle_spectrum` :func:`angle_spectrum` plots the wrapped version of this function. :func:`specgram` :func:`specgram` can plot the phase spectrum of segments within the signal in a colormap. Additional kwargs: hold = [True|False] overrides default hold state

psd

Plot the power spectral density. 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, return_line=None, **kwargs) The power spectral density :math:`P_{xx}` 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 :math:`P_{xx}`, with a scaling to correct for power loss due to windowing. If len(*x*) < *NFFT*, it will be zero padded to *NFFT*. *x*: 1-D array or sequence Array or sequence containing the data Keyword arguments: *Fs*: scalar The sampling frequency (samples per time unit). It is used to calculate the Fourier frequencies, freqs, in cycles per time unit. The default value is 2. *window*: callable or ndarray A function or a vector of length *NFFT*. To create window vectors see :func:`window_hanning`, :func:`window_none`, :func:`numpy.blackman`, :func:`numpy.hamming`, :func:`numpy.bartlett`, :func:`scipy.signal`, :func:`scipy.signal.get_window`, etc. The default is :func:`window_hanning`. If a function is passed as the argument, it must take a data segment as an argument and return the windowed version of the segment. *sides*: [ 'default' | 'onesided' | 'twosided' ] Specifies which sides of the spectrum to return. Default gives the default behavior, which returns one-sided for real data and both for complex data. 'onesided' forces the return of a one-sided spectrum, while 'twosided' forces two-sided. *pad_to*: integer The number of points to which the data segment is padded when performing the FFT. This can be different from *NFFT*, which specifies the number of data points used. While not increasing the actual resolution of the spectrum (the minimum distance between resolvable peaks), this can give more points in the plot, allowing for more detail. This corresponds to the *n* parameter in the call to fft(). The default is None, which sets *pad_to* equal to *NFFT* *NFFT*: integer The number of data points used in each block for the FFT. A power 2 is most efficient. The default value is 256. This should *NOT* be used to get zero padding, or the scaling of the result will be incorrect. Use *pad_to* for this instead. *detrend*: [ 'default' | 'constant' | 'mean' | 'linear' | 'none'] or callable The function applied to each segment before fft-ing, designed to remove the mean or linear trend. Unlike in MATLAB, where the *detrend* parameter is a vector, in matplotlib is it a function. The :mod:`~matplotlib.pylab` module defines :func:`~matplotlib.pylab.detrend_none`, :func:`~matplotlib.pylab.detrend_mean`, and :func:`~matplotlib.pylab.detrend_linear`, but you can use a custom function as well. You can also use a string to choose one of the functions. 'default', 'constant', and 'mean' call :func:`~matplotlib.pylab.detrend_mean`. 'linear' calls :func:`~matplotlib.pylab.detrend_linear`. 'none' calls :func:`~matplotlib.pylab.detrend_none`. *scale_by_freq*: boolean Specifies whether the resulting density values should be scaled by the scaling frequency, which gives density in units of Hz^-1. This allows for integration over the returned frequency values. The default is True for MATLAB compatibility. *noverlap*: integer The number of points of overlap between segments. The default value is 0 (no overlap). *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. *return_line*: bool Whether to include the line object plotted in the returned values. Default is False. If *return_line* is False, returns the tuple (*Pxx*, *freqs*). If *return_line* is True, returns the tuple (*Pxx*, *freqs*. *line*): *Pxx*: 1-D array The values for the power spectrum `P_{xx}` before scaling (real valued) *freqs*: 1-D array The frequencies corresponding to the elements in *Pxx* *line*: a :class:`~matplotlib.lines.Line2D` instance The line created by this function. Only returend if *return_line* is True. 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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number **Example:** .. plot:: mpl_examples/pylab_examples/psd_demo.py .. seealso:: :func:`specgram` :func:`specgram` differs in the default overlap; in not returning the mean of the segment periodograms; in returning the times of the segments; and in plotting a colormap instead of a line. :func:`magnitude_spectrum` :func:`magnitude_spectrum` plots the magnitude spectrum. :func:`csd` :func:`csd` plots the spectral density between two signals. Additional kwargs: hold = [True|False] overrides default hold state

spy

Plot the sparsity pattern on a 2-D array. ``spy(Z)`` plots the sparsity pattern of the 2-D array *Z*. Parameters ---------- Z : sparse array (n, m) The array to be plotted. precision : float, optional, default: 0 If *precision* is 0, any non-zero value will be plotted; else, values of :math:`|Z| > 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. origin : ["upper", "lower"], optional, default: "upper" Place the [0,0] index of the array in the upper left or lower left corner of the axes. aspect : ['auto' | 'equal' | scalar], optional, default: "equal" If 'equal', and `extent` is None, changes the axes aspect ratio to match that of the image. If `extent` is not `None`, the axes aspect ratio is changed to match that of the extent. If 'auto', changes the image aspect ratio to match that of the axes. If None, default to rc ``image.aspect`` value. 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* See also -------- imshow : for image options. plot : for plotting options Additional kwargs: hold = [True|False] overrides default hold state

vlines

Plot vertical lines. Plot vertical lines at each `x` from `ymin` to `ymax`. Parameters ---------- x : scalar or 1D array_like x-indexes where to plot the lines. ymin, ymax : scalar or 1D array_like Respective beginning and end of each line. If scalars are provided, all lines will have same length. colors : array_like of colors, optional, default: 'k' linestyles : ['solid' | 'dashed' | 'dashdot' | 'dotted'], optional label : string, optional, default: '' Returns ------- lines : `~matplotlib.collections.LineCollection` Other parameters ---------------- kwargs : `~matplotlib.collections.LineCollection` properties. See also -------- hlines : horizontal lines Examples --------- .. plot:: mpl_examples/pylab_examples/vline_hline_demo.py Additional kwargs: hold = [True|False] overrides default hold state

plot_date

Plot with data with dates. 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 | :class:`tzinfo` instance] 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: agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number .. 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. Additional kwargs: hold = [True|False] overrides default hold state

warnings

Python part of the warnings subsystem.

_imread

Read an image from a file into an array. *fname* may be a string path or a Python file-like object. If using a file object, it must be opened in binary mode. If *format* is provided, will try to read file of that type, otherwise the format is deduced from the filename. If nothing can be deduced, PNG is tried. Return value is a :class:`numpy.array`. For grayscale images, the return array is MxN. For RGB images, the return value is MxNx3. For RGBA images the return value is MxNx4. matplotlib can only read PNGs natively, but if `PIL <http://www.pythonware.com/products/pil/>`_ is installed, it will use it to load the image and return an array (if possible) which can be used with :func:`~matplotlib.pyplot.imshow`.

imread

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()

delaxes

Remove an axes from the current figure. If *ax* doesn't exist, an error will be raised. ``delaxes()``: delete the current axes

dedent

Remove excess indentation from docstring *s*. Discards any leading blank lines, then removes up to n whitespace characters from each line, where n is the number of leading whitespace characters in the first line. It differs from textwrap.dedent in its deletion of leading blank lines and its use of the first non-blank line to determine the indentation. It is also faster in most cases.

minorticks_off

Remove minor ticks from the current plot.

rcdefaults

Restore the default rc params. These are not the params loaded by the rc file, but mpl's internal params. See rc_file_defaults for reloading the default params from the rc file

fignum_exists

Return *True* if figure *num* exists.

is_string_like

Return True if *obj* looks like a string

rc_context

Return a context manager for managing rc settings. This allows one to do:: with mpl.rc_context(fname='screen.rc'): plt.plot(x, a) with mpl.rc_context(fname='print.rc'): plt.plot(x, b) plt.plot(x, c) The 'a' vs 'x' and 'c' vs 'x' plots would have settings from 'screen.rc', while the 'b' vs 'x' plot would have settings from 'print.rc'. A dictionary can also be passed to the context manager:: with mpl.rc_context(rc={'text.usetex': True}, fname='screen.rc'): plt.plot(x, a) The 'rc' dictionary takes precedence over the settings loaded from 'fname'. Passing a dictionary only is also valid.

get_figlabels

Return a list of existing figure labels.

get_fignums

Return a list of existing figure numbers.

subplot

Return a subplot axes positioned by the given grid definition. Typical call signature:: subplot(nrows, ncols, plot_number) Where *nrows* and *ncols* are used to notionally split the figure into ``nrows * ncols`` sub-axes, and *plot_number* is used to identify the particular subplot that this function is to create within the notional grid. *plot_number* starts at 1, increments across rows first and has a maximum of ``nrows * ncols``. In the case when *nrows*, *ncols* and *plot_number* are all less than 10, a convenience exists, such that the a 3 digit number can be given instead, where the hundreds represent *nrows*, the tens represent *ncols* and the units represent *plot_number*. For instance:: subplot(211) produces a subaxes in a figure which represents the top plot (i.e. the first) in a 2 row by 1 column notional grid (no grid actually exists, but conceptually this is how the returned subplot has been positioned). .. note:: Creating a new subplot with a position which is entirely inside a pre-existing axes will trigger the larger axes to be deleted:: import matplotlib.pyplot as plt # plot a line, implicitly creating a subplot(111) plt.plot([1,2,3]) # now create a subplot which represents the top plot of a grid # with 2 rows and 1 column. Since this subplot will overlap the # first, the plot (and its axes) previously created, will be removed plt.subplot(211) plt.plot(range(12)) plt.subplot(212, axisbg='y') # creates 2nd subplot with yellow background If you do not want this behavior, use the :meth:`~matplotlib.figure.Figure.add_subplot` method or the :func:`~matplotlib.pyplot.axes` function instead. Keyword arguments: *axisbg*: The background color of the subplot, which can be any valid color specifier. See :mod:`matplotlib.colors` for more information. *polar*: A boolean flag indicating whether the subplot plot should be a polar projection. Defaults to *False*. *projection*: A string giving the name of a custom projection to be used for the subplot. This projection must have been previously registered. See :mod:`matplotlib.projections`. .. seealso:: :func:`~matplotlib.pyplot.axes` For additional information on :func:`axes` and :func:`subplot` keyword arguments. :file:`examples/pie_and_polar_charts/polar_scatter_demo.py` For an example **Example:** .. plot:: mpl_examples/subplots_axes_and_figures/subplot_demo.py

isinteractive

Return status of interactive mode.

ishold

Return the hold status of the current axes.

get_backend

Return the name of the current backend.

get

Return the value of object's property. *property* is an optional string for the property you want to return Example usage:: getp(obj) # get all the object properties getp(obj, 'linestyle') # get the linestyle property *obj* is a :class:`Artist` instance, e.g., :class:`~matplotllib.lines.Line2D` or an instance of a :class:`~matplotlib.axes.Axes` or :class:`matplotlib.text.Text`. If the *property* is 'somename', this function returns obj.get_somename() :func:`getp` can be used to query all the gettable properties with ``getp(obj)``. Many properties have aliases for shorter typing, e.g. 'lw' is an alias for 'linewidth'. In the output, aliases and full property names will be listed as: property or alias = value e.g.: linewidth or lw = 2

getp

_show

Show all figures as SVG/PNG payloads sent to the IPython clients. Parameters ---------- close : bool, optional If true, a ``plt.close('all')`` call is automatically issued after sending all the figures. If this is set, the figures will entirely removed from the internal list of figures. block : Not used. The `block` parameter is a Matplotlib experimental parameter. We accept it in the function signature for compatibility with other backends.

_imsave

Save an array as in image file. The output formats available depend on the backend being used. Arguments: *fname*: A string containing a path to a filename, or a Python file-like object. If *format* is *None* and *fname* is a string, the output format is deduced from the extension of the filename. *arr*: An MxN (luminance), MxNx3 (RGB) or MxNx4 (RGBA) array. Keyword arguments: *vmin*/*vmax*: [ None | scalar ] *vmin* and *vmax* set the color scaling for the image by fixing the values that map to the colormap color limits. If either *vmin* or *vmax* is None, that limit is determined from the *arr* min/max value. *cmap*: cmap is a colors.Colormap instance, e.g., cm.jet. If None, default to the rc image.cmap value. *format*: One of the file extensions supported by the active backend. Most backends support png, pdf, ps, eps and svg. *origin* [ 'upper' | 'lower' ] Indicates where the [0,0] index of the array is in the upper left or lower left corner of the axes. Defaults to the rc image.origin value. *dpi* The DPI to store in the metadata of the file. This does not affect the resolution of the output image.

imsave

savefig

Save the current figure. Call signature:: savefig(fname, dpi=None, facecolor='w', edgecolor='w', orientation='portrait', papertype=None, format=None, transparent=False, bbox_inches=None, pad_inches=0.1, frameon=None) The output formats available depend on the backend being used. Arguments: *fname*: A string containing a path to a filename, or a Python file-like object, or possibly some backend-dependent object such as :class:`~matplotlib.backends.backend_pdf.PdfPages`. If *format* is *None* and *fname* is a string, the output format is deduced from the extension of the filename. If the filename has no extension, the value of the rc parameter ``savefig.format`` is used. If *fname* is not a string, remember to specify *format* to ensure that the correct backend is used. Keyword arguments: *dpi*: [ *None* | ``scalar > 0`` ] The resolution in dots per inch. If *None* it will default to the value ``savefig.dpi`` in the matplotlibrc file. *facecolor*, *edgecolor*: the colors of the figure rectangle *orientation*: [ 'landscape' | 'portrait' ] not supported on all backends; currently only on postscript output *papertype*: One of 'letter', 'legal', 'executive', 'ledger', 'a0' through 'a10', 'b0' through 'b10'. Only supported for postscript output. *format*: One of the file extensions supported by the active backend. Most backends support png, pdf, ps, eps and svg. *transparent*: If *True*, the axes patches will all be transparent; the figure patch will also be transparent unless facecolor and/or edgecolor are specified via kwargs. This is useful, for example, for displaying a plot on top of a colored background on a web page. The transparency of these patches will be restored to their original values upon exit of this function. *frameon*: If *True*, the figure patch will be colored, if *False*, the figure background will be transparent. If not provided, the rcParam 'savefig.frameon' will be used. *bbox_inches*: Bbox in inches. Only the given portion of the figure is saved. If 'tight', try to figure out the tight bbox of the figure. *pad_inches*: Amount of padding around the figure when bbox_inches is 'tight'. *bbox_extra_artists*: A list of extra artists that will be considered when the tight bbox is calculated.

_setp

Set a property on an artist object. matplotlib supports the use of :func:`setp` ("set property") and :func:`getp` to set and get object properties, as well as to do introspection on the object. For example, to set the linestyle of a line to be dashed, you can do:: >>> line, = plot([1,2,3]) >>> setp(line, linestyle='--') If you want to know the valid types of arguments, you can provide the name of the property you want to set without a value:: >>> setp(line, 'linestyle') linestyle: [ '-' | '--' | '-.' | ':' | 'steps' | 'None' ] If you want to see all the properties that can be set, and their possible values, you can do:: >>> setp(line) ... long output listing omitted :func:`setp` operates on a single instance or a list of instances. If you are in query mode introspecting the possible values, only the first instance in the sequence is used. When actually setting values, all the instances will be set. e.g., suppose you have a list of two lines, the following will make both lines thicker and red:: >>> x = arange(0,1.0,0.01) >>> y1 = sin(2*pi*x) >>> y2 = sin(4*pi*x) >>> lines = plot(x, y1, x, y2) >>> setp(lines, linewidth=2, color='r') :func:`setp` works with the MATLAB style string/value pairs or with python kwargs. For example, the following are equivalent:: >>> setp(lines, 'linewidth', 2, 'color', 'r') # MATLAB style >>> setp(lines, linewidth=2, color='r') # python style

setp

MultipleLocator

Set a tick on every integer that is multiple of base in the view interval

title

Set a title of the current axes. Set one of the three available axes titles. The available titles are positioned above the axes in the center, flush with the left edge, and flush with the right edge. .. seealso:: See :func:`~matplotlib.pyplot.text` for adding text to the current axes Parameters ---------- label : str Text to use for the title fontdict : dict A dictionary controlling the appearance of the title text, the default `fontdict` is: {'fontsize': rcParams['axes.titlesize'], 'fontweight' : rcParams['axes.titleweight'], 'verticalalignment': 'baseline', 'horizontalalignment': loc} loc : {'center', 'left', 'right'}, str, optional Which title to set, defaults to 'center' Returns ------- text : :class:`~matplotlib.text.Text` The matplotlib text instance representing the title Other parameters ---------------- kwargs : text properties Other keyword arguments are text properties, see :class:`~matplotlib.text.Text` for a list of valid text properties.

interactive

Set interactive mode to boolean b. If b is True, then draw after every plotting command, e.g., after xlabel

margins

Set or retrieve autoscaling margins. signatures:: margins() returns xmargin, ymargin :: margins(margin) margins(xmargin, ymargin) margins(x=xmargin, y=ymargin) margins(..., tight=False) All three forms above set the xmargin and ymargin parameters. All keyword parameters are optional. A single argument specifies both xmargin and ymargin. The *tight* parameter is passed to :meth:`autoscale_view`, which is executed after a margin is changed; the default here is *True*, on the assumption that when margins are specified, no additional padding to match tick marks is usually desired. Setting *tight* to *None* will preserve the previous setting. Specifying any margin changes only the autoscaling; for example, if *xmargin* is not None, then *xmargin* times the X data interval will be added to each end of that interval before it is used in autoscaling.

xlabel

Set the *x* axis label of the current axis. 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

ylabel

Set the *y* axis label of the current axis. 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.

clim

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)

sca

Set the current Axes instance to *ax*. The current Figure is updated to the parent of *ax*.

sci

Set the current image. This image will be the 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.

Set the current rc params. Group is the grouping for the rc, e.g., for ``lines.linewidth`` the group is ``lines``, for ``axes.facecolor``, the group is ``axes``, and so on. Group may also be a list or tuple of group names, e.g., (*xtick*, *ytick*). *kwargs* is a dictionary attribute name/value pairs, e.g.,:: rc('lines', linewidth=2, color='r') sets the current rc params and is equivalent to:: rcParams['lines.linewidth'] = 2 rcParams['lines.color'] = 'r' The following aliases are available to save typing for interactive users: ===== ================= Alias Property ===== ================= 'lw' 'linewidth' 'ls' 'linestyle' 'c' 'color' 'fc' 'facecolor' 'ec' 'edgecolor' 'mew' 'markeredgewidth' 'aa' 'antialiased' ===== ================= Thus you could abbreviate the above rc command as:: rc('lines', lw=2, c='r') Note you can use python's kwargs dictionary facility to store dictionaries of default parameters. e.g., you can customize the font rc as follows:: font = {'family' : 'monospace', 'weight' : 'bold', 'size' : 'larger'} rc('font', **font) # pass in the font dict as kwargs This enables you to easily switch between several configurations. Use :func:`~matplotlib.pyplot.rcdefaults` to restore the default rc params after changes.

set_cmap

Set the default colormap. Applies to the current image if any. See help(colormaps) for more information. *cmap* must be a :class:`~matplotlib.colors.Colormap` instance, or the name of a registered colormap. See :func:`matplotlib.cm.register_cmap` and :func:`matplotlib.cm.get_cmap`.

hold

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.

xscale

Set the scaling of the *x*-axis. call signature:: xscale(scale, **kwargs) The available scales are: 'linear' | 'log' | 'symlog' Different keywords may be accepted, depending on the scale: 'linear' 'log' *basex*/*basey*: The base of the logarithm *nonposx*/*nonposy*: ['mask' | 'clip' ] non-positive values in *x* or *y* can be masked as invalid, or clipped to a very small positive number *subsx*/*subsy*: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8 logarithmically spaced minor ticks between each major tick. 'symlog' *basex*/*basey*: The base of the logarithm *linthreshx*/*linthreshy*: The range (-*x*, *x*) within which the plot is linear (to avoid having the plot go to infinity around zero). *subsx*/*subsy*: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8 logarithmically spaced minor ticks between each major tick. *linscalex*/*linscaley*: This allows the linear range (-*linthresh* to *linthresh*) to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when *linscale* == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range.

yscale

Set the scaling of the *y*-axis. call signature:: yscale(scale, **kwargs) The available scales are: 'linear' | 'log' | 'symlog' Different keywords may be accepted, depending on the scale: 'linear' 'log' *basex*/*basey*: The base of the logarithm *nonposx*/*nonposy*: ['mask' | 'clip' ] non-positive values in *x* or *y* can be masked as invalid, or clipped to a very small positive number *subsx*/*subsy*: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8 logarithmically spaced minor ticks between each major tick. 'symlog' *basex*/*basey*: The base of the logarithm *linthreshx*/*linthreshy*: The range (-*x*, *x*) within which the plot is linear (to avoid having the plot go to infinity around zero). *subsx*/*subsy*: Where to place the subticks between each major tick. Should be a sequence of integers. For example, in a log10 scale: ``[2, 3, 4, 5, 6, 7, 8, 9]`` will place 8 logarithmically spaced minor ticks between each major tick. *linscalex*/*linscaley*: This allows the linear range (-*linthresh* to *linthresh*) to be stretched relative to the logarithmic range. Its value is the number of decades to use for each half of the linear range. For example, when *linscale* == 1.0 (the default), the space used for the positive and negative halves of the linear range will be equal to one decade in the logarithmic range.

switch_backend

Switch the default backend. This feature is **experimental**, and is only expected to work switching to an image backend. e.g., 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.

Figure

The Figure instance supports callbacks through a *callbacks* attribute which is a :class:`matplotlib.cbook.CallbackRegistry` instance. The events you can connect to are 'dpi_changed', and the callback will be called with ``func(fig)`` where fig is the :class:`Figure` instance. *patch* The figure patch is drawn by a :class:`matplotlib.patches.Rectangle` instance *suppressComposite* For multiple figure images, the figure will make composite images depending on the renderer option_image_nocomposite function. If suppressComposite is True|False, this will override the renderer.

FigureCanvasBase

The canvas the figure renders into. Public attributes *figure* A :class:`matplotlib.figure.Figure` instance

__spec__

The specification for a module, used for loading. A module's spec is the source for information about the module. For data associated with the module, including source, use the spec's loader. `name` is the absolute name of the module. `loader` is the loader to use when loading the module. `parent` is the name of the package the module is in. The parent is derived from the name. `is_package` determines if the module is considered a package or not. On modules this is reflected by the `__path__` attribute. `origin` is the specific location used by the loader from which to load the module, if that information is available. When filename is set, origin will match. `has_location` indicates that a spec's "origin" reflects a location. When this is True, `__file__` attribute of the module is set. `cached` is the location of the cached bytecode file, if any. It corresponds to the `__cached__` attribute. `submodule_search_locations` is the sequence of path entries to search when importing submodules. If set, is_package should be True--and False otherwise. Packages are simply modules that (may) have submodules. If a spec has a non-None value in `submodule_search_locations`, the import system will consider modules loaded from the spec as packages. Only finders (see importlib.abc.MetaPathFinder and importlib.abc.PathEntryFinder) should modify ModuleSpec instances.

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 built-in 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 turquoise title:: title('Is this the best color?', color='#afeeee')

matplotlib

This is an object-oriented plotting library. A procedural interface is provided by the companion pyplot module, which may be imported directly, e.g.:: import matplotlib.pyplot as plt or using ipython:: ipython at your terminal, followed by:: In [1]: %matplotlib In [2]: import matplotlib.pyplot as plt at the ipython shell prompt. For the most part, direct use of the object-oriented library is encouraged when programming; pyplot is primarily for working interactively. The exceptions are the pyplot commands :func:`~matplotlib.pyplot.figure`, :func:`~matplotlib.pyplot.subplot`, :func:`~matplotlib.pyplot.subplots`, and :func:`~pyplot.savefig`, which can greatly simplify scripting. Modules include: :mod:`matplotlib.axes` defines the :class:`~matplotlib.axes.Axes` class. Most pylab commands are wrappers for :class:`~matplotlib.axes.Axes` methods. The axes module is the highest level of OO access to the library. :mod:`matplotlib.figure` defines the :class:`~matplotlib.figure.Figure` class. :mod:`matplotlib.artist` defines the :class:`~matplotlib.artist.Artist` base class for all classes that draw things. :mod:`matplotlib.lines` defines the :class:`~matplotlib.lines.Line2D` class for drawing lines and markers :mod:`matplotlib.patches` defines classes for drawing polygons :mod:`matplotlib.text` defines the :class:`~matplotlib.text.Text`, :class:`~matplotlib.text.TextWithDash`, and :class:`~matplotlib.text.Annotate` classes :mod:`matplotlib.image` defines the :class:`~matplotlib.image.AxesImage` and :class:`~matplotlib.image.FigureImage` classes :mod:`matplotlib.collections` classes for efficient drawing of groups of lines or polygons :mod:`matplotlib.colors` classes for interpreting color specifications and for making colormaps :mod:`matplotlib.cm` colormaps and the :class:`~matplotlib.image.ScalarMappable` mixin class for providing color mapping functionality to other classes :mod:`matplotlib.ticker` classes for calculating tick mark locations and for formatting tick labels :mod:`matplotlib.backends` a subpackage with modules for various gui libraries and output formats The base matplotlib namespace includes: :data:`~matplotlib.rcParams` a global dictionary of default configuration settings. It is initialized by code which may be overridded by a matplotlibrc file. :func:`~matplotlib.rc` a function for setting groups of rcParams values :func:`~matplotlib.use` a function for setting the matplotlib backend. If used, this function must be called immediately after importing matplotlib for the first time. In particular, it must be called **before** importing pylab (if pylab is imported). matplotlib was initially written by John D. Hunter (1968-2012) and is now developed and maintained by a host of others. Occasionally the internal documentation (python docstrings) will refer to MATLAB®, a registered trademark of The MathWorks, Inc.

This module provides a large set of colormaps, functions for registering new colormaps and for getting a colormap by name, and a mixin class for adding color mapping functionality.

sys

This module provides access to some objects used or maintained by the interpreter and to functions that interact strongly with the interpreter. Dynamic objects: argv -- command line arguments; argv[0] is the script pathname if known path -- module search path; path[0] is the script directory, else '' modules -- dictionary of loaded modules displayhook -- called to show results in an interactive session excepthook -- called to handle any uncaught exception other than SystemExit To customize printing in an interactive session or to install a custom top-level exception handler, assign other functions to replace these. stdin -- standard input file object; used by input() stdout -- standard output file object; used by print() stderr -- standard error object; used for error messages By assigning other file objects (or objects that behave like files) to these, it is possible to redirect all of the interpreter's I/O. last_type -- type of last uncaught exception last_value -- value of last uncaught exception last_traceback -- traceback of last uncaught exception These three are only available in an interactive session after a traceback has been printed. Static objects: builtin_module_names -- tuple of module names built into this interpreter copyright -- copyright notice pertaining to this interpreter exec_prefix -- prefix used to find the machine-specific Python library executable -- absolute path of the executable binary of the Python interpreter float_info -- a struct sequence with information about the float implementation. float_repr_style -- string indicating the style of repr() output for floats hash_info -- a struct sequence with information about the hash algorithm. hexversion -- version information encoded as a single integer implementation -- Python implementation information. int_info -- a struct sequence with information about the int implementation. maxsize -- the largest supported length of containers. maxunicode -- the value of the largest Unicode code point platform -- platform identifier prefix -- prefix used to find the Python library thread_info -- a struct sequence with information about the thread implementation. version -- the version of this interpreter as a string version_info -- version information as a named tuple __stdin__ -- the original stdin; don't touch! __stdout__ -- the original stdout; don't touch! __stderr__ -- the original stderr; don't touch! __displayhook__ -- the original displayhook; don't touch! __excepthook__ -- the original excepthook; don't touch! Functions: displayhook() -- print an object to the screen, and save it in builtins._ excepthook() -- print an exception and its traceback to sys.stderr exc_info() -- return thread-safe information about the current exception exit() -- exit the interpreter by raising SystemExit getdlopenflags() -- returns flags to be used for dlopen() calls getprofile() -- get the global profiling function getrefcount() -- return the reference count for an object (plus one :-) getrecursionlimit() -- return the max recursion depth for the interpreter getsizeof() -- return the size of an object in bytes gettrace() -- get the global debug tracing function setcheckinterval() -- control how often the interpreter checks for events setdlopenflags() -- set the flags to be used for dlopen() calls setprofile() -- set the global profiling function setrecursionlimit() -- set the max recursion depth for the interpreter settrace() -- set the global debug tracing function

subplots_adjust

Tune the subplot layout. call signature:: subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) 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

ioff

Turn interactive mode off.

ion

Turn interactive mode on.

box

Turn the axes box on or off. *on* may be a boolean or a string, 'on' or 'off'. If *on* is *None*, toggle state.

grid

Turn the axes grids on or off. Call signature:: grid(self, b=None, which='major', axis='both', **kwargs) Set the axes grids on or off; *b* is a boolean. (For MATLAB compatibility, *b* may also be a string, 'on' or 'off'.) 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*. *which* can be 'major' (default), 'minor', or 'both' to control whether major tick grids, minor tick grids, or both are affected. *axis* can be 'both' (default), 'x', or 'y' to control which set of gridlines are drawn. *kwargs* are used to set the grid line properties, e.g.,:: ax.grid(color='r', linestyle='-', linewidth=2) Valid :class:`~matplotlib.lines.Line2D` kwargs are agg_filter: unknown alpha: float (0.0 transparent through 1.0 opaque) animated: [True | False] antialiased or aa: [True | False] axes: an :class:`~matplotlib.axes.Axes` instance clip_box: a :class:`matplotlib.transforms.Bbox` instance clip_on: [True | False] clip_path: [ (:class:`~matplotlib.path.Path`, :class:`~matplotlib.transforms.Transform`) | :class:`~matplotlib.patches.Patch` | None ] color or c: any matplotlib color contains: a callable function dash_capstyle: ['butt' | 'round' | 'projecting'] dash_joinstyle: ['miter' | 'round' | 'bevel'] dashes: sequence of on/off ink in points drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] figure: a :class:`matplotlib.figure.Figure` instance fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none'] gid: an id string label: string or anything printable with '%s' conversion. linestyle or ls: [``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``] linewidth or lw: float value in points lod: [True | False] marker: :mod:`A valid marker style <matplotlib.markers>` markeredgecolor or mec: any matplotlib color markeredgewidth or mew: float value in points markerfacecolor or mfc: any matplotlib color markerfacecoloralt or mfcalt: any matplotlib color markersize or ms: float markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float] path_effects: unknown picker: float distance in points or callable pick function ``fn(artist, event)`` pickradius: float distance in points rasterized: [True | False | None] sketch_params: unknown snap: unknown solid_capstyle: ['butt' | 'round' | 'projecting'] solid_joinstyle: ['miter' | 'round' | 'bevel'] transform: a :class:`matplotlib.transforms.Transform` instance url: a url string visible: [True | False] xdata: 1D array ydata: 1D array zorder: any number

xkcd

Turns on `xkcd <http://xkcd.com/>`_ sketch-style drawing mode. This will only have effect on things drawn after this function is called. For best results, the "Humor Sans" font should be installed: it is not included with matplotlib. Parameters ---------- scale : float, optional The amplitude of the wiggle perpendicular to the source line. length : float, optional The length of the wiggle along the line. randomness : float, optional The scale factor by which the length is shrunken or expanded. Notes ----- This function works by a number of rcParams, so it will probably override others you have set before. If you want the effects of this function to be temporary, it can be used as a context manager, for example:: with plt.xkcd(): # This figure will be in XKCD-style fig1 = plt.figure() # ... # This figure will be in regular style fig2 = plt.figure()

FormatStrFormatter

Use an old-style ('%' operator) format string to format the tick

FuncFormatter

User defined function for formatting

six

Utilities for writing code that runs on Python 2 and 3

_interactive_bk

list() -> new empty list list(iterable) -> new list initialized from iterable's items

silent_list

override repr when returning a list of matplotlib artists to prevent long, meaningless output. This is meant to be used for a homogeneous list of a given type

pylab_setup

return new_figure_manager, draw_if_interactive and show for pylab

is_numlike

return true if *obj* looks like a number

autumn

set the default colormap to autumn and apply to current image if any. See help(colormaps) for more information

bone

set the default colormap to bone and apply to current image if any. See help(colormaps) for more information

cool

set the default colormap to cool and apply to current image if any. See help(colormaps) for more information

copper

set the default colormap to copper and apply to current image if any. See help(colormaps) for more information

flag

set the default colormap to flag and apply to current image if any. See help(colormaps) for more information

gray

set the default colormap to gray and apply to current image if any. See help(colormaps) for more information

hot

set the default colormap to hot and apply to current image if any. See help(colormaps) for more information

hsv

set the default colormap to hsv and apply to current image if any. See help(colormaps) for more information

jet

set the default colormap to jet and apply to current image if any. See help(colormaps) for more information

pink

set the default colormap to pink and apply to current image if any. See help(colormaps) for more information

prism

set the default colormap to prism and apply to current image if any. See help(colormaps) for more information

spectral

set the default colormap to spectral and apply to current image if any. See help(colormaps) for more information

spring

set the default colormap to spring and apply to current image if any. See help(colormaps) for more information

summer

set the default colormap to summer and apply to current image if any. See help(colormaps) for more information

winter

set the default colormap to winter and apply to current image if any. See help(colormaps) for more information

matplot_plt

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