matplotlib 3.5.1

BackRef

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 functions .pyplot.figure , .pyplot.subplot , .pyplot.subplots , and .pyplot.savefig , which can greatly simplify scripting.

Modules include:

matplotlib.axes

The :None:None:`~.axes.Axes` class. Most pyplot functions are wrappers for :None:None:`~.axes.Axes` methods. The axes module is the highest level of OO access to the library.

matplotlib.figure

The .Figure class.

matplotlib.artist

The .Artist base class for all classes that draw things.

matplotlib.lines

The .Line2D class for drawing lines and markers.

matplotlib.patches

Classes for drawing polygons.

matplotlib.text

The :None:None:`.Text` and .Annotation classes.

matplotlib.image

The .AxesImage and .FigureImage classes.

matplotlib.collections

Classes for efficient drawing of groups of lines or polygons.

matplotlib.colors

Color specifications and making colormaps.

matplotlib.cm

Colormaps, and the .ScalarMappable mixin class for providing color mapping functionality to other classes.

matplotlib.ticker

Calculation of tick mark locations and formatting of tick labels.

matplotlib.backends

A subpackage with modules for various GUI libraries and output formats.

The base matplotlib namespace includes:

:None:None:`~matplotlib.rcParams`

Default configuration settings; their defaults may be overridden using a matplotlibrc file.

~matplotlib.use

Setting the Matplotlib backend. This should be called before any figure is created, because it is not possible to switch between different GUI backends after that.

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.

An object-oriented plotting library.

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 functions .pyplot.figure , .pyplot.subplot , .pyplot.subplots , and .pyplot.savefig , which can greatly simplify scripting.

Modules include:

matplotlib.axes

The :None:None:`~.axes.Axes` class. Most pyplot functions are wrappers for :None:None:`~.axes.Axes` methods. The axes module is the highest level of OO access to the library.

matplotlib.figure

The .Figure class.

matplotlib.artist

The .Artist base class for all classes that draw things.

matplotlib.lines

The .Line2D class for drawing lines and markers.

matplotlib.patches

Classes for drawing polygons.

matplotlib.text

The :None:None:`.Text` and .Annotation classes.

matplotlib.image

The .AxesImage and .FigureImage classes.

matplotlib.collections

Classes for efficient drawing of groups of lines or polygons.

matplotlib.colors

Color specifications and making colormaps.

matplotlib.cm

Colormaps, and the .ScalarMappable mixin class for providing color mapping functionality to other classes.

matplotlib.ticker

Calculation of tick mark locations and formatting of tick labels.

matplotlib.backends

A subpackage with modules for various GUI libraries and output formats.

The base matplotlib namespace includes:

:None:None:`~matplotlib.rcParams`

Default configuration settings; their defaults may be overridden using a matplotlibrc file.

~matplotlib.use

Setting the Matplotlib backend. This should be called before any figure is created, because it is not possible to switch between different GUI backends after that.

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.

Examples

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy

204 Elements
scipy.signal.exponential
scipy.signal.windows._windows.boxcar
scipy.signal._wavelets.morlet2
scipy.interpolate._polyint.krogh_interpolate
scipy.signal._filter_design.gammatone
scipy.signal._ltisys.dimpulse
scipy.spatial._qhull.ConvexHull
scipy.signal.bartlett
scipy.signal._bsplines.cspline1d_eval
scipy.interpolate._bsplines.make_lsq_spline
scipy.interpolate._bspl.evaluate_all_bspl
scipy.special._spherical_bessel.spherical_kn
scipy.signal.windows._windows.barthann
scipy.signal.chebwin
scipy.signal._czt.CZT
scipy.spatial.transform._rotation_spline.RotationSpline
scipy.signal.boxcar
scipy.interpolate._polyint.barycentric_interpolate
scipy.signal._arraytools.even_ext
scipy.signal._signaltools.lfilter
scipy.interpolate._ndgriddata.griddata
scipy.signal.windows._windows.hann
scipy.signal._waveforms.square
scipy.signal._bsplines.cspline1d
scipy.signal._filter_design.cheb2ord
scipy.signal._filter_design.iirdesign
scipy.signal._peak_finding.peak_widths
scipy.signal._ltisys.lsim2
scipy.optimize._optimize.rosen
scipy.signal.blackmanharris
scipy.interpolate._polyint.approximate_taylor_polynomial
scipy.signal._filter_design.lp2lp
scipy.fft._basic.fft
scipy.signal._signaltools.resample_poly
scipy.integrate._quadrature.cumulative_trapezoid
scipy.integrate._quad_vec.quad_vec
scipy.signal.windows._windows.chebwin
scipy.signal.parzen
scipy.signal._filter_design.cheb1ord
scipy.signal._filter_design.buttord
scipy.interpolate._interpolate.interp2d
scipy.signal._ltisys.dfreqresp
scipy.signal.windows._windows.dpss
scipy.signal._waveforms.sweep_poly
scipy.interpolate._interpolate.interp1d
scipy.signal.barthann
scipy.interpolate._bsplines.make_interp_spline
scipy.signal._signaltools.oaconvolve
scipy.signal._filter_design.iirfilter
scipy.signal._ltisys.dlti.bode
scipy.signal._filter_design.sosfreqz
scipy.signal._max_len_seq.max_len_seq
scipy.signal.windows._windows.general_cosine
scipy.signal._czt.zoom_fft
scipy.signal.flattop
scipy.signal._ltisys.dstep
scipy.signal._fir_filter_design.minimum_phase
scipy.signal.windows._windows.triang
scipy.signal._filter_design.ellip
scipy.special._orthogonal.jacobi
scipy.signal.windows._windows.exponential
scipy.signal._filter_design.lp2bs
scipy.special._orthogonal.genlaguerre
scipy.signal._signaltools.filtfilt
scipy.misc._common.face
scipy.signal.windows._windows.blackman
scipy.fft._basic.ifft
scipy.signal.windows._windows.cosine
scipy.signal.gaussian
scipy.signal._fir_filter_design.kaiserord
scipy.signal._waveforms.sawtooth
scipy.interpolate._fitpack2.UnivariateSpline
scipy.spatial._kdtree.KDTree.query_ball_point
scipy.signal._filter_design.butter
scipy.signal._ltisys.impulse
scipy.signal._filter_design.group_delay
scipy.signal._spectral_py.csd
scipy.signal.windows._windows.tukey
scipy.interpolate._fitpack_impl.splrep
scipy.interpolate._interpolate.lagrange
scipy.spatial._plotutils.voronoi_plot_2d
scipy.signal._filter_design.iircomb
scipy.signal._filter_design.bessel
scipy.signal._filter_design.bilinear
scipy.signal.hann
scipy.signal._lti_conversion.cont2discrete
scipy.spatial._kdtree.KDTree.query_pairs
scipy.signal._signaltools.correlate
scipy.signal.tukey
scipy.spatial._plotutils.convex_hull_plot_2d
scipy.signal._ltisys.step
scipy.signal.bohman
scipy.signal._spectral_py.istft
scipy.signal._wavelets.cwt
scipy.signal.hamming
scipy.interpolate._ndgriddata.NearestNDInterpolator
scipy.signal.windows._windows.kaiser
scipy.signal.cosine
scipy.misc._common.electrocardiogram
scipy.signal._waveforms.chirp
scipy.signal._czt.czt_points
scipy.signal._ltisys.lti.bode
scipy.signal._signaltools.correlate2d
scipy.signal._signaltools.wiener
scipy.signal._signaltools.sosfilt_zi
scipy.special._orthogonal.chebyu
scipy.signal._bsplines.qspline1d
scipy.signal._ltisys.lsim
scipy.signal._signaltools.resample
scipy.interpolate._bsplines.BSpline
scipy.linalg._basic.lstsq
scipy.spatial._qhull.tsearch
scipy.signal._wavelets.ricker
scipy.spatial._qhull.Voronoi
scipy.spatial._geometric_slerp.geometric_slerp
scipy.signal._ltisys.step2
scipy.signal._spectral_py.lombscargle
scipy.interpolate._fitpack2.LSQUnivariateSpline
scipy.signal._filter_design.freqz
scipy.signal._filter_design.iirpeak
scipy.special._orthogonal.chebyt
scipy.signal._signaltools.sosfiltfilt
scipy.interpolate._fitpack2.RectSphereBivariateSpline
scipy.signal._bsplines.qspline1d_eval
scipy.signal._ltisys.bode
scipy.signal._waveforms.unit_impulse
scipy.signal._fir_filter_design.firls
scipy.fft._basic.ifftn
scipy.signal._signaltools.decimate
scipy.signal._filter_design.freqz_zpk
scipy.special._orthogonal.laguerre
scipy.signal.windows._windows.taylor
scipy.signal._signaltools.sosfilt
scipy.spatial._qhull.Delaunay
scipy.signal._ltisys.impulse2
scipy.signal._spectral_py.welch
scipy.signal.windows._windows.bartlett
scipy.signal._filter_design.lp2hp
scipy.spatial._plotutils.delaunay_plot_2d
scipy.signal.triang
scipy.optimize._optimize.bracket
scipy.interpolate._fitpack2.InterpolatedUnivariateSpline
scipy.special._orthogonal.hermite
scipy.signal.windows._windows.bohman
scipy.signal._spectral_py.spectrogram
scipy.signal._arraytools.odd_ext
scipy.signal._czt.ZoomFFT
scipy.interpolate._rbfinterp.RBFInterpolator
scipy.signal._ltisys.place_poles
scipy.optimize._minpack_py.curve_fit
scipy.spatial._qhull.HalfspaceIntersection
scipy.signal._spectral_py.periodogram
scipy.interpolate._cubic.pchip_interpolate
scipy.signal.blackman
scipy.signal.windows._windows.nuttall
scipy.optimize._zeros_py.newton
scipy.signal._filter_design.freqs
scipy.signal.windows._windows.general_gaussian
scipy.spatial._kdtree.KDTree.query_ball_tree
scipy.signal.windows._windows.gaussian
scipy.signal.windows._windows.flattop
scipy.special._orthogonal.gegenbauer
scipy.signal._signaltools.fftconvolve
scipy.signal._waveforms.gausspulse
scipy.special._spherical_bessel.spherical_yn
scipy.signal._ltisys.freqresp
scipy.signal._filter_design.lp2bp
scipy.signal._filter_design.iirnotch
scipy.signal._peak_finding.find_peaks
scipy.signal.windows._windows.general_hamming
scipy.special._basic.diric
scipy.signal.kaiser
scipy.misc._common.ascent
scipy.signal._bsplines.spline_filter
scipy.signal._fir_filter_design.remez
scipy.interpolate._bsplines.BSpline.integrate
scipy.signal._spectral_py.coherence
scipy.signal.general_gaussian
scipy.signal._spectral_py.stft
scipy.signal.windows._windows.parzen
scipy.signal._signaltools.convolve
scipy.fft._basic.fftn
scipy.special._spherical_bessel.spherical_jn
scipy.interpolate._fitpack2.LSQSphereBivariateSpline
scipy.signal._peak_finding.peak_prominences
scipy.signal._filter_design.freqs_zpk
scipy.signal.nuttall
scipy.special._spherical_bessel.spherical_in
scipy.signal._filter_design.cheby1
scipy.signal._signaltools.hilbert
scipy.signal.windows._windows.blackmanharris
scipy.integrate._bvp.solve_bvp
scipy.integrate._odepack_py.odeint
scipy.signal._filter_design.ellipord
scipy.signal._arraytools.const_ext
scipy.integrate._ivp.ivp.solve_ivp
scipy.signal._filter_design.cheby2
scipy.signal._signaltools.convolve2d
scipy.interpolate._cubic.CubicSpline
scipy.interpolate._fitpack_py.splrep
scipy.interpolate.interpnd.LinearNDInterpolator
scipy.signal.windows._windows.hamming
scipy.signal._filter_design.bilinear_zpk
scipy.signal._ltisys.dbode

dask

32 Elements
dask.array.ufunc.wrap_elemwise.<locals>.wrapped
dask.array.random.RandomState.gamma
dask.array.random.RandomState.triangular
dask.array.random.RandomState.standard_gamma
dask.array.random.RandomState.standard_t
dask.array.ufunc.cosh
dask.array.random.RandomState.noncentral_chisquare
dask.array.ufunc.arccos
dask.array.random.RandomState.standard_cauchy
dask.array.random.RandomState.weibull
dask.array.random.RandomState.laplace
dask.array.random.RandomState.pareto
dask.array.random.RandomState.power
dask.array.random.RandomState.logistic
dask.array.random.RandomState.lognormal
dask.array.ufunc.sin
dask.array.random.RandomState.random_integers
dask.array.random.RandomState.poisson
dask.array.random.RandomState.zipf
dask.array.random.RandomState.hypergeometric
dask.array.random.RandomState.uniform
dask.array.creation.meshgrid
dask.array.random.RandomState.rayleigh
dask.array.ufunc.absolute
dask.array.random.RandomState.vonmises
dask.array.ufunc.exp
dask.array.random.RandomState.noncentral_f
dask.array.ufunc.arctan
dask.array.random.RandomState.wald
dask.array.random.RandomState.logseries
dask.array.random.RandomState.normal
dask.array.random.RandomState.gumbel

skimage

skimage.filters._gabor.gabor
skimage.filters._gabor.gabor_kernel
skimage.viewer.canvastools.painttool.PaintTool

networkx

networkx.drawing.nx_pylab.draw_networkx
networkx.drawing.nx_pylab.draw_networkx_edges

matplotlib

matplotlib.cbook.Grouper
matplotlib.widgets.SpanSelector
matplotlib.backends.backend_pdf.PdfPages
matplotlib.widgets.RectangleSelector

papyri

papyri.examples.example2
papyri.gen.Gen.get_example_data
papyri.examples.example1

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them


File: /matplotlib/__init__.py#0
type: <class 'module'>
Commit: