matplotlib 3.5.1

Other ParametersParametersReturnsBackRef
title(label, fontdict=None, loc=None, pad=None, *, y=None, **kwargs)

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.

Other Parameters

**kwargs : `.Text` properties

Other keyword arguments are text properties, see :None:None:`.Text` for a list of valid text properties.

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'],
 'color': rcParams['axes.titlecolor'],
 'verticalalignment': 'baseline',
 'horizontalalignment': loc}
loc : {'center', 'left', 'right'}, default: :rc:`axes.titlelocation`

Which title to set.

y : float, default: :rc:`axes.titley`

Vertical Axes loation for the title (1.0 is the top). If None (the default), y is determined automatically to avoid decorators on the Axes.

pad : float, default: :rc:`axes.titlepad`

The offset of the title from the top of the Axes, in points.

Returns

`.Text`

The matplotlib text instance representing the title

Set a title for the Axes.

Examples

See :

Back References

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

dask

dask.array.random.RandomState.power
dask.array.ufunc.wrap_elemwise.<locals>.wrapped
dask.array.random.RandomState.zipf
dask.array.ufunc.exp

scipy

64 Elements
scipy.signal.windows._windows.kaiser
scipy.signal.cosine
scipy.signal._waveforms.chirp
scipy.signal.exponential
scipy.signal.hamming
scipy.signal.windows._windows.boxcar
scipy.signal._filter_design.sosfreqz
scipy.signal._filter_design.gammatone
scipy.signal.windows._windows.general_cosine
scipy.signal.windows._windows.nuttall
scipy.signal.blackman
scipy.signal.flattop
scipy.signal.bartlett
scipy.signal._spectral_py.istft
scipy.signal.windows._windows.general_gaussian
scipy.signal.windows._windows.triang
scipy.signal.windows._windows.gaussian
scipy.signal._filter_design.ellip
scipy.signal.windows._windows.flattop
scipy.signal.windows._windows.exponential
scipy.signal.windows._windows.blackman
scipy.signal.windows._windows.cosine
scipy.signal.windows._windows.barthann
scipy.signal.gaussian
scipy.signal._fir_filter_design.kaiserord
scipy.signal._ltisys.step2
scipy.special._basic.diric
scipy.signal.kaiser
scipy.signal.chebwin
scipy.signal._filter_design.butter
scipy.spatial.transform._rotation_spline.RotationSpline
scipy.signal.boxcar
scipy.signal.general_gaussian
scipy.signal._spectral_py.stft
scipy.signal._filter_design.group_delay
scipy.signal.windows._windows.parzen
scipy.signal.windows._windows.tukey
scipy.interpolate._ndgriddata.griddata
scipy.signal.windows._windows.hann
scipy.signal.windows._windows.taylor
scipy.signal.nuttall
scipy.signal._filter_design.bessel
scipy.signal.hann
scipy.signal._filter_design.cheby1
scipy.signal.windows._windows.blackmanharris
scipy.signal.tukey
scipy.signal.windows._windows.bartlett
scipy.signal._ltisys.step
scipy.signal.blackmanharris
scipy.signal._filter_design.ellipord
scipy.signal.triang
scipy.integrate._ivp.ivp.solve_ivp
scipy.special._orthogonal.hermite
scipy.signal._filter_design.cheby2
scipy.signal.windows._windows.chebwin
scipy.signal.parzen
scipy.signal._filter_design.cheb1ord
scipy.signal._filter_design.buttord
scipy.signal.windows._windows.bohman
scipy.signal.bohman
scipy.signal.windows._windows.hamming
scipy.signal._waveforms.sweep_poly
scipy.signal._filter_design.cheb2ord
scipy.signal.barthann

matplotlib

matplotlib.pyplot.plotting

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


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