cm (colormap) matplotlib.cm 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. class matplotlib.cm.ScalarMappable(norm=None, cmap=None) Bases: object This is a mixin class to support scalar data to RGBA mapping. The ScalarMappable makes use of data normalization before returning RGBA colors from the given colormap. Parameters: norm : matplotlib.colors.Normalize

lines matplotlib.lines This module contains all the 2D line class which can draw with a variety of line styles, markers and colors. class matplotlib.lines.Line2D(xdata, ydata, linewidth=None, linestyle=None, color=None, marker=None, markersize=None, markeredgewidth=None, markeredgecolor=None, markerfacecolor=None, markerfacecoloralt='none', fillstyle=None, antialiased=None, dash_capstyle=None, solid_capstyle=None, dash_joinstyle=None, solid_joinstyle=None, pickradius=5, drawstyle=None, markev

type1font matplotlib.type1font This module contains a class representing a Type 1 font. This version reads pfa and pfb files and splits them for embedding in pdf files. It also supports SlantFont and ExtendFont transformations, similarly to pdfTeX and friends. There is no support yet for subsetting. Usage: >>> font = Type1Font(filename) >>> clear_part, encrypted_part, finale = font.parts >>> slanted_font = font.transform({'slant': 0.167}) >>> extended_font =

scale matplotlib.scale class matplotlib.scale.InvertedLog10Transform(shorthand_name=None) Bases: matplotlib.transforms.Transform Creates a new TransformNode. shorthand_name - a string representing the ?name? of this transform. The name carries no significance other than to improve the readability of str(transform) when DEBUG=True. base = 10.0 has_inverse = True input_dims = 1 inverted() is_separable = True output_dims = 1 transform_non_affine(a) class matplotlib.sc

path matplotlib.path A module for dealing with the polylines used throughout matplotlib. The primary class for polyline handling in matplotlib is Path. Almost all vector drawing makes use of Paths somewhere in the drawing pipeline. Whilst a Path instance itself cannot be drawn, there exists Artist subclasses which can be used for convenient Path visualisation - the two most frequently used of these are PathPatch and PathCollection. class matplotlib.path.Path(vertices, codes=None, _interpolati

projections matplotlib.projections class matplotlib.projections.ProjectionRegistry Bases: object Manages the set of projections available to the system. get_projection_class(name) Get a projection class from its name. get_projection_names() Get a list of the names of all projections currently registered. register(*projections) Register a new set of projection(s). matplotlib.projections.get_projection_class(projection=None) Get a projection class from its name. If projectio

afm (Adobe Font Metrics interface) matplotlib.afm This is a python interface to Adobe Font Metrics Files. Although a number of other python implementations exist, and may be more complete than this, it was decided not to go with them because they were either: copyrighted or used a non-BSD compatible license had too many dependencies and a free standing lib was needed Did more than needed and it was easier to write afresh rather than figure out how to get just what was needed. It is pretty e

sankey matplotlib.sankey Module for creating Sankey diagrams using matplotlib class matplotlib.sankey.Sankey(ax=None, scale=1.0, unit='', format='%G', gap=0.25, radius=0.1, shoulder=0.03, offset=0.15, head_angle=100, margin=0.4, tolerance=1e-06, **kwargs) Bases: object Sankey diagram in matplotlib Sankey diagrams are a specific type of flow diagram, in which the width of the arrows is shown proportionally to the flow quantity. They are typically used to visualize energy or material or cost

tight_layout matplotlib.tight_layout This module provides routines to adjust subplot params so that subplots are nicely fit in the figure. In doing so, only axis labels, tick labels, axes titles and offsetboxes that are anchored to axes are currently considered. Internally, it assumes that the margins (left_margin, etc.) which are differences between ax.get_tightbbox and ax.bbox are independent of axes position. This may fail if Axes.adjustable is datalim. Also, This will fail for some cases (

mpl_toolkits.axes_grid.axes_grid class mpl_toolkits.axes_grid.axes_grid.Grid(fig, rect, nrows_ncols, ngrids=None, direction='row', axes_pad=0.02, add_all=True, share_all=False, share_x=True, share_y=True, label_mode='L', axes_class=None) Build an Grid instance with a grid nrows*ncols Axes in Figure fig with rect=[left, bottom, width, height] (in Figure coordinates) or the subplot position code (e.g., ?121?). Optional keyword arguments: Keyword Default Description direction ?row? [ ?row? | ?