matplotlib is typically imported as the abbreviated alias mpl:
In [1]: import matplotlib as mplThe attribute __all__ is a list of identifiers that are imported when a * import is used:
In [2]: from matplotlib import *When a * import is used, all the variables display on the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| Name ▲ | Type | Size | Value |
| __version__ | str | 6 | '3.8.0' |
| __version_info__ | tuple | 3 | (3, 8, 0) |
| __bibtex__ | str | 256 | '@Article{matplotlib2024,...}' |
| colormaps | function | - | <function colormaps> |
| color_sequences | dict | 5 | {'default': [...], 'tab10': [...]} |
| MatplotlibDeprecationWarning | class | - | <class MatplotlibDeprecationWarning> |
| ExecutableNotFoundError | class | - | <class ExecutableNotFoundError> |
| set_loglevel | function | - | <function set_loglevel> |
| get_configdir | function | - | <function get_configdir> |
| get_cachedir | function | - | <function get_cachedir> |
| get_data_path | function | - | <function get_data_path> |
| use | function | - | <function use> |
| get_backend | function | - | <function get_backend> |
| interactive | function | - | <function interactive> |
| is_interactive | function | - | <function is_interactive> |
| set_interactive | function | - | <function set_interactive> |
| rc | function | - | <function rc> |
| rc_context | function | - | <function rc_context> |
| rcdefaults | function | - | <function rcdefaults> |
| rcParams | RcParams | - | RcParams({...}) |
| rcParamsDefault | RcParams | - | RcParams({...}) |
| rcParamsOrig | RcParams | - | RcParams({...}) |
| style | module | - | <module style> |
| use_line_collection | bool | 1 | True |
| is_interactive_backend | function | - | <function is_interactive_backend> |
The * import is typically not recommended and instead the identifiers are typically accessed from mpl:
In [4]: mpl.
# -------------------------------------
# Available Identifiers for Matplotlib:
# -------------------------------------
# 🔧 Version Information:
# - __version__ : The version number of the currently installed Matplotlib library.
# - __version_info__ : A tuple containing the version information of Matplotlib in a more
# : structured format.
# - __bibtex__ : The BibTeX entry for citing Matplotlib in academic papers or projects.
# 🎨 Colormap and Color Utilities:
# - colormaps : Provides access to Matplotlib's built-in colormap registry, allowing
# : retrieval of colormap instances (e.g., 'viridis', 'plasma') for coloring
# : plots based on data values.
# - color_sequences : Contains predefined color sequences used by Matplotlib for cycling through
# : line colors when creating multiple plots or lines.
# ⚠️ Warnings and Exceptions:
# - MatplotlibDeprecationWarning : A custom warning class for deprecation warnings specific to Matplotlib,
# : raised when a feature is deprecated and scheduled for removal.
# - ExecutableNotFoundError : An exception raised when a required executable (e.g., LaTeX engine) is
# : missing.
# 🔧 Configuration Management:
# - set_loglevel : Sets the logging level for Matplotlib, controlling the verbosity of log
# : messages ('debug', 'info', 'warning', 'error').
# - get_configdir : Returns the directory where Matplotlib stores its configuration files,
# : typically user-specific settings.
# - get_cachedir : Returns the directory where Matplotlib stores cache files, such as font
# : lists and compiled LaTeX formulas.
# - get_data_path : Returns the path to the directory containing essential Matplotlib data
# : files (e.g., default fonts, image resources).
# 🎛️ Interactive Mode:
# - interactive : Enables or disables interactive mode. In interactive mode, plots are
# : automatically updated after each command. In non-interactive mode,
# : plt.show() must be called to display plots.
# - is_interactive : Returns True if Matplotlib is in interactive mode, False otherwise.
# 🎨 Backend Management:
# - get_backend : Returns the name of the current Matplotlib backend used for rendering
# : (e.g., 'TkAgg', 'Qt5Agg').
# - use : Sets the Matplotlib backend, determining how plots are rendered (e.g.,
# : interactively or saved to files).
# 🔧 Configuration Files:
# - matplotlib_fname : Returns the full path to the matplotlibrc configuration file currently
# : in use, containing user-defined plot settings.
# 🔧 RcParams and Configuration Tools:
# - RcParams : A specialized dictionary-like object storing Matplotlib's runtime
# : configuration settings (e.g., figure size, font size, line width).
# - rc_params : Returns the current configuration as an RcParams object, allowing
# : retrieval of active settings.
# - rc_params_from_file : Loads Matplotlib configuration parameters from a file into an RcParams
# : object.
# - rcParamsDefault : The default configuration settings shipped with Matplotlib.
# - rcParamsOrig : Stores the original configuration parameters, allowing reversion to the
# : default state after customizations.
# - defaultParams : Alias for the default Matplotlib parameters, representing the
# : out-of-the-box settings.
# 🔧 Context Managers and Reset Functions:
# - rc_context : A context manager for temporarily setting Matplotlib configuration
# : parameters within a block of code.
# - rc : Updates individual parameters in the current configuration, such as
# : default figure size or line color.
# - rcdefaults : Resets all configuration parameters to their default values
# : (rcParamsDefault).
# - rc_file_defaults : Resets configuration parameters to those specified in the user-defined
# : matplotlibrc file.
# - rc_file : Loads and applies settings from a specific configuration file.The following data model attribute gives details about the version of matplotlib installed:
In [4]: mpl.__version__
Out[4]: 3.9.2It should be 3.9.2 or later. Most of the other identifiers shown alter the current configuration or recall parameters and will be discussed in more detail later.
mpl is compartmentalised into modules. Note however that the modules are not imported when a * import is used, because the data model attribute __all__ is defined and excludes these modules. The modules are typically imported independently:
In [5]: mpl.
# -------------------------------------
# Matplotlib Modules Overview
# -------------------------------------
# Interface Modules:
# - pyplot : Main plotting interface (state-based API), used to create and manipulate figures and plots.
# - mlab : Deprecated module that provided Matlab-like functions.
# Core Plotting and Figure Components:
# These modules form the basic building blocks of creating and managing plots, axes, and figures.
# - figure : Defines the figure class, which is the top-level container for all plot elements.
# - axes : Handles the individual plot areas (axes) where the data is displayed.
# - axis : Manages x and y axis properties (e.g., ticks, labels).
# - lines : Handles line plotting.
# - markers : Manages markers for data points.
# - collections : Efficiently handles collections of objects like lines, polygons, etc.
# - patches : Handles 2D shapes such as rectangles, circles, polygons, etc.
# - legend : Manages legends on plots.
# - legend_handler : Customizes how legend entries are rendered.
# - spines : Manages the borders (spines) of the axes.
# - ticker : Controls tick marks and labels on the axes.
# - table : Creates tables within a plot.
# - text : Handles text objects within plots.
# Data Transformation and Handling:
# Modules related to scaling, transforming, and managing different types of data.
# - transforms : Handles transformations between coordinate systems.
# - scale : Manages scaling on axes (e.g., linear, log).
# - units : Allows the use of custom units with plots.
# - dates : Specialized support for date-based plotting.
# - category : Handles categorical data (e.g., bar charts for named categories).
# Color and Styling:
# Modules that deal with color handling, colormaps, and styling.
# - colors : Defines color conversions and mapping between data values and colors.
# - cm : Colormap handling.
# - colorbar : Adds a colorbar to figures.
# - style : Supports predefined styles for customizing plot appearance.
# Geometry and Path Handling:
# Modules for managing geometric shapes, paths, and effects.
# - path : Handles paths (a series of possibly connected segments).
# - patheffects : Adds effects to paths (e.g., shadowing).
# - bezier : Handles Bezier curves and operations.
# - quiver : Plots 2D vector fields (arrows).
# - contour : Creates contour plots for 2D data.
# - tri : Handles triangulation and plotting data over triangular grids.
# Figure Layout and Grids:
# Modules to control figure layout, grids, and positioning of plot elements.
# - gridspec : Grid layout for arranging subplots within a figure.
# - layout_engine : Provides a framework for custom layout management.
# Special Plot Types:
# Modules for specialized types of plots.
# - image : Handles image display within plots.
# - sankey : Plots Sankey diagrams (flow charts).
# - animation : Supports creating animations from plots.
# Font and Text Handling:
# Modules related to text, fonts, and math rendering.
# - font_manager : Manages fonts and font properties.
# - ft2font : Low-level interface to FreeType (for rendering fonts).
# - mathtext : Renders mathematical expressions within plots.
# - texmanager : Manages LaTeX typesetting for text elements.
# Backends and Rendering:
# Modules related to rendering and backends for different output formats.
# - backends : Handles different backends (e.g., TkAgg, Agg) for rendering plots.
# - backend_bases : Base classes for creating backends.
# - backend_tools : Tools for interacting with the backend (e.g., zoom, pan).
# - backend_managers : Manages backend-related events and interactivity.
# Interactive and Widget Elements:
# Modules for interactive elements in plots.
# - widgets : Provides interactive widgets (e.g., sliders, buttons) for plots.
# - offsetbox : Used for placing offset boxes such as legends or annotations.
# Support and Utility Functions:
# General utility functions that support Matplotlib’s core features.
# - cbook : A collection of utility functions (helper functions, class decorators, etc.).
# - rcsetup : Handles matplotlibrc configuration.
# - testing : Tools for testing plots and comparing outputs.
# - typing : Type hints for Matplotlib.
# Sphinx Extensions:
# Modules used for generating documentation (via Sphinx) and embedding plots.
# - sphinxext.plot_directive : Directive to embed plots in Sphinx documentation.
# - sphinxext.mathmpl : Embeds math in plots in Sphinx-generated documentation.
# - sphinxext.figmpl_directive : A Sphinx extension to insert figures into documentation.
# - sphinxext.roles : Custom roles for Matplotlib’s Sphinx documentation.These modules are supplemented by the modules in the mpl_toolkits library:
In [5]: import mpl_toolkits
In [6]: mpl_toolkits.
# mplot3d : Toolkit for 3D plotting.
# axes_grid1 : Adds more features for arranging multiple axes in a figure.
# axisartist : Provides custom axis styling beyond the default Matplotlib axes.Typically the interface module pyplot is used and imported using the abbreviated alias plt:
In [7]: import matplotlib.pyplot as pltThe function get_plot_commands can be used to get details about the plot commands:
In [8]: plt.get_plot_commands()
# Figure Creation
# - figure : creates a new figure
# - gcf : gets the current figure
# - close : closes a figure
# - gca : gets the current axis
# Axes Creation
# - subplots : creates a figure and a set of subplots (axes)
# - subplot : adds a subplot to the figure
# - add_subplot : Adds an axes to the figure at the specified grid location
# - axes : Adds an axes to the figure at the specified position
# Figure Level Commands
# - suptitle : Adds a title to the entire figure
# - savefig : Saves the figure
# - tight_layout : Adjusts the layout of the figure
# - figlegend : Places a legend on the figure
# - show : Displays the figure
# - clf : Clears the figure
# - subplots_adjust: Adjust the spacing
# - margins : Sets the margins of a plot
# Axes Level Commands
## Plotting commands
# - plot : Plot lines and/or markers on the axes
# - scatter : Create a scatter plot
# - bar : Create a bar plot
# - barh : Create a bar plot
# - hist : Plot a histogram
# - boxplot : Create a box plot
# - violinplot : Create a violin plot
# - pie : Create a pie chart
# - step : Create step plots
# - fill : Fill between two lines
# - fill_between : Fill between two curves
# - errorbar : Plot error bars
# - stem : Create a stem plot
# - quiver : Create a 2D field of arrows
# - hexbin : Create hexagonal binning plots
# - contour : Plot contour lines
# - contourf : Plot filled contour lines
# - imshow : Display an image on the axes
# - matshow : Display a matrix of a new figure
# - streamplot : Draw streamlines of a vector flow
# - table : Adds a table to a matrix
## Axes Property Commands
# - xlim : Set the x-axis limits
# - ylim : Set the y-axis limits
# - xlabel : Set the x-axis label
# - ylabel : Set the y-axis label
# - title : Set the title of the axes
# - grid : Toggle the gridlines on/off
# - legend : Add a legend to the axes
# - xticks : Set the x-axis tick marks
# - yticks : Set the y-axis tick marks
# - axhline : Draw a horizontal line across the axes
# - axvline : Draw a vertical line across the axes
# - axhspan : Highlight a horizontal span across axes
# - axvspan : Highlight a vertical span across axes
# - text : Add text at an arbitrary position on the axes
# - annotate : Annotate a point with text
# - setp : Set properties of plot elements
# - xscale : Set the x-axis scale (e.g. logarithmic)
# - yscale : Set the y-axis scale (e.g. logarithmic)
# - twinx : Create a twin axes that shares the x-axis
# - twiny : Create a twin axes that shares the y-axis
## Color Axis Commands
# - colorbar : Adds a colorbar to the figure, which maps colors to the data values
# - clim : Sets the color limits to the current image
# - colormaps : Lists all available colormaps
# - set_cmap : Sets the default colormap for the current image
# - get_cmap : Returns the current colormap
## Specialised Plotting Functions
# - polar : Creates a polar plot
# - semilogx : Creates a plot with a logarithmic scale on the x-axis
# - semilogy : Creates a plot with a logarithmic scale on the y-axis
# - loglog : Make a plot with logarithmic scales on both axes
# - hist2d : Plot a 2D Histogram
# - tricontour : Draw contours on a triangular grid
# - tricontourf : Draw filled contours on a triangular grid
# - triplot : Plot a triangulation
# - psd : Plot a power spectral density
# - specgram : Plot a specgram
# - spy : Plots the sparsity of a matrixmatplotlib is based upon numpy and is part fo the scientific or numpy stack. As a consequence, the pyplot namespace includes np and a larger number of standard modules:
In [9]: plt.np
Out[9]: <module 'numpy' from 'C:\\Users\\phili\\AppData\\Local\\spyder-6\\envs\\spyder-runtime\\Lib\\site-packages\\numpy\\__init__.py'>The standard modules and np present in the plt namespace are used internally by the plotting functions in the plt namespace and are not typically used externally:
In [10]: plt.np.__version__
Out[10]: '2.1.2'If np function is desired, it is typically imported directly using:
In [11]: import numpy as np
In [12]: np.__version__
Out[12]: '2.1.2'
In [13]: exitAn ndarray is typically used to store data used for plots. The following imports are commonly used:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
In [2]: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])A Figure instance (blank canvas) with the interactive backend can instantiated using:
In [3]: plt.figure()
Out[3]: <Figure size 640x480 with 0 Axes>It has an int figure number which is 1st order indexed and takes on the next int value by default:
Details about the optional parameters can be seen using:
In [4]: plt.figure?
Signature:
plt.figure(
num: 'int | str | Figure | SubFigure | None' = None,
figsize: 'tuple[float, float] | None' = None,
dpi: 'float | None' = None,
*,
facecolor: 'ColorType | None' = None,
edgecolor: 'ColorType | None' = None,
frameon: 'bool' = True,
FigureClass: 'type[Figure]' = <class 'matplotlib.figure.Figure'>,
clear: 'bool' = False,
**kwargs,
) -> 'Figure'
Docstring:
Create a new figure, or activate an existing figure.The following command will create a subplot Axes instance on the currently selected Figure:
In [5]: plt.subplot()
Out[5]: <Axes: >
Details about the optional parameters can be seen using:
In [6]: plt.subplot?
Signature: plt.subplot(*args, **kwargs) -> 'Axes'
Docstring:
Add an Axes to the current figure or retrieve an existing Axes.
This is a wrapper of `.Figure.add_subplot` which provides additional
behavior when working with the implicit API (see the notes section).
Call signatures::
subplot(nrows, ncols, index, **kwargs)
subplot(pos, **kwargs)
subplot(**kwargs)
subplot(ax)The optional parameters nrows, ncols and index can be used to create multiple subplots on a Figure. If unspecified, these take on their default value of 1 which gives a single Axes which spans the entire Figure canvas.
The dependent y variable can be plotted with respect to the x variable using:
In [7]: plt.plot(x, y)
Out[7]: [<matplotlib.lines.Line2D at 0x1ff94f15310>]
Details about the optional parameters can be seen using:
In [8]: plt.plot?
Signature:
plt.plot(
*args: 'float | ArrayLike | str',
scalex: 'bool' = True,
scaley: 'bool' = True,
data=None,
**kwargs,
) -> 'list[Line2D]'
Docstring:
Plot y versus x as lines and/or markers.
Call signatures::
plot([x], y, [fmt], *, data=None, **kwargs)
plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)If the plot is examined, some Figure level options can be adjusted in the GUI:
The tight layout is often used to reduce whitespace and to fit in legends and titles:
This can be done programmatically using:
In [9]: plt.figure()
: plt.subplot()
: plt.plot(x, y)
: plt.tight_layout()
Notice the next int value 2 has been used. Returning to:
In [10]: plt.figure()
: plt.subplot()
: plt.plot(x, y)
Out[10]: [<matplotlib.lines.Line2D at 0x1ff96810150>]
The Figure instance (canvas) has an Axes (black box). The Axes (plural) has a title, x-axis and y-axis:
The logarithmic scale is often used to emphasise small (positive) numbers:
The above can be performed programmatically using:
In [11]: plt.figure()
: plt.subplot()
: plt.plot(x, y)
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
: #plt.xscale('linear')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')Setting the xscale explicitly to linear is not required as this is the default value.
The line properties are at the default values:
The label can be modified, the label is used to select each line on the Axes and the automatic legend can be regenerated with the new label:
This can be performed programmatically using:
In [12]: plt.figure()
: plt.subplot()
: plt.plot(x, y)
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend(['lineplot'])More typically label is supplied within the plot function:
In [12]: plt.figure()
: plt.subplot()
: plt.plot(x, y, label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()The linestyle can also be selected:
The width can also be specified:
The color (US spelling without the u) can be selected using the color picker:
The format displayed is as a #rrggbbaa where each color channel spans over a byte and is represented by two hexadecimal characters. The last channel alpha is used for transparancy where 00 is fully transparent and ff is non-transparent. When the alpha channel is not specified, ff is assumed. A CSS4 color is usually preferred instead of a #rrggbbaa as it makes the code more readible. The #rrggbbaa format is however more flexible:
Selecting apply will show the #rrggbbaa format:
A CSS4 color is a key in the dict instance matplotlib.colors.CSS4_COLORS. CSS4_COLORS is capitalised because it is a constant:
In [13]: from matplotlib.colors import CSS4_COLORSThe CSS4_COLORS dict can be seen in the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| CSS4_COLORS | dict | 148 | {'aliceblue': '#F0F8FF', 'antiquewhite': '#FAEBD7', 'aqua': '#00FFFF', 'a... |
This can be expanded:
| CSS4_COLORS - dict (148 elements) | ||||
|---|---|---|---|---|
| ▲ | Key | Type | Size | Value |
| 0 | aliceblue | str | 7 | #F0F8FF |
| 1 | antiquewhite | str | 7 | #FAEBD7 |
| 2 | aqua | str | 7 | #00FFFF |
| 3 | aquamarine | str | 7 | #7FFFD4 |
| 4 | azure | str | 7 | #F0FFFF |
| 5 | beige | str | 7 | #F5F5DC |
| 6 | bisque | str | 7 | #FFE4C4 |
| 7 | black | str | 7 | #000000 |
| 8 | blanchedalmond | str | 7 | #FFEBCD |
| 9 | blue | str | 7 | #0000FF |
When a CCS4 color is used, the key is looked up in the mapping CSS4_COLORS and returns the value in the form #RRGGBB, note the CSS4_color uses capital case for the hexadecimal str, Python prefers lower case for hexadecimal strings but accepts upper and lower case. To view these the following plot can be used:
In [14]: plt.figure()
: plt.subplot()
: for color in CSS4_COLORS:
: plt.plot([0, 0], [0, 0], color=color, linewidth=5, label=color)
: plt.legend(ncol=5)
: plt.axis('off')
: plt.tight_layout()
These changes can be made programmatically by supplying optional parameters to the plot function:
In [13]: plt.figure()
: plt.subplot()
: plt.plot(x, y, color='tomato', linewidth=5, linestyle='dotted', label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[13]: <matplotlib.legend.Legend at 0x1ffa1040cd0>
There is also the abbreviated form:
In [13]: plt.figure()
: plt.subplot()
: plt.plot(x, y, c='tomato', lw=5, ls=':', label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[14]: <matplotlib.legend.Legend at 0x1ffa1d44050>
There is also the option for a marker:
This can be programmatically input by supplying parameters to the plot function:
In [14]: plt.figure()
: plt.subplot()
: plt.plot(x, y, color='tomato', linewidth=5, linestyle='dotted',
: marker='*', markersize=15.0, markerfacecolor='royalblue', markeredgecolor='seagreen',
: label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[14]: <matplotlib.legend.Legend at 0x1ffa331b3d0>
Or using the abbreviated form:
In [15]: plt.figure()
: plt.subplot()
: plt.plot(x, y, c='tomato', lw=5, ls='dotted',
: marker='*', ms=15.0, mfc='royalblue', mec='seagreen',
: label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[15]: <matplotlib.legend.Legend at 0x1ffa3822c10>
With a marker present, it is easier to visualise the drawstyle:
This can be supplied programmatically, alongside some additional (less commonly used) marker parameters not available in the GUI:
In [16]: plt.figure()
: plt.subplot()
: plt.plot(x, y, color='tomato', linewidth=5, drawstyle='steps-mid', linestyle='dotted',
: marker='s', markersize=25.0, markerfacecolor='royalblue', markeredgecolor='seagreen',
: markeredgewidth=3.0, fillstyle='top', markerfacecoloralt='darkorchid',
: label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[16]: <matplotlib.legend.Legend at 0x1ff9ca11dd0>
The abbreviated form is:
In [16]: plt.figure()
: plt.subplot()
: plt.plot(x, y, color='tomato', lw=5, ds='steps-mid', ls='dotted',
: marker='s', ms=25.0, mfc='royalblue', mec='seagreen',
: mew=3.0, fillstyle='top', mfcalt='darkorchid',
: label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[16]: <matplotlib.legend.Legend at 0x1ff9ca11dd0>
The CSS4 colors map to a hexadecimal string of the format '#rrggbbaa' or '#rrggbb' where each color channel is a byte represented by 2 hexadecimal characters. A byte has the values 0:256 inclusive of the start 0 and exclusive of the stop 256. The last value is therefore 255. Each color channel can be expressed as a float and a normalised (r, g, b) tuple can be used. On a computer screen, every pixel is a RGB LED and this RGB value is used to create a color at that specified pixel:
Another color dict is BASE_COLORS which expresses the base colors as a (r, g, b) tuple:
In [17]: from matplotlib.colors import BASE_COLORSThis includes the primary base colors (blue, green and red), secondary base colors (cyan, magenta, yellow) absense of color (black) and all LEDs at maximum brightness (white) as a normalised 3 element (r, g, b) tuple:
| Variable Explorer | |||
|---|---|---|---|
| CSS4_COLORS | dict | 148 | {'aliceblue': '#F0F8FF', 'antiquewhite': '#FAEBD7', 'aqua': '#00FFFF', 'a... |
| BASE_COLORS | dict | 8 | {'b': (0, 0, 1), 'g': (0, 0.5, 0), 'r': (1, 0, 0), 'c': (0, 0.75, 0.75),... |
This can be expanded:
| BASE_COLORS - dict (8 elements) | ||||
|---|---|---|---|---|
| ▲ | Key | Type | Size | Value |
| 0 | b | tuple | 3 | (0, 0, 1) |
| 1 | g | tuple | 3 | (0, 0.5, 0) |
| 2 | r | tuple | 3 | (1, 0, 0) |
| 3 | c | tuple | 3 | (0, 0.75, 0.75) |
| 4 | m | tuple | 3 | (0.75, 0, 0.75) |
| 5 | y | tuple | 3 | (0.75, 0.75, 0) |
| 6 | k | tuple | 3 | (0, 0, 0) |
| 7 | w | tuple | 3 | (1, 1, 1) |
Note the human eye is about as twice as sensitive to green as red and blue, so a slight correction factor has been made in the values to compensate for this.
The plot function returns a list with a single line plot:
In [18]: plt.figure()
: plt.subplot()
: plt.plot(x, y,
: color='r',
: linestyle=':'
: marker='s')
Out[18]: [<matplotlib.lines.Line2D at 0x1ffa66eae10>] 
The color, linestyle and marker can be combined into a single parameter (the ordering in the combined parameter doesn't matter):
In [19]: plt.figure()
: plt.subplot()
: plt.plot(x, y, 'r:s')
Out[19]: [<matplotlib.lines.Line2D at 0x1ffa545b250>] 
This form can be used to plot multiple lines:
In [20]: plt.figure()
: plt.subplot()
: plt.plot(x, y, ':m*',
x, y+15000, '-co',
x, y+25000, '--y^',
x, y+35000, '-.ks')
Out[20]: [<matplotlib.lines.Line2D at 0x1ffa4bcd790>,
<matplotlib.lines.Line2D at 0x1ffa18dbed0>,
<matplotlib.lines.Line2D at 0x1ff9d5b3190>,
<matplotlib.lines.Line2D at 0x1ffa4f23dd0>] 
Note that specification of a named parameter, will update all the lines:
In [21]: plt.figure()
: plt.subplot()
: plt.plot(x, y, ':m*',
x, y+15000, '-co',
x, y+25000, '--y^',
x, y+35000, '-.ks',
linewidth=5.0)
Out[21]: [<matplotlib.lines.Line2D at 0x1ffa5894990>,
<matplotlib.lines.Line2D at 0x1ff9ea4e950>,
<matplotlib.lines.Line2D at 0x1ffa3040a50>,
<matplotlib.lines.Line2D at 0x1ffa1db4d50>] 
Notice the last line in In [21] is returned in Out[21]. When using matplotlib using procedural programming, where each plotting function is referenced from plt, the output is commonly suppressed. The last line of code can be suppressed using a semicolon ; for example:
In [22]: plt.figure()
: plt.subplot()
: plt.plot(x, y, ':m*',
x, y+15000, '-co',
x, y+25000, '--y^',
x, y+35000, '-.ks',
linewidth=5.0); 
Notice that the plot is still produced but the return value is not shown in the cell output because it is suppressed.
Starting with a blank Figure canavas:
In [23]: plt.figure()
Out[23]: <Figure size 640x480 with 0 Axes>
The function subplot was used to create a single subplot spanning the full Figure canvas. The subplot function has the optional parameters nrows, ncols and index and these all have a default value of 1 creating a single subplot spanning the full Figure canvas.
If nrows=2 and ncols=2, there will instead be a 2 by 2 grid of subplots spanning the full Figure canvas. The 4 subplots have a first-order index from 1 to 4 (inclusive of 4) and a row-major ordered:
In [24]: # subplot top left - add tomato line
: plt.subplot(2, 2, 1)
: plt.title('1')
: plt.plot(x, y, color='tomato')
Out[24]: [<matplotlib.lines.Line2D at 0x20a75b55950>]
In [25]: # subplot top right - add royalblue line
: plt.subplot(2, 2, 2)
: plt.title('2')
: plt.plot(x, y, color='royalblue')
Out[25]: [<matplotlib.lines.Line2D at 0x20a756b70d0>] 
In [26]: # subplot bottom left - add seagreen line
: plt.subplot(2, 2, 3)
: plt.title('3')
: plt.plot(x, y, color='seagreen')
Out[26]: [<matplotlib.lines.Line2D at 0x20a75ae75d0>]
In [27]: # subplot bottom right - add goldline
: plt.subplot(2, 2, 4)
: plt.title('4')
: plt.plot(x, y, color='gold')
Out[27]: [<matplotlib.lines.Line2D at 0x20a75d39a50>]
In [28]: # reselect subplot top left - add gray line
: plt.subplot(2, 2, 1)
: plt.plot(x, -y, color='gray')
Out[28]: [<matplotlib.lines.Line2D at 0x20a75799590>]
In [29]: exitIn procedural programming, none of the returned values are assigned to object names. For example:
In [1]: 'hello'
Out[1]: 'hello'
In [2]: 2
Out[2]: 2
In [3]: [1, 2, 3, 4, 5]
Out[3]: [1, 2, 3, 4, 5]Since these object instances were not instantiated to object names, they cannot be accessed and are immediately cleaned up by Python's garbage collection. If they are instead instantiated to object names:
In [4]: text = 'hello'
: num = 2
: nums = [1, 2, 3, 4, 5]Then they display on the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| text | str | 5 | hello |
| num | int | 1 | 2 |
| nums | list | 5 | [1, 2, 3, 4, 5] |
And methods can be called from them:
In [5]: text.upper()
Out[5]: 'HELLO'text and num are immutable and all methods return an instance. nums is mutable and has mutable methods which modify the method in place:
In [6]: nums.append(6)| Variable Explorer | |||
|---|---|---|---|
| text | str | 5 | hello |
| num | int | 1 | 2 |
| nums | list | 6 | [1, 2, 3, 4, 5, 6] |
In [7]: exitThe libraries will be imported and the data instantiated:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
In [2]: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])
Notice that the figure function returns a Figure instance:
In [3]: plt.figure()
Out[3]: <Figure size 640x480 with 0 Axes>The subplot function returns an Axes instance:
In [4]: plt.subplot()
Out[4]: <Axes: >The plot function returns a list of Line2D instances:
In [5]: plt.plot(x, y, ':m*',
x, y+15000, '-co',
x, y+25000, '--y^',
x, y+35000, '-.ks',
linewidth=5.0)
Out[5]:
[<matplotlib.lines.Line2D at 0x1ffa6003490>,
<matplotlib.lines.Line2D at 0x1ffa67a6890>,
<matplotlib.lines.Line2D at 0x1ffa6001710>,
<matplotlib.lines.Line2D at 0x1ff9d1ca510>]More typically an Object orientated syntax is used, where:
In [6]: fig = plt.figure()| Variable Explorer | |||
|---|---|---|---|
| x | array of int64 | (5, ) | [1 2 3 4 5] |
| y | array of int64 | (5, ) | [ 10 100 1000 10000 10000] |
| fig | Figure | 1 | <Figure size 640x480 with 0 Axes> |
Instead of using the function subplot, the Figure method add_subplot will be used:
In [7]: ax = fig.add_subplot()| Variable Explorer | |||
|---|---|---|---|
| x | array of int64 | (5, ) | [1 2 3 4 5] |
| y | array of int64 | (5, ) | [ 10 100 1000 10000 10000] |
| fig | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| ax | Axes | 1 | <Axes: > |
Instead of using the function plot, the Axes method plot will be used:
In [8]: lines = ax.plot(x, y, ':m*',
x, y+15000, '-co',
x, y+25000, '--y^',
x, y+35000, '-.ks',
linewidth=5.0)Note that the function plot and Axes method plot have consistent parameters to one another:
| Variable Explorer | |||
|---|---|---|---|
| x | array of int64 | (5, ) | [1 2 3 4 5] |
| y | array of int64 | (5, ) | [ 10 100 1000 10000 10000] |
| fig | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| ax | Axes | 1 | <Axes: > |
| lines | list | 4 | [Line2D, Line2D, Line2D, Line2D] |
The lines list instance can be expanded:
| lines - list (4 elements) | |||
|---|---|---|---|
| Index ▲ | Type | Size | Value |
| 0 | Line2D | 1 | <matplotlib.lines.Line2D at 0x1ffa6085310> |
| 1 | Line2D | 1 | <matplotlib.lines.Line2D at 0x1ffa60851d0> |
| 2 | Line2D | 1 | <matplotlib.lines.Line2D at 0x1ffa606bbd0> |
| 3 | Line2D | 1 | <matplotlib.lines.Line2D at 00x1ffa606b6d> |
Each of these is a object recognised by matplotlib that has properties. The function get properties getp can list these properties. The properties can be listed for the Figure instance fig:
In [9]: plt.getp(fig)
agg_filter = None
alpha = None
animated = False
axes = [<Axes: >]
children = [<matplotlib.patches.Rectangle object at 0x000001F...
clip_box = None
clip_on = True
clip_path = None
constrained_layout = False
constrained_layout_pads = (None, None, None, None)
default_bbox_extra_artists = [<Axes: >, <matplotlib.spines.Spine object at 0x00...
dpi = 100.0
edgecolor = (1.0, 1.0, 1.0, 1.0)
facecolor = (1.0, 1.0, 1.0, 1.0)
figheight = 4.8
figure = Figure(640x480)
figwidth = 6.4
frameon = True
gid = None
in_layout = True
label =
layout_engine = None
linewidth = 0.0
mouseover = False
path_effects = []
picker = None
rasterized = False
size_inches = [6.4 4.8]
sketch_params = None
snap = None
suptitle =
supxlabel =
supylabel =
tight_layout = False
tightbbox = TransformedBbox( Bbox(x0=25.90277777777777, y0...
transform = IdentityTransform()
transformed_clip_path_and_affine = (None, None)
url = None
visible = True
window_extent = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=6.4, ...
zorder = 0The properties can be listed for the Axes instance ax:
In [10]: plt.getp(ax)
adjustable = box
agg_filter = None
alpha = None
anchor = C
animated = False
aspect = auto
autoscale_on = True
autoscalex_on = True
autoscaley_on = True
axes_locator = None
axisbelow = line
box_aspect = None
children = [<matplotlib.lines.Line2D object at 0x000001FFA608...
clip_box = None
clip_on = True
clip_path = None
data_ratio = 11247.5
default_bbox_extra_artists = [<matplotlib.spines.Spine object at 0x000001FF9ED9...
facecolor or fc = (1.0, 1.0, 1.0, 1.0)
figure = Figure(640x480)
forward_navigation_events = auto
frame_on = True
gid = None
gridspec = GridSpec(1, 1)
images = <a list of 0 AxesImage objects>
in_layout = True
label =
legend = None
legend_handles_labels = ([], [])
lines = <a list of 4 Line2D objects>
mouseover = False
navigate = True
navigate_mode = None
path_effects = []
picker = None
position = Bbox(x0=0.125, y0=0.10999999999999999, x1=0.9, y1=...
rasterization_zorder = None
rasterized = False
shared_x_axes = <matplotlib.cbook.GrouperView object at 0x000001FF...
shared_y_axes = <matplotlib.cbook.GrouperView object at 0x000001FF...
sketch_params = None
snap = None
subplotspec = GridSpec(1, 1)[0:1, 0:1]
tightbbox = Bbox(x0=25.90277777777777, y0=29.077777777777776, ...
title =
transform = IdentityTransform()
transformed_clip_path_and_affine = (None, None)
url = None
visible = True
window_extent = TransformedBbox( Bbox(x0=0.125, y0=0.109999999...
xaxis = XAxis(80.0,52.8)
xaxis_transform = BlendedGenericTransform( CompositeGenericTrans...
xbound = (np.float64(0.8), np.float64(5.2))
xgridlines = <a list of 11 Line2D gridline objects>
xlabel =
xlim = (np.float64(0.8), np.float64(5.2))
xmajorticklabels = [Text(0.5, 0, '0.5'), Text(1.0, 0, '1.0'), Text(1....
xmargin = 0.05
xminorticklabels = []
xscale = linear
xticklabels = [Text(0.5, 0, '0.5'), Text(1.0, 0, '1.0'), Text(1....
xticklines = <a list of 22 Line2D ticklines objects>
xticks = [0.5 1. 1.5 2. 2.5 3. ]...
yaxis = YAxis(80.0,52.8)
yaxis_transform = BlendedGenericTransform( BboxTransformTo( ...
ybound = (np.float64(-2239.5), np.float64(47249.5))
ygridlines = <a list of 7 Line2D gridline objects>
ylabel =
ylim = (np.float64(-2239.5), np.float64(47249.5))
ymajorticklabels = [Text(0, -10000.0, '−10000'), Text(0, 0.0, '0'), T...
ymargin = 0.05
yminorticklabels = []
yscale = linear
yticklabels = [Text(0, -10000.0, '−10000'), Text(0, 0.0, '0'), T...
yticklines = <a list of 14 Line2D ticklines objects>
yticks = [-10000. 0. 10000. 20000. 30000. 40000.]....
zorder = 0The properties can be listed for the list of Line2D instances:
In [11]: plt.getp(lines)
agg_filter = None
alpha = None
animated = False
antialiased or aa = True
bbox = Bbox(x0=1.0, y0=10.0, x1=5.0, y1=10000.0)
children = []
clip_box = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=1.0, ...
clip_on = True
clip_path = None
color or c = m
dash_capstyle = butt
dash_joinstyle = round
data = (array([1, 2, 3, 4, 5]), array([ 10, 100, 100...
drawstyle or ds = default
figure = Figure(640x480)
fillstyle = full
gapcolor = None
gid = None
in_layout = True
label = _child0
linestyle or ls = :
linewidth or lw = 5.0
marker = *
markeredgecolor or mec = m
markeredgewidth or mew = 1.0
markerfacecolor or mfc = m
markerfacecoloralt or mfcalt = none
markersize or ms = 6.0
markevery = None
mouseover = False
path = Path(array([[1.e+00, 1.e+01], [2.e+00, 1.e+...
path_effects = []
picker = None
pickradius = 5
rasterized = False
sketch_params = None
snap = None
solid_capstyle = projecting
solid_joinstyle = round
tightbbox = Bbox(x0=98.37878787878786, y0=65.43333333333332, x...
transform = CompositeGenericTransform( TransformWrapper( ...
transformed_clip_path_and_affine = (None, None)
url = None
visible = True
window_extent = Bbox(x0=98.37878787878786, y0=65.43333333333332, x...
xdata = [1 2 3 4 5]
xydata = [[1.e+00 1.e+01] [2.e+00 1.e+02] [3.e+00 1.e+03]...
ydata = [ 10 100 1000 10000 10000]
zorder = 2The properties can be listed for a single Line2D instance:
In [12]: plt.getp(lines[0])
agg_filter = None
alpha = None
animated = False
antialiased or aa = True
bbox = Bbox(x0=1.0, y0=10.0, x1=5.0, y1=10000.0)
children = []
clip_box = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=1.0, ...
clip_on = True
clip_path = None
color or c = m
dash_capstyle = butt
dash_joinstyle = round
data = (array([1, 2, 3, 4, 5]), array([ 10, 100, 100...
drawstyle or ds = default
figure = Figure(640x480)
fillstyle = full
gapcolor = None
gid = None
in_layout = True
label = _child0
linestyle or ls = :
linewidth or lw = 5.0
marker = *
markeredgecolor or mec = m
markeredgewidth or mew = 1.0
markerfacecolor or mfc = m
markerfacecoloralt or mfcalt = none
markersize or ms = 6.0
markevery = None
mouseover = False
path = Path(array([[1.e+00, 1.e+01], [2.e+00, 1.e+...
path_effects = []
picker = None
pickradius = 5
rasterized = False
sketch_params = None
snap = None
solid_capstyle = projecting
solid_joinstyle = round
tightbbox = Bbox(x0=98.37878787878786, y0=65.43333333333332, x...
transform = CompositeGenericTransform( TransformWrapper( ...
transformed_clip_path_and_affine = (None, None)
url = None
visible = True
window_extent = Bbox(x0=98.37878787878786, y0=65.43333333333332, x...
xdata = [1 2 3 4 5]
xydata = [[1.e+00 1.e+01] [2.e+00 1.e+02] [3.e+00 1.e+03]...
ydata = [ 10 100 1000 10000 10000]
zorder = 2The fig instance created with object orientated programming, looks the same as the one created using procedural programming:
The set properties function setp can be used to set properties. For example the Figure property tight_layout can be assigned to True. Out[13] displays a list of return values for each property:
In [13]: plt.setp(fig, tight_layout=True)
Out[13]: [None]
The Axes labels, limits and scales can be set. Out[14] displays a list of return values for each property:
In [14]: plt.setp(ax,
xlabel='x',
ylabel='y',
xlim=(0, 5),
ylim=(1, 50000),
xscale='linear',
yscale='log')
In [14]:
[Text(0.5, 29.000000000000014, 'x'),
Text(28.999999999999993, 0.5, 'y'),
0.0,
5.0,
1,
50000,
None,
None]
Using setp on the list instance lines will set the property on each individual line. For example the linewidth and linestyle will be set to the same value for each Line2D:
In [15]: plt.setp(lines, linewidth=2, linestyle=':')
Out[15]: [None, None, None, None, None, None, None, None]
The properties of an individual Line2D instance can be set:
In [16]: plt.setp(lines[0],
color='royalblue',
linewidth=2,
linestyle='-.',
marker='^',
markersize=15,
markeredgewidth=2,
markeredgecolor='tomato',
fillstyle='top',
markerfacecoloralt='seagreen')
Out[16]: [None, None, None, None, None, None, None, None, None]
Previously the following plot was created using procedural programming:
In [17]: plt.figure()
: plt.subplot()
: plt.plot(x, y, color='tomato', lw=5, ds='steps-mid', ls='dotted',
: marker='s', ms=25.0, mfc='royalblue', mec='seagreen',
: mew=3.0, fillstyle='top', mfcalt='darkorchid',
: label='lineplot')
:
: plt.title('y=f(x)')
:
: plt.xlim(left=1, right=5)
: plt.xlabel('x')
:
: plt.ylim(bottom=1, top=110000)
: plt.ylabel('y')
: plt.yscale('log')
: plt.legend()
Out[17]: <matplotlib.legend.Legend at 0x20a71ea9410> 
In [18]: fig = plt.figure()
: ax = fig.add_subplot()
: lines = ax.plot(x, y, color='tomato', lw=5, ds='steps-mid', ls='dotted',
: marker='s', ms=25.0, mfc='royalblue', mec='seagreen',
: mew=3.0, fillstyle='top', mfcalt='darkorchid',
: label='lineplot')
:
: ax.set_title('y=f(x)')
:
: ax.set_xlim(left=1, right=5)
: ax.set_xlabel('x')
:
: ax.set_ylim(bottom=1, top=110000)
: ax.set_ylabel('y')
: ax.set_yscale('log')
: legend = ax.legend()| Variable Explorer | |||
|---|---|---|---|
| x | array of int64 | (5, ) | [1 2 3 4 5] |
| y | array of int64 | (5, ) | [ 10 100 1000 10000 10000] |
| fig | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| ax | Axes | 1 | <Axes: > |
| lines | list | 1 | [Line2D] |
| legend | Legend | 1 | < Legend at 0x20a757d33d0> |
Some of the methods used above also have a return value but are nto assigned to an object name:
In [19]: ax.set_title('y=f(x)')
Out[19]: Text(0.5, 1.0, 'y=f(x)')These are not commonly assigned to object names as they can be accessed as attributes from ax:
In [20]: ax.title
Out[20]: Text(0.5, 1.0, 'y=f(x)')Or from a complementary get method:
In [21]: ax.set_xlim(left=1, right=5)
Out[21]: (1.0, 5.0)
In [22]: ax.set_xlabel('x')
Out[22]: Text(0.5, 23.52222222222222, 'x')
In [23]: ax.get_xlim()
Out[23]: (np.float64(1.0), np.float64(5.0))
In [24]: ax.get_xlabel()
Out[24]: 'x'
In [25]: ax.get_xscale()
Out[25]: 'linear'Additional xaxis related attributes are compartmentalised under the attribute xaxis:
In [26]: ax.xaxis.label
Out[26]: Text(0.5, 23.52222222222222, 'x')
In [27]: ax.xaxis.get_label()
Out[27]: Text(0.5, 23.52222222222222, 'x')
In [28]: ax.xaxis.get_scale()
Out[28]: 'linear'Analagous yaxis related attributes are compartmentalised unde the attribute yaxis. The identifiers of ax can be examined:
In [29]: ax.
# -------------------------------
# Available Identifiers for `Axes`:
# ---------------------------------
# 🔍 Core Attributes:
# - figure : The `Figure` instance the Axes belong to.
# - dataLim : The data limits for the Axes.
# - viewLim : The visible limits for the Axes.
# - name : The name of the Axes instance.
# - xaxis : The `XAxis` instance for the Axes.
# - yaxis : The `YAxis` instance for the Axes.
# 🔧 Axes Properties:
# - patch : The `Patch` instance (background).
# - spines : A dictionary of `Spine` instances.
# - title : The title text of the Axes.
# - titleOffsetTrans : Transformation for title offset.
# 🔧 Axes Configuration and State:
# - adjustable : Defines whether to adjust the Axes' limits.
# - axis : Sets the axis properties (on/off/scaling).
# - autoscale : Enables or disables autoscaling.
# - box_aspect : Aspect ratio of the Axes box.
# - grid : Configures the Axes grid.
# - legend : Adds a legend to the Axes.
# - margins : Sets padding for the Axes' limits.
# - navigate : Enables navigation (zoom/pan).
# - tight : Sets the Axes tight layout.
# - transAxes : Transformation of Axes coordinates.
# - transData : Transformation of data coordinates.
# - transLimits : Transformation of Axes limits.
# - transScale : Transformation of Axes scaling.
# 🔍 Debugging and Interactivity:
# - pick : Processes pick events.
# - contains : Tests whether a mouse event is in the `Axes`.
# - format_coord : Formats the coordinate display.
# - drag_pan : Handles panning of the Axes.
# - start_pan : Begins panning.
# - end_pan : Ends panning.
# 🔍 Object Accessors:
# - lines : Returns the list of `Line2D` objects in the `Axes`.
# - patches : Returns the list of patches in the `Axes`.
# - images : Returns the list of images in the `Axes`.
# - texts : Returns the list of text objects in the `Axes`.
# - collections : Returns the collection of objects like polygons or paths.
# 🔧 Data Management Methods:
# - cla : Clears the Axes.
# - clear : Clears the Axes (alias of `cla`).
# - relim : Recomputes data limits.
# - autoscale_view : Updates view limits based on data.
# - ignore_existing_data_limits : Ignores the existing data limits.
# - update_datalim : Updates the data limits for the Axes.
# - update_from : Updates the Axes properties from another.
# 🔧 Plotting Methods:
# - acorr : Plots the autocorrelation function.
# - angle_spectrum : Plots the angle spectrum.
# - arrow : Draws an arrow on the Axes.
# - axhline : Draws a horizontal line across the Axes.
# - axhspan : Draws a horizontal span across the Axes.
# - axline : Draws an infinite line across the Axes.
# - axvline : Draws a vertical line across the Axes.
# - axvspan : Draws a vertical span across the Axes.
# - bar : Creates a bar plot.
# - bar_label : Adds labels to bar plots.
# - barbs : Plots wind barbs.
# - barh : Creates a horizontal bar plot.
# - broken_barh : Plots horizontal bars with breaks.
# - bxp : Draws a box plot.
# - cohere : Plots the coherence.
# - contour : Creates a contour plot.
# - contourf : Creates a filled contour plot.
# - csd : Plots the cross-spectral density.
# - errorbar : Plots error bars.
# - eventplot : Plots events as lines.
# - fill : Fills between curves.
# - fill_between : Fills between two horizontal curves.
# - fill_betweenx : Fills between two vertical curves.
# - hexbin : Creates a hexagonal binning plot.
# - hist : Plots a histogram.
# - hist2d : Plots a 2D histogram.
# - hlines : Plots horizontal lines.
# - imshow : Displays an image on the Axes.
# - legend_ : Manages legend properties.
# - loglog : Plots data on a log-log scale.
# - magnitude_spectrum : Plots the magnitude spectrum.
# - matshow : Displays a matrix as an image.
# - pcolor : Creates a pseudocolor plot.
# - pcolormesh : Creates a pseudocolor mesh plot.
# - phase_spectrum : Plots the phase spectrum.
# - pie : Creates a pie chart.
# - plot : Plots lines and/or markers.
# - plot_date : Plots data with date information.
# - psd : Plots the power spectral density.
# - quiver : Plots vector fields.
# - scatter : Creates a scatter plot.
# - semilogx : Plots data on a semi-log scale (X-axis log).
# - semilogy : Plots data on a semi-log scale (Y-axis log).
# - specgram : Plots a spectrogram.
# - spy : Plots a 2D array sparsity pattern.
# - stackplot : Creates a stacked area plot.
# - stairs : Creates step plots.
# - stem : Creates stem plots.
# - step : Creates step plots.
# - streamplot : Plots streamlines of a vector field.
# - table : Adds a table to the Axes.
# - text : Adds text at a given location.Note that the Axes attribute figure is the same as fig:
In [29]: ax.figure == fig
Out[29]: TrueThe identifiers of the xaxis attribute can be examined:
In [30]: ax.xaxis.
# ---------------------------------
# Available Identifiers for `xaxis`:
# ---------------------------------
# 🔍 Core Attributes:
# - axes : The parent `Axes` object.
# - figure : The parent `Figure` object.
# - axis_name : Name of the axis ('x' for `XAxis`).
# - label : The axis label text.
# - offsetText : The offset text displayed on the axis.
# - scale : The scaling of the axis.
# - units : Units assigned to the axis.
# 🔧 Axis Configuration and State:
# - labelpad : Padding between the axis and its label.
# - offset_text_position : Position of the offset text.
# - ticks_position : Position of ticks on the axis (top, bottom, etc.).
# - isDefault_label : Checks if the label is the default.
# - isDefault_majfmt : Checks if the major formatter is default.
# - isDefault_majloc : Checks if the major locator is default.
# - isDefault_minfmt : Checks if the minor formatter is default.
# - isDefault_minloc : Checks if the minor locator is default.
# 🔍 Accessors and Query Methods:
# - get_label : Returns the axis label.
# - get_label_position : Returns the label position.
# - get_label_text : Returns the text of the label.
# - get_major_formatter : Returns the major tick formatter.
# - get_major_locator : Returns the major tick locator.
# - get_minor_formatter : Returns the minor tick formatter.
# - get_minor_locator : Returns the minor tick locator.
# - get_majorticklabels : Returns the major tick labels.
# - get_minorticklabels : Returns the minor tick labels.
# - get_majorticklines : Returns the major tick lines.
# - get_minorticklines : Returns the minor tick lines.
# - get_majorticklocs : Returns the major tick locations.
# - get_minorticklocs : Returns the minor tick locations.
# - get_tick_padding : Returns padding between ticks and labels.
# - get_tick_params : Returns parameters for the ticks.
# - get_tick_space : Returns space available for ticks.
# - get_ticklabels : Returns all tick labels.
# - get_ticklines : Returns all tick lines.
# - get_ticklocs : Returns all tick locations.
# - get_ticks_direction : Returns the direction of the ticks.
# - get_ticks_position : Returns the position of the ticks.
# 🔧 Axis Manipulation:
# - set_label : Sets the axis label.
# - set_label_coords : Sets coordinates for the label.
# - set_label_position : Sets the label position.
# - set_label_text : Sets the label text.
# - set_major_formatter : Sets the major tick formatter.
# - set_major_locator : Sets the major tick locator.
# - set_minor_formatter : Sets the minor tick formatter.
# - set_minor_locator : Sets the minor tick locator.
# - set_ticks_position : Sets the position of ticks.
# - set_tick_params : Configures tick parameters.
# - set_ticklabels : Sets tick labels.
# - set_ticks : Sets tick locations.
# 🔧 Drawing and Layout:
# - draw : Draws the axis.
# - grid : Configures grid lines for the axis.
# - clear : Clears the axis.
# - format_cursor_data : Formats cursor data for display.
# - limit_range_for_scale : Limits the axis range for a scale.
# 🔍 Debugging and Interactivity:
# - pick : Processes pick events.
# - contains : Checks if a mouse event is in the axis.
# - callbacks : Manages callbacks for axis updates.
# - add_callback : Adds a callback function.
# - remove_callback : Removes a callback function.
# 🔧 Tick Management:
# - major : Configures major ticks.
# - minor : Configures minor ticks.
# - majorTicks : Returns the list of major ticks.
# - minorTicks : Returns the list of minor ticks.
# - minorticks_on : Enables minor ticks.
# - minorticks_off : Disables minor ticks.
# - reset_ticks : Resets the tick positions.
# 🔧 Appearance:
# - set_alpha : Sets the transparency of the axis.
# - set_visible : Sets the visibility of the axis.
# - set_zorder : Sets the drawing order of the axis.
# 🔍 Utility and Miscellaneous:
# - update : Updates the axis properties.
# - update_units : Updates the axis units.
# - convert_units : Converts data to the axis units.
# - have_units : Checks if the axis has units.
# - convert_xunits : Converts x-units.
# - convert_yunits : Converts y-units.
# 🔧 Object Accessors:
# - get_children : Returns all child artists of the axis.
# - get_figure : Returns the parent figure.
# - get_gridlines : Returns the grid lines.
# - get_view_interval : Returns the visible range of the axis.
# - get_data_interval : Returns the data range of the axis.
# - get_transform : Returns the transformation applied to the axis.The identifiers of the yaxis attribute can be examined:
In [30]: ax.yaxis.
# ---------------------------------
# Available Identifiers for `yaxis`:
# ---------------------------------
# 🔍 Core Attributes:
# - axes : The parent `Axes` object.
# - figure : The parent `Figure` object.
# - axis_name : Name of the axis ('y' for `YAxis`).
# - label : The axis label text.
# - offsetText : The offset text displayed on the axis.
# - scale : The scaling of the axis.
# - units : Units assigned to the axis.
# 🔧 Axis Configuration and State:
# - labelpad : Padding between the axis and its label.
# - offset_text_position : Position of the offset text.
# - ticks_position : Position of ticks on the axis (left, right, etc.).
# - isDefault_label : Checks if the label is the default.
# - isDefault_majfmt : Checks if the major formatter is default.
# - isDefault_majloc : Checks if the major locator is default.
# - isDefault_minfmt : Checks if the minor formatter is default.
# - isDefault_minloc : Checks if the minor locator is default.
# 🔍 Accessors and Query Methods:
# - get_label : Returns the axis label.
# - get_label_position : Returns the label position.
# - get_label_text : Returns the text of the label.
# - get_major_formatter : Returns the major tick formatter.
# - get_major_locator : Returns the major tick locator.
# - get_minor_formatter : Returns the minor tick formatter.
# - get_minor_locator : Returns the minor tick locator.
# - get_majorticklabels : Returns the major tick labels.
# - get_minorticklabels : Returns the minor tick labels.
# - get_majorticklines : Returns the major tick lines.
# - get_minorticklines : Returns the minor tick lines.
# - get_majorticklocs : Returns the major tick locations.
# - get_minorticklocs : Returns the minor tick locations.
# - get_tick_padding : Returns padding between ticks and labels.
# - get_tick_params : Returns parameters for the ticks.
# - get_tick_space : Returns space available for ticks.
# - get_ticklabels : Returns all tick labels.
# - get_ticklines : Returns all tick lines.
# - get_ticklocs : Returns all tick locations.
# - get_ticks_direction : Returns the direction of the ticks.
# - get_ticks_position : Returns the position of the ticks.
# 🔧 Axis Manipulation:
# - set_label : Sets the axis label.
# - set_label_coords : Sets coordinates for the label.
# - set_label_position : Sets the label position.
# - set_label_text : Sets the label text.
# - set_major_formatter : Sets the major tick formatter.
# - set_major_locator : Sets the major tick locator.
# - set_minor_formatter : Sets the minor tick formatter.
# - set_minor_locator : Sets the minor tick locator.
# - set_ticks_position : Sets the position of ticks.
# - set_tick_params : Configures tick parameters.
# - set_ticklabels : Sets tick labels.
# - set_ticks : Sets tick locations.
# 🔧 Drawing and Layout:
# - draw : Draws the axis.
# - grid : Configures grid lines for the axis.
# - clear : Clears the axis.
# - format_cursor_data : Formats cursor data for display.
# - limit_range_for_scale : Limits the axis range for a scale.
# 🔍 Debugging and Interactivity:
# - pick : Processes pick events.
# - contains : Checks if a mouse event is in the axis.
# - callbacks : Manages callbacks for axis updates.
# - add_callback : Adds a callback function.
# - remove_callback : Removes a callback function.
# 🔧 Tick Management:
# - major : Configures major ticks.
# - minor : Configures minor ticks.
# - majorTicks : Returns the list of major ticks.
# - minorTicks : Returns the list of minor ticks.
# - minorticks_on : Enables minor ticks.
# - minorticks_off : Disables minor ticks.
# - reset_ticks : Resets the tick positions.
# 🔧 Appearance:
# - set_alpha : Sets the transparency of the axis.
# - set_visible : Sets the visibility of the axis.
# - set_zorder : Sets the drawing order of the axis.
# 🔍 Utility and Miscellaneous:
# - update : Updates the axis properties.
# - update_units : Updates the axis units.
# - convert_units : Converts data to the axis units.
# - have_units : Checks if the axis has units.
# - convert_xunits : Converts x-units.
# - convert_yunits : Converts y-units.
# 🔧 Object Accessors:
# - get_children : Returns all child artists of the axis.
# - get_figure : Returns the parent figure.
# - get_gridlines : Returns the grid lines.
# - get_view_interval : Returns the visible range of the axis.
# - get_data_interval : Returns the data range of the axis.
# - get_transform : Returns the transformation applied to the axis.In [30]: fig.
# ----------------------------------
# Available Identifiers for `fig`:
# ----------------------------------
# 🔍 Figure Attributes:
# - axes : List of all axes in the figure.
# - artists : List of artists in the figure.
# - canvas : The canvas that the figure is drawn on.
# - dpi : The DPI of the figure.
# - figure : The figure object itself.
# - images : List of images in the figure.
# - legend : The legend for the figure.
# - lines : List of lines in the figure.
# - patches : List of patches in the figure.
# - texts : List of text objects in the figure.
# 🔧 Drawing and Rendering:
# - draw : Draws the figure.
# - draw_artist : Draws a specific artist within the figure.
# - draw_without_rendering : Draws without triggering rendering.
# - figimage : Adds a figure-level image.
# - format_cursor_data : Formats cursor data for the figure.
# - get_dpi : Gets the DPI of the figure.
# - get_edgecolor : Gets the edge color of the figure.
# - get_facecolor : Gets the face color of the figure.
# - get_size_inches : Gets the size of the figure in inches.
# - get_transform : Returns the transformation applied to the figure.
# - get_window_extent : Returns the extent of the figure window.
# - get_zorder : Gets the z-order for drawing the figure.
# 🔧 Axis and Subplot Management:
# - add_axes : Adds axes to the figure.
# - add_subplot : Adds a subplot to the figure.
# - add_subfigure : Adds a subfigure to the figure.
# - delaxes : Deletes axes from the figure.
# - get_axes : Returns the list of axes in the figure.
# - get_children : Returns the children of the figure.
# - get_suptitle : Gets the figure's suptitle.
# - subplots : Creates subplots within the figure.
# - subplots_adjust : Adjusts subplot parameters.
# 🔧 Layout and Positioning:
# - align_labels : Aligns labels in the figure.
# - align_titles : Aligns titles in the figure.
# - align_xlabels : Aligns x-axis labels in the figure.
# - align_ylabels : Aligns y-axis labels in the figure.
# - get_constrained_layout : Returns the constrained layout status.
# - get_layout_engine : Returns the layout engine of the figure.
# - set_constrained_layout : Sets the constrained layout status.
# - set_layout_engine : Sets the layout engine for the figure.
# - set_tight_layout : Sets the tight layout configuration.
# 🔧 Figure Customization:
# - set_alpha : Sets the alpha transparency for the figure.
# - set_frameon : Sets whether the figure has a frame.
# - set_figheight : Sets the figure height.
# - set_figwidth : Sets the figure width.
# - set_zorder : Sets the z-order for the figure.
# - set_visible : Sets the visibility of the figure.
# 🔧 Event and Interaction:
# - pick : Handles pick events.
# - contains : Checks if a point is inside the figure.
# - waitforbuttonpress : Waits for a button press event.
# - ginput : Captures mouse input.
# 🔧 Saving and Output:
# - savefig : Saves the figure to a file.
# - update_from : Updates the figure from another.
# - update : Updates the figure properties.
# 🔍 Utility Methods:
# - get_default_bbox_extra_artists : Gets the default extra artists for the bounding box.
# - get_tightbbox : Returns the tight bounding box for the figure.The add_subplot method has consistent optional parameters to the subplot function and the optional parameters nrows, ncols and index and these all have a default value of 1 creating a single subplot spanning the full Figure canvas:
In [30]: fig = plt.figure()
: ax = fig.add_subplot()
Multiple subplot Axes instances can be instantiated using repeated calls to the Figure method add_subplot:
In [31]: fig = plt.figure()
: ax_top = fig.add_subplot(2, 1, 1)
: ax_bottom = fig.add_subplot(2, 1, 2)
It is more convenient to group multiple Axes instance together in a datastructure such as a ndarray. This can be done using the method subplots. The parameters nrows and ncols have the default value of 1 creating a single subplot spanning the full Figure canvas:
In [32]: fig1, ax1 = plt.subplots()
| Variable Explorer | |||
|---|---|---|---|
| fig1 | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| ax1 | Axes | 1 | <Axes: > |
In [33]: fig2, ax2 = plt.subplots(2, 1)
| Variable Explorer | |||
|---|---|---|---|
| fig2 | Figure | 1 | <Figure size 640x480 with 2 Axes> |
| ax2 | Array of Axes | (2, ) | [<Axes: > <Axes: >] |
| ax2 - numpy Axes array | |
|---|---|
| Index ▲ | |
| 0 | <Axes: > |
| 1 | <Axes: > |
In [34]: fig3, ax3 = plt.subplots(1, 3)
| Variable Explorer | |||
|---|---|---|---|
| fig3 | Figure | 1 | <Figure size 640x480 with 3 Axes> |
| ax3 | Array of Axes | (3, ) | [<Axes: > <Axes: > <Axes: >] |
| ax3 - numpy Axes array | |
|---|---|
| Index ▲ | |
| 0 | <Axes: > |
| 1 | <Axes: > |
| 2 | <Axes: > |
In [35]: fig4, ax4 = plt.subplots(2, 3)
| Variable Explorer | |||
|---|---|---|---|
| fig4 | Figure | 1 | <Figure size 640x480 with 6 Axes> |
| ax4 | Array of Axes | (2, 3) | [[ <Axes: > <Axes: > <Axes: >] [ <Axes: > <Axes: > <Axes: >]] |
| ax4 - numpy Axes array | |||
|---|---|---|---|
| Index ▲ | 0 | 1 | 2 |
| 0 | <Axes: > | <Axes: > | <Axes: > |
| 1 | <Axes: > | <Axes: > | <Axes: > |
Since ax1 is an Axes instance, the Axes method plot can be used:
In [36]: ax1.plot(x, y)
Out[36]: [<matplotlib.lines.Line2D at 0x20b53544190>]
Since ax2 is an ndarray (1d array) of Axes, it can be indexed into to select an Axes and the Axes method plot can be used:
In [37]: ax2[0].plot(x, y);
In [38]: ax2[1].plot(x, -y);
ax3 is also an ndarray (1d array) of Axes, it can be indexed into to select an Axes and the Axes method plot can be used:
In [39]: ax3[0].plot(x, -y)
: ax3[2].plot(x, y)
ax4 is an ndarray (2d array) of Axes, it can be indexed into to select an Axes and the Axes method plot can be used:
In [40]: ax4[0, 0].plot(x, y)
: ax4[1, 2].plot(x, y)
When procedural programming is used to create plots and the Figure and Axes haven't been instantiated, the get current Figure gcf function and get current Axes gca function can be used to assign the currently selected Figure or Axes to an instance. gca is also available as a Figure method:
In [41]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])
In [2]: # Procedural Programming
: plt.figure()
: plt.subplot()
: plt.title(1)
: plt.plot(x, y, color='tomato');
In [3]: # select current figure
: fig = plt.gcf()
: ax = fig.gca()
| Variable Explorer | |||
|---|---|---|---|
| fig | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| ax | Axes | 1 | <Axes: > |
When multiple Figure instances are created using procedural programming:
In [4]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])
: # Procedural Programming
: plt.figure()
: plt.subplot()
: plt.title(1)
: plt.plot(x, y, color='tomato');
:
: plt.figure()
: plt.subplot(2, 1, 1)
: plt.title(2)
: plt.plot(x, y, color='royalblue')
: plt.subplot(2, 1, 2)
: plt.title(3)
: plt.plot(x, y, color='seagreen')
: plt.tight_layout();
The Figure instance can be reselected using the Figure number:
In [2]: fig1 = plt.figure(1)
: fig2 = plt.figure(2)| Variable Explorer | |||
|---|---|---|---|
| fig1 | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| fig2 | Figure | 1 | <Figure size 640x480 with 2 Axes> |
And the Figure attribute axes is a list of each Axes in the Figure listed by insertion order:
In [3]: fig2.axes
Out[3]: [<Axes: title={'center': '2'}>, <Axes: title={'center': '3'}>]
In [4]: fig2.axes[1].plot(x, -y, color='orange')
Out[4]: [<matplotlib.lines.Line2D at 0x18277aa6950>]
The Figure method subplots can be used to instantiate an ndarray of Axes:
In [5]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])
: # figure and subplots
: fig = plt.figure()
: ax = fig.subplots(2, 2)
: # subplot top left - add tomato line
: ax[0, 0].plot(x, y, color='tomato')
: # subplot top right - add royalblue line
: ax[0, 1].plot(x, y, color='royalblue')
: # subplot bottom left - add seagreen line
: ax[1, 0].plot(x, y, color='seagreen')
: # subplot bottom left - add gold line
: ax[1, 1].plot(x, y, color='gold');
The Figure method subplot_mosaic can alternatively be used to instantiate a dict of Axes:
In [2]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])
: # figure and subplot_mosaic
: mosaic = np.array([['tomato', 'royalblue'],
: ['seagreen', 'royalblue']])
: fig = plt.figure()
: ax = fig.subplot_mosaic(mosaic=mosaic)| Variable Explorer | |||
|---|---|---|---|
| fig | Figure | 1 | <Figure size 640x480 with 3 Axes> |
| mosaic | Array of str288 | (2, 2) | [[tomato royalblue] [seagreen royalblue]] |
| ax | dict | 3 | {'tomato': <Axes: >, 'royalblue': <Axes: >, 'seagreen': <Axes: >} |
| mosaic - numpy object array | ||
|---|---|---|
| Index ▲ | 0 | 1 |
| 0 | tomato | royalblue |
| 1 | seagreen | royalblue |
| ax - dict (3 elements) | ||||
|---|---|---|---|---|
| ▲ | Key | Type | Size | Value |
| 0 | tomato | Axes | 1 | <Axes: > |
| 1 | royalblue | Axes | 1 | <Axes: > |
| 2 | seagreen | Axes | 1 | <Axes: > |
Indexing into this dict with a key can be used to access an Axes and the Axes method plot can be used to create a plot:
In [2]: ax['tomato'].plot(x, y, color='tomato');
Notice that the key royalblue initially spanned two positions in the ndarray and therefore the Axes occupies twice the size of the other Axes:
In [3]: ax['royalblue'].plot(x, y, color='royalblue');
In [4]: ax['seagreen'].plot(x, y, color='seagreen');
The Figure method add_axes can be used to manually draw rectangular Axes using normalised Figure canvas co-ordinates. The method add_axes requires a tuple of the form (left, bottom, width, height) and for the purpose of readibility, a NamedTuple subclass AxesRect will be used:
In [5]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: # figure and add_axes
: from collections import namedtuple
: AxesRect = namedtuple('AxesRect', ['left', 'bottom', 'width', 'height'])
: fig = plt.figure()
: ax1 = fig.add_axes(rect=AxesRect(left=0, bottom=0, width=1, height=1), facecolor='royalblue')
: ax2 = fig.add_axes(rect=AxesRect(left=0.2, bottom=0.2, width=0.5, height=0.5), facecolor='tomato')
ax1 has the facecolor='royalblue' and spans the entire Figure canvas. The Axes black box spans the edges and the Axes spines and titles are outside the Figure canvas and therefore do not show.
The following Figure instances can be created:
In [2]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 2, 3, 4, 5])
: y = np.array([10, 100, 1000, 10000, 10000])
:
: fig1, ax1 = plt.subplots()
: ax1.plot(x, y, color='tomato')
:
: fig2, ax2 = plt.subplots(2, 2)
: ax2[0, 0].plot(x, y, color='tomato')
: ax2[0, 1].plot(x, y, color='royalblue')
: ax2[1, 0].plot(x, y, color='seagreen')
: ax2[1, 1].plot(x, y, color='gold')
:
: mosaic = np.array([['tomato', 'royalblue'],
: ['seagreen', 'royalblue']])
: fig3 = plt.figure()
: ax3 = fig3.subplot_mosaic(mosaic=mosaic)
: ax3['tomato'].plot(x, y, color='tomato')
: ax3['royalblue'].plot(x, y, color='royalblue')
: ax3['seagreen'].plot(x, y, color='seagreen');
The Figure and Axes methods clear, clear the Axes and the Figure respectively:
In [2]: ax3['royalblue'].clear()
In [3]: fig3.clear()
These methods are also available under the alias clear currently selected Figure clf and clear currently selected Axes cla however generally accessing the object and using the method clear is generally preferred. These methods are available for consistency with the functions clf and cla.
The close function closes a Figure using the Figure number or instance name. Alternatively the str 'all' can be used to close all currently opened Figure instances:
In [4]: plt.close(3)
In [5]: plt.close(fig2)
In [6]: plt.close('all')matplotlib has multiple backends, which are frameworks for displaying plots. The backends can be listed using the following ipython magic:
In [7]: %matplotlib --list
Available matplotlib backends: ['agg', 'auto', 'cairo', 'gtk3', 'gtk3agg', 'gtk3cairo', 'gtk4', 'gtk4agg', 'gtk4cairo', 'inline', 'macosx', 'nbagg', 'notebook', 'osx', 'pdf', 'pgf', 'ps', 'qt', 'qt5', 'qt5agg', 'qt5cairo', 'qt6', 'qtagg', 'qtcairo', 'svg', 'template', 'tk', 'tkagg', 'tkcairo', 'webagg', 'wx', 'wx', 'wxagg', 'wxcairo']So far an interactive backend has been assumed qtagg which uses the default version of the General User Interface (GUI) Library Qt.
In [8]: %matplotlib qtaggThis requires the equivalent GUI library to be installed in the Python environment:
The inline backend saves each plot as a static image:
In [9]: %matplotlib inline
As a static image it is not possible to interact with the Figure and modify the Figure line by line as previously demonstrated. The Spyder IDE used above is based on Qt and backend support varies between each Python IDE.
The inline backend is the most commonly used backend for an interactive Python notebook and the default backend in JupyterLab:
JupyterLab also supports an interactive python matplotlib backend, which nests an interactive matplotlib plot into the output of a notebook cell. This once again requires the equivalent GUI library to be installed in the Python environment:
In [10]: %matplotlib ipympl
The interactivity in the notebook cell is currently limited compared to that offered by the qtagg backend:
In [11]: %matplotlib qtagg
The VSCode IDE behaves similarly to Spyder when running a Python script file but behaves more similarly to JupyterLab when running the Python script file interactively or using an interactive Python notebook file.
The mpl namespace has a number of backend related identifiers such as:
In [12]: mpl.get_backend()
Out[12]: 'qtagg'And:
In [13]: mpl.use('inline')These commands get and set the backend and are typically used within a Python script file. These commands are typically used when a Python shell is used, opposed to an Interactive Python (ipython) shell. For an ipython shell, the ipython magic %matplotlib is often more reliable. The Spyder IDE and JupyterLab IDEs use an ipython shell by default.
If the following Figure is made:
In [14]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: theta = np.linspace(-2*np.pi, 2*np.pi, 100)
: y1 = np.sin(theta)
: y2 = np.cos(theta)
: fig, ax = plt.subplots()
: ax.plot(theta, y1, color='royalblue', label='y=sin(theta)')
: ax.plot(theta, y2, color='seagreen', label='y=cos(theta)')
: ax.set_xlabel('theta')
: ax.set_ylabel('y')
: ax.set_title('Trigometric Functions')
: ax.legend()
Supposing the Greek letter θ or a mathematical expression is desired in the Axes label. It can be supplied in TeX. The equation editor in Only Office Desktop Editors or Microsoft Word can be used to create an equation visually and convert it to TeX:
This gives \theta, recall that \t in a string is an instruction to insert a tab. To use a TeX expression, it is enclosed in $. $\theta$ This expression is usually incorporated into a raw string R'$\theta$'. Uppercase R is preferred for this raw string as it doesn't apply syntax highlighting. Lower case r applies syntax-highlighting for a regular expression which is the wrong syntax-highlighting for TeX:
In [2]: ax.set_xlabel(R'$\theta$')
Out[2]: Text(0.5, 23.52222222222222, '$\\theta$')
In [3]: ax.set_ylabel(R'$y$')
Out[3]: Text(22.097222222222214, 0.5, '$y$')
The default text rendering engine has limited support for TeX. A TeX rendering engine can instead be used by assigning the parameter usetex=True. This requires Tex to be setup on your Operating System.
On Windows, MikTex should be installed. The MikTex Console should be opened and updated. The MikTeX Console should be used to install the following styles type1cm, cm-super, geometry, underscore, zhmetrics.
On Ubuntu and Ubuntu based Linux distributions, TeX should be installed using the Terminal:
user@pc: $ sudo apt-get install texlive-xetex texlive-fonts-recommended texlive-plain-generic cm-super dvipngOn Fedora and Redhat based Linux distributions, TeX should be installed using the Terminal:
user@pc: $ sudo dnf install texlive-xetex texlive-fonts-recommended texlive-plain-generic cm-super dvipngIn [4]: ax.set_xlabel(R'$\theta$', usetex=True)
Out[4]: Text(0.5, 23.52222222222222, '$\\theta$')
In [5]: ax.set_ylabel(R'$y$', usetex=True)
Out[5]: Text(22.097222222222214, 0.5, '$y$')Unfortunately the legend method does not have a usetex parameter. To use TeX in the legend, the recall parameters for the current session can be changed for all text objects and usetex can be set to True. Session specific modifications to the recall parameters are normally done at the top, shortly after the library imports:
In [6]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: plt.rc('text', usetex=True)
: theta = np.linspace(-2*np.pi, 2*np.pi, 100)
: y1 = np.sin(theta)
: y2 = np.cos(theta)
: fig, ax = plt.subplots()
: ax.plot(theta, y1, color='royalblue', label=R'$y=\sin{(\theta)}$')
: ax.plot(theta, y2, color='seagreen', label=R'$y=\cos{(\theta)}$')
: ax.set_xlabel(R'$\theta$')
: ax.set_ylabel(R'$y$')
: ax.set_title('Trigometric Functions')
: ax.legend()The plot will display using the TeX rendering engine. Note that the TeX rendering engine is much slower, which is the reason it isn't used by default:
The default Axes has xticks positioned at the bottom and yticks positioned on the left:
In [2]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: theta = np.linspace(-2*np.pi, 2*np.pi, 100)
: y1 = np.sin(theta)
: y2 = np.cos(theta)
In [2]: fig, ax_left = plt.subplots()
: ax_left.plot(theta, y1, color='royalblue', label='y=sin(theta)')
: ax_left.plot(theta, y2, color='seagreen', label='y=cos(theta)')
: ax_left.set_xlabel('theta')
: ax_left.set_ylabel('y')
: ax_left.set_title('Trigometric Functions')
: ax_left.legend()
Sometimes it is desireable to create a plot that has a linked xaxis but different y units. A linked Axes can be created using the Figure method twinx:
In [3]: y3 = np.tan(theta)In [4]: ax_right = ax_left.twinx()
: ax_right.plot(theta, y3, color='tomato', label='y=tan(theta)')
: ax_right.set_ylabel('y3')
: ax_right.legend()
Note the Legend instance of ax_right is on top of the Legend instance ax_left. To combine these, legend can instead be called as a Figure method:
In [5]: fig.legend()
Out[5]: <matplotlib.legend.Legend at 0x24818796fd0>
The old Axes Legend instances are still present. These Legend instances were not instantiated to instance names however can be obtained using the Axes method get_legend:
In [6]: ax_left_legend = ax_left.get_legend()
: ax_right_legend = ax_right.get_legend()Once instantiated, Legend identifiers can be examined:
In [7]: ax_left_legend.
# ---------------------------------------------------------
# Available Identifiers for matplotlib.legend.Legend:
# ---------------------------------------------------------
# 🎨 Appearance and Styling:
# - get_frame : Retrieves the rectangle patch representing the legend's frame.
# - set_frame_on : Sets whether the legend's frame is displayed.
# - get_frame_on : Checks if the legend's frame is currently displayed.
# - get_frame_alpha : Retrieves the transparency (alpha) level of the legend frame.
# - set_frame_alpha : Sets the transparency (alpha) level of the legend frame.
# - get_facecolor : Retrieves the background color of the legend frame.
# - set_facecolor : Sets the background color of the legend frame.
# - get_edgecolor : Retrieves the border color of the legend frame.
# - set_edgecolor : Sets the border color of the legend frame.
# - get_shadow : Checks if the legend has a shadow.
# - set_shadow : Sets whether the legend has a shadow.
# - get_labelspacing : Retrieves the vertical spacing between labels in the legend.
# - set_labelspacing : Sets the vertical spacing between labels in the legend.
# - get_columnspacing : Retrieves the spacing between columns in a multi-column legend.
# - set_columnspacing : Sets the spacing between columns in a multi-column legend.
# - get_borderpad : Retrieves the padding inside the legend's border.
# - set_borderpad : Sets the padding inside the legend's border.
# - get_borderaxespad : Retrieves the padding between the legend and the axes.
# - set_borderaxespad : Sets the padding between the legend and the axes.
# - get_title_fontsize : Retrieves the font size of the legend title.
# - set_title_fontsize : Sets the font size of the legend title.
# 📐 Position and Layout:
# - get_bbox_to_anchor : Retrieves the bounding box for the legend anchor point.
# - set_bbox_to_anchor : Sets the bounding box for the legend anchor point.
# - get_loc : Retrieves the current location of the legend.
# - set_loc : Sets the location of the legend (e.g., 'upper right').
# - get_mode : Retrieves the current mode for expanding the legend.
# - set_mode : Sets the mode for expanding the legend (e.g., 'expand').
# 🖌️ Text and Labels:
# - get_texts : Retrieves a list of the legend's text instances.
# - get_title : Retrieves the text instance of the legend's title.
# - set_title : Sets the title text for the legend.
# - get_title_position : Retrieves the position of the title relative to the legend box.
# - set_title_position : Sets the position of the title relative to the legend box.
# - get_lines : Retrieves the line instances displayed in the legend.
# - get_patches : Retrieves the patch instances displayed in the legend.
# - get_labels : Retrieves a list of the labels displayed in the legend.
# - set_labels : Updates the text of the legend labels.
# 🔄 Handles and Items:
# - get_legend_handles_labels : Returns a tuple of the legend's handles and labels.
# - get_handles : Retrieves a list of the handles (e.g., markers, lines) in the legend.
# - update_default_handler_map : Updates the default handler map for legend entries.
# - add_artist : Adds an artist to the legend (e.g., for additional customization).
# 🛠️ Utilities and Configuration:
# - set_draggable : Enables or disables the draggable behavior of the legend.
# - get_draggable : Checks if the legend is draggable.
# - set_in_layout : Controls whether the legend is included in layout calculations.
# - get_in_layout : Checks if the legend is included in layout calculations.
# - contains : Checks if a point or object is contained within the legend.
# - contains_point : Checks if a specific point is within the legend's boundaries.
# - findobj : Finds child objects of the legend meeting specific criteria.
# - update : Updates the legend's appearance and layout.
# 🖌️ Rendering and Drawing:
# - draw : Renders the legend on the canvas.
# - get_transform : Retrieves the transformation matrix for the legend.
# - get_window_extent : Retrieves the bounding box of the legend in display coordinates.
# ❌ Removal:
# - remove : Removes the legend from the figure or axes it is attached to.The Legend method remove can be used to remove the old Legend instances:
In [7]: ax_left_legend.remove()
: ax_right_legend.remove()
There is no fig method get_legend because a Figure can contain multiple legends. The legends attribute is a list of Legend instances:
In [8]: fig.legends
Out[8]: [<matplotlib.legend.Legend at 0x24818796fd0>]It can be indexed into:
In [9]: fig_legend = fig.legends[0]
: fig_legend.remove()
The Figure method twinx instantiated a second Axes instance which shared the x-axis, displaying a second y-axis which had a different y-scale shown on the right.
The Figure method twiny can likewise instantiate a second Axes instance which has a shared y-axis which has a different x-scale shown on the top. This is less commonly used as typically x is the independent variable.
In [10]: fig, ax = plt.subplots()
: ax.plot(theta, y1, color='royalblue', label='y=sin(theta)')
: ax.legend()
Out[10]: <matplotlib.legend.Legend at 0x248187d1990> 
Recall an Axes has a single Legend, therefor repeated calls of the Axes method legend method will remove the old Legend instance and create a new one. The legend method has the optional parameter loc which can be sued to position the Legend instance on the Axes:
In [11]: ax.legend(loc='upper left')
Out[11]: <matplotlib.legend.Legend at 0x24818a60090>
In [12]: ax.legend(loc='center left')
Out[12]: <matplotlib.legend.Legend at 0x24818a63510>
In [13]: ax.legend(loc='lower left')
Out[13]: <matplotlib.legend.Legend at 0x2481b84f710>
In [14]: ax.legend(loc='upper center')
Out[14]: <matplotlib.legend.Legend at 0x2481a29da10>
The 9 locations are:
'upper left''center left''lower left''upper center''center''lower center''upper right''center right''lower right'
It is also possible to specify the Legend position with respect to the Axes boundary box, which is shown by default using a black line.
The parameter bbox_to_anchor can be set to a 2 element tuple of the form (x, y) or a 4 element tuple of the form (x, y, width, height). For convenience a NamedTuple subclass will be created for these two scenarios:
In [15]: from collections import namedtuple
: BBox2 = namedtuple('BBox2', ['x', 'y'])
: BBox4 = namedtuple('BBox4', ['x', 'y', 'width', 'height'])In [16]: ax.legend(bbox_to_anchor=BBox2(x=0.1, y=0.1));
The anchoring is easier to visualise, using a normalised Axes:
In [17]: fig, ax = plt.subplots()
In [18]: ax.plot(np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
: np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]),
: color='royalblue', label='normalised data')
: ax.set_xlim(left=0, right=1)
: ax.set_ylim(bottom=0, top=1)
: ax.minorticks_on()
: ax.grid(which='major')
: ax.grid(which='minor', linewidth=0.5, linestyle=':')
: ax.legend(bbox_to_anchor=BBox2(x=0.1, y=0.1));
In [19]: ax.legend(bbox_to_anchor=BBox2(x=0.5, y=0.5));
In [20]: ax.legend(bbox_to_anchor=BBox2(x=1.0, y=1.0));
An optional width and height offset can be added:
In [21]: ax.legend(bbox_to_anchor=BBox4(x=1, y=1, width=0.1, height=0.1));
Returning to:
In [22]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: theta = np.linspace(-2*np.pi, 2*np.pi, 100)
: y1 = np.sin(theta)
: y3 = np.tan(theta)
In [2]: fig, ax_left = plt.subplots()
: ax_left.plot(theta, y1, color='royalblue', label='y=sin(theta)')
: ax_left.set_xlabel('theta')
: ax_left.set_ylabel('y')
: ax_left.set_title('Trigometric Functions')
In [3]: ax_right = ax_left.twinx()
: ax_right.plot(theta, y3, color='tomato', label='y=tan(theta)')
: ax_right.set_ylabel('y3', color='tomato');
Each Axes has a Spines instance which can be accessed using the spines attribute:
In [4]: ax_left.spines
Out[4]: <matplotlib.spines.Spines at 0x2481d1df190>This is dict-like:
In [5]: dict(ax_left.spines)
Out[5]:
{'left': <matplotlib.spines.Spine at 0x2481d1f8950>,
'right': <matplotlib.spines.Spine at 0x2481d50cb10>,
'bottom': <matplotlib.spines.Spine at 0x2481d4d6b90>,
'top': <matplotlib.spines.Spine at 0x2481d1ebe50>}The key can be used to access each spine, the left Spine can be instantiated and its identifiers examined:
In [6]: spine_left = ax_left.spines['left']In [7]: spine_left.
# -------------------------------------------------------
# Available Identifiers for matplotlib.spines.Spine:
# -------------------------------------------------------
# 🎨 Appearance and Styling:
# - get_linestyle : Retrieves the current linestyle of the spine.
# - set_linestyle : Sets the linestyle of the spine.
# - get_linewidth : Retrieves the current line width of the spine.
# - set_linewidth : Sets the line width of the spine.
# - get_edgecolor : Retrieves the edge color of the spine.
# - set_edgecolor : Sets the edge color of the spine.
# - get_facecolor : Retrieves the face color of the spine.
# - set_facecolor : Sets the face color of the spine.
# - set_color : Sets both the edge and face colors of the spine.
# - get_hatch : Retrieves the hatch pattern of the spine.
# - set_hatch : Sets the hatch pattern of the spine.
# - get_capstyle : Retrieves the cap style of the spine's line.
# - set_capstyle : Sets the cap style of the spine's line.
# - get_joinstyle : Retrieves the join style of the spine's line.
# - set_joinstyle : Sets the join style of the spine's line.
# - get_fill : Checks if the spine is filled.
# - set_fill : Sets whether the spine is filled.
# - get_alpha : Retrieves the transparency (alpha) level.
# - set_alpha : Sets the transparency (alpha) level.
# 📐 Position and Transformation:
# - get_position : Retrieves the current position of the spine.
# - set_position : Sets the position of the spine.
# - get_bounds : Retrieves the bounds of the spine.
# - set_bounds : Sets the bounds of the spine.
# - get_transform : Retrieves the transformation matrix for the spine.
# - set_transform : Sets the transformation matrix for the spine.
# - get_spine_transform : Retrieves the spine-specific transformation.
# - set_patch_line : Sets the spine to be a line.
# - set_patch_circle : Sets the spine to be a circle.
# - set_patch_arc : Sets the spine to be an arc.
# 🖌️ Rendering and Drawing:
# - draw : Renders the spine on the canvas.
# - clear : Clears the spine's visual appearance.
# - get_path : Retrieves the path object used to define the spine.
# - set_path_effects : Sets visual effects (like shadow) for the spine.
# - get_path_effects : Retrieves the current path effects for the spine.
# - get_rasterized : Checks if the spine is rasterized.
# - set_rasterized : Sets whether the spine should be rasterized.
# - get_visible : Checks if the spine is visible.
# - set_visible : Sets whether the spine is visible.
# - get_zorder : Retrieves the z-order (stacking order) of the spine.
# - set_zorder : Sets the z-order (stacking order) of the spine.
# 🔧 Utilities and State:
# - update : Updates the spine with new properties.
# - update_from : Copies properties from another spine.
# - properties : Returns a dictionary of the spine's properties.
# - contains : Checks if a point or object is contained within the spine.
# - contains_point : Checks if a specific point is within the spine's boundaries.
# - contains_points : Checks if multiple points are within the spine's boundaries.
# - get_children : Retrieves all children artists of the spine.
# - findobj : Finds child objects meeting specific criteria.
# 🛠️ Callback and Event Handling:
# - add_callback : Adds a callback to respond to property changes.
# - remove_callback : Removes a callback previously added to the spine.
# - pchanged : Triggers a "property changed" event for the spine.
# - pick : Handles picking (selection) events for the spine.
# - get_picker : Retrieves the picker object associated with the spine.
# - set_picker : Sets the picker object for the spine.
# 🔄 Special Spine Types:
# - arc_spine : Creates an arc-shaped spine.
# - circular_spine : Creates a circular-shaped spine.
# - linear_spine : Creates a linear spine.The left spine can be updated:
In [7]: spine_left.set_linewidth(3)
: spine_left.set_color('gold');
This can be used to more clearly identify the axis used for each line:
In [7]: spine_left.set_color('royalblue')
: spine_right = ax_right.spines['right']
: spine_right.set_linewidth(3)
: spine_right.set_color('tomato')
The Axes attribute xaxis and yaxis will return an XAxis and a YAxis instance.
In [8]: ax_left.yaxis
Out[8]: <matplotlib.axis.YAxis at 0x2481d1e2350>The identifiers from the YAxis can be examined:
In [9]: ax_left.yaxis.
# ----------------------------------------
# Available Identifiers for Axes Y-axis:
# ----------------------------------------
# 🔧 Tick Parameters:
# - get_tick_params : Retrieves the current tick parameters for the y-axis.
# : Includes settings for major and minor ticks such as
# : visibility, size, color, and label size.
# - set_tick_params : Sets the tick parameters for the y-axis. Customize
# : aspects like which ticks to apply settings to
# : (major/minor), label size, direction, and more.
# 🔧 Tick Labels:
# - get_ticklabels : Returns a list of text objects representing the tick labels
# : on the y-axis. This can be used to modify the appearance
# : of the labels.
# - set_ticklabels : Sets the y-axis tick labels to specified strings. Allows
# : customization of tick labels beyond the default formatting.
# - get_label_text : Retrieves the text of the y-axis label.
# 🔧 Ticks and Labels Formatting:
# - set_ticks : Sets the positions of ticks on the y-axis. Optionally specify
# : whether to apply these positions to major or minor ticks.
# - get_major_formatter : Returns the formatter for the major tick labels on the y-axis.
# : Controls how tick labels are formatted (e.g., decimal, scientific).
# - set_major_formatter : Sets the formatter for the major tick labels on the y-axis.
# - get_minor_formatter : Returns the formatter for the minor tick labels on the y-axis.
# - set_minor_formatter : Sets the formatter for the minor tick labels on the y-axis.
# 🔧 Major and Minor Ticks:
# - get_major_ticks : Returns a list of major ticks on the y-axis, allowing for
# : further customization or inspection.
# - set_major_locator : Sets the locator for the major ticks on the y-axis,
# : which determines their placement.
# - get_minor_ticks : Returns a list of minor ticks on the y-axis for customization
# : or inspection.
# - set_minor_locator : Sets the locator for the minor ticks on the y-axis,
# : which determines their placement.
# - minorticks_on : Turns on minor ticks for the y-axis.
# - minorticks_off : Turns off minor ticks for the y-axis.
# 🔧 Limits and Scaling:
# - get_ylim : Returns the current y-axis limits as a tuple (min, max).
# - set_ylim : Sets the y-axis limits to specified values. Useful
# : for zooming in or out of the plot.
# - set_scale : Sets the scaling of the y-axis ('linear', 'log', 'symlog', etc.).
# - get_scale : Retrieves the current scale of the y-axis.
# 🔧 Label and Offset:
# - get_label : Retrieves the label text of the y-axis.
# - set_label : Sets the label text for the y-axis.
# - get_offset_text : Retrieves the offset text displayed for the y-axis.
# - set_offset_position : Sets the offset text position ('left' or 'right').
# 🔧 Visibility and Appearance:
# - label : Gets or sets the label of the y-axis. This is the text displayed
# : next to the y-axis.
# - get_visible : Returns whether the y-axis is currently visible or not.
# - set_visible : Sets the visibility of the y-axis.
# - get_alpha : Retrieves the transparency level of the y-axis.
# - set_alpha : Sets the transparency level of the y-axis.
# - get_zorder : Returns the drawing order (z-order) of the y-axis.
# - set_zorder : Sets the drawing order (z-order) of the y-axis.
# 🔧 Other Properties:
# - clear_ticks : Removes all ticks from the y-axis, providing a clean slate
# : for custom tick placement.
# - invert_yaxis : Inverts the direction of the y-axis.
# - is_inverted : Returns whether the y-axis is currently inverted.
# - grid : Toggles gridlines on the y-axis.
# - update : Updates y-axis properties based on a dictionary of parameters.Notice that there are major and minor ticks and only the major ticks are shown by default. The minorticks can be enabled using the Axes method minorticks_on:
In [9]: ax_left.yaxis.minorticks_on();
This isn't visible as the left spine width is set too wide, this can be narrowed:
In [10]: left_spine.set_linewidth(2);
Now the major ticks and minor ticks can be seen. Notice that the major ticks have a label and the minor ticks don't have a label. The major ticks can be set using the method set_ticks:
In [11]: ax_left.yaxis.set_ticks(np.linspace(-2, 2, 5))
Out[11]:
[<matplotlib.axis.YTick at 0x2481d216890>,
<matplotlib.axis.YTick at 0x2481d4e6dd0>,
<matplotlib.axis.YTick at 0x2481d23ef90>,
<matplotlib.axis.YTick at 0x2481d25f050>,
<matplotlib.axis.YTick at 0x2481d289cd0>]
The minor ticks can be changed using the method set_ticks with the optional parameter minor=True:
In [12]: ax_left.yaxis.set_ticks(np.linspace(-2, 2, 15), minor=True)
Out[12]:
[<matplotlib.axis.YTick at 0x2481d224590>,
<matplotlib.axis.YTick at 0x2481d282710>,
<matplotlib.axis.YTick at 0x2481d935610>,
<matplotlib.axis.YTick at 0x2481d958e50>,
<matplotlib.axis.YTick at 0x2481d95b490>,
<matplotlib.axis.YTick at 0x2481d969790>,
<matplotlib.axis.YTick at 0x2481d96ba50>,
<matplotlib.axis.YTick at 0x2481d1c6910>,
<matplotlib.axis.YTick at 0x2481d93d890>,
<matplotlib.axis.YTick at 0x2481d9707d0>,
<matplotlib.axis.YTick at 0x2481d9728d0>,
<matplotlib.axis.YTick at 0x2481d974bd0>,
<matplotlib.axis.YTick at 0x2481d96ec10>,
<matplotlib.axis.YTick at 0x2481d977910>,
<matplotlib.axis.YTick at 0x2481d97db10>]
The get_tick_params method displays the tick parameters:
In [13]: ax_left.yaxis.get_tick_params()
Out[13]:
{'left': True,
'right': False,
'labelleft': True,
'labelright': False,
'gridOn': False}The set_tick_params method can be used to change these parameters and also has a number of additional optional parameters:
In [14]: ax_left.yaxis.set_tick_params?
Signature: ax_left.yaxis.set_tick_params(**kwargs)
Docstring:
Change the appearance of ticks, tick labels, and gridlines.
Tick properties that are not explicitly set using the keyword
arguments remain unchanged unless *reset* is True. For the current
style settings, see `.Axis.yaxis.get_tick_params`.
Parameters
----------
which : {'major', 'minor', 'both'}, default: 'major'
The group of ticks to which the parameters are applied.
reset : bool, default: False
Whether to reset the ticks to defaults before updating them.
Other Parameters
----------------
direction : {'in', 'out', 'inout'}
Puts ticks inside the Axes, outside the Axes, or both.
length : float
Tick length in points.
width : float
Tick width in points.
color : :mpltype:`color`
Tick color.
pad : float
Distance in points between tick and label.
labelsize : float or str
Tick label font size in points or as a string (e.g., 'large').
labelcolor : :mpltype:`color`
Tick label color.
labelfontfamily : str
Tick label font.
colors : :mpltype:`color`
Tick color and label color.
zorder : float
Tick and label zorder.
bottom, top, left, right : bool
Whether to draw the respective ticks.
labelbottom, labeltop, labelleft, labelright : bool
Whether to draw the respective tick labels.
labelrotation : float
Tick label rotation
grid_color : :mpltype:`color`
Gridline color.
grid_alpha : float
Transparency of gridlines: 0 (transparent) to 1 (opaque).
grid_linewidth : float
Width of gridlines in points.
grid_linestyle : str
Any valid `.Line2D` line style spec.The major ticks can be changed:
In [15]: ax_left.yaxis.set_tick_params(length=10, width=5, direction='out', color='magenta', labelcolor='orange');
colors is used instead of color and labelcolor when both the color and labelcolor match:
In [16]: ax_left.yaxis.set_tick_params(length=5, width=3, direction='inout', colors='royalblue');
In [17]: ax_left.yaxis.set_tick_params(which='minor', length=4, width=2, direction='in', color='orange');
The method get_ticklabels can be used to retrieve a list of Text instances. Each Text instance has the form Text(x, y, 'label_text') where x=0 because the yaxis is positioned at the origin of the xaxis:
In [18]: ax_left.yaxis.get_ticklabels()
Out[18]:
[Text(0, -2.0, '−2'),
Text(0, -1.0, '−1'),
Text(0, 0.0, '0'),
Text(0, 1.0, '1'),
Text(0, 2.0, '2')]The text of these labels can be replaced by using the method set_ticklabels and supplying a list of Text instances or a list of str instances corresponding to the labels:
In [19]: ax_left.yaxis.set_ticklabels(['−two', '−one', 'zero', 'one', 'two'])
Out[19]:
[Text(0, -2.0, '−two'),
Text(0, -1.0, '−one'),
Text(0, 0.0, 'zero'),
Text(0, 1.0, 'one'),
Text(0, 2.0, 'two')]
The method grid is used to turn on the grid:
In [20]: ax_left.yaxis.grid()
grid has a number of parameters consistent to those found in plot:
In [21]: ax_left.yaxis.grid(color='cyan', linewidth=3, linestyle='-.')
which can be used to select the minor grid lines:
In [22]: ax_left.yaxis.grid(which='minor', color='khaki', linewidth=1.5, linestyle='--')
Returning to a simplified plot:
In [23]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: theta = np.linspace(-2*np.pi, 2*np.pi, 100)
: y1 = np.sin(theta)
: fig, ax = plt.subplots()
: ax.plot(theta, y1, color='royalblue', label=R'$y=\sin{(\theta)}$')
: ax.set_xlabel(R'$\theta$')
: ax.set_ylabel(R'$y$')
: ax.set_title('Trigometric Functions')
: fig.legend()
The xticks can be set to the following values, where 2*np.pi is also known as tau, the circle constant:
In [2]: ax.xaxis.set_ticks(np.linspace(-2*np.pi, 2*np.pi, 5))
Out[2]:
[<matplotlib.axis.XTick at 0x2481d8a65d0>,
<matplotlib.axis.XTick at 0x2481d8a4990>,
<matplotlib.axis.XTick at 0x2481d8d49d0>,
<matplotlib.axis.XTick at 0x2481d219a50>,
<matplotlib.axis.XTick at 0x2481d97c2d0>]
And these labels can be expressed as fractions of tau:
In [3]: ax.yaxis.set_ticklabels([R'$−\frac{2\tau}{2}$', R'$−\frac{\tau}{2}$', R'$0$', R'$\frac{\tau}{2}$', R'$\frac{2\tau}{2}$'])
Out[3]:
[Text(-6.283185307179586, 0, '$−\\frac{2\\tau}{2}$'),
Text(-3.141592653589793, 0, '$−\\frac{\\tau}{2}$'),
Text(0.0, 0, '$0$'),
Text(3.141592653589793, 0, '$\\frac{\\tau}{2}$'),
Text(6.283185307179586, 0, '$\\frac{2\\tau}{2}$')]
The Axes has the method tick_params which can be used to set the tick parameters of both the XAxis and YAxis. This method has the additional parameter axis='both'. If this is changed to 'x' or 'y', it will behave analogously to the set_tick_params method on the XAxis or YAxis respectively:
In [4]: ax.tick_params(direction='inout', length=10, width=3, color='magenta')
The Axes also has the method grid which can be used to set the grid on both the XAxis and YAxis. This method also has the additional parameter axis='both'. If this is changed to 'x' or 'y', it will behave analogously to the grid method on the XAxis or YAxis respectively:
In [5]: ax.grid()
The Axes also has the method minorticks_on which can be used to turn on the minor ticks on both the XAxis and YAxis. This method also has the additional parameter axis='both'. If this is changed to 'x' or 'y', it will behave analogously to the minorticks_on method on the XAxis or YAxis respectively:
In [6]: ax.minorticks_on()
In [7]: ax.grid(which='minor', linewidth=0.5, linestyle=':')
Supposing there are two data points with x, y, z co-ordinate:
In [8]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
In [2]: x = np.array([1, 2])
: y = np.array([3, 6])
: z = np.array([4, 8])The figure method, add_subplot has the optional parameter projection which can be used to instantiate a 3DAxes
In [3]: fig = plt.figure()
: fig.add_subplot(projection='3d')
The currently selected Axes3D can be selected using either plt.gca or by accessing it via the Figure attribute axes:
In [4]: ax = fig.axes[0]Axes3D is a child class of Axes and therefore inherits its methods while also adding additional methods specific to 3D plotting. These can be viewed using:
In [5]: ax.
# ----------------------------------------
# Available Identifiers for Axes3D:
# ----------------------------------------
# 🔧 Labels (Unique to Axes3D):
# - set_xlabel : Sets the label for the x-axis in 3D.
# - set_ylabel : Sets the label for the y-axis in 3D.
# - set_zlabel : Sets the label for the z-axis in 3D.
# 🔧 3D Plotting Methods:
# - plot3D : Plots lines in 3D space.
# - scatter3D : Creates a scatter plot in 3D.
# - bar3d : Creates a 3D bar plot.
# - contour3D : Creates contour plots in 3D.
# - plot_surface : Plots a 3D surface.
# - plot_wireframe : Plots a wireframe representation of a surface.
# 🔧 Setting Limits and View:
# - set_box_aspect : Sets the aspect ratio of the 3D plot.
# - set_xlim3d : Sets the limits for the x-axis in 3D.
# - set_ylim3d : Sets the limits for the y-axis in 3D.
# - set_zlim3d : Sets the limits for the z-axis in 3D.
# - view_init : Sets the elevation and azimuthal angle of the plot.
# 🔧 Accessing and Manipulating Axes:
# - get_proj : Returns the projection matrix used for 3D plots.
# - grid : Controls the visibility of the grid in 3D.
# 🔧 Visibility Control:
# - set_visible : Controls the visibility of the 3D axes.
# - set_zticks : Sets the ticks for the z-axis in 3D.
# 🔧 Advanced Plotting Control:
# - text3D : Adds text in 3D space.The method plot3D is consistent to plot but requires 3 dimensional data. In this example a single data point is plotted, the three axis labels can also be set:
In [5]: ax.plot3D(x, y, z, color='royalblue', marker='o')
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.set_zlabel('z');
In [6]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:The figure method, add_subplot optional parameter projection can also be used to instantiate a PolarAxes:
In [3]: fig = plt.figure()
: fig.add_subplot(projection='3d')
Out[3]: <PolarAxes: >
In [4]: ax = fig.axes[0]PolarAxes is also a child class of Axes. Its identifiers can be examined:
In [4]: ax.
# ----------------------------------------
# Available Identifiers for PolarAxes:
# ----------------------------------------
# 🔧 Theta Limits and Formatting:
# - set_thetamin : Sets the minimum theta value (in degrees) displayed on the plot.
# - set_thetamax : Sets the maximum theta value (in degrees) displayed on the plot.
# - set_theta_zero_location : Sets the location of the theta=0° line (e.g., 'N', 'E', 'S', 'W').
# - set_theta_direction : Sets the direction of increasing theta (1 for counterclockwise, -1 for clockwise).
# - get_theta_zero_location : Retrieves the current location of the theta=0° line.
# - get_theta_direction : Retrieves the current direction of increasing theta.
# 🔧 Radial Limits:
# - set_ylim : Sets the radial limits of the plot (minimum and maximum radius).
# - get_ylim : Retrieves the current radial limits.
# 🔧 Tick and Grid Customization:
# - set_rgrids : Sets the locations and labels of radial grid lines and ticks.
# - get_rgrids : Retrieves the current radial grid lines and labels.
# - set_thetagrids : Sets the locations and labels of angular grid lines and ticks.
# - get_thetagrids : Retrieves the current angular grid lines and labels.
# 🔧 Axis Labels:
# - set_xlabel : Sets the label for the x-axis.
# - set_ylabel : Sets the label for the y-axis.
# - get_xlabel : Retrieves the current label of the x-axis.
# - get_ylabel : Retrieves the current label of the y-axis.
# 🔧 Appearance and Visibility:
# - grid : Toggles grid lines on or off.
# - set_aspect : Sets the aspect ratio of the polar plot.
# - legend : Adds a legend to the plot.
# - get_aspect : Retrieves the current aspect ratio setting.
# - set_visible : Toggles visibility of the polar axes.
# 🔧 Data and Transformations:
# - plot : Plots data in polar coordinates.
# - scatter : Creates a scatter plot in polar coordinates.
# - bar : Creates a bar plot in polar coordinates.
# - fill : Fills the area under a curve in polar coordinates.
# 🔧 Conversion Utilities:
# - polar : Converts Cartesian data to polar coordinates.
# - inverse_polar : Converts polar data back to Cartesian coordinates.Instead of using an x and a y coordinate system it uses a theta and a radius co-ordinate system. The xaxis is the angles and the yaxis is the radius.
In [4]: ax.xaxis.get_ticklocs()
Out[4]:
array([0. , 0.78539816, 1.57079633, 2.35619449, 3.14159265,
3.92699082, 4.71238898, 5.49778714])
In [5]: ax.yaxis.get_ticklocs()
Out[5]:
[np.float64(0.2),
np.float64(0.4),
np.float64(0.6000000000000001),
np.float64(0.8),
np.float64(1.0)]Although theta is displayed in degrees, it actually uses radians. For clarity it can be changed into fractions of the circle constant tau. A list comprehension will be used to create the labels. Because the prefix R is sued to create a raw string, the prefix f cannot be used for a formatted string. Moreover attempting to do so would result in confusion because the {} is used for another purpose in TeX. Therefore str concatenation is carried out using the str operator +, which is a little less readible but more clear in Out[6]:
In [6]: ax.xaxis.set_ticklabels([R'$\frac{'+str(num)+R'}{8}\tau$' for num in range(8)])
C:\Users\phili\AppData\Local\Temp\ipykernel_5756\716684506.py:1: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
ax.xaxis.set_ticklabels([R'$\frac{'+str(num)+R'}{8}\tau$' for num in range(8)])
Out[6]:
[Text(0.0, 0, '$\\frac{0}{8}\\tau$'),
Text(0.7853981633974483, 0, '$\\frac{1}{8}\\tau$'),
Text(1.5707963267948966, 0, '$\\frac{2}{8}\\tau$'),
Text(2.356194490192345, 0, '$\\frac{3}{8}\\tau$'),
Text(3.141592653589793, 0, '$\\frac{4}{8}\\tau$'),
Text(3.9269908169872414, 0, '$\\frac{5}{8}\\tau$'),
Text(4.71238898038469, 0, '$\\frac{6}{8}\\tau$'),
Text(5.497787143782138, 0, '$\\frac{7}{8}\\tau$')]
Note a UserWarning is flagged stating that set_ticklabels should only be used after set_ticks, this can be addressed by using set_ticks to assign the ticks to the original positions before using set_ticklabels:
In [7]: ax.xaxis.set_ticks(ax.xaxis.get_ticklocs())
: ax.xaxis.set_ticklabels([R'$\frac{'+str(num)+R'}{8}\tau$' for num in range(8)])
Out[7]:
[Text(0.0, 0, '$\\frac{0}{8}\\tau$'),
Text(0.7853981633974483, 0, '$\\frac{1}{8}\\tau$'),
Text(1.5707963267948966, 0, '$\\frac{2}{8}\\tau$'),
Text(2.356194490192345, 0, '$\\frac{3}{8}\\tau$'),
Text(3.141592653589793, 0, '$\\frac{4}{8}\\tau$'),
Text(3.9269908169872414, 0, '$\\frac{5}{8}\\tau$'),
Text(4.71238898038469, 0, '$\\frac{6}{8}\\tau$'),
Text(5.497787143782138, 0, '$\\frac{7}{8}\\tau$')]The theta zero position is in the East by default and the direction is clockwise. A new Figure will be created so it is using default labels. The PolarAxes method set_theta_zero_location can be used to set the zero location to North and the method set_theta_direction can be used to display each angle clockwise:
In [8]: fig = plt.figure()
: ax = fig.add_subplot(projection='polar')
: ax.set_theta_zero_location('N')
: ax.set_theta_direction('clockwise')
The maximum theta value, in degrees can be set using the PolarAxes method set_theta. A restriction to 90 degrees is common when looking at angles in a triangle for example:
In [9]: fig = plt.figure()
: ax = fig.add_subplot(projection='polar')
: ax.set_theta_zero_location('N')
: ax.set_theta_direction('clockwise')
: ax.set_thetamax(90)
Cartesian co-ordinates can be compared with polar co-ordinates:
In [10]: fig = plt.figure()
: ax = fig.add_subplot(1, 2, 1)
: ax.set_aspect(1)
: ax_polar = fig.add_subplot(1, 2, 2, projection='polar')
: ax_polar.set_thetamax(90)
: ax_polar.xaxis.set_ticks(np.linspace(0, np.pi/2, 7))
: ax_polar.xaxis.set_ticklabels([R'$\frac{'+str(num)+R'}{24}\tau$' for num in range(7)]);
Assuming a normalised radius of 1, the effect of changing angle can be examined. The x and y values in the cartesian co-ordinate system can be calculated using the cosine and sine of the angles:
In [11]: theta = np.linspace(0, np.pi/2, 7)
: r = np.ones(7)
: x = np.cos(theta)
: y = np.sin(theta)| Variable Explorer | |||
|---|---|---|---|
| theta | array of float64 | (7, ) | [0. 0.26179939 0.52359878 0.78539816 1.04719755, 1.30899694 1.57079633] |
| r | array of float64 | (7, ) | [1., 1., 1., 1., 1., 1., 1.] |
| x | array of float64 | (7, ) | [1.00000000e+00 9.65925826e-01 8.66025404e-01 7.07106781e-01, 5.00000000e-01 2.58819045e-01 6.12323400e-17] |
| y | array of float64 | (7, ) | [0. 0.25881905 0.5 0.70710678 0.8660254 0.96592583 1. ] |
If a list of colors and markers are instantiated:
In [12]: colors = ['royalblue', 'gold', 'forestgreen', 'salmon', 'violet', 'peru', 'deeppink']
: markers = ['o', 's', 'p', 'h', '8', 'X', '*']Notice that in the variable explorer, that all of these have a length of 7:
| Variable Explorer | |||
|---|---|---|---|
| theta | array of float64 | (7, ) | [0. 0.26179939 0.52359878 0.78539816 1.04719755, 1.30899694 1.57079633] |
| r | array of float64 | (7, ) | [1., 1., 1., 1., 1., 1., 1.] |
| x | array of float64 | (7, ) | [1.00000000e+00 9.65925826e-01 8.66025404e-01 7.07106781e-01, 5.00000000e-01 2.58819045e-01 6.12323400e-17] |
| y | array of float64 | (7, ) | [0. 0.25881905 0.5 0.70710678 0.8660254 0.96592583 1. ] |
| colors | list | 7 | ['royalblue', 'gold', 'forestgreen', 'salmon', 'violet', 'peru', 'deeppink'] |
| markers | list | 7 | ['o', 's', 'p', 'h', '8', 'X', '*'] |
This means they can all be looped over:
In [8]: for index in range(7):
: print(colors[index])
royalblue
gold
forestgreen
salmon
violet
peru
deeppinkThe Axes and PolarAxes method plot can be used in a for loop:
In [9]: for index in range(7):
: ax.plot(x[index], y[index], color=colors[index],
: linestyle=None, marker=markers[index], markersize=10)
: ax_polar.plot(theta[index], r[index], color=colors[index],
: linestyle=None, marker=markers[index], markersize=10)
:
: ax.set_xlabel('x')
: ax.set_ylabel('y');
The radius currently looks like an xaxis, it would be clearer if the radial position was set to another angle:
In [10]: ax_polar.set_rlabel_position(35)
Unfortunately this method does not work properly when set_theta_max has been used:
In [11]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: fig = plt.figure()
: ax = fig.add_subplot(1, 2, 1)
: ax.set_aspect(1)
: ax_polar = fig.add_subplot(1, 2, 2, projection='polar')
: theta = np.linspace(0, np.pi/2, 7)
: r = np.ones(7)
: x = np.cos(theta)
: y = np.sin(theta)
: colors = ['royalblue', 'gold', 'forestgreen', 'salmon', 'violet', 'peru', 'deeppink']
: markers = ['o', 's', 'p', 'h', '8', 'X', '*']
: for index in range(7):
: ax.plot(x[index], y[index], color=colors[index],
: linestyle=None, marker=markers[index], markersize=10)
: ax_polar.plot(theta[index], r[index], color=colors[index],
: linestyle=None, marker=markers[index], markersize=10)
:
: ax.set_xlabel('x')
: ax.set_ylabel('y');
Setting labels for the PolarAxes does not work too well:
In [2]: ax_polar.set_xlabel(R'$\theta$')
Out[2]: Text(0.5, 92.87272727272723, '$\\theta$')
In [3]: ax_polar.set_ylabel(R'r')
Out[3]: Text(344.98989898989896, 0.5, 'r')
And therefore this information is better placed using a custom Text instance which can be placed on an Axes using the Axes method annotate:
In [4]: ax.annotate?
Signature:
ax.annotate(
text,
xy,
xytext=None,
xycoords='data',
textcoords=None,
arrowprops=None,
annotation_clip=None,
**kwargs,
)
Docstring:
Annotate the point *xy* with text *text*.For convenience a return to a more simpler plot will be used:
In [5]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: theta = np.linspace(-2*np.pi, 2*np.pi, 100)
: y = np.sin(theta)
: fig, ax = plt.subplots()
: ax.plot(theta, y, color='royalblue', label=R'$y=\sin{(\theta)}$')
: ax.set_xlabel(R'$\theta$')
: ax.set_ylabel(R'$y$')
: ax.set_title('Trigometric Functions')
: ax.grid()
: fig.legend()
An annotation can be added using:
In [2]: ax.plot(0.2, 0.2, color='tomato', marker='o')
: ax.annotate('noarrow data (0.2, 0.2)', xy=(0.2, 0.2), xycoords='data')
Out[2]: Text(0.2, 0.2, 'noarrow data (0.2, 0.2)')
In [3]: ax.annotate('noarrow axes fraction (0.2, 0.2)', xy=(0.2, 0.2), xycoords='axes fraction')
Out[3]: Text(0.2, 0.2, 'noarrow axes fraction (0.2, 0.2)')
In [4]: ax.annotate('noarrow figure fraction (0.2, 0.2)', xy=(0.2, 0.2), xycoords='figure fraction')
Out[4]: Text(0.2, 0.2, 'noarrow figure fraction (0.2, 0.2)')
Each of these Text instances can be found in the Axes attribute Text:
In [5]: ax.texts
Out[5]: <Axes.ArtistList of 3 texts>The annotate method has xy and xytext which allows the text to be repositioned at a different co-ordinate from being annotated:
In [6]: ax.plot(4, 0, color='orange', marker='o')
: ax.annotate('no arrow', xy=(0.2, 0.2), xytext=(4, 0),
: textcoords='data', xycoords='data',
: color='dodgerblue')
Out[6]: Text(4, 0, 'fancy arrow') 
Note that no arrow is shown because an arrow wasn't specified. The parameters for arrow properties are compartmentalised using a dict inwtance, typically the class is used here, so syntax highlighting is consistent. The coordinate systems are not specified and use the default value 'data':
In [7]: ax.plot(4, 0.5, color='purple', marker='o')
: ax.annotate('fancy arrow drawn', xy=(0.2, 0.2), xytext=(4, 0.5),
color='dodgerblue',
arrowprops=dict(color='magenta', arrowstyle='fancy'))
Out[7]: Text(4, 0.5, 'fancy arrow drawn')
The Text instance is added to the Axes attribute texts:
In [8]: ax.texts
Out[8]: <Axes.ArtistList of 5 texts>The last annotation can be removed by indexing into this list to select the annotation at index -1 and using the remove method:
In [9]: ax.texts[-1].remove()
The remaining annotations can be removed using:
In [10]: for annotation in ax.texts:
: annotation.remove()
A simple arrow can be examined:
In [11]: ax.annotate('simple arrow drawn', xy=(0.2, 0.2), xytext=(4, 0.5),
color='dodgerblue',
arrowprops=dict(color='seagreen', arrowstyle='simple'))
Out[11]: Text(4, 0.5, 'simple arrow drawn')
The fancyarrow has some additional customisation options which can be specified within arrowprops:
In [12]: ax.texts[-1].remove()
In [13]: ax.annotate('fancy arrow drawn', xy=(0.2, 0.2), xytext=(4, 0.5),
color='dodgerblue',
arrowprops=dict(facecolor='yellow', edgecolor='lime', arrowstyle='fancy'))
In [1]: import random
: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: from matplotlib.colors import CSS4_COLORS
: random.seed(0)
: colors = list(CSS4_COLORS)
:
: x = np.arange(0, 50, 1)
: y = x ** 2
: s = y + 1
: c = random.choices(colors, k=50)
:
: fig, ax = plt.subplots()
: path_collection = ax.scatter(x, y, s, c)
| Variable Explorer | |||
|---|---|---|---|
| x | array of int64 | (50, ) | [ 0 1 2 ... 47 48 49] |
| y | array of int64 | (50, ) | [ 0 1 4 ... 2209 2304 2401] |
| s | array of int64 | (50, ) | [ 1 2 5 ... 2210 2305 2402] |
| c | list | 50 | ['salmon', 'papayawhip', 'khaki','darkturquoise', 'lightsalmon', 'ind...] |
| fig | Figure | 1 | <Figure size 640x480 with 1 Axes> |
| ax | Axes | 1 | <Axes: > |
| line_collection | PathCollection | 1 | <PathCollection: > |
The properties of the PathCollection instance line_collection can be examined using the function get properties:
In [2]: plt.getp(path_collection)
agg_filter = None
alpha = None
animated = False
antialiased or aa or antialiaseds = [ True]
array = None
capstyle = None
children = []
clim = (None, None)
clip_box = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=1.0, ...
clip_on = True
clip_path = None
cmap = <matplotlib.colors.ListedColormap object at 0x0000...
edgecolor or ec or edgecolors = [[0.98039216 0.50196078 0.44705882 1. ] [1...
facecolor or facecolors or fc = [[0.98039216 0.50196078 0.44705882 1. ] [1...
figure = Figure(640x480)
fill = True
gid = None
hatch = None
in_layout = True
joinstyle = None
label = _child0
linestyle or dashes or linestyles or ls = [(np.float64(0.0), None)]
linewidth or linewidths or lw = [1.]
mouseover = False
offset_transform or transOffset = CompositeGenericTransform( TransformWrapper( ...
offsets = [[0.0 0.0] [1.0 1.0] [2.0 4.0] [3.0 9.0] [4.0 ...
path_effects = None
paths = (Path(array([[ 0. , -0.5 ], [ ...
picker = None
pickradius = 5.0
rasterized = False
sizes = [ 1 2 5 10 17 26]...
sketch_params = None
snap = None
tightbbox = Bbox(x0=80.0, y0=52.8, x1=576.0, y1=422.4)
transform = IdentityTransform()
transformed_clip_path_and_affine = (None, None)
transforms = [[[1.38888889 0. 0. ] [0. ...
url = None
urls = [None]
visible = True
window_extent = Bbox(x0=inf, y0=inf, x1=-inf, y1=-inf)
zorder = 1Notice that the properties of the LineCollection are similar to a Line2D. There are slight differences because the main focus of the Line2D as the name of the class suggests is the line and the marker is secondary. In the PathCollection, the main focus is the marker itself.
The following Axes methods can be used to label each axis and add gridlines:
In [3]: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.grid()
: ax.minorticks_on()
: ax.grid(which='minor', linewidth=0.5, linestyle=':');
In the above example x, y, s and c were all plural and of equal length. It is common to apply a consistent size and color to all markers in a LineCollection. This is done by applying a scalar to the third positional argument and supplying a single color to the optional parameter color:
In [4]: path_collection2 = ax.scatter(x, y, 30, color='tomato')
Each LineCollection can be found in the Axes attribute collections:
In [5]: ax.collections
Out[5]: <Axes.ArtistList of 2 collections>
In [6]: path_collection2 == ax.collections[1]
Out[6]: TrueThe identifiers of one of the PathCollection instances can be examined:
In [7]: path_collection.
# ----------------------------------------
# Available Identifiers for LineCollection:
# ----------------------------------------
# 🔧 General Attributes:
# - get_segments : Returns the segments that make up the collection. Each
# : segment is a list of (x, y) coordinate pairs.
# - set_segments : Sets the segments that define the collection. Input should
# : be a list of (x, y) coordinate pairs or a sequence of such lists.
# - get_paths : Retrieves the paths used in the collection. Each path
# : corresponds to a line or multiple connected lines.
# - get_offset_transform : Returns the transformation applied to the offsets in the
# : collection.
# 🎨 Colors and Appearance:
# - set_color : Sets the color(s) of the lines in the collection. Can be
# : a single color or a list of colors.
# - get_color : Returns the color(s) of the lines in the collection.
# - set_linestyle : Sets the line style(s) of the collection (e.g., solid, dashed).
# - get_linestyle : Retrieves the current line style(s) of the collection.
# - set_linewidth : Sets the width of the lines in the collection.
# - get_linewidth : Retrieves the width of the lines in the collection.
# 🔧 Offset and Transformation:
# - set_offsets : Sets the offsets for the collection. Offsets can be
# : applied to each line segment.
# - get_offsets : Retrieves the offsets applied to the collection.
# - set_transform : Sets the transformation applied to the collection's lines.
# - get_transform : Retrieves the transformation applied to the collection.
# 🔧 Alpha and Visibility:
# - set_alpha : Sets the transparency (alpha) level for the collection.
# - get_alpha : Retrieves the transparency (alpha) level for the collection.
# - set_visible : Sets the visibility of the collection.
# - get_visible : Checks whether the collection is currently visible.
# 📏 Clipping and Bounds:
# - set_clip_box : Sets the clipping box for the collection, defining the
# : area within which the lines are displayed.
# - get_clip_box : Retrieves the clipping box applied to the collection.
# - set_clip_path : Sets a clipping path for the collection.
# - get_clip_path : Retrieves the clipping path applied to the collection.
# 🛠️ Customization:
# - set_joinstyle : Sets the join style of the lines (e.g., miter, round, bevel).
# - get_joinstyle : Retrieves the join style of the lines in the collection.
# - set_capstyle : Sets the cap style of the lines (e.g., butt, round, projecting).
# - get_capstyle : Retrieves the cap style of the lines in the collection.
# - set_antialiased : Enables or disables anti-aliasing for the lines in the collection.
# - get_antialiased : Checks whether anti-aliasing is enabled for the collection.
# 🗑️ Removing and Updating:
# - remove : Removes the collection from its parent `Axes` or `Figure`.
# - update : Updates the collection's properties based on input.
# 🔧 Other Methods:
# - draw : Draws the collection onto a given renderer. Primarily used
# : internally by Matplotlib.
# - set_picker : Sets the picker function or tolerance for the collection.
# - get_picker : Retrieves the current picker function or tolerance.
# - contains : Checks whether a point is within the collection's boundaries.
# - set_zorder : Sets the z-order for the collection, which determines
# : the drawing priority.
# - get_zorder : Retrieves the current z-order of the collection.The previous PathCollection instance path_collection can be removed using the method remove:
In [7]: path_collection.remove()
The Axes method scatter has the optional parameters edgecolor and linewidth. linewidth is used opposed to markerwidth as the PathCollection has no other line:
In [8]: path_collection2.remove()
: path_collection3 = ax.scatter(x, y, 30, color='tomato',
: edgecolor='royalblue', linewidth=1.5)
The Axes method scatter has the optional parameter marker which is always singular and applies to every point in the entire PathCollection:
In [8]: path_collection3.remove()
: path_collection4 = ax.scatter(x, y, 100, color='tomato',
: marker='p', edgecolor='royalblue', linewidth=1.5)
To see more information about the markers, the MarkerStyle class can be imported. It has the attribute markers which is a dict:
In [9]: from matplotlib.markers import MarkerStyle
In [10]: markers = MarkerStyle.markers
In [11]: markers
Out[11]:
{'.': 'point',
',': 'pixel',
'o': 'circle',
'v': 'triangle_down',
'^': 'triangle_up',
'<': 'triangle_left',
'>': 'triangle_right',
'1': 'tri_down',
'2': 'tri_up',
'3': 'tri_left',
'4': 'tri_right',
'8': 'octagon',
's': 'square',
'p': 'pentagon',
'*': 'star',
'h': 'hexagon1',
'H': 'hexagon2',
'+': 'plus',
'x': 'x',
'D': 'diamond',
'd': 'thin_diamond',
'|': 'vline',
'_': 'hline',
'P': 'plus_filled',
'X': 'x_filled',
0: 'tickleft',
1: 'tickright',
2: 'tickup',
3: 'tickdown',
4: 'caretleft',
5: 'caretright',
6: 'caretup',
7: 'caretdown',
8: 'caretleftbase',
9: 'caretrightbase',
10: 'caretupbase',
11: 'caretdownbase',
'None': 'nothing',
'none': 'nothing',
' ': 'nothing',
'': 'nothing'}The dict has key and value pairs. It can be compared to the dict instance CSS4_COLORS:
In [12]: CSS4_COLORS['tomato']
Out[12]: '#FF6347'
In [13]: markers['.']
Out[13]: 'point'Notice in CSS4_COLORS the keys are more human-readible than the values and both the keys and values are accepted by the color parameters:
In [14]: ax.scatter(x, y, 100, color='#FF6347', marker='.', edgecolor='royalblue', linewidth=1.5)
Out[14]: <matplotlib.collections.PathCollection at 0x24383777c90>In markers, the values are more human-readible than the keys however unfortunately only the keys are accepted by the marker parameter. Using one of the values in the dict instance market flags up a ValueError because it is unrecognised:
In [12]: ax.scatter(x, y, 100, color='tomato', marker='point', edgecolor='royalblue', linewidth=1.5)
Traceback (most recent call last):
ValueError: Unrecognized marker style 'pixel'In [13]: exit
In [1]: import random
: import numpy as np
: import matplotlib.pyplot as plt
: from matplotlib.colors import CSS4_COLORS
: from matplotlib.markers import MarkerStyle
:
: random.seed(0)
:
: markers = list(MarkerStyle.markers)
: colors = list(CSS4_COLORS)
:
: markers_per_row = 5
: spacing = 2
:
: fig, ax = plt.subplots()
:
: for index, marker in enumerate(markers):
: row = index // markers_per_row
: col = index % markers_per_row
: x = col * spacing
: y = -row * spacing
:
: if marker is not None:
: ax.scatter(x, y, 150, color='tomato', edgecolor='royalblue',
: linewidth=3, marker=marker, label=marker)
:
: ax.set_xlim(-1, 16)
:
: legend = fig.legend(ncols=2)
: legend.set_frame_on(True)
: legend.get_frame().set_alpha(1.0)
UserWarning: You passed a edgecolor/edgecolors ('royalblue') for an unfilled marker (''). Matplotlib is ignoring the edgecolor in favor of the facecolor. This behavior may change in the future.
Supposing the following Figure is created:
In [2]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.linspace(-4*np.pi, 4*np.pi, 100)
: y1 = 3 * np.sin(x)
: y2 = 4 * np.cos(x)
:
: fig, ax = plt.subplots()
: ax.plot(x, y1, color='seagreen')
: ax.plot(x, y2, color='royalblue');
The Axes method vlines can be used to create a number of vertical lines. For vlines an ndarray or list of x values is required and a scalar is required for ymin and ymax:
In [2]: x = np.linspace(-4*np.pi, 4*np.pi, 5)
: ymin = -6
: ymax = 6
:
: ax.vlines(x, ymin, ymax, color='tomato')
Out[2]: <matplotlib.collections.LineCollection at 0x2458ded1350> 
The Axes attribute collections is a list of collections on the Axes instance and the last item in this list is the LineCollection, it can be instantiated using:
In [3]: line_collection = ax.collections[-1]Notice when get properties is used on this LineCollection, that many of the properties available are in singular and plural form because they refere to multiple lines:
In [4]: plt.getp(line_collection)
agg_filter = None
alpha = None
animated = False
antialiased or aa or antialiaseds = [ True]
array = None
capstyle = None
children = []
clim = (None, None)
clip_box = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=1.0, ...
clip_on = True
clip_path = None
cmap = <matplotlib.colors.ListedColormap object at 0x0000...
color = [[1. 0.38823529 0.27843137 1. ]]
colors = [[1. 0.38823529 0.27843137 1. ]]
edgecolor or ec or edgecolors = [[1. 0.38823529 0.27843137 1. ]]
facecolor or facecolors or fc = []
figure = Figure(640x480)
fill = False
gapcolor = None
gid = None
hatch = None
in_layout = True
joinstyle = None
label = _child2
linestyle or dashes or linestyles or ls = [(np.float64(0.0), None)]
linewidth or linewidths or lw = [1.5]
mouseover = False
offset_transform or transOffset = IdentityTransform()
offsets = [[0. 0.]]
path_effects = None
paths = [Path(array([[-12.56637061, -6. ], ...
picker = None
pickradius = 5.0
rasterized = False
segments = [array([[-12.56637061, -6. ], [-12....
sketch_params = None
snap = None
tightbbox = Bbox(x0=80.0, y0=52.8, x1=576.0, y1=422.4)
transform = CompositeGenericTransform( TransformWrapper( ...
transformed_clip_path_and_affine = (None, None)
transforms = []
url = None
urls = [None]
visible = True
window_extent = Bbox(x0=inf, y0=inf, x1=-inf, y1=-inf)
zorder = 2A scalar value is typically set to the singular parameters and is applied to each of the lines:
In [5]: plt.setp(line_collection, color='magenta', linewidth=2, linestyle='dotted')
Out[5]: [None, None, None]
For the plural parameters a list or ndarray of equal length to the number of lines in the LineCollection is used to customise each line:
In [6]: plt.setp(line_collection, colors=['tomato', 'orange', 'magenta', 'orange', 'tomato'],
: linewidths=[3, 2, 1, 2, 3], linestyles=['solid', 'dashdot', 'dotted', 'dashdot', 'solid'])
Out[6]: [None, None, None]
These parameters can also be assigned when the Axes method vlines is called. The Axes method hlines is consistent:
In [7]: y = [-4, -3, 3, 4]
: xmin = -2 * np.pi
: xmax = 2 * np.pi
In [7]: ax.hlines(y, xmin, xmax, colors=['darkviolet', 'dodgerblue', 'dodgerblue', 'darkviolet'],
: linewidths=[3, 2, 2, 3], linestyles=['solid', 'dotted', 'dotted', 'solid'])
Out[7]: <matplotlib.collections.LineCollection at 0x2458df03dd0>
Previously the Axes method plot was examined which returned a list of Line2D instances, there are a number of related plots that are based on this method but make slight modifications:
| Axes plotting method | Description |
|---|---|
plot |
plots a list of Line2D instances |
errorbar |
plots a list of Line2D instances and a LineCollection of errorbars |
plot_date |
plots a list of Line2D instances, x data is an array of dates |
step |
Same as plot but the parameter drawstyle='step' by default |
semilogx |
Same as plot but Axes.set_xscale('log') by default |
semilogy |
Same as plot but Axes.set_yscale('log') by default |
loglogx |
Same as plot but Axes.set_xscale('log') and Axes.set_yscale('log') by default |
There are also some additional Axes methods for plotting a straight line along the Axes:
| Axes plotting method | Description |
|---|---|
axvline |
plots a vertical line across an Axes returning a Line2D instance |
axhline |
plots a horizontal line across an Axes returning a Line2D instance |
In [8]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.linspace(-4*np.pi, 4*np.pi, 100)
: y1 = 3 * np.sin(x)
: y2 = 4 * np.cos(x)
:
: fig, ax = plt.subplots()
: ax.plot(x, y1, color='seagreen')
: ax.plot(x, y2, color='royalblue');
When the parameters of the Axes method axhline are left at their defaults, a horizontal line through the origin is drawn:
In [2]: ax.axhline()
Out[2]: <matplotlib.lines.Line2D at 0x26777512bd0>
Likewise when the parameters of the Axes method axhline are left at their defaults, a horizontal line through the origin is drawn:
In [3]: ax.axvline()
Out[3]: <matplotlib.lines.Line2D at 0x267774ce690>
Optional parameters can be specified. For more details the documentation can be examined:
In [4]: ax.axhline?
Signature: ax.axhline(y=0, xmin=0, xmax=1, **kwargs)
Docstring:
Add a horizontal line across the Axes.
Parameters
----------
y : float, default: 0
y position in data coordinates of the horizontal line.
xmin : float, default: 0
Should be between 0 and 1, 0 being the far left of the plot, 1 the
far right of the plot.
xmax : float, default: 1
Should be between 0 and 1, 0 being the far left of the plot, 1 the
far right of the plot.Note that y is in data co-ordinates, whereas xmin and xmax are in normalised Axes co-ordinates. The remaining paramters are consistent to those seen in the Axes method plot.
In [5]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: linestyles = ['solid', 'dotted', 'dashed', 'dashdot']
:
: fig, ax = plt.subplots()
: for index, linestyle in enumerate(linestyles):
: ax.axhline(y=-index, linestyle=linestyle, label=linestyle)
:
: legend = fig.legend()
: legend.set_frame_on(True)
: legend.get_frame().set_alpha(1.0)
Additional linestyles can be specified using a tuple of the form (m_pt_offset, (n_pt_on, o_pt_off, p_pt_on, ...)):
In [5]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: linestyle_dict = {
: 'loosely dotted': (0, (1, 10)),
: 'dotted': (0, (1, 1)),
: 'densely dotted': (0, (1, 1)),
: 'long dash with offset': (5, (10, 3)),
: 'loosely dashed': (0, (5, 10)),
: 'dashed': (0, (5, 5)),
: 'densely dashed': (0, (5, 1)),
: 'loosely dashdotted': (0, (3, 10, 1, 10)),
: 'dashdotted': (0, (3, 5, 1, 5)),
: 'densely dashdotted': (0, (3, 1, 1, 1)),
: 'dashdotdotted': (0, (3, 5, 1, 5, 1, 5)),
: 'loosely dashdotdotted': (0, (3, 10, 1, 10, 1, 10)),
: 'densely dashdotdotted': (0, (3, 1, 1, 1, 1, 1)),
: }
:
: fig, ax = plt.subplots()
: for index, linestyle in enumerate(linestyle_dict):
: ax.axhline(y=-index, linestyle=linestyle_dict[linestyle],
: label=f'{linestyle}, {linestyle_dict[linestyle]}')
:
: legend = fig.legend()
: legend.set_frame_on(True)
: legend.get_frame().set_alpha(1.0)
The Axes method bar can be used to create a bar graph:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots()
:
: x = np.array([1, 2, 3, 4, 5])
: heights = np.array([1, 3, 3, 4, 1])
: width = 0.5
In [2]: ax.bar(x, heights, width)
Out[2]: <BarContainer object of 5 artists>
The BarContainer instance is the last element in the Axes list attribute containers:
In [3]: bar_container = ax.containers[0]The properties of the BarContainer instance can be examined:
In [4]: plt.getp(bar_container)
agg_filter = None
alpha = None
angle = 0.0
animated = False
antialiased or aa = True
bbox = Bbox(x0=0.75, y0=0.0, x1=1.25, y1=1.0)
capstyle = butt
center = [1. 0.5]
children = []
clip_box = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=1.0, ...
clip_on = True
clip_path = None
corners = [[0.75 0. ] [1.25 0. ] [1.25 1. ] [0.75 1. ...
data_transform = CompositeGenericTransform( TransformWrapper( ...
edgecolor or ec = (0.0, 0.0, 0.0, 0.0)
extents = Bbox(x0=102.54545454545453, y0=52.8, x1=152.646464...
facecolor or fc = (0.12156862745098039, 0.4666666666666667, 0.705882...
figure = Figure(640x480)
fill = True
gid = None
hatch = None
height = 1
in_layout = True
joinstyle = miter
label = _nolegend_
linestyle or ls = solid
linewidth or lw = 1.0
mouseover = False
patch_transform = CompositeGenericTransform( BboxTransformTo( ...
path = Path(array([[0., 0.], [1., 0.], [1.,...
path_effects = []
picker = None
rasterized = False
sketch_params = None
snap = None
tightbbox = Bbox(x0=102.54545454545453, y0=52.8, x1=152.646464...
transform = CompositeGenericTransform( CompositeGenericTra...
transformed_clip_path_and_affine = (None, None)
url = None
verts = [[102.54545455 52.8 ] [152.64646465 52.8 ...
visible = True
width = 0.5
window_extent = Bbox(x0=102.54545454545453, y0=52.8, x1=152.646464...
x = 0.75
xy = (np.float64(0.75), np.int64(0))
y = 0
zorder = 1In a bar graph, the width is normally constant, however it is possible for each width to be assigned to a unique value:
In [5]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(2, 1)
:
: x = np.array([1, 2, 3, 4, 5])
: heights = np.array([1, 3, 3, 4, 1])
: width = 0.5
: widths = np.array([0.2, 0.3, 0.4, 0.5, 0.6])
:
: ax[0].bar(x, heights, width)
: ax[1].bar(x, heights, widths);
It is possible to create a stacked bar graph using the optional positional input argument bottom:
In [6]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: width = 0.8
: x1 = np.array([1, 2, 3, 4, 5])
: height1 = np.array([1, 3, 3, 4, 1])
:
: x2 = x1
: height2 = np.array([5, 1, 1, 2, 7])
: bottom2 = height1
:
: x3 = x1
: height3 = np.array([3, 3, 3, 3, 3])
: bottom3 = bottom2 + height2
:
: fig, ax = plt.subplots(1, 1)
: ax.bar(x1, height1)
: ax.bar(x2, height2, width, bottom2)
: ax.bar(x3, height3, width, bottom3);
A side by side bar graph can be created by offsetting x by the width:
In [7]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: width = 0.2
: x1 = np.array([1, 2, 3, 4, 5])
: height1 = np.array([1, 3, 3, 4, 1])
: x2 = x1 + width
: height2 = np.array([5, 1, 1, 2, 7])
: x3 = x2 + width
: height3 = np.array([3, 3, 3, 3, 3])
:
: fig, ax = plt.subplots(1, 1)
: ax.bar(x1, height1, width)
: ax.bar(x2, height2, width)
: ax.bar(x3, height3, width)
:
: ax.set_xlabel('x')
: ax.set_ylabel('height');
The tick locations and tick labels can be examined using the XAxis methods get_ticklocs and get_ticklabels:
In [8]: ax.xaxis.get_ticklocs()
Out[8]: array([0., 1., 2., 3., 4., 5., 6.])
In [9]: ax.xaxis.get_ticklabels()
Out[9]:
[Text(0.0, 0, '0'),
Text(1.0, 0, '1'),
Text(2.0, 0, '2'),
Text(3.0, 0, '3'),
Text(4.0, 0, '4'),
Text(5.0, 0, '5'),
Text(6.0, 0, '6')]The tick locations can be set to the position of the central bar using the XAxis method set_ticks:
In [10]: ax.xaxis.set_ticks(x2);
The tick labels can be set using the Axes method set_ticklabels and a list comprehension can be used to make the labels:
In [11]: ax.xaxis.set_ticklabels([f'group{index}' for index in range(1, 6)]);
Returning to a simple bar graph and assigning the BarContainer instance to bar_container:
In [12]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: width = 0.8
: x = np.array([1, 2, 3, 4, 5])
: height = np.array([1, 3, 3, 4, 1])
:
: fig, ax = plt.subplots(1, 1)
: bar_container = ax.bar(x, height, width)
The identifiers for the BarContainer instance can be examined:
In [13]: bar_container.
# -----------------------------------------
# Available Identifiers for BarContainer:
# -----------------------------------------
# 📦 Components and Attributes:
# - patches : List of `matplotlib.patches.Rectangle` instances,
# : representing the individual bars in the container.
# - errorbar : `None` or an `ErrorbarContainer` if error bars
# : are present for the bars.
# 🎨 Appearance and Style:
# - set_alpha : Sets the transparency (alpha) level for the container.
# - get_alpha : Retrieves the transparency (alpha) level for the container.
# - set_label : Sets the label for the container.
# - get_label : Returns the label of the container.
# 🛠️ Customization:
# - set_transform : Sets a transformation for the bar container.
# - get_transform : Retrieves the transformation applied to the container.
# 📏 Clipping and Bounds:
# - set_clip_box : Sets the clipping box for the container, defining
# : the area within which the bars are displayed.
# - get_clip_box : Retrieves the clipping box applied to the container.
# - set_clip_path : Sets a clipping path for the container.
# - get_clip_path : Retrieves the clipping path applied to the container.
# 🔧 Visibility and Drawing:
# - set_visible : Sets the visibility of the container.
# - get_visible : Checks whether the container is currently visible.
# - draw : Draws the container onto a given renderer. Primarily
# : used internally by Matplotlib.
# 🗑️ Removing and Updating:
# - remove : Removes the container from its parent `Axes` or `Figure`.
# - update : Updates the properties of the container.
# 🔧 General Methods:
# - get_children : Returns a list of all child Artists in the container.
# - contains : Checks whether a given point is within the container's boundaries.
# - set_picker : Sets the picker function or tolerance for the container.
# - get_picker : Retrieves the current picker function or tolerance.
# - set_zorder : Sets the z-order for the container, determining its drawing priority.
# - get_zorder : Retrieves the current z-order of the container.The attribute patches is a list of Rectangle patches:
In [13]: bar_container.patches
Out[13]:
[<matplotlib.patches.Rectangle at 0x26a5d739250>,
<matplotlib.patches.Rectangle at 0x26a59abf010>,
<matplotlib.patches.Rectangle at 0x26a5d6b1b90>,
<matplotlib.patches.Rectangle at 0x26a5d73a410>,
<matplotlib.patches.Rectangle at 0x26a5d73a990>]One of these Rectangle instances can be assigned to a rectangle:
In [14]: rectangle = bar_container.patches[0]The Rectangle instances identifiers can be examined:
In [15]: rectangle.
# ---------------------------------------
# Available Identifiers for Rectangle:
# ---------------------------------------
# 🔧 General Attributes:
# - get_x : Retrieves the x-coordinate of the rectangle's lower-left corner.
# - set_x : Sets the x-coordinate of the rectangle's lower-left corner.
# - get_y : Retrieves the y-coordinate of the rectangle's lower-left corner.
# - set_y : Sets the y-coordinate of the rectangle's lower-left corner.
# - get_width : Returns the width of the rectangle.
# - set_width : Sets the width of the rectangle.
# - get_height : Returns the height of the rectangle.
# - set_height : Sets the height of the rectangle.
# - get_bbox : Retrieves the bounding box of the rectangle as a `Bbox` object.
# - set_bounds : Sets the position and size of the rectangle as `(x, y, width, height)`.
# 🎨 Appearance and Style:
# - set_edgecolor : Sets the color of the rectangle's edge.
# - get_edgecolor : Retrieves the color of the rectangle's edge.
# - set_facecolor : Sets the color of the rectangle's face.
# - get_facecolor : Retrieves the color of the rectangle's face.
# - set_linewidth : Sets the width of the rectangle's edge line.
# - get_linewidth : Retrieves the width of the rectangle's edge line.
# - set_hatch : Sets the hatching pattern for the rectangle's face.
# - get_hatch : Retrieves the hatching pattern for the rectangle's face.
# - set_fill : Enables or disables filling of the rectangle's face.
# - get_fill : Checks whether the rectangle's face is filled.
# 🔧 Transformation and Positioning:
# - set_transform : Sets a transformation for the rectangle.
# - get_transform : Retrieves the transformation applied to the rectangle.
# - get_path : Returns the path defining the rectangle as a `Path` object.
# - get_verts : Retrieves the vertices of the rectangle as a list of `(x, y)` pairs.
# 🔧 Alpha and Visibility:
# - set_alpha : Sets the transparency (alpha) level for the rectangle.
# - get_alpha : Retrieves the transparency (alpha) level for the rectangle.
# - set_visible : Sets the visibility of the rectangle.
# - get_visible : Checks whether the rectangle is currently visible.
# 📏 Clipping and Bounds:
# - set_clip_box : Sets the clipping box for the rectangle.
# - get_clip_box : Retrieves the clipping box applied to the rectangle.
# - set_clip_path : Sets a clipping path for the rectangle.
# - get_clip_path : Retrieves the clipping path applied to the rectangle.
# 🗑️ Removing and Updating:
# - remove : Removes the rectangle from its parent `Axes` or `Figure`.
# - update : Updates the rectangle's properties based on input.
# 🛠️ Customization:
# - set_joinstyle : Sets the join style for the rectangle's edges (e.g., miter, round, bevel).
# - get_joinstyle : Retrieves the join style for the rectangle's edges.
# - set_capstyle : Sets the cap style for the rectangle's edges (e.g., butt, round, projecting).
# - get_capstyle : Retrieves the cap style for the rectangle's edges.
# - set_antialiased : Enables or disables anti-aliasing for the rectangle's edges.
# - get_antialiased : Checks whether anti-aliasing is enabled for the rectangle.
# 🔧 Other Methods:
# - draw : Draws the rectangle onto a given renderer. Primarily used internally.
# - set_picker : Sets the picker function or tolerance for the rectangle.
# - get_picker : Retrieves the current picker function or tolerance.
# - contains : Checks whether a given point is within the rectangle's boundaries.
# - set_zorder : Sets the z-order for the rectangle, determining its drawing priority.
# - get_zorder : Retrieves the current z-order of the rectangle.The properties of the Rectangle instance can also be examined:
In [16]: plt.getp(rectangle)
agg_filter = None
alpha = None
angle = 0.0
animated = False
antialiased or aa = True
bbox = Bbox(x0=0.6, y0=0.0, x1=1.4, y1=1.0)
capstyle = butt
center = [1. 0.5]
children = []
clip_box = TransformedBbox( Bbox(x0=0.0, y0=0.0, x1=1.0, ...
clip_on = True
clip_path = None
corners = [[0.6 0. ] [1.4 0. ] [1.4 1. ] [0.6 1. ]]
data_transform = CompositeGenericTransform( TransformWrapper( ...
edgecolor or ec = (0.0, 0.0, 0.0, 0.0)
extents = Bbox(x0=102.54545454545455, y0=52.8, x1=177.696969...
facecolor or fc = (0.12156862745098039, 0.4666666666666667, 0.705882...
figure = Figure(640x480)
fill = True
gid = None
hatch = None
height = 1
in_layout = True
joinstyle = miter
label = _nolegend_
linestyle or ls = solid
linewidth or lw = 1.0
mouseover = False
patch_transform = CompositeGenericTransform( BboxTransformTo( ...
path = Path(array([[0., 0.], [1., 0.], [1.,...
path_effects = []
picker = None
rasterized = False
sketch_params = None
snap = None
tightbbox = Bbox(x0=102.54545454545455, y0=52.8, x1=177.696969...
transform = CompositeGenericTransform( CompositeGenericTra...
transformed_clip_path_and_affine = (None, None)
url = None
verts = [[102.54545455 52.8 ] [177.6969697 52.8 ...
visible = True
width = 0.8
window_extent = Bbox(x0=102.54545454545455, y0=52.8, x1=177.696969...
x = 0.6
xy = (np.float64(0.6), np.int64(0))
y = 0
zorder = 1At current there is no publicly accessible dict in matplotlib.hatches of the form:
In [17]: hatches = {'/': 'diagonalhatch',
: '\\': 'backwarddiagonalhatch',
: '|': 'verticalhatch',
: '-': 'horizontalhatch',
: '+': 'crosshatch',
: 'x': 'crossedhatch',
: 'o': 'smallcirclehatch',
: 'O': 'largecirclehatch',
: '.': 'dotshatch',
: '*': 'starshatch'}Notice that each key is a single character, with the exception to '\\' becuase the \ is a special character in a Python str which gives an instruction to insert an escape character, the escape character to be inserted in this case is the \ itself.
The following plot can be created, the BarContainer can be looped through to customise each Rectangle using set properties:
In [18]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: width = 0.8
: x = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
: height = np.array([4, 3, 3, 4, 3, 2, 2, 5, 5, 3])
:
: hatches = {'/': 'diagonalhatch',
: '\\': 'backwarddiagonalhatch',
: '|': 'verticalhatch',
: '-': 'horizontalhatch',
: '+': 'crosshatch',
: 'x': 'crossedhatch',
: 'o': 'smallcirclehatch',
: 'O': 'largecirclehatch',
: '.': 'dotshatch',
: '*': 'starshatch'}
:
: hatch_styles = list(hatches)
: linewidths = [num / 2 for num in range(1, 11)]
: colors = ['tomato', 'seagreen', 'royalblue', 'cyan', 'fuchsia',
: 'gold', 'palegreen', 'darkviolet', 'hotpink', 'darkorange']
:
: fig, ax = plt.subplots()
: bar_container = ax.bar(x, height, width)
:
: for index, rectangle in enumerate(bar_container):
: plt.setp(rectangle, facecolor=colors[index], edgecolor='black', linewidth=linewidths[index],
: hatch=hatch_styles[index])
If the hatch srtyle is replicated using an int it becomes more densely packed:
In [19]: for index, rectangle in enumerate(bar_container):
: plt.setp(rectangle, facecolor=colors[index], edgecolor='black', linewidth=linewidths[index],
: hatch=2*hatch_styles[index])
In [20]: for index, rectangle in enumerate(bar_container):
: plt.setp(rectangle, facecolor=colors[index], edgecolor='black', linewidth=linewidths[index],
: hatch=3*hatch_styles[index])
The Axes method bar has a complementary method barh which can be used to create a horizontal bar graph:
In [21]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: height = 0.2
: y1 = np.array([1, 2, 3, 4, 5])
: width1 = np.array([1, 3, 3, 4, 1])
: y2 = y1 + height
: width2 = np.array([5, 1, 1, 2, 7])
: y3 = y2 + height
: width3 = np.array([3, 3, 3, 3, 3])
:
: fig, ax = plt.subplots(1, 1)
: ax.barh(y1, width1, height, facecolor='seagreen', edgecolor='black', linewidth=1, hatch='x')
: ax.barh(y2, width2, height, facecolor='royalblue', edgecolor='black', linewidth=1, hatch='.')
: ax.barh(y3, width3, height, facecolor='tomato', edgecolor='black', linewidth=1, hatch='+')
:
: ax.set_xlabel('width')
: ax.set_ylabel('y')
:
: ax.yaxis.set_ticks(y2)
: ax.yaxis.set_ticklabels([f'group{index}' for index in range(1, 6)]);
If the following bar graph is examined:
In [23]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 2, 3, 4, 5])
: heights = np.array([1, 3, 3, 4, 1])
: fig, ax = plt.subplots()
: ax.bar(x, heights);
The bars can also be shown visually using a pie chart. A pie chart can be created using the Axes method pie:
In [2]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 3, 3, 4, 1])
: fig, ax = plt.subplots()
: ax.pie(x)
Out[2]:
([<matplotlib.patches.Wedge at 0x27e0faa65d0>,
<matplotlib.patches.Wedge at 0x27e10397590>,
<matplotlib.patches.Wedge at 0x27e0fa9a3d0>,
<matplotlib.patches.Wedge at 0x27e10386210>,
<matplotlib.patches.Wedge at 0x27e102fa350>],
[Text(1.062518406843863, 0.2847009573534656, ''),
Text(0.28470084757827546, 1.062518436258031, ''),
Text(-1.0625185165806301, 0.2847005478099705, ''),
Text(5.376382425237028e-07, -1.0999999999998686, ''),
Text(1.0625186093477872, -0.2847002015974779, '')])
Notice that the return value is a 2 element tuple of 2 equally sized list instances. The first list contains each Wedge in the pie chart and the second list contains each Text instance associated with the wedge. Each of these are added to the Axes attributes patches and texts:
In [2]: ax.patches
Out[2]: <Axes.ArtistList of 5 patches>
In [3]: ax.texts
Out[3]: <Axes.ArtistList of 5 texts>The Axes method pie has a number of optional parameters:
Out[4]: ax.pie?
Signature:
ax.pie(
x,
explode=None,
labels=None,
colors=None,
autopct=None,
pctdistance=0.6,
shadow=False,
labeldistance=1.1,
startangle=0,
radius=1,
counterclock=True,
wedgeprops=None,
textprops=None,
center=(0, 0),
frame=False,
rotatelabels=False,
*,
normalize=True,
hatch=None,
data=None,
)The following parameters colors and hatch (singular but can be assigned to plural values) can be used:
In [2]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 3, 3, 4, 1])
: colors = ['tomato', 'seagreen', 'royalblue', 'cyan', 'fuchsia']
: hatches = ['/', '\\', '|', '-', '+']
: fig, ax = plt.subplots()
: ax.pie(x, colors=colors, hatch=hatches)
Out[2]:
([<matplotlib.patches.Wedge at 0x27e0faa65d0>,
<matplotlib.patches.Wedge at 0x27e10397590>,
<matplotlib.patches.Wedge at 0x27e0fa9a3d0>,
<matplotlib.patches.Wedge at 0x27e10386210>,
<matplotlib.patches.Wedge at 0x27e102fa350>],
[Text(1.062518406843863, 0.2847009573534656, ''),
Text(0.28470084757827546, 1.062518436258031, ''),
Text(-1.0625185165806301, 0.2847005478099705, ''),
Text(5.376382425237028e-07, -1.0999999999998686, ''),
Text(1.0625186093477872, -0.2847002015974779, '')])
A wedge can be emphasised using the explode parameter. The default explode value is 0 for each wedge but can be assigned to a normalised float which essentially pulls the wedge out from the center. The parameters labels is used to assign text to the Text instances associated with each Wedge. wedgeprops is a dict instance that is used to compartmentalise parameters that are assigned to all of the Wedge instances and textprops is another dict instance that is used to compartmentalise parameters that assigned to all of the Text instances:
In [5]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 3, 3, 4, 1])
: explode = np.array([0, 0, 0, 0.2, 0])
: labels = ['tomato', 'seagreen', 'royalblue', 'cyan', 'fuchsia']
: colors = ['tomato', 'seagreen', 'royalblue', 'cyan', 'fuchsia']
: hatches = ['/', '\\', '|', '-', '+']
: fig, ax = plt.subplots()
: ax.pie(x, explode=explode, labels=labels, colors=colors, hatch=hatches,
: wedgeprops=dict(edgecolor='black', linewidth=2),
: textprops=dict(fontsize=14, color='deepskyblue', weight='bold'))
Out[5]:
([<matplotlib.patches.Wedge at 0x27e111f9190>,
<matplotlib.patches.Wedge at 0x27e112aafd0>,
<matplotlib.patches.Wedge at 0x27e1364c090>,
<matplotlib.patches.Wedge at 0x27e14e5ce10>,
<matplotlib.patches.Wedge at 0x27e135f4410>],
[Text(1.062518406843863, 0.2847009573534656, 'tomato'),
Text(0.28470084757827546, 1.062518436258031, 'seagreen'),
Text(-1.0625185165806301, 0.2847005478099705, 'royalblue'),
Text(6.353906502552851e-07, -1.2999999999998448, 'cyan'),
Text(1.0625186093477872, -0.2847002015974779, 'fuchsia')])
The parameter radius species the radius to draw each Wedge from. By default each Wedge will be drawn from the radius to the origin. However wedgeprops can be assigned a width and the Wedge will be drawn from a radius towards the origin stopping at the specified width creating a donut:
In [6]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([1, 3, 3, 4, 1])
: colors = ['tomato', 'seagreen', 'royalblue', 'cyan', 'fuchsia']
: hatches = ['/', '\\', '|', '-', '+']
: fig, ax = plt.subplots()
: ax.pie(x, colors=colors, hatch=hatches,
: radius=1,
: wedgeprops=dict(edgecolor='black', linewidth=2, width=0.5))
Out[6]:
([<matplotlib.patches.Wedge at 0x184234693d0>,
<matplotlib.patches.Wedge at 0x18423a38e50>,
<matplotlib.patches.Wedge at 0x18423a3a010>,
<matplotlib.patches.Wedge at 0x18423a3b250>,
<matplotlib.patches.Wedge at 0x18423a486d0>],
[Text(1.062518406843863, 0.2847009573534656, ''),
Text(0.28470084757827546, 1.062518436258031, ''),
Text(-1.0625185165806301, 0.2847005478099705, ''),
Text(5.376382425237028e-07, -1.0999999999998686, ''),
Text(1.0625186093477872, -0.2847002015974779, '')])
The Axes method hist can be used to create a histogram plot. A histogram plot is essentially a bar graph and therefore the Axes method hist and bar have many consistent parameters. The input argument x is the data to be histogrammed. bins is an int used to specify the number of bins to be created:
In [7]: ax.hist?
Signature:
ax.hist(
x,
bins=None,
range=None,
density=False,
weights=None,
cumulative=False,
bottom=None,
histtype='bar',
align='mid',
orientation='vertical',
rwidth=None,
log=False,
color=None,
label=None,
stacked=False,
*,
data=None,
**kwargs,
)The third parameter range has the same identifier name as the builtins class range but in this case is a tuple of the form (start, stop). As the syntax highlighting gets confused, the first three parameters are provided positionally:
In [8]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0.4, 1.2, 1.3, 2.3, 2.3, 2.4, 3.5, 3.4, 4.1, 4.2,
: 4.7, 5.1, 5.1, 5.5, 5.7, 5.7, 5.8, 5.9, 5.9, 5.9,
: 6.1, 6.1, 6.1, 6.7, 6.2, 7.3, 7.4, 7.4, 7.4, 7.4,
: 8.8, 8.8, 8.8, 8.8, 8.8, 9.2, 9.2, 9.2, 9.2, 9.9])
:
: bins = 10
: start = 0
: stop = 10
:
: fig, ax = plt.subplots()
: ax.grid()
: ax.minorticks_on()
: ax.grid(which='minor', linestyle=':')
: ax.set_xlabel('bins')
: ax.set_ylabel('frequency')
:
: ax.hist(x, bins, (start, stop), facecolor='seagreen', edgecolor='black', hatch='oo')
Out[8]:
(array([1., 2., 3., 2., 3., 9., 5., 5., 5., 5.]),
array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]),
<BarContainer object of 10 artists>)
The return value is a 3 element tuple. Notice the first element is the height of each of the 10 bars (bins=10). The second element is the boundary of each bar, as there are 10 bars, and each bar has a lower and upper boundary, there are 10+1 boundaries. The third element is the BarContainer, which can be found under the Axes attribute containers. Note each bar in the BarContainer is a Rectangle:
In [9]: ax.containers
Out[9]: [<BarContainer object of 10 artists>]
In [10]: ax.containers[-1]
Out[10]: <BarContainer object of 10 artists>
In [11]: ax.containers[-1][0]
Out[11]: <matplotlib.patches.Rectangle at 0x27545d4c6d0>Since each bin is a unit in the bar plot above, the data can be physically counted. For a bar graph, the number of bins can be decreased or increased. When the number of bins is too small, the distribution may be blurred. When the number of bins is too large, the general trend may be obscured overemphasising noise:
In [12]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0.4, 1.2, 1.3, 2.3, 2.3, 2.4, 3.5, 3.4, 4.1, 4.2,
: 4.7, 5.1, 5.1, 5.5, 5.7, 5.7, 5.8, 5.9, 5.9, 5.9,
: 6.1, 6.1, 6.1, 6.7, 6.2, 7.3, 7.4, 7.4, 7.4, 7.4,
: 8.8, 8.8, 8.8, 8.8, 8.8, 9.2, 9.2, 9.2, 9.2, 9.9])
:
: bins = (5, 10, 50)
: start = 0
: stop = 10
: colors = ('tomato', 'seagreen', 'royalblue')
: hatches = ('xx', 'oo', '**')
:
: fix, ax = plt.subplots(3, 1)
:
: for index in range(3):
: ax[index].hist(x, bins[index], (start, stop),
: facecolor=colors[index], edgecolor='black', hatch=hatches[index])
: ax[index].set_xlabel('x')
: ax[index].set_ylabel('y')
: ax[index].grid()
: ax[index].minorticks_on()
: ax[index].grid(which='minor', linewidth=0.5, linestyle=':')
The x data above can be examined using statistical functions in np:
In [13]: import statistics
: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0.4, 1.2, 1.3, 2.3, 2.3, 2.4, 3.5, 3.4, 4.1, 4.2,
: 4.7, 5.1, 5.1, 5.5, 5.7, 5.7, 5.8, 5.9, 5.9, 5.9,
: 6.1, 6.1, 6.1, 6.7, 6.2, 7.3, 7.4, 7.4, 7.4, 7.4,
: 8.8, 8.8, 8.8, 8.8, 8.8, 9.2, 9.2, 9.2, 9.2, 9.9])The ndarray methods min and max can be used to calculate the minimum and maximum values:
In [14]: x.min()
Out[14]: np.float64(0.4)
In [15]: x.max()
Out[15]: np.float64(9.9)The median function can be used to calculate the median:
In [16]: np.median(x)
Out[16]: np.float64(6.0)The quantile function can be used to calculate the 25 % quantiles:
In [17]: np.quantile(x, [0, 0.25, 0.50, 0.75, 1.0])
Out[17]: array([0.4 , 4.575, 6. , 7.75 , 9.9 ])The Axes method boxplot essentially displays these quantiles:
In [18]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0.4, 1.2, 1.3, 2.3, 2.3, 2.4, 3.5, 3.4, 4.1, 4.2,
: 4.7, 5.1, 5.1, 5.5, 5.7, 5.7, 5.8, 5.9, 5.9, 5.9,
: 6.1, 6.1, 6.1, 6.7, 6.2, 7.3, 7.4, 7.4, 7.4, 7.4,
: 8.8, 8.8, 8.8, 8.8, 8.8, 9.2, 9.2, 9.2, 9.2, 9.9])
:
: fig, ax = plt.subplots()
: ax.grid()
: ax.minorticks_on()
: ax.grid(which='minor', linestyle=':')
: ax.boxplot(x)
Out[18]:
{'whiskers': [<matplotlib.lines.Line2D at 0x1c256b008d0>,
<matplotlib.lines.Line2D at 0x1c256af8c10>],
'caps': [<matplotlib.lines.Line2D at 0x1c256b01950>,
<matplotlib.lines.Line2D at 0x1c256b021d0>],
'boxes': [<matplotlib.lines.Line2D at 0x1c256af9090>],
'medians': [<matplotlib.lines.Line2D at 0x1c256b02ad0>],
'fliers': [<matplotlib.lines.Line2D at 0x1c256b033d0>],
'means': []}
The boxplot is constructed using a number of Line2D instances. Notice that the return value is a dict which gives a key to each Line2D value.
Multiple distributions can be compared:
In [19]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0.4, 1.2, 1.3, 2.3, 2.3, 2.4, 3.5, 3.4, 4.1, 4.2,
: 4.7, 5.1, 5.1, 5.5, 5.7, 5.7, 5.8, 5.9, 5.9, 5.9,
: 6.1, 6.1, 6.1, 6.7, 6.2, 7.3, 7.4, 7.4, 7.4, 7.4,
: 8.8, 8.8, 8.8, 8.8, 8.8, 9.2, 9.2, 9.2, 9.2, 9.9])
:
: fig, ax = plt.subplots()
: ax.grid()
: ax.minorticks_on()
: ax.grid(which='minor', linestyle=':')
: ax.boxplot([x, x+1, x+2])
Out[19]:
{'whiskers': [<matplotlib.lines.Line2D at 0x1c258492f10>,
<matplotlib.lines.Line2D at 0x1c2584939d0>,
<matplotlib.lines.Line2D at 0x1c2584a2cd0>,
<matplotlib.lines.Line2D at 0x1c2584a3550>,
<matplotlib.lines.Line2D at 0x1c2584aa710>,
<matplotlib.lines.Line2D at 0x1c2584aaf90>],
'caps': [<matplotlib.lines.Line2D at 0x1c2584a0350>,
<matplotlib.lines.Line2D at 0x1c2584a0c50>,
<matplotlib.lines.Line2D at 0x1c2584a3c90>,
<matplotlib.lines.Line2D at 0x1c2584a8610>,
<matplotlib.lines.Line2D at 0x1c2584ab8d0>,
<matplotlib.lines.Line2D at 0x1c2584b81d0>],
'boxes': [<matplotlib.lines.Line2D at 0x1c258492490>,
<matplotlib.lines.Line2D at 0x1c2584a24d0>,
<matplotlib.lines.Line2D at 0x1c2584a9f10>],
'medians': [<matplotlib.lines.Line2D at 0x1c2584a1490>,
<matplotlib.lines.Line2D at 0x1c2584a8c10>,
<matplotlib.lines.Line2D at 0x1c2584b8990>],
'fliers': [<matplotlib.lines.Line2D at 0x1c2584a1d10>,
<matplotlib.lines.Line2D at 0x1c2584a9610>,
<matplotlib.lines.Line2D at 0x1c2584b92d0>],
'means': []}
A violin plot is similar but instead of a box, gives a kernel density estimation which gives more details about the shape of the distribution. The Axes method violinplot can be used to create a violin plot:
In [20]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0.4, 1.2, 1.3, 2.3, 2.3, 2.4, 3.5, 3.4, 4.1, 4.2,
: 4.7, 5.1, 5.1, 5.5, 5.7, 5.7, 5.8, 5.9, 5.9, 5.9,
: 6.1, 6.1, 6.1, 6.7, 6.2, 7.3, 7.4, 7.4, 7.4, 7.4,
: 8.8, 8.8, 8.8, 8.8, 8.8, 9.2, 9.2, 9.2, 9.2, 9.9])
:
: dataset = [x, x+1, x+2]
: fig, ax = plt.subplots()
: ax.grid()
: ax.minorticks_on()
: ax.grid(which='minor', linestyle=':')
: ax.violinplot(dataset)
Out[20]:
{'bodies': [<matplotlib.collections.PolyCollection at 0x1c25855be90>,
<matplotlib.collections.PolyCollection at 0x1c258817090>,
<matplotlib.collections.PolyCollection at 0x1c258824490>],
'cmaxes': <matplotlib.collections.LineCollection at 0x1c2582bbcd0>,
'cmins': <matplotlib.collections.LineCollection at 0x1c258826e10>,
'cbars': <matplotlib.collections.LineCollection at 0x1c258834650>}
The Axes method hist2d can be used to create a 2 dimensional histogram plot. Instead of using a rectangle to represent each value, a square is used to represent each value in 2 dimensional space. Instead of a bar height to denote the occurance of a value, a color is used:
In [21]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([1, 1, 1, 1, 1,
: 2, 2, 2,
: 3, 3, 3, 3])
: y = np.array([1, 2, 3, 3, 3,
: 2, 2, 3,
: 1, 1, 1, 1])
:
: bins = (3, 3)
:
: fig, ax = plt.subplots()
: ax.hist2d(x, y, bins)
Out[2]:
(array([[1., 1., 3.],
[0., 2., 1.],
[4., 0., 0.]]),
array([1. , 1.66666667, 2.33333333, 3. ]),
array([1. , 1.66666667, 2.33333333, 3. ]),
<matplotlib.collections.QuadMesh at 0x1f53747fe90>)
The Axes method hexbin can be used to create a hexbin plot, which uses hexagonal bins instead of square ones. This can provide a better representation of data density when the data points are not uniformly distributed because hexagonal shapes reduce visual bias compared to square bins:
In [3]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([1, 1, 1, 1, 1,
: 2, 2, 2,
: 3, 3, 3, 3])
: y = np.array([1, 2, 3, 3, 3,
: 2, 2, 3,
: 1, 1, 1, 1])
:
: gridside = 3
:
: fig, ax = plt.subplots()
: ax.hexbin(x, y, gridside=gridsize)
Out[2]: <matplotlib.collections.PolyCollection at 0x1f5388e6e50>
Supposing the following row and column ndarray are instantiated to xrow and ycol:
In [3]: exit
In [1]: import numpy as np
: xrow = np.linspace(-2, 2, 10)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 10)[:, np.newaxis]These display on the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| xrow | array of float64 | (1, 11) | [[-2. -1.6 -1.2 ... 1.2 1.6 2. ]] |
| ycol | array of float64 | (11, 1) | [[-2] [-1.6] [-1.2] |
And can be expanded. IDEs such as Spyder will display the data using a colormap which can be used to visualise changes in the underlying data:
| xrow - numpy float64 array | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Index ▲ | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| ycol - numpy float64 array | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Index ▲ | ||||||||||
| 0 | -2 | |||||||||
| 1 | -1.6 | |||||||||
| 2 | -1.2 | |||||||||
| 3 | -0.8 | |||||||||
| 4 | -0.4 | |||||||||
| 5 | 0 | |||||||||
| 6 | 0.4 | |||||||||
| 7 | 0.8 | |||||||||
| 8 | 1.2 | |||||||||
| 8 | 1.6 | |||||||||
| 8 | 2.0 | |||||||||
The meshgrid function can be used to project xrow for every row in ycol and to project ycol for every column in xrow returning a 2-element tuple of the matrices xmat and ymat:
In [2]: xmat, ymat = np.meshgrid(xrow, ycol)These can be viewed in the Variable Explorer:
| Variable Explorer | |||
|---|---|---|---|
| xmat | array of float64 | (11, 11) | [[-2. -1.6 -1.2 -0.8 -0.4 0. 0.4 0.8 1.2 1.6 2. ] [-2. -1.6 -1.2 -0.8 -0.4 0. 0.4 0.8 1.2 1.6 2. ] ... ] |
| ymat | array of float64 | (11, 11) | [[-2. -2. -2. -2. -2. -2. -2. -2. -2. -2. -2.] [-1.6 -1.6 -1.6 -1.6 -1.6 -1.6 -1.6 -1.6 -1.6 -1.6 -1.6] ... ] |
And can be expanded. IDEs such as Spyder will display the data using a colormap which can be used to visualise changes in the underlying data:
| xmat - numpy float64 array | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Index ▲ | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| 0 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | -2 | |
| 1 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | -1.6 | |
| 2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | -1.2 | |
| 3 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | -0.8 | |
| 4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | -0.4 | |
| 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| 6 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 | |
| 7 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | 0.8 | |
| 8 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | 1.2 | |
| 9 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | 1.6 | |
| 10 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | |
| ymat - numpy float64 array | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Index ▲ | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| 0 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 1 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 2 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 3 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 4 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 5 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 6 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 7 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 8 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 9 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
| 10 | -2 | -1.6 | -1.2 | -0.8 | -0.4 | 0 | 0.4 | 0.8 | 1.2 | 1.6 | 2.0 | |
A mathematical expression to generate zdata can be used in the form of a function:
In [3]: def fun(x, y):
: return x * np.exp(-x**2 - y**2)This can be called, supplying xmat and ymat to get zmat:
In [4]: zmat = fun(xmat, ymat)| zmat - numpy float64 array | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Index ▲ | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | |
| 0 | -0.0007 | -0.0022 | -0.0052 | -0.0077 | -0.0064 | 0.0000 | 0.0064 | 0.0077 | 0.0052 | 0.0023 | 0.0007 | |
| 1 | -0.0028 | -0.0096 | -0.0220 | -0.0326 | -0.0263 | 0.0000 | 0.0263 | 0.0326 | 0.0220 | 0.0096 | 0.0028 | |
| 2 | -0.0087 | -0.0293 | -0.0674 | -0.0999 | -0.0808 | 0.0000 | 0.0808 | 0.0999 | 0.0674 | 0.0293 | 0.0087 | |
| 3 | -0.0193 | -0.0652 | -0.1499 | -0.2224 | -0.1797 | 0.0000 | 0.1797 | 0.2224 | 0.1499 | 0.0652 | 0.0193 | |
| 4 | -0.0312 | -0.1054 | -0.2423 | -0.3595 | -0.2905 | 0.0000 | 0.2905 | 0.3595 | 0.2423 | 0.1054 | 0.0312 | |
| 5 | -0.0366 | -0.1237 | -0.2843 | -0.4218 | -0.3409 | 0.0000 | 0.3409 | 0.4218 | 0.2843 | 0.1237 | 0.0366 | |
| 6 | -0.0312 | -0.1054 | -0.2423 | -0.3595 | -0.2905 | 0.0000 | 0.2905 | 0.3595 | 0.2423 | 0.1054 | 0.0312 | |
| 7 | -0.0193 | -0.0652 | -0.1499 | -0.2224 | -0.1797 | 0.0000 | 0.1797 | 0.2224 | 0.1499 | 0.0652 | 0.0193 | |
| 8 | -0.0087 | -0.0292 | -0.0674 | -0.0999 | -0.0808 | 0.0000 | 0.0808 | 0.0999 | 0.0674 | 0.0292 | 0.0087 | |
| 9 | -0.0028 | -0.0906 | -0.0220 | -0.0326 | -0.0263 | 0.0000 | 0.0263 | 0.0326 | 0.0220 | 0.0906 | 0.0028 | |
| 10 | -0.0007 | -0.0023 | -0.0052 | -0.0077 | -0.0062 | 0.0000 | 0.0062 | 0.0077 | 0.0052 | 0.0023 | 0.0007 | |
More commonly this is done using a lambda expression, which recall is a way of defining the function on a single line:
In [3]: def fun(x, y):
: return x * np.exp(-x**2 - y**2)In [5]: fun = lambda x, y: x * np.exp(-x**2 - y**2)The lambda expression is often anonymous, therefore:
In [6]: lambda x, y: x * np.exp(-x**2 - y**2)
Out[6]: <function __main__.<lambda>(x, y)>And is often defined and called on a single line:
In [7]: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)Notice that xmat, ymat and zmat values are now in matrix form which is the form expected by many plotting functions.
Other plotting functions will however expect flattened arrays that are 1 dimensional. These can be obtained using the ndarray method flatten:
In [8]: xarray = xmat.flatten()
: yarray = ymat.flatten()
: zarray = zmat.flatten()| Variable Explorer | |||
|---|---|---|---|
| xarray | array of float64 | (121,) | [-2. -1.6 -1.2 ... 1.2 1.6 2. ] |
| yarray | array of float64 | (121,) | [-2. -2. -2. ... 2. 2. 2. ] |
| zarray | array of float64 | (121,) | [-0.0007 -0.002 -0.005 ... 0.005 0.002 0.0007] |
The Axes plotting method matshow can be used to visualise a matrix using a colormap, in a similar manner to the Variable Explorer in IDEs like Spyder:
In [9]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 11)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 11)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig, ax = plt.subplots()
: ax.set_xlabel('column')
: ax.xaxis.set_label_position('top')
: ax.set_ylabel('row')
: ax.matshow(zmat)
Out[1]: <matplotlib.image.AxesImage at 0x2017e5b2350>Notice that the x and y values display the pixel column and row respectively:
The return value is an AxesImages which is stored in the Axes attribute images:
In [2]: ax.images
Out[2]: <Axes.ArtistList of 1 images>This can be assigned to an instance name:
In [3]: ax_image = ax.images[0]The identifiers of the ax_image can be examined:
In [15]: ax_image.
# ---------------------------------------
# Available Identifiers for AxesImage:
# ---------------------------------------
# 🔧 General Attributes:
# - get_array : Retrieves the image data array.
# - get_cmap : Retrieves the colormap of the image.
# - get_norm : Retrieves the normalization instance of the image.
# - get_interpolation : Retrieves the interpolation method used for the image.
# - get_origin : Retrieves the origin setting for the image.
# - get_extent : Retrieves the bounding box for the image.
# 🎨 Appearance and Style:
# - set_array(array) : Updates the data array and redraws the image.
# - set_cmap(cmap) : Sets the colormap for the image.
# - set_norm(norm) : Sets the normalization for the image data.
# - set_interpolation(interpolation) : Sets the interpolation method for the image.
# - set_origin(origin) : Sets the origin for the image.
# - set_extent(extent) : Sets the bounding box for the image.
# 🔧 Color Mapping:
# - set_clim(vmin, vmax) : Sets the color limits for the image.
# 🔧 Other Methods:
# - set_data(data) : Sets the data for the image.
# - get_clim() : Retrieves the current color limits of the image.
# - contains(x, y) : Checks whether a point is within the image's display area.
# - set_picker(picker) : Sets the picker function or tolerance for the image.
# - get_picker() : Retrieves the current picker function or tolerance.
# - set_zorder(zorder) : Sets the z-order for the image, determining its drawing priority.
# - get_zorder() : Retrieves the current z-order of the image.
# 🗑️ Removing and Updating:
# - remove() : Removes the image from its parent `Axes` or `Figure`.
# - update() : Updates the image's properties based on input.
# 🔧 Transformation and Positioning:
# - set_transform(transform) : Sets a transformation for the image.
# - get_transform() : Retrieves the transformation applied to the image.
# - get_path() : Returns the path defining the image's bounding box as a `Path` object.The Figure method colorbar can be used to display a colorbar on the Figure. This method requires the AxesImage and Axes instances (if the Axes instance isn't supplied, it is implied from the AxesImage attribute axes):
In [4]: fig.colorbar(ax_image, ax=ax)
Out[4]: <matplotlib.colorbar.Colorbar at 0x2010575e650>
The Colorbar instance is not available as a Figure attribute, however it is a child of the Figure instance. In this example it is the last index and can be assigned to an instance name:
In [5]: fig.get_children()
Out[5]:
[<matplotlib.patches.Rectangle at 0x20104abcd10>,
<Axes: xlabel='column', ylabel='row'>,
<Axes: label='<colorbar>'>]
In [6]: cbar = fig.get_children()[-1]The identifiers of the cbar can be examined:
In [7]: cbar.
# ---------------------------------------
# Available Identifiers for Colorbar:
# ---------------------------------------
# 🔧 General Attributes:
# - get_clim : Retrieves the current color limits of the colorbar.
# - set_clim : Sets the color limits for the colorbar.
# - get_ticks : Retrieves the ticks used for the colorbar.
# - set_ticks : Sets the ticks for the colorbar.
# - get_ticklabels : Retrieves the tick labels for the colorbar.
# - set_ticklabels : Sets the tick labels for the colorbar.
# - get_orientation : Retrieves the orientation of the colorbar ('vertical' or 'horizontal').
# - set_orientation : Sets the orientation of the colorbar.
# - get_label : Retrieves the label of the colorbar.
# - set_label : Sets the label of the colorbar.
# 🎨 Appearance and Style:
# - set_alpha : Sets the transparency (alpha) level for the colorbar.
# - get_alpha : Retrieves the transparency (alpha) level for the colorbar.
# - set_cmap : Sets the colormap for the colorbar.
# - get_cmap : Retrieves the colormap used by the colorbar.
# - set_extend : Sets the extension type for the colorbar (e.g., 'neither', 'both', 'min', 'max').
# - get_extend : Retrieves the extension type for the colorbar.
# - set_format : Sets the format of the tick labels.
# - get_format : Retrieves the format of the tick labels.
# 🔧 Positioning and Size:
# - set_bounds : Sets the bounding box for the colorbar.
# - get_bounds : Retrieves the bounding box of the colorbar.
# 🔧 Updating and Redrawing:
# - refresh : Refreshes the colorbar.
# - draw : Redraws the colorbar, updating its appearance.
# 🛠️ Customization:
# - set_ticks_position : Sets the position of the ticks ('left', 'right', 'top', 'bottom').
# - get_ticks_position : Retrieves the current position of the ticks.
# - set_position : Sets the position of the colorbar within the figure.
# - get_position : Retrieves the position of the colorbar within the figure.
# 🔧 Color Mapping:
# - to_rgba : Converts a scalar value to an RGBA color using the colormap.
# - get_array : Retrieves the array of data values associated with the colorbar.
# 📏 Other Methods:
# - remove : Removes the colorbar from its parent `Axes` or `Figure`.
# - set_color : Sets the color for the colorbar (not commonly used).
# 🗑️ Visibility:
# - set_visible : Sets the visibility of the colorbar.
# - get_visible : Checks whether the colorbar is currently visible.If the cbar is removed using the Colorbar method remove:
In [8]: cbar.remove()
Another Colorbar instance can be added, this time the location of the colorbar can be specified:
In [9]: cbar = fig.colorbar(ax_image, location='bottom')
The AxesImage attribute cmap will display the colormap:
In [10]: ax_image.cmap
Out[10]: 
This can be assigned to an instance name:
In [11]: cmap = ax_image.cmapThe identifiers of the Colormap instance can be examined:
In [12]: cmap.
# ---------------------------------------
# Available Identifiers for Colormap:
# ---------------------------------------
# 🎨 General Attributes:
# - name : The name of the colormap.
# - colors : An array of RGBA colors in the colormap (available for ListedColormap).
# - N : The number of entries in the colormap.
# 🔧 Colormap Functions:
# - __call__ : Maps a scalar or array of values to RGBA colors.
# - set_over : Sets the color for values above the colormap range.
# - set_under : Sets the color for values below the colormap range.
# - set_bad : Sets the color for masked values.
# - set_extremes : Sets the color limits for the colormap.
# 🔍 Information:
# - get_array : Retrieves the array of colors for the colormap.
# - get_over : Retrieves the color for values above the colormap range.
# - get_under : Retrieves the color for values below the colormap range.
# - get_bad : Retrieves the color for masked values.
# 🛠️ Customization:
# - with_extremes : Returns a new colormap that includes specified extremes.The name attribute gives the name of the Colormap. The default colormap 'viridis' is used:
In [13]: cmap.name
Out[14]: 'viridis'The N attribute gives the number of colors in the Colormap:
In [15]: cmap.N
Out[15]: 256The colors attribute gives each color in the Coloromap as a list. Each element in the list is a nested 3-element list which has normalised [r, g, b] values:
In [16]: cmap.colors[:10]
Out[16]:
[[0.267004, 0.004874, 0.329415],
[0.26851, 0.009605, 0.335427],
[0.269944, 0.014625, 0.341379],
[0.271305, 0.019942, 0.347269],
[0.272594, 0.025563, 0.353093],
[0.273809, 0.031497, 0.358853],
[0.274952, 0.037752, 0.364543],
[0.276022, 0.044167, 0.370164],
[0.277018, 0.050344, 0.375715],
[0.277941, 0.056324, 0.381191]]These can be viewed as hexadecimal strings of the format '#rrggbb' by using, the to_hex function:
In [17]: from matplotlib.colors import to_hex
In [18]: [to_hex(color) for color in cmap.colors[:10]]
Out[18]:
['#440154',
'#440256',
'#450457',
'#450559',
'#46075a',
'#46085c',
'#460a5d',
'#460b5e',
'#470d60',
'#470e61']The function colormaps displays a list of the colormaps provided by matplotlib:
In [19]: plt.colormaps
Out[19]: ['magma', 'inferno', 'plasma', 'viridis', 'cividis',
'twilight', 'twilight_shifted', 'turbo', 'Blues',
'BrBG', 'BuGn', 'BuPu', 'CMRmap', 'GnBu',
'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn',
'PiYG', 'PuBu', 'PuBuGn', 'PuOr', 'PuRd',
'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu',
'RdYlGn', 'Reds', 'Spectral', 'Wistia', 'YlGn',
'YlGnBu', 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn',
'binary', 'bone', 'brg', 'bwr', 'cool',
'coolwarm', 'copper', 'cubehelix', 'flag', 'gist_earth',
'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow',
'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2',
'gray', 'hot', 'hsv', 'jet', 'nipy_spectral',
'ocean', 'pink', 'prism', 'rainbow', 'seismic',
'spring', 'summer', 'terrain', 'winter', 'Accent',
'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1',
'Set2', 'Set3', 'tab10', 'tab20', 'tab20b',
'tab20c', 'grey', 'gist_grey', 'gist_yerg', 'Grays',
'magma_r', 'inferno_r', 'plasma_r', 'viridis_r',
'cividis_r', 'twilight_r', 'twilight_shifted_r',
'turbo_r', 'Blues_r', 'BrBG_r', 'BuGn_r',
'BuPu_r', 'CMRmap_r', 'GnBu_r', 'Greens_r',
'Greys_r', 'OrRd_r', 'Oranges_r', 'PRGn_r',
'PiYG_r', 'PuBu_r', 'PuBuGn_r', 'PuOr_r',
'PuRd_r', 'Purples_r', 'RdBu_r', 'RdGy_r',
'RdPu_r', 'RdYlBu_r', 'RdYlGn_r', 'Reds_r',
'Spectral_r', 'Wistia_r', 'YlGn_r', 'YlGnBu_r',
'YlOrBr_r', 'YlOrRd_r', 'afmhot_r', 'autumn_r',
'binary_r', 'bone_r', 'brg_r', 'bwr_r',
'cool_r', 'coolwarm_r', 'copper_r', 'cubehelix_r',
'flag_r', 'gist_earth_r', 'gist_gray_r',
'gist_heat_r', 'gist_ncar_r', 'gist_rainbow_r',
'gist_stern_r', 'gist_yarg_r', 'gnuplot_r',
'gnuplot2_r', 'gray_r', 'hot_r', 'hsv_r',
'jet_r', 'nipy_spectral_r', 'ocean_r',
'pink_r', 'prism_r', 'rainbow_r', 'seismic_r',
'spring_r', 'summer_r', 'terrain_r', 'winter_r',
'Accent_r', 'Dark2_r', 'Paired_r', 'Pastel1_r',
'Pastel2_r', 'Set1_r', 'Set2_r', 'Set3_r',
'tab10_r', 'tab20_r', 'tab20b_r', 'tab20c_r']Each of these can be used with the AxesImage method set_cmap:
In [20]: ax_image.set_cmap('RdBu')
Note that changing the colormap does not change the underlying data and the values on the colorbar are the same. Some of the more common colormaps can be examined:
In [21]: ax_image.set_cmap('magma')
In [22]: ax_image.set_cmap('jet')
In [23]: ax_image.set_cmap('bone')
plt.colormaps is a list of colormap names. mpl.colormaps is a colormap register which can be used to view colormaps in the IPython console:
In [24]: mpl.colormaps
Out[24]: <matplotlib.cm.ColormapRegistry at 0x139d7a009d0>The following colormaps are perceptually uniform sequential colormaps:
In [25]: mpl.colormaps['viridis']
In [26]: mpl.colormaps['plasma']
In [27]: mpl.colormaps['inferno']
In [28]: mpl.colormaps['magma']
In [29]: mpl.colormaps['cividis']
In a uniform sequential colormap the lightness value increases monotonically throughout the colormap:
In [30]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(5, 1)
:
: colormaps = ['viridis', 'plasma', 'inferno', 'magma', 'cividis']
:
: for index, colormap in enumerate(colormaps):
: ax[index].imshow(np.array([np.arange(0, 256)]),
: cmap=colormap, aspect=10)
: ax[index].set_title(colormap, fontsize=12)
: ax[index].axis('off')
:
: fig.suptitle('Uniform Sequential Colormaps')
: fig.tight_layout()
The non-uniform sequential colormaps often include sharp transitions or nonlinear progressions, which can create visual artifacts or bias in data interpretation:
In [31]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(9, 2)
:
: colormaps = ['Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
: 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
: 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']
:
: for index, colormap in enumerate(colormaps):
: if (index<9):
: ax[index, 0].imshow(np.array([np.arange(0, 256)]), cmap=colormap, aspect=10)
: ax[index, 0].set_title(colormap, fontsize=8)
: ax[index, 0].axis('off')
: else:
: ax[index-9, 1].imshow(np.array([np.arange(0, 256)]), cmap=colormap,
: aspect=10)
: ax[index-9, 1].set_title(colormap, fontsize=8)
: ax[index-9, 1].axis('off')
:
: fig.suptitle('Sequential Colormaps')
: fig.tight_layout()The non-uniform sequential 2 colormaps are not completely sequential and therefore the lightness values in some of these colormaps can plateau or trend up and down which can create visual artifacts or bias in data interpretation:
In [32]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(8, 2)
:
: colormaps = ['binary', 'gist_yarg', 'gist_gray', 'gray', 'bone',
: 'pink', 'spring', 'summer', 'autumn', 'winter', 'cool',
: 'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper']
:
: for index, colormap in enumerate(colormaps):
: if (index<8):
: ax[index, 0].imshow(np.array([np.arange(0, 256)]), cmap=colormap, aspect=10)
: ax[index, 0].set_title(colormap, fontsize=8)
: ax[index, 0].axis('off')
: else:
: ax[index-8, 1].imshow(np.array([np.arange(0, 256)]), cmap=colormap,
: aspect=10)
: ax[index-8, 1].set_title(colormap, fontsize=8)
: ax[index-8, 1].axis('off')
:
: fig.suptitle('Sequential 2 Colormaps')
: fig.tight_layout()
A diverging colormap occurs between two colors with a neutral color in the middle:
In [33]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(6, 2)
:
: colormaps = ['PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu',
: 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']
:
: for index, colormap in enumerate(colormaps):
: if (index<6):
: ax[index, 0].imshow(np.array([np.arange(0, 256)]), cmap=colormap, aspect=10)
: ax[index, 0].set_title(colormap, fontsize=8)
: ax[index, 0].axis('off')
: else:
: ax[index-6, 1].imshow(np.array([np.arange(0, 256)]), cmap=colormap,
: aspect=10)
: ax[index-6, 1].set_title(colormap, fontsize=8)
: ax[index-6, 1].axis('off')
:
: fig.suptitle('Diverging Colormaps')
: fig.tight_layout()
A cyclic colormap starts and ends with the same value:
In [34]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(3, 1)
:
: colormaps = ['twilight', 'twilight_shifted', 'hsv']
:
: for index, colormap in enumerate(colormaps):
: ax[index].imshow(np.array([np.arange(0, 256)]),
: cmap=colormap, aspect=10)
: ax[index].set_title(colormap, fontsize=12)
: ax[index].axis('off')
:
: fig.suptitle('Cyclic Colormaps')
: fig.tight_layout()
Qualitative colormaps are typically used to represent data that is in distinct categories and does not follow an inherent order or gradient:
In [35]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots(6, 2)
:
: colormaps = ['Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2',
: 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b',
: 'tab20c']
:
: for index, colormap in enumerate(colormaps):
: if (index<6):
: ax[index, 0].imshow(np.array([np.arange(0, 256)]), cmap=colormap, aspect=10)
: ax[index, 0].set_title(colormap, fontsize=8)
: ax[index, 0].axis('off')
: else:
: ax[index-6, 1].imshow(np.array([np.arange(0, 256)]), cmap=colormap,
: aspect=10)
: ax[index-6, 1].set_title(colormap, fontsize=8)
: ax[index-6, 1].axis('off')
:
: fig.suptitle('Qualitative Colormaps')
: fig.tight_layout()
A qualitative colormap 'tab10' for example is the default colormap used to distinguish each line below:
In [36]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.array([0, 1, 2, 3, 4])
: y = np.array([0, 2, 4, 6, 8])
:
: fig, ax = plt.subplots()
: ax.plot(x, y-1,
: x, y-2,
: x, y-3,
: x, y-4,
: x, y-5,
: x, y-6,
: x, y-7,
: x, y-8,
: x, y-9,
: x, y-10,
: x, y-15,
: x, y-16,
: linewidth=5);
Note that this colormap only contains 10 discrete values and after these are exhausted, these repeat. To get the list of colors in the form of hex values, the following list comprehension can be used:
In [37]: [to_hex(color) for color in mpl.colormaps['tab10'].colors]
Out[37]:
['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf']A custom colormap can be created using a list of hex values:
In [38]: colors = ['#a33c46', '#a33c5c', '#a33c71', '#a33c86',
'#a33c9b', '#883cab', '#733cab', '#5e3cab',
'#493cab', '#3840ab']
: from matplotlib.colors import LinearSegmentedColormap
: cmap = LinearSegmentedColormap.from_list('custom_cmap', colors)Figure instance 1 can be reselected and the colormap applied using:
In [39]: plt.figure(1)
: ax_image.set_cmap(cmap);
The matshow displays a matrix, displaying the column number on x and the row number on y. The pseudo-color plot is similar but can be used to display x and y
In [40]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 11)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 11)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.pcolor(xmat, ymat, zmat)
Out[1]: <matplotlib.collections.PolyQuadMesh at 0x18ad1fb6410>
In [2]: ax.collections
Out[2]: <Axes.ArtistList of 1 collections>
In [3]: ax.collections[0]
Out[3]: <matplotlib.collections.PolyQuadMesh at 0x18ad1fb6410>
In [4]: polyquadmesh = ax.collections[0]The identifiers of the PolyQuadMesh are largely consistent with the AxesImage:
In [5]: polyquadmesh.
# ---------------------------------------
# Available Identifiers for PolyQuadMesh:
# ---------------------------------------
# 🔧 General Attributes:
# - get_array : Retrieves the mesh data array.
# - get_cmap : Retrieves the colormap of the mesh.
# - get_norm : Retrieves the normalization instance of the mesh.
# - get_clim : Retrieves the color limits (vmin, vmax) of the mesh.
# 🎨 Appearance and Style:
# - set_cmap(cmap) : Sets the colormap for the mesh.
# - set_norm(norm) : Sets the normalization for the mesh data.
# - set_clim(vmin, vmax) : Sets the color limits for the mesh.
# 🗑️ Removing and Updating:
# - remove() : Removes the mesh from its parent `Axes` or `Figure`.
# 🔧 Transformation and Positioning:
# - set_transform(transform) : Sets a transformation for the mesh.
# - get_transform() : Retrieves the transformation applied to the mesh.
# 🔧 Other Methods:
# - contains(x, y) : Checks whether a point is within the mesh's display area.
# - set_picker(picker) : Sets the picker function or tolerance for the mesh.
# - get_picker() : Retrieves the current picker function or tolerance.
# - set_zorder(zorder) : Sets the z-order for the mesh, determining its drawing priority.
# - get_zorder() : Retrieves the current z-order of the mesh.A colorbar can be added:
In [6]: colorbar = fig.colorbar(polyquadmesh, location='bottom')
The limits of the color axis can be examined using the method get_clim:
In [7]: polyquadmesh.get_clim()
Out[7]: (-0.4218339392344389, 0.4218339392344388)These can be assigned to new minimum and maximum values using the complementary method set_clim. vmin is used to select the lowest boundary, anything below this value can be considered as noise and is not shown:
In [8]: polyquadmesh.set_clim(vmin=0, vmax=0.5)
vmax is the highest boundary and anything above this value is considered as saturated:
In [9]: polyquadmesh.set_clim(vmin=-0.5, vmax=0)
vmin and vmax can be assigned as optional parameters for the Axes method pcolor.
For a larger dataset the Axes method pcolormesh should be preferentially used over pcolor as pcolr can be quite slow for large datasets:
In [10]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: quadmesh = ax.pcolormesh(xmat, ymat, zmat)
: colorbar = fig.colorbar(quadmesh, location='bottom')
Inclines and declines in data can be represented using contours, and contours are commonly used on maps to represent 3D topography such as hills and valleys. The input arguments for the Axes method countour are largely consistent with pcolor, however the additional parameter levels can be used to display the number of lines representing the levels:
In [10]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: contour = ax.contour(xmat, ymat, zmat, vmin=-0.5, vmax=0.5, levels=9)
: colorbar = fig.colorbar(contour, location='bottom')
The Axes method contourf is similar but creates a filled contour:
In [11]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: contourf = ax.contourf(xmat, ymat, zmat, vmin=-0.5, vmax=0.5, levels=9)
: colorbar = fig.colorbar(contourf, location='bottom')
The identifiers for the QuadContourSet can be examined. Many of these are consistent with the AxesImage however because the contour is a collection of lines, there are line related parameters related to the linestyle and linewidth:
In [12]: contour.
# ---------------------------------------
# Available Identifiers for QuadContourSet:
# ---------------------------------------
# 🔧 General Attributes:
# - get_array : Retrieves the data array used for contour levels.
# - get_clim : Retrieves the color limits for the contour set.
# - get_levels : Retrieves the contour levels.
# - get_linewidths : Retrieves the linewidths of the contour lines.
# - get_linestyles : Retrieves the line styles of the contour lines.
# - get_norm : Retrieves the normalization instance used for the contour set.
# 🎨 Appearance and Style:
# - set_array(array) : Updates the data array and redraws the contours.
# - set_clim(vmin, vmax) : Sets the color limits for the contour set.
# - set_levels(levels) : Sets the contour levels.
# - set_linewidths(linewidths) : Sets the linewidths for the contour lines (individual).
# - set_linewidth(linewidth) : Sets a single linewidth for all contour lines.
# - set_linestyles(linestyles) : Sets the line styles for the contour lines (individual).
# - set_linestyle(linestyle) : Sets a single line style for all contour lines.
# - set_color(color) : Sets the color for the contour lines.
# - set_facecolor(color) : Sets the face color for filled contours.
# - set_edgecolor(color) : Sets the edge color for contours.
# 🔧 Color Mapping:
# - set_cmap(cmap) : Sets the colormap for the contour set.
# 🔧 Other Methods:
# - contains(x, y) : Checks whether a point is within the contour set's display area.
# - get_picker() : Retrieves the current picker function or tolerance.
# - set_picker(picker) : Sets the picker function or tolerance for the contour set.
# - set_zorder(zorder) : Sets the z-order for the contour set, determining its drawing priority.
# - get_zorder() : Retrieves the current z-order of the contour set.
# 🗑️ Removing and Updating:
# - remove() : Removes the contour set from its parent `Axes` or `Figure`.
# - update() : Updates the contour properties based on input.
# 🔧 Transformation and Positioning:
# - set_transform(transform) : Sets a transformation for the contour set.
# - get_transform() : Retrieves the transformation applied to the contour set.
# - get_paths() : Returns the paths defining the contours as a list of `Path` objects.The singular form will update all the lines in the contour, the plural form can be used to create a list of properties to update each line. Note the plural form expects a list that is 2 elements larger than the number of levels, although the first and last lines at the boundaries are not shown:
In [11]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
: linewidths = np.array([8,
: 5, 4, 3, 2,
: 1,
: 2, 3, 4, 5,
: 8])
: linestyles = [':',
: ':', ':', ':', ':',
: '--',
: '-', '-', '-', '-',
: '-']
: colors = ['royalblue',
: 'royalblue', 'royalblue', 'royalblue', 'royalblue',
: 'black',
: 'tomato', 'tomato', 'tomato', 'tomato',
: 'tomato']
:
: fig, ax = plt.subplots()
: quadcontourset = ax.contour(xmat, ymat, zmat, levels=9, colors=colors,
: linewidths=linewidths, linestyles=linestyles)
: ax.clabel(quadcontourset, fontsize=9, inline=True)
: ax.set_xlabel('x')
: ax.set_ylabel('y');
If an Axes3D instance is used, the 3D data can be plotted using the Axes3D method plot_surface:
In [12]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig = plt.figure()
: ax = fig.add_subplot(projection='3d')
: ax.plot_surface(xmat, ymat, zmat)
Out[1]: <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1baabac4a90>
The Poly3DCollection is the last element in the Axes3D attribute attribute collections:
In [2]: ax.collections
Out[2]: <Axes.ArtistList of 1 collections>
In [3]: ax.collections[0]
Out[3]: <mpl_toolkits.mplot3d.art3d.Poly3DCollection at 0x1baabac4a90>
In [4]: poly3dcollection = ax.collections[0]Notice that the Poly3DCollection is essentially a grid which spans the surface. Each part of the grid is approximately a square that has line like properties and a face. The identifiers for the Poly3DCollection can be examined:
In [5]: poly3dcollection.
# ---------------------------------------
# Available Identifiers for Poly3DCollection:
# ---------------------------------------
# 🔧 General Attributes:
# - get_verts : Retrieves the vertices of the polygons in the collection.
# - get_edgecolor : Retrieves the edge color of the polygons.
# - get_facecolor : Retrieves the face color of the polygons.
# - get_array : Retrieves the data array associated with the collection (for colormapping).
# - get_clim : Retrieves the color limits (vmin, vmax) for the collection.
# - get_norm : Retrieves the normalization instance for colormapping.
# 🎨 Appearance and Style:
# - set_verts(verts) : Sets the vertices for the polygons in the collection.
# - set_edgecolor(color) : Sets the edge color of the polygons.
# - set_facecolor(color) : Sets the face color of the polygons.
# - set_alpha(alpha) : Sets the alpha (transparency) value for the collection.
# - set_array(array) : Sets the data array for colormapping.
# - set_clim(vmin, vmax) : Sets the color limits (vmin, vmax) for colormapping.
# - set_cmap(cmap) : Sets the colormap for the collection.
# - set_norm(norm) : Sets the normalization for colormapping.
# - set_linewidth(linewidth) : Sets the linewidth for all edges in the collection.
# - set_linestyle(linestyle) : Sets the line style for all edges in the collection.
# 🔧 Other Methods:
# - set_picker(picker) : Sets the picker function or tolerance for the collection.
# - get_picker() : Retrieves the current picker function or tolerance.
# - set_zorder(zorder) : Sets the z-order of the collection, determining its drawing priority.
# - get_zorder() : Retrieves the current z-order of the collection.
# 🗑️ Removing and Updating:
# - remove() : Removes the collection from its parent `Axes` or `Figure`.
# - update() : Updates the collection's properties based on input.
# 🔧 Transformation and Positioning:
# - set_transform(transform) : Sets a transformation for the collection.
# - get_transform() : Retrieves the transformation applied to the collection.
# 🌐 3D-Specific Methods:
# - set_sort_zpos(zpos) : Sets the z-position used for depth sorting.
# - get_sort_zpos() : Retrieves the z-position used for depth sorting.
# 🔧 Path and Geometry:
# - get_paths() : Retrieves the paths representing the polygons in the collection.
# - set_paths(paths) : Sets the paths for the polygons in the collection.A uniform color can be selected:
In [5]: poly3dcollection.set_edgecolor('black')
: poly3dcollection.set_linewidth(1)
: poly3dcollection.set_facecolor('tomato')
Instead of setting a uniform color, it is also possible to set a colormap:
In [6]: poly3dcollection.set_cmap('cool')Note this method does not appear to work properly. Setting the colormap when using the Axes3D method plot_surface directly appears to work:
In [7]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig = plt.figure()
: ax = fig.add_subplot(projection='3d')
: poly3dcollection = ax.plot_surface(xmat, ymat, zmat, cmap='cool')
The Axes3D method plot_wireframe instead plots a wireframe which is a frame structure similar to a surface but instead of each surface havign a face is empty:
In [6]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig = plt.figure()
: ax = fig.add_subplot(projection='3d')
: ax.plot_wireframe(xmat, ymat, zmat)
Out[6]: <mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x1baa4e042d0>
The Line3DCollection is the last element in the Axes3D attribute attribute collections:
In [7]: ax.collections
Out[7]: <Axes.ArtistList of 1 collections>
In [8]: ax.collections[0]
Out[8]: <mpl_toolkits.mplot3d.art3d.Line3DCollection at 0x1baa4e042d0>
In [9]: line3dcollection = ax.collections[0]The identifiers for the Line3DCollection instance can be examined and are largely consistent with thsoe from Poly3DCollection:
In [10]: python
# ---------------------------------------
# Available Identifiers for Line3DCollection:
# ---------------------------------------
# 🔧 General Attributes:
# - get_segments : Retrieves the line segments in the collection.
# - get_edgecolor : Retrieves the edge color for the lines.
# - get_facecolor : Retrieves the face color for the lines.
# - get_linewidth : Retrieves the linewidth for the lines.
# - get_linestyle : Retrieves the linestyle for the lines.
# - get_alpha : Retrieves the alpha (transparency) value.
# - get_array : Retrieves the data array associated with the collection (for colormapping).
# - get_clim : Retrieves the color limits (vmin, vmax) for colormapping.
# - get_cmap : Retrieves the colormap used for colormapping.
# - get_norm : Retrieves the normalization instance for colormapping.
# 🎨 Appearance and Style:
# - set_segments(segments) : Sets the line segments in the collection.
# - set_edgecolor(color) : Sets the edge color for the lines.
# - set_facecolor(color) : Sets the face color for the lines.
# - set_linewidth(linewidth) : Sets the linewidth for all lines in the collection.
# - set_linestyle(linestyle) : Sets the linestyle for all lines in the collection.
# - set_alpha(alpha) : Sets the alpha (transparency) value for the collection.
# - set_array(array) : Sets the data array for colormapping.
# - set_clim(vmin, vmax) : Sets the color limits (vmin, vmax) for colormapping.
# - set_cmap(cmap) : Sets the colormap for the collection.
# - set_norm(norm) : Sets the normalization for colormapping.
# 🔧 Other Methods:
# - set_picker(picker) : Sets the picker function or tolerance for the collection.
# - get_picker() : Retrieves the current picker function or tolerance.
# - set_zorder(zorder) : Sets the z-order of the collection, determining its drawing priority.
# - get_zorder() : Retrieves the current z-order of the collection.
# 🗑️ Removing and Updating:
# - remove() : Removes the collection from its parent `Axes` or `Figure`.
# - update() : Updates the collection's properties based on input.
# 🔧 Transformation and Positioning:
# - set_transform(transform) : Sets a transformation for the collection.
# - get_transform() : Retrieves the transformation applied to the collection.
# 🌐 3D-Specific Methods:
# - set_sort_zpos(zpos) : Sets the z-position used for depth sorting.
# - get_sort_zpos() : Retrieves the z-position used for depth sorting.
# 🔧 Path and Geometry:
# - get_paths() : Retrieves the paths representing the lines in the collection.
# - set_paths(paths) : Sets the paths for the lines in the collection.The Axes3D instance has the attributes elev, azim and roll which give the elevation angle, aximuthal angle and rolling angle respectively:
In [10]: ax.elev
Out[10]: 30
In [11]: ax.azim
Out[11]: -60
In [12]: ax.roll
Out[12]: 0The Axes3D method view_init can be used to set the initialisation angles used for the Axes3D. The effect of the elevation angle can be examined:
In [13]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig = plt.figure()
: for index in range(1, 13):
: ax = fig.add_subplot(3, 4, index, projection='3d')
: ax.plot_surface(xmat, ymat, zmat, cmap='cool')
: ax.view_init(elev=30*(index-1), azim=90, roll=60)
: ax.set_title(f'elev={30*(index-1)}')
: ax.set_axis_off()
: fig = plt.figure()
: for index in range(1, 13):
: ax = fig.add_subplot(3, 4, index, projection='3d')
: ax.plot_surface(xmat, ymat, zmat, cmap='cool')
: ax.view_init(elev=30, azim=30*(index-1), roll=60)
: ax.set_title(f'axim={30*(index-1)}')
: ax.set_axis_off()
: fig = plt.figure()
: for index in range(1, 13):
: ax = fig.add_subplot(3, 4, index, projection='3d')
: ax.plot_surface(xmat, ymat, zmat, cmap='cool')
: ax.view_init(elev=30, azim=90, roll=30*(index-1))
: ax.set_title(f'roll={30*(index-1)}')
: ax.set_axis_off()
In [14]: exitIn order for an animation, the Python environment requires the library ffmpeg. This library is not included in the conda-forge Python environment which is supplied with the Spyder IDE. A custom Python environment must be made. To do this it is recommended to use Miniforge which has a base Python environment which includes the conda package manager and uses the community channel conda-forge:
conda create -n spyder-env spyder-kernels cython seaborn scikit-learn pyarrow sympy openpyxl xlrd xlsxwriter lxml sqlalchemy tabulate pyqt ffmpeg ghostscript ruffOnce the Python environment is setup, the FuncAnimation class can be imported. To instantiate this class, a Figure instance and update method are required, alongside the frames and the frame interval. An example is given below:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: from matplotlib.animation import FuncAnimation
:
: xrow = np.linspace(-2, 2, 1001)[np.newaxis, :]
: ycol = np.linspace(-2, 2, 1001)[:, np.newaxis]
: xmat, ymat = np.meshgrid(xrow, ycol)
: zmat = (lambda x, y: x * np.exp(-x**2 - y**2))(xmat, ymat)
:
: fig = plt.figure()
: ax = fig.add_subplot(projection='3d')
: plot_wireframe = ax.plot_wireframe(xmat, ymat, zmat)
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.set_zlabel('z')
:
: def update(frame):
: if (frame<360):
: ax.set_title(f'elev={frame}, azim=90, roll=60')
: ax.view_init(elev=frame, azim=90, roll=60)
: return ax
: elif (frame<720):
: ax.set_title(f'elev=30, azim={frame-360}, roll=60')
: ax.view_init(elev=30, azim=frame, roll=60)
: return ax
: else:
: ax.set_title(f'elev=30, azim=90, roll={frame-720}')
: ax.view_init(elev=30, azim=frame, roll=60)
: return ax
:
: ani = FuncAnimation(fig, update, frames=np.arange(0, 1081), interval=10)
: fig.show()
: ani.save('image_sequence.gif')
: ani.save('image_sequence.mp4', fps=10)
In [2]: exitAn image called hill.jpg is saved in an image_files subfolder that is found beside the current Python script file:
It can be opened using the function imread. The relative file path is typically supplied using a raw string. Capital R is used so regular expression syntax highlighting isn't applied to the raw string. In a relative file path, the initial . indicates the file is in the same folder as the current script file and .. would instead be an instruction to go up a level to the parent folder. The \ is the default file separator used in the Windows Operating System. On a Linux or Mac Operating System, the / would instead be used a file separator:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: img = plt.imread(R'.\images\hill.jpg')| Variable Explorer | |||
|---|---|---|---|
| img | array of uint8 | (3072, 4080, 3) | [[ 82 150 223] [ 82 150 223] [ 81 149 232]] |
Notice that img is an ndarray of shape (3072, 4080, 3) where 3072 is the number of pages, 4080 is the number of rows and 3 is the number of columns.
For an img the last dimension which has 3 elements correspond to the uint8 r, g, b values, recall that this is essentially '#rrggbb' and corresponds to a color of a pixel. The remaining two dimensions which we previously considered as pages and rows can now be considered as rows and columns (of color pixels). These are typically displayed on a computer screen.
The image can be shown on a Axes using the Axes method imshow:
In [2]: fig, ax = plt.subplots()
: ax.imshow(img)
Out[2]: <matplotlib.image.AxesImage at 0x2b8730d01a0>
Note that this is an AxesImage instance and the identifiers of this class have already been examined. If a .png was loaded instead of a .jpg it would have the fourth channel, alpha which gives the transparancy value.
In [3]: exitThe Axes method table can be used to add a table to an Axes. Its docstring can be examined:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: fig, ax = plt.subplots()
: ax.table?
Signature:
ax.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,
edges='closed',
**kwargs,
)Notice that the table specific paramters are provided using camelCase. The data is generally provided using an outer list of rows where each row is a list instance of equal length:
In [2]: data = [['x', 'y', 'z'],
: [0, 1, 2],
: [1, 2, 3],
: [2, 3, 6]]
:
In [3]: ax.table(cellText=data)
Out[3]: <matplotlib.table.Table at 0x2b80177bd90>This table is added to the Axes, at the bottom by default:
This Table instance is the last element in the Axes attribute tables:
In [4]: table = ax.tables[-1]The identifiers of the Table instance can be examined:
In [5]: table.
In [5]: table.
# ---------------------------------------
# Available Identifiers for Table Class:
# ---------------------------------------
# 🔧 General Attributes and Methods:
# - figure : The figure to which the table belongs.
# - axes : The axes to which the table belongs.
# - remove() : Removes the table from its parent axes.
# - update(props) : Updates the table with specified properties.
# - update_from(other) : Updates table properties from another object.
# 🗂️ Managing Table Cells:
# - add_cell(row, col, width, height, **kwargs) : Adds a new cell at the specified location.
# - get_celld() : Retrieves a dictionary of all table cells.
# 🖌️ Appearance and Styling:
# - set_fontsize(size) : Sets the font size for the table.
# - set_alpha(alpha) : Sets the transparency of the table.
# - set_visible(visible) : Sets whether the table is visible.
# - set_zorder(zorder) : Adjusts the drawing order of the table.
# 🗺️ Positioning and Transformation:
# - set_transform(transform) : Applies a transformation to the table.
# - get_transform() : Retrieves the current transformation.
# - get_window_extent(renderer) : Returns the bounding box of the table.
# - scale(x_scale, y_scale) : Scales the table by the given factors.
# 📏 Clip and Bounding Box:
# - set_clip_box(bbox) : Sets a clipping box for the table.
# - get_clip_box() : Retrieves the current clipping box.
# - set_clip_on(clip) : Enables or disables clipping for the table.
# - get_clip_on() : Checks if clipping is enabled.
# 🖌️ Cell-Level Customization:
# - get_children() : Returns the child artists (e.g., cells) of the table.
# - get_cell(row, col) : Retrieves a specific cell for further customization.
# 🎨 Advanced Graphics Options:
# - set_path_effects(effects) : Applies path effects to the table.
# - set_rasterized(rasterized) : Enables rasterization for the table.
# - get_rasterized() : Checks if the table is rasterized.
# 🔍 Picking and Interaction:
# - contains(mouseevent) : Checks if the table contains the given mouse event.
# - pick(mouseevent) : Picks the table based on a mouse event.
# - get_picker() : Retrieves the picker function or tolerance for the table.
# - set_picker(picker) : Sets the picker function or tolerance for the table.
# 🔧 Miscellaneous:
# - stale : Indicates if the table needs re-rendering.
# - add_callback(callback) : Adds a callback for property changes.
# - remove_callback(callback) : Removes a previously added callback.Most of the identifiers are used to change the text positioning within the Table and not the position of the Table on the Axes itself. Table positioning is typically carried out when using the Axes method table with the optional parameter loc. This optional parameter is consistent to the optional parameter loc in the Axes method legend which was previously examined and can be assigned to a string or a boundary box with respect to normalised Axes co-ordianates:
In [6]: table.remove()
: table = ax.table(cellText=data, loc='center')
To hide the boundary box, ticks, and grid lines, the Axes method axis can be set to 'off':
In [7]: ax.axis('off')
Out[7]: (np.float64(0.0), np.float64(1.0), np.float64(0.0), np.float64(1.0))
A Table is often placed using a subplot. For example:
In [8]: exit
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
:
: x = np.linspace(0, np.pi/2, 100)
: y = np.sin(x)
:
: x1 = np.linspace(0, np.pi/2, 7)
: y1 = np.sin(x1)
:
: data = [[R'$x$', R'$y$']]
: for num in range(7):
: data.append([R'$\frac{'+str(num)+R'}{24}\tau$',
: np.round(np.sin(2*np.pi*num/24), 3)])
:
: fig, ax = plt.subplots(2, 1)
: lines = ax[0].plot(x, y)
: path_collection = ax[0].scatter(x1, y1, color='tomato')
: ax[0].set_xlabel('x')
: ax[0].set_ylabel('y')
: ax[0].xaxis.set_ticks(np.linspace(0, np.pi/2, 7))
: ax[0].xaxis.set_ticklabels([R'$\frac{'+str(num)+R'}{24}\tau$' for num in range(7)])
: ax[0].grid()
:
: table = ax[1].table(cellText=data, loc='center')
: ax[1].axis('off')
: table.set_fontsize(8)
:
: fig.tight_layout()
The following plot is made using the default recall parameters:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([0, 1, 2, 3, 4, 5])
: y = np.array([0, 2, 4, 6, 8, 10])
:
: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.set_title('y=f(x)')
: ax.plot(x, y, x, y+1, x, y+2, x, y+3)
The currently used recall parameters are found in rcParams which is dict-like:
In [2]: recall_parameters = mpl.rc_params()| Variable Explorer | |||
|---|---|---|---|
| recall_parameters | RCParams | 318 | RcParams({'_internal.classic_mode': False, 'agg.path.chunksize': 0, 'animation.bitrate': -1, ... |
The items in recall_parameters can be examined in an ipython cell:
In [3]: recall_parameters
Out[3]: RcParams({'_internal.classic_mode': False,
'agg.path.chunksize': 0,
'animation.bitrate': -1,
'animation.codec': 'h264',
'animation.convert_args': ['-layers', 'OptimizePlus'],
'animation.convert_path': 'convert',
'animation.embed_limit': 20.0,
'animation.ffmpeg_args': [],
'animation.ffmpeg_path': 'ffmpeg',
'animation.frame_format': 'png',
'animation.html': 'none',
'animation.writer': 'ffmpeg',
'axes.autolimit_mode': 'data',
'axes.axisbelow': 'line',
'axes.edgecolor': 'black',
'axes.facecolor': 'white',
'axes.formatter.limits': [-5, 6],
'axes.formatter.min_exponent': 0,
'axes.formatter.offset_threshold': 4,
'axes.formatter.use_locale': False,
'axes.formatter.use_mathtext': False,
'axes.formatter.useoffset': True,
'axes.grid': False,
'axes.grid.axis': 'both',
'axes.grid.which': 'major',
'axes.labelcolor': 'black',
'axes.labelpad': 4.0,
'axes.labelsize': 'medium',
'axes.labelweight': 'normal',
'axes.linewidth': 0.8,
'axes.prop_cycle': cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']),
'axes.spines.bottom': True,
'axes.spines.left': True,
'axes.spines.right': True,
'axes.spines.top': True,
'axes.titlecolor': 'auto',
'axes.titlelocation': 'center',
'axes.titlepad': 6.0,
'axes.titlesize': 'large',
'axes.titleweight': 'normal',
'axes.titley': None,
'axes.unicode_minus': True,
'axes.xmargin': 0.05,
'axes.ymargin': 0.05,
'axes.zmargin': 0.05,
'axes3d.automargin': False,
'axes3d.grid': True,
'axes3d.xaxis.panecolor': (0.95, 0.95, 0.95, 0.5),
'axes3d.yaxis.panecolor': (0.9, 0.9, 0.9, 0.5),
'axes3d.zaxis.panecolor': (0.925, 0.925, 0.925, 0.5),
'backend': 'qtagg',
'backend_fallback': True,
'boxplot.bootstrap': None,
'boxplot.boxprops.color': 'black',
'boxplot.boxprops.linestyle': '-',
'boxplot.boxprops.linewidth': 1.0,
'boxplot.capprops.color': 'black',
'boxplot.capprops.linestyle': '-',
'boxplot.capprops.linewidth': 1.0,
'boxplot.flierprops.color': 'black',
'boxplot.flierprops.linestyle': 'none',
'boxplot.flierprops.linewidth': 1.0,
'boxplot.flierprops.marker': 'o',
'boxplot.flierprops.markeredgecolor': 'black',
'boxplot.flierprops.markeredgewidth': 1.0,
'boxplot.flierprops.markerfacecolor': 'none',
'boxplot.flierprops.markersize': 6.0,
'boxplot.meanline': False,
'boxplot.meanprops.color': 'C2',
'boxplot.meanprops.linestyle': '--',
'boxplot.meanprops.linewidth': 1.0,
'boxplot.meanprops.marker': '^',
'boxplot.meanprops.markeredgecolor': 'C2',
'boxplot.meanprops.markerfacecolor': 'C2',
'boxplot.meanprops.markersize': 6.0,
'boxplot.medianprops.color': 'C1',
'boxplot.medianprops.linestyle': '-',
'boxplot.medianprops.linewidth': 1.0,
'boxplot.notch': False,
'boxplot.patchartist': False,
'boxplot.showbox': True,
'boxplot.showcaps': True,
'boxplot.showfliers': True,
'boxplot.showmeans': False,
'boxplot.vertical': True,
'boxplot.whiskerprops.color': 'black',
'boxplot.whiskerprops.linestyle': '-',
'boxplot.whiskerprops.linewidth': 1.0,
'boxplot.whiskers': 1.5,
'contour.algorithm': 'mpl2014',
'contour.corner_mask': True,
'contour.linewidth': None,
'contour.negative_linestyle': 'dashed',
'date.autoformatter.day': '%Y-%m-%d',
'date.autoformatter.hour': '%m-%d %H',
'date.autoformatter.microsecond': '%M:%S.%f',
'date.autoformatter.minute': '%d %H:%M',
'date.autoformatter.month': '%Y-%m',
'date.autoformatter.second': '%H:%M:%S',
'date.autoformatter.year': '%Y',
'date.converter': 'auto',
'date.epoch': '1970-01-01T00:00:00',
'date.interval_multiples': True,
'docstring.hardcopy': False,
'errorbar.capsize': 0.0,
'figure.autolayout': False,
'figure.constrained_layout.h_pad': 0.04167,
'figure.constrained_layout.hspace': 0.02,
'figure.constrained_layout.use': False,
'figure.constrained_layout.w_pad': 0.04167,
'figure.constrained_layout.wspace': 0.02,
'figure.dpi': 100.0,
'figure.edgecolor': 'white',
'figure.facecolor': 'white',
'figure.figsize': [6.4, 4.8],
'figure.frameon': True,
'figure.hooks': [],
'figure.labelsize': 'large',
'figure.labelweight': 'normal',
'figure.max_open_warning': 20,
'figure.raise_window': True,
'figure.subplot.bottom': 0.11,
'figure.subplot.hspace': 0.2,
'figure.subplot.left': 0.125,
'figure.subplot.right': 0.9,
'figure.subplot.top': 0.88,
'figure.subplot.wspace': 0.2,
'figure.titlesize': 'large',
'figure.titleweight': 'normal',
'font.cursive': ['Apple Chancery',
'Textile',
'Zapf Chancery',
'Sand',
'Script MT',
'Felipa',
'Comic Neue',
'Comic Sans MS',
'cursive'],
'font.family': ['sans-serif'],
'font.fantasy': ['Chicago',
'Charcoal',
'Impact',
'Western',
'xkcd script',
'fantasy'],
'font.monospace': ['DejaVu Sans Mono',
'Bitstream Vera Sans Mono',
'Computer Modern Typewriter',
'Andale Mono',
'Nimbus Mono L',
'Courier New',
'Courier',
'Fixed',
'Terminal',
'monospace'],
'font.sans-serif': ['DejaVu Sans',
'Bitstream Vera Sans',
'Computer Modern Sans Serif',
'Lucida Grande',
'Verdana',
'Geneva',
'Lucid',
'Arial',
'Helvetica',
'Avant Garde',
'sans-serif'],
'font.serif': ['DejaVu Serif',
'Bitstream Vera Serif',
'Computer Modern Roman',
'New Century Schoolbook',
'Century Schoolbook L',
'Utopia',
'ITC Bookman',
'Bookman',
'Nimbus Roman No9 L',
'Times New Roman',
'Times',
'Palatino',
'Charter',
'serif'],
'font.size': 10.0,
'font.stretch': 'normal',
'font.style': 'normal',
'font.variant': 'normal',
'font.weight': 'normal',
'grid.alpha': 1.0,
'grid.color': '#b0b0b0',
'grid.linestyle': '-',
'grid.linewidth': 0.8,
'hatch.color': 'black',
'hatch.linewidth': 1.0,
'hist.bins': 10,
'image.aspect': 'equal',
'image.cmap': 'viridis',
'image.composite_image': True,
'image.interpolation': 'antialiased',
'image.interpolation_stage': 'data',
'image.lut': 256,
'image.origin': 'upper',
'image.resample': True,
'interactive': False,
'keymap.back': ['left', 'c', 'backspace', 'MouseButton.BACK'],
'keymap.copy': ['ctrl+c', 'cmd+c'],
'keymap.forward': ['right', 'v', 'MouseButton.FORWARD'],
'keymap.fullscreen': ['f', 'ctrl+f'],
'keymap.grid': ['g'],
'keymap.grid_minor': ['G'],
'keymap.help': ['f1'],
'keymap.home': ['h', 'r', 'home'],
'keymap.pan': ['p'],
'keymap.quit': ['ctrl+w', 'cmd+w', 'q'],
'keymap.quit_all': [],
'keymap.save': ['s', 'ctrl+s'],
'keymap.xscale': ['k', 'L'],
'keymap.yscale': ['l'],
'keymap.zoom': ['o'],
'legend.borderaxespad': 0.5,
'legend.borderpad': 0.4,
'legend.columnspacing': 2.0,
'legend.edgecolor': '0.8',
'legend.facecolor': 'inherit',
'legend.fancybox': True,
'legend.fontsize': 'medium',
'legend.framealpha': 0.8,
'legend.frameon': True,
'legend.handleheight': 0.7,
'legend.handlelength': 2.0,
'legend.handletextpad': 0.8,
'legend.labelcolor': 'None',
'legend.labelspacing': 0.5,
'legend.loc': 'best',
'legend.markerscale': 1.0,
'legend.numpoints': 1,
'legend.scatterpoints': 1,
'legend.shadow': False,
'legend.title_fontsize': None,
'lines.antialiased': True,
'lines.color': 'C0',
'lines.dash_capstyle': <CapStyle.butt: 'butt'>,
'lines.dash_joinstyle': <JoinStyle.round: 'round'>,
'lines.dashdot_pattern': [6.4, 1.6, 1.0, 1.6],
'lines.dashed_pattern': [3.7, 1.6],
'lines.dotted_pattern': [1.0, 1.65],
'lines.linestyle': '-',
'lines.linewidth': 1.5,
'lines.marker': 'None',
'lines.markeredgecolor': 'auto',
'lines.markeredgewidth': 1.0,
'lines.markerfacecolor': 'auto',
'lines.markersize': 6.0,
'lines.scale_dashes': True,
'lines.solid_capstyle': <CapStyle.projecting: 'projecting'>,
'lines.solid_joinstyle': <JoinStyle.round: 'round'>,
'macosx.window_mode': 'system',
'markers.fillstyle': 'full',
'mathtext.bf': 'sans:bold',
'mathtext.bfit': 'sans:italic:bold',
'mathtext.cal': 'cursive',
'mathtext.default': 'it',
'mathtext.fallback': 'cm',
'mathtext.fontset': 'dejavusans',
'mathtext.it': 'sans:italic',
'mathtext.rm': 'sans',
'mathtext.sf': 'sans',
'mathtext.tt': 'monospace',
'patch.antialiased': True,
'patch.edgecolor': 'black',
'patch.facecolor': 'C0',
'patch.force_edgecolor': False,
'patch.linewidth': 1.0,
'path.effects': [],
'path.simplify': True,
'path.simplify_threshold': 0.111111111111,
'path.sketch': None,
'path.snap': True,
'pcolor.shading': 'auto',
'pcolormesh.snap': True,
'pdf.compression': 6,
'pdf.fonttype': 3,
'pdf.inheritcolor': False,
'pdf.use14corefonts': False,
'pgf.preamble': '',
'pgf.rcfonts': True,
'pgf.texsystem': 'xelatex',
'polaraxes.grid': True,
'ps.distiller.res': 6000,
'ps.fonttype': 3,
'ps.papersize': 'letter',
'ps.useafm': False,
'ps.usedistiller': None,
'savefig.bbox': None,
'savefig.directory': '~',
'savefig.dpi': 'figure',
'savefig.edgecolor': 'auto',
'savefig.facecolor': 'auto',
'savefig.format': 'png',
'savefig.orientation': 'portrait',
'savefig.pad_inches': 0.1,
'savefig.transparent': False,
'scatter.edgecolors': 'face',
'scatter.marker': 'o',
'svg.fonttype': 'path',
'svg.hashsalt': None,
'svg.image_inline': True,
'text.antialiased': True,
'text.color': 'black',
'text.hinting': 'force_autohint',
'text.hinting_factor': 8,
'text.kerning_factor': 0,
'text.latex.preamble': '',
'text.parse_math': True,
'text.usetex': False,
'timezone': 'UTC',
'tk.window_focus': False,
'toolbar': 'toolbar2',
'webagg.address': '127.0.0.1',
'webagg.open_in_browser': True,
'webagg.port': 8988,
'webagg.port_retries': 50,
'xaxis.labellocation': 'center',
'xtick.alignment': 'center',
'xtick.bottom': True,
'xtick.color': 'black',
'xtick.direction': 'out',
'xtick.labelbottom': True,
'xtick.labelcolor': 'inherit',
'xtick.labelsize': 'medium',
'xtick.labeltop': False,
'xtick.major.bottom': True,
'xtick.major.pad': 3.5,
'xtick.major.size': 3.5,
'xtick.major.top': True,
'xtick.major.width': 0.8,
'xtick.minor.bottom': True,
'xtick.minor.ndivs': 'auto',
'xtick.minor.pad': 3.4,
'xtick.minor.size': 2.0,
'xtick.minor.top': True,
'xtick.minor.visible': False,
'xtick.minor.width': 0.6,
'xtick.top': False,
'yaxis.labellocation': 'center',
'ytick.alignment': 'center_baseline',
'ytick.color': 'black',
'ytick.direction': 'out',
'ytick.labelcolor': 'inherit',
'ytick.labelleft': True,
'ytick.labelright': False,
'ytick.labelsize': 'medium',
'ytick.left': True,
'ytick.major.left': True,
'ytick.major.pad': 3.5,
'ytick.major.right': True,
'ytick.major.size': 3.5,
'ytick.major.width': 0.8,
'ytick.minor.left': True,
'ytick.minor.ndivs': 'auto',
'ytick.minor.pad': 3.4,
'ytick.minor.right': True,
'ytick.minor.size': 2.0,
'ytick.minor.visible': False,
'ytick.minor.width': 0.6,
'ytick.right': False})To update the recall parameters, the rc function is used:
In [4]: recall_parameters['lines.marker']
Out[4]: 'None'
In [5]: recall_parameters['lines.linewidth']
Out[5]: 1.5
In [6]: recall_parameters['lines.markersize']
Out[6]: 6.0The default marker is None, meaning no marker is applied.
The default linewidth is 1.5.
To update the currently used recall_parameters, the rc function is used:
In [7]: mpl.rc('lines', linewidth=5)
: mpl.rc('lines', marker='*')
: mpl.rc('lines', markersize=50)The current parameters can be examined:
In [8]: current_parameters = mpl.rcParams
In [9]: current_parameters['lines.linewidth']
Out[9]: 5.0
In [10]: current_parameters['lines.marker']
Out[10]: '*'
In [11]: current_parameters['lines.markersize']
Out[11]: 50Now when the following Figure is made, it is made with the updated recall parameters:
In [12]: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.set_title('y=f(x)')
: ax.plot(x, y, x, y+1, x, y+2, x, y+3)
The recall parameter for lines.color displays a single color:
In [13]: recall_parameters['lines.color']
Out[13]: 'C0'This is overridden by axes.prop_cycle which applies cycles through tab10 using a color cycle:
In [14]: recall_parameters['axes.prop_cycle']
Out[14]: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])The function colormaps can be used to visualise the colormaps 'tab20' and 'tab10' in ipython cells:
In [15]: mpl.colormaps['tab20']
In [16]: mpl.colormaps['tab10']
The colors for 'tab10' can be observed where each color is a normalised (r, g, b) tuple:
In [17]: mpl.colormaps['tab10'].colors
Out[17]:
((0.12156862745098039, 0.4666666666666667, 0.7058823529411765),
(1.0, 0.4980392156862745, 0.054901960784313725),
(0.17254901960784313, 0.6274509803921569, 0.17254901960784313),
(0.8392156862745098, 0.15294117647058825, 0.1568627450980392),
(0.5803921568627451, 0.403921568627451, 0.7411764705882353),
(0.5490196078431373, 0.33725490196078434, 0.29411764705882354),
(0.8901960784313725, 0.4666666666666667, 0.7607843137254902),
(0.4980392156862745, 0.4980392156862745, 0.4980392156862745),
(0.7372549019607844, 0.7411764705882353, 0.13333333333333333),
(0.09019607843137255, 0.7450980392156863, 0.8117647058823529))The function to_hex can be used to convert these to the hexadecimal format '#rrggbb':
In [18]: from matplotlib.colors import to_hex
In [19]: [to_hex(color) for color in mpl.colormaps['tab10'].colors]
Out[19]:
['#1f77b4',
'#ff7f0e',
'#2ca02c',
'#d62728',
'#9467bd',
'#8c564b',
'#e377c2',
'#7f7f7f',
'#bcbd22',
'#17becf']
In [20]: [to_hex(color) for color in mpl.colormaps['tab20'].colors]
Out[20]:
['#1f77b4',
'#aec7e8',
'#ff7f0e',
'#ffbb78',
'#2ca02c',
'#98df8a',
'#d62728',
'#ff9896',
'#9467bd',
'#c5b0d5',
'#8c564b',
'#c49c94',
'#e377c2',
'#f7b6d2',
'#7f7f7f',
'#c7c7c7',
'#bcbd22',
'#dbdb8d',
'#17becf',
'#9edae5']A new cycler instance can be made for the 'tab20' colors:
In [21]: mpl.cycler(color=[to_hex(color) for color in mpl.colormaps['tab20'].colors])
Out[21]: cycler('color', ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5'])A new cycler instance can be made for 20 markers:
In [22]: mpl.cycler(marker = [
: '.', # Point marker
: ',', # Pixel marker
: 'o', # Circle marker
: 'v', # Triangle down
: '^', # Triangle up
: '<', # Triangle left
: '>', # Triangle right
: '1', # Tri-down (tripod)
: '2', # Tri-up (tripod)
: '3', # Tri-left (tripod)
: '4', # Tri-right (tripod)
: 's', # Square
: 'p', # Pentagon
: '*', # Star
: 'h', # Hexagon1
: 'H', # Hexagon2
: '+', # Plus
: 'x', # X
: 'D', # Diamond
: 'd', # Thin diamond
: ]
: )
Out[22]: cycler('marker', ['.', ',', 'o', 'v', '^', '<', '>', 1, 2, 3, 4, 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd'])These cycler instances can be added together:
In [23]: mpl.cycler('color', ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']) + mpl.cycler('marker', ['.', ',', 'o', 'v', '^', '<', '>', 1, 2, 3, 4, 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd'])
Out[23]: (cycler('color', ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']) + cycler('marker', ['.', ',', 'o', 'v', '^', '<', '>', 1, 2, 3, 4, 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd']))axes.prop_cycle can be assigned to this cycler instance:
In [24]: mpl.rc('axes', prop_cycle=mpl.cycler('color', ['#1f77b4', '#aec7e8', '#ff7f0e', '#ffbb78', '#2ca02c', '#98df8a', '#d62728', '#ff9896', '#9467bd', '#c5b0d5', '#8c564b', '#c49c94', '#e377c2', '#f7b6d2', '#7f7f7f', '#c7c7c7', '#bcbd22', '#dbdb8d', '#17becf', '#9edae5']) + mpl.cycler('marker', ['.', ',', 'o', 'v', '^', '<', '>', 1, 2, 3, 4, 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd']))Now when the following Figure is made, it is made with the updated recall parameters:
In [25]: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.set_title('y=f(x)')
: ax.plot(x, y, x, y+1, x, y+2, x, y+3)
There are three rcParams instances rcParams, the current recall parameters rcParamsDefault, the default recall parameters and rcParamsOrig, the original recall provided by matplotlib.
The function matplotlib_fname will get the file name for the original recall parameters:
In [27]: mpl.matplotlib_fname()
Out[27]: 'C:\\Users\\phili\\miniforge3\\envs\\spyder-env\\Lib\\site-packages\\matplotlib\\mpl-data\\matplotlibrc'This can be printed, to process, the escape characters:
In [28]: print(mpl.matplotlib_fname())
C:\Users\phili\miniforge3\envs\spyder-env\Lib\site-packages\matplotlib\mpl-data\matplotlibrcIf the parent folder mpl-data is opened in Windows Explorer:
The matplotlibrc can be opened in a text editor such as Notepad++:
Note the file should not be modified and instead a copy of it should be placed in matplotlibs configuration directory. The function get_configdir will display matplotlibs configuration directory, this will be printed once again to process the escape characters:
In [29]: print(mpl.get_configdir())
C:\Users\phili\.matplotlibThe matplotlibrc file can be copied to the configuration directory:
The copied matplotlibrc file can be opened in a text editor such as Notepad++. The top of the recall file, outlines a table of recall groupings:
If the LINES grouping is examined, many of the properties seen before are listed:
This file format is similar to an ini file and has the following item format:
key: value
The # is used to indicate a comment:
# unused_key: unused_value
Numbers are provided in the form 1 and strings are provided in the form hello. A list of parameters are defined using a cycle instance.
key1: cycler('values1': ['value1', 'value2', 'value3'])
key2: (cycler('values1': ['value1', 'value2', 'value3']) + cycler('values2', ['value1', 'value2', 'value3']))
Modifications in the matplotlibrc file can be made by commenting out the relevant lines and amending to the desired recall parameters:
For example changing:
#lines.linewidth: 1.5 # line width in points
#lines.linestyle: - # solid line
#lines.color: C0 # has no affect on plot(); see axes.prop_cycle
#lines.marker: None # the default marker
#axes.prop_cycle: cycler('color', ['1f77b4', 'ff7f0e', '2ca02c', 'd62728', '9467bd', '8c564b', 'e377c2', '7f7f7f', 'bcbd22', '17becf'])
To:
lines.linewidth: 5 # line width in points
lines.linestyle: : # solid line
# lines.color: C0 # has no affect on plot(); see axes.prop_cycle
# lines.marker: * # the default marker
axes.prop_cycle: (cycler('color', ['1f77b4', 'aec7e8', 'ff7f0e', 'ffbb78', '2ca02c', '98df8a', 'd62728', 'ff9896', '9467bd', 'c5b0d5', '8c564b', 'c49c94', 'e377c2', 'f7b6d2', '7f7f7f', 'c7c7c7', 'bcbd22', 'dbdb8d', '17becf', '9edae5']) + cycler('marker', ['.', ',', 'o', 'v', '^', '<', '>', '1', '2', '3', '4', 's', 'p', '*', 'h', 'H', '+', 'x', 'D', 'd']))
Note that an incorrect modification will result in an error when restarting the kernel in the Spyder IDE:
The Spyder IDE will not display this error with a valid matplotlibrc file. When example code is ran, the recall parameters will be applied:
In [1]: import numpy as np
: import matplotlib as mpl
: import matplotlib.pyplot as plt
: x = np.array([0, 1, 2, 3, 4, 5])
: y = np.array([0, 2, 4, 6, 8, 10])
:
: fig, ax = plt.subplots()
: ax.set_xlabel('x')
: ax.set_ylabel('y')
: ax.set_title('y=f(x)')
: ax.plot(x, y, x, y+1, x, y+2, x, y+3)