====== Working with matplotlib (JYP version) ======

<note tip>Note: [[https://matplotlib.org/cheatsheets/|Matplotlib cheatsheets]] ([[https://github.com/matplotlib/cheatsheets#cheatsheets-for-matplotlib-users|pdf version]])</note>

**Summary**: there are lots of python libraries that you can use for plotting, but Matplotlib has become a //de facto// standard

You can get more **information about python** on the [[other:python:jyp_steps|JYP's recommended steps for learning python]] page.

You can get more **information on plotting maps** in the [[other:python:jyp_steps#cartopy_iris|Cartopy + Iris section]] and the [[other:python:jyp_steps#basemap|basemap section]], with examples on the [[other:python:maps_by_jyp|JYP's map room]] page. Note: there will be no python3 support in basemap, and basemap is being slowly replaced by cartopy

matplotlib official website: [[http://matplotlib.org|Matplotlib web site]]

matplotlib help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/matplotlib|matplotlib help]]

===== Starting (and more) with matplotlib =====

The matplotlib documentation is good, but not always easy to use : the package is powerful, there are lots of ways to do the same thing and it's easy to get lost.

There are lots of online tutorials that often don't show much more than a simple scatter plot with basic page setup.

This page will hopefully give you some keys to become (progressively) a matplotlib power user.

<wrap hi>A good way to start with matplotlib</wrap> is to quickly read this section, practice, and read this section again (and again)

  - Have a quick look at the [[https://matplotlib.org/stable/gallery/index.html|matplotlib gallery]] to get an idea of all you can do with matplotlib. Later, when you need to plot something, go back to the gallery to find some examples that are close to what you need and click on them to view their source code
    * some examples are more //pythonic// (ie object oriented) than others, and some examples mix different styles of coding, which can be quite confusing. Try to [[http://matplotlib.org/faq/usage_faq.html#coding-styles|use an object oriented way of doing things]]!
  - Use the free hints provided by JY!
    - You will usually **initialize matplotlib** with: ''import matplotlib.pyplot as plt''
      * in some cases you may also need: ''import matplotlib as mpl''
      * later, you may need other matplotlib related modules, for advanced usage
    - You need to know some **matplotlib specific vocabulary**:
      * a Matplotlib **//Figure//** (or //canvas//) is a **graphical window** or //page// in which you create your plots...
        * example: ''my_page = plt.figure()''
        * if you need several display windows at the same time, create several figures!\\ <code>win_1 = plt.figure()
win_2 = plt.figure()</code>
        * the [[https://matplotlib.org/stable/gallery/showcase/anatomy.html|parts of a figure]] are usually positioned in //normalized coordinates//: ''(0, 0)'' is the bottom left of the figure, and ''(1, 1)'' is the top right
        * You don't really specify the **page orientation** (//portrait// or //landscape//) of a plot. If you want a portrait plot, it's up to you to create a plot that will look higher than it is large. The idea is not to worry about this and just check the final resulting plot: create a plot, save it, display the resulting png/pdf and then adjust the creation script
          * If you do have an idea of the layout of what you want to plot, it may be easier to explicitly specify the figure size/ratio at creation time, and then try to //fill// the normalized coordinates space of the figure
          * ''my_page = plt.figure()'': the ratio of the default figure is ''landscape'', because it is 33% larger than it is high. Creating a default figure will be OK most of the time!
          * ''my_page = plt.figure(figsize=(width, height))'': create a figure with a custom ratio
            * The specified ''width'' and ''height'' are supposed to be in inches (1 inch = 2.54 cm)
            * ''my_page = plt.figure(figsize=(8.3, 11.7))'': create a figure that will theoretically fill an A4 size page in portrait mode (check [[https://www.papersizes.org/a-paper-sizes.htm|Dimensions Of A Series Paper Sizes]] if you need more details about standard paper sizes)
      * a Matplotlib **//Axes//** (not to be confused with an //**axis**//) is a **(sub-)plot** inside a Figure... [[https://matplotlib.org/stable/api/axes_api.html|(much) More details]]
        * reserve space for **one plot** that will use most of the available area of the figure/page:
          * ''my_plot = my_page.add_subplot(1, 1, 1)'' or ''my_plot = my_page.subplot**s**()''
          * the syntax for **more than one plot** is: ''add_subplot(nrows, ncols, index)''
        * create **3 plots on 1 column** (each plot uses the full width of the figure):
          * <code>top_plot = my_page.add_subplot(3, 1, 1)
mid_plot = my_page.add_subplot(3, 1, 2)
bot_plot = my_page.add_subplot(3, 1, 3)</code>
          * creating an //array of plots// with ''subplots'' is **more efficient** than ''add_subplot'', when there are lots of plots on a page<code>plot_array = my_page.subplots(3, 1)
top_plot = plot_array[0]
mid_plot = plot_array[1]
bot_plot = plot_array[2]</code>
          * it is **even more efficient** to create a //figure// **and** //axes// with a single line:
            * one plot on one page: ''my_page, my_plot = **plt**.subplots()''
            * three plots on one A4 portrait page:\\ <code>
my_page, plot_array = plt.subplots(nrows=3, ncols=1,
                                   figsize=(8.3, 11.) # A4 portrait)
top_plot = plot_array[0]
mid_plot = plot_array[1]
bot_plot = plot_array[2]
</code>
            * if you need to specify the same additional //keyword// parameters (that will be passed //behind the scene// to the ''add_subplot'' function), you can use the ''subplot_kw'' parameter. For example, if all the plots will be created with cartopy, using the //Robinson// projection, the example above becomes:\\ <code>
my_page, plot_array = plt.subplots(nrows=3, ncols=1,
                                   figsize=(8.3, 11.), # A4 portrait
                                   subplot_kw=dict(projection=ccrs.Robinson()))
</code>
        * use [[https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_axes|my_page.add_axes(...)]] to add an axis in an arbirary location of the page\\ ''my_page.add_axes([left, bottom, width, height])''
      * a Matplotlib **//Artist//** or //Patch// is //something// (e.g a line, a group of markers, text, the legend...) plotted  on the Figure/Axis
      * **clearing** the //page// (or part of it): you probably won't need that...
        * ''my_page.clear()'' or ''my_page.clf()'' or ''plt.clf()'': clear the (current) figure
        * ''my_plot.clear()'' or ''my_plot.cla()'': clear the (current) axis
    - some resources for having multiple plots on the same figure
      * [[https://matplotlib.org/stable/gallery/recipes/create_subplots.html|Easily creating subplots]]
        * [[https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_subplot|fig.add_subplot(...)]]
        * [[https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_axes|fig.add_axes(...)]]
        * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html|plt.subplot(...)]]
        * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots.html|plt.subplots(...)]] with an **s** at the end ([[https://matplotlib.org/stable/gallery/subplots_axes_and_figures/subplots_demo.html|demo]])
        * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots_adjust.html|subplots_adjust]] can be used to change the overall boundaries of the subplots on the figure, and the spacing between the subplots\\ ''plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)''\\ or ''my_page.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)''
          * ''hspace''/''wspace'' is the amount of height/width between the subplots
            * ''hspace=0.1'' is enough for just displaying the ticks and the labels, without the axis name
            * use ''hspace=0'' to stick the plots together vertically
              * do not forget to disable the ticks where there is no space to plot them: ''my_plot.set_xticks([])''
          * ''my_page.subplots_adjust(right=0.75)'' will leave 25% on the right of the page for adding a legend outside of a plot
        * You can also **resize an existing (sub)plot** the following way:
          - Get the current size information: ''pl_x_bottomleft, pl_y_bottomleft, pl_width, pl_height = my_plot.get_position().bounds''
          - Set the new size: e.g reduce the height with ''my_plot.set_position( (pl_x_bottomleft, pl_y_bottomleft, pl_width, pl_height  * 0.5) )''
      * [[https://matplotlib.org/stable/gallery/index.html#subplots-axes-and-figures|Subplots, axes and figures]] gallery
      * [[https://matplotlib.org/tutorials/intermediate/gridspec.html#sphx-glr-tutorials-intermediate-gridspec-py|Customizing Figure Layouts Using GridSpec and Other Functions]], [[https://matplotlib.org/tutorials/intermediate/constrainedlayout_guide.html|constrained layout]] and [[https://matplotlib.org/tutorials/intermediate/tight_layout_guide.html|tight layout]]
    - use [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html|my_page.savefig(...)]] to save a figure
      *  <wrap hi>savefig(...) must be called **before** plt.show()!</wrap>
      * ''my_page.savefig('my_plot.pdf')'': save the figure to a pdf file
      * ''my_page.savefig('my_plot.png', dpi=200, transparent=True, bbox_inches='tight')'': save the figure to a png file at a higher resolution than the default (default is 100 dots per inch), with a transparent background and no extra space around the figure
    - **display** the figure and its plots, and **start interacting** (zooming, panning...) with them:\\ ''plt.show()''
    - it may be hard to (remember how to) **work with colors //and colorbars//**. Some examples from the [[https://matplotlib.org/stable/gallery/index.html#color|matplotlib Gallery]] can help you!\\ Note: A **reversed version of each colormap** is available by appending ''_r'' to the name, e.g., ''viridis_r''
      * [[https://matplotlib.org/stable/gallery/specialty_plots/leftventricle_bulleye.html|leftventricle_bulleye.py]]: associating different types of colormaps to a plot and colorbar
      * [[https://matplotlib.org/examples/api/colorbar_only.html|colorbar_only.py]]: the different types of colorbars (or plotting only a colorbar)
      * [[https://matplotlib.org/stable/gallery/color/colormap_reference.html|colormaps_reference.py]]: pre-defined colormaps
      * [[https://matplotlib.org/stable/gallery/color/named_colors.html|named_colors.py]]: named colors
      * More details about colors and colorbars below, in the [[#useful_matplotlib_reference_pages|Useful matplotlib reference pages]] section and the [[#graphics_related_resources|Graphics related resources]] section
    - if you don't see a part of what you have plotted, maybe it's hidden behind other elements! Use the [[https://matplotlib.org/examples/pylab_examples/zorder_demo.html|zorder parameter]] to explicitly **specify the plotting order/layers/depth**
      * things should automatically work //as expected// if //zorder// is not explicitly specified
      * Use the ''zorder=NN'' parameter when creating objects. ''NN'' is an integer where 0 is the lowest value (the farthest from the eye), and objects are plotted above objects with a lower //zorder// value
      * Use ''matplotlib_object.set_order(NN)'' to change the order after an object has been created
    - you can use **transparency** to partially show what is behind some markers or other objects. Many //artists// accept the ''alpha'' parameter where ''0.0'' means that the object is completely transparent, and ''1.0'' means completely opaque\\ e.g. ''my_plot.scatter(..., alpha=0.7)''
    - sometimes the results of the python/matplolib commands are displayed immediately, sometimes not. It depends if you are in [[http://matplotlib.org/faq/usage_faq.html#what-is-interactive-mode|interactive or non-interactive]] mode
    - if your matplotlib is executed in a batch script, it will generate an error when trying to create (''show()'') a plot, because matplotlib expects to be able to display the figure on a screen (with an X server running) by default.\\ Find out how to deal with this in [[other:python:matplotlib_by_jyp#creating_a_plot_offline|Creating a plot offline]].
    - the documentation may mention [[http://matplotlib.org/faq/usage_faq.html#what-is-a-backend|backends]]. What?? Basically, you use python commands to create a plot, and the backend is the //thing// that will render your plot on the screen or in a file (png, pdf, etc...)
  - Read the [[https://github.com/rougier/matplotlib-tutorial|Matplotlib tutorial by Nicolas Rougier]]
  - Download the [[http://matplotlib.org/contents.html|pdf version of the manual]]. **Do not print** the 2300+ pages of the manual! Read the beginner's guide (Chapter //FIVE// of //Part II//) and have a super quick look at the table of contents of the whole document.

===== Useful matplotlib reference pages =====

  * Some plot types:
    * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html|plot(...)]]: Plot y versus x as lines and/or markers
    * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html|scatter(...)]]: A scatter plot of y vs x with varying marker size and/or color
    * The ''plot'' function will be faster for scatterplots where markers don't vary in size or color
    * [[https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.contourf.html|contour(...) and contourf(...)]]: draw contour lines and filled contours
  * **X and Y axes parameters** (see also [[https://matplotlib.org/stable/gallery/showcase/anatomy.html|Anatomy of a figure]]):
    * **Axis range**: ''my_plot.set_xlim(x_leftmost_value, x_rightmost_value)''
      * Use the leftmost and rightmost values to specify the orientation of the axis (i.e the rightmost value can be smaller than the leftmost)
    * **Axis label**: ''my_plot.set_xlabel(x_label_string, fontsize=axis_label_fontsize)''
      * Use the extra labelpad parameter to move the label closer (negative value) to the axis or farther (positive value): e.g. ''my_plot.set_xlabel('A closer label', labelpad=-20'')
      * <wrap hi>cartopy dirty trick</wrap>: you have to use ''my_plot.set_xticks([])'', otherwise the X axis label will not be printed
        * [[https://stackoverflow.com/questions/35479508/cartopy-set-xlabel-set-ylabel-not-ticklabels|Trick source]]
        * Trick needs to be used with ''cartopy 0.17.0''\\ Remember to update/remove this information in the future
    * Major (and minor) **tick marks location**: ''my_plot.set_xticks(x_ticks_values, minor=False)'' ([[https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xticks.html|set_xticks]])
      * Use an empty list if you don't want tick marks: ''my_plot.set_xticks([])''
    * **Tick labels**: ''my_plot.set_xticklabels(x_tick_labels, minor=False, fontsize=ticklabels_fontsize)'' ([[https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xticklabels.html|set_xticklabels]])
      * If you do not specify labels, the //default labels// will just be the values specifying the ticks' location
      * ''x_tick_labels'' is a list of strings that has the same length as ''x_ticks_values''.\\ Use an empty string in the positions where you don't want a label
      * The default numerical labels may be too long, due to numerical approximations. You can try to explicitly round the values, or generate correct label strings from the values\\ <code>>>> x_tick_values = np.arange(0, 1, 0.2)
>>> x_tick_values.tolist()
[0.0, 0.2, 0.4, 0.6000000000000001, 0.8]
>>> x_tick_values.round(decimals=1).tolist()
[0.0, 0.2, 0.4, 0.6, 0.8]
>>> x_tick_labels = [ '%.1f' % (t_val,) for t_val in x_tick_values ]
>>> x_tick_labels
['0.0', '0.2', '0.4', '0.6', '0.8']
>>> x_tick_labels[0] = 'START'
>>> x_tick_labels[-1] = 'END'
>>> x_tick_labels
['START', '0.2', '0.4', '0.6', 'END']
</code> 
      * You can also use fancy [[https://matplotlib.org/stable/gallery/ticks_and_spines/tick-formatters.html|tick formatters]]
      * Many more options for ticks, labels, orientation, ...
    * **Grid lines**:
      * Their position is determined by the values used for ''set_xticks'' and ''set_yticks''
      * Activate all (horizontal **and** vertical) grid lines with: ''my_plot.grid(True, linestyle="%%--%%", linewidth=0.5, color='.25',zorder=some_value)''\\ You can adjust the ''zorder'' value to determine if the grid lines should be above or below other parts of the plot!
      * Plot only the horizontal **or** vertical lines with:\\ ''ax.yaxis.grid(True)''\\ or ''ax.xaxis.grid(True)''
      * Note: <wrap hi>special case of //cartopy// plots</wrap>: the location of the gridlines, and the properties of the associated labels are determined by ''myplot.gridlines''! See [[https://scitools.org.uk/cartopy/docs/latest/matplotlib/gridliner.html|Cartopy map gridlines and tick labels]]
  * [[https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html|line]] parameters
    * ''linestyle'': ''solid'', ''None'', [[https://matplotlib.org/api/_as_gen/matplotlib.lines.Line2D.html#matplotlib.lines.Line2D.set_linestyle|other]] ([[https://matplotlib.org/examples/lines_bars_and_markers/line_styles_reference.html|default styles example]], [[https://matplotlib.org/examples/lines_bars_and_markers/linestyles.html|custom styles example]])
  * [[https://matplotlib.org/api/markers_api.html|marker types]]
    * Default marker size and edge width:
      * ''mpl.rcParams['lines.markersize'] %%**%% 2'' => 36
      * ''mpl.rcParams['lines.linewidth']'' => 1.5
    * Other marker attributes. For ''plot'', all the markers have the same attributes, and for ''scatter'' the attributes can be the same, or specified for each marker
      * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html|plot(...)]]: //fmt// (see documentation) or ''marker'' and ''markerfacecolor''/''mfc'' (and ''markerfacecoloralt''/''mfcalt'' for dual color markers), ''markersize'', ''markeredgewidth''/''mew'', ''markeredgecolor'' (use ''markeredgecolor='none''' if you don't want to plot the edge of the markers), ''fillstyle'' (''full'', ''None'', [[https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.htm|other]])
      * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html|scatter(...)]]: ''marker'' (marker type), ''c'' (color), ''s'' (size), ''linewidths'' (linewidth of the marker edges), ''edgecolors''
  * [[https://matplotlib.org/stable/api/colors_api.html|colors]] and colormaps
    * [[https://matplotlib.org/stable/gallery/color/color_demo.html|color demo]]
    * [[https://matplotlib.org/stable/gallery/color/named_colors.html#sphx-glr-gallery-color-named-colors-py|named colors]]
    * [[https://www.w3schools.com/colors/colors_picker.asp|HTML color picker]] and different ways of choosing colors
    * Reverting the colors: add ''_r'' at the end of the colormap name
    * Number of colors in the //my_cmap// colormap (usually 256): ''my_cmap.N''
      * Accessing the RGB color definition by index, from ''0'' to ''my_cmap.N - 1''. Note that the index will //saturate// below ''0'' and above ''my_cmap.N - 1''\\ <code>>>> my_cmap.N
256
>>> my_cmap(-1) # Same as ano_cmap(0)
(0.3686274509803922, 0.30980392156862746, 0.6352941176470588, 1.0)
>>> my_cmap(0)
(0.3686274509803922, 0.30980392156862746, 0.6352941176470588, 1.0)
>>> my_cmap(1)
(0.36186082276047676, 0.3185697808535179, 0.6394463667820068, 1.0)
>>> my_cmap(255)
(0.6196078431372549, 0.00392156862745098, 0.25882352941176473, 1.0)
>>> my_cmap(256) # Same as ano_cmap(255)
(0.6196078431372549, 0.00392156862745098, 0.25882352941176473, 1.0)
>>> my_cmap(257) # Same as ano_cmap(255)
(0.6196078431372549, 0.00392156862745098, 0.25882352941176473, 1.0)
</code>
    * Special colormap colors
      * ''my_cmap.set_bad(color='k')'': color to be used for **masked** values
      * ''my_cmap.set_over(color='k')'': color to be used for //high out-of-range values// **if** ''extend'' is specified and is //'both'// or  //'max'//. Default color is ''my_cmap(my_cmap.N - 1)''
      * ''my_cmap.set_under(color='k')'': color to be used for //low out-of-range values// **if** ''extend'' is specified and is //'both'// or  //'min'//. Default color is ''my_cmap(0)''
  * [[https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure.colorbar|colorbar]] (see also the [[https://matplotlib.org/stable/api/colorbar_api.html|colorbar api]])
    * [[https://matplotlib.org/stable/gallery/subplots_axes_and_figures/colorbar_placement.html|Placing colorbars demo]]
    * [[https://matplotlib.org/stable/gallery/images_contours_and_fields/contourf_demo.html|contourf + colorbar demo]]
    * Changing the font size of a colorbar (i.e. //changing [[https://matplotlib.org/stable/api/axes_api.html#ticks-and-tick-labels|ticks and tick labels]]//):
      * This can be done by manipulating the properties of the //Axes// where the colorbar is plotted\\ e.g. change the tick labels font size with\\ ''cb.ax.tick_params(labelsize='xx-large')'' (where ''cb'' is a //colorbar// object)
  * [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.text.html|text(...)]] and [[https://matplotlib.org/tutorials/text/annotations.html|annotations]]
    * Some titles:
      * **Title at the top of the page**: [[https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.suptitle|Figure title]]: ''my_figure.suptitle('Figure title', x=xloc_in_normalized_coordinates, y=yloc_in_normalized_coordinates, ...)''
      * **Title above each plot**:[[https://matplotlib.org/api/axes_api.html#axis-labels-title-and-legend|Axis Labels, title, and legend]]: ''my_plot.set_title('Plot title', ...)''
        * [[https://matplotlib.org/stable/gallery/text_labels_and_annotations/titles_demo.html|Title and labels positions demo]]
    * ''fontsize'': size in points, or (better!) string specifying a relative size (''xx-small'', ''x-small'', ''small'', ''medium'', ''large'', ''x-large'', ''xx-large'')
    * [[https://matplotlib.org/stable/api/text_api.html|all the text properties]]
  * [[https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.legend.html|legend(...)]] ([[https://matplotlib.org/stable/gallery/text_labels_and_annotations/legend_demo.html|legend demo]], [[https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html|advanced legend guide]])
    * The legend will //show// the lines (or other objects) that were associated with a //label// with the ''label='' keyword when creating/updating a plot
      * If there are some elements of a plot that you do not want to associate with a legend (e.g. there are several lines with the same color and markers, but you want to plot the legend only once), do not specify a ''label='' keyword for these elements, or add a ''_'' at the front of the label strings
    * The legend is positioned somewhere (that can be specified) **inside** the plot. In order to place a legend **outside** the plot, use the ''bbox_to_anchor'' parameter
      * the parameters of ''bbox_to_anchor'' are in normalized coordinates of the current (sub)plot:
        * ''(0, 0)'' is the lower left corner of the plot, and ''(1, 1)'' the upper right corner
        * ''legend(... bbox_to_anchor=(1.05, 1.), loc='upper left', ...)'' will put the upper left corner of the legend slightly right (''(1.05, 1.)'') of the upper right corner (''(1, 1)'') of the plot
      * if the legend is outside of the plot, you have to **explicitly provide enough space for the legend on the page**
        * e.g. with [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplots_adjust.html|subplots_adjust]], ''plt.subplots_adjust(right=0.75)'' will make all the plots use 75% on the left of the page, and leave 25% on the right for the legend
  * The [[https://matplotlib.org/api/_as_gen/matplotlib.figure.Figure.html|figure(...)]] and the associated methods
  * The [[https://matplotlib.org/stable/api/axes_api.html|axes]] and the associated methods
  * [[https://matplotlib.org/tutorials/introductory/customizing.html#matplotlib-rcparams|matplotlib default config/settings]] can be queried and updated
    * example: the default figure size (inches) is ''mpl.rcParams['figure.figsize']'' (''[6.4, 4.8]'')
    * current settings' file:  ''mpl.matplotlib_fname()''
  * [[https://matplotlib.org/api/animation_api.html|Animations]] ([[https://matplotlib.org/stable/gallery/index.html#animation|demo]])

===== Misc Matplotlib tricks =====

==== Creating a plot offline ====

You may need to create a plot offline when your network connection is not good enough, you don't have an X server running to display the plot (possibly because the script is running on a cluster), etc... This is easily done with the following code:

<code># offline_plot = False
offline_plot = True

import matplotlib as mpl
if offline_plot:
    # Define the graphic back-end BEFORE importing pyplot
    mpl.use('Agg')
    
# Import the rest of the matplotlib based modules
import matplotlib.pyplot as plt

[ ...your actual code... ] 

# Done at last! Save the result
my_page.savefig(out_name, dpi=300, transparent=True, bbox_inches='tight')

if not offline_plot:
    # Enter the interactive mode to display the plot
    plt.show()</code>

Note: see also [[https://matplotlib.org/stable/gallery/user_interfaces/canvasagg.html|CanvasAgg demo]] for a pure offline plot, and [[https://matplotlib.org/faq/howto_faq.html?highlight=web#howto-webapp|How to use Matplotlib in a web application server]]. But the code above is much easier!

==== Specifying the background color of a plot ====

e.g. You need to plot a masked variable, but you don't want the masked areas to be white

<code># make the background dark gray (call this before the contourf)
plt.gca().patch.set_color('.25')
plt.contourf(d)
plt.show()</code>

[[https://stackoverflow.com/questions/9797520/masking-part-of-a-contourf-plot-in-matplotlib|trick source]]

===== Unsorted matplotlib stuff =====

Some useful notes and links that cannot be placed (yet) in a section of the main page

==== Plotting arcs (segments of ellipses) ====

  * [[https://matplotlib.org/stable/api/_as_gen/matplotlib.patches.Arc.html|Offical patches.Arc documentation]]
  * A nice [[https://stackoverflow.com/questions/54849976/can-someone-explain-the-different-parameters-in-matplotlib-patches-arc|ellipses and arcs tutorial]] on stackoverflow

==== Using hatches with contourf ====

  * [[https://matplotlib.org/stable/gallery/shapes_and_collections/hatch_style_reference.html|Hatch style reference]]
  * [[https://matplotlib.org/stable/gallery/shapes_and_collections/hatch_demo.html|Hatch demo]]
  * [[https://matplotlib.org/stable/gallery/images_contours_and_fields/contourf_hatching.html|Contourf hatching]]
  * //Collections// trick for [[https://fantashit.com/hatching-color-in-contourf-function/|changing the colors of hatches]]

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