User Tools

Site Tools



This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
other:python:jyp_steps [2019/08/09 11:42]
jypeter [Useful matplotlib reference pages]
other:python:jyp_steps [2019/11/18 12:04] (current)
jypeter Replaced files links with sharebox links
Line 18: Line 18:
     * the tutorial is very detailed about string handling, because strings offer an easy way to practice working with indices (indexing and slicing), before indexing numpy arrays. And our usual pre/​post-processing scripts often need to do a lot of string handling in order to generate the file/​variable/​experiment names     * the tutorial is very detailed about string handling, because strings offer an easy way to practice working with indices (indexing and slicing), before indexing numpy arrays. And our usual pre/​post-processing scripts often need to do a lot of string handling in order to generate the file/​variable/​experiment names
   * after reading this tutorial, you should practice with the following:   * after reading this tutorial, you should practice with the following:
-    * [[​public.php?​service=files&​t=9731fdad4521ac5fa6e84b392d3a2e44|Basic python training test (ipython notebook version)]]+    * [[​index.php/​s/​S3EO8cLrhVDeQWA|Basic python training test (ipython notebook version)]]
     * {{:​other:​python:​tp_intro_python_oct2013_no_solutions.pdf|Basic python training test (pdf version)}}     * {{:​other:​python:​tp_intro_python_oct2013_no_solutions.pdf|Basic python training test (pdf version)}}
     * {{:​other:​python:​tp_intro_python_oct2013_full.pdf|Basic python training test (pdf version, with answers)}}     * {{:​other:​python:​tp_intro_python_oct2013_full.pdf|Basic python training test (pdf version, with answers)}}
Line 173: Line 173:
 ===== Matplotlib ===== ===== Matplotlib =====
 +<note important>​
 +The full content of this //​matplotlib//​ section has been moved to\\ [[other:​python:​matplotlib_by_jyp|Working with matplotlib (JYP version)]]\\ after becoming too big to manage here</​note>​
 Summary: there are lots of python libraries that you can use for plotting, but Matplotlib has become a //de facto// standard Summary: there are lots of python libraries that you can use for plotting, but Matplotlib has become a //de facto// standard
Line 179: Line 182:
 Help on //stack overflow//: [[https://​​questions/​tagged/​matplotlib|matplotlib help]] Help on //stack overflow//: [[https://​​questions/​tagged/​matplotlib|matplotlib help]]
-The matplotlib documentation is good, but not always easy to use. <wrap hi>A good way to start with matplotlib</​wrap>​ is to quickly read the following, practice, and read this section again 
-  - Have a quick look at the [[https://​​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://​​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** 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 [[http://​​faq/​usage_faq.html#​parts-of-a-figure|parts of a figure]] are often positioned in //​normalized coordinates//:​ ''​(0,​ 0)''​ is the bottom left of the figure, and ''​(1,​ 1)''​ 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 (sizes are considered to be in inches) 
-            * ''​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://​​a-paper-sizes.htm|Dimensions Of A Series Paper Sizes]] if you need more size details) 
-      * a Matplotlib **//​Axis//​** is a **plot** inside a Figure... [[http://​​faq/​usage_faq.html#​parts-of-a-figure|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)'':​ syntax is ''​add_subplot(nrows,​ ncols, index)''​ 
-          * ''​my_plot = my_page.subplot**s**()''​ 
-        * 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) 
-middle_plot = my_page.add_subplot(3,​ 1, 2) 
-bottom_plot = my_page.add_subplot(3,​ 1, 3)</​code>​ 
-          * the following method 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] 
-middle_plot = plot_array[1] 
-bottom_plot = plot_array[2]</​code>​ 
-          * creating a figure and axes with a single line: ''​my_page,​ plot_array = **plt**.subplots(3,​ 1)''​ 
-        * use [[https://​​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://​​gallery/​recipes/​create_subplots.html#​sphx-glr-gallery-recipes-create-subplots-py|Easily creating subplots]] 
-        * [[https://​​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.add_subplot|fig.add_subplot(...)]] 
-        * [[https://​​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.add_axes|fig.add_axes(...)]] 
-        * [[https://​​api/​_as_gen/​matplotlib.pyplot.subplot.html|plt.subplot(...)]] 
-        * [[https://​​api/​_as_gen/​matplotlib.pyplot.subplots.html|plt.subplots(...)]] with an **s** at the end ([[https://​​gallery/​subplots_axes_and_figures/​subplots_demo.html|demo]]) 
-        * [[https://​​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://​​gallery/​index.html#​subplots-axes-and-figures|Subplots,​ axes and figures]] gallery 
-      * [[https://​​tutorials/​intermediate/​gridspec.html#​sphx-glr-tutorials-intermediate-gridspec-py|Customizing Figure Layouts Using GridSpec and Other Functions]],​ [[https://​​tutorials/​intermediate/​constrainedlayout_guide.html|constrained layout]] and [[https://​​tutorials/​intermediate/​tight_layout_guide.html|tight layout]] 
-    - use [[https://​​api/​_as_gen/​matplotlib.pyplot.savefig.html|my_page.savefig(...)]] to save a figure 
-      *  <wrap hi>​savefig(...) must be called **before**!</​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:\\ ''​''​ 
-    - it may be hard to (remember how to) **work with colors //and colorbars//​**. Some examples from the [[https://​​gallery/​index.html|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://​​gallery/​specialty_plots/​leftventricle_bulleye.html|]]:​ associating different types of colormaps to a plot and colorbar 
-      * [[https://​​examples/​api/​colorbar_only.html|]]:​ the different types of colorbars (or plotting only a colorbar) 
-      * [[https://​​gallery/​color/​colormap_reference.html|]]:​ pre-defined colormaps 
-      * [[https://​​gallery/​color/​named_colors.html|]]:​ 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://​​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://​​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 by default. 
-      * Check how you can [[https://​​faq/​howto_faq.html?​highlight=web#​generate-images-without-having-a-window-appear|generate images offline]] 
-    - the documentation may mention [[http://​​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://​​rougier/​matplotlib-tutorial|Matplotlib tutorial by Nicolas Rougier]] 
-  - Download the [[http://​​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://​​api/​_as_gen/​matplotlib.pyplot.plot.html|plot(...)]]:​ Plot y versus x as lines and/or markers 
-    * [[https://​​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://​​api/​_as_gen/​matplotlib.axes.Axes.contourf.html|contour(...) and contourf(...)]]:​ draw contour lines and filled contours 
-  * X and Y axes parameters 
-    * 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''​ 
-    * Major (and minor) tick marks location: ''​my_plot.set_xticks(x_ticks_values,​ minor=False)''​ 
-      * Use an empty list if you don't want tick marks: ''​my_plot.set_xticks([])''​ 
-    * Tick labels (if you don't want the default values): ''​my_plot.set_xticklabels(x_ticks_labels,​ minor=False,​ fontsize=ticklabels_fontsize)''​ 
-      * ''​x_ticks_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 
-      * Many more options for ticks, labels, orientation,​ ... 
-  * [[https://​​api/​_as_gen/​matplotlib.lines.Line2D.html|line]] parameters 
-    * ''​linestyle'':​ ''​solid'',​ ''​None'',​ [[https://​​api/​_as_gen/​matplotlib.lines.Line2D.html#​matplotlib.lines.Line2D.set_linestyle|other]] ([[https://​​examples/​lines_bars_and_markers/​line_styles_reference.html|default styles example]], [[https://​​examples/​lines_bars_and_markers/​linestyles.html|custom styles example]]) 
-  * [[https://​​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://​​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'',​ ''​fillstyle''​ (''​full'',​ ''​None'',​ [[https://​​gallery/​lines_bars_and_markers/​marker_fillstyle_reference.html|other]]) 
-      * [[https://​​api/​_as_gen/​matplotlib.pyplot.scatter.html|scatter(...)]]:​ ''​marker''​ (marker type), ''​c''​ (color), ''​s''​ (size), ''​linewidths''​ (linewidth of the marker edges), ''​edgecolors''​ 
-  * [[https://​​api/​colors_api.html|colors]] and colormaps 
-    * [[https://​​gallery/​color/​color_demo.html|color demo]] 
-    * [[https://​​examples/​color/​named_colors.html|named colors]] 
-    * Reverting the colors: add ''​_r''​ at the end of the colormap name 
-    * Special colormap colors 
-      * ''​cmap.set_bad(color='​k'​)'':​ color to be used for masked values 
-      * ''​cmap.set_over(color='​k'​)'':​ color to be used for high out-of-range values 
-      * ''​cmap.set_under(color='​k'​)'':​ color to be used for low out-of-range values 
-  * [[https://​​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.colorbar|colorbar]] 
-    * [[https://​​gallery/​subplots_axes_and_figures/​colorbar_placement.html|Placing colorbars demo]] 
-    * [[https://​​gallery/​images_contours_and_fields/​contourf_demo.html|contourf + colorbar demo]] 
-  * [[https://​​api/​_as_gen/​matplotlib.pyplot.text.html|text(...)]] and [[https://​​tutorials/​text/​annotations.html|annotations]] 
-    * Some titles: 
-      * [[https://​​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.suptitle|Figure title]]: ''​my_figure.suptitle('​Figure title',​ ...)''​ 
-      * [[https://​​api/​axes_api.html#​axis-labels-title-and-legend|Axis Labels, title, and legend]]: ''​my_plot.set_title('​Plot title',​ ...)''​ 
-    * ''​fontsize'':​ size in points, or (better!) string specifying a relative size (''​xx-small'',​ ''​x-small'',​ ''​small'',​ ''​medium'',​ ''​large'',​ ''​x-large'',​ ''​xx-large''​) 
-    * [[https://​​api/​text_api.html#​matplotlib.text.Text|all the text properties]] 
-  * [[https://​​api/​pyplot_api.html#​matplotlib.pyplot.legend|legend(...)]] ([[https://​​examples/​pylab_examples/​legend_demo3.html|legend demo]], [[https://​​users/​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://​​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://​​api/​_as_gen/​matplotlib.figure.Figure.html|figure(...)]] and the associated methods 
-  * The [[https://​​api/​axes_api.html|axes]] and the associated methods 
-  * [[https://​​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://​​api/​animation_api.html|Animations]] ([[https://​​gallery/​index.html#​animation|demo]]) 
-==== Misc Matplotlib tricks ==== 
-  * Specifying the background color of a plot (e.g. when plotting a masked variable and you don't want the masked areas to be white) 
-    * ''#​ make the background dark gray (call this before the contourf)''​\\ ''​plt.gca().patch.set_color('​.25'​)''​\\ ''​plt.contourf(d)''​\\ ''​''​ 
-    * [[https://​​questions/​9797520/​masking-part-of-a-contourf-plot-in-matplotlib|trick source]] 
 ===== Graphics related resources ===== ===== Graphics related resources =====
Line 328: Line 190:
     * See also: [[https://​​community/​tutorials/​seaborn-python-tutorial|     * See also: [[https://​​community/​tutorials/​seaborn-python-tutorial|
 Python Seaborn Tutorial For Beginners]] Python Seaborn Tutorial For Beginners]]
 +  * Communicating/​displaying/​plotting your data (possibly for people not of your field):
 +    * [[https://​​introduction-to-designing-data-visualizations-part-1-31c056556133|Introduction to Designing Data Visualizations — Part 1]]
 +    * [[https://​​tables-other-charts-data-visualization-part-2-cfc582e4712c|Tables & Other Charts — Data Visualization Part 2]]
 +    * [[https://​​tables-other-charts-data-visualization-part-3-5bfab15ce525|Tables & Other Charts — Data Visualization Part 3]]
   * Working with colors   * Working with colors
     * [[https://​​users/​colormaps.html|Choosing colormaps]]     * [[https://​​users/​colormaps.html|Choosing colormaps]]
-    * [[https://​​cmocean/​|Beautiful colormaps for oceanography: ​cmocean]]+    * [[https://​​cmocean/​|cmocean: ​Beautiful colormaps for oceanography]] 
 +    * [[https://​​palettable/​|Palettable:​ Color palettes for Python]]
     * [[http://​|ColorBrewer 2.0]] is a tool that can help you understand, and experiment with //​sequential//,​ //​diverging//​ and //​qualitative//​ colormaps     * [[http://​|ColorBrewer 2.0]] is a tool that can help you understand, and experiment with //​sequential//,​ //​diverging//​ and //​qualitative//​ colormaps
other/python/jyp_steps.1565343768.txt.gz · Last modified: 2019/08/09 11:42 by jypeter