other:python:jyp_steps
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other:python:jyp_steps [2019/05/27 09:28] jypeter [Matplotlib] Improved |
other:python:jyp_steps [2019/09/05 14:22] jypeter Moved the bulk of the matplotlib section to its own page when it became too long |
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| ===== Matplotlib ===== | ===== Matplotlib ===== | ||
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| + | <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 | ||
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| Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/matplotlib|matplotlib help]] | Help on //stack overflow//: [[https://stackoverflow.com/questions/tagged/matplotlib|matplotlib help]] | ||
| - | |||
| - | The documentation is good, but not always easy to use. <wrap hi>A good way to start with matplotlib</wrap> is to: | ||
| - | - Look at the [[http://matplotlib.org/gallery.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 | ||
| - | - Use the free hints provided by JY! | ||
| - | - You need to know some matplotlib specific vocabulary: | ||
| - | * a Matplotlib //Figure// is a graphical window in which you make your plots... | ||
| - | * the [[http://matplotlib.org/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 can't explicitly specify the orientation (//portrait// or //landscape//) of a plot. If you want a portrait plot, it's up to you to create a plot that will the higher than it is large. The idea is not to worry about this: create a plot and save it, and then adjust things if need be | ||
| - | * use [[https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html|fig.savefig(...)]] to save a figure | ||
| - | * a Matplotlib //Axis// is a plot inside a Figure... [[http://matplotlib.org/faq/usage_faq.html#parts-of-a-figure|More details]] | ||
| - | * a Matplotlib //Artist// or //Patch// is something (e.g a line, a group of markers, ...) plotted on the Figure/Axis | ||
| - | - some resources for having multiple plots on the same figure | ||
| - | * [[https://matplotlib.org/gallery/recipes/create_subplots.html#sphx-glr-gallery-recipes-create-subplots-py|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/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 boundaries | ||
| - | * [[https://matplotlib.org/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]] | ||
| - | - some examples are more //pythonic// (ie object oriented) than others, some example mix different styles of coding, all this can be confusing. Try to [[http://matplotlib.org/faq/usage_faq.html#coding-styles|use an object oriented way of doing things]]! | ||
| - | - it may be hard to (remember how to) work with colors. Some examples from the [[http://matplotlib.org/gallery.html|Gallery]] can help you! | ||
| - | * [[https://matplotlib.org/examples/pylab_examples/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/examples/color/colormaps_reference.html|colormaps_reference.py]]: pre-defined colormaps | ||
| - | * [[https://matplotlib.org/examples/color/named_colors.html|named_colors.py]]: named colors | ||
| - | * More details about the colors below, in the [[#graphics_related_resources|Resources section]] | ||
| - | - 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 by default. | ||
| - | * Check how you can [[https://matplotlib.org/faq/howto_faq.html?highlight=web#generate-images-without-having-a-window-appear|generate images 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...) | ||
| - | - 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 | ||
| - | * 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 | ||
| - | - Read the [[http://www.labri.fr/perso/nrougier/teaching/matplotlib/|Matplotlib tutorial by Nicolas Rougier]] | ||
| - | - Download the [[http://matplotlib.org/contents.html|pdf version of the manual]]. **Do not print** the 2800+ 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 reference pages ==== | ||
| - | |||
| - | * [[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.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]] | ||
| - | * ''fillstyle'': ''full'', ''None'', [[https://matplotlib.org/gallery/lines_bars_and_markers/marker_fillstyle_reference.html|other]] | ||
| - | * Other attributes: ''markersize'', ''markerfacecolor'' (and ''markerfacecoloralt'' for dual color markers), ''markeredgecolor'', ''markeredgewidth'' | ||
| - | * [[https://matplotlib.org/api/colors_api.html|colors]] | ||
| - | * [[https://matplotlib.org/gallery/color/color_demo.html|color demo]] | ||
| - | * [[https://matplotlib.org/examples/color/named_colors.html|named colors]] | ||
| - | * [[https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend|legend(...)]] ([[https://matplotlib.org/examples/pylab_examples/legend_demo3.html|legend demo]]) | ||
| - | * 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 | ||
| - | |||
| - | ==== 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)''\\ ''plt.show()'' | ||
| - | * [[https://stackoverflow.com/questions/9797520/masking-part-of-a-contourf-plot-in-matplotlib|trick source]] | ||
| ===== Graphics related resources ===== | ===== Graphics related resources ===== | ||
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| Where: [[http://pandas.pydata.org|Pandas web site]] | Where: [[http://pandas.pydata.org|Pandas web site]] | ||
| - | JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. You should at least have a quick look at: | + | JYP's comment: pandas is supposed to be quite good for loading, processing and plotting time series, without writing custom code. It is **very convenient for processing tables in xlsx files** (or csv, etc...). You should at least have a quick look at: |
| - | * The [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines Pandas, [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | + | |
| - | * the cheat sheet on the [[https://www.enthought.com/services/training/pandas-mastery-workshop/|Enthought workshops advertising page]] | + | * Some //Cheat Sheets// (in the following order): |
| - | * the cheat sheet on the [[https://github.com/pandas-dev/pandas/tree/master/doc/cheatsheet|github Pandas doc page]] | + | - Basics: [[http://datacamp-community-prod.s3.amazonaws.com/dbed353d-2757-4617-8206-8767ab379ab3|Pandas basics]] (associated with the [[https://www.datacamp.com/community/blog/python-pandas-cheat-sheet|Pandas Cheat Sheet for Data Science in Python]] pandas introduction page) |
| + | - Intermediate: [[https://github.com/pandas-dev/pandas/tree/master/doc/cheatsheet|github Pandas doc page]] | ||
| + | - Advanced: the cheat sheet on the [[https://www.enthought.com/services/training/pandas-mastery-workshop/|Enthought workshops advertising page]] | ||
| + | * Some tutorials: | ||
| + | * [[https://www.datacamp.com/community/blog/python-pandas-cheat-sheet|Pandas Cheat Sheet for Data Science in Python]] pandas introduction page | ||
| + | * The [[http://www.scipy-lectures.org/packages/statistics/index.html|Statistics in Python]] tutorial that combines Pandas, [[http://statsmodels.sourceforge.net/|Statsmodels]] and [[http://seaborn.pydata.org/|Seaborn]] | ||
| ===== Scipy Lecture Notes ===== | ===== Scipy Lecture Notes ===== | ||
other/python/jyp_steps.txt · Last modified: 2024/03/07 10:15 by jypeter
