other:python:jyp_steps
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other:python:jyp_steps [2019/06/27 09:31] jypeter [Useful matplotlib reference pages] Added axis ticks and labels |
other:python:jyp_steps [2019/08/09 09:42] jypeter Improved colors/colorbars |
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| * ''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 | * ''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()'' | - **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**. Some examples from the [[https://matplotlib.org/gallery/index.html]] can help you! | + | - it may be hard to (remember how to) **work with colors //and colorbars//**. Some examples from the [[https://matplotlib.org/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://matplotlib.org/examples/pylab_examples/leftventricle_bulleye.html|leftventricle_bulleye.py]]: associating different types of colormaps to a plot and colorbar | + | * [[https://matplotlib.org/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/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/gallery/color/colormap_reference.html|colormaps_reference.py]]: pre-defined colormaps |
| - | * [[https://matplotlib.org/examples/color/named_colors.html|named_colors.py]]: named colors | + | * [[https://matplotlib.org/gallery/color/named_colors.html|named_colors.py]]: named colors |
| - | * More details about the colors below, in the [[#graphics_related_resources|Resources section]] | + | * 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 | + | - 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 | * 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 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 | * 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 | - 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. | - 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. | ||
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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
