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other:python:jyp_steps [2019/06/03 18:32]
jypeter [Matplotlib] improved
other:python:jyp_steps [2019/08/09 11:42] (current)
jypeter [Useful matplotlib reference pages]
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 bottom_plot = plot_array[2]</​code>​ bottom_plot = plot_array[2]</​code>​
           * creating a figure and axes with a single line: ''​my_page,​ plot_array = **plt**.subplots(3,​ 1)''​           * creating a figure and axes with a single line: ''​my_page,​ plot_array = **plt**.subplots(3,​ 1)''​
 +        * 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       * 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...       * **clearing** the //page// (or part of it): you probably won't need that...
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         * [[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.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 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)''​         * [[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/​gallery/​index.html#​subplots-axes-and-figures|Subplots,​ axes and figures]] gallery       * [[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]]       * [[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]]
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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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 ==== Useful matplotlib reference pages ==== ==== Useful matplotlib reference pages ====
  
-  * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.plot.html|plot(...)]]:​ Plot y versus x as lines and/or markers +  ​* Some plot types: 
-  * [[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+    ​* [[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     * 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
 +    * 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://​matplotlib.org/​api/​_as_gen/​matplotlib.lines.Line2D.html|line]] parameters   * [[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]])     * ''​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]]   * [[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]] +    ​* Default marker size and edge width: 
-    Other attributes: ''​markersize'',​ ''​markerfacecolor''​ (and ''​markerfacecoloralt'' ​for dual color markers), ''​markeredgecolor'',​ ''​markeredgewidth''​ +      ​* ''​mpl.rcParams['lines.markersize'] %%**%% 2''​ => 36 
-  * [[https://​matplotlib.org/​api/​colors_api.html|colors]]+      * ''​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'',​ ''​fillstyle''​ (''​full'',​ ''​None'',​ [[https://​matplotlib.org/​gallery/​lines_bars_and_markers/​marker_fillstyle_reference.html|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/​api/​colors_api.html|colors]] ​and colormaps
     * [[https://​matplotlib.org/​gallery/​color/​color_demo.html|color demo]]     * [[https://​matplotlib.org/​gallery/​color/​color_demo.html|color demo]]
     * [[https://​matplotlib.org/​examples/​color/​named_colors.html|named colors]]     * [[https://​matplotlib.org/​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://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.colorbar|colorbar]]
 +    * [[https://​matplotlib.org/​gallery/​subplots_axes_and_figures/​colorbar_placement.html|Placing colorbars demo]]
 +    * [[https://​matplotlib.org/​gallery/​images_contours_and_fields/​contourf_demo.html|contourf + colorbar demo]]
   * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.text.html|text(...)]] and [[https://​matplotlib.org/​tutorials/​text/​annotations.html|annotations]]   * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.pyplot.text.html|text(...)]] and [[https://​matplotlib.org/​tutorials/​text/​annotations.html|annotations]]
 +    * Some titles:
 +      * [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html#​matplotlib.figure.Figure.suptitle|Figure title]]: ''​my_figure.suptitle('​Figure title',​ ...)''​
 +      * [[https://​matplotlib.org/​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''​)     * ''​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/​api/​text_api.html#​matplotlib.text.Text|all the text properties]]     * [[https://​matplotlib.org/​api/​text_api.html#​matplotlib.text.Text|all the text properties]]
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   * The [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html|figure(...)]] and the associated methods   * The [[https://​matplotlib.org/​api/​_as_gen/​matplotlib.figure.Figure.html|figure(...)]] and the associated methods
   * The [[https://​matplotlib.org/​api/​axes_api.html|axes]] and the associated methods   * The [[https://​matplotlib.org/​api/​axes_api.html|axes]] and the associated methods
-  * [[https://​matplotlib.org/​tutorials/​introductory/​customizing.html#​matplotlib-rcparams|matplotlib default settings]] can be queried and updated+  * [[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]''​)     * example: the default figure size (inches) is ''​mpl.rcParams['​figure.figsize'​]''​ (''​[6.4,​ 4.8]''​)
     * current settings'​ file:  ''​mpl.matplotlib_fname()''​     * current settings'​ file:  ''​mpl.matplotlib_fname()''​
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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.1559579561.txt.gz · Last modified: 2019/06/03 18:32 by jypeter