Differences

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--- other:python:matplotlib_by_jyp [2020/02/11 12:00] – [Starting (and more) with matplotlib] jypeter
+++ other:python:matplotlib_by_jyp [2020/03/31 16:26] – [Starting (and more) with matplotlib] Improved offline plot creation jypeter
@@ Line 47: / Line 47: @@
 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)
+          * 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:
+          * 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>
@@ Line 59: / Line 59: @@
 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])''
@@ Line 99: / Line 104: @@
     - 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 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 (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]].
-      * 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...)
   - Read the [[https://github.com/rougier/matplotlib-tutorial|Matplotlib tutorial by Nicolas Rougier]]
@@ Line 182: / Line 186: @@
 ===== 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)
+==== Creating a plot offline ====
-    * ''# 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]]
+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/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]]