Differences

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--- other:python:matplotlib_by_jyp [2020/03/31 12:49] – [Misc Matplotlib tricks] Improved jypeter
+++ other:python:matplotlib_by_jyp [2020/04/10 12:46] – [Useful matplotlib reference pages] Improved 'X and Y axes parameters' and added grid lines jypeter
@@ Line 104: / 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 117: / Line 116: @@
     * 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
+  * **X and Y axes parameters** (see also [[https://matplotlib.org/examples/showcase/anatomy.html|Anatomy of a figure]]):
-    * Axis range: ''my_plot.set_xlim(x_leftmost_value, x_rightmost_value)''
+    * **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)''
+    * **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)''
+    * 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)''
+    * **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, ...
+    * **Grid lines**:
+      * Their position is determined by the values used for ''set_xticks'' and ''set_yticks''
+      * Activate all (horizontal **and** vertical) grid lines with: ''my_plot.grid(True, linestyle="%%--%%", linewidth=0.5, color='.25',zorder=some_value)''\\ You can adjust the ''zorder'' value to determine if the grid lines should be above or below other parts of the plot!
+      * Plot only the horizontal **or** vertical lines with:\\ ''ax.yaxis.grid(True)''\\ or ''ax.xaxis.grid(True)''
+      * Note: <wrap hi>special case of //cartopy// plots</wrap>: the location of the gridlines, and the properties of the associated labels are determined by ''myplot.gridlines''! See [[https://scitools.org.uk/cartopy/docs/latest/matplotlib/gridliner.html|Cartopy map gridlines and tick labels]]
   * [[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]])
@@ Line 186: / Line 190: @@
 ===== Misc Matplotlib tricks =====
+==== Creating a plot offline ====
+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 ====