Update 3.4.0rc3 docs.

Author: QuLogic

3.4.0/.buildinfo

@@ -271,7 +271,7 @@ def my_plotter(ax, data1, data2, param_dict):
 # plotting windows pop up when they type commands.  Some people run
 # `Jupyter <https://jupyter.org>`_ notebooks and draw inline plots for
 # quick data analysis. Others embed Matplotlib into graphical user
-# interfaces like wxpython or pygtk to build rich applications.  Some
+# interfaces like PyQt or PyGObject to build rich applications.  Some
 # people use Matplotlib in batch scripts to generate postscript images
 # from numerical simulations, and still others run web application
 # servers to dynamically serve up graphs.
@@ -281,8 +281,8 @@ def my_plotter(ax, data1, data2, param_dict):
 # "frontend" is the user facing code, i.e., the plotting code, whereas the
 # "backend" does all the hard work behind-the-scenes to make the figure.
 # There are two types of backends: user interface backends (for use in
-# pygtk, wxpython, tkinter, qt4, qt5, or macosx; also referred to as
-# "interactive backends") and hardcopy backends to make image files
+# PyQt/PySide, PyGObject, Tkinter, wxPython, or macOS/Cocoa); also referred to
+# as "interactive backends") and hardcopy backends to make image files
 # (PNG, SVG, PDF, PS; also referred to as "non-interactive backends").
 #
 # Selecting a backend

3.4.0/Matplotlib.pdf

@@ -1,4 +1,6 @@
 """
+.. redirect-from:: /users/customizing
+
 Customizing Matplotlib with style sheets and rcParams
 =====================================================
 

3.4.0/_downloads/003118d8eb1e7df55671bcdf18bfac3c/sample_plots.ipynb

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')\nflierprops = dict(marker='o', markerfacecolor='green', markersize=12,\n                  linestyle='none')\nmedianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')\nmeanpointprops = dict(marker='D', markeredgecolor='black',\n                      markerfacecolor='firebrick')\nmeanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')\n\nfig, axs = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)\naxs[0, 0].boxplot(data, boxprops=boxprops)\naxs[0, 0].set_title('Custom boxprops', fontsize=fs)\n\naxs[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops)\naxs[0, 1].set_title('Custom medianprops\\nand flierprops', fontsize=fs)\n\naxs[0, 2].boxplot(data, whis=(0, 100))\naxs[0, 2].set_title('whis=(0, 100)', fontsize=fs)\n\naxs[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False,\n                  showmeans=True)\naxs[1, 0].set_title('Custom mean\\nas point', fontsize=fs)\n\naxs[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,\n                  showmeans=True)\naxs[1, 1].set_title('Custom mean\\nas line', fontsize=fs)\n\naxs[1, 2].boxplot(data, whis=[15, 85])\naxs[1, 2].set_title('whis=[15, 85]\\n#percentiles', fontsize=fs)\n\nfor ax in axs.flat:\n    ax.set_yscale('log')\n    ax.set_yticklabels([])\n\nfig.suptitle(\"I never said they'd be pretty\")\nfig.subplots_adjust(hspace=0.4)\nplt.show()"
+        "boxprops = dict(linestyle='--', linewidth=3, color='darkgoldenrod')\nflierprops = dict(marker='o', markerfacecolor='green', markersize=12,\n                  markeredgecolor='none')\nmedianprops = dict(linestyle='-.', linewidth=2.5, color='firebrick')\nmeanpointprops = dict(marker='D', markeredgecolor='black',\n                      markerfacecolor='firebrick')\nmeanlineprops = dict(linestyle='--', linewidth=2.5, color='purple')\n\nfig, axs = plt.subplots(nrows=2, ncols=3, figsize=(6, 6), sharey=True)\naxs[0, 0].boxplot(data, boxprops=boxprops)\naxs[0, 0].set_title('Custom boxprops', fontsize=fs)\n\naxs[0, 1].boxplot(data, flierprops=flierprops, medianprops=medianprops)\naxs[0, 1].set_title('Custom medianprops\\nand flierprops', fontsize=fs)\n\naxs[0, 2].boxplot(data, whis=(0, 100))\naxs[0, 2].set_title('whis=(0, 100)', fontsize=fs)\n\naxs[1, 0].boxplot(data, meanprops=meanpointprops, meanline=False,\n                  showmeans=True)\naxs[1, 0].set_title('Custom mean\\nas point', fontsize=fs)\n\naxs[1, 1].boxplot(data, meanprops=meanlineprops, meanline=True,\n                  showmeans=True)\naxs[1, 1].set_title('Custom mean\\nas line', fontsize=fs)\n\naxs[1, 2].boxplot(data, whis=[15, 85])\naxs[1, 2].set_title('whis=[15, 85]\\n#percentiles', fontsize=fs)\n\nfor ax in axs.flat:\n    ax.set_yscale('log')\n    ax.set_yticklabels([])\n\nfig.suptitle(\"I never said they'd be pretty\")\nfig.subplots_adjust(hspace=0.4)\nplt.show()"
       ]
     },
     {

3.4.0/_downloads/13b7d80b1bb64b045c82435ce9688fe8/usage.py

@@ -385,7 +385,7 @@
 EEG GUI
 =======
 
-You can embed Matplotlib into pygtk, wx, Tk, or Qt applications.
+You can embed Matplotlib into Qt, GTK, Tk, or wxWidgets applications.
 Here is a screenshot of an EEG viewer called `pbrain
 <https://github.com/nipy/pbrain>`__.
 

3.4.0/_downloads/2b8686c30ce6bcda15c2e17de950c17c/customizing.py

@@ -92,7 +92,7 @@
 # with requested margins.
 #
 # While sticky edges don't increase the axis limits through extra margins,
-# negative margins are still taken into accout. This can be seen in
+# negative margins are still taken into account. This can be seen in
 # the reduced limits of the third image.
 #
 # Controlling autoscale

3.4.0/_downloads/43dfdc3242d6e3f7d2a54971e771b799/boxplot.ipynb

@@ -5,6 +5,13 @@
 
 Matplotlib can wrap text automatically, but if it's too long, the text will be
 displayed slightly outside of the boundaries of the axis anyways.
+
+Note: Auto-wrapping does not work together with
+``savefig(..., bbox_inches='tight')``. The 'tight' setting rescales the canvas
+to accommodate all content and happens before wrapping. This affects
+``%matplotlib inline`` in IPython and Jupyter notebooks where the inline
+setting uses ``bbox_inches='tight'`` by default when saving the image to
+embed.
 """
 
 import matplotlib.pyplot as plt