diff --git a/README.html b/README.html index a5ce63e..3b5d989 100644 --- a/README.html +++ b/README.html @@ -3,7 +3,7 @@ - + Matplotlib tutorial @@ -12,17 +12,17 @@

Matplotlib tutorial

Nicolas P. Rougier

-https://zenodo.org/badge/doi/10.5281/zenodo.28747.png +https://zenodo.org/badge/doi/10.5281/zenodo.28747.svg
-

Table of Contents

+

Table of Contents

Sources are available from @@ -30,15 +30,14 @@

Nicolas P. Rougier

All code and material is licensed under a Creative Commons Attribution-ShareAlike 4.0.

You can test your installation before the tutorial using the check-installation.py script.

-

Tutorial can be read at http://www.labri.fr/perso/nrougier/teaching/matplotlib/matplotlib.html

See also:

-

Introduction

+

Introduction

matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats. We are going to explore @@ -47,7 +46,7 @@

Introduction

IPython

IPython is an enhanced interactive Python shell that has lots of interesting features including named inputs and outputs, access to -shell commands, improved debugging and many more. It allows +shell commands, improved debugging and much more. It allows interactive matplotlib sessions that have Matlab/Mathematica-like functionality.

@@ -59,18 +58,18 @@

pyplot

-

Simple plot

+

Simple plot

In this section, we want to draw the cosine and sine functions on the same plot. Starting from the default settings, we'll enrich the figure step by step to make it nicer.

-

First step is to get the data for the sine and cosine functions:

+

The first step is to get the data for the sine and cosine functions:

 import numpy as np
 
-X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
-C,S = np.cos(X), np.sin(X)
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+C, S = np.cos(X), np.sin(X)
-

X is now a numpy array with 256 values ranging from -π to +π (included). C is +

X is now a NumPy array with 256 values ranging from -π to +π (included). C is the cosine (256 values) and S is the sine (256 values).

To run the example, you can download each of the examples and run it using:

@@ -79,7 +78,7 @@ 
Simple plot

 
You can get source for each step by clicking on the corresponding figure.

 

 
Using defaults

-

+

 
Documentation

 
    
 
  • plot tutorial
    • 
@@ -94,10 +93,10 @@ 
    Using defaults
    
 are rather good in most cases, you may want to modify some properties for
 specific cases.
    
 
    -import numpy as np
-import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.pyplot as plt
 
-X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
+X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
 C,S = np.cos(X), np.sin(X)
 
 plt.plot(X,C)
@@ -108,7 +107,7 @@ 
    Using defaults
    
 

 

 
Instantiating defaults

-

+

 
Documentation

 
    
 
  • Customizing matplotlib
    • 
@@ -121,16 +120,16 @@ 
    Instantiating defaults
    
 to explore their affect (see Line properties and Line styles below).
    
 
     # Imports
-import numpy as np
-import matplotlib.pyplot as plt
+import numpy as np
+import matplotlib.pyplot as plt
 
 # Create a new figure of size 8x6 points, using 100 dots per inch
-plt.figure(figsize=(8,6), dpi=80)
+plt.figure(figsize=(8,6), dpi=100)
 
 # Create a new subplot from a grid of 1x1
 plt.subplot(111)
 
-X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
+X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
 C,S = np.cos(X), np.sin(X)
 
 # Plot cosine using blue color with a continuous line of width 1 (pixels)
@@ -143,13 +142,13 @@ 
    Instantiating defaults
    
 plt.xlim(-4.0,4.0)
 
 # Set x ticks
-plt.xticks(np.linspace(-4,4,9,endpoint=True))
+plt.xticks(np.linspace(-4,4,9,endpoint=True))
 
 # Set y limits
 plt.ylim(-1.0,1.0)
 
 # Set y ticks
-plt.yticks(np.linspace(-1,1,5,endpoint=True))
+plt.yticks(np.linspace(-1,1,5,endpoint=True))
 
 # Save figure using 72 dots per inch
 # savefig("../figures/exercice_2.png",dpi=72)
@@ -160,7 +159,7 @@ 
    Instantiating defaults
    
 

 

 
Changing colors and line widths

-

+

 
Documentation

 
 

 figures/exercice_3.png
-
First step, we want to have the cosine in blue and the sine in red and a
+
As a first step, we want to have the cosine in blue and the sine in red and a
 slightly thicker line for both of them. We'll also slightly alter the figure
 size to make it more horizontal.

 
@@ -181,7 +180,7 @@ 
Changing colors and line widths

 

 

 
Setting limits

-

+

 
Documentation

 

 

 
Setting ticks

-

+

 
Documentation

 

 

 
Setting tick labels

-

+

 
Documentation

 

 

 
Moving spines

-

+

 
Documentation

 
    
 
  • Spines
    • 
@@ -278,7 +277,7 @@ 
    Moving spines
    
 

 

 
Adding a legend

-

+

 
Documentation

 

 

 
Annotate some points

-

+

 
Documentation

 
 

 figures/exercice_9.png
-
Let's annotate some interesting points using the annotate command. We chose the
+
Let's annotate some interesting points using the annotate command. We choose the
 2π/3 value and we want to annotate both the sine and the cosine. We'll first
 draw a marker on the curve as well as a straight dotted line. Then, we'll use
 the annotate command to display some text with an arrow.

@@ -337,7 +336,7 @@ 
Annotate some points

 

 

 
Devil is in the details

-

+

 
Documentation

 
    
 
  • Artists
    • 
@@ -359,7 +358,7 @@ 
    Devil is in the details
    
 

 

 

-
Figures, Subplots, Axes and Ticks

+
Figures, Subplots, Axes and Ticks

 
So far we have used implicit figure and axes creation. This is handy for fast
 plots. We can have more control over the display using figure, subplot, and
 axes explicitly. A figure in matplotlib means the whole window in the user
@@ -418,7 +417,7 @@ 
Figures

 
The defaults can be specified in the resource file and will be used most of the
 time. Only the number of the figure is frequently changed.

 
When you work with the GUI you can close a figure by clicking on the x in the
-upper right corner. But you can close a figure programmatically by calling
+upper right corner. You can also close a figure programmatically by calling
 close. Depending on the argument it closes (1) the current figure (no
 argument), (2) a specific figure (figure number or figure instance as
 argument), or (3) all figures (all as argument).

@@ -448,13 +447,13 @@ 
Ticks

 figures. Matplotlib provides a totally configurable system for ticks. There are
 tick locators to specify where ticks should appear and tick formatters to give
 ticks the appearance you want. Major and minor ticks can be located and
-formatted independently from each other. Per default minor ticks are not shown,
+formatted independently from each other. By default minor ticks are not shown,
 i.e. there is only an empty list for them because it is as NullLocator (see
 below).

 

 
Tick Locators

 
There are several locators for different kind of requirements:

-
+
@@ -510,13 +509,13 @@ 
Tick Locators

-
Animation

+
Animation

 
For quite a long time, animation in matplotlib was not an easy task and was
 done mainly through clever hacks. However, things have started to change since
 version 1.1 and the introduction of tools for creating animation very
 intuitively, with the possibility to save them in all kind of formats (but don't
-expect to be able to run very complex animation at 60 fps though).

-

+expect to be able to run very complex animations at 60 fps though).

+

 
Documentation

 
    
 
  • See Animation
    • 
@@ -538,14 +537,14 @@ 
    Drip drop
    
 fig = plt.figure(figsize=(6,6), facecolor='white')
 
 # New axis over the whole figure, no frame and a 1:1 aspect ratio
-ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)
+ax = fig.add_axes([0,0,1,1], frameon=False, aspect=1)
 
 
    Next, we need to create several rings. For this, we can use the scatter plot
 object that is generally used to visualize points cloud, but we can also use it
-to draw rings by specifying we don't have a facecolor. We have also to take
-care of initial size and color for each ring such that we have all size between
-a minimum and a maximum size and also to make sure the largest ring is almost
-transparent.
    
+to draw rings by specifying we don't have a facecolor. We also have to take
+care of initial size and color for each ring such that we have all sizes between
+a minimum and a maximum size. In addition, we need to make sure the largest ring
+is almost transparent.
    
 figures/rain-static.png
 
     # Number of ring
@@ -573,10 +572,10 @@ 
    Drip drop
    
 ax.set_ylim(0,1), ax.set_yticks([])
 
    
 
    Now, we need to write the update function for our animation. We know that at
-each time step each ring should grow be more transparent while largest ring
-should be totally transparent and thus removed. Of course, we won't actually
-remove the largest ring but re-use it to set a new ring at a new random
-position, with nominal size and color. Hence, we keep the number of ring
+each time step each ring should grow and become more transparent while the
+largest ring should be totally transparent and thus removed. Of course, we won't
+actually remove the largest ring but re-use it to set a new ring at a new random
+position, with nominal size and color. Hence, we keep the number of rings
 constant.
    
 figures/rain.gif
 
    @@ -606,7 +605,7 @@ 
    Drip drop
    
 
    Last step is to tell matplotlib to use this function as an update function for
 the animation and display the result or save it as a movie:
    
 
    -animation = FuncAnimation(fig, update, interval=10, blit=True, frames=200)
+animation = FuncAnimation(fig, update, interval=10, blit=True, frames=200)
 # animation.save('rain.gif', writer='imagemagick', fps=30, dpi=40)
 plt.show()
 
    
@@ -616,14 +615,14 @@ 
    Earthquakes
    
 
    We'll now use the rain animation to visualize earthquakes on the planet from
 the last 30 days. The USGS Earthquake Hazards Program is part of the National
 Earthquake Hazards Reduction Program (NEHRP) and provides several data on their
-website. Those data are sorted according to
+website. Those data are sorted according to
 earthquakes magnitude, ranging from significant only down to all earthquakes,
 major or minor. You would be surprised by the number of minor earthquakes
 happening every hour on the planet. Since this would represent too much data
 for us, we'll stick to earthquakes with magnitude > 4.5. At the time of writing,
 this already represent more than 300 earthquakes in the last 30 days.
    
 
    First step is to read and convert data. We'll use the urllib library that
-allows to open and read remote data. Data on the website use the CSV format
+allows us to open and read remote data. Data on the website use the CSV format
 whose content is given by the first line:
    
 
     time,latitude,longitude,depth,mag,magType,nst,gap,dmin,rms,net,id,updated,place,type
@@ -634,10 +633,9 @@ 
    Earthquakes
    
 time of event (ok, that's bad, feel free to send me a PR).
    
 
     import urllib
-from mpl_toolkits.basemap import Basemap
 
-# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
-feed = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
+# -> https://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+feed = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
 
 # Significant earthquakes in the last 30 days
 # url = urllib.request.urlopen(feed + "significant_month.csv")
@@ -653,41 +651,52 @@ 
    Earthquakes
    
 
 # Reading and storage of data
 data = url.read()
-data = data.split(b'\n')[+1:-1]
+data = data.split(b'\n')[+1:-1]
 E = np.zeros(len(data), dtype=[('position',  float, 2),
-                               ('magnitude', float, 1)])
+                               ('magnitude', float)])
 
 for i in range(len(data)):
-    row = data[i].split(',')
+    row = data[i].split(b',')
     E['position'][i] = float(row[2]),float(row[1])
     E['magnitude'][i] = float(row[4])
 
    
-
    Now, we need to draw earth on a figure to show precisely where the earthquake
+
    Now, we need to draw the earth on a figure to show precisely where the earthquake
 center is and to translate latitude/longitude in some coordinates matplotlib
-can handle. Fortunately, there is the basemap project (that tends to be replaced by the
-more complete cartopy) that is really
+can handle. Fortunately, there is the basemap project (which is now deprecated in favor
+of the cartopy project) that is really
 simple to install and to use. First step is to define a projection to draw the
 earth onto a screen (there exists many different projections) and we'll stick
 to the mill projection which is rather standard for non-specialist like me.
    
 
    +from mpl_toolkits.basemap import Basemap
 fig = plt.figure(figsize=(14,10))
 ax = plt.subplot(1,1,1)
 
-earth = Basemap(projection='mill')
+map = Basemap(projection='mill')
 
    
 
    Next, we request to draw coastline and fill continents:
    
 
    -earth.drawcoastlines(color='0.50', linewidth=0.25)
-earth.fillcontinents(color='0.95')
+map.drawcoastlines(color='0.50', linewidth=0.25)
+map.fillcontinents(color='0.95')
+
    
+
    For cartopy, the steps are quite similar:
    
+
    +import cartopy
+ax = plt.axes(projection=cartopy.crs.Miller())
+ax.coastlines(color='0.50', linewidth=0.25)
+ax.add_feature(cartopy.feature.LAND, color='0.95')
+ax.set_global()
+trans = cartopy.crs.PlateCarree()
 
    
-
    The earth object will also be used to translate coordinate quite
-automatically. We are almost finished. Last step is to adapt the rain code and
-put some eye candy:
    
+
    We are almost finished. Last step is to adapt the rain code and
+put some eye candy. For basemap we use the map object to
+transform the coordinates whereas for cartopy we use the transform_point
+function of the chosen Miller projection:
    
 
     P = np.zeros(50, dtype=[('position', float, 2),
-                         ('size',     float, 1),
-                         ('growth',   float, 1),
-                         ('color',    float, 4)])
+                         ('size',    float),
+                         ('growth',  float),
+                         ('color',   float, 4)])
 scat = ax.scatter(P['position'][:,0], P['position'][:,1], P['size'], lw=0.5,
                   edgecolors = P['color'], facecolors='None', zorder=10)
 
@@ -699,7 +708,8 @@ 
    Earthquakes
    
     P['size'] += P['growth']
 
     magnitude = E['magnitude'][current]
-    P['position'][i] = earth(*E['position'][current])
+    P['position'][i] = map(*E['position'][current]) if use_basemap else \
+        cartopy.crs.Miller().transform_point(*E['position'][current], cartopy.crs.PlateCarree())
     P['size'][i] = 5
     P['growth'][i]= np.exp(magnitude) * 0.1
 
@@ -714,7 +724,7 @@ 
    Earthquakes
    
     return scat,
 
 
-animation = FuncAnimation(fig, update, interval=10)
+animation = FuncAnimation(fig, update, interval=10, blit=True)
 plt.show()
 
    
 
    If everything went well, you should obtain something like this (with animation):
    
@@ -722,7 +732,7 @@ 
    Earthquakes
    
 

 

 

-
Other Types of Plots

+
Other Types of Plots

 figures/plot.png
 figures/scatter.png
 figures/bar.png
@@ -738,13 +748,13 @@ 
Other Types of Plots

 

 
Regular Plots

 figures/plot_ex.png
-

+

 
Hints

 
You need to use the fill_between
 command.

 

 
Starting from the code below, try to reproduce the graphic on the right taking
-care of filled areas:

+care of filled areas.

 
 import numpy as np
 import matplotlib.pyplot as plt
@@ -762,7 +772,7 @@ 
Regular Plots

 

 
Scatter Plots

 figures/scatter_ex.png
-

+

 
Hints

 
Color is given by angle of (X,Y).

 

@@ -784,7 +794,7 @@ 
Scatter Plots

 

 
Bar Plots

 figures/bar_ex.png
-

+

 
Hints

 
You need to take care of text alignment.

 

@@ -813,7 +823,7 @@ 
Bar Plots

 

 
Contour Plots

 figures/contour_ex.png
-

+

 
Hints

 
You need to use the clabel
 command.

@@ -840,10 +850,10 @@ 
Contour Plots

 

 
Imshow

 figures/imshow_ex.png
-

+

 
Hints

 
You need to take care of the origin of the image in the imshow command and
-use a colorbar

+use a colorbar.

 

 
Starting from the code below, try to reproduce the graphic on the right taking
 care of colormap, image interpolation and origin.

@@ -865,7 +875,7 @@ 
Imshow

 

 
Pie Charts

 figures/pie_ex.png
-

+

 
Hints

 
You need to modify Z.

 

@@ -885,7 +895,7 @@ 
Pie Charts

 

 
Quiver Plots

 figures/quiver_ex.png
-

+

 
Hints

 
You need to draw arrows twice.

 

@@ -924,7 +934,7 @@ 
Grids

 

 
Multi Plots

 figures/multiplot_ex.png
-

+

 
Hints

 
You can use several subplots with different partition.

 

@@ -944,9 +954,9 @@ 
Multi Plots

 

 
Polar Axis

 figures/polar_ex.png
-

+

 
Hints

-
You only need to modify the axes line

+
You only need to modify the axes line.

 

 
Starting from the code below, try to reproduce the graphic on the right.

 
@@ -972,9 +982,9 @@ 
Polar Axis

 

 
3D Plots

 figures/plot3d_ex.png
-

+

 
Hints

-
You need to use contourf

+
You need to use contourf.

 

 
Starting from the code below, try to reproduce the graphic on the right.

 
@@ -999,16 +1009,16 @@ 
3D Plots

 

 
Text

 figures/text_ex.png
-

+

 
Hints

 
Have a look at the matplotlib logo.

 

-
Try to do the same from scratch !

+
Try to do the same from scratch!

 
Click on figure for solution.

 

 

 

-
Beyond this tutorial

+
Beyond this tutorial

 
Matplotlib benefits from extensive documentation as well as a large
 community of users and developpers. Here are some links of interest:

 

@@ -1126,11 +1136,11 @@ 
Mailing lists

 

 

 

-
Quick references

+
Quick references

 
Here is a set of tables that show main properties and styles.

 

 
Line properties

-

 
 
 

 
 
 

+
@@ -1217,7 +1227,7 @@ 
Line properties

 
Line styles

-

 
 
 

 
 

+
@@ -1360,7 +1370,7 @@ 
Line styles

 
Markers

-

 
 
 

 
 

+
@@ -1540,11 +1550,11 @@ 
Markers
Colormaps
All colormaps can be reversed by appending _r. For instance, gray_r is
 the reverse of gray.

-
If you want to know more about colormaps, checks Documenting the matplotlib
+
If you want to know more about colormaps, see Documenting the matplotlib
 colormaps.

 
Base

-

 
 
 

 
 
 

+
@@ -1620,7 +1630,7 @@ 
Base

 
GIST

-

 
 
 

 
 

+
@@ -1662,9 +1672,9 @@ 
GIST

 
 
 

 
 

 

-

+

 
Diverging

-
+
@@ -1714,9 +1724,9 @@ 
Diverging

 
 
 

 
 

 

-

+

 
Sequential

-
+
@@ -1804,7 +1814,7 @@ 
Sequential

 
Qualitative

-

 
 
 

 
 

+
@@ -1852,7 +1862,7 @@ 
Qualitative

 
Miscellaneous

-

 
 
 

 
 

+
diff --git a/README.rst b/README.rst
index 1db5a91..067f70e 100644
--- a/README.rst
+++ b/README.rst
@@ -657,7 +657,7 @@ Earthquakes
 We'll now use the rain animation to visualize earthquakes on the planet from
 the last 30 days. The USGS Earthquake Hazards Program is part of the National
 Earthquake Hazards Reduction Program (NEHRP) and provides several data on their
-`website `_. Those data are sorted according to
+`website `_. Those data are sorted according to
 earthquakes magnitude, ranging from significant only down to all earthquakes,
 major or minor. You would be surprised by the number of minor earthquakes
 happening every hour on the planet. Since this would represent too much data
@@ -675,15 +675,14 @@ whose content is given by the first line::
 
 We are only interested in latitude, longitude and magnitude and we won't parse
 time of event (ok, that's bad, feel free to send me a PR).
-  
+
 
 .. code:: python
 
    import urllib
-   from mpl_toolkits.basemap import Basemap
 
-   # -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
-   feed = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
+   # -> https://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+   feed = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
 
    # Significant earthquakes in the last 30 days
    # url = urllib.request.urlopen(feed + "significant_month.csv")
@@ -701,10 +700,10 @@ time of event (ok, that's bad, feel free to send me a PR).
    data = url.read()
    data = data.split(b'\n')[+1:-1]
    E = np.zeros(len(data), dtype=[('position',  float, 2),
-                                  ('magnitude', float, 1)])
+                                  ('magnitude', float)])
 
    for i in range(len(data)):
-       row = data[i].split(',')
+       row = data[i].split(b',')
        E['position'][i] = float(row[2]),float(row[1])
        E['magnitude'][i] = float(row[4])
 
@@ -712,39 +711,53 @@ time of event (ok, that's bad, feel free to send me a PR).
 Now, we need to draw the earth on a figure to show precisely where the earthquake
 center is and to translate latitude/longitude in some coordinates matplotlib
 can handle. Fortunately, there is the `basemap
-`_ project (that tends to be replaced by the
-more complete `cartopy `_) that is really
+`_ project (which is now deprecated in favor
+of the `cartopy `_ project) that is really
 simple to install and to use. First step is to define a projection to draw the
 earth onto a screen (there exists many different projections) and we'll stick
 to the `mill` projection which is rather standard for non-specialist like me.
-       
+
 
 .. code:: python
 
+   from mpl_toolkits.basemap import Basemap
    fig = plt.figure(figsize=(14,10))
    ax = plt.subplot(1,1,1)
 
-   earth = Basemap(projection='mill')
+   map = Basemap(projection='mill')
 
 
 Next, we request to draw coastline and fill continents:
 
 .. code:: python
-          
-   earth.drawcoastlines(color='0.50', linewidth=0.25)
-   earth.fillcontinents(color='0.95')
 
-The `earth` object will also be used to translate coordinates quite
-automatically. We are almost finished. Last step is to adapt the rain code and
-put some eye candy:
+   map.drawcoastlines(color='0.50', linewidth=0.25)
+   map.fillcontinents(color='0.95')
+
+For cartopy, the steps are quite similar:
+
+.. code:: python
+
+    import cartopy
+    ax = plt.axes(projection=cartopy.crs.Miller())
+    ax.coastlines(color='0.50', linewidth=0.25)
+    ax.add_feature(cartopy.feature.LAND, color='0.95')
+    ax.set_global()
+    trans = cartopy.crs.PlateCarree()
+
+
+We are almost finished. Last step is to adapt the rain code and
+put some eye candy. For basemap we use the map object to
+transform the coordinates whereas for cartopy we use the transform_point
+function of the chosen Miller projection:
 
 
 .. code:: python
 
    P = np.zeros(50, dtype=[('position', float, 2),
-                            ('size',     float, 1),
-                            ('growth',   float, 1),
-                            ('color',    float, 4)])
+                            ('size',    float),
+                            ('growth',  float),
+                            ('color',   float, 4)])
    scat = ax.scatter(P['position'][:,0], P['position'][:,1], P['size'], lw=0.5,
                      edgecolors = P['color'], facecolors='None', zorder=10)
 
@@ -756,7 +769,8 @@ put some eye candy:
        P['size'] += P['growth']
 
        magnitude = E['magnitude'][current]
-       P['position'][i] = earth(*E['position'][current])
+       P['position'][i] = map(*E['position'][current]) if use_basemap else \
+           cartopy.crs.Miller().transform_point(*E['position'][current], cartopy.crs.PlateCarree())
        P['size'][i] = 5
        P['growth'][i]= np.exp(magnitude) * 0.1
 
@@ -770,8 +784,8 @@ put some eye candy:
        scat.set_offsets(P['position'])
        return scat,
 
-       
-   animation = FuncAnimation(fig, update, interval=10)
+
+   animation = FuncAnimation(fig, update, interval=10, blit=True)
    plt.show()
 
 
@@ -781,7 +795,7 @@ If everything went well, you should obtain something like this (with animation):
    :target: scripts/earthquakes.py
    :width: 50%
 
-   
+
 Other Types of Plots
 ====================
 
diff --git a/scripts/check-installation.py b/scripts/check-installation.py
index 759394f..9cd856b 100644
--- a/scripts/check-installation.py
+++ b/scripts/check-installation.py
@@ -37,23 +37,30 @@
 else:
     print("ok")
 
-# Check for basemap
+# Check for basemap or cartopy
 try:
-    import mpl_toolkits.basemap as basemap 
+    import mpl_toolkits.basemap as basemap
+    check_for_cartopy = False
 except:
-    print("This tutorial requires basemap\n")
-    sys.exit()
-print("Check for basemap: ", end="")
-if LooseVersion(basemap.__version__) < LooseVersion("1.0"):
-    print("basemape is too old (< 1.0) \n")
-    sys.exit()
-else:
-    print("ok")
-        
-# Check for urllib
-try:
-    import urllib
-except:
-    print("This tutorial requires urllib")
+    check_for_cartopy = True
 else:
-    print("Check for urllib: ok")
+    print("Check for basemap: ", end="")
+    if LooseVersion(basemap.__version__) < LooseVersion("1.0"):
+        print("basemap is too old (< 1.0) \n")
+        sys.exit()
+    else:
+        print("ok")
+
+if check_for_cartopy:
+    try:
+        import cartopy
+    except:
+        print("This tutorial requires either basemap or cartopy\n")
+        sys.exit()
+
+    print("Check for cartopy: ", end="")
+    if LooseVersion(cartopy.__version__) < LooseVersion("0.15"):
+        print("cartopy is too old (< 0.15) \n")
+        sys.exit()
+    else:
+        print("ok")
diff --git a/scripts/earthquakes.py b/scripts/earthquakes.py
index 079c986..d1cebbd 100644
--- a/scripts/earthquakes.py
+++ b/scripts/earthquakes.py
@@ -10,14 +10,20 @@
 import matplotlib
 matplotlib.rcParams['toolbar'] = 'None'
 import matplotlib.pyplot as plt
-from mpl_toolkits.basemap import Basemap
 from  matplotlib.animation import FuncAnimation
+try:
+    from mpl_toolkits.basemap import Basemap
+    use_basemap = True
+except ImportError:
+    import cartopy
+    use_basemap = False
+
 
 
 # Open the earthquake data
 # -------------------------
-# -> http://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
-feed = "http://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
+# -> https://earthquake.usgs.gov/earthquakes/feed/v1.0/csv.php
+feed = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/"
 
 # Significant earthquakes in the past 30 days
 # url = urllib.urlopen(feed + "significant_month.csv")
@@ -35,7 +41,7 @@
 data = url.read()
 data = data.split(b'\n')[+1:-1]
 E = np.zeros(len(data), dtype=[('position',  float, 2),
-                               ('magnitude', float, 1)])
+                               ('magnitude', float)])
 for i in range(len(data)):
     row = data[i].split(b',')
     E['position'][i] = float(row[2]),float(row[1])
@@ -43,16 +49,24 @@
 
 
 fig = plt.figure(figsize=(14,10))
-ax = plt.subplot(1,1,1)
 P = np.zeros(50, dtype=[('position', float, 2),
-                        ('size',     float, 1),
-                        ('growth',   float, 1),
+                        ('size',     float),
+                        ('growth',   float),
                         ('color',    float, 4)])
 
-# Basemap projection
-map = Basemap(projection='mill')
-map.drawcoastlines(color='0.50', linewidth=0.25)
-map.fillcontinents(color='0.95')
+if use_basemap:
+    ax = plt.subplot(1,1,1)
+    # Basemap projection
+    map = Basemap(projection='mill')
+    map.drawcoastlines(color='0.50', linewidth=0.25)
+    map.fillcontinents(color='0.95')
+else:
+    # Cartopy projection
+    ax = plt.axes(projection=cartopy.crs.Miller())
+    ax.coastlines(color='0.50', linewidth=0.25)
+    ax.add_feature(cartopy.feature.LAND, color='0.95')
+    ax.set_global()
+
 scat = ax.scatter(P['position'][:,0], P['position'][:,1], P['size'], lw=0.5,
                   edgecolors = P['color'], facecolors='None', zorder=10)
 
@@ -65,7 +79,8 @@ def update(frame):
     P['size'] += P['growth']
 
     magnitude = E['magnitude'][current]
-    P['position'][i] = map(*E['position'][current])
+    P['position'][i] = map(*E['position'][current]) if use_basemap else \
+        cartopy.crs.Miller().transform_point(*E['position'][current], cartopy.crs.PlateCarree())
     P['size'][i] = 5
     P['growth'][i]= np.exp(magnitude) * 0.1
 
@@ -77,7 +92,8 @@ def update(frame):
     scat.set_facecolors(P['color']*(1,1,1,0.25))
     scat.set_sizes(P['size'])
     scat.set_offsets(P['position'])
+    return scat,
 
 plt.title("Earthquakes > 4.5 in the last 30 days")
-animation = FuncAnimation(fig, update, interval=10)
+animation = FuncAnimation(fig, update, interval=10, blit=True)
 plt.show()