Update figures #

Author: rougier

02-introduction.rst

@@ -56,13 +56,14 @@ each random steps.
 
 **Vectorized approach**
 
-But, we can further simplifying things by considering a random walk to be
-composed of a number of steps and corresponding positions are the cumulative
-sum of these steps.
+But, we can further simplify things by considering a random walk to be composed
+of a number of steps and corresponding positions are the cumulative sum of
+these steps.
 
 .. code:: python
        
    steps = 2*np.random.randint(0, 2, size=n) - 1
    walk = np.cumsum(steps)
 
-   
+This book is about vectorization, be it at the level of code or problem. We'll
+see the difference is important before looking at custom vectorization.

04-code-vectorization.rst

@@ -79,7 +79,7 @@ an array of cells that are connected together with the notion of neighbour and
 their vectorization is straightforward. Let me first define the game and we'll
 see how to vectorize it.
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 4.1**
    :class: legend
 
    Conus textile snail exhibits a cellular automaton pattern on its shell.
@@ -185,7 +185,7 @@ The figure below shows 4 iterations on a 4x4 area where the initial state is a
 `glider <https://en.wikipedia.org/wiki/Glider_(Conway%27s_Life)>`_, a structure
 discovered by Richard K. Guy in 1970.
 
-.. admonition:: **Figure 1**
+.. admonition:: **Figure 4.2**
    :class: legend
 
    The glider pattern is known to replicate itself one step diagonally in 4
@@ -278,7 +278,7 @@ If you look at the `birth` and `survive` lines, you'll see that these two
 variables are arrays that can be used to set `Z` values to 1 after having
 cleared it.
 
-.. admonition:: **Figure 2**
+.. admonition:: **Figure 4.3**
    :class: legend
 
    The Game of Life. Gray levels indicate how miuch a cell has been active in
@@ -349,7 +349,7 @@ Zebrafish     0.16  0.08  0.035 0.060
 The figure below show some animation of the model for a specific set of parameters.
 
 
-.. admonition:: **Figure 3**
+.. admonition:: **Figure 4.4**
    :class: legend
 
    Reaction-diffusion Gray-Scott model. From left to right, *Bacteria 1*, *Coral* and
@@ -417,7 +417,7 @@ which, if the number is still within some bounds, it is considerered non
 divergent. Of course, the more iteration you do, the more precision you get.
 
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 4.5**
    :class: legend
 
    Romanesco broccoli, showing self-similar form approximating a natural fractal.
@@ -555,7 +555,7 @@ escape cutoff. See reference below about the renormalization of the escape
 count. Here is a picture of the result where we use recount normalization,
 and added a power normalized colormap (gamma=0.3) as well as light shading.
 
-.. admonition:: **Figure 4**
+.. admonition:: **Figure 4.6**
    :class: legend
 
    The Mandelbrot as rendered by maplotlib using recount normalization, power
@@ -584,7 +584,7 @@ the exercise is to write a function using Numpy that takes a two-dimensional
 float array and return the dimension. We'll consider values in the array to be
 normalized (i.e. all values are between 0 and 1).
 
-.. admonition:: **Figure 5**
+.. admonition:: **Figure 4.7**
    :class: legend
 
    The Minkowski–Bouligand dimension of the Great Britain coastlines is
@@ -623,7 +623,7 @@ update at each iteration. This the case for example in particle systems where
 particles interact mostly with local neighbours. This is also the case for
 boids that simulate flocking behaviors.
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 4.8**
    :class: legend
 
    Flocking birds are an example of self-organization in biology.
@@ -656,7 +656,7 @@ rules. The rules applied in the simplest Boids world are as follows:
   local flockmates
 
 
-.. admonition:: **Figure 7**
+.. admonition:: **Figure 4.9**
    :class: legend
 
    Boids are governed by a set of three local rules (separation, cohesion and
@@ -874,17 +874,17 @@ velocity and position:
 
 We finally visualize the result using a custom oriented scatter plot.
 
-.. admonition:: **Figure 6**
+.. admonition:: **Figure 4.10**
    :class: legend
 
    Boids is an artificial life program, developed by Craig Reynolds in 1986,
    which simulates the flocking behaviour of birds.
 
 .. raw:: html
 
-         <video width="100%" class="bordered" controls>
-         <source src="data/boids.mp4" type="video/mp4">
-         Your browser does not support the video tag. </video>
+   <video width="100%" class="bordered" controls>
+   <source src="data/boids.mp4" type="video/mp4">
+   Your browser does not support the video tag. </video>
 
 
 

05-problem-vectorization.rst

@@ -171,7 +171,7 @@ split in two distinct problems: to find a path between two nodes in a graph and
 to find the shortest path. We'll illustrate this through path finding in a
 maze. First task is thus to build a maze.
 
-.. admonition:: **Figure 8**
+.. admonition:: **Figure 5.1**
    :class: legend
 
    A hedge maze at Longleat stately home in England.
@@ -247,7 +247,7 @@ maze is easy to solve. With low density, the maze has more "big empty rooms".
 
 Here is an animation showing the generation process.
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 5.2**
    :class: legend
 
    Progressive maze building with complexity and density control.
@@ -333,7 +333,7 @@ illustrated below (reading from left to right, top to bottom). Once this is
 done, we can ascent the gradient from the starting node. You can check on the
 figure this leads to the shortest path.
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 5.3**
    :class: legend
 
    Value iteration algorithm on a simple maze. Once entrance has been reached,
@@ -424,7 +424,7 @@ solution, reusing part of the game of life code:
 Once this is done, we can ascent the gradient to find the shortest path as
 illustrated on the figure below:
 
-.. admonition:: **Figure 8**
+.. admonition:: **Figure 5.4**
    :class: legend
 
    Path finding using the Bellman-Ford algorithm. Gradient colors indicate
@@ -455,7 +455,7 @@ Fluid Dynamics
 
 
 
-.. admonition:: **Figure 9**
+.. admonition:: **Figure 5.5**
    :class: legend
 
    Hydrodynamic flow at two different zoom levels, Neckar river, Heidelberg,
@@ -525,7 +525,7 @@ original paper as well as the explanation by `Philip Rideout
 using this technique.
 
 
-.. admonition:: **Figure 9**
+.. admonition:: **Figure 5.6**
    :class: legend
 
    Smoke simulation using the stable fluids algorithm by Jos Stam.  Right most
@@ -578,7 +578,7 @@ methods have been proposed to achieve such noise whose most simple is certainly
 the DART method.
 
 
-.. admonition:: **Figure 10**
+.. admonition:: **Figure 5.7**
    :class: legend
 
    Detail of "The Starry Night", Vincent van Gogh, 1889. The detail has been
@@ -685,7 +685,7 @@ reader. Note that not only this method is fast, but it also offers a better
 quality (more samples) than the DART method even with a high `k`
 parameter.
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 5.8**
    :class: legend
 
    Comparison of uniform, grid-jittered and Bridson sampling.

06-custom-vectorization.rst

@@ -268,7 +268,7 @@ Glumpy
 visualization library in Python whose goal is to make it easy to create fast,
 scalable, beautiful, interactive and dynamic visualizations.
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 6.1**
    :class: legend
 
    Simulation of a spiral galaxy using the density wave theory.
@@ -279,7 +279,7 @@ scalable, beautiful, interactive and dynamic visualizations.
 
 |
 
-.. admonition:: **Figure**
+.. admonition:: **Figure 6.2**
    :class: legend
 
    Tiger display using collections and 2 GL calls

book.rst

@@ -67,7 +67,7 @@ correct it as soon as you tell me.
 .. ----------------------------------------------------------------------------
 .. include:: 01-preface.rst
 .. include:: 02-introduction.rst
-.. .. include:: 03-anatomy.rst
+.. include:: 03-anatomy.rst
 .. include:: 04-code-vectorization.rst
 .. include:: 05-problem-vectorization.rst
 .. include:: 06-custom-vectorization.rst