merge

Author: hildobby

README.md

@@ -1,10 +1,38 @@
-# Complex System Simulation: Expanding and exploring the Bak-Snappen model
+Expanding and exploring the Bak-Sneppen model written in Python 3+
+=========================================
 
-2 Dimensions Bak-Snappen model, evaluation of the resulting model behaviour and effect of various parameters.
+2 dimensions and 3 dimensions Bak-Snappen model, evaluation of the resulting model behaviour and effect of various parameters.
 
 By: [Hildebert Moulié](https://github.com/hildobby), [Philippe Nicolau](https://github.com/PNicolau96), [Louis Weyland](https://github.com/LouisWW) & [Binjie Zhou](https://github.com/binjiezhou).
 
-## Setup required packages
+<p float="left" class="center">
+  <img src="https://github.com/hildobby/Complex_System/blob/master/src/figures/lattice_itr%3D1.png" width="400" />
+  <img src="https://github.com/hildobby/Complex_System/blob/master/src/figures/lattice-age_itr%3D2000.png" width="400" /> 
+</p>
+
+## Project Plan
+
+1. Bring the 1 dimension Bak-Snappen model to 2 dimensions and 3 dimensions
+2. Evaluate different results from the model:
+    * Avalanche time and compare it between several models
+    * Mutation distance
+    * Cluster sizes
+    * Evolutionary time
+3. Test the data with the [powerlaw](https://pypi.org/project/powerlaw/) package
+
+## Overview of some of the results
+
+### Fitness over time
+![Fitness over time](https://github.com/hildobby/Complex_System/blob/master/src/figures/average_fitness_s%3D(20%2C%2020)_itr%3D2000_rep%3D10_std%3D0.3.png)
+
+### Impact of percentage of empty node
+![Impact of percentage of empty node](https://github.com/hildobby/Complex_System/blob/master/presentation_content/images/emptynode1.png)
+
+
+### Cluster size distribution for different grid sizes
+![Cluster size distribution for different grid sizes](https://github.com/hildobby/Complex_System/blob/master/src/figures/)
+
+## Working with the repository
 
 Gathering the required packages
 ```
@@ -21,27 +49,9 @@ Generating the presentation from the `results_presentation.ipynb` Jupyter notebo
 jupyter nbconvert results_presentation.ipynb --to slides --post serve
 ```
 
-An overview of the results can also be found in [the Jupyter notebook file](https://github.com/hildobby/Complex_System/blob/master/results_overview.ipynb) named `results_overview.ipynb`.
-
-## Project Plan
-
-1. Bring the 1 dimension Bak-Snappen model to 2 dimensions and 3 dimensions
-2. Evaluate different results from the model:
-    * Avalanche time and compare it between several models
-    * Mutation distance (Check if power still applies here)
-    * Cluster sizes
-    * Evolutionary time
-3. Test the data withe the [powerlaw](https://pypi.org/project/powerlaw/) package
-
-
-
-## An overview of some of the results
-
-
-
 ## References
 
+* Alstott, J., Bullmore, E., & Plenz, D. (2014). powerlaw: a Python package for analysis of heavy-tailed distributions. PloS one, 9(1), e85777.
 * Bak, P., & Sneppen, K. (1993). Punctuated equilibrium and criticality in a simple model of evolution. Physical review letters, 71(24), 4083.
 * Fraiman, D. (2018). Bak-Sneppen model: Local equilibrium and critical value. Physical Review E, 97(4), 042123.
 * Paczuski, M., Maslov, S., & Bak, P. (1996). Avalanche dynamics in evolution, growth, and depinning models. Physical Review E, 53(1), 414.
-* Alstott, J., Bullmore, E., & Plenz, D. (2014). powerlaw: a Python package for analysis of heavy-tailed distributions. PloS one, 9(1), e85777.

results_overview.ipynb

@@ -553,16 +553,3 @@ def get_clusters(self):
         plt.savefig(path.join(dir_path, 'figures/lattice-age_itr={}.png'.format(iterations)), dpi=300)
         #
 
-        """
-        number_of_frames = 2000
-        def update_hist(num):
-            plt.cla()
-            plt.hist(np.concatenate([lattice.cluster_size[num][x] for x in lattice.cluster_size[num]]),bins=50)
-
-
-        fig = plt.figure()
-        hist = plt.hist(np.concatenate([lattice.cluster_size[0][x] for x in lattice.cluster_size[0]]),bins=50)
-
-        animation = animation.FuncAnimation(fig, update_hist, number_of_frames,interval=20)
-        plt.show()
-        """