clarified what we do

928fb7e4 · Prof. Dr. Robert Jäschke · a09d85d4 · 928fb7e4 · 928fb7e4
Commit 928fb7e4 authored 2 years ago by Prof. Dr. Robert Jäschke
--- a/Art.ipynb
+++ b/Art.ipynb
 {
 "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "framed-filename",
+   "metadata": {},
+   "source": [
+    "# Computer-generated Art"
+   ]
+  },
  {
   "cell_type": "markdown",
   "id": "apart-death",
@@ -44,11 +52,11 @@
    "\n",
    "def f(x, left, right, width, height):\n",
    "    if x < left or x > right:   # left + right\n",
-    "        return np.abs(np.random.normal(0, .5))\n",
+    "        return np.abs(np.random.normal(0, 0.5))\n",
    "    if x < left + width:        # ascent\n",
-    "        return sq(x, left, left + width, 0, height) + np.abs(np.random.normal(0, 1))\n",
+    "        return np.abs(np.random.normal(0, 1.0)) + sq(x, left, left + width, 0, height)\n",
    "    if x > right - width:       # descent\n",
-    "        return sq(x, right - width, right, 0, height, True) + np.abs(np.random.normal(0, 1))\n",
+    "        return np.abs(np.random.normal(0, 1.0)) + sq(x, right - width, right, 0, height, True)\n",
    "    else:                       # middle\n",
    "        return np.random.exponential(2) + height\n",
    "\n",

+%% Cell type:markdown id:framed-filename tags:
+
+# Computer-generated Art
+
 %% Cell type:markdown id:apart-death tags:

 ## Unknown Pleasures

 This artwork is inspired by [Peter Saville's](https://en.wikipedia.org/wiki/Peter_Saville_(graphic_designer)) cover of the [Joy Division](https://en.wikipedia.org/wiki/Joy_Division) album [Unknown Pleasures](https://en.wikipedia.org/wiki/Unknown_Pleasures). The original [is based on some interesting real data](https://theconversation.com/joy-division-40-years-on-from-unknown-pleasures-astronomers-have-revisited-the-pulsar-from-the-iconic-album-cover-119861) but here we try to imitate it with random data (creating unique pictures). The code demonstrates some mathematical approaches like translation, scaling and composition of functions.

 %% Cell type:code id:desperate-hands tags:

 ``` 
 import numpy as np
 import matplotlib.pyplot as plt

 k = 50 # number of horizontal points
 n = 80 # number of vertical lines

 plt.rcParams['figure.figsize'] = (15, 15)
 plt.rcParams['figure.dpi'] = 140
 plt.style.use('dark_background')
 plt.axis('off')
 plt.xlim(-100, 200)
 plt.ylim(-60, 240)

 xs = np.linspace(0, 100, k)

 def sq(x, minx, maxx, miny, maxy, invert=False):
    x = (x - minx) / (maxx - minx)
    if invert:
        x = 1 - x
    if x < 0.5:                 # ascent
        fx = 2*x**2
    else:                       # descent
        fx = (1 - 2*(1-x)**2)
    return fx * (maxy - miny) + miny

 def f(x, left, right, width, height):
    if x < left or x > right:   # left + right
-        return np.abs(np.random.normal(0, .5))
+        return np.abs(np.random.normal(0, 0.5))
    if x < left + width:        # ascent
-        return sq(x, left, left + width, 0, height) + np.abs(np.random.normal(0, 1))
+        return np.abs(np.random.normal(0, 1.0)) + sq(x, left, left + width, 0, height)
    if x > right - width:       # descent
-        return sq(x, right - width, right, 0, height, True) + np.abs(np.random.normal(0, 1))
+        return np.abs(np.random.normal(0, 1.0)) + sq(x, right - width, right, 0, height, True)
    else:                       # middle
        return np.random.exponential(2) + height

 for i in range(1, n+1):
    data = [f(x, 25, 75, 10, 5) + i*2 for x in xs]
    plt.plot(xs, data, color="white", zorder=n-i, linewidth=1)
    plt.fill_between(xs, data, color="black", zorder=n-i)

 plt.show()
 ```

--- a/README.org
+++ b/README.org
@@ -28,7 +28,8 @@ their difficulty (☆ = simple, ☆☆ = advanced, ☆☆☆ = sophisticated):
     module "Datenanalyse & -auswertung" (☆☆)
 - [[file:amazon_reviews.ipynb][Amazon reviews]] :: crawling web sites with [[https://scrapy.org/][Scrapy]], processing JSON
     data, basic statistics and visualisation (☆☆)
- [[file:Art.ipynb][Computer-generated art]] :: inspired by famous artists (☆☆)
+- [[file:Art.ipynb][Computer-generated art]] :: translation, scaling and composition of
+  functions (☆☆)
 - [[file:classification.ipynb][Classification]] :: basic machine learning classification example (☆)
 - [[file:community_detection.ipynb][Community detection]] :: applying community detection algorithms to
     network graphs (☆☆)