first relmath prototype

exercises/relmath/relmath.ipynb

@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Relational Mathematics\n",
+    "\n",
+    "### WARNING: MEANINGLESS DRAFT !!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'CIAO'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " getattr(\"ciao\", \"upper\")()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'relmath.MatMul'>\n",
+      "[[9, 0, 6], [0, 5, 7]]\n",
+      "[[9, 0], [0, 5], [6, 7]]\n",
+      "['a', 'b', 'x', 'y', 'z', 'a', 'b']\n",
+      "[{'value': 9, 'source': 0, 'target': 2}, {'value': 0, 'source': 0, 'target': 3}, {'value': 6, 'source': 0, 'target': 4}, {'value': 0, 'source': 1, 'target': 2}, {'value': 5, 'source': 1, 'target': 3}, {'value': 7, 'source': 1, 'target': 4}, {'value': 9, 'source': 2, 'target': 5}, {'value': 0, 'source': 2, 'target': 6}, {'value': 0, 'source': 3, 'target': 5}, {'value': 5, 'source': 3, 'target': 6}, {'value': 6, 'source': 4, 'target': 5}, {'value': 7, 'source': 4, 'target': 6}]\n",
+      "[100, 100, 200, 200, 200, 300, 300]\n",
+      "[0, 1, 0, 1, 2, 0, 1]\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "89f836499223426c81eaeea3d92bf4a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Figure(fig_margin={'bottom': 60, 'left': 60, 'right': 60, 'top': 60}, layout=Layout(height='500px', width='960…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from bqplot import *\n",
+    "from bqplot.marks import Graph\n",
+    "from ipywidgets import Layout\n",
+    "from relmath import * \n",
+    "type(M)\n",
+    "\n",
+    "fig_layout = Layout(width='960px', height='500px')\n",
+    "def disp(expr):\n",
+    "    \n",
+    "    print(type(expr))\n",
+    "    if type(expr) is MatMul:\n",
+    "        binop = expr\n",
+    "        \n",
+    "        left =  binop.left.simp()\n",
+    "        right = binop.right.simp()\n",
+    "        \n",
+    "        if type(left) is not Rel:\n",
+    "            raise ValueError(\"Can't simplify left operand to a Rel ! Found %s \" % left)\n",
+    "\n",
+    "        if type(right) is not Rel:\n",
+    "            raise ValueError(\"Can't simplify right operand to a Rel ! Found %s \" % right)\n",
+    "            \n",
+    "        print(left)\n",
+    "        print(right)\n",
+    "            \n",
+    "        node_data =  left.dom + left.cod + right.cod\n",
+    "        print(node_data)\n",
+    "\n",
+    "        link_data = []\n",
+    "        n = len(left.dom)\n",
+    "        m = len(left.cod)\n",
+    "        w = len(right.cod)\n",
+    "        for i in range(n):\n",
+    "            for j in range(m):\n",
+    "                link_data.append({'source': i, 'target': n+j, 'value': left.g[i][j].val})\n",
+    "\n",
+    "        for i in range(m):\n",
+    "            for j in range(w):\n",
+    "                link_data.append({'source': n+i, 'target': n+m+j, 'value': right.g[i][j].val})\n",
+    "                \n",
+    "                \n",
+    "        print(link_data)\n",
+    "\n",
+    "        xs = LinearScale()\n",
+    "        ys = LinearScale()\n",
+    "        lcs = ColorScale(scheme='Greens')\n",
+    "        x = ([100] * n) + ([200]*m) + ([300]*w)\n",
+    "        y = list(range(n)) + list(range(m)) + list(range(w))\n",
+    "        print(x)\n",
+    "        print(y)\n",
+    "        graph = Graph(node_data=node_data, link_data=link_data, link_type='line',\n",
+    "                      colors=['orange'], directed=False, \n",
+    "                      scales={'x': xs, 'y': ys, 'link_color': lcs}, \n",
+    "                      x=x, y=y,  color=np.random.rand(len(node_data)))\n",
+    "        return Figure(marks=[graph], layout=fig_layout)    \n",
+    "        \n",
+    "    \n",
+    "    elif type(expr) is Rel:\n",
+    "        node_data = expr.dom() + expr.cod()\n",
+    "        #print(node_data)\n",
+    "\n",
+    "        link_data = []\n",
+    "        for i in range(len(expr.dom)):\n",
+    "            for j in range(len(expr.cod)):\n",
+    "                link_data.append({'source': i, 'target': i+j, 'value': expr.g[i][j].val})\n",
+    "\n",
+    "        #print(link_data)\n",
+    "\n",
+    "        xs = LinearScale()\n",
+    "        ys = LinearScale()\n",
+    "        lcs = ColorScale(scheme='Greens')\n",
+    "        x = ([100] * len(expr.dom)) + ([200]*len(expr.cod))\n",
+    "        y = list(range(len(expr.dom))) + list(range(len(expr.cod)))\n",
+    "        #print(x)\n",
+    "        #print(y)\n",
+    "        graph = Graph(node_data=node_data, link_data=link_data, link_type='line',\n",
+    "                      colors=['orange'], directed=False, \n",
+    "                      scales={'x': xs, 'y': ys, 'link_color': lcs}, \n",
+    "                      x=x, y=y,  color=np.random.rand(len(node_data)))\n",
+    "        return Figure(marks=[graph], layout=fig_layout)    \n",
+    "    else:\n",
+    "        raise ValueError(\"not supported type: %s\" % type(expr) )\n",
+    "    \n",
+    "disp(E)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.rand(len(node_data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "M.nodes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_data = list('ABCDEFG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "node_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "link_data = [{'source': s, 'target': t, 'value': np.random.rand()} for s, t in np.random.randint(0, 7, (20, 2))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "link_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}