Bootstraping repo, and setting up the RPDE notebook.

numba_examples/rpde.ipynb

@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        requirejs.config({\n",
+       "            paths: {\n",
+       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
+       "            }\n",
+       "        });\n",
+       "        require(['plotly'], function(Plotly) {\n",
+       "            window._Plotly = Plotly;\n",
+       "        });\n",
+       "        }\n",
+       "        </script>\n",
+       "        "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import librosa\n",
+    "import numpy as np\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objs as go\n",
+    "from plotly.offline import  init_notebook_mode,  iplot\n",
+    "init_notebook_mode(connected=True)\n",
+    "from numpy.linalg import norm\n",
+    "\n",
+    "from tqdm import tqdm\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### First things first\n",
+    "\n",
+    "_Loading the audio data, and embedding the audio time series_\n",
+    " Our algorithm operates on time series\n",
+    "(here, it's audio, what a coincidence!).\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# loads the sound data into a float32\n",
+    "data, rate = librosa.core.load(\"pho_a.wav\")\n",
+    "\n",
+    "px.line(data, x=range(len(data)), y=data)\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def embed_time_series(data: np.ndarray, dim: int, tau: int):\n",
+    "    \"\"\"This creates an special embedding for the input data. The\n",
+    "    output shape of this function is (N, D) with\n",
+    "    N = len(data) - (dim - 1) * tau\n",
+    "    D = dim\"\"\"\n",
+    "    embed_points_nb = data.shape[0] - (dim - 1) * tau\n",
+    "    embed_mask = np.arange(embed_points_nb)[:, np.newaxis] + np.arange(dim)\n",
+    "    tau_offsets = np.arange(dim) * (tau - 1)\n",
+    "    embed_mask = embed_mask + tau_offsets\n",
+    "    return data[embed_mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this creates an embedding of a slice of the time series\n",
+    "ts = embed_time_series(data[:2000], dim=4, tau=22)\n",
+    "print(ts.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "trace = go.Scatter3d(\n",
+    "        x=ts[:,0],\n",
+    "        y=ts[:,1],\n",
+    "        z=ts[:,2],\n",
+    "        name=\"Pho_a\",\n",
+    "        mode='markers',\n",
+    "        marker=dict(\n",
+    "            color=\"blue\",\n",
+    "            size=2,\n",
+    "            symbol='circle',\n",
+    "            line=dict(\n",
+    "                color='rgb(204, 204, 204)',\n",
+    "                width=1\n",
+    "            ),\n",
+    "            opacity=0.7\n",
+    "        )\n",
+    "    )\n",
+    "layout = go.Layout(margin=dict(l=0,r=0,b=0,t=0),\n",
+    "                   scene = dict(\n",
+    "                        xaxis={\"title\":\"f1\"},\n",
+    "                        yaxis={\"title\":\"f2\"},\n",
+    "                        zaxis={\"title\":\"f3\"})\n",
+    "                  )\n",
+    "fig = go.Figure(data=[trace], layout=layout)\n",
+    "iplot(fig, filename=\"/tmp/pho_a_trajectory.png\")"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### This is where the fun begins\n",
+    "\n",
+    "Now, let's implement the RPDE algorithm.\n",
+    "I've provided 4 different implementations:\n",
+    "\n",
+    "1. The first one is a plain python implem of the recurrence diagram\n",
+    "2. The second one is numba implem without any parallelization\n",
+    "2. The third one is a naïve parallelized numba implem. It's \"almost\" working,\n",
+    "as it outputs a value close to the actual value, but some race conditions make it\n",
+    "undeterministic.\n",
+    "3. The third one is a fixed parallelized version of the previous numba implementation,\n",
+    "fully adapted to work in parallel"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "def plain_python_recurrence_histogram(ts: np.ndarray, epsilon: float, t_max: int):\n",
+    "    distances_histogram = np.zeros(len(ts))\n",
+    "    epsilon = 0.25\n",
+    "    for i in tqdm(np.arange(len(ts))):\n",
+    "        # finding the first \"out of ball\" index\n",
+    "        first_out = len(ts) # security\n",
+    "        for j in np.arange(i + 1, len(ts)):\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d > epsilon:\n",
+    "                first_out = j\n",
+    "                break\n",
+    "\n",
+    "        # finding the first \"back to the ball\" index\n",
+    "        for j in np.arange(first_out + 1, len(ts)):\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d < epsilon:\n",
+    "                distances_histogram[j - i] += 1\n",
+    "                break\n",
+    "    return distances_histogram"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numba import jit, float32, prange, int32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@jit(int32[:](float32[:,:], float32, int32), nopython=True)\n",
+    "def recurrence_histogram(ts: np.ndarray, epsilon: float, t_max: int):\n",
+    "    distances_histogram = np.zeros(len(ts), dtype=np.int32)\n",
+    "    for i in np.arange(len(ts)):\n",
+    "        # finding the first \"out of ball\" index\n",
+    "        first_out = len(ts) # security\n",
+    "        for j in np.arange(i + 1, len(ts)):\n",
+    "            if t_max > 0 and j - i > t_max:\n",
+    "                break\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d > epsilon:\n",
+    "                first_out = j\n",
+    "                break\n",
+    "\n",
+    "        # finding the first \"back to the ball\" index\n",
+    "        for j in np.arange(first_out + 1, len(ts)):\n",
+    "            if 0 < t_max < j - i:\n",
+    "                break\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d < epsilon:\n",
+    "                distances_histogram[j - i] += 1\n",
+    "                break\n",
+    "    return distances_histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "# Here, we naively just switched out all \"np.arange\" occurences to numba.prange.\n",
+    "@jit(int32[:](float32[:,:], float32, int32), nopython=True)\n",
+    "def wrong_parallel_recurrence_histogram(ts: np.ndarray, epsilon: float, t_max: int):\n",
+    "    distances_histogram = np.zeros(len(ts), dtype=np.int32)\n",
+    "    for i in prange(len(ts)):\n",
+    "        # finding the first \"out of ball\" index\n",
+    "        first_out = len(ts) # security\n",
+    "        for j in prange(i + 1, len(ts)):\n",
+    "            if t_max > 0 and j - i > t_max:\n",
+    "                break\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d > epsilon:\n",
+    "                first_out = j\n",
+    "                break\n",
+    "\n",
+    "        # finding the first \"back to the ball\" index\n",
+    "        for j in prange(first_out + 1, len(ts)):\n",
+    "            if 0 < t_max < j - i:\n",
+    "                break\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d < epsilon:\n",
+    "                distances_histogram[j - i] += 1\n",
+    "                break\n",
+    "    return distances_histogram"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@jit(int32[:](float32[:,:], float32, int32), parallel=True, nopython=True)\n",
+    "def parallel_recurrence_histogram(ts: np.ndarray, epsilon: float, t_max: int):\n",
+    "    return_distances = np.zeros(len(ts), dtype=np.int32)\n",
+    "    for i in prange(len(ts)):\n",
+    "        # finding the first \"out of ball\" index\n",
+    "        first_out = len(ts) # security\n",
+    "        for j in np.arange(i + 1, len(ts)):\n",
+    "            if t_max > 0 and j - i > t_max:\n",
+    "                break\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d > epsilon:\n",
+    "                first_out = j\n",
+    "                break\n",
+    "\n",
+    "        # finding the first \"back to the ball\" index\n",
+    "        for j in np.arange(first_out + 1, len(ts)):\n",
+    "            if t_max > 0 and j - i > t_max:\n",
+    "                break\n",
+    "            d = norm(ts[i] - ts[j])\n",
+    "            if d < epsilon:\n",
+    "                return_distances[i] = j - i\n",
+    "                break\n",
+    "                \n",
+    "    # building histogram, can't be parallel\n",
+    "    distance_histogram = np.zeros(len(ts), dtype=np.int32)\n",
+    "    for i in np.arange(len(ts)):\n",
+    "        if return_distances[i] != 0:\n",
+    "            distance_histogram[return_distances[i]] += 1\n",
+    "    return distance_histogram"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### Running stuff\n",
+    "\n",
+    "Let's run the good ol' python implementation, just to take a look at what does\n",
+    "the algorithm's output look like.\n",
+    "\n",
+    "Also, let's check that the various numba implementations's outputs match the plain\n",
+    "python \"ground truth\"."
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 30s, sys: 67.9 ms, total: 1min 30s\n",
+      "Wall time: 55.6 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "distances_histogram = plain_python_recurrence_histogram(ts, 0.12, 10000)\n",
+    "px.line(distances_histogram, x=range(len(distances_histogram)), y=distances_histogram)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "### The ~~hour~~ seconds of truth"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%time plain_python_recurrence_histogram(ts, 0.12, 10000)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%time numba_recurrence_histogram(ts, 0.12, 10000)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%time parallel_recurrence_histogram(ts, 0.12, 10000)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 16min 10s, sys: 3.01 s, total: 16min 13s\n",
+      "Wall time: 6min 59s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

requirements.txt

@@ -0,0 +1,8 @@
+sly
+numba
+scipy
+numpy
+tqdm
+librosa
+jupyter
+dataclasses >= 0.7; python_version <'3.7'

sly_examples/json_parser.py

@@ -0,0 +1,107 @@
+from sly import Lexer, Parser
+import pprint
+
+"""
+Implementation with SLY of a very simple JSON parser.
+Little reminder: here's the grammar
+
+    json := object | array
+    object := '{' members '}'
+    members := pair | pair ',' members
+    array := '[' elements ']'
+    elements := value | value "," elements
+    pair := STRING ':' value
+    value := STRING | INTEGER | FLOAT | object | array
+
+"""
+
+
+class JSONLexer(Lexer):
+    tokens = {"FLOAT", "INTEGER", "STRING"}
+
+    literals = {'{', '}', '[', ']', ',', ':'}
+    ignore = " \t\n"
+
+    @_(r"\".*?\"")
+    def STRING(self, t):
+        t.value = t.value.strip("\"")
+        return t
+
+    @_(r"\d+\.\d*")
+    def FLOAT(self, t):
+        t.value = float(t.value)
+        return t
+
+    @_(r"\d+")
+    def INTEGER(self, t):
+        t.value = int(t.value)
+        return t
+
+
+class JSONParser(Parser):
+    tokens = JSONLexer.tokens
+    start = "json"
+
+    @_('object',
+       'array')
+    def json(self, p):
+        return p[0]
+
+    @_('"{" members "}"')
+    def object(self, p):
+        return {key: value for key, value in p.members}
+
+    @_('pair')
+    def members(self, p):
+        return [p.pair]
+
+    @_('pair "," members')
+    def members(self, p):
+        return [p.pair] + p.members
+
+    @_('STRING ":" value')
+    def pair(self, p):
+        return p.STRING, p.value
+
+    @_('"[" elements "]"')
+    def array(self, p):
+        return p.elements
+
+    @_('value')
+    def elements(self, p):
+        return [p.value]
+
+    @_('value "," elements')
+    def elements(self, p):
+        return [p.value] + p.elements
+
+    @_('STRING',
+       'INTEGER',
+       'FLOAT',
+       'object',
+       'array')
+    def value(self, p):
+        return p[0]
+
+    def error(self, p):
+        raise ValueError("Parsing error at token %s" % str(p))
+
+
+if __name__ == "__main__":
+    lexer = JSONLexer()
+    parser = JSONParser()
+    json_text = """{"menu": {
+          "id": "file",
+          "value": "File",
+          "popup": {
+            "menuitem": [
+              {"value": "New", "onclick": "CreateNewDoc()"},
+              {"value": "Open", "onclick": "OpenDoc()"},
+              {"value": "Close", "onclick": "CloseDoc()"}
+            ],
+            "delay" : 1.5
+          }
+        }}
+        """
+    result = parser.parse(lexer.tokenize(json_text))
+    pprint.pprint(result)

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+   outer loop
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+  endfacet
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+  endfacet
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+   outer loop
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+  endfacet
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+  endfacet
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+    vertex 1.29904 0.25 0
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+  endfacet
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+    vertex 1.78814e-006 0 0.75
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+  endfacet
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+  endfacet
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+   outer loop
+    vertex 1.78814e-006 0 0.75
+    vertex 0.36904 0 1.5
+    vertex 1.78814e-006 0.25 0.75
+   endloop
+  endfacet
+  facet normal -0.897262 0 0.441498
+   outer loop
+    vertex 0.36904 0 1.5
+    vertex 0.36904 0.25 1.5
+    vertex 1.78814e-006 0.25 0.75
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 0.48049 0.35 1.5
+    vertex 0.155677 0.35 2.16012
+    vertex 0.889765 0.35 2.20888
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 0.889765 0.35 0.791115
+    vertex 0.155677 0.35 0.839879
+    vertex 0.48049 0.35 1.5
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 0.889765 0.35 2.20888
+    vertex 1.29904 0.35 2.82024
+    vertex 1.70831 0.35 2.20888
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 1.29904 0.35 0.179758
+    vertex 0.889765 0.35 0.791115
+    vertex 1.70831 0.35 0.791115
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 2.11759 0.35 1.5
+    vertex 1.70831 0.35 2.20888
+    vertex 2.4424 0.35 2.16012
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 1.70831 0.35 0.791115
+    vertex 2.11759 0.35 1.5
+    vertex 2.4424 0.35 0.839879
+   endloop
+  endfacet
+  facet normal 0 1 0
+   outer loop
+    vertex 0.889765 0.35 0.791115
+    vertex 0.48049 0.35 1.5
+    vertex 0.889765 0.35 2.20888
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+  endfacet
+  facet normal 0 1 0
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+    vertex 0.889765 0.35 2.20888
+    vertex 1.70831 0.35 2.20888
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+  endfacet
+  facet normal 0 1 0
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+    vertex 1.70831 0.35 2.20888
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+  endfacet
+  facet normal 0 1 0
+   outer loop
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+    vertex 2.11759 0.35 1.5
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+  endfacet
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+    vertex 0.36904 0.25 1.5
+    vertex 0.155677 0.35 0.839879
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+  endfacet
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+    vertex 0.36904 0.25 1.5
+    vertex 0.48049 0.35 1.5
+    vertex 0.155677 0.35 0.839879
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+  endfacet
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+    vertex 0.36904 0.25 1.5
+    vertex 0.155677 0.35 2.16012
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+  endfacet
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+    vertex 0.155677 0.35 2.16012
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+  endfacet
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+    vertex 2.22904 0.25 1.5
+    vertex 2.11759 0.35 1.5
+    vertex 2.4424 0.35 2.16012
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+    vertex 2.4424 0.35 0.839879
+    vertex 2.22904 0.25 1.5
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+    vertex 2.11759 0.35 1.5
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+  endfacet
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+    vertex 2.4424 0.35 0.839879
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+  endfacet
+  facet normal 0.0468684 0.707107 -0.705552
+   outer loop
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+    vertex 1.70831 0.35 0.791115
+    vertex 2.4424 0.35 0.839879
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+    vertex 1.29904 0.35 0.179758
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+    vertex 0.889765 0.35 0.791115
+    vertex 1.29904 0.35 0.179758
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+    vertex 1.29904 0.35 0.179758
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+  endfacet
+  facet normal -0.0468684 0.707107 -0.705552
+   outer loop
+    vertex 0.83404 0.25 0.694596
+    vertex 0.155677 0.35 0.839879
+    vertex 0.889765 0.35 0.791115
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+  endfacet
+  facet normal -0.0468685 0.707107 -0.705552
+   outer loop
+    vertex 0.83404 0.25 0.694596
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+  endfacet
+endsolid Star

sly_examples/stl_parser.py

@@ -0,0 +1,115 @@
+import pprint
+from dataclasses import dataclass
+from typing import List, Tuple
+
+from sly import Lexer, Parser
+
+
+@dataclass
+class Vertex:
+    coords: Tuple[float, float, float]
+
+    def __repr__(self):
+        return "VERTEX(%f,%f,%f)" % self.coords
+
+
+@dataclass
+class Facet:
+    vertices: Tuple[Vertex, Vertex, Vertex]
+    normal: Vertex
+
+    def __repr__(self):
+        return "\t FACET({normal})\n\t\t{vertices}".format(
+            normal=repr(self.normal),
+            vertices="\n\t\t".join(repr(vertex) for vertex in self.vertices)
+        )
+
+
+@dataclass
+class Solid:
+    name: str
+    facets: List[Facet]
+
+    def __repr__(self):
+        return "SOLID({name})\n{facets}\n".format(
+            name=self.name,
+            facets="\n".join(repr(facet) for facet in self.facets)
+        )
+
+
+class STLLexer(Lexer):
+    tokens = {"SOLID_START", "SOLID_END",
+              "FACET_START", "FACET_END", "NAME_LITERAL",
+              "LOOP_START", "LOOP_END",
+              "NAME_LITERAL", "VERTEX_TAG", "FLOAT"}
+
+    @_(r"[ \t\n]+")
+    def ignore_whitespace(self, t):
+        self.lineno += t.value.count("\n")
+
+    SOLID_START = r"solid"
+    SOLID_END = r"endsolid"
+    FACET_START = r"facet normal"
+    FACET_END = r"endfacet"
+    LOOP_START = r"outer loop"
+    LOOP_END = r"endloop"
+    VERTEX_TAG = r"vertex"
+
+    @_("[+-]?\d+(\.\d*(e[+-]?\d+)?)?")
+    def FLOAT(self, t):
+        t.value = float(t.value)
+        return t
+
+    NAME_LITERAL = r"\w+"
+
+
+class STLParser(Parser):
+    tokens = STLLexer.tokens
+    start = "stl"
+
+    @_("solid")
+    def stl(self, p):
+        return p.solid
+
+    @_("SOLID_START NAME_LITERAL facets_list SOLID_END")
+    @_("SOLID_START NAME_LITERAL facets_list SOLID_END NAME_LITERAL")
+    def solid(self, p):
+        return Solid(name=p[1], facets=p.facets_list)
+
+    @_("facet")
+    def facets_list(self, p):
+        return [p.facet]
+
+    @_("facet facets_list")
+    def facets_list(self, p):
+        return [p.facet] + p.facets_list
+
+    @_("FACET_START triplet loop FACET_END")
+    def facet(self, p):
+        return Facet(vertices=p.loop, normal=Vertex(p.triplet))
+
+    @_("LOOP_START vertex vertex vertex LOOP_END")
+    def loop(self, p):
+        return p[1], p[2], p[3]
+
+    @_("VERTEX_TAG triplet")
+    def vertex(self, p):
+        return Vertex(p.triplet)
+
+    @_("FLOAT FLOAT FLOAT")
+    def triplet(self, p):
+        return p[0], p[1], p[2]
+
+    def error(self, token):
+        pass
+
+
+if __name__ == "__main__":
+    lexer = STLLexer()
+    parser = STLParser()
+
+    with open("star.stl") as stl_file:
+        # pprint.pprint(list(lexer.tokenize(stl_file.read())))
+        solid = parser.parse(lexer.tokenize(stl_file.read()))
+
+    print(solid)