{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construction du manche"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On code les notes de la manière suivante : un chiffre entre 0 (C) et 11 (B)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def note2int(note):\n",
    "    \"\"\" returns int associated with a given note\"\"\"\n",
    "    notes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n",
    "    ints = range(12)\n",
    "    return dict(zip(notes, ints))[note]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "note2int('D')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def int2note(integer):\n",
    "    \"\"\" returns note associated to a given int\"\"\"\n",
    "    notes = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']\n",
    "    ints = range(12)\n",
    "    return dict(zip(ints, notes))[integer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'E'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int2note(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut construire les cordes de la guitare en empilant des lignes qui correspondent à chaque corde, alors que les colonnes de la matrice correspondent aux cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "guitar = vstack((((arange(12) + 4) % 12), # mi aigu\n",
    "                 ((arange(12) + 11) % 12),\n",
    "                 ((arange(12) + 7) % 12),\n",
    "                 ((arange(12) + 2) % 12),\n",
    "                 ((arange(12) + 9) % 12),\n",
    "                 ((arange(12) + 4) % 12)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['E', 'B', 'G', 'D', 'A', 'E']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[int2note(guitar[i, 0]) for i in range(6)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Par comparaison booléenne, on peut n'afficher que certains notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x106829668>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1wAAADtCAYAAACrm3SJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10671fef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 6))\n",
    "imshow(guitar == note2int('C'), interpolation = 'nearest', aspect=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut étendre ce principe à plusieurs notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def guitar_with_notes(notes):\n",
    "    guitar_with_notes = zeros(guitar.shape, dtype=bool8)\n",
    "    for note in notes:\n",
    "        guitar_with_notes += (guitar == note2int(note))\n",
    "    return guitar_with_notes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x106b52ef0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1wAAADtCAYAAACrm3SJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0aIGg8MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1067ff208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 6))\n",
    "imshow(guitar_with_notes(['C', 'D']), interpolation = 'nearest', aspect=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalement, on peut afficher un accord de A mineur 7 b5, les notes étant : [A, C, Eb==D#, G]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x106d32d68>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1wAAADtCAYAAACrm3SJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAMonABAAAM8r/eJzrxIKyU/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b78518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 6))\n",
    "imshow(guitar_with_notes(['A', 'C', 'D#', 'G']), interpolation = 'nearest', aspect=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voir même avec des couleurs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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YcjNnBnpJkiSpY02n3HTBQC9JkiR1bD5H6F22UpIkSerYnloy7XawJKcl+UaSLUm+m+TK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\nGeglSZKkEWaglyRJkkbY/weI3zKsxN4meQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d0b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 6))\n",
    "imshow(guitar_with_notes(['A', 'C', 'D#', 'G']) * (guitar + 1), interpolation = 'nearest', aspect=.5, cmap=cm.Accent)\n",
    "cbar = colorbar(shrink=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enfin, nous pouvons également expérimenter avec le dessin des cordes individuelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x106d73cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 6))\n",
    "def plot_neck():\n",
    "    scale = 0.15\n",
    "    delta = 0.05\n",
    "    # lignes horizontales\n",
    "    for i in range(7):\n",
    "        plot([0, 1], array([i, i]) / 6. * scale, 'black')\n",
    "    # lignes verticales\n",
    "    for i in range(13):\n",
    "        plot(array([1, 1]) * i/12., [scale, 0], 'black')\n",
    "    \n",
    "    xlim(-delta, 1 + delta)\n",
    "    ylim(-delta, scale + delta)\n",
    "\n",
    "plot_neck()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x107075ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_notes(notes):\n",
    "    guitar_neck = guitar_with_notes(notes)\n",
    "    for i in range(6):\n",
    "        for j in range(12):\n",
    "            if guitar_neck[i, j] == True:\n",
    "                plot([(j + 0.5) / 12.], [i], 'bo')\n",
    "\n",
    "plot_notes(['C'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting it all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class guitar_plotter(object):\n",
    "    def __init__(self, scale=0.3, delta=0.1):\n",
    "        self.scale = scale\n",
    "        self.delta = delta\n",
    "    \n",
    "    def plot_neck(self):\n",
    "        for i in range(6):\n",
    "            plot([0, 1], array([i, i]) / 5. * self.scale, 'black')\n",
    "\n",
    "        for i in range(13):\n",
    "            \n",
    "            line = plot(array([1, 1]) * i/12., [self.scale, 0], 'black')\n",
    "            if i == 0 or i == 12:\n",
    "                setp(line, linewidth=4.0)\n",
    "            \n",
    "        xlim(-self.delta, 1 + self.delta)\n",
    "        ylim(-self.delta, self.scale + self.delta)\n",
    "        \n",
    "    def plot_notes(self, notes, labels=[]):\n",
    "        guitar_neck = guitar_with_notes(notes)\n",
    "        for i in range(6):\n",
    "            for j in range(12):\n",
    "                if guitar_neck[i, j] == True:\n",
    "                    plot([(j - 0.5) / 12.], [(5 - i) / 5. * self.scale], 'bo')\n",
    "                    if labels != []:\n",
    "                        text = dict(zip(notes, labels))[int2note(guitar[i, j])]\n",
    "                    else:\n",
    "                        text = int2note(guitar[i, j])\n",
    "                    annotate(text, ((j - 0.5) / 12., (5 - i + 0.15) / 5. * self.scale))\n",
    "\n",
    "    def plot_neck_and_notes(self, notes, labels=[]):\n",
    "        self.plot_neck()\n",
    "        self.plot_notes(notes, labels)\n",
    "        gca().set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bzFx3f5Lk56nrAK6su8+tLu5MclOSnyYsB65HTV0ALIXQBgATqqodVfV5ku+TfNjdX01d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\n9Rxi9j4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106b7b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 4))\n",
    "gp = guitar_plotter()\n",
    "gp.plot_neck_and_notes(['C', 'E', 'G'], ['1', '3', '5'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application aux accords de la gamme majeure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Majeur 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1070a4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 4))\n",
    "gp = guitar_plotter()\n",
    "gp.plot_neck_and_notes(['C', 'E', 'G', 'B'], ['1', '3', '5', '7'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mineur 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1073f8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 4))\n",
    "gp = guitar_plotter()\n",
    "gp.plot_neck_and_notes(['D', 'F', 'A', 'C'], ['1', 'b3', '5', 'b7'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accord de dominante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cnY//mOS57v7vMGUBTEdVXVJVv5iP9yW5PcmxYasCpkBoW9wvkxyd36f+ZpLnu/vlgWuC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\nUJEnybM2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1077616a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 4))\n",
    "gp = guitar_plotter()\n",
    "gp.plot_neck_and_notes(['G', 'B', 'D', 'F'], ['1', '3', '5', 'b7'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accord mineur 7 quinte bémol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LVZ2b2XvKe0MUBUA/hLYTs7uq/jffvjPJR1W1J/n7mbabk3w6VHGwgUVz97zk77l7axLf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\nAICO/T97dBl2sr/uYgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107c37a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 4))\n",
    "gp = guitar_plotter()\n",
    "gp.plot_neck_and_notes(['B', 'D', 'F', 'A'], ['1', 'b3', 'b5', 'b7'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accord mineur -> accord sus 2 (1789)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10806ebe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(15, 4))\n",
    "gp = guitar_plotter()\n",
    "gp.plot_neck_and_notes(['A', 'B', 'C', 'E'], ['1', '2', 'b3', '5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Au vu du diagramme ci-dessus, il me semble que la meilleure position pour jouer l'accord mineur vers suspendu soit celle avec la tonique sur la corde de La, en barré."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Diagramme interactif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n"
     ]
    }
   ],
   "source": [
    "from IPython.html.widgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def show_notes(notes):\n",
    "    notes = notes.split(',')\n",
    "    gp = guitar_plotter()\n",
    "    gp.plot_neck_and_notes(notes, notes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1081e60b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(show_notes,\n",
    "         notes=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\"A,\".split(',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}