compact_sets_sandbox/compact_sets_extended_simplified.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "806f6f69-1e0b-4d34-aac9-695c8531cdb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import chain, combinations, permutations, product, pairwise\n",
    "from math import prod, log\n",
    "from copy import deepcopy\n",
    "import networkx as nx\n",
    "from fractions import Fraction\n",
    "import json\n",
    "from operator import add\n",
    "\n",
    "def hs_array_to_fr(hs_array):\n",
    "    return prod([pow(dims[d], hs_array[d]) for d in range(len(dims))])\n",
    "\n",
    "def hs_array_to_cents(hs_array):\n",
    "    return (1200 * log(hs_array_to_fr(hs_array), 2))\n",
    "\n",
    "def expand_pitch(hs_array):\n",
    "    expanded_pitch = list(hs_array)\n",
    "    frequency_ratio = hs_array_to_fr(hs_array)\n",
    "    if frequency_ratio < 1:\n",
    "        while frequency_ratio < 1:\n",
    "            frequency_ratio *= 2\n",
    "            expanded_pitch[0] += 1\n",
    "    elif frequency_ratio >= 2:\n",
    "        while frequency_ratio >= 2:\n",
    "            frequency_ratio *= 1/2\n",
    "            expanded_pitch[0] += -1\n",
    "    return tuple(expanded_pitch)\n",
    "\n",
    "def expand_chord(chord):\n",
    "    return tuple(expand_pitch(p) for p in chord)\n",
    "\n",
    "def collapse_pitch(hs_array):\n",
    "    collapsed_pitch = list(hs_array)\n",
    "    collapsed_pitch[0] = 0\n",
    "    return tuple(collapsed_pitch)\n",
    "\n",
    "def collapse_chord(chord):\n",
    "    return tuple(collapse_pitch(p) for p in chord)\n",
    "\n",
    "def transpose_pitch(pitch, trans):\n",
    "    return tuple(map(add, pitch, trans))\n",
    "\n",
    "def transpose_chord(chord, trans):\n",
    "    return tuple(transpose_pitch(p, trans) for p in chord)\n",
    "\n",
    "def cent_difference(hs_array1, hs_array2):\n",
    "    return hs_array_to_cents(hs_array2) - hs_array_to_cents(hs_array1)\n",
    "\n",
    "def pitch_difference(hs_array1, hs_array2):\n",
    "    return transpose_pitch(hs_array1, [p * -1 for p in hs_array2])\n",
    "\n",
    "# this is modified for different chord sizes like original version\n",
    "def grow_chords(chord, root, min_chord_size, max_chord_size):\n",
    "    #this could use the tranpose_pitch function\n",
    "    branches = [branch for alt in [-1, 1] for d in range(1, len(root)) if (branch:=(*(r:=root)[:d], r[d] + alt, *r[(d + 1):])) not in chord]\n",
    "    subsets = chain.from_iterable(combinations(branches, r) for r in range(1, max_chord_size - len(chord) + 1))\n",
    "    for subset in subsets:\n",
    "        extended_chord = chord + subset\n",
    "        if(len(extended_chord) < max_chord_size):\n",
    "            for branch in subset:\n",
    "                yield from grow_chords(extended_chord, branch, min_chord_size, max_chord_size)\n",
    "        if(len(extended_chord) >= min_chord_size):\n",
    "            yield tuple(sorted(extended_chord, key=hs_array_to_fr))\n",
    "\n",
    "def chords(chord, root, min_chord_size, max_chord_size):\n",
    "    # this will filter out the 4x dups of paths that are loops, there might be a faster way to test this\n",
    "    return set(grow_chords(chord, root, min_chord_size, max_chord_size))\n",
    "\n",
    "# this is very slow, I have an idea in mind that my be faster by simply growing the chords to max_chord_size + max_sim_diff\n",
    "# technically at that point you have generated both chords and can get the second chord from the first\n",
    "def edges(chords, min_symdiff, max_symdiff, max_chord_size):  \n",
    "\n",
    "    def reverse_movements(movements):\n",
    "        return {value['destination']:{'destination':key, 'cent_difference':value['cent_difference'] * -1} for key, value in movements.items()}\n",
    "\n",
    "    def is_directly_tunable(intersection, diff):\n",
    "        # this only works for now when intersection if one element - need to fix that\n",
    "        return max([sum(abs(p) for p in collapse_pitch(pitch_difference(d, list(intersection)[0]))) for d in diff]) == 1\n",
    "\n",
    "    for combination in combinations(chords, 2):\n",
    "        [expanded_base, expanded_comp] = [expand_chord(chord) for chord in combination]\n",
    "        edges = []\n",
    "        transpositions = set(pitch_difference(pair[0], pair[1]) for pair in set(product(expanded_base, expanded_comp)))\n",
    "        for trans in transpositions:\n",
    "            expanded_comp_transposed = transpose_chord(expanded_comp, trans)\n",
    "            intersection = set(expanded_base) & set(expanded_comp_transposed)\n",
    "            symdiff_len = sum([len(chord) - len(intersection) for chord in [expanded_base, expanded_comp_transposed]])\n",
    "            if (min_symdiff <= symdiff_len <= max_symdiff):\n",
    "                rev_trans = tuple(t * -1 for t in trans)\n",
    "                [diff1, diff2] = [list(set(chord) - intersection) for chord in [expanded_base, expanded_comp_transposed]]\n",
    "                base_map = {val: {'destination':transpose_pitch(val, rev_trans), 'cent_difference': 0} for val in intersection}\n",
    "                base_map_rev = reverse_movements(base_map)\n",
    "                maps = []\n",
    "                diff1 += [None] * (max_chord_size - len(diff1) - len(intersection))\n",
    "                perms = [list(perm) + [None] * (max_chord_size - len(perm) - len(intersection)) for perm in set(permutations(diff2))]\n",
    "                for p in perms:\n",
    "                    appended_map = {\n",
    "                        diff1[index]:\n",
    "                        {\n",
    "                            'destination': transpose_pitch(val, rev_trans) if val != None else None, \n",
    "                            'cent_difference': cent_difference(diff1[index], val) if None not in [diff1[index], val] else None\n",
    "                        } for index, val in enumerate(p)}\n",
    "                    yield (tuple(expanded_base), tuple(expanded_comp), {\n",
    "                        'transposition': trans,\n",
    "                        'symmetric_difference': symdiff_len, \n",
    "                        'is_directly_tunable': is_directly_tunable(intersection, diff2),\n",
    "                        'movements': base_map | appended_map\n",
    "                    },)\n",
    "                    yield (tuple(expanded_comp), tuple(expanded_base), {\n",
    "                        'transposition': rev_trans,\n",
    "                        'symmetric_difference': symdiff_len, \n",
    "                        'is_directly_tunable': is_directly_tunable(intersection, diff1),\n",
    "                        'movements': base_map_rev | reverse_movements(appended_map)\n",
    "                    },)\n",
    "\n",
    "def graph_from_edges(edges):\n",
    "    g = nx.MultiDiGraph()\n",
    "    g.add_edges_from(edges)\n",
    "    return g\n",
    "\n",
    "def generate_graph(chord_set, min_symdiff, max_symdiff, max_chord_size):\n",
    "    #chord_set = chords(pitch_set, min_chord_size, max_chord_size)\n",
    "    edge_set = edges(chord_set, min_symdiff, max_symdiff, max_chord_size)\n",
    "    res_graph = graph_from_edges(edge_set)\n",
    "    return res_graph\n",
    "\n",
    "def display_graph(graph):\n",
    "    show_graph = nx.Graph(graph)\n",
    "    pos = nx.draw_spring(show_graph, node_size=5, width=0.1)\n",
    "    plt.figure(1, figsize=(12,12)) \n",
    "    nx.draw(show_graph, pos, node_size=5, width=0.1)\n",
    "    plt.show()\n",
    "    #plt.savefig('compact_sets.png', dpi=150)\n",
    "\n",
    "def path_to_chords(path, start_root):\n",
    "    current_root = start_root\n",
    "    start_chord = tuple(sorted(path[0][0], key=hs_array_to_fr))\n",
    "    chords = ((start_chord, start_chord,),)\n",
    "    for edge in path:\n",
    "        trans = edge[2]['transposition']\n",
    "        movements = edge[2]['movements']\n",
    "        current_root = transpose_pitch(current_root, trans)\n",
    "        current_ref_chord = chords[-1][0]\n",
    "        next_ref_chord = tuple(movements[pitch]['destination'] for pitch in current_ref_chord)\n",
    "        next_transposed_chord = tuple(transpose_pitch(pitch, current_root) for pitch in next_ref_chord)\n",
    "        chords += ((next_ref_chord, next_transposed_chord,),)\n",
    "    return tuple(chord[1] for chord in chords)\n",
    "\n",
    "def write_chord_sequence(path):\n",
    "    file = open(\"seq.txt\", \"w+\")\n",
    "    content = json.dumps(path)\n",
    "    content = content.replace(\"[[[\", \"[\\n\\t[[\")\n",
    "    content = content.replace(\", [[\", \",\\n\\t[[\")\n",
    "    content = content.replace(\"]]]\", \"]]\\n]\")\n",
    "    file.write(content)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "id": "aea5215c-8551-4685-b761-11c2dc74cf22",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice, choices\n",
    "\n",
    "# This is for the static version\n",
    "def stochastic_hamiltonian(graph):\n",
    "        \n",
    "    def movement_size_weights(edges):\n",
    "        \n",
    "        def max_cent_diff(edge):\n",
    "            res = max([abs(v) for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None])\n",
    "            return res\n",
    "            \n",
    "        def min_cent_diff(edge):\n",
    "            res = [abs(v) for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            res.remove(0)\n",
    "            return min(res)\n",
    "            \n",
    "        for e in edges:\n",
    "            yield 1000 if ((max_cent_diff(e) < 200) and (min_cent_diff(e)) > 50) else 1\n",
    "\n",
    "    def hamiltonian_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[1] not in [path_edge[0] for path_edge in path] else 1 / graph.nodes[e[1]]['count']\n",
    "    \n",
    "    def contrary_motion_weights(edges):\n",
    "\n",
    "        def is_contrary(edge):\n",
    "            cent_diffs = [v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            cent_diffs.sort()\n",
    "            return (cent_diffs[0] < 0) and (cent_diffs[1] == 0) and (cent_diffs[2] > 0)\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if is_contrary(e) else 1\n",
    "    \n",
    "    def is_directly_tunable_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[2]['is_directly_tunable'] else 0\n",
    "\n",
    "    def voice_crossing_weights(edges):\n",
    "        \n",
    "        def has_voice_crossing(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            source_order = [ordered_source.index(p) for p in source]\n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            destination_order = [ordered_destination.index(p) for p in destination]\n",
    "            return source_order != destination_order\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if not has_voice_crossing(e) else 0\n",
    "\n",
    "    def is_bass_rooted(chord):\n",
    "        return max([sum(abs(p) for p in collapse_pitch(pitch_difference(chord[0], p))) for p in chord[1:]]) == 1\n",
    "        \n",
    "    check_graph = graph.copy()\n",
    "    next_node = choice([node for node in graph.nodes() if is_bass_rooted(node)])\n",
    "    check_graph.remove_node(next_node)\n",
    "    for node in graph.nodes(data=True):\n",
    "        node[1]['count'] = 1\n",
    "    path = []\n",
    "    while (nx.number_of_nodes(check_graph) > 0) and (len(path) < 5000):\n",
    "        out_edges = list(graph.out_edges(next_node, data=True))\n",
    "        #print([l for l in zip(movement_size_weights(out_edges), hamiltonian_weights(out_edges))])\n",
    "        factors = [\n",
    "            movement_size_weights(out_edges), \n",
    "            hamiltonian_weights(out_edges), \n",
    "            contrary_motion_weights(out_edges), \n",
    "            is_directly_tunable_weights(out_edges),\n",
    "            voice_crossing_weights(out_edges)\n",
    "        ]\n",
    "        weights = [prod(a) for a in zip(*factors)]\n",
    "        edge = choices(out_edges, weights=weights)[0]\n",
    "        #edge = random.choice(out_edges)\n",
    "        next_node = edge[1]\n",
    "        node[1]['count'] += 1\n",
    "        path.append(edge)\n",
    "        if next_node in check_graph.nodes:\n",
    "            check_graph.remove_node(next_node)\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "id": "4e3ef738-7f64-47c3-9129-0450fd031375",
   "metadata": {},
   "outputs": [],
   "source": [
    "dims = (2, 3, 5, 7, 11)\n",
    "root = (0, 0, 0, 0, 0)\n",
    "chord = (root,)\n",
    "chord_set = chords(chord, root, 3, 3)\n",
    "graph = generate_graph(chord_set, 4, 4, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "id": "6e0de0e5-7973-4a15-ae90-5bbb4a1ef49d",
   "metadata": {},
   "outputs": [],
   "source": [
    "path = stochastic_hamiltonian(graph)\n",
    "#for edge in path:\n",
    "#    print(edge)\n",
    "write_chord_sequence(path_to_chords(path, root))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f2d356f-6377-46cf-bbb1-32111be90f4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "7f141bf5-fdcb-4c01-a10b-3e86d3d1a7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "chord_set = chords(chord, root, 3, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "88850b8c-a743-44d0-b863-7cd9066690d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timer unit: 1e-09 s\n",
       "\n",
       "Total time: 0.112228 s\n",
       "File: /tmp/ipykernel_515812/2679284550.py\n",
       "Function: edge_data at line 74\n",
       "\n",
       "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
       "==============================================================\n",
       "    74                                           def edge_data(chords, min_symdiff, max_symdiff, max_chord_size):\n",
       "    75       990   29603044.0  29902.1     26.4      [expanded_base, expanded_comp] = [expand_chord(chord) for chord in chords]\n",
       "    76       990     229527.0    231.8      0.2      edges = []\n",
       "    77       990   23648371.0  23887.2     21.1      transpositions = set(pitch_difference(pair[0], pair[1]) for pair in set(product(expanded_base, expanded_comp)))\n",
       "    78      9193    2315267.0    251.9      2.1      for trans in transpositions:\n",
       "    79      8203   33386775.0   4070.1     29.7          expanded_comp_transposed = transpose_chord(expanded_comp, trans)\n",
       "    80      8203    8393773.0   1023.3      7.5          intersection = set(expanded_base) & set(expanded_comp_transposed)\n",
       "    81      8203   11812057.0   1440.0     10.5          symdiff_len = sum([len(chord) - len(intersection) for chord in [expanded_base, expanded_comp_transposed]])\n",
       "    82      8203    2530596.0    308.5      2.3          if (min_symdiff <= symdiff_len <= max_symdiff):\n",
       "    83                                                       rev_trans = tuple(t * -1 for t in trans)\n",
       "    84                                                       [diff1, diff2] = [list(set(chord) - intersection) for chord in [expanded_base, expanded_comp_transposed]]\n",
       "    85                                                       base_map = {val: {'destination':transpose_pitch(val, rev_trans), 'cent_difference': 0} for val in intersection}\n",
       "    86                                                       base_map_rev = reverse_movements(base_map)\n",
       "    87                                                       tunability = is_directly_tunable(intersection, diff2)\n",
       "    88                                                       maps = []\n",
       "    89                                                       diff1 += [None] * (max_chord_size - len(diff1) - len(intersection))\n",
       "    90                                                       perms = [list(perm) + [None] * (max_chord_size - len(perm) - len(intersection)) for perm in set(permutations(diff2))]\n",
       "    91                                                       for p in perms:\n",
       "    92                                                           appended_map = {\n",
       "    93                                                               diff1[index]:\n",
       "    94                                                               {\n",
       "    95                                                                   'destination': transpose_pitch(val, rev_trans) if val != None else None, \n",
       "    96                                                                   'cent_difference': cent_difference(diff1[index], val) if None not in [diff1[index], val] else None\n",
       "    97                                                               } for index, val in enumerate(p)}\n",
       "    98                                                           edges.append((tuple(expanded_base), tuple(expanded_comp), {\n",
       "    99                                                               'transposition': trans,\n",
       "   100                                                               'symmetric_difference': symdiff_len, \n",
       "   101                                                               'is_directly_tunable': tunability,\n",
       "   102                                                               'movements': base_map | appended_map\n",
       "   103                                                           }))\n",
       "   104                                                           edges.append((tuple(expanded_comp), tuple(expanded_base), {\n",
       "   105                                                               'transposition': rev_trans,\n",
       "   106                                                               'symmetric_difference': symdiff_len, \n",
       "   107                                                               'is_directly_tunable': tunability,\n",
       "   108                                                               'movements': base_map_rev | reverse_movements(appended_map)\n",
       "   109                                                           }))\n",
       "   110       990     308812.0    311.9      0.3      return edges if edges != [] else None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lprun -f edge_data edges(chord_set, 3, 3, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "id": "6e4ecb10-344b-4721-b2f4-68de91d712db",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice, choices\n",
    "\n",
    "# This is for the rising version / yitgadal\n",
    "def stochastic_hamiltonian(graph):\n",
    "        \n",
    "    def movement_size_weights(edges):\n",
    "        \n",
    "        def max_cent_diff(edge):\n",
    "            res = max([v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None])\n",
    "            return res\n",
    "            \n",
    "        def min_cent_diff(edge):\n",
    "            res = [v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            res.remove(0)\n",
    "            return min(res)\n",
    "            \n",
    "        for e in edges:\n",
    "            yield 1000 if ((max_cent_diff(e) < 175) and (min_cent_diff(e)) >= 0) else 1\n",
    "\n",
    "    def hamiltonian_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[1] not in [path_edge[0] for path_edge in path] else 1 / graph.nodes[e[1]]['count']\n",
    "    \n",
    "    def contrary_motion_weights(edges):\n",
    "\n",
    "        def is_contrary(edge):\n",
    "            cent_diffs = [v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            cent_diffs.sort()\n",
    "            return (cent_diffs[0] < 0) and (cent_diffs[1] == 0) and (cent_diffs[2] > 0)\n",
    "\n",
    "        for e in edges:\n",
    "            yield 2 if is_contrary(e) else 1\n",
    "    \n",
    "    def is_directly_tunable_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[2]['is_directly_tunable'] else 0\n",
    "\n",
    "    def transposition_weight(edges):\n",
    "        for e in edges:\n",
    "            yield 1000 if 0 <= hs_array_to_cents(e[2]['transposition']) < 100 else 0\n",
    "\n",
    "    def is_sustained_voice(edges, voice):\n",
    "        \n",
    "        def is_sustained(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            return ordered_source[voice] == ordered_destination[voice]\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if is_sustained(e) else 0\n",
    "\n",
    "    def voice_crossing_weights(edges):\n",
    "        \n",
    "        def has_voice_crossing(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            source_order = [ordered_source.index(p) for p in source]\n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            destination_order = [ordered_destination.index(p) for p in destination]\n",
    "            return source_order != destination_order\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if not has_voice_crossing(e) else 0\n",
    "\n",
    "    def is_bass_rooted(chord):\n",
    "        return max([sum(abs(p) for p in collapse_pitch(pitch_difference(chord[0], p))) for p in chord[1:]]) == 1\n",
    "        \n",
    "    check_graph = graph.copy()\n",
    "    #next_node = choice([node for node in graph.nodes() if is_bass_rooted(node)])\n",
    "    next_node = choice(list(graph.nodes()))\n",
    "    print(next_node)\n",
    "    check_graph.remove_node(next_node)\n",
    "    for node in graph.nodes(data=True):\n",
    "        node[1]['count'] = 1\n",
    "    path = []\n",
    "    while (nx.number_of_nodes(check_graph) > 0) and (len(path) < 500):\n",
    "        out_edges = list(graph.out_edges(next_node, data=True))\n",
    "        #print([l for l in zip(movement_size_weights(out_edges), hamiltonian_weights(out_edges))])\n",
    "        factors = [\n",
    "            movement_size_weights(out_edges), \n",
    "            hamiltonian_weights(out_edges), \n",
    "            #contrary_motion_weights(out_edges), \n",
    "            #is_directly_tunable_weights(out_edges),\n",
    "            voice_crossing_weights(out_edges),\n",
    "            #transposition_weight(out_edges)\n",
    "            #is_sustained_voice(out_edges, 0)\n",
    "        ]\n",
    "        weights = [prod(a) for a in zip(*factors)]\n",
    "        #print(weights)\n",
    "        edge = choices(out_edges, weights=weights)[0]\n",
    "        #print(edge)\n",
    "        #edge = random.choice(out_edges)\n",
    "        next_node = edge[1]\n",
    "        node[1]['count'] += 1\n",
    "        path.append(edge)\n",
    "        if next_node in check_graph.nodes:\n",
    "            check_graph.remove_node(next_node)\n",
    "    print(len(check_graph.nodes()))\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a76dc0f3-02e2-4739-9014-b53d3a590e3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dims = (2, 3, 5, 7, 11)\n",
    "root = (0, 0, 0, 0, 0)\n",
    "chord = (root,)\n",
    "chord_set = chords(chord, root, 3, 3)\n",
    "graph = generate_graph(chord_set, 2, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 690,
   "id": "7b76d848-fe53-4b60-b414-46cfe570f78b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "((3, 0, 0, -1, 0), (0, 0, 0, 0, 0), (-2, 0, 0, 1, 0))\n",
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "163"
      ]
     },
     "execution_count": 690,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = stochastic_hamiltonian(graph)\n",
    "#for edge in path:\n",
    "#    print(edge)\n",
    "write_chord_sequence(path_to_chords(path, root))\n",
    "len(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91fd1700-6f41-4a4c-98cb-8355075d44d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ffmpeg -i video.mkv -acodec pcm_s16le -ar 16000 -ac 2 bereshit_chant.wav\n",
    "#sox bereshit_chant.wav bereshit_chant_snippet.wav trim 4 60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51be5ff2-2b7f-4350-878f-09e79d4bff1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import crepe\n",
    "from scipy.io import wavfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9b8e1e21-c5f5-4d77-8d1c-094b073b8322",
   "metadata": {},
   "outputs": [],
   "source": [
    "sr, audio = wavfile.read('/home/mwinter/Downloads/bereshit_chant_snippet.wav')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "01f8f969-fc3c-46a2-acfd-b1f956e438bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-05-22 19:43:58.758829: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-05-22 19:43:58.911551: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2024-05-22 19:43:59.465441: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-05-22 19:44:00.873378: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 301ms/step\n"
     ]
    }
   ],
   "source": [
    "time, frequency, confidence, activation = crepe.predict(audio, sr, step_size = 25)\n",
    "#time, frequency, confidence = [list(time), list(frequency), list(confidence)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a89c32ee-6413-400a-b3c9-a875f2768954",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[(1.05, 51.0, 0.0),\n",
       " (0.3, 53.0, 200.0),\n",
       " (0.125, 51.0, -200.0),\n",
       " (1.375, 51.0, 0.0),\n",
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       " (0.1, 52.0, 100.0),\n",
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       " (0.625, 48.0, -300.0),\n",
       " (0.625, 48.0, 0.0),\n",
       " (0.5, 53.0, 500.0),\n",
       " (0.125, 53.0, 0.0),\n",
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       " (0.875, 49.0, 0.0),\n",
       " (0.5, 51.0, 200.0),\n",
       " (0.375, 51.0, 0.0),\n",
       " (0.375, 48.0, -300.0),\n",
       " (0.625, 48.0, 0.0),\n",
       " (0.075, 51.0, 300.0),\n",
       " (0.15, 52.0, 100.0),\n",
       " (0.5, 51.0, -100.0),\n",
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       " (0.2, 53.0, 200.0),\n",
       " (1.125, 52.0, -100.0),\n",
       " (0.0, 52.0, 0.0),\n",
       " (0.05, 50.0, -200.0),\n",
       " (0.625, 49.0, -100.0),\n",
       " (0.375, 49.0, 0.0),\n",
       " (0.125, 52.0, 300.0),\n",
       " (1.5, 52.0, 0.0),\n",
       " (0.375, 51.0, -100.0),\n",
       " (0.5, 51.0, 0.0),\n",
       " (0.45, 53.0, 200.0),\n",
       " (1.0, 49.0, -400.0),\n",
       " (0.5, 49.0, 0.0),\n",
       " (0.125, 56.0, 700.0),\n",
       " (0.375, 56.0, 0.0),\n",
       " (0.075, 55.0, -100.0),\n",
       " (0.3, 54.0, -100.0),\n",
       " (0.2, 53.0, -100.0),\n",
       " (0.35, 51.0, -200.0),\n",
       " (0.25, 53.0, 200.0),\n",
       " (0.75, 51.0, -200.0),\n",
       " (1.25, 51.0, 0.0),\n",
       " (0.45, 49.0, -200.0),\n",
       " (0.3, 48.0, -100.0),\n",
       " (0.2, 49.0, 100.0),\n",
       " (1.0, 51.0, 200.0),\n",
       " (1.25, 51.0, 0.0),\n",
       " (0.3, 53.0, 200.0),\n",
       " (0.05, 52.0, -100.0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import librosa \n",
    "import numpy as np\n",
    "from scipy.signal import savgol_filter\n",
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "\n",
    "def filter_data(time, frequency, confidence):\n",
    "    data = tuple(data for data in zip(list(time), list(frequency), list(confidence)) if data[2] > 0.1)\n",
    "    dev = 0\n",
    "    dur = 0\n",
    "    for (p1, p2) in pairwise(data):\n",
    "        t_diff = (p2[0] - p1[0])\n",
    "        dur = round(dur + (p2[0] - p1[0]), 2)\n",
    "        if(True):\n",
    "            freq1, conf1 = p1[1:3]\n",
    "            midi1 = librosa.hz_to_midi(freq1)\n",
    "            r_midi1 = np.around(midi1)\n",
    "            freq2, conf2 = p2[1:3]\n",
    "            midi2 = librosa.hz_to_midi(freq2)\n",
    "            r_midi2 = np.around(midi2)\n",
    "            #if(r_midi2 != r_midi1):\n",
    "            if(True):\n",
    "                yield(dur, freq1, conf1, midi1, r_midi1, dev * 100)\n",
    "                dev = r_midi2 - r_midi1\n",
    "                dur = 0\n",
    "                \n",
    "def filter_data(time, frequency, confidence):\n",
    "    data = tuple(data for data in zip(list(time), list(frequency), list(confidence)) if data[2] > 0.9)\n",
    "    #durs = tuple(round(y[0]-x[0], 2) for (x, y) in pairwise(data)) + ((1,))\n",
    "    #data = tuple((d,) + data[i][1:] for i, d in enumerate(durs) if d > 0.01)\n",
    "    #print(data)\n",
    "    m, rm = [librosa.hz_to_midi(data[0][1]), np.around(librosa.hz_to_midi(data[0][1]))]\n",
    "    yield(data[0] + (m, rm, 0,))\n",
    "    for i, [t, f, c] in enumerate(data):\n",
    "        nm, nrm = [librosa.hz_to_midi(f), np.around(librosa.hz_to_midi(f))]\n",
    "        if((0 < i < len(data) - 1) and (nrm != rm)):\n",
    "            m, rm, d = [nm, nrm, nrm - rm]\n",
    "            yield(t, f, c, m, rm, d * 100)\n",
    "            \n",
    "t_min = 50\n",
    "t_max = 600\n",
    "f_time, f_frequency, f_confidence = [time[t_min:], frequency[t_min:], confidence[t_min:]]\n",
    "s_confidence = savgol_filter(f_confidence, 30, 4)\n",
    "plt.plot(f_time, f_confidence)\n",
    "plt.plot(f_time, s_confidence, color='red')\n",
    "plt.show()\n",
    "\n",
    "def filter_midi(f_time, f_frequency, s_confidence):\n",
    "    last_confident_value = 51\n",
    "    for t, f, c in zip(f_time, f_frequency, s_confidence):\n",
    "        if c > 0.9:\n",
    "            last_confident_value = librosa.hz_to_midi(f)\n",
    "        yield last_confident_value\n",
    "            \n",
    "#m_time, midi = zip(*[[t, librosa.hz_to_midi(f)] for t, f, c in zip(f_time, f_frequency, s_confidence) if c > 0.0])\n",
    "#nr_midi = np.interp(f_time, m_time, midi)\n",
    "#r_midi = np.interp(f_time, m_time, np.around(midi))\n",
    "#midi\n",
    "\n",
    "midi = list(filter_midi(f_time, f_frequency, s_confidence))\n",
    "\n",
    "def snap_midi(midi):\n",
    "    r_midi = np.around(midi)\n",
    "    s_midi = np.around(savgol_filter(midi, 30, 5))\n",
    "    last_val = s_midi[0]\n",
    "    for i, (r1, r2) in enumerate(pairwise(r_midi)):\n",
    "        if r1 != r2:\n",
    "            last_val = s_midi[i+5]\n",
    "        yield last_val\n",
    "    yield last_val\n",
    "\n",
    "def filter_midi(midi):\n",
    "    last_val = midi[0]\n",
    "    dur = 1\n",
    "    dev = 0.0\n",
    "    first_flag = True\n",
    "    for i, (m1, m2) in enumerate(pairwise(midi)):\n",
    "        if m1 != m2:\n",
    "            #yield (dur / 40, last_val, dev * 100)\n",
    "            if first_flag or dur < 20:\n",
    "                yield (dur / 40, last_val, dev * 100)\n",
    "                first_flag = False\n",
    "            else:\n",
    "                size = int(dur / 5)\n",
    "                sep = randint(1, size)\n",
    "                yield (5 / 40.0 * sep, last_val, dev * 100)\n",
    "                yield (5 / 40.0 * (size - sep), last_val, 0.0)\n",
    "            last_val = m2\n",
    "            dur = 1\n",
    "            dev = m2 - m1\n",
    "        else:\n",
    "            dur += 1\n",
    "            \n",
    "    \n",
    "\n",
    "plt.plot(f_time, midi)\n",
    "plt.plot(f_time, savgol_filter(midi, 30, 5), color='yellow')\n",
    "plt.plot(f_time, np.around(savgol_filter(midi, 30, 5)), color='red')\n",
    "plt.plot(f_time, np.around(midi), color='green')\n",
    "plt.plot(f_time, list(snap_midi(midi)), color='blue')\n",
    "#plt.show()\n",
    "\n",
    "target_melody_data = list(filter_midi(list(snap_midi(midi))))\n",
    "target_melody_data\n",
    "\n",
    "#target_melody_data = list(filter_data(time, frequency, confidence))\n",
    "#target_melody_data\n",
    "#durs = tuple(round(y[0]-x[0], 2) for (x, y) in pairwise(target_melody_data)) + ((1,))\n",
    "#print(durs)\n",
    "#target_melody_data = tuple((d,) + target_melody_data[i][1:] for i, d in enumerate(durs) if d > 0.1)\n",
    "#list(target_melody_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "b0d17cc2-a181-4212-aba5-72b90cab2a84",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice, choices\n",
    "\n",
    "# This is for the beginning / breysheet\n",
    "def stochastic_hamiltonian(graph):\n",
    "\n",
    "    #try making this omit the moving voice\n",
    "    def movement_size_weights(edges):\n",
    "        \n",
    "        def max_cent_diff(edge):\n",
    "            res = max([abs(v) for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None])\n",
    "            return res\n",
    "            \n",
    "        def min_cent_diff(edge):\n",
    "            res = [abs(v) for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            res.remove(0)\n",
    "            return min(res)\n",
    "            \n",
    "        for e in edges:\n",
    "            yield 4 if ((max_cent_diff(e) < 300) and (min_cent_diff(e)) >= 0) else 1\n",
    "\n",
    "    def hamiltonian_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[1] not in [path_edge[0] for path_edge in path] else 1 / graph.nodes[e[1]]['count']\n",
    "    \n",
    "    def contrary_motion_weights(edges):\n",
    "\n",
    "        def is_contrary(edge):\n",
    "            cent_diffs = [v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            cent_diffs.sort()\n",
    "            return (cent_diffs[0] < 0) and (cent_diffs[1] == 0) and (cent_diffs[2] > 0)\n",
    "\n",
    "        for e in edges:\n",
    "            yield 2 if is_contrary(e) else 1\n",
    "    \n",
    "    def is_directly_tunable_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[2]['is_directly_tunable'] else 1\n",
    "\n",
    "    def symdiff_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 1000 if e[2]['symmetric_difference'] == 2 else 1\n",
    "\n",
    "    def transposition_weight(edges):\n",
    "        for e in edges:\n",
    "            yield 100 if 0 <= hs_array_to_cents(e[2]['transposition']) < 100 else 1\n",
    "\n",
    "    def is_sustained_voice(edges, voice):\n",
    "        \n",
    "        def is_sustained(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            return ordered_source[voice] == ordered_destination[voice]\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if is_sustained(e) else 1\n",
    "\n",
    "    def voice_crossing_weights(edges):\n",
    "        \n",
    "        def has_voice_crossing(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            source_order = [ordered_source.index(p) for p in source]\n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            destination_order = [ordered_destination.index(p) for p in destination]\n",
    "            return source_order != destination_order\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if not has_voice_crossing(e) else 0\n",
    "\n",
    "    def is_bass_rooted(chord):\n",
    "        return max([sum(abs(p) for p in collapse_pitch(pitch_difference(chord[0], p))) for p in chord[1:]]) == 1\n",
    "\n",
    "    def target_melody_weights(edges, target, c_devs, voice):\n",
    "\n",
    "        def target_weight(edge, target, c_devs, voice):\n",
    "            candidate_diffs = []\n",
    "            for idx, dev in enumerate(c_devs):\n",
    "                if(idx == 2):\n",
    "                    source = list(edge[0])\n",
    "                    ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "                    candidate_diff = edge[2]['movements'][ordered_source[idx]]['cent_difference']\n",
    "                    candidate_diffs += [abs(dev + candidate_diff - target)]\n",
    "            #return 1/pow(1.1, min(candidate_diffs))\n",
    "            return 10 if min(candidate_diffs) < 40 else 1/pow(1.1, min(candidate_diffs))\n",
    "            \n",
    "        for e in edges:\n",
    "            yield target_weight(e, target, c_devs, voice)\n",
    "        \n",
    "    check_graph = graph.copy()\n",
    "    next_node = choice(list(graph.nodes()))\n",
    "    check_graph.remove_node(next_node)\n",
    "    for node in graph.nodes(data=True):\n",
    "        node[1]['count'] = 1\n",
    "    path = []\n",
    "    s_next_node = sorted(next_node, key=hs_array_to_fr)\n",
    "    c_devs = (cent_difference(s_next_node[2], s_next_node[0]), cent_difference(s_next_node[2], s_next_node[1]), 0,)\n",
    "    #c_devs = (0, cent_difference(s_next_node[0], s_next_node[1]), cent_difference(s_next_node[0], s_next_node[2]),)\n",
    "    print(c_devs)\n",
    "    while (nx.number_of_nodes(check_graph) > 0) and (len(path) < len(target_melody_data)-1):\n",
    "        out_edges = list(graph.out_edges(next_node, data=True))\n",
    "        factors = [\n",
    "            movement_size_weights(out_edges), \n",
    "            #hamiltonian_weights(out_edges), \n",
    "            #contrary_motion_weights(out_edges), \n",
    "            #is_directly_tunable_weights(out_edges),\n",
    "            voice_crossing_weights(out_edges),\n",
    "            #transposition_weight(out_edges),\n",
    "            #is_sustained_voice(out_edges, 0),\n",
    "            target_melody_weights(out_edges, target_melody_data[len(path)+1][-1], c_devs, 2),\n",
    "            #symdiff_weights(out_edges)\n",
    "        ]\n",
    "        weights = [prod(a) for a in zip(*factors)]\n",
    "        edge = choices(out_edges, weights=weights)[0]\n",
    "        next_node = edge[1]\n",
    "        node[1]['count'] += 1\n",
    "        path.append(edge)\n",
    "        s_chord = tuple(sorted(edge[0], key=hs_array_to_fr))\n",
    "        c_devs = tuple(c_devs[pdx] + edge[2]['movements'][pitch]['cent_difference'] - target_melody_data[len(path)][-1] for pdx, pitch in enumerate(s_chord))\n",
    "        print(s_chord)\n",
    "        print(c_devs)\n",
    "        print(target_melody_data[len(path)][-1])\n",
    "        if next_node in check_graph.nodes:\n",
    "            check_graph.remove_node(next_node)\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a8592bc9-7e9e-4b6a-9eaa-4c4e3b69ce91",
   "metadata": {},
   "outputs": [],
   "source": [
    "dims = (2, 3, 5, 7, 11, 13)\n",
    "root = (0, 0, 0, 0, 0, 0)\n",
    "chord = (root,)\n",
    "chord_set = chords(chord, root, 3, 3)\n",
    "graph = generate_graph(chord_set, 2, 2, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "fb2ad9ad-7a8c-4f84-ab0c-9c1f1ecdb8a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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       " (0.25, 52.0, 100.0),\n",
       " (0.25, 51.0, -100.0),\n",
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       " (0.25, 51.0, 500.0),\n",
       " (1.0, 51.0, 0.0),\n",
       " (0.25, 56.0, 500.0),\n",
       " (0.5, 57.0, 100.0),\n",
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       " (0.25, 53.0, -100.0),\n",
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       " (0.25, 48.0, -300.0),\n",
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       " (1.25, 51.0, 300.0),\n",
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       " (0.25, 51.0, -100.0),\n",
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       " (0.5, 48.0, -300.0),\n",
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       " (0.25, 51.0, -100.0),\n",
       " (0.25, 51.0, 0.0),\n",
       " (0.25, 53.0, 200.0),\n",
       " (1.0, 52.0, -100.0),\n",
       " (0.0, 52.0, 0.0),\n",
       " (0.25, 50.0, -200.0),\n",
       " (0.5, 49.0, -100.0),\n",
       " (0.5, 49.0, 0.0),\n",
       " (1.0, 52.0, 300.0),\n",
       " (0.5, 52.0, 0.0),\n",
       " (0.5, 51.0, -100.0),\n",
       " (0.25, 51.0, 0.0),\n",
       " (0.25, 53.0, 200.0),\n",
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       " (1.0, 49.0, 0.0),\n",
       " (0.5, 56.0, 700.0),\n",
       " (0.0, 56.0, 0.0),\n",
       " (0.25, 55.0, -100.0),\n",
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       " (0.0, 54.0, 0.0),\n",
       " (0.25, 53.0, -100.0),\n",
       " (0.25, 51.0, -200.0),\n",
       " (0.0, 51.0, 0.0),\n",
       " (0.25, 53.0, 200.0),\n",
       " (0.0, 53.0, 0.0),\n",
       " (1.75, 51.0, -200.0),\n",
       " (0.25, 51.0, 0.0),\n",
       " (0.25, 49.0, -200.0),\n",
       " (0.0, 49.0, 0.0),\n",
       " (0.25, 48.0, -100.0),\n",
       " (0.0, 48.0, 0.0),\n",
       " (0.25, 49.0, 100.0),\n",
       " (1.0, 51.0, 200.0),\n",
       " (1.25, 51.0, 0.0),\n",
       " (0.25, 53.0, 200.0),\n",
       " (0.0, 53.0, 0.0),\n",
       " (0.25, 52.0, -100.0)]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import librosa \n",
    "import numpy as np\n",
    "from scipy.signal import savgol_filter\n",
    "import matplotlib.pyplot as plt\n",
    "from random import randint\n",
    "\n",
    "def filter_data(time, frequency, confidence):\n",
    "    data = tuple(data for data in zip(list(time), list(frequency), list(confidence)) if data[2] > 0.1)\n",
    "    dev = 0\n",
    "    dur = 0\n",
    "    for (p1, p2) in pairwise(data):\n",
    "        t_diff = (p2[0] - p1[0])\n",
    "        dur = round(dur + (p2[0] - p1[0]), 2)\n",
    "        if(True):\n",
    "            freq1, conf1 = p1[1:3]\n",
    "            midi1 = librosa.hz_to_midi(freq1)\n",
    "            r_midi1 = np.around(midi1)\n",
    "            freq2, conf2 = p2[1:3]\n",
    "            midi2 = librosa.hz_to_midi(freq2)\n",
    "            r_midi2 = np.around(midi2)\n",
    "            #if(r_midi2 != r_midi1):\n",
    "            if(True):\n",
    "                yield(dur, freq1, conf1, midi1, r_midi1, dev * 100)\n",
    "                dev = r_midi2 - r_midi1\n",
    "                dur = 0\n",
    "                \n",
    "def filter_data(time, frequency, confidence):\n",
    "    data = tuple(data for data in zip(list(time), list(frequency), list(confidence)) if data[2] > 0.9)\n",
    "    #durs = tuple(round(y[0]-x[0], 2) for (x, y) in pairwise(data)) + ((1,))\n",
    "    #data = tuple((d,) + data[i][1:] for i, d in enumerate(durs) if d > 0.01)\n",
    "    #print(data)\n",
    "    m, rm = [librosa.hz_to_midi(data[0][1]), np.around(librosa.hz_to_midi(data[0][1]))]\n",
    "    yield(data[0] + (m, rm, 0,))\n",
    "    for i, [t, f, c] in enumerate(data):\n",
    "        nm, nrm = [librosa.hz_to_midi(f), np.around(librosa.hz_to_midi(f))]\n",
    "        if((0 < i < len(data) - 1) and (nrm != rm)):\n",
    "            m, rm, d = [nm, nrm, nrm - rm]\n",
    "            yield(t, f, c, m, rm, d * 100)\n",
    "            \n",
    "t_min = 50\n",
    "t_max = 600\n",
    "f_time, f_frequency, f_confidence = [time[t_min:], frequency[t_min:], confidence[t_min:]]\n",
    "s_confidence = savgol_filter(f_confidence, 30, 4)\n",
    "plt.plot(f_time, f_confidence)\n",
    "plt.plot(f_time, s_confidence, color='red')\n",
    "plt.show()\n",
    "\n",
    "def filter_midi(f_time, f_frequency, s_confidence):\n",
    "    last_confident_value = 51\n",
    "    for t, f, c in zip(f_time, f_frequency, s_confidence):\n",
    "        if c > 0.9:\n",
    "            last_confident_value = librosa.hz_to_midi(f)\n",
    "        yield last_confident_value\n",
    "            \n",
    "#m_time, midi = zip(*[[t, librosa.hz_to_midi(f)] for t, f, c in zip(f_time, f_frequency, s_confidence) if c > 0.0])\n",
    "#nr_midi = np.interp(f_time, m_time, midi)\n",
    "#r_midi = np.interp(f_time, m_time, np.around(midi))\n",
    "#midi\n",
    "\n",
    "midi = list(filter_midi(f_time, f_frequency, s_confidence))\n",
    "\n",
    "def snap_midi(midi):\n",
    "    r_midi = np.around(midi)\n",
    "    s_midi = np.around(savgol_filter(midi, 30, 5))\n",
    "    last_val = s_midi[0]\n",
    "    for i, (r1, r2) in enumerate(pairwise(r_midi)):\n",
    "        if r1 != r2:\n",
    "            last_val = s_midi[i+5]\n",
    "        yield last_val\n",
    "    yield last_val\n",
    "\n",
    "def filter_midi(midi):\n",
    "    last_val = midi[0]\n",
    "    dur = 1\n",
    "    dev = 0.0\n",
    "    first_flag = True\n",
    "    for i, (m1, m2) in enumerate(pairwise(midi)):\n",
    "        if m1 != m2:\n",
    "            #yield (dur / 40, last_val, dev * 100)\n",
    "            if first_flag or dur < 10:\n",
    "                yield (10 / 40.0, last_val, dev * 100)\n",
    "                first_flag = False\n",
    "            else:\n",
    "                size = int(dur / 10)\n",
    "                \n",
    "                sep = randint(1, size)\n",
    "                yield (10 / 40.0 * sep, last_val, dev * 100)\n",
    "                yield (10 / 40.0 * (size - sep), last_val, 0.0)\n",
    "\n",
    "                #yield (10 / 40.0, last_val, dev * 100)\n",
    "                #for i in range(size):\n",
    "                #    yield (10 / 40.0, last_val, 0.0)\n",
    "                    \n",
    "            last_val = m2\n",
    "            dur = 1\n",
    "            dev = m2 - m1\n",
    "        else:\n",
    "            dur += 1\n",
    "            \n",
    "    \n",
    "\n",
    "plt.plot(f_time, midi)\n",
    "plt.plot(f_time, savgol_filter(midi, 30, 5), color='yellow')\n",
    "plt.plot(f_time, np.around(savgol_filter(midi, 30, 5)), color='red')\n",
    "plt.plot(f_time, np.around(midi), color='green')\n",
    "plt.plot(f_time, list(snap_midi(midi)), color='blue')\n",
    "#plt.show()\n",
    "\n",
    "target_melody_data = list(filter_midi(list(snap_midi(midi))))\n",
    "target_melody_data\n",
    "\n",
    "#target_melody_data = list(filter_data(time, frequency, confidence))\n",
    "#target_melody_data\n",
    "#durs = tuple(round(y[0]-x[0], 2) for (x, y) in pairwise(target_melody_data)) + ((1,))\n",
    "#print(durs)\n",
    "#target_melody_data = tuple((d,) + target_melody_data[i][1:] for i, d in enumerate(durs) if d > 0.1)\n",
    "#list(target_melody_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "be01e4ae-e629-42ff-9d95-f77d510c13bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-551.3179423647567, -165.0042284999219, 0)\n",
      "((0, 0, 0, 0, 0, 0), (-2, 0, 1, 0, 0, 0), (-3, 0, 0, 0, 1, 0))\n",
      "(-751.3179423647567, -365.0042284999219, 21.309485364912916)\n",
      "200.0\n",
      "((0, 0, 0, 0, 0, 0), (-2, 0, 1, 0, 0, 0), (-4, 0, 2, 0, 0, 0))\n",
      "(-724.4765667306114, -365.0042284999219, 21.309485364912916)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (2, 0, 1, 0, 0, -1))\n",
      "(-524.4765667306114, -165.0042284999219, 26.841375634145265)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (-3, 0, 0, 0, 1, 0))\n",
      "(-524.4765667306114, -204.33271789672978, 26.841375634145265)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-6, 0, 0, 1, 1, 0), (-3, 0, 0, 0, 1, 0))\n",
      "(-475.11362523124217, -4.3327178967297755, 226.84137563414527)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (-4, 1, 0, 1, 0, 0), (-1, 1, 0, 0, 0, 0))\n",
      "(-421.8406820010981, -4.3327178967297755, 226.84137563414527)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (1, 0, 0, 1, -1, 0), (4, 0, 0, 0, -1, 0))\n",
      "(-135.50681142760473, 95.66728210327022, 326.84137563414527)\n",
      "-100.0\n",
      "((0, 0, 0, 0, 0, 0), (3, 0, 0, -1, 0, 0), (6, 0, 0, -2, 0, 0))\n",
      "(-135.50681142760473, 95.66728210327022, 223.9655268030848)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (3, 0, 0, -1, 0, 0), (4, 0, 0, 0, 0, -1))\n",
      "(-235.50681142760473, -107.20856672779013, 123.9655268030848)\n",
      "100.0\n",
      "((0, 0, 0, 0, 0, 0), (1, 0, 0, 1, 0, -1), (4, 0, 0, 0, 0, -1))\n",
      "(-920.8948528629555, -107.20856672779013, 123.9655268030848)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (3, 0, -1, 0, 0, 0), (6, 0, -1, -1, 0, 0))\n",
      "(-574.0794723315278, -307.2085667277901, -76.0344731969152)\n",
      "200.0\n",
      "((0, 0, 0, 0, 0, 0), (-1, -1, 0, 1, 0, 0), (2, -1, 0, 0, 0, 0))\n",
      "(-889.7207593320804, -307.2085667277901, -76.0344731969152)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (0, 0, -1, 1, 0, 0), (3, 0, -1, 0, 0, 0))\n",
      "(-947.7362284971007, -107.20856672779013, 123.9655268030848)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 0, 1), (0, 0, 0, -1, 0, 1))\n",
      "(-847.7362284971007, -7.208566727790128, 121.0896779720241)\n",
      "-100.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 0, 1), (-2, 0, 0, 1, 0, 0))\n",
      "(-847.7362284971007, -110.08441555885082, 121.0896779720241)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-5, 0, 0, 2, 0, 0), (-2, 0, 0, 1, 0, 0))\n",
      "(-1019.4379837972862, -410.0844155588508, -178.9103220279759)\n",
      "300.0\n",
      "((0, 0, 0, 0, 0, 0), (-6, 0, 0, 1, 0, 1), (-3, 0, 0, 0, 0, 1))\n",
      "(-1019.4379837972862, -410.0844155588508, -50.612077328161604)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-6, 0, 0, 1, 0, 1), (-2, 0, 0, 1, 0, 0))\n",
      "(-1219.437983797286, -1091.1397390974719, -250.6120773281616)\n",
      "200.0\n",
      "((0, 0, 0, 0, 0, 0), (1, 0, 0, 1, 0, -1), (-2, 0, 0, 1, 0, 0))\n",
      "(-1219.437983797286, -570.7559261620431, -250.6120773281616)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, -1, 0), (-2, 0, 0, 1, 0, 0))\n",
      "(-1019.437983797286, -370.7559261620431, 15.557787702791757)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, -1, 0), (2, 0, 1, 0, -1, 0))\n",
      "(-730.2282643927321, -370.7559261620431, 15.557787702791757)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (2, 0, 1, 0, 0, -1))\n",
      "(-430.22826439273206, -70.75592616204312, -43.9145505278978)\n",
      "-300.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (-2, 0, 1, 0, 0, 0))\n",
      "(-884.4422122972078, -70.75592616204312, -43.9145505278978)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (3, 0, -1, 0, 0, 0), (-3, 0, 0, 0, 0, 1))\n",
      "(-1384.4422122972078, -570.7559261620431, -211.28358793135357)\n",
      "500.0\n",
      "((0, 0, 0, 0, 0, 0), (3, 0, -1, 0, 0, 0), (7, 0, -1, 0, 0, -1))\n",
      "(-1051.8112497006637, -570.7559261620431, -211.28358793135357)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-7, 0, 0, 0, 0, 2), (-3, 0, 0, 0, 0, 1))\n",
      "(-651.8112497006637, -42.45768146222866, 188.71641206864643)\n",
      "-400.0\n",
      "((0, 0, 0, 0, 0, 0), (-6, 0, 0, 1, 0, 1), (-3, 0, 0, 0, 0, 1))\n",
      "(-170.7559261620425, -42.45768146222866, 188.71641206864643)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (1, 0, 0, 1, 0, -1), (4, 0, 0, 0, 0, -1))\n",
      "(-728.7713953270627, -342.45768146222866, -111.28358793135357)\n",
      "300.0\n",
      "((0, 0, 0, 0, 0, 0), (-2, 0, 1, 0, 0, 0), (1, 0, 1, -1, 0, 0))\n",
      "(-828.7713953270627, -442.45768146222866, -15.085109191897857)\n",
      "100.0\n",
      "((0, 0, 0, 0, 0, 0), (-2, 0, 1, 0, 0, 0), (3, 0, -1, 0, 0, 0))\n",
      "(-728.7713953270627, -342.45768146222866, -26.81639446167577)\n",
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      "(-728.7713953270627, -524.8613935962886, -26.81639446167577)\n",
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      "(-1073.5434512315312, -424.8613935962886, 73.18360553832423)\n",
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      "(-1173.5434512315312, -524.8613935962886, -26.81639446167577)\n",
      "100.0\n",
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      "(-1073.5434512315312, -424.8613935962886, -38.54767973145334)\n",
      "-100.0\n",
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      "(-873.5434512315312, -224.86139359628862, -33.015789462220994)\n",
      "-200.0\n",
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      "(-873.5434512315312, -514.0711130008425, -33.015789462220994)\n",
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      "(-573.5434512315312, -214.0711130008425, -22.225508866774874)\n",
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      "(-573.5434512315312, -408.53922273161, -22.225508866774874)\n",
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      "(-1073.5434512315312, -908.5392227316099, -259.85716509636626)\n",
      "500.0\n",
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      "(-1573.5434512315312, -1214.0711130008424, -400.38482686567676)\n",
      "500.0\n",
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      "(-1545.2452065317168, -1314.0711130008424, -500.38482686567676)\n",
      "100.0\n",
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      "(-1545.2452065317168, -1314.0711130008424, -473.5434512315314)\n",
      "0.0\n",
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      "(-1145.2452065317168, -914.0711130008424, -100.38482686567676)\n",
      "-400.0\n",
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      "(-1145.2452065317168, -914.0711130008424, -73.54345123153138)\n",
      "0.0\n",
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      "(-1142.3693577006563, -1014.0711130008424, -173.54345123153138)\n",
      "100.0\n",
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      "(-1042.3693577006563, -393.68730006541364, -73.54345123153138)\n",
      "-100.0\n",
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      "(-1042.3693577006563, -393.68730006541364, -7.373586200578018)\n",
      "0.0\n",
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      "(-1342.3693577006563, -693.6873000654136, -195.64230093080045)\n",
      "300.0\n",
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      "(-1136.1699627001112, -793.6873000654136, -295.64230093080045)\n",
      "100.0\n",
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      "(-1291.7322992000259, -793.6873000654136, -295.64230093080045)\n",
      "0.0\n",
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      "(-691.7322992000259, -193.68730006541364, 10.22270166536191)\n",
      "-600.0\n",
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      "(-691.7322992000259, -376.0910121994732, 10.22270166536191)\n",
      "0.0\n",
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      "(-791.7322992000259, -476.0910121994732, 21.953986935139824)\n",
      "100.0\n",
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      "(-974.1360113340859, -476.0910121994732, 21.953986935139824)\n",
      "0.0\n",
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      "(-1074.1360113340859, -576.0910121994732, -24.77306983471601)\n",
      "100.0\n",
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      "(-935.5633504301628, -576.0910121994732, -24.77306983471601)\n",
      "0.0\n",
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      "(-735.5633504301628, -376.0910121994732, -16.61867396878347)\n",
      "-200.0\n",
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      "(-402.93238783361875, -376.0910121994732, -16.61867396878347)\n",
      "0.0\n",
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      "(-889.7772983346388, -76.0910121994732, 283.38132603121653)\n",
      "-300.0\n",
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      "(-889.7772983346388, -76.0910121994732, -49.249636565327705)\n",
      "0.0\n",
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      "(-1189.7772983346388, -376.0910121994732, -16.61867396878347)\n",
      "300.0\n",
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      "(-1078.046013064861, -376.0910121994732, -16.61867396878347)\n",
      "0.0\n",
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      "(-1278.046013064861, -576.0910121994732, -189.77729833463832)\n",
      "200.0\n",
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      "(-1003.4635844698039, -576.0910121994732, -189.77729833463832)\n",
      "0.0\n",
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      "(-707.2651057303485, -476.0910121994732, -89.77729833463832)\n",
      "-100.0\n",
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      "(-607.2651057303485, -220.95139186551336, 10.222701665361683)\n",
      "-100.0\n",
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      "(-638.4593559698822, -220.95139186551336, 10.222701665361683)\n",
      "0.0\n",
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      "(-1138.4593559698822, -720.9513918655134, -169.6334495007568)\n",
      "500.0\n",
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      "(-1238.4593559698822, -740.4143568352691, -269.6334495007568)\n",
      "100.0\n",
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      "(-1138.4593559698822, -640.4143568352691, 8.267700799974136)\n",
      "-100.0\n",
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      "(-832.2599609693369, -640.4143568352691, 8.267700799974136)\n",
      "0.0\n",
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      "(-1238.4593559698822, -740.4143568352691, -91.73229920002586)\n",
      "100.0\n",
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      "(-793.6873000654139, -740.4143568352691, -91.73229920002586)\n",
      "0.0\n",
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      "(-393.68730006541387, -340.41435683526913, 157.63064229934344)\n",
      "-400.0\n",
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      "(-891.7322992000263, -340.41435683526913, 157.63064229934344)\n",
      "0.0\n",
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      "(-691.7322992000263, -140.41435683526913, 10.22270166536157)\n",
      "-200.0\n",
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      "(-691.7322992000263, -220.95139186551347, 10.22270166536157)\n",
      "0.0\n",
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      "(-930.3049601039494, -320.9513918655135, -89.77729833463843)\n",
      "100.0\n",
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      "(-452.12548539638885, -220.95139186551347, 10.22270166536157)\n",
      "-100.0\n",
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      "(-541.0952406993954, -220.95139186551347, 10.22270166536157)\n",
      "0.0\n",
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      "(-1022.9063927309014, -320.9513918655135, -89.77729833463843)\n",
      "100.0\n",
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      "(-1022.9063927309014, -320.9513918655135, 38.520946365176314)\n",
      "0.0\n",
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      "(-922.9063927309014, -220.95139186551347, 10.22270166536157)\n",
      "-100.0\n",
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      "(-541.0952406993954, -220.95139186551347, 10.22270166536157)\n",
      "0.0\n",
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      "(-658.6032048037637, 79.04860813448653, 310.22270166536157)\n",
      "-300.0\n",
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      "(-76.09101219947331, 79.04860813448653, 310.22270166536157)\n",
      "0.0\n",
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      "(-1030.304960103949, -420.9513918655135, -189.77729833463843)\n",
      "500.0\n",
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      "(-1030.304960103949, -420.9513918655135, -61.47905363482414)\n",
      "0.0\n",
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      "(-630.304960103949, -20.95139186551347, 210.22270166536157)\n",
      "-400.0\n",
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      "(-861.479053634824, -20.95139186551347, 210.22270166536157)\n",
      "0.0\n",
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      "(-487.8222974692509, -220.95139186551347, 10.22270166536157)\n",
      "200.0\n",
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      "(-958.6032048037633, -220.95139186551347, 10.22270166536157)\n",
      "0.0\n",
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      "(-658.6032048037633, -76.09101219947314, 310.22270166536157)\n",
      "-300.0\n",
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      "(-658.6032048037633, -76.09101219947314, 155.083081331402)\n",
      "0.0\n",
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      "(-958.6032048037633, -376.09101219947314, 10.22270166536157)\n",
      "300.0\n",
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      "(-1058.6032048037632, -356.64820393837596, -89.77729833463843)\n",
      "100.0\n",
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      "(-958.6032048037632, -541.0952406993952, 10.22270166536157)\n",
      "-100.0\n",
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      "(-958.6032048037632, -830.3049601039487, 10.22270166536157)\n",
      "0.0\n",
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      "(-1158.6032048037632, -509.92114716851995, -189.77729833463843)\n",
      "200.0\n",
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      "(-1058.6032048037632, -409.92114716851995, -23.60743330368507)\n",
      "-100.0\n",
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      "(-574.9253756684418, -409.92114716851995, -23.60743330368507)\n",
      "0.0\n",
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      "(-792.43333977281, -209.92114716851995, 176.39256669631493)\n",
      "-200.0\n",
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      "(-692.43333977281, -109.92114716851995, 121.25294636235537)\n",
      "-100.0\n",
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      "(-376.79205277225725, -109.92114716851995, 121.25294636235537)\n",
      "0.0\n",
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      "(-676.7920527722572, -317.31971454156775, -178.74705363764463)\n",
      "300.0\n",
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      "(-676.7920527722572, -317.31971454156775, 25.162948093130353)\n",
      "0.0\n",
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      "(-576.7920527722572, -217.31971454156775, -25.474110407500348)\n",
      "-100.0\n",
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      "(-576.7920527722572, -217.31971454156775, 13.85437898930752)\n",
      "0.0\n",
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      "(-776.7920527722572, -417.31971454156775, -31.006000676732697)\n",
      "200.0\n",
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      "(-844.6922868118982, -417.31971454156775, -31.006000676732697)\n",
      "0.0\n",
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      "(-515.3647136761801, -17.31971454156775, 368.9939993232673)\n",
      "-400.0\n",
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      "(-515.3647136761801, -17.31971454156775, 35.9532286885767)\n",
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      "(-1215.3647136761801, -717.3197145415677, -68.6376569063242)\n",
      "700.0\n",
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      "(-1076.7920527722572, -717.3197145415677, -68.6376569063242)\n",
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      "(-976.7920527722572, -328.1099951370138, 31.362343093675804)\n",
      "-100.0\n",
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      "(-876.7920527722572, -228.1099951370138, 3.064098393861059)\n",
      "-100.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, -1, 0), (7, 0, 0, -1, -1, 0))\n",
      "(-965.7618080752637, -228.1099951370138, 3.064098393861059)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-5, 0, 0, 2, 0, 0), (-2, 0, 0, 1, 0, 0))\n",
      "(-710.622187741304, -128.1099951370138, 103.06409839386106)\n",
      "-100.0\n",
      "((0, 0, 0, 0, 0, 0), (0, 0, -1, 1, 0, 0), (3, 0, -1, 0, 0, 0))\n",
      "(-194.98090074075122, 71.89000486298619, 303.06409839386106)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (-1, -1, 0, 1, 0, 0), (2, -1, 0, 0, 0, 0))\n",
      "(-768.6376569063243, 71.89000486298619, 303.06409839386106)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 0, 1), (0, 0, 0, -1, 0, 1))\n",
      "(-968.6376569063243, -128.1099951370138, 0.1882495628003653)\n",
      "200.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 0, 1), (-2, 0, 0, 1, 0, 0))\n",
      "(-968.6376569063243, -609.1653186756349, 0.1882495628003653)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (-2, 0, 0, 1, 0, 0))\n",
      "(-768.6376569063243, -409.1653186756349, -22.851604810800154)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (2, 0, 1, 0, 0, -1))\n",
      "(-960.4832610403917, -409.1653186756349, -22.851604810800154)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 1, 0), (-5, 0, 1, 0, 1, 0))\n",
      "(-760.4832610403917, -209.16531867563492, 22.00877485523995)\n",
      "-200.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 1, 0), (0, 0, 0, -1, 1, 0))\n",
      "(-337.4635633754494, -209.16531867563492, 22.00877485523995)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (1, 0, 0, 1, 0, -1), (4, 0, 0, 0, 0, -1))\n",
      "(-579.9462260101475, -109.16531867563492, 122.00877485523995)\n",
      "-100.0\n",
      "((0, 0, 0, 0, 0, 0), (-4, 1, 0, 1, 0, 0), (-1, 1, 0, 0, 0, 0))\n",
      "(-468.63765690632454, -109.16531867563492, 122.00877485523995)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, 0, -1), (7, 0, 0, -1, 0, -1))\n",
      "(-857.8473763108782, -209.16531867563492, 22.00877485523995)\n",
      "100.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, -1, 0), (7, 0, 0, -1, -1, 0))\n",
      "(-1057.8473763108782, -409.1653186756349, -22.85160481080004)\n",
      "200.0\n",
      "((0, 0, 0, 0, 0, 0), (4, 0, 0, 0, -1, 0), (2, 0, 1, 0, -1, 0))\n",
      "(-960.4832610403915, -409.1653186756349, -22.85160481080004)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 1, 0), (-5, 0, 1, 0, 1, 0))\n",
      "(-1160.4832610403914, -609.1653186756349, -57.84737631087819)\n",
      "200.0\n",
      "((0, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 1, 0), (-6, 0, 0, 0, 2, 0))\n",
      "(-1026.673282780003, -609.1653186756349, -57.84737631087819)\n",
      "0.0\n",
      "((0, 0, 0, 0, 0, 0), (1, 0, 0, 1, -1, 0), (-2, 0, 0, 1, 0, 0))\n",
      "(-895.4790325404695, -509.1653186756349, 42.15262368912181)\n",
      "-100.0\n"
     ]
    }
   ],
   "source": [
    "path = stochastic_hamiltonian(graph)\n",
    "#durs = tuple(round(y[0]-x[0], 2) for (x, y) in pairwise(target_melody_data)) + ((1,))\n",
    "durs = tuple(d[0] for d in target_melody_data)\n",
    "path_to_chords(path, root)\n",
    "write_chord_sequence(list(zip(durs, path_to_chords(path, root))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "57c834bf-fee7-4ef4-b648-2173099fbb56",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import choice, choices\n",
    "\n",
    "def next_edges(source):  \n",
    "\n",
    "    def transpose_to_nearest(hs_array1, hs_array2):\n",
    "        expanded_pitch = hs_array2\n",
    "        frequency_ratio = hs_array_to_fr(list(hs_array2)) / hs_array_to_fr(list(hs_array1))\n",
    "        if frequency_ratio < 1:\n",
    "            while frequency_ratio < 1:\n",
    "                frequency_ratio *= 2\n",
    "                expanded_pitch[0] += 1\n",
    "        elif frequency_ratio >= 2:\n",
    "            while frequency_ratio >= 2:\n",
    "                frequency_ratio *= 1/2\n",
    "                expanded_pitch[0] += -1\n",
    "        return tuple(expanded_pitch)\n",
    "        \n",
    "    def gen_candidates(chord):\n",
    "        for sdx, s_pitch in enumerate(chord):\n",
    "            for cdx, c_pitch in enumerate(chord):\n",
    "                if sdx != cdx:   \n",
    "                    for ddx, dim in enumerate(c_pitch[1:]):\n",
    "                        for alt in [-1, 1]:\n",
    "                            new_pitch = list(c_pitch)\n",
    "                            new_pitch[ddx+1] += alt\n",
    "                            new_pitch_up = transpose_to_nearest(s_pitch, new_pitch)\n",
    "                            new_pitch_down = list(new_pitch_up)\n",
    "                            new_pitch_down[0] += -1\n",
    "                            new_chord = list(chord)\n",
    "                            new_chord[sdx] = tuple(new_pitch_up)\n",
    "                            yield tuple(new_chord)\n",
    "                            new_chord[sdx] = tuple(new_pitch_down)\n",
    "                            yield tuple(new_chord)\n",
    "\n",
    "    for candidate in gen_candidates(source):\n",
    "\n",
    "        movements = {\n",
    "            pitch:\n",
    "            {\n",
    "                'destination': candidate[index], \n",
    "                'cent_difference': cent_difference(pitch, candidate[index])\n",
    "            } for index, pitch in enumerate(source)}\n",
    "        \n",
    "        yield (tuple(source), tuple(candidate), {'transposition': root, 'movements': movements},)\n",
    "\n",
    "\n",
    "# This is for the beginning / breysheet - redone to calculate next chord as a function and not from the graph\n",
    "def stochastic_hamiltonian(graph):\n",
    "\n",
    "    #try making this omit the moving voice\n",
    "    def movement_size_weights(edges):\n",
    "        \n",
    "        def max_cent_diff(edge):\n",
    "            res = max([abs(v) for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None])\n",
    "            return res\n",
    "            \n",
    "        def min_cent_diff(edge):\n",
    "            res = [abs(v) for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            res.remove(0)\n",
    "            return min(res)\n",
    "            \n",
    "        for e in edges:\n",
    "            yield 7 if ((max_cent_diff(e) < 100) and (min_cent_diff(e)) >= 0) else 1\n",
    "\n",
    "    def hamiltonian_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[1] not in [path_edge[0] for path_edge in path] else 1 / graph.nodes[e[1]]['count']\n",
    "    \n",
    "    def contrary_motion_weights(edges):\n",
    "\n",
    "        def is_contrary(edge):\n",
    "            cent_diffs = [v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\n",
    "            cent_diffs.sort()\n",
    "            return (cent_diffs[0] < 0) and (cent_diffs[1] == 0) and (cent_diffs[2] > 0)\n",
    "\n",
    "        for e in edges:\n",
    "            yield 2 if is_contrary(e) else 1\n",
    "    \n",
    "    def is_directly_tunable_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 10 if e[2]['is_directly_tunable'] else 1\n",
    "\n",
    "    def symdiff_weights(edges):\n",
    "        for e in edges:\n",
    "            yield 1000 if e[2]['symmetric_difference'] == 2 else 1\n",
    "\n",
    "    def transposition_weight(edges):\n",
    "        for e in edges:\n",
    "            yield 100 if 0 <= hs_array_to_cents(e[2]['transposition']) < 100 else 1\n",
    "\n",
    "    def in_range(edges):\n",
    "        for e in edges:\n",
    "            yield 5 if hs_array_to_fr(e[0][0]) >= 0 else 0\n",
    "\n",
    "    def is_sustained_voice(edges, voice):\n",
    "        \n",
    "        def is_sustained(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            return ordered_source[voice] == ordered_destination[voice]\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if is_sustained(e) else 1\n",
    "\n",
    "    def voice_crossing_weights(edges):\n",
    "        \n",
    "        def has_voice_crossing(edge):\n",
    "            source = list(edge[0])\n",
    "            ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "            source_order = [ordered_source.index(p) for p in source]\n",
    "            destination = [transpose_pitch(edge[2]['movements'][p]['destination'], edge[2]['transposition']) for p in source]\n",
    "            ordered_destination = sorted(destination, key=hs_array_to_fr)\n",
    "            destination_order = [ordered_destination.index(p) for p in destination]\n",
    "            return source_order != destination_order\n",
    "\n",
    "        for e in edges:\n",
    "            yield 10 if not has_voice_crossing(e) else 0\n",
    "\n",
    "    def is_bass_rooted(chord):\n",
    "        return max([sum(abs(p) for p in collapse_pitch(pitch_difference(chord[0], p))) for p in chord[1:]]) == 1\n",
    "\n",
    "    def target_melody_weights(edges, target, c_devs, voice):\n",
    "\n",
    "        def target_weight(edge, target, c_devs, voice):\n",
    "            candidate_diffs = []\n",
    "            for idx, dev in enumerate(c_devs):\n",
    "                if(idx == 2):\n",
    "                    source = list(edge[0])\n",
    "                    ordered_source = sorted(source, key=hs_array_to_fr)    \n",
    "                    candidate_diff = edge[2]['movements'][ordered_source[idx]]['cent_difference']\n",
    "                    candidate_diffs += [abs(dev + candidate_diff - target)]\n",
    "            #return 1/pow(1.1, min(candidate_diffs))\n",
    "            return 10 if min(candidate_diffs) < 40 else 1/pow(1.1, min(candidate_diffs))\n",
    "            \n",
    "        for e in edges:\n",
    "            yield target_weight(e, target, c_devs, voice)\n",
    "        \n",
    "    check_graph = graph.copy()\n",
    "    next_node = choice(list(graph.nodes()))\n",
    "    check_graph.remove_node(next_node)\n",
    "    for node in graph.nodes(data=True):\n",
    "        node[1]['count'] = 1\n",
    "    path = []\n",
    "    s_next_node = sorted(next_node, key=hs_array_to_fr)\n",
    "    c_devs = (cent_difference(s_next_node[2], s_next_node[0]), cent_difference(s_next_node[2], s_next_node[1]), 0,)\n",
    "    #c_devs = (0, cent_difference(s_next_node[0], s_next_node[1]), cent_difference(s_next_node[0], s_next_node[2]),)\n",
    "    print(c_devs)\n",
    "    while (nx.number_of_nodes(check_graph) > 0) and (len(path) < len(target_melody_data)-1):\n",
    "        #out_edges = list(graph.out_edges(next_node, data=True))\n",
    "        out_edges = list(next_edges(next_node))\n",
    "        factors = [\n",
    "            movement_size_weights(out_edges), \n",
    "            #hamiltonian_weights(out_edges), \n",
    "            #contrary_motion_weights(out_edges), \n",
    "            #is_directly_tunable_weights(out_edges),\n",
    "            voice_crossing_weights(out_edges),\n",
    "            #transposition_weight(out_edges),\n",
    "            #is_sustained_voice(out_edges, 0),\n",
    "            target_melody_weights(out_edges, target_melody_data[len(path)+1][-1], c_devs, 2),\n",
    "            #symdiff_weights(out_edges),\n",
    "            in_range(out_edges)\n",
    "        ]\n",
    "        weights = [prod(a) for a in zip(*factors)]\n",
    "        edge = choices(out_edges, weights=weights)[0]\n",
    "        next_node = edge[1]\n",
    "        node[1]['count'] += 1\n",
    "        path.append(edge)\n",
    "        s_chord = tuple(sorted(edge[0], key=hs_array_to_fr))\n",
    "        c_devs = tuple(c_devs[pdx] + edge[2]['movements'][pitch]['cent_difference'] - target_melody_data[len(path)][-1] for pdx, pitch in enumerate(s_chord))\n",
    "        print(s_chord)\n",
    "        print(c_devs)\n",
    "        #print(target_melody_data[len(path)][-1])\n",
    "        if next_node in check_graph.nodes:\n",
    "            check_graph.remove_node(next_node)\n",
    "    return path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "8912c650-a43d-4539-ae24-b5acf9bc543d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "((0, 2, 2, -4, 2, -1), (-8, 4, 2, -2, 2, -1), (-2, 2, 2, -3, 2, -1))\n",
      "(-905.4838799249776, 36.07793474404673, 63.34202654414776)\n",
      "((0, 2, 2, -4, 2, -1), (-8, 4, 2, -2, 2, -1), (-2, 2, 2, -3, 2, -1))\n",
      "(-905.4838799249776, -546.0115416942884, 63.34202654414776)\n",
      "((0, 2, 2, -4, 2, -1), (4, 2, 2, -4, 2, -2), (-2, 2, 2, -3, 2, -1))\n",
      "(-1005.4838799249776, -646.0115416942884, -36.65797345585224)\n",
      "((0, 2, 2, -4, 2, -1), (4, 2, 2, -4, 2, -2), (-2, 2, 2, -3, 2, -1))\n",
      "(-1205.4838799249776, -846.0115416942884, -5.483879924977373)\n",
      "((0, 2, 2, -4, 2, -1), (4, 2, 2, -4, 2, -2), (1, 2, 2, -4, 2, -1))\n",
      "(-1205.4838799249776, -364.95621815566716, -5.483879924977373)\n",
      "((0, 2, 2, -4, 2, -1), (-3, 2, 2, -4, 2, 0), (1, 2, 2, -4, 2, -1))\n",
      "(-1405.4838799249776, -564.9562181556671, -13.63827579091003)\n",
      "((0, 2, 2, -4, 2, -1), (-3, 2, 2, -4, 2, 0), (-6, 2, 2, -4, 3, 0))\n",
      "(-1444.8123693217854, -564.9562181556671, -13.63827579091003)\n",
      "((-10, 2, 2, -3, 3, 0), (-3, 2, 2, -4, 2, 0), (-6, 2, 2, -4, 3, 0))\n",
      "(-1344.8123693217854, -464.9562181556671, 33.08878097894603)\n"
     ]
    }
   ],
   "source": [
    "path = stochastic_hamiltonian(graph)\n",
    "#durs = tuple(round(y[0]-x[0], 2) for (x, y) in pairwise(target_melody_data)) + ((1,))\n",
    "durs = tuple(d[0] for d in target_melody_data)\n",
    "path_to_chords(path, root)\n",
    "write_chord_sequence(list(zip(durs, path_to_chords(path, root))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a13bec1-1a48-4647-821e-f1779976d80f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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