From eadd9c092f6cb7f308385ce5d61d436469109f7c Mon Sep 17 00:00:00 2001 From: mwinter Date: Fri, 31 May 2024 03:26:26 +0200 Subject: [PATCH] major cleanup / redo of the beginning --- compact_sets_extended_simplified.ipynb | 14075 +++++++++++++++++++---- compact_sets_play.scd | 12 +- 2 files changed, 11942 insertions(+), 2145 deletions(-) diff --git a/compact_sets_extended_simplified.ipynb b/compact_sets_extended_simplified.ipynb index 56f64b9..8f27f6b 100644 --- a/compact_sets_extended_simplified.ipynb +++ b/compact_sets_extended_simplified.ipynb @@ -1,9 +1,19 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "06774c1b-6cc6-4f6e-8f4a-bafd572f8ac3", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "# Topological Functions" + ] + }, { "cell_type": "code", - "execution_count": 236, - "id": "806f6f69-1e0b-4d34-aac9-695c8531cdb1", + "execution_count": 21, + "id": "5178de6f-a93c-45b1-9608-b0bbe8a771f2", "metadata": {}, "outputs": [], "source": [ @@ -164,9 +174,27 @@ " file.close()" ] }, + { + "cell_type": "markdown", + "id": "b0722b0d-43fd-4c06-a932-248bf62fab6f", + "metadata": {}, + "source": [ + "# This is the static version, which will be the middle piece" + ] + }, + { + "cell_type": "markdown", + "id": "ea38a1da-7eac-4434-97b6-3208379affa0", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## path functions" + ] + }, { "cell_type": "code", - "execution_count": 683, + "execution_count": 2, "id": "aea5215c-8551-4685-b761-11c2dc74cf22", "metadata": {}, "outputs": [], @@ -233,7 +261,6 @@ " 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", @@ -252,9 +279,19 @@ " return path" ] }, + { + "cell_type": "markdown", + "id": "17c03eb3-b670-494b-a6db-225b1ac10525", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## model" + ] + }, { "cell_type": "code", - "execution_count": 684, + "execution_count": 3, "id": "4e3ef738-7f64-47c3-9129-0450fd031375", "metadata": {}, "outputs": [], @@ -268,7 +305,7 @@ }, { "cell_type": "code", - "execution_count": 685, + "execution_count": 4, "id": "6e0de0e5-7973-4a15-ae90-5bbb4a1ef49d", "metadata": {}, "outputs": [], @@ -279,6 +316,16 @@ "write_chord_sequence(path_to_chords(path, root))" ] }, + { + "cell_type": "markdown", + "id": "b2e73f16-563f-4783-a6e9-0e5130be80e0", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## profiling " + ] + }, { "cell_type": "code", "execution_count": 11, @@ -372,9 +419,27 @@ "lprun -f edge_data edges(chord_set, 3, 3, 4)" ] }, + { + "cell_type": "markdown", + "id": "94ab1f6a-d020-444a-b67d-e62428ed416f", + "metadata": {}, + "source": [ + "# This is for the rising version/yitgadal, which will be either the last of the 3 pieces (with option of the pieces played in reverse)" + ] + }, + { + "cell_type": "markdown", + "id": "6af2ac39-ffae-4403-af81-c214ac257aac", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## path functions" + ] + }, { "cell_type": "code", - "execution_count": 688, + "execution_count": 5, "id": "6e4ecb10-344b-4721-b2f4-68de91d712db", "metadata": {}, "outputs": [], @@ -463,17 +528,14 @@ " factors = [\n", " movement_size_weights(out_edges), \n", " hamiltonian_weights(out_edges), \n", + " voice_crossing_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", @@ -483,9 +545,19 @@ " return path" ] }, + { + "cell_type": "markdown", + "id": "62a5c827-d57f-4eea-a43d-56e24c0e2ff3", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## model" + ] + }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "id": "a76dc0f3-02e2-4739-9014-b53d3a590e3d", "metadata": {}, "outputs": [], @@ -499,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 690, + "execution_count": 9, "id": "7b76d848-fe53-4b60-b414-46cfe570f78b", "metadata": {}, "outputs": [ @@ -507,17 +579,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "((3, 0, 0, -1, 0), (0, 0, 0, 0, 0), (-2, 0, 0, 1, 0))\n", + "((3, 0, -1, 0, 0), (2, -1, 0, 0, 0), (0, 0, 0, 0, 0))\n", "0\n" ] }, { "data": { "text/plain": [ - "163" + "178" ] }, - "execution_count": 690, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -530,9 +602,25 @@ "len(path)" ] }, + { + "cell_type": "markdown", + "id": "4a9a4688-c648-4339-a328-4c514fca75f2", + "metadata": {}, + "source": [ + "# This is for the beginning/breysheet, which will be the first of the set of 3 (with option in reverse)" + ] + }, + { + "cell_type": "markdown", + "id": "6849a0ac-cb9b-45f2-b32d-5695a06549d9", + "metadata": {}, + "source": [ + "## functions for analyzing a recording and filtering the data" + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "91fd1700-6f41-4a4c-98cb-8355075d44d2", "metadata": {}, "outputs": [], @@ -543,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 22, "id": "51be5ff2-2b7f-4350-878f-09e79d4bff1f", "metadata": {}, "outputs": [], @@ -554,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 23, "id": "9b8e1e21-c5f5-4d77-8d1c-094b073b8322", "metadata": {}, "outputs": [], @@ -564,26 +652,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 24, "id": "01f8f969-fc3c-46a2-acfd-b1f956e438bc", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-05-24 17:13:22.147078: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2024-05-24 17:13:22.471453: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n", - "2024-05-24 17:13:23.474683: 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", - "2024-05-24 17:13:25.296611: 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[1m36s\u001b[0m 471ms/step\n" + "\u001b[1m76/76\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 292ms/step\n" ] } ], @@ -593,159 +670,19 @@ ] }, { - "cell_type": "code", - "execution_count": 379, - "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 == voice):\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": 374, - "id": "a8592bc9-7e9e-4b6a-9eaa-4c4e3b69ce91", - "metadata": {}, - "outputs": [], + "cell_type": "markdown", + "id": "6909f6c9-b8b7-4fe8-bca3-57fb8b8851b3", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ - "dims = (2, 3, 5, 7)\n", - "root = (0, 0, 0, 0)\n", - "chord = (root,)\n", - "chord_set = chords(chord, root, 3, 3)\n", - "graph = generate_graph(chord_set, 2, 2, 3)" + "## legacy stuff" ] }, { "cell_type": "code", - "execution_count": 342, - "id": "fb2ad9ad-7a8c-4f84-ab0c-9c1f1ecdb8a8", + "execution_count": 14, + "id": "63d4b87f-7782-4aec-9066-6f2710f3d997", "metadata": { "scrolled": true }, @@ -754,874 +691,830 @@ "name": "stdout", "output_type": "stream", "text": [ - "[(39.0, 44.0, 51.0), (39.0, 44.0, 53.0)]\n", - "51.0\n", - "2\n", - "[(39.0, 44.0, 53.0), (39.0, 44.0, 51.0)]\n", - "53.0\n", "2\n", - "[(39.0, 44.0, 51.0), (39.0, 46.0, 51.0)]\n", "51.0\n", - "2\n", - "[(39.0, 46.0, 51.0), 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(0.2, 41.0, 0, (0,), 100.0),\n", + " (0.8, 44.0, 1, (0,), 300.0),\n", + " (0.075, 40.0, 0, (0,), -400.0),\n", + " (0.125, 52.0, 2, (0,), 1200.0),\n", + " (0.025, 39.0, 0, (0,), -1300.0),\n", + " (0.25, 53.0, 2, (0,), 1400.0),\n", + " (0.05, 52.0, 2, (0,), -100.0),\n", + " (0.45, 51.0, 2, (0,), -100.0),\n", + " (0.025, 52.0, 2, (0,), 100.0),\n", + " (0.325, 45.0, 1, (0,), -700.0)]" ] }, - "execution_count": 342, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1734,39 +1627,92 @@ " else:\n", " dur += 1\n", "\n", + "#need to figure out how to make each note hold for a bit and then release\n", "def filter_midi(midi):\n", " last_val = midi[0][-1]\n", " ins = 2\n", " dur = 1\n", + " total_dur = 0\n", " dev = 0.0\n", + " remaining_durs = [100, 100, 100]\n", " first_flag = True\n", " for i, (m1, m2) in enumerate(pairwise(midi)):\n", " if m1 != m2:\n", - " print([m1, m2])\n", - " print(last_val)\n", + " #print([m1, m2])\n", + " #print(last_val)\n", + " #print(ins)\n", + " #yield (dur / 40, last_val, ins, dev * 100)\n", + "\n", + " if first_flag or dur < 20:\n", + " allowed_ins = (ins,)\n", + " print((10 / 40.0, last_val, ins, allowed_ins, dev * 100))\n", + " yield (10 / 40.0, last_val, ins, allowed_ins, dev * 100)\n", + " first_flag = False\n", + " else:\n", + " size = int(dur / 10) \n", + "\n", + " #b_dur = randint(1, size)\n", + " #yield ((b_dur * 10) / 40.0, last_val, ins, dev * 100)\n", + " #yield (((size - b_dur) * 10) / 40.0, last_val, ins, 0)\n", + "\n", + " #yield (((size - 1) * 10) / 40.0, last_val, ins, dev * 100)\n", + " #yield (10 / 40.0, last_val, ins, 0)\n", + "\n", + " #allowed_ins = tuple(ibx for ibx, r_dur in enumerate(remaining_durs) if r_dur < 20 or ibx == ins)\n", + " allowed_ins = (ins,)\n", + " yield (randint(5, 10) / 40.0, last_val, ins, allowed_ins, dev * 100)\n", + " print((randint(5, 10) / 40.0, last_val, ins, allowed_ins, dev * 100))\n", + " for irx in range(size - 1):\n", + " allowed_ins = tuple(ibx for ibx, r_dur in enumerate(remaining_durs) if r_dur < 20 and ibx != ins)\n", + " yield (randint(5, 10) / 40.0, last_val, ins, allowed_ins, 0.0)\n", + " print((randint(5, 10) / 40.0, last_val, ins, allowed_ins, 0.0))\n", + "\n", + " remaining_durs[ins] = 0\n", " print(ins)\n", - " yield (dur / 40, last_val, ins, dev * 100)\n", - "\n", - " #if first_flag or dur < 10:\n", - " # yield (10 / 40.0, last_val, ins, dev * 100)\n", - " # first_flag = False\n", - " #else:\n", - " # size = int(dur / 10)\n", - "\n", - " # b_dur = randint(5, 10)\n", - " # yield (randint(5, 10) / 40.0, last_val, ins, dev * 100)\n", - " # for i in range(size):\n", - " # yield (randint(5, 10) / 40.0, last_val, ins, 0.0)\n", - " \n", + " print(midi[i + remaining_durs[ins]])\n", + " print(midi[i + 1 + remaining_durs[ins]])\n", + " while midi[i + 1 + remaining_durs[ins]][ins] == midi[i + 2 + remaining_durs[ins]][ins] and midi[total_dur + 2 + remaining_durs[ins]][ins] < len(midi):\n", + " remaining_durs[ins] += 1\n", "\n", - " ins = [i for i, m in enumerate(m1) if m1[i] != m2[i]][0]\n", + " ins = [imx for imx, m in enumerate(m1) if m1[imx] != m2[imx]][0]\n", " #ins = 2\n", " dur = 1\n", " dev = m2[ins] - last_val\n", " last_val = m2[ins]\n", " else:\n", " dur += 1\n", + " remaining_durs = [r_dur - 1 for r_dur in remaining_durs]\n", + " print(remaining_durs)\n", + " print(midi[i])\n", + " total_dur += 1\n", "\n", + "def filter_midi(midi):\n", + " remainder_durs = [1000, 1000, 1000]\n", + " for imx, m in enumerate(midi):\n", + " if m != midi[imx - 1]:\n", + " if imx == 0:\n", + " ins = 2\n", + " val = m[2]\n", + " last_val = val\n", + " else:\n", + " ins = [pmx for pmx, p in enumerate(m) if p != midi[imx - 1][pmx]][0]\n", + " val = m[ins]\n", + " note_dur = 0\n", + " print(ins)\n", + " print(val)\n", + " while imx + note_dur < len(midi) and midi[imx + note_dur][ins] == val:\n", + " note_dur += 1\n", + " next_dur = 0\n", + " while imx + next_dur < len(midi) and midi[imx + next_dur] == m:\n", + " next_dur += 1\n", + " remainder_durs[ins] = note_dur - next_dur\n", + " dev = val - last_val\n", + " allowed_ins = (0,)\n", + " \n", + " yield (next_dur / 40.0, val, ins, allowed_ins, dev * 100)\n", + " \n", + " last_val = val\n", + " remainder_durs = [rd - 1 for rd in remainder_durs]\n", " \n", "midi = list(freqs_to_midi(f_frequency, f_confidence))\n", "\n", @@ -1778,20 +1724,26 @@ "#plt.show()\n", "\n", "midi = tuple(snap_midi(midi))\n", + "\n", + "#print(len(midi))\n", + "#print(midi)\n", + "\n", + "fs = 2351 # sample rate \n", + "f = 20 # the frequency of the signal\n", + "\n", "#midi = zip(\n", - "# tuple(midi[0] - 12 for i in range(2 * 40)) + tuple(m - 12 for m in midi for r in range(2)),\n", - "# tuple(midi[0] - 7 for i in range(1 * 40)) + tuple(m - 7 for m in midi for r in range(2)) + tuple(midi[-1] - 12 for i in range(5 * 40)),\n", - "# tuple(m - 0 for m in midi) + tuple(midi[-1] - 0 for i in range(10 * 40)))\n", + "# tuple(midi[0] - 12 for i in range(2 * 40)) + tuple(chain(*tuple([m - 12 for r in range(randint(2, 4))] for m in midi))),\n", + "# tuple(midi[0] - 7 for i in range(1 * 40)) + tuple(chain(*tuple([m - 7 for r in range(randint(1, 3))] for m in midi))) + tuple(midi[-1] - 12 for i in range(5 * 40)),\n", + "# tuple(chain(*tuple([m for r in range(int(1 - np.cos(2*np.pi*f * (imx/fs))) * 5 + 1)] for imx, m in enumerate(midi)))) + tuple(midi[-1] - 0 for i in range(10 * 40)))\n", "\n", "midi = zip(\n", - " tuple(midi[0] - 12 for i in range(2 * 40)) + tuple(chain(*tuple([m - 12 for r in range(randint(2, 4))] for m in midi))),\n", - " tuple(midi[0] - 7 for i in range(1 * 40)) + tuple(chain(*tuple([m - 7 for r in range(randint(1, 3))] for m in midi))) + tuple(midi[-1] - 12 for i in range(5 * 40)),\n", - " tuple(chain(*tuple([m for r in range(randint(1, 2))] for m in midi))) + tuple(midi[-1] - 0 for i in range(10 * 40)))\n", + " tuple(midi[0] - 12 for i in range(2 * 40)) + tuple(chain(*tuple([m - 12 for r in range(int(1 - np.sin(2*np.pi*14 * (imx/fs))) * 3 + 1)] for imx, m in enumerate(midi)))),\n", + " tuple(midi[0] - 7 for i in range(1 * 40)) + tuple(chain(*tuple([m - 7 for r in range(int(1 - np.sin(2*np.pi*12 * (imx/fs))) * 3 + 1)] for imx, m in enumerate(midi)))),\n", + " tuple(chain(*tuple([m for r in range(int(1 - np.cos(0.5 * np.pi + 2*np.pi*10 * (imx/fs))) * 3 + 1)] for imx, m in enumerate(midi))))\n", + ")\n", "\n", - "#midi = zip(\n", - "# tuple(m - 24 for m in midi) + tuple(midi[-1] - 24 for i in range(5 * 40)),\n", - "# tuple(midi[0] - 12 for i in range(3 * 40)) + tuple(m - 12 for m in midi) + tuple(midi[-1] - 12 for i in range(2 * 40)),\n", - "# tuple(midi[0] - 0 for i in range(5 * 40)) + tuple(m - 0 for m in midi))\n", + "#for m in list(midi):\n", + "# print(m)\n", "\n", "target_melody_data = list(filter_midi(list(midi)))\n", "target_melody_data\n", @@ -1807,29 +1759,70 @@ }, { "cell_type": "code", - "execution_count": 319, - "id": "2b311f14-5061-4f51-9ee7-bd19a1d938fc", + "execution_count": 33, + "id": "762c7dd3-7c20-47a5-aa12-c090f243833a", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 ... 2348 2349 2350]\n" + ] + }, { "data": { "text/plain": [ - "(1, 1, 2, 2, 2, 2, 3, 3, 3)" + "range(0, 1)" ] }, - "execution_count": 319, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "tuple(chain(*tuple([m for r in range(randint(2, 4))] for m in [1, 2, 3])))" + "import matplotlib.pyplot as plt # For ploting\n", + "import numpy as np # to work with numerical data efficiently\n", + "\n", + "fs = 2351 # sample rate \n", + "f = 3 # the frequency of the signal\n", + "\n", + "x = np.arange(fs) # the points on the x axis for plotting\n", + "print(x)\n", + "# compute the value (amplitude) of the sin wave at the for each sample\n", + "y = (1 - np.sin(0.5 * np.pi + 2*np.pi*f * ((x)/fs))) * 2 + 0\n", + "\n", + "#this instruction can only be used with IPython Notbook. \n", + "# showing the exact location of the smaples\n", + "plt.stem(x,y, 'r', )\n", + "plt.plot(x,y)\n", + "np.cos(2*np.pi*f * (10/fs)) * 1 + 2\n", + "range(int((1 - np.sin(2*np.pi*f * (50/fs))) * + 2))" + ] + }, + { + "cell_type": "markdown", + "id": "1d9d26d1-caa8-43f3-a4da-803ae55b3502", + "metadata": {}, + "source": [ + "## cleanup (this is what is being used)" ] }, { "cell_type": "code", - "execution_count": 380, - "id": "be01e4ae-e629-42ff-9d95-f77d510c13bf", + "execution_count": 151, + "id": "fb2ad9ad-7a8c-4f84-ab0c-9c1f1ecdb8a8", "metadata": { "scrolled": true }, @@ -1838,665 +1831,6963 @@ "name": "stdout", "output_type": "stream", "text": [ - "(-701.9550008653874, -386.31371386483454, 0)\n", - "((0, 0, 0, 0), (1, 1, -1, 0), (-1, 1, 0, 0))\n", - "(-901.9550008653874, -586.3137138648345, -88.26871473022209)\n", - "200.0\n", - "((0, 0, 0, 0), (1, 1, -1, 0), (3, 0, -1, 0))\n", - "(-701.9550008653874, -386.31371386483454, 0.0)\n", - "-200.0\n", - "((0, 0, 0, 0), (1, 1, -1, 0), (-1, 1, 0, 0))\n", - "(-313.68628613516523, 113.68628613516546, 500.0)\n", + "146.9\n", + "0.0 51.0 2 51.0\n", + "4.125\n", + "here\n", + "3.175 51.0 2 None\n", + "4.125\n", + "here\n", + "3.5 51.0 2 None\n", + "4.125\n", + "here\n", + "3.875 51.0 2 None\n", + "4.125\n", + "2.125 46.0 1 46.0\n", + "0.3\n", + "2.425 44.0 1 44.0\n", + "2.7\n", + "here\n", + "4.575 44.0 1 None\n", + "2.7\n", + "here\n", + "4.9 44.0 1 None\n", + "2.7\n", + "3.125 41.0 0 41.0\n", + "0.3\n", + "3.425 39.0 0 39.0\n", + "3.75\n", + "here\n", + "6.4 39.0 0 None\n", + "3.75\n", + "here\n", + "6.775 39.0 0 None\n", + "3.75\n", + "here\n", + "7.075 39.0 0 None\n", + "3.75\n", + "4.125 53.0 2 53.0\n", + "1.2\n", + "5.125 42.0 1 42.0\n", + "1.7\n", + "here\n", + "6.075 42.0 1 None\n", + "1.7\n", + "here\n", + "6.45 42.0 1 None\n", + "1.7\n", + "here\n", + "6.7 42.0 1 None\n", + "1.7\n", + "5.325 51.0 2 51.0\n", + "6.0\n", + "here\n", + "10.8 51.0 2 None\n", + "6.0\n", + "here\n", + "11.175 51.0 2 None\n", + "6.0\n", + "6.825 41.0 1 41.0\n", + "3.9\n", + "here\n", + "9.975 41.0 1 None\n", + "3.9\n", + "here\n", + "10.275 41.0 1 None\n", + "3.9\n", + "here\n", + "10.525 41.0 1 None\n", + "3.9\n", + "7.175 37.0 0 37.0\n", + "1.7\n", + "here\n", + "8.25 37.0 0 None\n", + "1.7\n", + "here\n", + "8.5 37.0 0 None\n", + "1.7\n", + "here\n", + "8.75 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"execute_result" + } + ], + "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", + " \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", + "\n", + "def freqs_to_midi(freqs, confs):\n", + " last_confident_value = 51\n", + " for f, c in zip(freqs, confs):\n", + " if c > 0.9:\n", + " last_confident_value = librosa.hz_to_midi(f)\n", + " yield last_confident_value\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", + "\n", + " def integrate(midi):\n", + " print(len(midi) / 40)\n", + " for imx, m in enumerate(midi):\n", + " if m != midi[imx - 1] or imx == 0:\n", + " if imx == 0:\n", + " ins = 2\n", + " val = m[2]\n", + " last_val = val\n", + " else:\n", + " ins = [pmx for pmx, p in enumerate(m) if p != midi[imx - 1][pmx]][0]\n", + " val = m[ins]\n", + " note_dur = 0\n", + " #print(ins)\n", + " #print(val)\n", + " while imx + note_dur < len(midi) and midi[imx + note_dur][ins] == val:\n", + " note_dur += 1\n", + " dev = val - last_val\n", + "\n", + " print (imx / 40.0, val, ins, val)\n", + " print(note_dur / 40.0)\n", + " yield (imx / 40.0, val, ins, val)\n", + "\n", + " tail = randint(20, 40)\n", + " if note_dur / 2 > tail:\n", + " #if False:\n", + " t_sum = 0\n", + " while t_sum < tail:\n", + " print(\"here\")\n", + " print ((imx + note_dur - tail + t_sum) / 40, val, ins, None)\n", + " print(note_dur / 40.0)\n", + " yield ((imx + note_dur - tail + t_sum) / 40, val, ins, None)\n", + " t_sum += randint(10, 15)\n", + " \n", + " last_val = val\n", + "\n", + " def redifferentiate(sorted_midi):\n", + " for imx, sm in enumerate(sorted_midi):\n", + " print(\"---\")\n", + " print(sm)\n", + " if imx < len(sorted_midi) - 1:\n", + " dur = round(sorted_midi[imx + 1][0] - sm[0], 3)\n", + " else:\n", + " dur = 40\n", + " [val, ins] = sm[1:-1]\n", + " dev = 0\n", + " if imx == 0:\n", + " dev = 0.0\n", + " last_val = val\n", + " elif sm[-1] == None:\n", + " dev = None\n", + " else:\n", + " dev = (sm[-1] - last_val) * 100.0\n", + " last_val = val\n", + " print((dur, val, ins, dev))\n", + " yield (dur, val, ins, dev)\n", + " \n", + " sorted_midi = sorted(integrate(midi))\n", + " #for sm in sorted_midi:\n", + " # print(sm)\n", + " redifferentiated_midi = redifferentiate(sorted_midi)\n", + " return redifferentiated_midi\n", + " \n", + "midi = list(freqs_to_midi(f_frequency, f_confidence))\n", + "midi = tuple(snap_midi(midi))\n", + "\n", + "fs = 2351 # sample rate \n", + "f = 20 # the frequency of the signal\n", + "\n", + "midi = zip(\n", + " tuple(midi[0] - 12 for i in range(2 * 40)) + tuple(chain(*tuple([m - 12 for r in range(int(1 - np.sin(2*np.pi*14 * (imx/fs))) * 3 + 1)] for imx, m in enumerate(midi)))),\n", + " tuple(midi[0] - 7 for i in range(1 * 40)) + tuple(chain(*tuple([m - 7 for r in range(int(1 - np.sin(2*np.pi*12 * (imx/fs))) * 3 + 1)] for imx, m in enumerate(midi)))),\n", + " tuple(chain(*tuple([m for r in range(int(1 - np.cos(0.5 * np.pi + 2*np.pi*10 * (imx/fs))) * 3 + 1)] for imx, m in enumerate(midi))))\n", + ")\n", + "\n", + "target_melody_data = list(filter_midi(list(midi)))\n", + "target_melody_data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "c1e314cf-c928-4df5-b0da-8a7b13b2225f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2168.8259064691247, -968.8259064691249, 0]\n", + "1\n", "-500.0\n", - "((0, 0, 0, 0), (5, 0, -2, 0), (3, 0, -1, 0))\n", - 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"[-4.52271854198716, 262.34818706174997, 964.3031879271375]\n", + "0\n", + "None\n", + "[-4.52271854198716, 262.34818706174997, 964.3031879271375]\n", + "((3, 1, 0, -2), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "((-1, 0, 0, 0), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "[31.174093530875325, 262.34818706174997, 964.3031879271375]\n", + "0\n", + "-400.0\n", + "[31.174093530875325, 262.34818706174997, 964.3031879271375]\n", + "((-1, 0, 0, 0), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "((3, -1, 0, -1), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "[-39.60681380363735, 662.34818706175, 1364.3031879271375]\n", + "0\n", + "None\n", + "[-39.60681380363735, 662.34818706175, 1364.3031879271375]\n", + "((3, -1, 0, -1), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "((-3, 1, 0, 0), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "[-66.87090560373713, 662.34818706175, 1364.3031879271375]\n", + "0\n", + "None\n", + "[-66.87090560373713, 662.34818706175, 1364.3031879271375]\n", + "((-3, 1, 0, 0), (2, 0, 0, -1), (1, 1, 0, -1))\n", + "((3, -1, 0, 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864.3031879271375]\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (1, 1, 0, -1))\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (7, -1, 0, -2))\n", + "[-539.6068138036374, -41.56181466902467, 891.5672797272375]\n", + "2\n", + "None\n", + "[-539.6068138036374, -41.56181466902467, 891.5672797272375]\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (7, -1, 0, -2))\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (3, -2, 0, 0))\n", + "[-539.6068138036374, -41.56181466902467, 927.2640918001001]\n", + "2\n", "700.0\n", - "((0, 0, 0, 0), (2, -1, 0, 0), (-1, 1, 0, 0))\n", - "(-584.3587129994469, -113.5778056649346, 117.59628786593998)\n", + "[-539.6068138036374, -41.56181466902467, 927.2640918001001]\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (3, -2, 0, 0))\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (4, -1, 0, -1))\n", + "[-1239.6068138036374, -741.5618146690247, -39.60681380363735]\n", + "2\n", "-100.0\n", - "((0, 0, 0, 0), (-4, 1, 0, 1), (-1, 1, 0, 0))\n", - "(284.467193469678, 986.4221943350653, 1217.59628786594)\n", + "[-1239.6068138036374, -741.5618146690247, -39.60681380363735]\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (4, -1, 0, -1))\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (8, -2, -1, -1))\n", + "[-1139.6068138036374, -641.5618146690247, 172.1244714661405]\n", + "0\n", + "None\n", + "[-1139.6068138036374, -641.5618146690247, 172.1244714661405]\n", + "((3, -1, 0, -1), (5, -2, 0, -1), (8, -2, -1, -1))\n", + "((4, -2, -1, 0), (5, -2, 0, -1), (8, -2, -1, -1))\n", + "[-1259.0496220647344, -641.5618146690247, 172.1244714661405]\n", + "0\n", + "None\n", + "[-1259.0496220647344, -641.5618146690247, 172.1244714661405]\n", + "((4, -2, -1, 0), (5, -2, 0, -1), (8, -2, -1, -1))\n", + "((6, -3, 0, -1), (5, -2, 0, -1), (8, -2, -1, -1))\n", + "[-1343.516815534412, -641.5618146690247, 172.1244714661405]\n", + "0\n", "-1100.0\n", - "((0, 0, 0, 0), (-1, 1, 0, 0), (2, 1, 0, -1))\n", - "(-415.532806530322, 286.42219433506534, 398.15347960484314)\n", + "[-1343.516815534412, -641.5618146690247, 172.1244714661405]\n", + 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"None\n", + "[-13.794766605395353, 484.2502325292173, 1186.2052333946046]\n", + "((-5, -2, 3, 0), (-3, -3, 3, 0), (-4, -2, 3, 0))\n", + "((-5, -2, 3, 0), (-3, -3, 3, 0), (0, -3, 2, 0))\n", + "[-13.794766605395353, 484.2502325292173, 1297.9365186643824]\n", + "2\n", + "1400.0\n", + "[-13.794766605395353, 484.2502325292173, 1297.9365186643824]\n", + "((-5, -2, 3, 0), (-3, -3, 3, 0), (0, -3, 2, 0))\n", + "((-5, -2, 3, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "[-1413.7947666053954, -915.7497674707827, 17.379326925479518]\n", + "0\n", + "-1300.0\n", + "[-1413.7947666053954, -915.7497674707827, 17.379326925479518]\n", + "((-5, -2, 3, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "((-1, -3, 2, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "[-2.0634813356175528, 384.2502325292173, 1317.3793269254795]\n", + "1\n", + "None\n", + "[-2.0634813356175528, 384.2502325292173, 1317.3793269254795]\n", + "((-1, -3, 2, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "((-1, -3, 2, 0), (1, -2, 3, -2), (-1, -2, 3, -1))\n", + "[-2.0634813356175528, 348.5534204563545, 1317.3793269254795]\n", + "1\n", + "None\n", + "[-2.0634813356175528, 348.5534204563545, 1317.3793269254795]\n", + "((-1, -3, 2, 0), (1, -2, 3, -2), (-1, -2, 3, -1))\n", + "((-1, -3, 2, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "[-2.0634813356175528, 384.2502325292173, 1317.3793269254795]\n", + "1\n", + "None\n", + "[-2.0634813356175528, 384.2502325292173, 1317.3793269254795]\n", + "((-1, -3, 2, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "((-1, -3, 2, 0), (1, -2, 3, -2), (-1, -2, 3, -1))\n", + "[-2.0634813356175528, 348.5534204563545, 1317.3793269254795]\n", + "1\n", + "None\n", + "[-2.0634813356175528, 348.5534204563545, 1317.3793269254795]\n", + "((-1, -3, 2, 0), (1, -2, 3, -2), (-1, -2, 3, -1))\n", + "((-1, -3, 2, 0), (-3, -3, 3, 0), (-1, -2, 3, -1))\n", + "[-2.0634813356175528, 384.2502325292173, 1317.3793269254795]\n", + "1\n", + "500.0\n", + "[-2.0634813356175528, 384.2502325292173, 1317.3793269254795]\n", + "((-1, -3, 2, 0), 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(-1, -1, 1, 0))\n", + "[-353.29309993880224, 25.3090909347128, 615.5328065303224]\n", + "1\n", "-200.0\n", - "((0, 0, 0, 0), (3, 0, 0, -1), (-1, 1, 0, 0))\n", - "(-734.9756147914197, -419.3343277908667, -33.020613926032524)\n", - "0.0\n", - "((0, 0, 0, 0), (1, 1, -1, 0), (-1, 1, 0, 0))\n", - "(-301.8465203951573, 280.6656722091333, 666.9793860739675)\n", - "-700.0\n", - "((0, 0, 0, 0), (0, 0, -1, 1), (-2, 0, 0, 1))\n", - "(-801.8465203951573, -219.33432779086672, 11.83976574000792)\n", - "500.0\n", - "((0, 0, 0, 0), (0, 0, -1, 1), (3, 0, -1, 0))\n", - "(-517.3793269254793, -19.334327790866723, 211.83976574000792)\n", + "[-353.29309993880224, 25.3090909347128, 615.5328065303224]\n", + "((1, -1, 1, -1), (4, -3, 0, 0), (-1, -1, 1, 0))\n", + "((1, -1, 1, -1), (-4, -1, 2, 0), (-1, -1, 1, 0))\n", + "[-153.29309993880224, 1.8465203951568583, 815.5328065303224]\n", + "0\n", + "None\n", + "[-153.29309993880224, 1.8465203951568583, 815.5328065303224]\n", + "((1, -1, 1, -1), (-4, -1, 2, 0), (-1, -1, 1, 0))\n", + "((-7, -1, 2, 1), (-4, -1, 2, 0), (-1, -1, 1, 0))\n", + "[-229.32757313571778, 1.8465203951568583, 815.5328065303224]\n", + "2\n", + "None\n", + "[-229.32757313571778, 1.8465203951568583, 815.5328065303224]\n", + "((-7, -1, 2, 1), (-4, -1, 2, 0), (-1, -1, 1, 0))\n", + "((-7, -1, 2, 1), (-4, -1, 2, 0), (-9, -1, 2, 2))\n", + "[-229.32757313571778, 1.8465203951568583, 739.4983333334069]\n", + "1\n", + "-100.0\n", + "[-229.32757313571778, 1.8465203951568583, 739.4983333334069]\n", + "((-7, -1, 2, 1), (-4, -1, 2, 0), (-9, -1, 2, 2))\n", + "((-7, -1, 2, 1), (-12, -1, 3, 2), (-9, -1, 2, 2))\n", + "[-129.32757313571778, 25.81204719824177, 839.4983333334069]\n", + "0\n", + "None\n", + "[-129.32757313571778, 25.81204719824177, 839.4983333334069]\n", + "((-7, -1, 2, 1), (-12, -1, 3, 2), (-9, -1, 2, 2))\n", + "((-15, -1, 3, 3), (-12, -1, 3, 2), (-9, -1, 2, 2))\n", + "[-205.36204633263287, 25.81204719824177, 839.4983333334069]\n", + "1\n", "-200.0\n", - "((0, 0, 0, 0), (2, -1, 0, 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-721.2893286562545)\n", + "[-5.362046332632872, 70.67242686428199, 963.4638601364917]\n", + "((-15, -1, 3, 3), (-7, -1, 2, 1), (-17, -1, 3, 4))\n", + "((-10, -1, 2, 2), (-7, -1, 2, 1), (-17, -1, 3, 4))\n", + "[-260.5016666665929, -29.327573135718012, 863.4638601364917]\n", + "2\n", + "None\n", + "[-260.5016666665929, -29.327573135718012, 863.4638601364917]\n", + "((-10, -1, 2, 2), (-7, -1, 2, 1), (-17, -1, 3, 4))\n", + "((-10, -1, 2, 2), (-7, -1, 2, 1), (-9, -1, 2, 2))\n", + "[-260.5016666665929, -29.327573135718012, 939.4983333334069]\n", + "2\n", + "None\n", + "[-260.5016666665929, -29.327573135718012, 939.4983333334069]\n", + "((-10, -1, 2, 2), (-7, -1, 2, 1), (-9, -1, 2, 2))\n", + "((-10, -1, 2, 2), (-7, -1, 2, 1), (-4, -1, 1, 1))\n", + "[-260.5016666665929, -29.327573135718012, 784.3587129994473]\n", + "0\n", + "-100.0\n", + "[-260.5016666665929, -29.327573135718012, 784.3587129994473]\n", + "((-10, -1, 2, 2), (-7, -1, 2, 1), (-4, -1, 1, 1))\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (-4, -1, 1, 1))\n", + "[-84.46719346967757, 70.67242686428199, 884.3587129994473]\n", + "2\n", "1300.0\n", - "((0, 0, 0, 0), (-4, 1, 0, 1), (-2, 0, 0, 1))\n", - "(-1890.115235125379, -1152.463422187129, -921.2893286562545)\n", - "200.0\n", - "((0, 0, 0, 0), (-5, 0, 0, 2), (-2, 0, 0, 1))\n", - "(-1519.3343277908666, -1252.463422187129, -1021.2893286562545)\n", - "100.0\n", - "((0, 0, 0, 0), (-1, -1, 0, 1), (2, -1, 0, 0))\n", - "(-34.9756147914195, 547.536577812871, 778.7106713437455)\n", - "-1800.0\n", - "((0, 0, 0, 0), (0, 0, -1, 1), (3, 0, -1, 0))\n", - "(-1434.9756147914195, -852.463422187129, -466.14970832229494)\n", - "1400.0\n", - "((0, 0, 0, 0), (0, 0, -1, 1), (-2, 0, 0, 1))\n", - "(-634.9756147914195, -52.46342218712903, 178.7106713437455)\n", - "-800.0\n", - "((0, 0, 0, 0), (0, 0, -1, 1), (3, 0, -1, 0))\n", - "(-538.777136051964, -152.46342218712903, 78.7106713437455)\n", + "[-84.46719346967757, 70.67242686428199, 884.3587129994473]\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (-4, -1, 1, 1))\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (-9, -1, 2, 2))\n", + "[-1384.4671934696776, -1229.327573135718, -260.5016666665931]\n", + "2\n", "100.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (1, 0, 1, -1))\n", - "(-438.777136051964, -52.46342218712903, 59.26786308264832)\n", + "[-1384.4671934696776, -1229.327573135718, -260.5016666665931]\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (-9, -1, 2, 2))\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (2, -1, 1, -1))\n", + "[-1484.4671934696776, -1329.327573135718, -53.2930999388027]\n", + "2\n", "-100.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (2, -1, 0, 0))\n", - "(-338.777136051964, -71.90623044822655, 159.26786308264832)\n", + "[-1484.4671934696776, -1329.327573135718, -53.2930999388027]\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (2, -1, 1, -1))\n", + "((-2, -1, 1, 0), (-7, -1, 2, 1), (-3, -1, 2, 0))\n", + "[-1384.4671934696776, -1229.327573135718, 201.8465203951571]\n", + "0\n", + "-1500.0\n", + "[-1384.4671934696776, -1229.327573135718, 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1570.672426864282]\n", + "2\n", + "1600.0\n", + "[-15.641287000552438, 482.40371213406024, 1570.672426864282]\n", + "((-5, -1, 1, 1), (-3, -2, 1, 1), (-6, -1, 2, 1))\n", + "((-5, -1, 1, 1), (-3, -2, 1, 1), (-2, -2, 1, 1))\n", + "[-1615.6412870005524, -1117.5962878659398, 82.40371213406002]\n", + "2\n", "-100.0\n", - "((0, 0, 0, 0), (-1, -1, 0, 1), (2, -1, 0, 0))\n", - "(-1638.7771360519641, -1371.9062304482266, -669.9512295828392)\n", - "1300.0\n", - "((0, 0, 0, 0), (-1, -1, 0, 1), (-2, 0, 0, 1))\n", - "(-1438.7771360519641, -1052.463422187129, -469.95122958283923)\n", + "[-1615.6412870005524, -1117.5962878659398, 82.40371213406002]\n", + "((-5, -1, 1, 1), (-3, -2, 1, 1), (-2, -2, 1, 1))\n", + "((-5, -1, 1, 1), (-3, -2, 1, 1), (-6, -1, 2, 1))\n", + "[-1515.6412870005524, -1017.5962878659398, 70.6724268642819]\n", + "1\n", + "None\n", + "[-1515.6412870005524, -1017.5962878659398, 70.6724268642819]\n", + "((-5, -1, 1, 1), (-3, -2, 1, 1), (-6, -1, 2, 1))\n", + "((-5, -1, 1, 1), (-4, -1, 2, 0), (-6, -1, 2, 1))\n", + "[-1515.6412870005524, -898.1534796048431, 70.6724268642819]\n", + "2\n", "-200.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (-2, 0, 0, 1))\n", - "(-1338.7771360519641, -952.463422187129, -250.50842132174193)\n", + "[-1515.6412870005524, -898.1534796048431, 70.6724268642819]\n", + "((-5, -1, 1, 1), (-4, -1, 2, 0), (-6, -1, 2, 1))\n", + "((-5, -1, 1, 1), (-4, -1, 2, 0), (-5, 0, 2, 0))\n", + "[-1315.6412870005524, -698.1534796048431, 3.801521260544547]\n", + "2\n", "-100.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (-3, 1, 1, 0))\n", - "(-438.77713605196413, -52.46342218712903, 59.26786308264832)\n", - "-900.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (2, -1, 0, 0))\n", - "(-1038.7771360519641, -652.463422187129, 49.491578678258065)\n", - "600.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (-3, 1, 1, 0))\n", - "(-338.77713605196413, 47.53657781287097, 159.26786308264832)\n", - "-700.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (2, -1, 0, 0))\n", - "(-138.77713605196413, 92.39695747891113, 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0))\n", + "[-1215.6412870005524, -598.1534796048431, -100.10848047023043]\n", + "1\n", + "None\n", + "[-1215.6412870005524, -598.1534796048431, -100.10848047023043]\n", + "((-5, -1, 1, 1), (-4, -1, 2, 0), (-2, -2, 2, 0))\n", + "((-5, -1, 1, 1), (-6, 0, 1, 1), (-2, -2, 2, 0))\n", + "[-1215.6412870005524, -513.6862861351653, -100.10848047023043]\n", + "0\n", + "-1400.0\n", + "[-1215.6412870005524, -513.6862861351653, -100.10848047023043]\n", + "((-5, -1, 1, 1), (-6, 0, 1, 1), (-2, -2, 2, 0))\n", + "((-9, 0, 2, 1), (-6, 0, 1, 1), (-2, -2, 2, 0))\n", + "[72.62742772966976, 886.3137138648347, 1299.8915195297695]\n", + "2\n", + "1200.0\n", + "[72.62742772966976, 886.3137138648347, 1299.8915195297695]\n", + "((-9, 0, 2, 1), (-6, 0, 1, 1), (-2, -2, 2, 0))\n", + "((-9, 0, 2, 1), (-6, 0, 1, 1), (-8, 0, 2, 1))\n", + "[-1127.3725722703302, -313.68628613516535, 72.62742772966931]\n", + "1\n", "-200.0\n", - "((0, 0, 0, 0), (3, 0, -1, 0), (6, 0, -1, -1))\n", - "(-921.2893286562544, 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"[-355.13962033395933, 142.90537880065267, 1111.7312852697778]\n", + "((1, 0, -1, 0), (3, -1, -1, 0), (1, -1, -1, 1))\n", + "((-3, -1, -1, 2), (3, -1, -1, 0), (1, -1, -1, 1))\n", + "[-319.44280826109707, 142.90537880065267, 1111.7312852697778]\n", + "0\n", + "-400.0\n", + "[-319.44280826109707, 142.90537880065267, 1111.7312852697778]\n", + "((-3, -1, -1, 2), (3, -1, -1, 0), (1, -1, -1, 1))\n", + "((1, 0, -1, 0), (3, -1, -1, 0), (1, -1, -1, 1))\n", + "[44.86037966604067, 542.9053788006527, 1511.7312852697778]\n", + "1\n", + "None\n", + "[44.86037966604067, 542.9053788006527, 1511.7312852697778]\n", + "((1, 0, -1, 0), (3, -1, -1, 0), (1, -1, -1, 1))\n", + "((1, 0, -1, 0), (-1, 0, 0, 0), (1, -1, -1, 1))\n", + "[44.86037966604067, 431.1740935308751, 1511.7312852697778]\n", + "1\n", + "None\n", + "[44.86037966604067, 431.1740935308751, 1511.7312852697778]\n", + "((1, 0, -1, 0), (-1, 0, 0, 0), (1, -1, -1, 1))\n", + "((1, 0, -1, 0), (3, -1, -1, 0), (1, -1, -1, 1))\n", + "[44.86037966604067, 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"((16, -1, -2, -4), (19, -1, -3, -4), (17, -1, -2, -4))\n", + "((20, -2, -3, -4), (19, -1, -3, -4), (17, -1, -2, -4))\n", + "[-706.9806756709036, -5.02567480551636, 381.28803905931863]\n", + "1\n", + "None\n", + "[-706.9806756709036, -5.02567480551636, 381.28803905931863]\n", + "((20, -2, -3, -4), (19, -1, -3, -4), (17, -1, -2, -4))\n", + "((20, -2, -3, -4), (15, 0, -2, -4), (17, -1, -2, -4))\n", + "[-706.9806756709036, -116.75696007529405, 381.28803905931863]\n", + "0\n", + "None\n", + "[-706.9806756709036, -116.75696007529405, 381.28803905931863]\n", + "((20, -2, -3, -4), (15, 0, -2, -4), (17, -1, -2, -4))\n", + "((13, 1, -2, -4), (15, 0, -2, -4), (17, -1, -2, -4))\n", + "[-614.8019592099067, -116.75696007529405, 381.28803905931863]\n", + "0\n", "-500.0\n", - "((0, 0, 0, 0), (-2, 0, 1, 0), (3, 0, -1, 0))\n", - "(-858.8313810468205, -472.5176671819861, 25.527331952626355)\n", + "[-614.8019592099067, -116.75696007529405, 381.28803905931863]\n", + "((13, 1, -2, -4), (15, 0, -2, -4), 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"[29.841459515673023, 416.1551733805079, 914.2001725151206]\n", + "2\n", + "1200.0\n", + "[29.841459515673023, 416.1551733805079, 914.2001725151206]\n", + "((21, -3, 1, -7), (19, -3, 2, -7), (21, -4, 2, -7))\n", + "((21, -3, 1, -7), (19, -3, 2, -7), (22, -3, 1, -7))\n", + "[-1170.158540484327, -783.8448266194921, 29.841459515673023]\n", + "0\n", + "-1300.0\n", + "[-1170.158540484327, -783.8448266194921, 29.841459515673023]\n", + "((21, -3, 1, -7), (19, -3, 2, -7), (22, -3, 1, -7))\n", + "((17, -2, 2, -7), (19, -3, 2, -7), (22, -3, 1, -7))\n", + "[18.11017424589545, 516.1551733805079, 1329.841459515673]\n", + "2\n", + "1400.0\n", + "[18.11017424589545, 516.1551733805079, 1329.841459515673]\n", + "((17, -2, 2, -7), (19, -3, 2, -7), (22, -3, 1, -7))\n", + "((17, -2, 2, -7), (19, -3, 2, -7), (21, -2, 2, -8))\n", + "[-1381.8898257541045, -883.8448266194921, 49.28426777677055]\n", + "1\n", + "None\n", + "[-1381.8898257541045, -883.8448266194921, 49.28426777677055]\n", + "((17, -2, 2, -7), (19, -3, 2, -7), (21, -2, 2, -8))\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (21, -2, 2, -8))\n", + "[-1381.8898257541045, -919.5416386923544, 49.28426777677055]\n", + "2\n", "-100.0\n", - "((0, 0, 0, 0), (4, -1, -1, 0), (2, -1, 0, 0))\n", - "(-574.4726680473734, -188.15895418253854, 198.15475968229566)\n", - "-300.0\n", - "(1.525, 0.425, 1.225, 0.65, 0.375, 0.65, 0.175, 0.925, 0.4, 0.4, 0.7, 0.25, 0.625, 0.1, 1.175, 0.475, 0.1, 0.225, 0.625, 0.525, 0.2, 0.625, 0.05, 0.175, 0.65, 1.0, 0.075, 0.075, 1.2, 0.525, 0.3, 0.075, 1.225, 0.325, 0.3, 0.15, 0.075, 0.525, 0.225, 2.275, 0.125, 0.025, 0.225, 0.025, 0.125, 0.25, 0.25, 0.8, 0.125, 0.325, 0.275, 0.375, 0.425, 0.125, 1.1, 0.375, 0.05, 0.1, 0.4, 1.15, 0.15, 0.1, 0.075, 0.75, 0.575, 0.05, 0.325, 0.9, 0.375, 0.575, 0.05, 0.125, 0.075, 0.125, 0.4, 0.275, 0.125, 0.525, 0.65, 0.975, 0.725, 0.275, 0.175, 1.65, 0.7, 0.1, 0.05, 0.225, 0.4, 0.375, 0.325, 0.2, 0.225, 0.425, 2.05, 0.025, 0.125, 0.075, 0.45, 0.075, 0.225, 0.075, 0.35, 0.15, 0.3, 0.175, 0.1, 0.525, 0.55, 0.325, 0.375, 0.075, 0.5, 0.525, 0.7, 0.3, 0.05, 0.55, 0.65, 0.675, 0.25, 0.7, 0.85, 0.9, 1.15, 0.25, 0.025, 0.1, 0.475, 0.125, 1.0, 0.35, 0.075, 0.725, 0.3, 0.2, 0.1, 0.25, 0.2, 0.975, 0.55, 0.025, 0.225, 0.425, 0.525, 1.45, 0.025, 0.175, 0.275, 0.025, 0.35, 0.4, 0.4, 0.075, 0.7, 0.35, 0.1, 0.4, 0.175, 0.275, 1.425, 0.775, 0.475, 0.85, 1.375, 0.25, 0.55, 0.075, 1.425, 0.325, 0.525, 0.3, 0.825, 0.525, 0.15, 0.075, 0.375, 0.125, 0.05, 0.3, 0.675, 1.4, 0.9, 0.475, 0.425, 0.475, 0.275, 1.175, 0.3, 0.45, 1.375, 0.225, 0.25, 0.1, 0.05, 0.15, 0.6, 0.225, 0.55, 0.45, 0.275, 0.15, 0.05, 1.45, 0.225, 0.225, 0.75, 0.25, 0.55, 0.525, 0.925, 1.4, 0.725, 0.8, 0.775)\n" + "[-1381.8898257541045, -919.5416386923544, 49.28426777677055]\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (21, -2, 2, -8))\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (26, -2, 1, -9))\n", + "[-1281.8898257541045, -819.5416386923544, -5.855352557189008]\n", + "2\n", + "-100.0\n", + "[-1281.8898257541045, -819.5416386923544, -5.855352557189008]\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (26, -2, 1, -9))\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (22, -1, 2, -9))\n", + "[-1181.8898257541045, -719.5416386923544, -17.586637826967177]\n", + "1\n", + "None\n", + "[-1181.8898257541045, -719.5416386923544, -17.586637826967177]\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (22, -1, 2, -9))\n", + "((17, -2, 2, -7), (19, -3, 2, -7), (22, -1, 2, -9))\n", + "[-1181.8898257541045, -683.8448266194921, -17.586637826967177]\n", + "1\n", + "None\n", + "[-1181.8898257541045, -683.8448266194921, -17.586637826967177]\n", + "((17, -2, 2, -7), (19, -3, 2, -7), (22, -1, 2, -9))\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (22, -1, 2, -9))\n", + "[-1181.8898257541045, -719.5416386923544, -17.586637826967177]\n", + "2\n", + "100.0\n", + "[-1181.8898257541045, -719.5416386923544, -17.586637826967177]\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (22, -1, 2, -9))\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (26, -2, 1, -9))\n", + "[-1281.8898257541045, -819.5416386923544, -5.855352557189008]\n", + "1\n", + "-700.0\n", + "[-1281.8898257541045, -819.5416386923544, -5.855352557189008]\n", + "((17, -2, 2, -7), (23, -2, 2, -9), (26, -2, 1, -9))\n", + "((17, -2, 2, -7), (27, -3, 1, -9), (26, -2, 1, -9))\n", + "[-581.8898257541045, -7.810353422576782, 694.144647442811]\n" ] } ], "source": [ - "path = stochastic_hamiltonian(graph)\n", + "path = stochastic_hamiltonian(root)\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", - "print(durs)\n", "write_chord_sequence(list(zip(durs, path_to_chords(path, root))))" ] }, + { + "cell_type": "markdown", + "id": "85eda9de-ad78-4009-bf0e-c4d45e806bc4", + "metadata": {}, + "source": [ + "## function-based approach" + ] + }, + { + "cell_type": "markdown", + "id": "0c249f37-5fd5-45d3-9f52-313dd9ae3250", + "metadata": {}, + "source": [ + "### path functions" + ] + }, + { + "cell_type": "markdown", + "id": "df57f38b-be7a-4cac-b0af-33b36c97f388", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "#### legacy" + ] + }, { "cell_type": "code", - "execution_count": 381, + "execution_count": 15, "id": "57c834bf-fee7-4ef4-b648-2173099fbb56", "metadata": {}, "outputs": [], @@ -2511,7 +8802,7 @@ " distances += [sum([abs(dist) * log(dims[idx], 2) for idx, dist in enumerate(pitch_difference(chord[i], chord[j]))])]\n", " return sum(distances)\n", " \n", - "def next_edges(source): \n", + "def next_edges(source, last_chords): \n", "\n", " def transpose_to_nearest(hs_array1, hs_array2):\n", " expanded_pitch = hs_array2\n", @@ -2526,28 +8817,44 @@ " expanded_pitch[0] += -1\n", " return tuple(expanded_pitch)\n", " \n", - " def gen_candidates(chord):\n", + " def gen_candidates(chord, last_chords):\n", " for sdx, s_pitch in enumerate(chord):\n", " for cdx, c_pitch in enumerate(chord):\n", - " if sdx != cdx: \n", + " if cdx != sdx: \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_chord_up = list(chord)\n", + " new_chord_up[sdx] = tuple(new_pitch_up)\n", + " #print(\"here\")\n", + " #print(new_pitch_up)\n", + " #print(tuple(l[sdx] for l in last_chords))\n", + " #print(new_pitch_up not in tuple(l[sdx] for l in last_chords))\n", + " \n", + " #if tuple(new_chord_up) != chord and (tuple(new_pitch_up) not in tuple(l[sdx] for l in last_chords)) and max(new_pitch_up[1:]) <= 1 and min(new_pitch_up[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", + " if tuple(new_chord_up) != chord and max(new_pitch_up[1:]) <= 1 and min(new_pitch_up[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", + " #if tuple(new_chord_up) != chord and tuple(new_pitch_up) not in tuple(l[sdx] for l in last_chords):\n", + " #if tuple(new_chord_up) != chord:\n", + " yield tuple(new_chord_up)\n", + "\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", - " #if tuple(new_chord) != chord and max(new_pitch_up[1:]) <= 1 and min(new_pitch_up[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", - " if tuple(new_chord) != chord:\n", - " yield tuple(new_chord)\n", - " new_chord[sdx] = tuple(new_pitch_down)\n", - " if tuple(new_chord) != chord:\n", - " #if tuple(new_chord) != chord and max(new_pitch_down[1:]) <= 1 and min(new_pitch_down[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", - " yield tuple(new_chord)\n", - "\n", - " for candidate in gen_candidates(source):\n", + " new_chord_down = list(chord)\n", + " new_chord_down[sdx] = tuple(new_pitch_down)\n", + "\n", + " #print(\"here2\")\n", + " #print(new_pitch_down)\n", + " #print(tuple(l[sdx] for l in last_chords))\n", + " #print(new_pitch_down not in tuple(l[sdx] for l in last_chords))\n", + " #if tuple(new_chord_down) != chord:\n", + " #if tuple(new_chord_down) != chord and new_pitch_down not in tuple(l[sdx] for l in last_chords):\n", + " if tuple(new_chord_down) != chord and max(new_pitch_down[1:]) <= 1 and min(new_pitch_down[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", + " #if tuple(new_chord_down) != chord and (tuple(new_pitch_down) not in tuple(l[sdx] for l in last_chords)) and max(new_pitch_down[1:]) <= 1 and min(new_pitch_down[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", + " yield tuple(new_chord_down)\n", + "\n", + " for candidate in gen_candidates(source, last_chords):\n", "\n", " movements = {\n", " pitch:\n", @@ -2559,23 +8866,3755 @@ " 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", + "# This is for the beginning / breysheet - redone to calculate next chord as a function and not from the graph\n", + "def stochastic_hamiltonian(root):\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 100 if ((max_cent_diff(e) < 200) and (min_cent_diff(e)) >= 50) else 1/pow(1.1, max_cent_diff(e))\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", + " s_chord = sorted(e[1], key=hs_array_to_fr)\n", + " yield 5 if hs_array_to_fr(sorted(e[1], key=hs_array_to_fr)[0]) >= 0.25 else 0\n", + "\n", + " def hd_weight(edges):\n", + " for e in edges:\n", + " yield 100 * (1/pow(hd_sum(e[1]), 2))\n", + "\n", + " def permission_weight(edges, allowed_ins):\n", + " print(\"here\")\n", + " print(allowed_ins)\n", + " print(\"---\")\n", + " for e in edges:\n", + " diff = [ibx for ibx, pitch in enumerate(e[0]) if pitch != e[1][ibx]][0]\n", + " yield 1 if diff in allowed_ins else 0\n", + "\n", + " def dca_weight(edges, ins, last_chords):\n", + " for e in edges:\n", + " #print(tuple(e[1][ins]))\n", + " #print(ins)\n", + " #print(tuple(l[ins] for l in last_chords))\n", + " diff_e = [i for i, item in enumerate(e[0]) if item != e[1][i]][0]\n", + " if ins == diff_e:\n", + " yield 1\n", + " else:\n", + " yield 100 if tuple(e[1][diff_e]) not in tuple(l[diff_e] for l in last_chords) 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 favor_bass(edges, ins):\n", + "\n", + " def ins_check(edge, ins):\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", + " if ins == 2:\n", + " return 1\n", + " elif ins == 1 and ordered_source[0] != ordered_destination[0]:\n", + " return 1\n", + " else:\n", + " return 0\n", + "\n", + " for e in edges:\n", + " yield ins_check(e, ins)\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 == voice):\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 100 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", + " next_node = ((-2, 0, 0, 0, 0, 0), (-1, 0, 0, 0, 0, 0), (-3, 0, 0, 0, 1, 0))\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", + " last_chords = (tuple(s_next_node),)\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", + " while (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, last_chords))\n", + " target = target_melody_data[len(path)+1][-1]\n", + " ins = target_melody_data[len(path)+1][2]\n", + " print(target)\n", + " print(ins)\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, c_devs, ins),\n", + " #symdiff_weights(out_edges),\n", + " hd_weight(out_edges),\n", + " dca_weight(out_edges, ins, last_chords),\n", + " permission_weight(out_edges, target_melody_data[len(path)+1][3])\n", + " #favor_bass(out_edges, ins),\n", + " #in_range(out_edges)\n", + " ]\n", + " weights = [prod(a) for a in zip(*factors)]\n", + " if sum(weights) != 0:\n", + " edge = choices(out_edges, weights=weights)[0]\n", + " else:\n", + " movements = {\n", + " pitch:\n", + " {\n", + " 'destination': pitch, \n", + " 'cent_difference': 0\n", + " } for index, pitch in enumerate(edge[1])}\n", + " edge = (edge[1], edge[1], {'transposition': (0, 0, 0, 0, 0, 0), 'movements': movements},)\n", + " \n", + " next_node = edge[1]\n", + " last_chords = last_chords + (next_node,)\n", + " if len(last_chords) > 10:\n", + " last_chords = last_chords[-10:]\n", + " print(last_chords)\n", + " #node[1]['count'] += 1\n", + " path.append(edge)\n", + " s_chord = tuple(sorted(edge[0], key=hs_array_to_fr))\n", + " print(c_devs)\n", + " #for pdx, pitch in enumerate(s_chord):\n", + " # print(\"here\")\n", + " # print(c_devs[pdx])\n", + " # print(edge[2]['movements'][pitch]['cent_difference'])\n", + " # print(target_melody_data[len(path)][-1])\n", + " if sum(weights) != 0:\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(edge[1])\n", + " print(c_devs)\n", + " print(target_melody_data[len(path)][2])\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": "markdown", + "id": "24267739-0381-4651-8804-4b4665592de2", + "metadata": {}, + "source": [ + "#### cleanup (this is what is being used)" + ] + }, + { + "cell_type": "code", + "execution_count": 236, + "id": "81f20bd9-e87a-4918-a61b-27b04b6d5aee", + "metadata": {}, + "outputs": [], + "source": [ + "from random import choice, choices\n", + "\n", + "def hd_sum(chord):\n", + " distances = []\n", + " size = len(chord)\n", + " for i in range(size):\n", + " for j in range(i+1, size):\n", + " distances += [sum([abs(dist) * log(dims[idx], 2) for idx, dist in enumerate(pitch_difference(chord[i], chord[j]))])]\n", + " return sum(distances)\n", + " \n", + "def next_edges(source, ins): \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, ins):\n", + " for cdx, c_pitch in enumerate(chord):\n", + " if cdx != ins: \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", + " s_pitch = chord[ins]\n", + " new_pitch_up = transpose_to_nearest(s_pitch, new_pitch)\n", + " new_chord_up = list(chord)\n", + " new_chord_up[ins] = tuple(new_pitch_up)\n", + " \n", + " if tuple(new_chord_up) != chord and max(new_pitch_up[1:]) <= 1 and min(new_pitch_up[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", + " #if tuple(new_chord_up) != chord:\n", + " yield tuple(new_chord_up)\n", + "\n", + " new_pitch_down = list(new_pitch_up)\n", + " new_pitch_down[0] += -1\n", + " new_chord_down = list(chord)\n", + " new_chord_down[ins] = tuple(new_pitch_down)\n", + "\n", + " if tuple(new_chord_down) != chord and max(new_pitch_down[1:]) <= 1 and min(new_pitch_down[1:]) >= 0 and sum(new_pitch_up[1:]) <= 2:\n", + " #if tuple(new_chord_down) != chord:\n", + " yield tuple(new_chord_down)\n", + "\n", + " for candidate in gen_candidates(source, ins):\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(root):\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 100 if ((max_cent_diff(e) < 200) and (min_cent_diff(e)) >= 50) else 1/pow(1.1, max_cent_diff(e))\n", + "\n", + " def in_range(edges):\n", + " for e in edges:\n", + " s_chord = sorted(e[1], key=hs_array_to_fr)\n", + " yield 5 if hs_array_to_fr(sorted(e[1], key=hs_array_to_fr)[0]) >= 0.25 else 0\n", + "\n", + " def hd_weight(edges):\n", + " for e in edges:\n", + " yield 10 * (1/pow(hd_sum(e[1]), 3))\n", + "\n", + " def permission_weight(edges, allowed_ins):\n", + " print(\"here\")\n", + " print(allowed_ins)\n", + " print(\"---\")\n", + " for e in edges:\n", + " diff = [ibx for ibx, pitch in enumerate(e[0]) if pitch != e[1][ibx]][0]\n", + " yield 1 if diff in allowed_ins else 0\n", + "\n", + " def dca_weight(edges, ins, dev, last_chords):\n", + " for e in edges:\n", + " #if dev != None:\n", + " if False:\n", + " yield 1\n", + " else:\n", + " yield 100 if tuple(e[1][ins]) not in tuple(l[ins] for l in last_chords) 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 target_melody_weights(edges, target_dev, c_devs, ins):\n", + "\n", + " def target_weight(edge, target_dev, c_devs, ins):\n", + " source = list(edge[0])\n", + " ordered_source = sorted(source, key=hs_array_to_fr) \n", + " candidate_diff = abs(c_devs[ins] + edge[2]['movements'][ordered_source[ins]]['cent_difference'] - target_dev)\n", + " return 100 if candidate_diff < 40 else 1/pow(1.1, candidate_diff)\n", + " \n", + " for e in edges:\n", + " yield target_weight(e, target_dev, c_devs, ins) if target_dev != None else 1\n", + " \n", + " next_node = ((-2, 0, 0, 0, 0), (-1, 0, 0, 0, 0), (-1, 1, 0, 0, 9))\n", + " path = []\n", + " s_next_node = sorted(next_node, key=hs_array_to_fr)\n", + " last_chords = (tuple(s_next_node),)\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", + " for dur, val, ins, dev in target_melody_data[1:]:\n", + " print(ins)\n", + " print(dev)\n", + " out_edges = list(next_edges(next_node, ins))\n", + " factors = [\n", + " movement_size_weights(out_edges), \n", + " voice_crossing_weights(out_edges),\n", + " target_melody_weights(out_edges, dev, c_devs, ins),\n", + " hd_weight(out_edges),\n", + " dca_weight(out_edges, ins, dev, last_chords)\n", + " #permission_weight(out_edges, target_melody_data[len(path)+1][3])\n", + " ]\n", + " weights = [prod(a) for a in zip(*factors)]\n", + " if sum(weights) != 0:\n", + " edge = choices(out_edges, weights=weights)[0]\n", + " else:\n", + " movements = {\n", + " pitch:\n", + " {\n", + " 'destination': pitch, \n", + " 'cent_difference': 0\n", + " } for index, pitch in enumerate(edge[1])}\n", + " edge = (edge[1], edge[1], {'transposition': (0, 0, 0, 0, 0), 'movements': movements},)\n", + " \n", + " next_node = edge[1]\n", + " last_chords = last_chords + (next_node,)\n", + " if len(last_chords) > 4:\n", + " last_chords = last_chords[-4:]\n", + " path.append(edge)\n", + " s_chord = tuple(sorted(edge[0], key=hs_array_to_fr))\n", + " print(c_devs)\n", + " #for pdx, pitch in enumerate(s_chord):\n", + " # print(\"here\")\n", + " # print(c_devs[pdx])\n", + " # print(edge[2]['movements'][pitch]['cent_difference'])\n", + " # print(target_melody_data[len(path)][-1])\n", + " if sum(weights) != 0:\n", + " if dev != None:\n", + " c_devs = [c_devs[pdx] + edge[2]['movements'][pitch]['cent_difference'] - dev for pdx, pitch in enumerate(s_chord)]\n", + " else:\n", + " c_devs[ins] += edge[2]['movements'][s_chord[ins]]['cent_difference']\n", + " print(s_chord)\n", + " print(edge[1])\n", + " print(c_devs)\n", + " return path" + ] + }, + { + "cell_type": "markdown", + "id": "3102630d-3e61-4653-8482-4e50e0e26858", + "metadata": {}, + "source": [ + "### model" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "id": "f8ef1a93-4616-4a83-bbae-23ed13ba4f41", + "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": 238, + "id": "8912c650-a43d-4539-ae24-b5acf9bc543d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + 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"[-31.174093530875552, 466.8709056037376, 2053.1846194685727]\n", + "((-6, 1, 0, 1, 0), (-4, 0, 0, 1, 0), (-5, 0, 1, 1, 0))\n", + "((-6, 1, 0, 1, 0), (-3, 1, 0, 0, 0), (-5, 0, 1, 1, 0))\n", + "[-331.17409353087555, -99.99999999999977, 1753.1846194685727]\n", + "1\n", + "400.0\n", + "[-331.17409353087555, -99.99999999999977, 1753.1846194685727]\n", + "((-6, 1, 0, 1, 0), (-3, 1, 0, 0, 0), (-5, 0, 1, 1, 0))\n", + "((-6, 1, 0, 1, 0), (-3, 0, 1, 0, 0), (-5, 0, 1, 1, 0))\n", + "[-731.1740935308756, 384.35871299944745, 1353.1846194685727]\n", + "1\n", + "200.0\n", + "[-731.1740935308756, 384.35871299944745, 1353.1846194685727]\n", + "((-6, 1, 0, 1, 0), (-3, 0, 1, 0, 0), (-5, 0, 1, 1, 0))\n", + "((-6, 1, 0, 1, 0), (-2, 1, 0, 0, 0), (-5, 0, 1, 1, 0))\n", + "[-931.1740935308756, 500.0000000000001, 1153.1846194685727]\n", + "1\n", + "-100.0\n", + "[-931.1740935308756, 500.0000000000001, 1153.1846194685727]\n", + "((-6, 1, 0, 1, 0), (-2, 1, 0, 0, 0), (-5, 0, 1, 1, 0))\n", + "((-6, 1, 0, 1, 0), (-3, 0, 0, 1, 0), (-5, 0, 1, 1, 0))\n", + "[-831.1740935308756, 866.8709056037376, 1253.1846194685727]\n", + "2\n", + "600.0\n", + "[-831.1740935308756, 866.8709056037376, 1253.1846194685727]\n", + "((-6, 1, 0, 1, 0), (-3, 0, 0, 1, 0), (-5, 0, 1, 1, 0))\n", + "((-6, 1, 0, 1, 0), (-3, 0, 0, 1, 0), (0, 0, 0, 0, 0))\n", + "[-1431.1740935308756, 266.8709056037376, 498.0449991346129]\n", + "0\n", + "-1600.0\n", + "[-1431.1740935308756, 266.8709056037376, 498.0449991346129]\n", + "((-6, 1, 0, 1, 0), (-3, 0, 0, 1, 0), (0, 0, 0, 0, 0))\n", + "((-8, 0, 0, 1, 1), (-3, 0, 0, 1, 0), (0, 0, 0, 0, 0))\n", + "[18.18884796849352, 1866.8709056037376, 2098.044999134613]\n", + "2\n", + "1800.0\n", + "[18.18884796849352, 1866.8709056037376, 2098.044999134613]\n", + "((-8, 0, 0, 1, 1), (-3, 0, 0, 1, 0), (0, 0, 0, 0, 0))\n", + "((-8, 0, 0, 1, 1), (-3, 0, 0, 1, 0), (-6, 0, 0, 1, 1))\n", + "[-1781.8111520315065, 66.87090560373758, 618.1888479684949]\n", + "0\n", + "-1400.0\n", + "[-1781.8111520315065, 66.87090560373758, 618.1888479684949]\n", + "((-8, 0, 0, 1, 1), (-3, 0, 0, 1, 0), (-6, 0, 0, 1, 1))\n", + "((-5, 0, 0, 0, 1), (-3, 0, 0, 1, 0), (-6, 0, 0, 1, 1))\n", + "[-150.63705850063116, 1466.8709056037376, 2018.1888479684949]\n", + "0\n", + "200.0\n", + "[-150.63705850063116, 1466.8709056037376, 2018.1888479684949]\n", + "((-5, 0, 0, 0, 1), (-3, 0, 0, 1, 0), (-6, 0, 0, 1, 1))\n", + "((-4, 0, 0, 1, 0), (-3, 0, 0, 1, 0), (-6, 0, 0, 1, 1))\n", + "[66.87090560373713, 1266.8709056037376, 1818.1888479684949]\n", + "1\n", + "500.0\n", + "[66.87090560373713, 1266.8709056037376, 1818.1888479684949]\n", + "((-4, 0, 0, 1, 0), (-3, 0, 0, 1, 0), (-6, 0, 0, 1, 1))\n", + "((-4, 0, 0, 1, 0), (-7, 0, 0, 1, 1), (-6, 0, 0, 1, 1))\n", + "[-433.1290943962629, 118.18884796849431, 1318.1888479684949]\n", + "0\n", + "-600.0\n", + "[-433.1290943962629, 118.18884796849431, 1318.1888479684949]\n", + "((-4, 0, 0, 1, 0), (-7, 0, 0, 1, 1), (-6, 0, 0, 1, 1))\n", + "((-5, 0, 0, 1, 0), (-7, 0, 0, 1, 1), (-6, 0, 0, 1, 1))\n", + "[-1033.129094396263, 718.1888479684943, 1918.1888479684949]\n", + "1\n", + "500.0\n", + "[-1033.129094396263, 718.1888479684943, 1918.1888479684949]\n", + "((-5, 0, 0, 1, 0), (-7, 0, 0, 1, 1), (-6, 0, 0, 1, 1))\n", + "((-5, 0, 0, 1, 0), (-6, 0, 1, 1, 0), (-6, 0, 0, 1, 1))\n", + "[-1533.129094396263, 53.18461946857224, 1418.1888479684949]\n", + "2\n", + "700.0\n", + "[-1533.129094396263, 53.18461946857224, 1418.1888479684949]\n", + "((-5, 0, 0, 1, 0), (-6, 0, 1, 1, 0), (-6, 0, 0, 1, 1))\n", + "((-5, 0, 0, 1, 0), (-6, 0, 1, 1, 0), (-5, 0, 1, 1, 0))\n", + "[-2233.129094396263, -646.8153805314278, 553.1846194685729]\n", + "1\n", + "-1000.0\n", + "[-2233.129094396263, -646.8153805314278, 553.1846194685729]\n", + "((-5, 0, 0, 1, 0), (-6, 0, 1, 1, 0), (-5, 0, 1, 1, 0))\n", + "((-5, 0, 0, 1, 0), (-4, 0, 0, 1, 0), (-5, 0, 1, 1, 0))\n", + "[-1233.129094396263, -33.12909439626242, 1553.184619468573]\n", + "0\n", + "-100.0\n", + "[-1233.129094396263, -33.12909439626242, 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0))\n", + "[-2964.3233446357963, -2315.6412870005524, -1346.8153805314275]\n", + "2\n", + "-400.0\n", + "[-2964.3233446357963, -2315.6412870005524, -1346.8153805314275]\n", + "((-8, 0, 1, 0, 1), (-4, 0, 1, 0, 0), (-6, 0, 1, 1, 0))\n", + "((-8, 0, 1, 0, 1), (-4, 0, 1, 0, 0), (-4, 1, 1, 0, 0))\n", + "[-2564.3233446357963, -1915.6412870005524, -13.686286135164892]\n", + "1\n", + "0.0\n", + "[-2564.3233446357963, -1915.6412870005524, -13.686286135164892]\n", + "((-8, 0, 1, 0, 1), (-4, 0, 1, 0, 0), (-4, 1, 1, 0, 0))\n", + "((-8, 0, 1, 0, 1), (-5, 0, 0, 0, 1), (-4, 1, 1, 0, 0))\n", + "[-2564.3233446357963, -1750.6370585006305, -13.686286135164892]\n", + "1\n", + "-200.0\n", + "[-2564.3233446357963, -1750.6370585006305, -13.686286135164892]\n", + "((-8, 0, 1, 0, 1), (-5, 0, 0, 0, 1), (-4, 1, 1, 0, 0))\n", + "((-8, 0, 1, 0, 1), (-3, 1, 0, 0, 0), (-4, 1, 1, 0, 0))\n", + "[-2364.3233446357963, -1399.9999999999995, 186.3137138648351]\n", + "1\n", + "-100.0\n", + "[-2364.3233446357963, 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"path_to_chords(path, root)\n", + "write_chord_sequence(list(zip(durs, path_to_chords(path, root))))" + ] + }, + { + "cell_type": "markdown", + "id": "fe46a139-9c18-4e18-98d2-5b34a2ffb6ee", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, + "source": [ + "## graph based approach" + ] + }, + { + "cell_type": "markdown", + "id": "046e48b9-7f25-4833-b0d9-c53963d164f4", + "metadata": {}, + "source": [ + "### path functions" + ] + }, + { + "cell_type": "code", + "execution_count": 379, + "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([v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None])\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 = [v for val in edge[2]['movements'].values() if (v:=val['cent_difference']) is not None]\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 10 if ((max_cent_diff(e) < 200) and (min_cent_diff(e)) >= 50) else 1\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", @@ -2603,15 +12642,6 @@ " 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", - " s_chord = sorted(e[1], key=hs_array_to_fr)\n", - " yield 5 if hs_array_to_fr(sorted(e[1], key=hs_array_to_fr)[0]) >= 0.25 else 0\n", - "\n", - " def hd_weight(edges):\n", - " for e in edges:\n", - " yield 2 * (1/pow(hd_sum(e[1]), 1))\n", - "\n", " def is_sustained_voice(edges, voice):\n", " \n", " def is_sustained(edge):\n", @@ -2624,23 +12654,6 @@ " for e in edges:\n", " yield 10 if is_sustained(e) else 1\n", "\n", - " def favor_bass(edges, ins):\n", - "\n", - " def ins_check(edge, ins):\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", - " if ins == 2:\n", - " return 1\n", - " elif ins == 1 and ordered_source[0] != ordered_destination[0]:\n", - " return 1\n", - " else:\n", - " return 0\n", - "\n", - " for e in edges:\n", - " yield ins_check(e, ins)\n", - "\n", " def voice_crossing_weights(edges):\n", " \n", " def has_voice_crossing(edge):\n", @@ -2685,23 +12698,17 @@ " #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", - " target = target_melody_data[len(path)+1][-1]\n", - " ins = target_melody_data[len(path)+1][-2]\n", + " out_edges = list(graph.out_edges(next_node, data=True))\n", " factors = [\n", - " #movement_size_weights(out_edges), \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, c_devs, ins),\n", - " #symdiff_weights(out_edges),\n", - " hd_weight(out_edges),\n", - " #favor_bass(out_edges, ins),\n", - " #in_range(out_edges)\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", @@ -2712,880 +12719,690 @@ " 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)][-2])\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": "markdown", + "id": "5e4318db-bd43-434a-8c27-5a110e0bd6c4", + "metadata": {}, + "source": [ + "### model" + ] + }, { "cell_type": "code", - "execution_count": 382, - "id": "8912c650-a43d-4539-ae24-b5acf9bc543d", + "execution_count": 374, + "id": "a8592bc9-7e9e-4b6a-9eaa-4c4e3b69ce91", "metadata": {}, + "outputs": [], + "source": [ + "dims = (2, 3, 5, 7)\n", + "root = (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": 380, + "id": "be01e4ae-e629-42ff-9d95-f77d510c13bf", + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "(-1044.8603796660402, -231.17409353087487, 0)\n", - "((0, 0, 0, 0), (3, 0, -1, 0), (6, 0, -1, -1))\n", - "(-1244.8603796660402, -431.17409353087487, -44.86037966604022)\n", - "2\n", + "(-701.9550008653874, -386.31371386483454, 0)\n", + "((0, 0, 0, 0), (1, 1, -1, 0), (-1, 1, 0, 0))\n", + "(-901.9550008653874, -586.3137138648345, -88.26871473022209)\n", "200.0\n", - "((0, 0, 0, 0), (3, 0, -1, 0), (1, 0, 0, 0))\n", - "(-1044.8603796660402, -231.17409353087487, 0.0)\n", - "2\n", + "((0, 0, 0, 0), (1, 1, -1, 0), (3, 0, -1, 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b/compact_sets_play.scd @@ -30,7 +30,7 @@ hsArrayDimDiff = { if(fArray.sum == 0, {1}, {(primes[fArray.abs.indexOf(1) + 1] * fArray.sum)}) }; -file = File("/home/mwinter/Sketches/compact_sets/seq.txt".standardizePath,"r"); +file = File("/home/mwinter/Sketches/compact_sets/seq_bak.txt".standardizePath,"r"); seq = file.readAllString.interpret; //seq = seq.collect({arg item; item.sort}); @@ -191,9 +191,9 @@ chords.postln; musicData = chords.flop.collect({arg voice, v; var phrases, freqs, vDurs, delays, attacks, rels, sustains, amps, refs; phrases = voice.postln.separate({arg a, b; a != b}); - freqs = phrases.postln.collect({arg phrase; if(phrase[0] != ["Rest"], {45.midicps * pow(2, ([0, 1, 1][v]).clip(0, 2)) * hsArrayToFreq.value(phrase[0])}, {Rest(0)})}); + freqs = phrases.postln.collect({arg phrase; if(phrase[0] != ["Rest"], {60.midicps * pow(2, ([1, 1, 1][v]).clip(0, 2)) * hsArrayToFreq.value(phrase[0])}, {Rest(0)})}); vDurs = durs.clumps(phrases.collect({arg phrase; phrase.size})).collect({arg c; c.sum}); - amps = freqs.collect({rrand(0.6, 0.7) / [1, 2, 2, 2][v]}); + amps = freqs.collect({rrand(0.6, 0.7) / [2, 2, 3, 2][v]}); [freqs, vDurs, amps].flop; }); @@ -204,10 +204,10 @@ musicData = chords.flop.collect({arg voice, v; var freqs, durs, attacks, delays, sustains, rels, amps; # freqs, durs, amps = voice.flop; //# durs, attacks, delays, sustains, rels = [durs, attacks, delays, sustains, rels].collect({arg data; data / 16}); - durs = durs * 16; + durs = durs; durs.postln; - rels = (durs / 2).clip(0, 3); - attacks = (durs / 2).clip(0, 3); + rels = (durs / 2).clip(0, 0.01); + attacks = (durs / 2).clip(0, 0.05); sustains = durs - rels; Pbind( \instrument, \string_model,