{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import omusic\n", "import omusic.chord as chord\n", "import omusic.modes as modes\n", "from omusic.scale import scale\n", "from omusic import note_i2s\n", "from omusic import note_s2i\n", "from omusic import interval_s2i\n", "from omusic import interval_i2s\n", "from omusic import name_interval\n", "from omusic import invert\n", "from omusic import reach\n", "from omusic import same_class" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['C0', 'D0', 'E0', 'F0', 'G0', 'A0', 'B0']\n", "['C0', 'D#0', 'F0', 'F#0', 'G0', 'A#0']\n", "['C0', 'D0', 'D#0', 'E0', 'G0', 'A0']\n" ] } ], "source": [ "print(scale(\"C\", modes.MAJOR))\n", "print(scale(\"C\", modes.MINOR_BLUES))\n", "print(scale(\"C\", modes.MAJOR_BLUES))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Music Theory" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Music Space\n", "\n", "There are two pre-defined \"music spaces\":\n", "\n", "* `NOTES_MIDI` represents scientific names where `NOTES_MIDI[0]` is $\\mathrm{C}_{0}$ and `NOTES_MIDI[127]` is $\\mathrm{C}_{10}$. Also, `NOTES_MIDI[i]` is the $i^\\mathrm{th}$ MIDI sound. This is the default option.\n", "\n", "* `NOTES_INTEGER` represents pitch classes, where `NOTES_INTEGER[0]` is $\\mathrm{C}$ and `NOTES_INTEGER[11]` is $\\mathrm{B}$. Also, `NOTES_MIDI[i]` is the integer notation for that class.\n", "\n", "To use a music space, assign the corresponding variable to `omusic.NOTES`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "assert omusic.NOTES == omusic.NOTES_MIDI" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For readability, notes are often represented as strings. \n", "\n", "\n", "The functions To convert \n", "\n", "\n", "\n", "(a)\n", "as a string (\"C\") or (b) as an integer (\"0\"). \n", "\n", "The following cells define two functions\n", "`note_s2i` and `note_i2s` that convert between\n", "these representations." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "for i in range(len(omusic.NOTES)):\n", " assert note_i2s(i) == omusic.NOTES[i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check that `key_s2i` and `key_i2s` are\n", "each others' inverse:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "for i in range(len(omusic.NOTES)):\n", " assert note_i2s(i) == omusic.NOTES[i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the name of a note does not end with an octave (`0` to `10`), it is understood to be on the $0^{\\mathrm{th}}$ octave. If the octave $o$ is not in range $[0,\\cdots,10]$, then it is replaced with $o~(\\mathrm{mod}~10)$." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "if omusic.NOTES == omusic.NOTES_MIDI:\n", " assert note_s2i(\"C\") == note_s2i(\"C0\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from omusic import Pitch, Interval" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Intervals\n", "\n", "An **interval** is the musical distance between two notes. An interval between two notes is **harmonic** if these notes are played at the same time; otherwise, the interval is **melodic**. The **half-step** is the smallest apartness commonly used in Western omusic. A **whole-step** equals two half-steps.\n", "\n", "In integer notation, an interval be denoted by a simple integer. In text however, intervals are often communicated by name. The following sections explain how these names are formed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generic Intervals\n", "\n", "**Generic intervals** measure the difference between the staff positions of two notes. In practice, this measure ignores accidentals: $\\mathrm{C}\\text{--}\\mathrm{D}$ are one genetic step apart, but so are $\\mathrm{C}\\text{--}\\mathrm{D}\\#$. This is because $\\mathrm{D}$ and $\\mathrm{D}\\#$ are on the same staff.\n", "\n", "Generic intervals, like people, have names. \n", "For example, $\\mathrm{C}\\text{--}\\mathrm{D}$ are a second apart. The following table lists these names.\n", "\n", "|Step difference|Name of Interval|\n", "|-|-|\n", "| 0 | First / Prime |\n", "| 1 | Seconds |\n", "| 2 | Thrids |\n", "| 3 | Fourths |\n", "| 4 | Fifths |\n", "| 5 | Sixths |\n", "| 6 | Sevens |\n", "| 7 | Eights |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Specific Intervals\n", "\n", "**Specific intervals** are measured on both staff and half steps. For example, recall that $\\mathrm{C}\\text{--}\\mathrm{D}$ are a generic second apart. Because they are also 2 half steps apart, they are a _major second apart_. $\\mathrm{B}\\text{--}\\mathrm{C}$ on the other hand are a _minor second_ apart because they are a genetic second apart while being just one half step apart. Some examples follow:\n", "\n", "|Apartness|Name|\n", "|-|-|\n", "| 2 | Major Second |\n", "| 4 | Major Third |\n", "| 5 | Perfect Fourth |\n", "| 7 | Perfect Fifth |\n", "| 9 | Major Sixth |\n", "| 11 | Major Seventh |\n", "| 12 | Perfect Eighth (Perfect octave) |\n", "\n", "The terms \"major\" and \"perfect\" refer to the interval's quality. Only the 2nds, 3rds, 4ths, 6ths, and 7ths are **major interval**s. The rest (1sts, 4ths, 5ths, and 8ths) are **perfect interval**s instead.\n", "\n", "A **minor interval** has 1 fewer half step than a major interval. An **augmented interval** has one more than a major interval. An **augmented interval** has one more half step than a perfect interval. A **diminished interval** has one less half step. Minor intervals can be diminished by subtracting yet another half-step. The following figure illustrates these relations:\n", "\n", "![image](../../media/music_steps.svg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell seeks to capture this behaviour. In particular:\n", "\n", "* `INTERVALS` maps every name of an interval to an apartness (by half steps).\n", "\n", "* `apartness_to_name` maps every apartness to a set of names.\n", "\n", "* `name_apartness`, when given two notes, returns a probable name for hte interval between them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check if the aforementioned intervals are correctly named:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The interval from C# to D is minor 2\n", "The interval from C to D is major 2\n", "The interval from C to D# is augmented 2\n", "The interval from B to C is minor 2\n" ] } ], "source": [ "_pairs: list[tuple[str, str]] = [\n", " ('C#', 'D'),\n", " ('C', 'D'),\n", " ('C', 'D#'),\n", " ('B', 'C'),\n", "]\n", "\n", "for __from, to in _pairs:\n", " print(f\"The interval from {__from}\"\n", " f\" to {to} is\"\n", " f\" {name_interval(__from, to)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cheat Sheet\n", "\n", "Having suffered through the Library of Alexandria, you have earned yourself access to a cheat sheet. Enjoy :D" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Half-Step DifferenceName Half-Step DifferenceName
0augmented 7 5perfect 4
0diminished 2 6augmented 4
0perfect 8 6diminished 5
0prime 1 7diminished 6
1augmented 1 7perfect 5
1augmented 8 8augmented 5
1minor 2 8minor 6
2diminished 3 9diminished 7
2major 2 9major 6
3augmented 2 10augmented 6
3minor 3 10minor 7
4diminished 4 11diminished 1
4major 3 11diminished 8
5augmented 3 11major 7
" ], "text/plain": [ "'\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n
Half-Step DifferenceName Half-Step DifferenceName
0augmented 7 5perfect 4
0diminished 2 6augmented 4
0perfect 8 6diminished 5
0prime 1 7diminished 6
1augmented 1 7perfect 5
1augmented 8 8augmented 5
1minor 2 8minor 6
2diminished 3 9diminished 7
2major 2 9major 6
3augmented 2 10augmented 6
3minor 3 10minor 7
4diminished 4 11diminished 1
4major 3 11diminished 8
5augmented 3 11major 7
'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "persephone: list[tuple[int, str]]\\\n", " = sorted([(b, a) for a, b\n", " in omusic.INTERVALS.items()])\n", "\n", "import tabulate\n", "tabulate.tabulate((p := persephone,\n", " [(*x, *y) for (x, y)\n", " in zip(p[:int(len(p)/2)],\n", " p[int(len(p)/2):])])[1],\n", "\n", " headers=[\"Half-Step Difference\",\n", " \"Name\"]*2,\n", " tablefmt=\"html\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `reach` function \"reaches up\" from a given note by either the given apartness (in half steps) of an interval specified by name.\n", "\n", "To show the correctness of `reach`, assuming that `name_interval` is correct, try to \"reach\" from every note to every other note by the name of the interval between them." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "for name_x in omusic.NOTE_NAMES:\n", " for name_y in omusic.NOTE_NAMES:\n", " same_class(reach(name_x, name_interval(name_x, name_y)), name_y)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inverting Intervals\n", "\n", "To **invert** a group of notes is to move the lowest note an octave higher. This is easy to implement.\n", "\n", "Inverting an interval carries a related meaning. Suppose that inverting C-G gives G-C: _inverting the interval_ between C-G should yield the interval between G-C. The `invert_interval` function captures this behaviour." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perfect intervals always invert to perfect intervals. A factoid: inverting the perfect 4th and the perfect 5th give each other." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "assert invert(omusic.INTERVALS[\"perfect 5\"])\\\n", " == omusic.INTERVALS[\"perfect 4\"]\\\n", " and invert(omusic.INTERVALS[\"perfect 4\"])\\\n", " == omusic.INTERVALS[\"perfect 5\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reaching \"up\" from a note by a given interval, then again by the invert of that interval, should produce that note (albeit an octave higher). Together, `reach` and `invert_interval` allows us to test this:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "for note, interval in zip(omusic.NOTES,\n", " omusic.INTERVALS.values()):\n", " assert same_class(note,\n", " reach(reach(note, interval),\n", " invert(interval)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scales\n", "\n", "This section is developed with help from _Play Guitar in 14 Days_ by Troy Nelson.\n", "\n", "A **scale** is an ordered sequence of notes. In western music, a scale (particularly a _diatonic scale_) is constructed by counting notes from a starting note. This starting note is its **home note** (or _**tonic**_); the pattern of counting is either its key (if the pattern is major or minor) or its mood (if the pattern is, for example, ionian). These inconsistencies are due to historical reasons.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scale Degrees 音级\n", "\n", "The scale degree is the position of a particular note on a scale, up from the tonic. The $i^\\mathrm{th}$ degree can be denoted as $\\hat{i}$.\n", "\n", "For a heptatonic scale, these degrees have the following names:\n", "\n", "| Position | Name |\n", "|-|-|\n", "| 8 | Tonic (again) |\n", "| 7 | Leading Tone / Subtonic |\n", "| 6 | Submediant |\n", "| 5 | Dominant |\n", "| 4 | Subdominant |\n", "| 3 | Mediant |\n", "| 2 | Supertonic |\n", "| 1 | Tonic |\n", "\n", "Note some peculiarities: the submediant (6th) does not lead into the mediant (3rd); rather, it is the \"mediant\" of the dominant (5th) and the subtonic (7th).\n", "\n", "Also, the 7th node can have two names (\"may\", since some tutorials just call it the supertonic): If it is one half step below the tonic, then it is the leading tone; if it is one whole step below the tonic, then it is the subtonic.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Major and Minor Scales\n", "\n", "The **major scale** is a seven-note scale constructed form a specific pattern of half steps and full steps. Here, such patters are represented as a sequence of `2`s and `1`s. The minor scale uses a similar pattern.\n", "\n", "{TODO}\n", "\n", "For scales, the word **key** can communicate two things: (a) if a scale is major or minor (\"the scale is in major key\"), or (b) which note is the tonic of the scale (\"the scale is in the key of A\"). In the latter case, the scale itself is the key. Better not think too hard about it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Modes\n", "\n", "The adjectives \"major\" and \"minor\" can apply to many things, from intervals to keys (modes) to scales. For convenience, this library describes all sequences of intervals as modes. See `omusic.modes` for pre-defined modes." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "import omusic.modes as modes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Constructing Scales\n", "\n", "To construct a scale from a tonic and a mode, start counting from the tonic according to the mode. The\n", "`construct_scale` function captures this\n", "behaviour." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['C0', 'D0', 'D#0', 'F0', 'G0', 'A0', 'B0']" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scale(\"C\", modes.MINOR_MELODIC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relative Scales\n", "\n", "Relative scales contain the same notes, though not arranged in the same order. To build evidence that `construct_scale` is correct, see if it correctly constructs relatives.\n", "\n", "Here's every pair of relatives from the circle\n", "of fifthssss. Ssss. Hisssss." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interval from C to A is major 6, or 9 half steps.\n", "Interval from G to E is major 6, or 9 half steps.\n", "Interval from D to B is major 6, or 9 half steps.\n", "Interval from A to F# is major 6, or 9 half steps.\n", "Interval from E to C# is major 6, or 9 half steps.\n", "Interval from B to G# is major 6, or 9 half steps.\n", "Interval from F# to D# is major 6, or 9 half steps.\n", "Interval from C# to A# is major 6, or 9 half steps.\n", "Interval from G# to F is diminished 7, or 9 half steps.\n", "Interval from D# to C is diminished 7, or 9 half steps.\n", "Interval from A# to G is diminished 7, or 9 half steps.\n" ] } ], "source": [ "from omusic import same_class, name_interval\n", "\n", "CIRCLE_OF_LIFE: list[tuple[str, str]]\\\n", " = [(\"C\", \"A\"),\n", " (\"G\", \"E\"),\n", " (\"D\", \"B\"),\n", " (\"A\", \"F#\"),\n", " (\"E\", \"C#\"),\n", " (\"B\", \"G#\"),\n", " (\"F#\", \"D#\"),\n", " (\"C#\", \"A#\"),\n", " (\"G#\", \"F\"),\n", " (\"D#\", \"C\"),\n", " (\"A#\", \"G\")]\n", "\n", "for major_key, minor_key in CIRCLE_OF_LIFE:\n", " assert same_class(\n", " scale(major_key, modes.MAJOR),\n", " scale(minor_key, modes.MINOR),)\n", " \n", " interval_name: str = name_interval(major_key, minor_key)\n", "\n", " print(f\"Interval from {major_key} to {minor_key}\"\n", " f\" is {name_interval(major_key, minor_key)},\"\n", " f\" or {omusic.INTERVALS[interval_name]} half steps.\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['major 6', 'diminished 7']" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "interval_i2s(9)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'minor 7'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "name_interval(\"A\", \"G\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pentatonic Scales\n", "\n", "A pentatonic scale is a scale with five tones instead of seven. To construct a pentatonic scale from a major heptatonic scale, take items at indices $[1, 2, 3, 5, 6]$ (assuming 1-based indexing). Constructing pentatonic minor scales is similar, but uses indices $[1, 3, 4, 5, 7]$.\n", "\n", "The function `pentatonic_major` and \n", "`pentatonic_minor` capture these behaviours. These\n", "functions can also construct pentatonic scales from other modes.\n", "\n", "For convenience, be free to use\n", "`MAJOR_PENTATONIC` and `MINOR_PENTATONIC`." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from omusic.modes import _pentatonic_major\n", "from omusic.modes import _pentatonic_minor\n", "\n", "assert modes.MAJOR_PENTATONIC \\\n", " == _pentatonic_major(modes.MAJOR)\n", "\n", "assert modes.MINOR_PENTATONIC \\\n", " == _pentatonic_minor(modes.MINOR)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "assert same_class(\n", " scale(\"C\",\n", " _pentatonic_major(modes.MAJOR)),\n", " ['C', 'D', 'E', 'G', 'A'])\n", "\n", "assert same_class(scale(\"A\",\n", " _pentatonic_minor(modes.MINOR)),\n", " ['A', 'C', 'D', 'E', 'G'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Blues Scales\n", "\n", "Blues scales are 6-notes long. A blues minor scale is the pentatonic scale with an extra flat 5th. For example, whereas the A pentatonic scale is ['A', 'C', 'D', 'E', 'G'], the A blues scale is ['A', 'C', 'D', 'D#', 'E', 'G'] A blues major scale gets a flat third instead." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from omusic.modes import _blues_major\n", "from omusic.modes import _blues_minor" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "assert same_class(\n", " scale('C',\n", " _blues_major(modes.MAJOR)),\n", " ['C', 'D', 'D#', 'E', 'G', 'A'])\n", "assert same_class(\n", " scale('A',\n", " _blues_minor(modes.MINOR)),\n", " ['A', 'C', 'D', 'D#', 'E', 'G'])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['A0', 'B0', 'C1', 'D1', 'E1', 'F1', 'G1']" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scale(\"A\", modes.MINOR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Harmonic and Melodic Minors\n", "\n", "I see your harmonic minor and raise you\n", "a melodic minor.\n", "\n", "The harmonic minor has a \n", "\n", "Recall that the A minor is ['A', 'B', 'C', 'D', 'E', 'F', 'G']. Its harmonic minor is [..., 'E', 'F', 'G#']; its melodic minor is [... 'E', 'F#', 'G#']" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "from omusic.modes import _harmonic_minor\n", "from omusic.modes import _melodic_minor" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "assert same_class(\n", " scale(\"A\", _melodic_minor(modes.MINOR)),\n", " ['A', 'B', 'C', 'D', 'E', 'F#', 'G#'])\n", "\n", "assert same_class(\n", " scale(\"A\", _harmonic_minor(modes.MINOR)),\n", " ['A', 'B', 'C', 'D', 'E', 'F', 'G#'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Augmented and Diminished Scales\n", "\n", "There are two kinds of diminished scales: the\n", "typical whole-half diminished scale (of \n", "intervals [2, 1, 2, 1, ...]) and the half-whole diminished scale (of intervals [1, 2, 1, 2,...]).\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "from omusic.modes import DIMINISHED_WHOLE_HALF\n", "from omusic.modes import DIMINISHED_HALF_WHOLE\n", "from omusic.modes import AUGMENTED" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "assert same_class(\n", " scale(\"A\", modes.DIMINISHED_HALF_WHOLE),\n", " ['A', 'A#', 'C', 'C#', 'D#', 'E', 'F#', 'G'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chords\n", "\n", "A **chord** is a combination of three or more notes. There are many ways to construct chords, such as constructing triads on a scale.\n", "\n", "As you know, a triad is a triple of topological spaces $\\{P, A, B\\};~A,B\\prec P$ where $P=\\mathrm{int}(A)\\cup\\mathrm{int}(B)$.\n", "\n", "What you might not know is the fact that the **triad** of a scale is a subset of notes in that scale at the prime, a third, and a fifth. This \"prime\" is the **root** of the triad." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "from omusic.chord import count_triad" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A **major triad** takes a major third and a perfect fifth. Other triads are constructed with different choices of thirds and fifths'." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "from omusic.chord import count_triad_major" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "assert same_class(\n", " count_triad_major(\"C\"),\n", " ['C', 'E', 'G'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Inverting Triads\n", "\n", "Like inverting intervals, inverting a triad moves the lowest note up an octave.\n", "\n", "When the bass note, the lowest note in the triad, is its root, the triad is in root position. Inverting the triad once moves it into **first inversion**; inverting again moves it to the **second inversion**.\n", "\n", "Alternatively, the degree of inversion can be denoted by its bass note. For example, the F major triad is `['F', 'A', 'C']`; its first inversion `['A', 'C', 'F']` can be denoted by F/A.\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# invert_triad\n", "# invert_triad_to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Seventh Chords\n", "\n", "A seventh chord combines a triad with an interval of a seventh. There are five types of common seventh chords:\n", "\n", "* The dominant seventh uses a major triad and a minor seventh ...\n", "\n", "* ... and so on. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "assert omusic.same_class(\n", " chord.count_seventh_dominant(\"C\"),\n", " ['C', 'E', 'G', 'A#'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Diatonic Triads\n", "\n", "Every major and minor scale have seven **diatonic triads**, which are formed from notes on that scale.\n", "\n", "The first triad uses the 1st, 3rd, and 5th notes counting up from the root (the root itself being the 1st). The nth triad instead counts from the nth note instead." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "from omusic.chord import triad\n", "from omusic.chord import seventh" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['D1', 'F1', 'A1', 'C2']" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seventh(\"C\", modes.MAJOR, order=8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this method is identical to the aforementioned approach of counting intervals." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "assert chord.count_triad_major(\"C\")\\\n", " == triad(\"C\", modes.MAJOR)\n", "\n", "assert chord.count_seventh_augmented_major(\"C\")\\\n", " == seventh(\"C\", modes.AUGMENTED, \"major\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Roman Numeral Analysis: Seventh Chords\n", "\n", "I honestly don't know what this means." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Neapolitan Chords\n", "\n", "To construct a Neapolitan chord, construct a major triad (1, 3, 5) starting with the second scale degree of another scale.\n", "\n" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "from omusic.chord import neapolitan_chord" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['A#0', 'D1', 'F1']" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "neapolitan_chord(\"A\", modes.MAJOR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Nonharmonic Tones" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "\n", "# for winner in list_of_scales[5:]:\n", "# print(f\"{winner[0]} {winner[1]} has matches {note_i2s(list(winner[2]))}, but not {note_i2s(list(winner[3]))}\")\n", "\n", "# # Prog rock -- scales\n", "# # Two finger picking\n", "# # Just go faster\n", "# # Chord transition\n", "# # > Major scale harmony\n", "# # > Minor scale harmony\n", "# # Pentatonic scale - make it easier to \n", "# # Figure out songs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualisation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To visualise the fretboard, first find out which notes are on it.\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format='retina'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Drawing the Fretboard\n", "\n", "Begin with drawing an empty fretboard." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { 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", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 281, "width": 484 } }, "output_type": "display_data" } ], "source": [ "import omusic.guitar as guitar\n", "from omusic.guitar import draw_scale\n", "\n", "draw_scale(omusic.NOTES, 0, 6)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['C5', 'E5', 'G5']" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "triad(\"C5\", modes.MAJOR)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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5+XHsscfudNlJkyYlzVx0/PHHp5TnuHHjktpnn312RESMGTMmfvGLX5T60rDNmzfH6tWrY+HChfHmm2/G73//++jQoUNcc8018bOf/Sxq1vTILEB1lJ/pBAAAssXXX39dIla3bt0MZFJ+Y8eOjY4dO8bkyZMznQoAabKzm9RlUZU3qcuruLg45syZE9dff320a9cuhgwZUqKwAwDIDaVdZ++1114ZyKRiXGsDAFBWmRxrmjBhQtJ9tB49esTzzz8fhx9+eIwaNarUYojSbNy4MYYPHx5HHHFEPPnkk+lKF4AMqlevXonYqlWryrTuxIkTE58PO+ywXc76vf24VETqY02vvvpqUvvII4+MSy+9NPr167fTGdRL88knn8SgQYPimGOOiQULFqSUAwC5QbkbAMB/lfYwaabeDrBw4cISsc2bN8eqVati2bJlMX369Hj77bdj1KhR8cUXXyQtV1hYGKeeempMnDgxDjvssCrKGICqsrOb1GV523JV3qSuDF9//XVce+218cILL8Rzzz2Xc4WKALCnK+06u1atWrtdb+7cufHwww+XeTs33HDDLn/XbONaGwCAdMvkWNOUKVOS2mvXro1+/frF5s2bE7HmzZtHjx49on379lG/fv1YtWpVfPHFF/Hee+/FkiVLktZftWpV9O/fP9avXx8DBgyokr8BgKpR2v2Z7c8XO7Ns2bKYM2dOor27saPtx5oOOuig2HvvvVPIMuK9995Lag8fPrzErBGHHHJIdOnSJTFOtnLlyvj3v/8dU6dOLVGoOHny5DjmmGPi7bffjnbt2qWUCwDZTUEEAMB/lXbRv2bNmmjcuHEGsimpVq1a0bx582jevHkceuih0b9//xgyZEiMGDEibr755qSHNVasWBE/+MEPYurUqZGfb1IwgOokV25Sb693795RXFxcIr527dpYs2ZNzJ8/P6ZPnx7jxo2Ll19+ucTf8+qrr0a/fv1i7NixZXqIEgDIDqWdt7efgWpn5s+fH4MHDy7zdi6//PIyFUSUxrU2AACVKZNjTdOmTUtqb/9W7VNPPTX+3//7f9G7d++d/pZ9//33Y/DgwfHUU08lxX/605/GN7/5zTj66KMrPWcAMmPdunUlYg0bNtztetu/eCviP+M/O7Nq1ar44IMPEu1UX7y1dOnSWLp0aVJsWzFEgwYN4qqrrorLLrss9ttvv1LX37RpU4wcOTLuvPPO+PjjjxPxxYsXx2mnnRbvv/++MSeAasQdewCA/yrtrdNlnRYyU2rUqBEDBgyIadOmRefOnZO+mz59eowYMSJDmQGQLrlwk7qsGjRoEHvvvXf06tUrBg4cGM8991wsWrQoLrvsshIDsy+99FLcd999ackDAEiPOnXqlIht2LAhA5mkxrU2AADllcmxpnnz5pWINWrUKMaMGRMvvPBCfOc739llYW+3bt1i1KhRMXz48MjLy0vEt2zZEoMGDSr1hScA5KYdx5ry8/OjQYMGu11v+7Gm/Pz8OPbYY3e67Ouvvx5FRUWJdqpjTaWd1yIijj766Jg9e3b89re/3WkxRERE7dq14+KLL46pU6fGaaedlvTdzJkz46GHHkopHwCymxkiyKiGDRuWOmUk6bd169bdLnPEEUd40xlJSrvJ1aZNm6QbYrC9TOwz8+bNi7Zt25Zr3dLeJrly5cpo3759RdNKu9atW8f48ePjkEMOiRUrViTijzzySPzoRz+qlG0sWrQoDjjggErpa2ccZ0iVfYZUlWefqci5JR1y4SZ1RbRs2TL++te/ximnnBL9+/ePjRs3Jr779a9/HQMGDCj3G6B3VJZzm+MMqbLPkCr7DKmq6n2msq+zCwsLK5pSlXGtzZ7KPkMq7C+kyj5Dqow1ld2iRYuS2o0aNYrXXnstjjjiiJT6GTBgQCxcuDDuvPPOROy9996Ll156KU455ZQK5+j3L9nGPkOqqsNY01dffZXUbtiwYVL+q1evjt/+9rcl1hs7dmzSOnfddddOt/HWW28ltV955ZWYOnVqol23bt247bbbdrr+Z599ViJ24oknxnPPPVfqSzh2pkGDBjFy5Mg48sgj41//+lci/pvf/CYGDhxY4WfjjDWRDrm4z9SuXTvWrFmT6TTYgymIAAD4r9atW5eIffrpp3HYYYdlIJvUtWrVKm6//fa4+uqrE7E33ngjVq1aVSVTMQNQNXLhJnVl6Nu3b9x///3xk5/8JBFbuXJljB07Nn74wx+mddsAQOVo1apVidiSJUt2u953v/vdXb599pJLLqmyWRpcawMAkIpMjTWtWbOmxGxsDz74YMrFENvccMMNMXTo0Pjyyy8TsWeeeabCBREAZIcFCxYktffff/+k9rvvvhuDBw/eZR+rVq3a7TLbGzZsWFL7uOOO2+VY0/bnoIj/vFBr5MiRKRVDbFO/fv2444474rzzzkvEli5dGu+8804cddRRKfcHQPZREEFGqQjLnNmzZ0enTp12ucy0adOiY8eOVZQRueDLL7+Mli1bJsUWL14cLVq0yFBGZLtc22dKm05xypQpcfrpp2cgm/I5//zzY9CgQYmZgLZs2RIfffRR9OjRo8J9t23bNukt3emQa/sMmWefIVXVYZ/JhZvUleXSSy+NoUOHJhVjvPDCC5VWEFGWc1t12GeoWvYZUmWfIVW5tM/Uq1cvmjdvHsuXL0/E5s+fn8GMyse1Nnsa+wypsL+QKvsMqcq1fSZTY03r168vEevbt2+5+6tfv35873vfi+HDhydir776arn728bvX7KRfYZUVYd9Zsexph2fIXvnnXfSnsMxxxyzy+93PLf17t27QjOIn3nmmVG3bt34+uuvE7FXX321wgURxppIB/sMpK5i8/0AAFQjpRWBTZkyJQOZlF+rVq1KTLu8bNmyDGUDQDrkwk3qypKXlxf9+/dPis2aNatKtg0AVI6DDz44qT1z5swMZVJ+rrUBACirTI01bd68uUSsbt26FepzxwdEFy9eXKH+AMgO8+bNixUrViTFdhxrevfdd9Oex+7GmnY8t1X0vFa7du3o1q1bUsy5DaD6UBABAPBfbdq0ib333jsp9tZbb5WYYjjb7Tgd86pVqzKUCQCVLVduUlem7t27J7U9fAgAuaVr165J7aVLl5Yo8MwFrrUBACiLTI01NWnSpERs+zdgl0erVq2S2hs2bIi1a9dWqE8AMq+0GX+OPPLIpPazzz4bxcXFSf995zvfSXy/9957l/h++//uvffepP7ef//9EsucdNJJu8xzx3NbRc9rESXPbV9++WWF+wQgOyiIAADYzvYX8RH/ecDh6aefzlA25bPj1JEVfVMCANkjV25SVyYPHwJAbjvuuONKxMaNG5eBTCrGtTYAAGWVibGmBg0axF577ZUU++KLLyrUZ40aNZLa+fn5fgcDVAMvvvhiUrt+/fpx9NFH73KdoqKipBnKe/Xqtcvl33zzzcTnBg0axOGHH55yni1atEhqV/S8FlHy3Fa/fv0K9wlAdlAQAQCwnbPOOqtEbNiwYeXub/ny5fHMM88k/ps6dWoFsiubpUuXJrXbtWuX9m0CUDVy5SZ1ZfLwIQDktj59+kSdOnWSYo899liGsik/19oAAJRVpsaa2rdvn9Su6Eyyn3/+eVK7efPmJR4kBSC3fPTRR/Hcc88lxfr16xe1a9fe5XrTp0+PdevWJdo9e/bc5fJvvfVW4nOPHj3Kdf7Y8bw2derUKCoqSrmf7e14btvxpVwA5C4FEQAA2znjjDOiadOmSbHXX389XnjhhXL1N2vWrOjbt2/iv/vuu68y0typf/3rX7Fs2bJEu1atWnHooYemdZsAVI1cukldmTx8CAC5rXHjxnHqqacmxd5+++14++23M5RR6lxrAwCQikyNNfXu3Tup/fzzz5dre9tMnDgxqd2tW7cK9QdA5v3mN7+JrVu3JsUGDhy42/W2HzuK2PXLtxYsWBBLliwp07K7cuSRR0aDBg0S7TVr1sRrr71Wrr4iItatWxfvvfdeUsy5DaD6UBABALCdOnXqlHrBP3DgwFi7dm0GMkrNP/7xj6R2r169TPMIUE3k0k3qyrTjze1Mz1gBAKTuyiuvLBG79tprS/y2yVautQEASEWmxppOPPHEpPbIkSOT7vWlorCwMMaOHZsU27HQGYDc8tJLL5WYtfOUU06Jb3/727tdd/uxprp160aXLl12uuz2M5FHlH+sqVatWnHcccclxYYMGVKuviIinnzyydiwYUOiXaNGjTjppJPK3R8A2UVBBADADgYNGhQFBQVJsU8//TQuvPDC2LRpU4ay2r3FixfHvffemxS77LLLMpQNAJUp125SV5YtW7bEU089lRQ7+eSTM5QNAFBeffr0KfG7ZfLkyXHddddlKKOyc60NAEB5ZGKs6bTTTovWrVsn2ps2bYorrriiXH3deuutsXr16kS7Xr16ce6551Y4RwAyY/LkyXHeeedFcXFxIlavXr2dzjq0o+3Hj7p37x61atUq07L5+fllGsvamR//+MdJ7eeffz6eeeaZlPtZuXJl3H777UmxM888M5o0aVLu3ADILgoiAAB20KxZsxg8eHCJ+HPPPRd9+/aNdevWpW3b29+ASMXq1avj9NNPT8qte/fu0b9//8pKDYAMydWb1JXhz3/+c8yfPz/Rbtq0aZxzzjkZzAgAKK8//elPkZ+fPCQxZMiQuOmmm9K+bdfaAABUtUyMNdWtWzeuv/76pNizzz4bN998c0r9DBs2LP785z8nxa644opo1apVhXMEoGpt3bo1HnzwwTj++ONjzZo1Sd/df//9ceCBB+62j8WLF8enn36aaO/uZVrbv6irY8eO0bhx4xSz/j9nnXVWHHHEEUmxH/3oRzF58uQy9/H111/HueeeG4sWLUrE8vLy4rbbbit3XgBkHwURAACluPTSS+P73/9+ifiLL74YBx98cIwcOTIt273mmmvi4osvjlmzZpV5nddffz26desW06ZNS8Rq1aoVDzzwQOTl5aUhSwC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" ] }, "metadata": { "image/png": { "height": 281, "width": 1492 } }, "output_type": "display_data" } ], "source": [ "draw_scale(triad(\"A5\", modes.MINOR), 0, 19)\n", "# draw_scale(construct_scale(\"C7\", MAJOR), 0, 19)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "image/png": { "height": 281, "width": 1492 } }, "output_type": "display_data" } ], "source": [ "draw_scale(seventh(\"E4\", modes.MAJOR), 0, 19, strict=True)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "from omusic.modes import *\n", "\n", "from omusic import NOTE_NAMES\n", "\n", "TEST_SCALES = [\n", "[MAJOR, \"major\"],\n", "[MINOR, \"minor\"],\n", "[MINOR_NATURAL, \"minor_natural\"], \n", "[MINOR_HARMONIC, \"minor_harmonic\"],\n", "[MINOR_MELODIC, \"minor_melodic\"],\n", "[IONIAN, \"ionian\"],\n", "[DORIAN, \"dorian\"],\n", "[PHRYGIAN, \"phrygian\"],\n", "[LYDIAN, \"lydian\"],\n", "[MIXOLYDIAN, \"mixolydian\"],\n", "[AEOLIAN, \"aeolian\"],\n", "[LOCRIAN, \"locrian\"],]\n", "\n", "list_of_scales = []\n", "\n", "for key in NOTE_NAMES:\n", " for _scale in TEST_SCALES:\n", " this_scale_set = set([x[:-1] for x in scale(key, _scale[0])])\n", " that_scale_set = set([\"C\", \"F\", \"G\"])\n", " list_of_scales.append([\n", " key,\n", " _scale[1],\n", " this_scale_set.intersection(that_scale_set),\n", " this_scale_set.difference(that_scale_set),\n", " that_scale_set.difference(this_scale_set)\n", " ])\n", "\n", "list_of_scales.sort(key=lambda x: len(x[2]),\n", " reverse=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.10" } }, "nbformat": 4, "nbformat_minor": 2 }