{ "metadata": { "name": "", "signature": "sha256:7177323aa4ea3d489a3b9953dca67d73781befa3fbb1ffce6458000cb48f7e39" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Preamble" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was taken and slightly adapted from the example section of the abjad documentation. I am not the author of the content (only the porting to noteook format). I have removed all diacritics for now." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import copy\n", "from abjad import *\n", "%load_ext abjad.ext.ipython" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will add a few imports here and a support function" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def make_part_lilypond_file():\n", " r'''Makes P\u00e4rt LilyPond file.\n", " '''\n", "\n", " score_template = PartCantusScoreTemplate()\n", " score = score_template()\n", "\n", " add_bell_music_to_score(score)\n", " add_string_music_to_score(score)\n", "\n", " apply_bowing_marks(score)\n", " apply_dynamics(score)\n", " apply_expressive_marks(score)\n", " apply_page_breaks(score)\n", " apply_rehearsal_marks(score)\n", " apply_final_bar_lines(score)\n", "\n", " configure_score(score)\n", " lilypond_file = lilypondfiletools.make_basic_lilypond_file(score)\n", " configure_lilypond_file(lilypond_file)\n", "\n", " return lilypond_file" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Part - Cantus in Memory of Benjamin Britten" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The score template" ] }, { "cell_type": "code", "collapsed": false, "input": [ "class PartCantusScoreTemplate(abctools.AbjadObject):\n", " r'''P\u00e4rt Cantus score template.\n", " '''\n", "\n", " ### SPECIAL METHODS ###\n", "\n", " def __call__(self):\n", " '''Calls score template.\n", "\n", " Returns LilyPond file.\n", " '''\n", "\n", " # make bell voice and staff\n", " bell_voice = scoretools.Voice(name='Bell Voice')\n", " bell_staff = scoretools.Staff([bell_voice], name='Bell Staff')\n", " clef = indicatortools.Clef('treble')\n", " attach(clef, bell_staff)\n", " bells = instrumenttools.Instrument(\n", " instrument_name='Campana in La',\n", " short_instrument_name='Camp.',\n", " pitch_range='[C4, C6]',\n", " )\n", " attach(bells, bell_staff)\n", " tempo = indicatortools.Tempo((1, 4), (112, 120))\n", " attach(tempo, bell_staff)\n", " time_signature = indicatortools.TimeSignature((6, 4))\n", " attach(time_signature, bell_staff)\n", "\n", " # make first violin voice and staff\n", " first_violin_voice = scoretools.Voice(name='First Violin Voice')\n", " first_violin_staff = scoretools.Staff(\n", " [first_violin_voice],\n", " name='First Violin Staff',\n", " )\n", " clef = indicatortools.Clef('treble')\n", " attach(clef, first_violin_staff)\n", " violin = instrumenttools.Violin(\n", " instrument_name_markup='Violin I',\n", " short_instrument_name_markup='Vl. I'\n", " )\n", " attach(violin, first_violin_staff)\n", "\n", " # make second violin voice and staff\n", " second_violin_voice = scoretools.Voice(name='Second Violin Voice')\n", " second_violin_staff = scoretools.Staff(\n", " [second_violin_voice],\n", " name='Second Violin Staff',\n", " )\n", " clef = indicatortools.Clef('treble')\n", " attach(clef, second_violin_staff)\n", " violin = instrumenttools.Violin(\n", " instrument_name_markup='Violin II',\n", " short_instrument_name_markup='Vl. II'\n", " )\n", " attach(violin, second_violin_staff)\n", "\n", " # make viola voice and staff\n", " viola_voice = scoretools.Voice(name='Viola Voice')\n", " viola_staff = scoretools.Staff([viola_voice], name='Viola Staff')\n", " clef = indicatortools.Clef('alto')\n", " attach(clef, viola_staff)\n", " viola = instrumenttools.Viola()\n", " attach(viola, viola_staff)\n", "\n", " # make cello voice and staff\n", " cello_voice = scoretools.Voice(name='Cello Voice')\n", " cello_staff = scoretools.Staff([cello_voice], name='Cello Staff')\n", " clef = indicatortools.Clef('bass')\n", " attach(clef, cello_staff)\n", " cello = instrumenttools.Cello()\n", " attach(cello, cello_staff)\n", "\n", " # make bass voice and staff\n", " bass_voice = scoretools.Voice(name='Bass Voice')\n", " bass_staff = scoretools.Staff([bass_voice], name='Bass Staff')\n", " clef = indicatortools.Clef('bass')\n", " attach(clef, bass_staff)\n", " contrabass = instrumenttools.Contrabass(\n", " short_instrument_name_markup='Cb.'\n", " )\n", " attach(contrabass, bass_staff)\n", "\n", " # make strings staff group\n", " strings_staff_group = scoretools.StaffGroup([\n", " first_violin_staff,\n", " second_violin_staff,\n", " viola_staff,\n", " cello_staff,\n", " bass_staff,\n", " ],\n", " name='Strings Staff Group',\n", " )\n", "\n", " # make score\n", " score = scoretools.Score([\n", " bell_staff,\n", " strings_staff_group,\n", " ],\n", " name='P\u00e4rt Cantus Score'\n", " )\n", "\n", " # return P\u00e4rt Cantus score\n", " return score\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The bell music" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add_bell_music_to_score(score):\n", " r'''Adds bell music to score.\n", " '''\n", "\n", " bell_voice = score['Bell Voice']\n", "\n", " def make_bell_phrase():\n", " phrase = []\n", " for _ in range(3):\n", " phrase.append(scoretools.Measure((6, 4), r\"r2. a'2. \\laissezVibrer\"))\n", " phrase.append(scoretools.Measure((6, 4), 'R1.'))\n", " for _ in range(2):\n", " phrase.append(scoretools.Measure((6, 4), 'R1.'))\n", " return phrase\n", "\n", " for _ in range(11):\n", " bell_voice.extend(make_bell_phrase())\n", "\n", " for _ in range(19):\n", " bell_voice.append(scoretools.Measure((6, 4), 'R1.'))\n", "\n", " bell_voice.append(scoretools.Measure((6,4), r\"a'1. \\laissezVibrer\"))\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The string music" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating the music for the strings is a bit more involved, but conceptually falls into two steps. First, we\u2019ll procedurally generate basic pitches and rhythms for all string voices. Then, we\u2019ll make edits to the generated material by hand. The entire process is encapsulated in the following function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def add_string_music_to_score(score):\n", " r'''Adds string music to score.\n", " '''\n", "\n", " # generate some pitch and rhythm information\n", " pitch_contour_reservoir = create_pitch_contour_reservoir()\n", " shadowed_contour_reservoir = shadow_pitch_contour_reservoir(\n", " pitch_contour_reservoir)\n", " durated_reservoir = durate_pitch_contour_reservoir(\n", " shadowed_contour_reservoir)\n", "\n", " # add six dotted-whole notes and the durated contours to each string voice\n", " for instrument_name, descents in durated_reservoir.items():\n", " instrument_voice = score['%s Voice' % instrument_name]\n", " instrument_voice.extend(\"R1. R1. R1. R1. R1. R1.\")\n", " for descent in descents:\n", " instrument_voice.extend(descent)\n", "\n", " # apply instrument-specific edits\n", " edit_first_violin_voice(score, durated_reservoir)\n", " edit_second_violin_voice(score, durated_reservoir)\n", " edit_viola_voice(score, durated_reservoir)\n", " edit_cello_voice(score, durated_reservoir)\n", " edit_bass_voice(score, durated_reservoir)\n", "\n", " # chop all string parts into 6/4 measures\n", " strings_staff_group = score['Strings Staff Group']\n", " with systemtools.ForbidUpdate(score):\n", " for voice in iterate(strings_staff_group).by_class(scoretools.Voice):\n", " shards = mutate(voice[:]).split([(6, 4)], cyclic=True)\n", " for shard in shards:\n", " scoretools.Measure((6, 4), shard)\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pitch material is the same for all of the strings: a descending a-minor scale, generally decorated with diads. But, each instrument uses a different overall range, with the lower instrument playing slower and slower than the higher instruments, creating a sort of mensuration canon.\n", "\n", "For each instrument, the descending scale is fragmented into what we\u2019ll call \u201cdescents\u201d. The first descent uses only the first note of that instrument\u2019s scale, while the second descent adds the second note, and the third another. We\u2019ll generate as many descents per instruments as there are pitches in its overall scale:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def create_pitch_contour_reservoir():\n", " r'''Creates pitch contour reservoir.\n", " '''\n", "\n", " scale = tonalanalysistools.Scale('a', 'minor')\n", " pitch_ranges = {\n", " 'First Violin': pitchtools.PitchRange('[C4, A6]'),\n", " 'Second Violin': pitchtools.PitchRange('[A3, A5]'),\n", " 'Viola': pitchtools.PitchRange('[E3, A4]'),\n", " 'Cello': pitchtools.PitchRange('[A2, A3]'),\n", " 'Bass': pitchtools.PitchRange('[C3, A3]'),\n", " }\n", "\n", " reservoir = {}\n", " for instrument_name, pitch_range in pitch_ranges.items():\n", " pitch_set = scale.create_named_pitch_set_in_pitch_range(pitch_range)\n", " pitches = sorted(pitch_set, reverse=True)\n", " pitch_descents = []\n", " for i in range(len(pitches)):\n", " descent = tuple(pitches[:i + 1])\n", " pitch_descents.append(descent)\n", " reservoir[instrument_name] = tuple(pitch_descents)\n", "\n", " return reservoir\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here\u2019s what the first 10 descents for the first violin look like:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "reservoir = create_pitch_contour_reservoir()\n", "for i in range(10):\n", " descent = reservoir['First Violin'][i]\n", " print ' '.join(str(x) for x in descent)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "a'''\n", "a''' g'''\n", "a''' g''' f'''\n", "a''' g''' f''' e'''\n", "a''' g''' f''' e''' d'''\n", "a''' g''' f''' e''' d''' c'''\n", "a''' g''' f''' e''' d''' c''' b''\n", "a''' g''' f''' e''' d''' c''' b'' a''\n", "a''' g''' f''' e''' d''' c''' b'' a'' g''\n", "a''' g''' f''' e''' d''' c''' b'' a'' g'' f''\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we add diads to all of the descents, except for the viola\u2019s. We\u2019ll use a dictionary as a lookup table, to tell us what interval to add below a given pitch class:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def shadow_pitch_contour_reservoir(pitch_contour_reservoir):\n", " r'''Shadows pitch contour reservoir.\n", " '''\n", "\n", " shadow_pitch_lookup = {\n", " pitchtools.NamedPitchClass('a'): -5, # add a P4 below\n", " pitchtools.NamedPitchClass('g'): -3, # add a m3 below\n", " pitchtools.NamedPitchClass('f'): -1, # add a m2 below\n", " pitchtools.NamedPitchClass('e'): -4, # add a M3 below\n", " pitchtools.NamedPitchClass('d'): -2, # add a M2 below\n", " pitchtools.NamedPitchClass('c'): -3, # add a m3 below\n", " pitchtools.NamedPitchClass('b'): -2, # add a M2 below\n", " }\n", "\n", " shadowed_reservoir = {}\n", "\n", " for instrument_name, pitch_contours in pitch_contour_reservoir.items():\n", " # The viola does not receive any diads\n", " if instrument_name == 'Viola':\n", " shadowed_reservoir['Viola'] = pitch_contours\n", " continue\n", "\n", " shadowed_pitch_contours = []\n", "\n", " for pitch_contour in pitch_contours[:-1]:\n", " shadowed_pitch_contour = []\n", " for pitch in pitch_contour:\n", " pitch_class = pitch.named_pitch_class\n", " shadow_pitch = pitch + shadow_pitch_lookup[pitch_class]\n", " diad = (shadow_pitch, pitch)\n", " shadowed_pitch_contour.append(diad)\n", " shadowed_pitch_contours.append(tuple(shadowed_pitch_contour))\n", "\n", " # treat the final contour differently: the last note does not become a diad\n", " final_shadowed_pitch_contour = []\n", " for pitch in pitch_contours[-1][:-1]:\n", " pitch_class = pitch.named_pitch_class\n", " shadow_pitch = pitch + shadow_pitch_lookup[pitch_class]\n", " diad = (shadow_pitch, pitch)\n", " final_shadowed_pitch_contour.append(diad)\n", " final_shadowed_pitch_contour.append(pitch_contours[-1][-1])\n", " shadowed_pitch_contours.append(tuple(final_shadowed_pitch_contour))\n", "\n", " shadowed_reservoir[instrument_name] = tuple(shadowed_pitch_contours)\n", "\n", " return shadowed_reservoir" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we\u2019ll add rhythms to the pitch contours we\u2019ve been constructing. Each string instrument plays twice as slow as the string instrument above it in the score. Additionally, all the strings start with some rests, and use a \u201clong-short\u201d pattern for their rhythms:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def durate_pitch_contour_reservoir(pitch_contour_reservoir):\n", " r'''Durates pitch contour reservoir.\n", " '''\n", "\n", " instrument_names = [\n", " 'First Violin',\n", " 'Second Violin',\n", " 'Viola',\n", " 'Cello',\n", " 'Bass',\n", " ]\n", "\n", " durated_reservoir = {}\n", "\n", " for i, instrument_name in enumerate(instrument_names):\n", " long_duration = Duration(1, 2) * pow(2, i)\n", " short_duration = long_duration / 2\n", " rest_duration = long_duration * Multiplier(3, 2)\n", "\n", " div = rest_duration // Duration(3, 2)\n", " mod = rest_duration % Duration(3, 2)\n", "\n", " initial_rest = scoretools.MultimeasureRest((3, 2)) * div\n", " if mod:\n", " initial_rest += scoretools.make_rests(mod)\n", "\n", " durated_contours = [tuple(initial_rest)]\n", "\n", " pitch_contours = pitch_contour_reservoir[instrument_name]\n", " durations = [long_duration, short_duration]\n", " counter = 0\n", " for pitch_contour in pitch_contours:\n", " contour = []\n", " for pitch in pitch_contour:\n", " contour.extend(scoretools.make_leaves([pitch], [durations[counter]]))\n", " counter = (counter + 1) % 2\n", " durated_contours.append(tuple(contour))\n", "\n", " durated_reservoir[instrument_name] = tuple(durated_contours)\n", "\n", " return durated_reservoir" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let\u2019s see what a few of those look like. First, we\u2019ll build the entire reservoir from scratch, so you can see the process:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pitch_contour_reservoir = create_pitch_contour_reservoir()\n", "shadowed_contour_reservoir = shadow_pitch_contour_reservoir(pitch_contour_reservoir)\n", "durated_reservoir = durate_pitch_contour_reservoir(shadowed_contour_reservoir)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we\u2019ll grab the sub-reservoir for the first violins, taking the first ten descents (which includes the silences we\u2019ve been adding as well). We\u2019ll label each descent with some markup, to distinguish them, throw them into a Staff and give them a 6/4 time signature, just so they line up properly." ] }, { "cell_type": "code", "collapsed": false, "input": [ "descents = durated_reservoir['First Violin'][:10]\n", "for i, descent in enumerate(descents[1:], 1):\n", " markup = markuptools.Markup(r'\\rounded-box \\bold {}'.format(i), Up,)\n", " attach(markup, descent[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "staff = Staff(sequencetools.flatten_sequence(descents))\n", "time_signature = TimeSignature((6, 4))\n", "attach(time_signature, staff)\n", "show(staff)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Pqh09qQVfh4AGzykhIDtD/gvkXcNr3w289tVoPM5v1Gaqjcr6hvU/AGs7hd/IETi9h12I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\nUEwgR2hvc3RzY3JpcHQgOS4xNJ2zXFgAAAAASUVORK5CYII=\n" } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let\u2019s look at the second violins too:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "descents = durated_reservoir['Second Violin'][:10]\n", "for i, descent in enumerate(descents[1:], 1):\n", " markup = markuptools.Markup(r'\\rounded-box \\bold {}'.format(i), Up)\n", " attach(markup, descent[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "staff = Staff(sequencetools.flatten_sequence(descents))\n", "time_signature = TimeSignature((6, 4))\n", "attach(time_signature, staff)\n", "show(staff)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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IEJGPEHaztt5WTypEPYC9JAlM4fQkaLrlZnd4hlbe5oDR6IJjE+hy/2LfvXLpbfUQQNRD\n9ghQK5XIQihPhHXHOVp5W4OkGI0OOBBAPxDIpajKyPWBYO9t9RJAt05zexXtBqKrodRB9dOtJfMl\nuZPdjtAkRfmG0eiACwF8m4QpYXrPnCa9u/QSwJyNACWLOpuh90eddL3k4UmKkmM0OuBGAE+bZha5\nhA70ZfsIYF4KCAKUH+RMAP9WXniSojsB9hCNBnMQgPzL2ttq35rlYw2yd6o/DySSICDOe/51IOT8\nU/46kcPfLvv/SQ7v6r+/6R8wni6Ad/eg7iAaDaJ3eJs0/nlVVeK1f4+yTYBCNLhqvfUrIW8CBMra\n9/OJsehH9/YreXz/tyNVOJ5tNmuDx5M2eabke/deb+zx/fc3pvB2hSlT22e/A5klGm2fHaNxs8cs\n0WCt6wkbjbbP48D6PWm57ef18ciOx8OxN9KC/OxpnJSkNNoDkPhGLG1T2bjhKvzdDdokxdskzB6W\nXSHH9o8kt0TD2+fGni0aj1UpoaPx8HksBppAjdu+XvNflj4R6b2KLKviZvqyySCWtmnjhrPwdzP4\nl01gbS4MuKQ4gQCWaHj7fLd3sgmszZ0EHo1FCODtNT9Z+kT9BIjFcMfLT44iFjmqccNV+Gv0raNF\nUmwuDLikOJ4AXyzR8PW5Ccb5myUaDQFGRGNYX12EAN5e2yI9QICU0YHOs29VRCxy1BMBZP3LYBEb\nfeufljM/3Uwulh9wbrZ5Qh4s0fD1uQnGd1EN44kAXtGw6auLEcDL68PZIoP2E6DQiSp9dPeuimxt\n06cmkIvwdzd45ZYzPzUnQCXF8QRw7rc4+tzYs8r/T00gr2jY9NXFmkBeXpNfFhn0mQAtGfSXajzR\nXuXNMFHjD/Xl7CBHkfdhOeq90Ukdhb/DzaBVUrxGnpYb2CTFd3dt9+VAaomGr8/3YMjrgN5nEF39\nLDsHo+XzWAzIoI3bvtfQKoOSXz0yqLr/ye+vmlEjQWkCGCHqektLtYFoke7rj97tbUnRSej6djN4\ntalv80mK3j43Bx4t0fCVbu/BEG821botg3pFw6Y2A8jNM8mgg5HulUHj+zK2LbTa/f/tXRVZtk9Q\nVCw7zCcp+ku3/j57wr0J5BsNawLrdLnZRQb1hfXtck8fQN/XMUs//Na0+//yzMrbAAF8JcWzTVL0\nl269ffaFXx/AJxrkaklgnS43b4gAejx8XInn6UdfJChPGdSyfQEC+EqKf9hyNr2lW3+ffeFOAN9o\nRCdLAuskubn2TfZKrUsTIDIC0MsgeX8ZsX0ey/aFCODgfyMp/rRZ9pZu/X32hR8BfKJhFVgnyM33\nc8leqXVhAiT1uMXyOQ/CX0Zsn8eyfaEmkI+kaLXsLd36++wLcBnUXWCdIDfffZO9UutSBLjJoIrD\nRuw6Xd9/thQjXwnqSW2ybB8vKULLoI2kaLXsLd2O8NkT4DKou8A6Xm5ufPuPXqk1sp2916Ytw7RL\nBv2qV3VX/5mh7Ie2Fjo+f9CWODkps3IQoyVFq+UxGaz+PnvBSwaFFVhHyM0PGKmVHnqlVjY2KZnZ\n7tUuGTQ2NVd8H3gPshLHBppA4JbDlkGhLU+QQW9Nf9YvtS7bB4j0QGDdgGOEOk49YcWnJkCIMii4\n5Qky6L3p3687L0sAoudCYSQqKpZpjyDmBvrUBAhRBgW3PF4GbaTW/+zVnZcmgKxKQpUjF2EIPfbs\nbZuW7cETQNa/IAH8ZVCHgevLEoArT6S5602hOMTUKJ+aACHKoOCWx8ugDtOELSuD/qblPaKFJzMy\n/i8yVqJsq02W7UvIoNCWw5ZBoS2Pl0EP77aB64vLoFchTgetThGtS5GxEmVbbbJsX0YGhbU8Pht0\nGzIorOXx2aDfLLmxU9weJYNWuh+em1lBqa5BIAYGf+om0HZ83kITaA4snAohdAc41WIuT/R1TqaX\nAAmwiM9IgNcDpWWHnmQ4PRYyTyqzZsaFAswziQRYxGckwOuB0rJDNwHK6DYaWJUqJRBrOCABFvEZ\nCfB6oLTs0DMgpuTULL6RRRcKskoSEmARn5EArwdKyw69g+L/PNWZQL/9lBD43DLoZnzehAw6A5aV\nQW/4cb1evwIooE74ATRD5Ud8/zbyQKtYuUGfrfj2feSBVhl0Pp+nuG3F9f+G63Gg5QFChPsk5ntA\niAsOuKHzFs/iuOjfug8gAdrYFwFEs5gjEgBhsCsClM0KlDkSAGGwKwLIhgApW9u99YAEaGNXBKgM\nAaT6EoOMhwwTSIA29kWAC4m4fv8Ls1A8ArFhdBGgoCYVIndYyhmBCBud3dyc0VhcQIbDIxCbRrfO\nU6YxF9j+QXx+7FjoRCCQAIidAwmA2DWQAIhdAwmA2DWQAIhdAwmA2DWQAGAo09ZQI4ipBBALAAkA\nhZwlrcGmIgKYTQYxP5AAUGDPd3y240zakIAEAIIULz8ATKqNmB9IACDI5OUHvrZHCBcgAYBQE6AU\njF6k+YGv7RHCBUgAINQEiEhS8Xo2Sb62RwgXIAGAYAiQ6hXVBDFDqfnaHiFcgAQAgiGA0AvEiXpA\nNV/bI4QLkABAuBNA1wBmSQW+tkcIFyABgGAIUBASS0aInlKbr+0RwgVIACDUnWAZkUuCTaCAgAQA\nQk2AIjc9Yf1SmK/tEcIFSAAg3JtAqeoDmHVy+doeIVyABACCIYBUBChZPbEwX9sjhAuQAECom0Ax\niRnLzQ98bY8QLkACAOGWC1TK+3RKfG2PEC5AAgDhQzIcZoMGASQAFF7GA8jL2g4hXIAEgEIWpa0R\nYckZR4QFASQAGIqkNSY4xfs/DCgCyFwvgKCAs+EidgeSxvrFPROC73g9JMRuQfRy8VUuOmQMBOLz\noyZAoVo/F2y1IvaHmgAK8sM0HgUyAvHp0RDg8jqXWU752s4hEHPjToCCvDzvc1qP60D4IRNcQci1\n/UC44U6A9PWFQEQIQV3IFxkjqimZq+DdUuIQGwdJBeGiqhL6/HtiVsvGKsAPqYqZvvFLdltqHLF1\nNA/+l7dg6grSJMGELnfkquWjHxq6BcRP6suBcwdlrUgunMeoN6yFnlSIXN3/OS4V34YQw9vvBIg0\nASJHApSxCnRa2mzvHHOGp4cAqi7P1OUx83sgDFyugm4CXe5fpINRRRShSJJh6ugQViCA6hiYrXhh\nGjhdBdUJJpeiKiO3TnBKmKy04pasXbpNYxUC6NcCOentBOxP7XO8Cl6BqdtICfVqamLoIdFLAP2Z\nkZ7Ne1T75rgK+uWLjqKHaQw9LPoIYFRRQWjn1l2qfXNcBfpeCNUL9rCMoQdGmwDFYzzHlepPSlhr\njIf4/Y0pvF3V7+qhpXFWX07CCd/PJ8aiH247bxFvb067NUFywfHIjsfD0R4XDP1MKNoEKB8j+t6J\n+viTkOvjp78O5Dcp/z4RctBX4b3ejZCzdMCv39SD7s9f12v35ujqYmQSJp/i2m/AROs1SH+7OPU/\nw3G5GQ489LXDE+AU+nEoe5pApWqd6lr20cx8qnt91T6b3MdnrOOgTjFQD8t7FH0bKPqF2VBcasOh\nh35yU80l9KOd6/mdZVVMai20hn70mC86SeLNT+2zyn2fhQBPQXKQkL9Y4lIbDj30QRIgFi+PGHr/\nU5pr66fF2eS+zqsAK/ctRoBWkKw4nIfj8iBAy2pwoQ+SACmjz+qc90vOp7NY5L6OqwAt9y3WBPIJ\nEvk1HJdHEyjk0AdJgEJntTxlsni+5Hzyksphue/jVQCX+xYhgG+QbDLozfCE0IsNhD4YArRk0B8H\nFbRvL5KRl8LXxvV4Gpb72F3nmiL3Dat9bKqUNqDFXcnIIB2PyuOv/QJlY3h06N/+sX7oib/bL4Vw\nC/0Y9Mmguux/gelg75GbFjdF7rOcYSEZ1BORRSCcKvJpr9cPfYAyaKzVNrgcdcmdtLhJcp8tr3KZ\nJpAnuKVkkyt45fX6oQ+mCdRAlzxmcMPBpE3uq6/CFLnPmle5VwJ8XT/0wRFAj4ePKxF7G+v18uCk\nxU2R+6x5lT0EcD9JoARg64e+hwDrhr5xruO3yAhAGfU21u+lkxY3Re6z5lV2690egl+gBNhA6DsN\nrh36xrmPP6laUK9yVRKwdFtXGXSC3EcsZ+gkgJfgFygBNhD6rtCuHvrGudb3mwyqqsOz/v/wdv0+\nUb+6a1XH07Dc91GL8wWxqH1tGdRb8DMq3+Hf+ss3VotjloJOFflqGXTt0Ldk0O2E/oYOGfRP1QDS\n/x20GnAC0ULfI8sO01MSibMM6i343Sx/+dJfvi3LoDYX5g99I4NuKfQ3dMigsamSYnIDRFdYcssO\n0zOyiKsM6i/43Sz/fuov34abQMLmgm0HK6yhv4d2U6FvnOs4aVS7xYimqc9wvV4vuWUHgKvgKoN6\nC36NynfuL9++CeAqg24q9I1zHeVJ9LxYUVGxTPduAOYGWoIArjKot+DXqHzf+8u3bwK4yqALhd4v\nlbWLALIqCVWnvghTFUyO0CIEcJVBvQW/u+W3fh93TgBXGXSR0Pumsn6MMFdMlqY0gps/p8+PuAQB\nnGVQX8GP3SwHKoMKyw4LhL652RcIvXcq60cZNNLCEtHimBkZ/zuZLMWJK7PsMDlVU2txg2pfpwzq\nhG83y3OkJC4gg9oiu0Dou2RQJ4wIvW8qa6cM+i7l+aS+/SS/tIg1WYpbRAa17bDbbNC542IP/ZLZ\noL6prF3ZoLoyy82soFTXIe2BwSOxRBPItsNek+FsxV4g9Esmw/n3MzpOpzvAqe5K8ESfIZkaISRA\nr1NyeDsSwFaIrk6wVz+jMxlOj4XMEz0yvqoudPq4gNAJYObwj370lw8JMLiDdLDSgxGh/6+3g8Lb\n+3jvy+g2GlhwVRN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} ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And, last we\u2019ll take a peek at the violas. They have some longer notes, so we\u2019ll split their music cyclically every 3 half notes, just so nothing crosses the bar lines accidentally:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "descents = durated_reservoir['Viola'][:10]\n", "for i, descent in enumerate(descents[1:], 1):\n", " markup = markuptools.Markup(\n", " r'\\rounded-box \\bold {}'.format(i),\n", " Up,\n", " )\n", " attach(markup, descent[0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "staff = Staff(sequencetools.flatten_sequence(descents))\n", "shards = mutate(staff[:]).split([(3, 2)], cyclic=True)\n", "time_signature = indicatortools.TimeSignature((6, 4))\n", "attach(time_signature, staff)\n", "show(staff)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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HWgEJTILzSzhhB16x+GshmzWCGNAV+3XsGACMJecSiL0DDuJ/HhDaeI0gzwwbYiAQUo4s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\nAElFTkSuQmCC\n" } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see how each part is twice as slow as the previous, and starts a little bit later too." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The edits" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def edit_first_violin_voice(score, durated_reservoir):\n", " r'''Edits first violin voice.\n", " '''\n", "\n", " voice = score['First Violin Voice']\n", " descents = durated_reservoir['First Violin']\n", " descents = selectiontools.ContiguousSelection(descents)\n", "\n", " last_descent = select(descents[-1], contiguous=True)\n", " copied_descent = mutate(last_descent).copy()\n", " voice.extend(copied_descent)\n", "\n", " final_sustain_rhythm = [(6, 4)] * 43 + [(1, 2)]\n", " final_sustain_notes = scoretools.make_notes([\"c'\"], final_sustain_rhythm)\n", " voice.extend(final_sustain_notes)\n", " tie = spannertools.Tie()\n", " attach(tie, final_sustain_notes)\n", " voice.extend('r4 r2.')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "def edit_second_violin_voice(score, durated_reservoir):\n", " r'''Edits second violin voice.\n", " '''\n", "\n", " voice = score['Second Violin Voice']\n", " descents = durated_reservoir['Second Violin']\n", "\n", " last_descent = select(descents[-1], contiguous=True)\n", " copied_descent = mutate(last_descent).copy()\n", " copied_descent = list(copied_descent)\n", " copied_descent[-1].written_duration = durationtools.Duration(1, 1)\n", " copied_descent.append(scoretools.Note('a2'))\n", " for leaf in copied_descent:\n", " articulation = indicatortools.Articulation('accent')\n", " attach(articulation, leaf)\n", " articulation = indicatortools.Articulation('tenuto')\n", " attach(articulation, leaf)\n", " voice.extend(copied_descent)\n", "\n", " final_sustain = []\n", " for _ in range(32):\n", " final_sustain.append(scoretools.Note('a1.'))\n", " final_sustain.append(scoretools.Note('a2'))\n", " articulation = indicatortools.Articulation('accent')\n", " attach(articulation, final_sustain[0])\n", " articulation = indicatortools.Articulation('tenuto')\n", " attach(articulation, final_sustain[0])\n", "\n", " voice.extend(final_sustain)\n", " tie = spannertools.Tie()\n", " attach(tie, final_sustain)\n", " voice.extend('r4 r2.')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "def edit_viola_voice(score, durated_reservoir):\n", " r'''Edits viola voice.\n", " '''\n", "\n", " voice = score['Viola Voice']\n", " descents = durated_reservoir['Viola']\n", "\n", " for leaf in descents[-1]:\n", " articulation = indicatortools.Articulation('accent')\n", " attach(articulation, leaf)\n", " articulation = indicatortools.Articulation('tenuto')\n", " attach(articulation, leaf)\n", " last_descent = select(descents[-1], contiguous=True)\n", " copied_descent = mutate(last_descent).copy()\n", " for leaf in copied_descent:\n", " if leaf.written_duration == durationtools.Duration(4, 4):\n", " leaf.written_duration = durationtools.Duration(8, 4)\n", " else:\n", " leaf.written_duration = durationtools.Duration(4, 4)\n", " voice.extend(copied_descent)\n", "\n", " bridge = scoretools.Note('e1')\n", " articulation = indicatortools.Articulation('tenuto')\n", " attach(articulation, bridge)\n", " articulation = indicatortools.Articulation('accent')\n", " attach(articulation, bridge)\n", " voice.append(bridge)\n", "\n", " final_sustain_rhythm = [(6, 4)] * 21 + [(1, 2)]\n", " final_sustain_notes = scoretools.make_notes(['e'], final_sustain_rhythm)\n", " articulation = indicatortools.Articulation('accent')\n", " attach(articulation, final_sustain_notes[0])\n", " articulation = indicatortools.Articulation('tenuto')\n", " attach(articulation, final_sustain_notes[0])\n", " voice.extend(final_sustain_notes)\n", " tie = spannertools.Tie()\n", " attach(tie, final_sustain_notes)\n", " voice.extend('r4 r2.')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "def edit_cello_voice(score, durated_reservoir):\n", " r'''Edits cello voice.\n", " '''\n", "\n", " voice = score['Cello Voice']\n", " descents = durated_reservoir['Cello']\n", "\n", " logical_tie = inspect_(voice[-1]).get_logical_tie()\n", " for leaf in logical_tie.leaves:\n", " parent = leaf._get_parentage().parent\n", " index = parent.index(leaf)\n", " parent[index] = scoretools.Chord(['e,', 'a,'], leaf.written_duration)\n", "\n", " selection = voice[-len(descents[-1]):]\n", " unison_descent = mutate(selection).copy()\n", " voice.extend(unison_descent)\n", " for chord in unison_descent:\n", " index = inspect_(chord).get_parentage().parent.index(chord)\n", " parent[index] = scoretools.Note(\n", " chord.written_pitches[1], chord.written_duration)\n", " articulation = indicatortools.Articulation('accent')\n", " attach(articulation, parent[index])\n", " articulation = indicatortools.Articulation('tenuto')\n", " attach(articulation, parent[index])\n", "\n", " voice.extend('a,1. ~ a,2')\n", " voice.extend('b,1 ~ b,1. ~ b,1.')\n", " voice.extend('a,1. ~ a,1. ~ a,1. ~ a,1. ~ a,1. ~ a,2')\n", " voice.extend('r4 r2.')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "def edit_bass_voice(score, durated_reservoir):\n", " r'''Edits bass voice.\n", " '''\n", "\n", " voice = score['Bass Voice']\n", "\n", " voice[-3:] = '\\maxima \\longa \\maxima \\longa \\maxima r4 r2.'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 21 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The marks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we\u2019ll apply various kinds of marks, including dynamics, articulations, bowing indications, expressive instructures, page breaks and rehearsal marks.\n", "\n", "We\u2019ll start with the bowing marks. This involves creating a piece of custom markup to indicate rebowing. We accomplish this by aggregating together some markuptools.MarkupCommand and markuptools.MusicGlyph objects. The completed markuptools.Markup object is then copied and attached at the correct locations in the score.\n", "\n", "Why copy it? A Mark can only be attached to a single Component. If we attached the original piece of markup to each of our target components in turn, only the last would actually receive the markup, as it would have be detached from the preceding components.\n", "\n", "Let\u2019s take a look:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def apply_bowing_marks(score):\n", " r'''Applies bowing marks to score.\n", " '''\n", "\n", " # apply alternating upbow and downbow for first two sounding bars\n", " # of the first violin\n", " for measure in score['First Violin Voice'][6:8]:\n", " for i, chord in enumerate(iterate(measure).by_class(Chord)):\n", " if i % 2 == 0:\n", " articulation = indicatortools.Articulation('downbow')\n", " attach(articulation, chord)\n", " else:\n", " articulation = indicatortools.Articulation('upbow')\n", " attach(articulation, chord)\n", "\n", " # create and apply rebowing markup\n", " rebow_markup = markuptools.Markup(\n", " markuptools.MarkupCommand(\n", " 'concat', [\n", " markuptools.MusicGlyph('scripts.downbow'),\n", " markuptools.MarkupCommand('hspace', 1),\n", " markuptools.MusicGlyph('scripts.upbow'),\n", " ]))\n", " markup = copy.copy(rebow_markup)\n", " attach(markup, score['First Violin Voice'][64][0])\n", " markup = copy.copy(rebow_markup)\n", " attach(markup, score['Second Violin Voice'][75][0])\n", " markup = copy.copy(rebow_markup)\n", " attach(markup, score['Viola Voice'][86][0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 22 }, { "cell_type": "markdown", "metadata": {}, "source": [ "After dealing with custom markup, applying dynamics is easy. Just instantiate and attach:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def apply_dynamics(score):\n", " r'''Applies dynamics to score.\n", " '''\n", "\n", " voice = score['Bell Voice']\n", " dynamic = indicatortools.Dynamic('ppp')\n", " attach(dynamic, voice[0][1])\n", " dynamic = indicatortools.Dynamic('pp')\n", " attach(dynamic, voice[8][1])\n", " dynamic = indicatortools.Dynamic('p')\n", " attach(dynamic, voice[18][1])\n", " dynamic = indicatortools.Dynamic('mp')\n", " attach(dynamic, voice[26][1])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[34][1])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[42][1])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[52][1])\n", " dynamic = indicatortools.Dynamic('fff')\n", " attach(dynamic, voice[60][1])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[68][1])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[76][1])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[84][1])\n", " dynamic = indicatortools.Dynamic('pp')\n", " attach(dynamic, voice[-1][0])\n", "\n", " voice = score['First Violin Voice']\n", " dynamic = indicatortools.Dynamic('ppp')\n", " attach(dynamic, voice[6][1])\n", " dynamic = indicatortools.Dynamic('pp')\n", " attach(dynamic, voice[15][0])\n", " dynamic = indicatortools.Dynamic('p')\n", " attach(dynamic, voice[22][3])\n", " dynamic = indicatortools.Dynamic('mp')\n", " attach(dynamic, voice[31][0])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[38][3])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[47][0])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[55][2])\n", " dynamic = indicatortools.Dynamic('fff')\n", " attach(dynamic, voice[62][2]) \n", "\n", " voice = score['Second Violin Voice']\n", " dynamic = indicatortools.Dynamic('pp')\n", " attach(dynamic, voice[7][0])\n", " dynamic = indicatortools.Dynamic('p')\n", " attach(dynamic, voice[12][0])\n", " dynamic = indicatortools.Dynamic('p')\n", " attach(dynamic, voice[16][0])\n", " dynamic = indicatortools.Dynamic('mp')\n", " attach(dynamic, voice[25][1])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[34][1])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[44][1])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[54][0])\n", " dynamic = indicatortools.Dynamic('fff')\n", " attach(dynamic, voice[62][1])\n", "\n", " voice = score['Viola Voice']\n", " dynamic = indicatortools.Dynamic('p')\n", " attach(dynamic, voice[8][0])\n", " dynamic = indicatortools.Dynamic('mp')\n", " attach(dynamic, voice[19][1])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[30][0])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[36][0])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[42][0])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[52][0])\n", " dynamic = indicatortools.Dynamic('fff')\n", " attach(dynamic, voice[62][0])\n", "\n", " voice = score['Cello Voice']\n", " dynamic = indicatortools.Dynamic('p')\n", " attach(dynamic, voice[10][0])\n", " dynamic = indicatortools.Dynamic('mp')\n", " attach(dynamic, voice[21][0])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[31][0])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[43][0])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[52][1])\n", " dynamic = indicatortools.Dynamic('fff')\n", " attach(dynamic, voice[62][0])\n", "\n", " voice = score['Bass Voice']\n", " dynamic = indicatortools.Dynamic('mp')\n", " attach(dynamic, voice[14][0])\n", " dynamic = indicatortools.Dynamic('mf')\n", " attach(dynamic, voice[27][0])\n", " dynamic = indicatortools.Dynamic('f')\n", " attach(dynamic, voice[39][0])\n", " dynamic = indicatortools.Dynamic('ff')\n", " attach(dynamic, voice[51][0])\n", " dynamic = indicatortools.Dynamic('fff')\n", " attach(dynamic, voice[62][0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 23 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We apply expressive marks the same way we applied our dynamics:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def apply_expressive_marks(score):\n", " r'''Applies expressive marks to score.\n", " '''\n", "\n", " voice = score['First Violin Voice']\n", " markup = markuptools.Markup(\n", " r'\\left-column { div. \\line { con sord. } }', Up)\n", " attach(markup, voice[6][1])\n", " markup = markuptools.Markup('sim.', Up)\n", " attach(markup, voice[8][0])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[58][3])\n", " markup = markuptools.Markup('div.', Up)\n", " attach(markup, voice[59][0])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[63][3])\n", "\n", " voice = score['Second Violin Voice']\n", " markup = markuptools.Markup('div.', Up)\n", " attach(markup, voice[7][0])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[66][1])\n", " markup = markuptools.Markup('div.', Up)\n", " attach(markup, voice[67][0])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[74][0])\n", "\n", " voice = score['Viola Voice']\n", " markup = markuptools.Markup('sole', Up)\n", " attach(markup, voice[8][0])\n", "\n", " voice = score['Cello Voice']\n", " markup = markuptools.Markup('div.', Up)\n", " attach(markup, voice[10][0])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[74][0])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[84][1])\n", " markup = markuptools.Markup(r'\\italic { espr. }', Down)\n", " attach(markup, voice[86][0])\n", " markup = markuptools.Markup(r'\\italic { molto espr. }', Down)\n", " attach(markup, voice[88][1])\n", "\n", " voice = score['Bass Voice']\n", " markup = markuptools.Markup('div.', Up)\n", " attach(markup, voice[14][0])\n", " markup = markuptools.Markup(r'\\italic { espr. }', Down)\n", " attach(markup, voice[86][0])\n", " mutate(voice[88][:]).split([Duration(1, 1), Duration(1, 2)])\n", " markup = markuptools.Markup(r'\\italic { molto espr. }', Down)\n", " attach(markup, voice[88][1])\n", " markup = markuptools.Markup('uniti', Up)\n", " attach(markup, voice[99][1])\n", "\n", " strings_staff_group = score['Strings Staff Group']\n", " for voice in iterate(strings_staff_group).by_class(scoretools.Voice):\n", " markup = markuptools.Markup(r'\\italic { (non dim.) }', Down)\n", " attach(markup, voice[102][0])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the indicatortools.LilyPondCommandClass to create LilyPond system breaks, and attach them to measures in the percussion part. After this, our score will break in the exact same places as the original:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def apply_page_breaks(score):\n", " r'''Applies page breaks to score.\n", " '''\n", "\n", " bell_voice = score['Bell Voice']\n", "\n", " measure_indices = [\n", " 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 72,\n", " 79, 86, 93, 100,\n", " ]\n", "\n", " for measure_index in measure_indices:\n", " command = indicatortools.LilyPondCommand('break', 'after')\n", " attach(command, bell_voice[measure_index])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We\u2019ll make the rehearsal marks the exact same way we made our line breaks:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def apply_rehearsal_marks(score):\n", " r'''Applies rehearsal marks to score.\n", " '''\n", "\n", " bell_voice = score['Bell Voice']\n", "\n", " measure_indices = [\n", " 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84,\n", " 90, 96, 102,\n", " ]\n", "\n", " for measure_index in measure_indices:\n", " command = indicatortools.LilyPondCommand(r'mark \\default', 'before')\n", " attach(command, bell_voice[measure_index])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then we add our final bar lines. indicatortools.BarLine objects inherit from indicatortools.Mark, so you can probably guess by now how we add them to the score... instantiate and attach:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def apply_final_bar_lines(score):\n", " r'''Applies final bar lines to score.\n", " '''\n", "\n", " for voice in iterate(score).by_class(scoretools.Voice):\n", " bar_line = indicatortools.BarLine('|.')\n", " attach(bar_line, voice[-1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 27 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "The LilyPond file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we create some functions to apply formatting directives to our Score object, then wrap it into a LilyPondFile and apply some more formatting.\n", "\n", "In our configure_score() functions, we use layouttools.make_spacing_vector() to create the correct Scheme construct to tell LilyPond how to handle vertical space for its staves and staff groups. You should consult LilyPond\u2019s vertical spacing documentation for a complete explanation of what this Scheme code means:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def configure_lilypond_file(lilypond_file):\n", " r'''Configures LilyPond file.\n", " '''\n", "\n", " lilypond_file.global_staff_size = 8\n", "\n", " context_block = lilypondfiletools.ContextBlock()\n", " context_block.context_name = r'Staff \\RemoveEmptyStaves'\n", " override(context_block).vertical_axis_group.remove_first = True\n", " lilypond_file.layout_block.items.append(context_block)\n", "\n", " slash_separator = indicatortools.LilyPondCommand('slashSeparator')\n", " lilypond_file.paper_block.system_separator_markup = slash_separator\n", "\n", " bottom_margin = lilypondfiletools.LilyPondDimension(0.5, 'in')\n", " lilypond_file.paper_block.bottom_margin = bottom_margin\n", "\n", " top_margin = lilypondfiletools.LilyPondDimension(0.5, 'in')\n", " lilypond_file.paper_block.top_margin = top_margin\n", "\n", " left_margin = lilypondfiletools.LilyPondDimension(0.75, 'in')\n", " lilypond_file.paper_block.left_margin = left_margin\n", "\n", " right_margin = lilypondfiletools.LilyPondDimension(0.5, 'in')\n", " lilypond_file.paper_block.right_margin = right_margin\n", "\n", " paper_width = lilypondfiletools.LilyPondDimension(5.25, 'in')\n", " lilypond_file.paper_block.paper_width = paper_width\n", "\n", " paper_height = lilypondfiletools.LilyPondDimension(7.25, 'in')\n", " lilypond_file.paper_block.paper_height = paper_height\n", "\n", " lilypond_file.header_block.composer = markuptools.Markup('Arvo P\u00e4rt')\n", " title = 'Cantus in Memory of Benjamin Britten (1980)'\n", " lilypond_file.header_block.title = markuptools.Markup(title)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [ "spacing_vector = layouttools.make_spacing_vector(0, 0, 8, 0)\n", "f(spacing_vector)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "#'((basic-distance . 0) (minimum-distance . 0) (padding . 8) (stretchability . 0))\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "def configure_score(score):\n", " r'''Configures score.\n", " '''\n", "\n", " spacing_vector = layouttools.make_spacing_vector(0, 0, 8, 0)\n", " override(score).vertical_axis_group.staff_staff_spacing = spacing_vector\n", " override(score).staff_grouper.staff_staff_spacing = spacing_vector\n", " override(score).staff_symbol.thickness = 0.5\n", " set_(score).mark_formatter = schemetools.Scheme('format-mark-box-numbers')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In our configure_lilypond_file() function, we need to construct a ContextBlock definition in order to tell LilyPond to hide empty staves, and additionally to hide empty staves if they appear in the first system:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let\u2019s run our original toplevel function to build the complete score:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "lilypond_file = make_part_lilypond_file()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here we show it (note this will produce a multi-page/multi-image output):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "show(lilypond_file)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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PJsLKRyVElrELWPloqBnAupGzC7srH4OhO6xPr7pxyb4GT7tev7cNi4b+OfN0qEu2YcmJ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\n3ORGGpy1Qf7zvcs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}, { "metadata": {}, "output_type": "display_data", "png": 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M77fzXHon6HAbXi5ZgxR45HJ0XIlNEr99KQuD77pSjYk9comV98hVllmHXBVn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\nKINKGruW0Rb9zyKJw0wMVev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AI2uvunFbdU5vkNobrVV8nt+eSPB5tN5o22MfmDKROdSmHhpjOLNP/eieNhx+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\nOL7dncXhPffs8M5IlsjGBF9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uRaa5qY+Cm2UW6zeo5MRlDAJF51qegyT37IV4cllO1OPWnwRls+kA9cgssvaO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\n7f8Uwkez3kbtOQZSIUmmRq4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tv3xMyApplkTWUqhOXQ99vZ/FudwkA7+b4e9wnbGYvb8wFv4+/XlmvK6F6tT1YNsy+yr5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\nsjrYNCCLBZDVZgeALCCrg02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PVyAL6+MVyML6eAWysD5egSysj1cgC+vjFcjC+ngFsrA+XoEsrI9XIAvr4xXIwvp4BbKw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\nCjIGfSGk645cP8xSfbSYusi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bNTsoz5Q5oNORtRt+lUZbhZW24srpcEBjK/7k1VQsc4xdnGlFJG9hvDyxmNiLlZxPq8Ta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\n+wCxCtpg3G7Ko8YEwkLG7OT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FFgcBmobQ1YVx5xWaRoFnITyf6SZizehGVtJ7iewPJi0z0TRT14YYWd52aYqC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\nFtLYcxFZ0Lqdx2SFMf2Bnho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