diff --git a/homepage/.nojekyll b/homepage/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/homepage/Makefile b/homepage/Makefile new file mode 100644 index 0000000..ed88099 --- /dev/null +++ b/homepage/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/homepage/README.md b/homepage/README.md new file mode 100644 index 0000000..1b87752 --- /dev/null +++ b/homepage/README.md @@ -0,0 +1,32 @@ +# corpus_docs +Sphinx project template to create a corpus documentation homepage including rendered Jupytext notebooks + +## Usage + + +1. `git clone --recurse-submodules git@github.com:DCMLab/corpus_docs.git` will include https://github.com/DCMLab/data_reports + as the submodule `notebooks` +1. `pip install -r requirements.txt` will install the requirements for both the Sphinx project and the notebooks. +1. [set the environment variable] `CORPUS_PATH` to the path where the corpus repo or metarepo is located +1. [set the environment variable] `ANNOTATED_ONLY` to one of `false|f|0` if non-annotated files are to be included +1. `make html` builds the homepage which you can view at `_build/html/index.html`. + +## Next steps + +This sphinx project includes that need to be completed using the Jinja templating library: https://realpython.com/primer-on-jinja-templating/ +Currently these placeholders include: +* `repo_name`: repository name as in the URLs, e.g. `beethoven_piano_sonatas` +* `pretty_repo_name`: title, e.g. `Ludwig van Beethoven - The piano sonatas` +* `zenodo_badge_id`: ID assigned by Zenodo to ensure that the badge always shows the DOI of the latest version. +* `example_fname`: one of the pieces' file names (without extension) to be used in the docs (e.g. for file paths) +* `example_full_title`: full title of the example, to be included in the text, e.g. `the first movement of the first sonata Op. 2 no. 1` +* `example_subcorpus`: (only for meta-corpora) name of the submodule to which the example belongs + +The `index.rst` includes the file `repo_readme.md` which is a dummy file. During the actual building it should be +replaced by the actual README of the repo. Nevertheless, the dummy could be used to create this README from the +placeholder variables. + +`introduction.rst` requires that for metarepos (which include submodules), the `git clone` command be extended with `--recurse-submodules -j8` + +`analyses.rst` needs to be adapted such that non-annotated repos (setting yet to be introduced), the `annotations` and `cadences` notebooks are not included. +Could also be done with two different .rst files for each of the cases. \ No newline at end of file diff --git a/homepage/_static/custom.css b/homepage/_static/custom.css new file mode 100644 index 0000000..7564589 --- /dev/null +++ b/homepage/_static/custom.css @@ -0,0 +1,9 @@ +.bd-main .bd-content .bd-article-container { + max-width: 100%; /* default is 60em */ +} +.bd-page-width { + max-width: 100%; /* maximizes overall horizontal space */ +} +.bd-sidebar-primary { + max-width: var(--pst-sidebar-secondary); /* same maximal width for the left side bar as for the right one */ +} \ No newline at end of file diff --git a/homepage/analyses.rst b/homepage/analyses.rst new file mode 100644 index 0000000..ee63cd1 --- /dev/null +++ b/homepage/analyses.rst @@ -0,0 +1,14 @@ +******** +Analyses +******** + +The following notebooks contain a couple of standard analysis and have been rendered for +display as a static homepage: + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + notebooks/overview + notebooks/notes_stats + diff --git a/homepage/conf.py b/homepage/conf.py new file mode 100644 index 0000000..aa331d1 --- /dev/null +++ b/homepage/conf.py @@ -0,0 +1,45 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "Claude Debussy – Préludes" +copyright = '2023, Johannes Hentschel' +author = 'Johannes Hentschel' +release = 'v0.9.1' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "myst_nb", # rendering Jupyter notebooks + "jupyter_sphinx", # rendering interactive Plotly in notebooks +] + +templates_path = ['_templates'] +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**README.md'] + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'pydata_sphinx_theme' +html_static_path = ['_static'] + +html_css_files = [ + 'custom.css', +] + +# -- MyST Notebook configuration----------------------------------------------- +# https://myst-nb.readthedocs.io/en/latest/configuration.html + +nb_execution_mode = "cache" +nb_execution_timeout = 300 +nb_execution_allow_errors = False +nb_execution_show_tb = True +# toggle text: +nb_code_prompt_show = "Show {type}" +nb_code_prompt_hide = "Hide {type}" \ No newline at end of file diff --git a/homepage/index.rst b/homepage/index.rst new file mode 100644 index 0000000..8f00d45 --- /dev/null +++ b/homepage/index.rst @@ -0,0 +1,13 @@ +.. contents:: README + :local: + +.. include:: repo_readme.md + :parser: myst_parser.sphinx_ + +.. toctree:: + :maxdepth: 1 + :caption: Further information: + + introduction + analyses + diff --git a/homepage/introduction.rst b/homepage/introduction.rst new file mode 100644 index 0000000..e919f6d --- /dev/null +++ b/homepage/introduction.rst @@ -0,0 +1,184 @@ +********** +How to use +********** + +.. contents:: Contents + :local: + +Getting the data +================ + +With full version history +------------------------- + +The dataset is version-controlled via `git `__. In order to download the files with all revisions they have gone through, git needs to be installed on your machine. +Then you can clone this repository using the command + +.. code:: bash + + + git clone https://github.com/DCMLab/debussy_preludes.git + + +Without full version history +---------------------------- + + +If you are only interested in the current version of the corpus, you can download and unpack: + +* `Claude Debussy – Préludes `__ + + +Data Formats +============ + +Each piece in this corpus is represented by four files with identical names, each in its own folder. For example, the first prelude from the first book, *Danseuses de Delphes*, has the following files: + +- ``MS3/l117-01_preludes_danseuses.mscx``: Uncompressed MuseScore file including the music and annotation labels. +- ``notes/l117-01_preludes_danseuses.tsv``: A table of all note heads contained in the score and their relevant features (not each of them represents an onset, some are tied together) +- ``measures/l117-01_preludes_danseuses.tsv``: A table with relevant information about the measures in the score. +- ``harmonies/l117-01_preludes_danseuses.tsv``: A list of the included harmony labels (including cadences and phrases) with their positions in the score. + +Opening Scores +-------------- + +After navigating to your local copy, you can open the scores in the folder ``MS3`` with the free and open source score editor `MuseScore `__. Please note that the scores have been edited, annotated and tested with `MuseScore 3.6.2 `__. MuseScore 4 has since been released and preliminary tests suggest that it renders them correctly. + +Opening TSV files in a spreadsheet +---------------------------------- + +Tab-separated value (TSV) files are like Comma-separated value (CSV) files and can be opened with most modern text editors. However, for correctly displaying the columns, you might want to use a spreadsheet or an addon for your favourite text editor. When you use a spreadsheet such as Excel, it might annoy you by interpreting fractions as dates. This can be circumvented by using ``Data --> From Text/CSV`` or the free alternative `LibreOffice Calc `__. Other than that, TSV data can be loaded with every modern programming language. + +Loading TSV files in Python +--------------------------- + +Since the TSV files contain null values, lists, fractions, and numbers that are to be treated as strings, you may want to use this code to load any TSV files related to this repository (provided you’re doing it in Python). After a quick ``pip install -U ms3`` (requires Python 3.10) you’ll be able to load any TSV like this: + +.. code:: python + + import ms3 + + labels = ms3.load_tsv('harmonies/l117-01_preludes_danseuses.tsv') + notes = ms3.load_tsv('notes/l117-01_preludes_danseuses.tsv') + +How to read ``metadata.tsv`` +============================ + +This section explains the meaning of the columns contained in ``metadata.tsv``. + +File information +---------------- + ++------------------------+------------------------------------------------------------+ +| column | content | ++========================+============================================================+ +| **fname** | name without extension (for referencing related files) | ++------------------------+------------------------------------------------------------+ +| **rel_path** | relative file path of the score, including extension | ++------------------------+------------------------------------------------------------+ +| **subdirectory** | folder where the score is located | ++------------------------+------------------------------------------------------------+ +| **last_mn** | last measure number | ++------------------------+------------------------------------------------------------+ +| **last_mn_unfolded** | number of measures when playing all repeats | ++------------------------+------------------------------------------------------------+ +| **length_qb** | length of the piece, measured in quarter notes | ++------------------------+------------------------------------------------------------+ +| **length_qb_unfolded** | length of the piece when playing all repeats | ++------------------------+------------------------------------------------------------+ +| **volta_mcs** | measure counts of first and second endings | ++------------------------+------------------------------------------------------------+ +| **all_notes_qb** | summed up duration of all notes, measured in quarter notes | ++------------------------+------------------------------------------------------------+ +| **n_onsets** | number of note onsets | ++------------------------+------------------------------------------------------------+ +| **n_onset_positions** | number of unique note onsets (“slices”) | ++------------------------+------------------------------------------------------------+ + +Composition information +----------------------- + ++--------------------+---------------------------+ +| column | content | ++====================+===========================+ +| **composer** | composer name | ++--------------------+---------------------------+ +| **workTitle** | work title | ++--------------------+---------------------------+ +| **composed_start** | earliest composition date | ++--------------------+---------------------------+ +| **composed_end** | latest composition date | ++--------------------+---------------------------+ +| **workNumber** | Catalogue number(s) | ++--------------------+---------------------------+ +| **movementNumber** | 1, 2, or 3 | ++--------------------+---------------------------+ +| **movementTitle** | title of the movement | ++--------------------+---------------------------+ + +Score information +----------------- + ++-----------------+--------------------------------------------------------+ +| column | content | ++=================+========================================================+ +| **label_count** | number of chord labels | ++-----------------+--------------------------------------------------------+ +| **KeySig** | key signature(s) (negative = flats, positive = sharps) | ++-----------------+--------------------------------------------------------+ +| **TimeSig** | time signature(s) | ++-----------------+--------------------------------------------------------+ +| **musescore** | MuseScore version | ++-----------------+--------------------------------------------------------+ +| **source** | URL to the first typesetter’s file | ++-----------------+--------------------------------------------------------+ +| **typesetter** | first typesetter | ++-----------------+--------------------------------------------------------+ +| **annotators** | creator(s) of the chord labels | ++-----------------+--------------------------------------------------------+ +| **reviewers** | reviewer(s) of the chord labels | ++-----------------+--------------------------------------------------------+ + +Identifiers +----------- + +These columns provide a mapping between multiple identifiers for the sonatas (not for individual movements). + ++-----------------+------------------------------------------------------------------------------------------------------------+ +| column | content | ++=================+============================================================================================================+ +| **wikidata** | URL of the `WikiData `__ item | ++-----------------+------------------------------------------------------------------------------------------------------------+ +| **viaf** | URL of the Virtual International Authority File (`VIAF `__) entry | ++-----------------+------------------------------------------------------------------------------------------------------------+ +| **musicbrainz** | `MusicBrainz `__ identifier | ++-----------------+------------------------------------------------------------------------------------------------------------+ +| **imslp** | URL to the wiki page within the International Music Score Library Project (`IMSLP `__) | ++-----------------+------------------------------------------------------------------------------------------------------------+ + +Generating all TSV files from the scores +======================================== + +When you have made changes to the scores and want to update the TSV files accordingly, you can use the following command (provided you have pip-installed `ms3 `__): + +.. code:: python + + ms3 extract -M -N -X -F -D # for measures, notes, expanded harmony labels, form labels, and metadata + +If, in addition, you want to generate the reviewed scores with out-of-label notes colored in red, you can do + +.. code:: python + + ms3 review -M -N -X -F -D # for extracting measures, notes, expanded harmony labels, form labels, and metadata + +By adding the flag ``-c`` to the review command, it will additionally compare the (potentially modified) annotations in the score with the ones currently present in the harmonies TSV files and reflect the comparison in the reviewed scores. + +Questions, Suggestions, Corrections, Bug Reports +================================================ + +For questions, remarks etc., please `create an issue `__ and feel free to fork and submit pull requests. + +License +======= + +Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (`CC BY-NC-SA 4.0 `__). \ No newline at end of file diff --git a/homepage/make.bat b/homepage/make.bat new file mode 100644 index 0000000..207019d --- /dev/null +++ b/homepage/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/homepage/notebooks/LICENSE b/homepage/notebooks/LICENSE new file mode 100644 index 0000000..0ad25db --- /dev/null +++ b/homepage/notebooks/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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We use this format because + +* Jupytext notebooks are great for version control +* MyST-flavoured can be integrated in a [Sphinx](https://www.sphinx-doc.org/) homepage and treated + like any other document + +## Running the notebooks + +* clone the corpus: `git clone --recurse-submodules -j8 git@github.com:DCMLab/dcml_corpora.git` +* create new environment, make it visible to your Jupyter + * for conda do `conda create --name {name} python=3.10` + * activate it and install `pip install ipykernel` + * `ipython kernel install --user --name={name}` +* within the new environment, install requirements, e.g. `pip install -r requirements.txt` +* open up jupyter notebook or jupyter lab and open the `.md` + ([documentation](https://jupytext.readthedocs.io/en/latest/paired-notebooks.html#how-to-open-scripts-with-either-the-text-or-notebook-view-in-jupyter)) +* change the value `~/dcml_corpora` in the second cell to your local clone. + +If the plots are not displayed and you are in JupyterLab, use [this guide](https://plotly.com/python/getting-started/#jupyterlab-support). \ No newline at end of file diff --git a/homepage/notebooks/annotations.md b/homepage/notebooks/annotations.md new file mode 100644 index 0000000..7d9246e --- /dev/null +++ b/homepage/notebooks/annotations.md @@ -0,0 +1,575 @@ +--- +jupytext: + formats: md:myst,ipynb + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.15.0 +kernelspec: + display_name: corpus_docs + language: python + name: corpus_docs +--- + +# Annotations + +```{code-cell} ipython3 +--- +mystnb: + code_prompt_hide: Hide imports + code_prompt_show: Show imports +tags: [hide-cell] +--- +import os +from collections import defaultdict, Counter +from fractions import Fraction + +from git import Repo +import dimcat as dc +import ms3 +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go + +from utils import STD_LAYOUT, CADENCE_COLORS, CORPUS_COLOR_SCALE, TYPE_COLORS, chronological_corpus_order, color_background, corpus_mean_composition_years, get_corpus_display_name, get_repo_name, resolve_dir, value_count_df, get_repo_name, print_heading, resolve_dir +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +CORPUS_PATH = os.path.abspath(os.path.join('..', '..')) +print_heading("Notebook settings") +print(f"CORPUS_PATH: {CORPUS_PATH!r}") +CORPUS_PATH = resolve_dir(CORPUS_PATH) +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +repo = Repo(CORPUS_PATH) +print_heading("Data and software versions") +print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}") +print(f"dimcat version {dc.__version__}") +print(f"ms3 version {ms3.__version__}") +``` + +```{code-cell} ipython3 +:tags: [remove-output] + +dataset = dc.Dataset() +dataset.load(directory=CORPUS_PATH, parse_tsv=False) +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +annotated_view = dataset.data.get_view('annotated') +annotated_view.include('facets', 'measures', 'notes$', 'expanded') +annotated_view.fnames_with_incomplete_facets = False +dataset.data.set_view(annotated_view) +dataset.data.parse_tsv(choose='auto') +dataset.get_indices() +dataset.data +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +print(f"N = {dataset.data.count_pieces()} annotated pieces, {dataset.data.count_parsed_tsvs()} parsed dataframes.") +``` + +```{code-cell} ipython3 +all_metadata = dataset.data.metadata() +assert len(all_metadata) > 0, "No pieces selected for analysis." +print(f"Metadata covers {len(all_metadata)} of the {dataset.data.count_pieces()} scores.") +mean_composition_years = corpus_mean_composition_years(all_metadata) +chronological_order = mean_composition_years.index.to_list() +corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE)) +corpus_names = {corp: get_corpus_display_name(corp) for corp in chronological_order} +chronological_corpus_names = list(corpus_names.values()) +corpus_name_colors = {corpus_names[corp]: color for corp, color in corpus_colors.items()} +``` + +## DCML harmony labels + +```{code-cell} ipython3 +:tags: [hide-input] + +try: + all_annotations = dataset.get_facet('expanded') +except Exception: + all_annotations = pd.DataFrame() +n_annotations = len(all_annotations.index) +includes_annotations = n_annotations > 0 +if includes_annotations: + display(all_annotations.head()) + print(f"Concatenated annotation tables contain {all_annotations.shape[0]} rows.") + no_chord = all_annotations.root.isna() + if no_chord.sum() > 0: + print(f"{no_chord.sum()} of them are not chords. Their values are: {all_annotations.label[no_chord].value_counts(dropna=False).to_dict()}") + all_chords = all_annotations[~no_chord].copy() + print(f"Dataset contains {all_chords.shape[0]} tokens and {len(all_chords.chord.unique())} types over {len(all_chords.groupby(level=[0,1]))} documents.") + all_annotations['corpus_name'] = all_annotations.index.get_level_values(0).map(corpus_names) + all_chords['corpus_name'] = all_chords.index.get_level_values(0).map(corpus_names) +else: + print(f"Dataset contains no annotations.") +``` + +## Phrases +### Presence of phrase annotation symbols per dataset: + +```{code-cell} ipython3 +all_annotations.groupby(["corpus"]).phraseend.value_counts() +``` + +### Presence of legacy phrase endings + +```{code-cell} ipython3 +legacy = all_annotations[all_annotations.phraseend == r'\\'] +legacy.groupby(level=0).size() +``` + +### A table with the extents of all annotated phrases +**Relevant columns:** +* `quarterbeats`: start position for each phrase +* `duration_qb`: duration of each phrase, measured in quarter notes +* `phrase_slice`: time interval of each annotated phrases (for segmenting chord progressions and notes) + +```{code-cell} ipython3 +phrase_segmented = dc.PhraseSlicer().process_data(dataset) +phrases = phrase_segmented.get_slice_info() +print(f"Overall number of phrases is {len(phrases.index)}") +phrases.head(10).style.apply(color_background, subset=["quarterbeats", "duration_qb"]) +``` + +### A table with the chord sequences of all annotated phrases + +```{code-cell} ipython3 +phrase_segments = phrase_segmented.get_facet('expanded') +phrase_segments +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +phrase2timesigs = phrase_segments.groupby(level=[0,1,2]).timesig.unique() +n_timesignatures_per_phrase = phrase2timesigs.map(len) +uniform_timesigs = phrase2timesigs[n_timesignatures_per_phrase == 1].map(lambda l: l[0]) +more_than_one = n_timesignatures_per_phrase > 1 +print(f"Filtered out the {more_than_one.sum()} phrases incorporating more than one time signature.") +n_timesigs = n_timesignatures_per_phrase.value_counts() +display(n_timesigs.reset_index().rename(columns=dict(index='#time signatures', timesig='#phrases'))) +uniform_timesig_phrases = phrases.loc[uniform_timesigs.index] +timesig_in_quarterbeats = uniform_timesigs.map(Fraction) * 4 +exact_measure_lengths = uniform_timesig_phrases.duration_qb / timesig_in_quarterbeats +uniform_timesigs = pd.concat([exact_measure_lengths.rename('duration_measures'), uniform_timesig_phrases], axis=1) +fig = px.histogram(uniform_timesigs, x='duration_measures', log_y=True, + labels=dict(duration_measures='phrase length bin in number of measures'), + color_discrete_sequence=CORPUS_COLOR_SCALE, + ) +fig.update_traces(xbins=dict( # bins used for histogram + #start=0.0, + #end=100.0, + size=1 + )) +fig.update_layout(**STD_LAYOUT) +fig.update_xaxes(dtick=4, gridcolor='lightgrey') +fig.update_yaxes(gridcolor='lightgrey') +fig.show() +``` + +### Local keys per phrase + +```{code-cell} ipython3 +local_keys_per_phrase = phrase_segments.groupby(level=[0,1,2]).localkey.unique().map(tuple) +n_local_keys_per_phrase = local_keys_per_phrase.map(len) +phrases_with_keys = pd.concat([n_local_keys_per_phrase.rename('n_local_keys'), + local_keys_per_phrase.rename('local_keys'), + phrases], axis=1) +phrases_with_keys.head(10).style.apply(color_background, subset=['n_local_keys', 'local_keys']) +``` + +#### Number of unique local keys per phrase + +```{code-cell} ipython3 +count_n_keys = phrases_with_keys.n_local_keys.value_counts().rename("#phrases").to_frame() +count_n_keys.index.rename("unique keys", inplace=True) +count_n_keys +``` + +#### The most frequent keys for non-modulating phrases + +```{code-cell} ipython3 +unique_key_selector = phrases_with_keys.n_local_keys == 1 +phrases_with_unique_key = phrases_with_keys[unique_key_selector].copy() +phrases_with_unique_key.local_keys = phrases_with_unique_key.local_keys.map(lambda t: t[0]) +value_count_df(phrases_with_unique_key.local_keys, counts="#phrases") +``` + +#### Most frequent modulations within one phrase + +```{code-cell} ipython3 +two_keys_selector = phrases_with_keys.n_local_keys > 1 +phrases_with_unique_key = phrases_with_keys[two_keys_selector].copy() +value_count_df(phrases_with_unique_key.local_keys, "modulations") +``` + +## Key areas + +```{code-cell} ipython3 +from ms3 import roman_numeral2fifths, transform, resolve_all_relative_numerals, replace_boolean_mode_by_strings +keys_segmented = dc.LocalKeySlicer().process_data(dataset) +keys = keys_segmented.get_slice_info() +print(f"Overall number of key segments is {len(keys.index)}") +keys["localkey_fifths"] = transform(keys, roman_numeral2fifths, ['localkey', 'globalkey_is_minor']) +keys.head(5).style.apply(color_background, subset="localkey") +``` + +### Durational distribution of local keys + +All durations given in quarter notes + +```{code-cell} ipython3 +key_durations = keys.groupby(['globalkey_is_minor', 'localkey']).duration_qb.sum().sort_values(ascending=False) +print(f"{len(key_durations)} keys overall including hierarchical such as 'III/v'.") +``` + +```{code-cell} ipython3 +keys_resolved = resolve_all_relative_numerals(keys) +key_resolved_durations = keys_resolved.groupby(['globalkey_is_minor', 'localkey']).duration_qb.sum().sort_values(ascending=False) +print(f"{len(key_resolved_durations)} keys overall after resolving hierarchical ones.") +key_resolved_durations +``` + +#### Distribution of local keys for piece in major and in minor + +`globalkey_mode=minor` => Piece is in Minor + +```{code-cell} ipython3 +pie_data = replace_boolean_mode_by_strings(key_resolved_durations.reset_index()) +px.pie(pie_data, names='localkey', values='duration_qb', facet_col='globalkey_mode') +``` + +#### Distribution of intervals between localkey tonic and global tonic + +```{code-cell} ipython3 +localkey_fifths_durations = keys.groupby(['localkey_fifths', 'localkey_is_minor']).duration_qb.sum() +bar_data = replace_boolean_mode_by_strings(localkey_fifths_durations.reset_index()) +bar_data.localkey_fifths = bar_data.localkey_fifths.map(ms3.fifths2iv) +fig = px.bar(bar_data, x='localkey_fifths', y='duration_qb', color='localkey_mode', log_y=True, barmode='group', + labels=dict(localkey_fifths='Roots of local keys as intervallic distance from the global tonic', + duration_qb='total duration in quarter notes', + localkey_mode='mode' + ), + color_discrete_sequence=CORPUS_COLOR_SCALE, + ) +fig.update_layout(**STD_LAYOUT) +fig.update_yaxes(gridcolor='lightgrey') +fig.show() +``` + +### Ratio between major and minor key segments by aggregated durations +#### Overall + +```{code-cell} ipython3 +keys.duration_qb = pd.to_numeric(keys.duration_qb) +maj_min_ratio = keys.groupby("localkey_is_minor").duration_qb.sum().to_frame() +maj_min_ratio['fraction'] = (100.0 * maj_min_ratio.duration_qb / maj_min_ratio.duration_qb.sum()).round(1) +maj_min_ratio +``` + +#### By dataset + +```{code-cell} ipython3 +segment_duration_per_dataset = keys.groupby(["corpus", "localkey_is_minor"]).duration_qb.sum().round(2) +norm_segment_duration_per_dataset = 100 * segment_duration_per_dataset / segment_duration_per_dataset.groupby(level="corpus").sum() +maj_min_ratio_per_dataset = pd.concat([segment_duration_per_dataset, + norm_segment_duration_per_dataset.rename('fraction').round(1).astype(str)+" %"], + axis=1) +maj_min_ratio_per_dataset['corpus_name'] = maj_min_ratio_per_dataset.index.get_level_values('corpus').map(corpus_names) +maj_min_ratio_per_dataset['mode'] = maj_min_ratio_per_dataset.index.get_level_values('localkey_is_minor').map({False: 'major', True: 'minor'}) +``` + +```{code-cell} ipython3 +fig = px.bar(maj_min_ratio_per_dataset.reset_index(), + x="corpus_name", + y="duration_qb", + color="mode", + text='fraction', + labels=dict(dataset='', duration_qb="duration in 𝅘𝅥", corpus_name='Key segments grouped by corpus'), + category_orders=dict(dataset=chronological_order) + ) +fig.update_layout(**STD_LAYOUT) +fig.show() +``` + +### Tone profiles for all major and minor local keys + +```{code-cell} ipython3 +notes_by_keys = keys_segmented.get_facet("notes") +notes_by_keys +``` + +```{code-cell} ipython3 +keys = keys[[col for col in keys.columns if col not in notes_by_keys]] +notes_joined_with_keys = notes_by_keys.join(keys, on=keys.index.names) +notes_by_keys_transposed = ms3.transpose_notes_to_localkey(notes_joined_with_keys) +mode_tpcs = notes_by_keys_transposed.reset_index(drop=True).groupby(['localkey_is_minor', 'tpc']).duration_qb.sum().reset_index(-1).sort_values('tpc').reset_index() +mode_tpcs['sd'] = ms3.fifths2sd(mode_tpcs.tpc) +mode_tpcs['duration_pct'] = mode_tpcs.groupby('localkey_is_minor', group_keys=False).duration_qb.apply(lambda S: S / S.sum()) +mode_tpcs['mode'] = mode_tpcs.localkey_is_minor.map({False: 'major', True: 'minor'}) +``` + +```{code-cell} ipython3 +#mode_tpcs = mode_tpcs[mode_tpcs['duration_pct'] > 0.001] +#sd_order = ['b1', '1', '#1', 'b2', '2', '#2', 'b3', '3', 'b4', '4', '#4', '##4', 'b5', '5', '#5', 'b6','6', '#6', 'b7', '7'] +xaxis = dict( + tickmode = 'array', + tickvals = mode_tpcs.tpc, + ticktext = mode_tpcs.sd + ) +legend=dict( + yanchor="top", + y=0.99, + xanchor="right", + x=0.99 +) +fig = px.bar(mode_tpcs, + x='tpc', + y='duration_pct', + color='mode', + barmode='group', + labels=dict(duration_pct='normalized duration', + tpc="Notes transposed to the local key, as major-scale degrees", + ), + #log_y=True, + #category_orders=dict(sd=sd_order) + ) +fig.update_layout(**STD_LAYOUT, xaxis=xaxis, legend=legend) +fig.show() +``` + +## Harmony labels +### Unigrams +For computing unigram statistics, the tokens need to be grouped by their occurrence within a major or a minor key because this changes their meaning. To that aim, the annotated corpus needs to be sliced into contiguous localkey segments which are then grouped into a major (`is_minor=False`) and a minor group. + +```{code-cell} ipython3 +root_durations = all_chords[all_chords.root.between(-5,6)].groupby(['root', 'chord_type']).duration_qb.sum() +# sort by stacked bar length: +#root_durations = root_durations.sort_values(key=lambda S: S.index.get_level_values(0).map(S.groupby(level=0).sum()), ascending=False) +bar_data = root_durations.reset_index() +bar_data.root = bar_data.root.map(ms3.fifths2iv) +px.bar(bar_data, x='root', y='duration_qb', color='chord_type') +``` + +```{code-cell} ipython3 +relative_roots = all_chords[['numeral', 'duration_qb', 'relativeroot', 'localkey_is_minor', 'chord_type']].copy() +relative_roots['relativeroot_resolved'] = transform(relative_roots, ms3.resolve_relative_keys, ['relativeroot', 'localkey_is_minor']) +has_rel = relative_roots.relativeroot_resolved.notna() +relative_roots.loc[has_rel, 'localkey_is_minor'] = relative_roots.loc[has_rel, 'relativeroot_resolved'].str.islower() +relative_roots['root'] = transform(relative_roots, roman_numeral2fifths, ['numeral', 'localkey_is_minor']) +chord_type_frequency = all_chords.chord_type.value_counts() +replace_rare = ms3.map_dict({t: 'other' for t in chord_type_frequency[chord_type_frequency < 500].index}) +relative_roots['type_reduced'] = relative_roots.chord_type.map(replace_rare) +#is_special = relative_roots.chord_type.isin(('It', 'Ger', 'Fr')) +#relative_roots.loc[is_special, 'root'] = -4 +``` + +```{code-cell} ipython3 +root_durations = relative_roots.groupby(['root', 'type_reduced']).duration_qb.sum().sort_values(ascending=False) +bar_data = root_durations.reset_index() +bar_data.root = bar_data.root.map(ms3.fifths2iv) +root_order = bar_data.groupby('root').duration_qb.sum().sort_values(ascending=False).index.to_list() +fig = px.bar(bar_data, x='root', y='duration_qb', color='type_reduced', barmode='group', log_y=True, + color_discrete_map=TYPE_COLORS, + category_orders=dict(root=root_order, + type_reduced=relative_roots.type_reduced.value_counts().index.to_list(), + ), + labels=dict(root="intervallic difference between chord root to the local or secondary tonic", + duration_qb="duration in quarter notes", + type_reduced="chord type", + ), + width=1000, + height=400, + ) +fig.update_layout(**STD_LAYOUT, + legend=dict( + orientation='h', + xanchor="right", + x=1, + y=1, + ) + ) +fig.update_yaxes(gridcolor='lightgrey') +fig.show() +``` + +```{code-cell} ipython3 +print(f"Reduced to {len(set(bar_data.iloc[:,:2].itertuples(index=False, name=None)))} types. Paper cites the sum of types in major and types in minor (see below), treating them as distinct.") +``` + +```{code-cell} ipython3 +dim_or_aug = bar_data[bar_data.root.str.startswith("a") | bar_data.root.str.startswith("d")].duration_qb.sum() +complete = bar_data.duration_qb.sum() +print(f"On diminished or augmented scale degrees: {dim_or_aug} / {complete} = {dim_or_aug / complete}") +``` + +```{code-cell} ipython3 +mode_slices = dc.ModeGrouper().process_data(keys_segmented) +``` + +### Whole dataset + +```{code-cell} ipython3 +mode_slices.get_slice_info() +``` + +```{code-cell} ipython3 +unigrams = dc.ChordSymbolUnigrams(once_per_group=True).process_data(mode_slices) +``` + +```{code-cell} ipython3 +unigrams.group2pandas = "group_of_series2series" +``` + +```{code-cell} ipython3 +unigrams.get(as_pandas=True) +``` + +```{code-cell} ipython3 +k = 20 +modes = {True: 'MINOR', False: 'MAJOR'} +for (is_minor,), ugs in unigrams.iter(): + print(f"TOP {k} {modes[is_minor]} UNIGRAMS\n{ugs.shape[0]} types, {ugs.sum()} tokens") + print(ugs.head(k).to_string()) +``` + +```{code-cell} ipython3 +ugs_dict = {modes[is_minor].lower(): (ugs/ugs.sum() * 100).round(2).rename('%').reset_index() for (is_minor,), ugs in unigrams.iter()} +ugs_df = pd.concat(ugs_dict, axis=1) +ugs_df.columns = ['_'.join(map(str, col)) for col in ugs_df.columns] +ugs_df.index = (ugs_df.index + 1).rename('k') +print(ugs_df.iloc[:50].to_markdown()) +``` + +### Per corpus + +```{code-cell} ipython3 +corpus_wise_unigrams = dc.Pipeline([dc.CorpusGrouper(), dc.ChordSymbolUnigrams(once_per_group=True)]).process_data(mode_slices) +``` + +```{code-cell} ipython3 +corpus_wise_unigrams.get() +``` + +```{code-cell} ipython3 +for (is_minor, corpus_name), ugs in corpus_wise_unigrams.iter(): + print(f"{corpus_name} {modes[is_minor]} unigrams ({ugs.shape[0]} types, {ugs.sum()} tokens)") + print(ugs.head(5).to_string()) +``` + +```{code-cell} ipython3 +types_shared_between_corpora = {} +for (is_minor, corpus_name), ugs in corpus_wise_unigrams.iter(): + if is_minor in types_shared_between_corpora: + types_shared_between_corpora[is_minor] = types_shared_between_corpora[is_minor].intersection(ugs.index) + else: + types_shared_between_corpora[is_minor] = set(ugs.index) +types_shared_between_corpora = {k: sorted(v, key=lambda x: unigrams.get()[(k, x)], reverse=True) for k, v in types_shared_between_corpora.items()} +n_types = {k: len(v) for k, v in types_shared_between_corpora.items()} +print(f"Chords which occur in all corpora, sorted by descending global frequency:\n{types_shared_between_corpora}\nCounts: {n_types}") +``` + +### Per piece + +```{code-cell} ipython3 +piece_wise_unigrams = dc.Pipeline([dc.PieceGrouper(), dc.ChordSymbolUnigrams(once_per_group=True)]).process_data(mode_slices) +``` + +```{code-cell} ipython3 +piece_wise_unigrams.get() +``` + +```{code-cell} ipython3 +types_shared_between_pieces = {} +for (is_minor, corpus_name), ugs in piece_wise_unigrams.iter(): + if is_minor in types_shared_between_pieces: + types_shared_between_pieces[is_minor] = types_shared_between_pieces[is_minor].intersection(ugs.index) + else: + types_shared_between_pieces[is_minor] = set(ugs.index) +print(types_shared_between_pieces) +``` + +## Bigrams + ++++ + +### Whole dataset + +```{code-cell} ipython3 +bigrams = dc.ChordSymbolBigrams(once_per_group=True).process_data(mode_slices) +``` + +```{code-cell} ipython3 +bigrams.get() +``` + +```{code-cell} ipython3 +modes = {True: 'MINOR', False: 'MAJOR'} +for (is_minor,), ugs in bigrams.iter(): + print(f"{modes[is_minor]} BIGRAMS\n{ugs.shape[0]} transition types, {ugs.sum()} tokens") + print(ugs.head(20).to_string()) +``` + +### Per corpus + +```{code-cell} ipython3 +corpus_wise_bigrams = dc.Pipeline([dc.CorpusGrouper(), dc.ChordSymbolBigrams(once_per_group=True)]).process_data(mode_slices) +``` + +```{code-cell} ipython3 +corpus_wise_bigrams.get() +``` + +```{code-cell} ipython3 +for (is_minor, corpus_name), ugs in corpus_wise_bigrams.iter(): + print(f"{corpus_name} {modes[is_minor]} bigrams ({ugs.shape[0]} transition types, {ugs.sum()} tokens)") + print(ugs.head(5).to_string()) +``` + +```{code-cell} ipython3 +normalized_corpus_unigrams = {group: (100 * ugs / ugs.sum()).round(1).rename("frequency") for group, ugs in corpus_wise_unigrams.iter()} +``` + +```{code-cell} ipython3 +transitions_from_shared_types = { + False: {}, + True: {} +} +for (is_minor, corpus_name), bgs in corpus_wise_bigrams.iter(): + transitions_normalized_per_from = bgs.groupby(level="from", group_keys=False).apply(lambda S: (100 * S / S.sum()).round(1)) + most_frequent_transition_per_from = transitions_normalized_per_from.rename('fraction').reset_index(level=1).groupby(level=0).nth(0) + most_frequent_transition_per_shared = most_frequent_transition_per_from.loc[types_shared_between_corpora[is_minor]] + unigram_frequency_of_shared = normalized_corpus_unigrams[(is_minor, corpus_name)].loc[types_shared_between_corpora[is_minor]] + combined = pd.concat([unigram_frequency_of_shared, most_frequent_transition_per_shared], axis=1) + transitions_from_shared_types[is_minor][corpus_name] = combined +``` + +```{code-cell} ipython3 +pd.concat(transitions_from_shared_types[False].values(), keys=transitions_from_shared_types[False].keys(), axis=1) +``` + +```{code-cell} ipython3 +pd.concat(transitions_from_shared_types[True].values(), keys=transitions_from_shared_types[False].keys(), axis=1) +``` + +### Per piece + +```{code-cell} ipython3 +piece_wise_bigrams = dc.Pipeline([dc.PieceGrouper(), dc.ChordSymbolBigrams(once_per_group=True)]).process_data(mode_slices) +``` + +```{code-cell} ipython3 +piece_wise_bigrams.get() +``` \ No newline at end of file diff --git a/homepage/notebooks/cadences.md b/homepage/notebooks/cadences.md new file mode 100644 index 0000000..c6a7c74 --- /dev/null +++ b/homepage/notebooks/cadences.md @@ -0,0 +1,478 @@ +--- +jupytext: + formats: md:myst,ipynb + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.15.0 +kernelspec: + display_name: corpus_docs + language: python + name: corpus_docs +--- + +# Cadences + +```{code-cell} ipython3 +--- +mystnb: + code_prompt_hide: Hide imports + code_prompt_show: Show imports +tags: [hide-cell] +--- +import os +from collections import defaultdict, Counter + +from git import Repo +import dimcat as dc +import ms3 +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go + +from utils import STD_LAYOUT, CADENCE_COLORS, color_background, value_count_df, get_repo_name, print_heading, resolve_dir +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +CORPUS_PATH = os.path.abspath(os.path.join('..', '..')) +print_heading("Notebook settings") +print(f"CORPUS_PATH: {CORPUS_PATH!r}") +CORPUS_PATH = resolve_dir(CORPUS_PATH) +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +repo = Repo(CORPUS_PATH) +print_heading("Data and software versions") +print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}") +print(f"dimcat version {dc.__version__}") +print(f"ms3 version {ms3.__version__}") +``` + +```{code-cell} ipython3 +:tags: [remove-output] + +dataset = dc.Dataset() +dataset.load(directory=CORPUS_PATH, parse_tsv=False) +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +annotated_view = dataset.data.get_view('annotated') +annotated_view.include('facets', 'expanded') +annotated_view.fnames_with_incomplete_facets = False +dataset.data.set_view(annotated_view) +dataset.data.parse_tsv(choose='auto') +dataset.get_indices() +dataset.data +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +print(f"N = {dataset.data.count_pieces()} annotated pieces, {dataset.data.count_parsed_tsvs()} parsed dataframes.") +``` + +## Metadata + +```{code-cell} ipython3 +all_metadata = dataset.data.metadata() +assert len(all_metadata) > 0, "No pieces selected for analysis." +print(f"Concatenated 'metadata.tsv' files cover {len(all_metadata)} of the {dataset.data.count_pieces()} scores.") +all_metadata.reset_index(level=1).groupby(level=0).nth(0).iloc[:,:20] +``` + +## All annotation labels from the selected pieces + +```{code-cell} ipython3 +all_labels = dataset.data.get_facet('expanded') + +print(f"{len(all_labels.index)} hand-annotated harmony labels:") +all_labels.iloc[:20].style.apply(color_background, subset="chord") +``` + +### Filtering out pieces without cadence annotations + +```{code-cell} ipython3 +hascadence = dc.HasCadenceAnnotationsFilter().process_data(dataset) +assert () in hascadence.indices and len(hascadence.indices[()]) > 0, "No cadences found." +print(f"Before: {len(dataset.indices[()])} pieces; after removing those without cadence labels: {len(hascadence.indices[()])}") +``` + +### Show corpora containing pieces with cadence annotations + +```{code-cell} ipython3 +grouped_by_corpus = dc.CorpusGrouper().process_data(hascadence) +corpora = {group[0]: f"{len(ixs)} pieces" for group, ixs in grouped_by_corpus.indices.items()} +print(f"{len(corpora)} corpora with {sum(map(len, grouped_by_corpus.indices.values()))} pieces containing cadence annotations:") +corpora +``` + +### All annotation labels from the selected pieces + +```{code-cell} ipython3 +all_labels = hascadence.get_facet('expanded') + +print(f"{len(all_labels.index)} hand-annotated harmony labels:") +all_labels.iloc[:10, 13:].style.apply(color_background, subset="chord") +``` + +### Metadata + +```{code-cell} ipython3 +dataset_metadata = hascadence.data.metadata() +hascadence_metadata = dataset_metadata.loc[hascadence.indices[()]] +hascadence_metadata.index.rename('dataset', level=0, inplace=True) +hascadence_metadata.head() +``` + +```{code-cell} ipython3 +mean_composition_years = hascadence_metadata.groupby(level=0).composed_end.mean().astype(int).sort_values() +chronological_order = mean_composition_years.index.to_list() +bar_data = pd.concat([mean_composition_years.rename('year'), + hascadence_metadata.groupby(level='dataset').size().rename('pieces')], + axis=1 + ).reset_index() +fig = px.bar(bar_data, x='year', y='pieces', color='dataset', title='Pieces contained in the dataset') +fig.update_traces(width=5) +``` + +## Overall + +* **PAC**: Perfect Authentic Cadence +* **IAC**: Imperfect Authentic Cadence +* **HC**: Half Cadence +* **DC**: Deceptive Cadence +* **EC**: Evaded Cadence +* **PC**: Plagal Cadence + +```{code-cell} ipython3 +print(f"{all_labels.cadence.notna().sum()} cadence labels.") +value_count_df(all_labels.cadence) +``` + +```{code-cell} ipython3 +px.pie(all_labels[all_labels.cadence.notna()], names="cadence", color="cadence", color_discrete_map=CADENCE_COLORS) +``` + +## Per dataset + +```{code-cell} ipython3 +cadence_count_per_dataset = all_labels.groupby("corpus").cadence.value_counts() +cadence_fraction_per_dataset = cadence_count_per_dataset / cadence_count_per_dataset.groupby(level=0).sum() +px.bar(cadence_fraction_per_dataset.rename('count').reset_index(), x='corpus', y='count', color='cadence', + color_discrete_map=CADENCE_COLORS, category_orders=dict(dataset=chronological_order)) +``` + +```{code-cell} ipython3 +fig = px.pie(cadence_count_per_dataset.rename('count').reset_index(), names='cadence', color='cadence', values='count', + facet_col='corpus', facet_col_wrap=4, height=2000, color_discrete_map=CADENCE_COLORS) +fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) +fig.update_layout(**STD_LAYOUT) +``` + +## Per phrase +### Number of cadences per phrase + +```{code-cell} ipython3 +segmented = dc.PhraseSlicer().process_data(grouped_by_corpus) +phrases = segmented.get_slice_info() +phrase_segments = segmented.get_facet("expanded") +phrase_gpb = phrase_segments.groupby(level=[0,1,2]) +local_keys_per_phrase = phrase_gpb.localkey.unique().map(tuple) +n_local_keys_per_phrase = local_keys_per_phrase.map(len) +phrases_with_keys = pd.concat([n_local_keys_per_phrase.rename('n_local_keys'), + local_keys_per_phrase.rename('local_keys'), + phrases], axis=1) +phrases_with_cadences = pd.concat([ + phrase_gpb.cadence.nunique().rename('n_cadences'), + phrase_gpb.cadence.unique().rename('cadences').map(lambda l: tuple(e for e in l if not pd.isnull(e))), + phrases_with_keys +], axis=1) +value_count_df(phrases_with_cadences.n_cadences, counts="#phrases") +``` + +```{code-cell} ipython3 +n_cad = phrases_with_cadences.groupby(level='corpus').n_cadences.value_counts().rename('counts').reset_index().sort_values('n_cadences') +n_cad.n_cadences = n_cad.n_cadences.astype(str) +fig = px.bar(n_cad, x='corpus', y='counts', color='n_cadences', height=800, barmode='group', + labels=dict(n_cadences="#cadences in a phrase"), + category_orders=dict(dataset=chronological_order) + ) +fig.show() +``` + +### Combinations of cadence types for phrases with more than one cadence + +```{code-cell} ipython3 +value_count_df(phrases_with_cadences[phrases_with_cadences.n_cadences > 1].cadences) +``` + +### Positioning of cadences within phrases + +```{code-cell} ipython3 +df_rows = [] +y_position = 0 +for ix in phrases_with_cadences[phrases_with_cadences.n_cadences > 0].sort_values('duration_qb').index: + df = phrase_segments.loc[ix] + description = str(ix) + if df.cadence.notna().any(): + interval = ix[2] + df_rows.append((y_position, interval.length, "end of phrase", description)) + start_pos = interval.left + cadences = df.loc[df.cadence.notna(), ['quarterbeats', 'cadence']] + cadences.quarterbeats -= start_pos + for cadence_x, cadence_type in cadences.itertuples(index=False, name=None): + df_rows.append((y_position, cadence_x, cadence_type, description)) + y_position += 1 + #else: + # df_rows.append((y_position, pd.NA, pd.NA, description)) + +data = pd.DataFrame(df_rows, columns=["phrase_ix", "x", "marker", "description"]) +``` + +```{code-cell} ipython3 +fig = px.scatter(data[data.x.notna()], x='x', y="phrase_ix", color="marker", hover_name="description", height=3000, + labels=dict(marker='legend'), color_discrete_map=CADENCE_COLORS) +fig.update_traces(marker_size=5) +fig.update_yaxes(autorange="reversed") +fig.show() +``` + +## Cadence ultima + +```{code-cell} ipython3 +phrase_segments = segmented.get_facet("expanded") +cadence_selector = phrase_segments.cadence.notna() +missing_chord_selector = phrase_segments.chord.isna() +cadence_with_missing_chord_selector = cadence_selector & missing_chord_selector +missing = phrase_segments[cadence_with_missing_chord_selector] +expanded = ms3.expand_dcml.expand_labels(phrase_segments[cadence_with_missing_chord_selector], propagate=False, chord_tones=True, skip_checks=True) +phrase_segments.loc[cadence_with_missing_chord_selector] = expanded +print(f"Ultima harmony missing for {(phrase_segments.cadence.notna() & phrase_segments.bass_note.isna()).sum()} cadence labels.") +``` + +### Ultimae as Roman numeral + +```{code-cell} ipython3 +def highlight(row, color="#ffffb3"): + if row.counts < 10: + return [None, None, None, None] + else: + return ["background-color: {color};"] * 4 + +cadence_counts = all_labels.cadence.value_counts() +ultima_root = phrase_segments.groupby(['localkey_is_minor', 'cadence']).numeral.value_counts().rename('counts').to_frame().reset_index() +ultima_root.localkey_is_minor = ultima_root.localkey_is_minor.map({False: 'in major', True: 'in minor'}) +#ultima_root.style.apply(highlight, axis=1) +``` + +```{code-cell} ipython3 +fig = px.pie(ultima_root, names='numeral', values='counts', + facet_row='cadence', facet_col='localkey_is_minor', + height=1500, + category_orders={'cadence': cadence_counts.index}, + ) +fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) +fig.update_traces(textposition='inside', textinfo='percent+label') +fig.update_layout(**STD_LAYOUT) +fig.show() +``` + +```{code-cell} ipython3 +#phrase_segments.groupby(level=[0,1,2], group_keys=False).apply(lambda df: df if ((df.cadence == 'PAC') & (df.numeral == 'V')).any() else None) +``` + +### Ultimae bass note as scale degree + +```{code-cell} ipython3 +ultima_bass = phrase_segments.groupby(['localkey_is_minor','cadence']).bass_note.value_counts().rename('counts').reset_index() +ultima_bass.bass_note = ms3.transform(ultima_bass, ms3.fifths2sd, dict(fifths='bass_note', minor='localkey_is_minor')) +ultima_bass.localkey_is_minor = ultima_bass.localkey_is_minor.map({False: 'in major', True: 'in minor'}) +#ultima_bass.style.apply(highlight, axis=1) +``` + +```{code-cell} ipython3 +fig = px.pie(ultima_bass, names='bass_note', values='counts', + facet_row='cadence', facet_col='localkey_is_minor', + height=1500, + category_orders={'cadence': cadence_counts.index}, + ) +fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) +fig.update_traces(textposition='inside', textinfo='percent+label') +fig.update_layout(**STD_LAYOUT) +fig.show() +``` + +## Chord progressions + ++++ + +### PACs with ultima I/i + +```{code-cell} ipython3 +def remove_immediate_duplicates(l): + return tuple(a for a, b in zip(l, (None, ) + l) if a != b) + +def get_progressions(selected='PAC', last_row={}, feature='chord', dataset=None, as_series=True, remove_duplicates=False): + """Uses the nonlocal variable phrase_segments.""" + last_row = {k: v if isinstance(v, tuple) else (v,) for k, v in last_row.items()} + progressions = [] + + for (corp, fname, *_), df in phrase_segments[phrase_segments[feature].notna()].groupby(level=[0,1,2]): + if dataset is not None and dataset not in corp: + continue + if (df.cadence == selected).fillna(False).any(): + # remove chords after the last cadence label + df = df[df.cadence.fillna(method='bfill').notna()] + # group segments leading up to a cadence label + cadence_groups = df.cadence.notna().shift().fillna(False).cumsum() + for i, cadence in df.groupby(cadence_groups): + last_r = cadence.iloc[-1] + typ = last_r.cadence + if typ != selected: + continue + if any(last_r[feat] not in values for feat, values in last_row.items()): + continue + if remove_duplicates: + progressions.append(remove_immediate_duplicates(cadence[feature].to_list())) + else: + progressions.append(tuple(cadence[feature])) + if as_series: + return pd.Series(progressions, dtype='object') + return progressions +``` + +```{code-cell} ipython3 +chord_progressions = get_progressions('PAC', dict(numeral=('I', 'i')), 'chord') +print(f"Progressions for {len(chord_progressions)} cadences:") +value_count_df(chord_progressions, "chord progressions") +``` + +```{code-cell} ipython3 +numeral_progressions = get_progressions('PAC', dict(numeral=('I', 'i')), 'numeral') +value_count_df(numeral_progressions, "numeral progressions") +``` + +```{code-cell} ipython3 +numeral_prog_no_dups = numeral_progressions.map(remove_immediate_duplicates) +value_count_df(numeral_prog_no_dups) +``` + +### PACs ending on scale degree 1 + +**Scale degrees expressed w.r.t. major scale, regardless of actual key.** + +```{code-cell} ipython3 +bass_progressions = get_progressions('PAC', dict(bass_note=0), 'bass_note') +bass_prog = bass_progressions.map(ms3.fifths2sd) +print(f"Progressions for {len(bass_progressions)} cadences:") +value_count_df(bass_prog, "bass progressions") +``` + +```{code-cell} ipython3 +bass_prog_no_dups = bass_prog.map(remove_immediate_duplicates) +value_count_df(bass_prog_no_dups) +``` + +```{code-cell} ipython3 +def make_sankey(data, labels, node_pos=None, margin={'l': 10, 'r': 10, 'b': 10, 't': 10}, pad=20, color='auto', **kwargs): + if color=='auto': + unique_labels = set(labels) + color_step = 100 / len(unique_labels) + unique_colors = {label: f'hsv({round(i*color_step)}%,100%,100%)' for i, label in enumerate(unique_labels)} + color = list(map(lambda l: unique_colors[l], labels)) + fig = go.Figure(go.Sankey( + arrangement = 'snap', + node = dict( + pad = pad, + #thickness = 20, + #line = dict(color = "black", width = 0.5), + label = labels, + x = [node_pos[i][0] if i in node_pos else 0 for i in range(len(labels))] if node_pos is not None else None, + y = [node_pos[i][1] if i in node_pos else 0 for i in range(len(labels))] if node_pos is not None else None, + color = color, + ), + link = dict( + source = data.source, + target = data.target, + value = data.value + ), + ), + ) + + fig.update_layout(margin=margin, **kwargs) + return fig + +def progressions2graph_data(progressions, cut_at_stage=None): + stage_nodes = defaultdict(dict) + edge_weights = Counter() + node_counter = 0 + for progression in progressions: + previous_node = None + for stage, current in enumerate(reversed(progression)): + if cut_at_stage and stage > cut_at_stage: + break + if current in stage_nodes[stage]: + current_node = stage_nodes[stage][current] + else: + stage_nodes[stage][current] = node_counter + current_node = node_counter + node_counter += 1 + if previous_node is not None: + edge_weights.update([(current_node, previous_node)]) + previous_node = current_node + return stage_nodes, edge_weights + +def graph_data2sankey(stage_nodes, edge_weights): + data = pd.DataFrame([(u, v, w) for (u, v), w in edge_weights.items()], columns = ['source', 'target', 'value']) + node2label = {node: label for stage, nodes in stage_nodes.items() for label, node in nodes.items()} + labels = [node2label[i] for i in range(len(node2label))] + return make_sankey(data, labels) + +def plot_progressions(progressions, cut_at_stage=None): + stage_nodes, edge_weights = progressions2graph_data(progressions, cut_at_stage=cut_at_stage) + return graph_data2sankey(stage_nodes, edge_weights) +``` + +#### Chordal roots for the 3 last stages + +```{code-cell} ipython3 +plot_progressions(numeral_prog_no_dups, cut_at_stage=3) +``` + +#### Complete chords for the last four stages in major + +```{code-cell} ipython3 +pac_major = get_progressions('PAC', dict(numeral='I', localkey_is_minor=False), 'chord') +plot_progressions(pac_major, cut_at_stage=4) +``` + +#### Bass degrees for the last 6 stages. + +```{code-cell} ipython3 +plot_progressions(bass_prog_no_dups, cut_at_stage=7) +``` + +#### Bass degrees without accidentals + +```{code-cell} ipython3 +def remove_sd_accidentals(t): + return tuple(map(lambda sd: sd[-1], t)) + +bass_prog_no_acc_no_dup = bass_prog.map(remove_sd_accidentals).map(remove_immediate_duplicates) +plot_progressions(bass_prog_no_acc_no_dup, cut_at_stage=7) +``` + +### HCs ending on V + +```{code-cell} ipython3 +half = get_progressions('HC', dict(numeral='V'), 'bass_note').map(ms3.fifths2sd) +print(f"Progressions for {len(half)} cadences:") +plot_progressions(half.map(remove_immediate_duplicates), cut_at_stage=5) +``` \ No newline at end of file diff --git a/homepage/notebooks/notes_stats.md b/homepage/notebooks/notes_stats.md new file mode 100644 index 0000000..7880737 --- /dev/null +++ b/homepage/notebooks/notes_stats.md @@ -0,0 +1,290 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.15.0 +kernelspec: + display_name: corpus_docs + language: python + name: corpus_docs +--- + +# Notes + +```{code-cell} ipython3 +--- +mystnb: + code_prompt_hide: Hide imports + code_prompt_show: Show imports +tags: [hide-cell] +--- +import os +from collections import defaultdict, Counter + +from git import Repo +import dimcat as dc +import ms3 +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go + +from utils import STD_LAYOUT, CADENCE_COLORS, CORPUS_COLOR_SCALE, chronological_corpus_order, color_background, get_corpus_display_name, get_repo_name, resolve_dir, value_count_df, get_repo_name, print_heading, resolve_dir +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +CORPUS_PATH = os.path.abspath(os.path.join('..', '..')) +ANNOTATED_ONLY = os.getenv("ANNOTATED_ONLY", "True").lower() in ('true', '1', 't') +print_heading("Notebook settings") +print(f"CORPUS_PATH: {CORPUS_PATH!r}") +print(f"ANNOTATED_ONLY: {ANNOTATED_ONLY}") +CORPUS_PATH = resolve_dir(CORPUS_PATH) +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +repo = Repo(CORPUS_PATH) +print_heading("Data and software versions") +print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}") +print(f"dimcat version {dc.__version__}") +print(f"ms3 version {ms3.__version__}") +``` + +```{code-cell} ipython3 +:tags: [remove-output] + +dataset = dc.Dataset() +dataset.load(directory=CORPUS_PATH, parse_tsv=False) +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +if ANNOTATED_ONLY: + annotated_view = dataset.data.get_view('annotated') + annotated_view.include('facets', 'measures', 'notes$', 'expanded') + annotated_view.fnames_with_incomplete_facets = False + dataset.data.set_view(annotated_view) +dataset.data.parse_tsv(choose='auto') +dataset.get_indices() +dataset.data +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +print(f"N = {dataset.data.count_pieces()} annotated pieces, {dataset.data.count_parsed_tsvs()} parsed dataframes.") +``` + +## Metadata + +```{code-cell} ipython3 +all_metadata = dataset.data.metadata() +print(f"Concatenated 'metadata.tsv' files cover {len(all_metadata)} of the {dataset.data.count_pieces()} scores.") +all_metadata.reset_index(level=1).groupby(level=0).nth(0).iloc[:,:20] +``` + +**Compute chronological order** + +```{code-cell} ipython3 +chronological_order = chronological_corpus_order(all_metadata) +corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE)) +chronological_order +``` + +```{code-cell} ipython3 +all_notes = dataset.data.get_all_parsed('notes', force=True, flat=True) +print(f"{len(all_notes.index)} notes over {len(all_notes.groupby(level=[0,1]))} files.") +all_notes.head() +``` + +```{code-cell} ipython3 +def weight_notes(nl, group_col='midi', precise=True): + summed_durations = nl.groupby(group_col).duration_qb.sum() + shortest_duration = summed_durations[summed_durations > 0].min() + summed_durations /= shortest_duration # normalize such that the shortest duration results in 1 occurrence + if not precise: + # This simple trick reduces compute time but also precision: + # The rationale is to have the smallest value be slightly larger than 0.5 because + # if it was exactly 0.5 it would be rounded down by repeat_notes_according_to_weights() + summed_durations /= 1.9999999 + return repeat_notes_according_to_weights(summed_durations) + +def repeat_notes_according_to_weights(weights): + try: + counts = weights.round().astype(int) + except Exception: + return pd.Series(dtype=int) + counts_reflecting_weights = [] + for pitch, count in counts.items(): + counts_reflecting_weights.extend([pitch]*count) + return pd.Series(counts_reflecting_weights) +``` + +## Ambitus + +```{code-cell} ipython3 +corpus_names = {corp: get_corpus_display_name(corp) for corp in chronological_order} +chronological_corpus_names = list(corpus_names.values()) +corpus_name_colors = {corpus_names[corp]: color for corp, color in corpus_colors.items()} +all_notes['corpus_name'] = all_notes.index.get_level_values(0).map(corpus_names) +``` + +```{code-cell} ipython3 +grouped_notes = all_notes.groupby('corpus_name') +weighted_midi = pd.concat([weight_notes(nl, 'midi', precise=False) for _, nl in grouped_notes], keys=grouped_notes.groups.keys()).reset_index(level=0) +weighted_midi.columns = ['dataset', 'midi'] +weighted_midi +``` + +```{code-cell} ipython3 +yaxis=dict(tickmode= 'array', + tickvals= [12, 24, 36, 48, 60, 72, 84, 96], + ticktext = ["C0", "C1", "C2", "C3", "C4", "C5", "C6", "C7"], + gridcolor='lightgrey', + ) +fig = px.violin(weighted_midi, + x='dataset', + y='midi', + color='dataset', + box=True, + labels=dict( + dataset='', + midi='distribution of pitches by duration' + ), + category_orders=dict(dataset=chronological_corpus_names), + color_discrete_map=corpus_name_colors, + width=1000, height=600, + ) +fig.update_traces(spanmode='hard') # do not extend beyond outliers +fig.update_layout(yaxis=yaxis, + **STD_LAYOUT, + showlegend=False) +fig.show() +``` + +## Tonal Pitch Classes (TPC) + +```{code-cell} ipython3 +weighted_tpc = pd.concat([weight_notes(nl, 'tpc') for _, nl in grouped_notes], keys=grouped_notes.groups.keys()).reset_index(level=0) +weighted_tpc.columns = ['dataset', 'tpc'] +weighted_tpc +``` + +### As violin plot + +```{code-cell} ipython3 +yaxis=dict( + tickmode= 'array', + tickvals= [-12, -9, -6, -3, 0, 3, 6, 9, 12, 15, 18], + ticktext = ["Dbb", "Bbb", "Gb", "Eb", "C", "A", "F#", "D#", "B#", "G##", "E##"], + gridcolor='lightgrey', + zerolinecolor='lightgrey', + zeroline=True + ) +fig = px.violin(weighted_tpc, + x='dataset', + y='tpc', + color='dataset', + box=True, + labels=dict( + dataset='', + tpc='distribution of tonal pitch classes by duration' + ), + category_orders=dict(dataset=chronological_corpus_names), + color_discrete_map=corpus_name_colors, + width=1000, + height=600, + ) +fig.update_traces(spanmode='hard') # do not extend beyond outliers +fig.update_layout(yaxis=yaxis, + **STD_LAYOUT, + showlegend=False) +fig.show() +``` + +### As bar plots + +```{code-cell} ipython3 +bar_data = all_notes.groupby('tpc').duration_qb.sum().reset_index() +x_values = list(range(bar_data.tpc.min(), bar_data.tpc.max()+1)) +x_names = ms3.fifths2name(x_values) +fig = px.bar(bar_data, x='tpc', y='duration_qb', + labels=dict(tpc='Named pitch class', + duration_qb='Duration in quarter notes' + ), + color_discrete_sequence=CORPUS_COLOR_SCALE, + width=1000, height=300, + ) +fig.update_layout(**STD_LAYOUT) +fig.update_yaxes(gridcolor='lightgrey') +fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='grey', tickmode='array', + tickvals=x_values, ticktext = x_names, dtick=1, ticks='outside', tickcolor='black', + minor=dict(dtick=6, gridcolor='grey', showgrid=True), + ) +fig.show() +``` + +```{code-cell} ipython3 +scatter_data = all_notes.groupby(['corpus_name', 'tpc']).duration_qb.sum().reset_index() +fig = px.bar(scatter_data, x='tpc', y='duration_qb', color='corpus_name', + labels=dict( + duration_qb='duration', + tpc='named pitch class', + ), + category_orders=dict(dataset=chronological_corpus_names), + color_discrete_map=corpus_name_colors, + width=1000, height=500, + ) +fig.update_layout(**STD_LAYOUT) +fig.update_yaxes(gridcolor='lightgrey') +fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='grey', tickmode='array', + tickvals=x_values, ticktext = x_names, dtick=1, ticks='outside', tickcolor='black', + minor=dict(dtick=6, gridcolor='grey', showgrid=True), + ) +fig.show() +``` + +### As scatter plots + +```{code-cell} ipython3 +fig = px.scatter(scatter_data, x='tpc', y='duration_qb', color='corpus_name', + labels=dict( + duration_qb='duration', + tpc='named pitch class', + ), + category_orders=dict(dataset=chronological_corpus_names), + color_discrete_map=corpus_name_colors, + facet_col='corpus_name', facet_col_wrap=3, facet_col_spacing=0.03, + width=1000, height=1000, + ) +fig.update_traces(mode='lines+markers') +fig.for_each_annotation(lambda a: a.update(text=a.text.split("=")[-1])) +fig.update_layout(**STD_LAYOUT, showlegend=False) +fig.update_xaxes(gridcolor='lightgrey', zerolinecolor='lightgrey', tickmode='array', tickvals= [-12, -6, 0, 6, 12, 18], + ticktext = ["Dbb", "Gb", "C", "F#", "B#", "E##"], visible=True, ) +fig.update_yaxes(gridcolor='lightgrey', zeroline=False, matches=None, showticklabels=True) +fig.show() +``` + +```{code-cell} ipython3 +no_accidental = bar_data[bar_data.tpc.between(-1,5)].duration_qb.sum() +with_accidental = bar_data[~bar_data.tpc.between(-1,5)].duration_qb.sum() +``` + +```{code-cell} ipython3 +entire = no_accidental + with_accidental +f"Fraction of note duration without accidental of the entire durations: {no_accidental} / {entire} = {no_accidental / entire}" +``` + +### Notes and staves + +```{code-cell} ipython3 +print("Distribution of notes over staves:") +value_count_df(all_notes.staff) +``` \ No newline at end of file diff --git a/homepage/notebooks/overview.md b/homepage/notebooks/overview.md new file mode 100644 index 0000000..d6e03e7 --- /dev/null +++ b/homepage/notebooks/overview.md @@ -0,0 +1,223 @@ +--- +jupytext: + formats: ipynb,md:myst + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.15.0 +kernelspec: + display_name: corpus_docs + language: python + name: corpus_docs +--- + +# Overview + +This notebook gives a general overview of the features included in the dataset. + +```{code-cell} ipython3 +--- +mystnb: + code_prompt_hide: Hide imports + code_prompt_show: Show imports +tags: [hide-cell] +--- +import os +from collections import defaultdict, Counter +from fractions import Fraction + +from git import Repo +import dimcat as dc +import ms3 +import pandas as pd +import plotly.express as px +import plotly.graph_objects as go + +from utils import CADENCE_COLORS, CORPUS_COLOR_SCALE, STD_LAYOUT, TYPE_COLORS, color_background, corpus_mean_composition_years, value_count_df, get_corpus_display_name, get_repo_name, print_heading, resolve_dir +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +CORPUS_PATH = os.path.abspath(os.path.join('..', '..')) +ANNOTATED_ONLY = os.getenv("ANNOTATED_ONLY", "True").lower() in ('true', '1', 't') +print_heading("Notebook settings") +print(f"CORPUS_PATH: {CORPUS_PATH!r}") +print(f"ANNOTATED_ONLY: {ANNOTATED_ONLY}") +CORPUS_PATH = resolve_dir(CORPUS_PATH) +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +repo = Repo(CORPUS_PATH) +print_heading("Data and software versions") +print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}") +print(f"dimcat version {dc.__version__}") +print(f"ms3 version {ms3.__version__}") +``` + +```{code-cell} ipython3 +:tags: [remove-output] + +dataset = dc.Dataset() +dataset.load(directory=CORPUS_PATH, parse_tsv=False) +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +if ANNOTATED_ONLY: + annotated_view = dataset.data.get_view('annotated') + annotated_view.include('facets', 'measures', 'notes$', 'expanded') + annotated_view.fnames_with_incomplete_facets = False + dataset.data.set_view(annotated_view) +dataset.data.parse_tsv(choose='auto') +dataset.get_indices() +dataset.data +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +print(f"N = {dataset.data.count_pieces()} annotated pieces, {dataset.data.count_parsed_tsvs()} parsed dataframes.") +``` + +```{code-cell} ipython3 +--- +mystnb: + code_prompt_hide: Hide data loading + code_prompt_show: Show data loading +tags: [hide-cell] +--- +all_metadata = dataset.data.metadata() +assert len(all_metadata) > 0, "No pieces selected for analysis." +print(f"Metadata covers {len(all_metadata)} of the {dataset.data.count_pieces()} scores.") +all_notes = dataset.get_facet('notes') +all_measures = dataset.get_facet('measures') +mean_composition_years = corpus_mean_composition_years(all_metadata) +chronological_order = mean_composition_years.index.to_list() +corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE)) +corpus_names = {corp: get_corpus_display_name(corp) for corp in chronological_order} +chronological_corpus_names = list(corpus_names.values()) +corpus_name_colors = {corpus_names[corp]: color for corp, color in corpus_colors.items()} +``` + +## Composition dates + +This section relies on the dataset's metadata. + +```{code-cell} ipython3 +valid_composed_start = pd.to_numeric(all_metadata.composed_start, errors='coerce') +valid_composed_end = pd.to_numeric(all_metadata.composed_end, errors='coerce') +print(f"Composition dates range from {int(valid_composed_start.min())} {valid_composed_start.idxmin()} " + f"to {int(valid_composed_end.max())} {valid_composed_end.idxmax()}.") +``` + +### Mean composition years per corpus + +```{code-cell} ipython3 +:tags: [hide-input] + +summary = all_metadata.copy() +summary.length_qb = all_measures.groupby(level=[0,1]).act_dur.sum() * 4.0 +summary = pd.concat([summary, + all_notes.groupby(level=[0,1]).size().rename('notes'), + ], axis=1) +bar_data = pd.concat([mean_composition_years.rename('year'), + summary.groupby(level='corpus').size().rename('pieces')], + axis=1 + ).reset_index() +fig = px.bar(bar_data, x='year', y='pieces', color='corpus', + color_discrete_map=corpus_colors, + ) +fig.update_traces(width=5) +fig.update_layout(**STD_LAYOUT) +fig.update_yaxes(gridcolor='lightgrey') +fig.update_traces(width=5) +``` + +### Composition years histogram + +```{code-cell} ipython3 +:tags: [hide-input] + +hist_data = summary.reset_index() +hist_data.corpus = hist_data.corpus.map(corpus_names) +fig = px.histogram(hist_data, x='composed_end', color='corpus', + labels=dict(composed_end='decade', + count='pieces', + ), + color_discrete_map=corpus_name_colors, + ) +fig.update_traces(xbins=dict( + size=10 +)) +fig.update_layout(**STD_LAYOUT) +fig.update_yaxes(gridcolor='lightgrey') +fig.show() +``` + +## Dimensions + +### Overview + +```{code-cell} ipython3 +:tags: [hide-input] + +corpus_metadata = summary.groupby(level=0) +n_pieces = corpus_metadata.size().rename('pieces') +absolute_numbers = dict( + measures = corpus_metadata.last_mn.sum(), + length = corpus_metadata.length_qb.sum(), + notes = corpus_metadata.notes.sum(), + labels = corpus_metadata.label_count.sum(), +) +absolute = pd.DataFrame.from_dict(absolute_numbers) +absolute = pd.concat([n_pieces, absolute], axis=1) +sum_row = pd.DataFrame(absolute.sum(), columns=['sum']).T +absolute = pd.concat([absolute, sum_row]) +relative = absolute.div(n_pieces, axis=0) +complete_summary = pd.concat([absolute, relative, absolute.iloc[:1,2:].div(absolute.measures, axis=0)], axis=1, keys=['absolute', 'per piece', 'per measure']) +complete_summary = complete_summary.apply(pd.to_numeric).round(2) +complete_summary.index = complete_summary.index.map(dict(corpus_names, sum='sum')) +complete_summary +``` + +### Measures + +```{code-cell} ipython3 +print(f"{len(all_measures.index)} measures over {len(all_measures.groupby(level=[0,1]))} files.") +all_measures.head() +``` + +```{code-cell} ipython3 +print("Distribution of time signatures per XML measure (MC):") +all_measures.timesig.value_counts(dropna=False) +``` + +### Harmony labels + +All symbols, independent of the local key (the mode of which changes their semantics). + +```{code-cell} ipython3 +try: + all_annotations = dataset.get_facet('expanded') +except Exception: + all_annotations = pd.DataFrame() +n_annotations = len(all_annotations.index) +includes_annotations = n_annotations > 0 +if includes_annotations: + display(all_annotations.head()) + print(f"Concatenated annotation tables contains {all_annotations.shape[0]} rows.") + no_chord = all_annotations.root.isna() + if no_chord.sum() > 0: + print(f"{no_chord.sum()} of them are not chords. Their values are: {all_annotations.label[no_chord].value_counts(dropna=False).to_dict()}") + all_chords = all_annotations[~no_chord].copy() + print(f"Dataset contains {all_chords.shape[0]} tokens and {len(all_chords.chord.unique())} types over {len(all_chords.groupby(level=[0,1]))} documents.") + all_annotations['corpus_name'] = all_annotations.index.get_level_values(0).map(get_corpus_display_name) + all_chords['corpus_name'] = all_chords.index.get_level_values(0).map(get_corpus_display_name) +else: + print(f"Dataset contains no annotations.") +``` \ No newline at end of file diff --git a/homepage/notebooks/requirements.txt b/homepage/notebooks/requirements.txt new file mode 100644 index 0000000..7b210af --- /dev/null +++ b/homepage/notebooks/requirements.txt @@ -0,0 +1,14 @@ +colorlover==0.3.0 +dimcat==0.3.0 +ipython==8.11.0 +jupytext==1.14.5 +ipykernel==6.25.0 +kaleido==0.2.1 +matplotlib==3.7.1 +ms3>=2.2.0 +nbformat==5.7.3 +numpy==1.24.2 +pandas==1.5.3 +plotly==5.13.1 +jinja2==3.1.2 +tabulate==0.9.0 diff --git a/homepage/notebooks/scale_degrees.md b/homepage/notebooks/scale_degrees.md new file mode 100644 index 0000000..5988742 --- /dev/null +++ b/homepage/notebooks/scale_degrees.md @@ -0,0 +1,495 @@ +--- +jupytext: + formats: md:myst,ipynb + text_representation: + extension: .md + format_name: myst + format_version: 0.13 + jupytext_version: 1.15.0 +kernelspec: + display_name: corpus_docs + language: python + name: corpus_docs +--- + +# Annotations + +```{code-cell} ipython3 +--- +mystnb: + code_prompt_hide: Hide imports + code_prompt_show: Show imports +tags: [hide-cell] +--- +import os +from collections import defaultdict, Counter +from fractions import Fraction + +from git import Repo +import dimcat as dc +import ms3 +import pandas as pd +pd.set_option('display.max_rows', 500) +pd.set_option('display.max_columns', 100) +import plotly.express as px +import plotly.graph_objects as go + +from utils import STD_LAYOUT, CADENCE_COLORS, CORPUS_COLOR_SCALE, TYPE_COLORS, chronological_corpus_order, color_background, corpus_mean_composition_years, get_corpus_display_name, get_repo_name, resolve_dir, value_count_df, get_repo_name, print_heading, resolve_dir +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +CORPUS_PATH = os.getenv('CORPUS_PATH', "/home/hentsche/tmp/all_subcorpora/") +print_heading("Notebook settings") +print(f"CORPUS_PATH: {CORPUS_PATH!r}") +CORPUS_PATH = resolve_dir(CORPUS_PATH) +``` + +```{code-cell} ipython3 +:tags: [hide-input] + +repo = Repo(CORPUS_PATH) +print_heading("Data and software versions") +print(f"Data repo '{get_repo_name(repo)}' @ {repo.commit().hexsha[:7]}") +print(f"dimcat version {dc.__version__}") +print(f"ms3 version {ms3.__version__}") +``` + +```{code-cell} ipython3 +:tags: [remove-output] + +dataset = dc.Dataset() +dataset.load(directory=CORPUS_PATH, parse_tsv=False) +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +annotated_view = dataset.data.get_view('annotated') +annotated_view.include('facets', 'measures', 'expanded') +annotated_view.fnames_with_incomplete_facets = False +dataset.data.set_view(annotated_view) +dataset.data.parse_tsv(choose='auto') +dataset.get_indices() +dataset.data +``` + +```{code-cell} ipython3 +:tags: [remove-input] + +print(f"N = {dataset.data.count_pieces()} annotated pieces, {dataset.data.count_parsed_tsvs()} parsed dataframes.") +``` + +```{code-cell} ipython3 +all_metadata = dataset.data.metadata() +assert len(all_metadata) > 0, "No pieces selected for analysis." +print(f"Metadata covers {len(all_metadata)} of the {dataset.data.count_pieces()} scores.") +mean_composition_years = corpus_mean_composition_years(all_metadata) +chronological_order = mean_composition_years.index.to_list() +corpus_colors = dict(zip(chronological_order, CORPUS_COLOR_SCALE)) +corpus_names = {corp: get_corpus_display_name(corp) for corp in chronological_order} +chronological_corpus_names = list(corpus_names.values()) +corpus_name_colors = {corpus_names[corp]: color for corp, color in corpus_colors.items()} +``` + +## DCML harmony labels + +```{code-cell} ipython3 +:tags: [hide-input] + +try: + all_annotations = dataset.get_facet('expanded') +except Exception: + all_annotations = pd.DataFrame() +n_annotations = len(all_annotations.index) +includes_annotations = n_annotations > 0 +if includes_annotations: + display(all_annotations.head()) + print(f"Concatenated annotation tables contain {all_annotations.shape[0]} rows.") + no_chord = all_annotations.root.isna() + if no_chord.sum() > 0: + print(f"{no_chord.sum()} of them are not chords. Their values are: {all_annotations.label[no_chord].value_counts(dropna=False).to_dict()}") + all_chords = all_annotations[~no_chord].copy() + print(f"Dataset contains {all_chords.shape[0]} tokens and {len(all_chords.chord.unique())} types over {len(all_chords.groupby(level=[0,1]))} documents.") + all_annotations['corpus_name'] = all_annotations.index.get_level_values(0).map(corpus_names) + all_chords['corpus_name'] = all_chords.index.get_level_values(0).map(corpus_names) +else: + print(f"Dataset contains no annotations.") +``` + +## Key areas + +```{code-cell} ipython3 +from ms3 import roman_numeral2fifths, transform, resolve_all_relative_numerals, replace_boolean_mode_by_strings +keys_segmented = dc.LocalKeySlicer().process_data(dataset) +keys = keys_segmented.get_slice_info() +print(f"Overall number of key segments is {len(keys.index)}") +keys["localkey_fifths"] = transform(keys, roman_numeral2fifths, ['localkey', 'globalkey_is_minor']) +keys.head(5).style.apply(color_background, subset="localkey") +``` + +### Durational distribution of local keys + +All durations given in quarter notes + +```{code-cell} ipython3 +key_durations = keys.groupby(['globalkey_is_minor', 'localkey']).duration_qb.sum().sort_values(ascending=False) +print(f"{len(key_durations)} keys overall including hierarchical such as 'III/v'.") +``` + +```{code-cell} ipython3 +keys_resolved = resolve_all_relative_numerals(keys) +key_resolved_durations = keys_resolved.groupby(['globalkey_is_minor', 'localkey']).duration_qb.sum().sort_values(ascending=False) +print(f"{len(key_resolved_durations)} keys overall after resolving hierarchical ones.") +key_resolved_durations +``` + +#### Distribution of local keys for piece in major and in minor + +`globalkey_mode=minor` => Piece is in Minor + +```{code-cell} ipython3 +pie_data = replace_boolean_mode_by_strings(key_resolved_durations.reset_index()) +px.pie(pie_data, names='localkey', values='duration_qb', facet_col='globalkey_mode') +``` + +#### Distribution of intervals between localkey tonic and global tonic + +```{code-cell} ipython3 +localkey_fifths_durations = keys.groupby(['localkey_fifths', 'localkey_is_minor']).duration_qb.sum() +bar_data = replace_boolean_mode_by_strings(localkey_fifths_durations.reset_index()) +bar_data.localkey_fifths = bar_data.localkey_fifths.map(ms3.fifths2iv) +fig = px.bar(bar_data, x='localkey_fifths', y='duration_qb', color='localkey_mode', log_y=True, barmode='group', + labels=dict(localkey_fifths='Roots of local keys as intervallic distance from the global tonic', + duration_qb='total duration in quarter notes', + localkey_mode='mode' + ), + color_discrete_sequence=CORPUS_COLOR_SCALE, + ) +fig.update_layout(**STD_LAYOUT) +fig.update_yaxes(gridcolor='lightgrey') +fig.show() +``` + +### Ratio between major and minor key segments by aggregated durations +#### Overall + +```{code-cell} ipython3 +keys.duration_qb = pd.to_numeric(keys.duration_qb) +maj_min_ratio = keys.groupby("localkey_is_minor").duration_qb.sum().to_frame() +maj_min_ratio['fraction'] = (100.0 * maj_min_ratio.duration_qb / maj_min_ratio.duration_qb.sum()).round(1) +maj_min_ratio +``` + +#### By dataset + +```{code-cell} ipython3 +segment_duration_per_dataset = keys.groupby(["corpus", "localkey_is_minor"]).duration_qb.sum().round(2) +norm_segment_duration_per_dataset = 100 * segment_duration_per_dataset / segment_duration_per_dataset.groupby(level="corpus").sum() +maj_min_ratio_per_dataset = pd.concat([segment_duration_per_dataset, + norm_segment_duration_per_dataset.rename('fraction').round(1).astype(str)+" %"], + axis=1) +maj_min_ratio_per_dataset['corpus_name'] = maj_min_ratio_per_dataset.index.get_level_values('corpus').map(corpus_names) +maj_min_ratio_per_dataset['mode'] = maj_min_ratio_per_dataset.index.get_level_values('localkey_is_minor').map({False: 'major', True: 'minor'}) +``` + +```{code-cell} ipython3 +fig = px.bar(maj_min_ratio_per_dataset.reset_index(), + x="corpus_name", + y="duration_qb", + color="mode", + text='fraction', + labels=dict(dataset='', duration_qb="duration in 𝅘𝅥", corpus_name='Key segments grouped by corpus'), + category_orders=dict(dataset=chronological_order) + ) +fig.update_layout(**STD_LAYOUT) +fig.show() +``` + +## Harmony labels +### Unigrams +For computing unigram statistics, the tokens need to be grouped by their occurrence within a major or a minor key because this changes their meaning. To that aim, the annotated corpus needs to be sliced into contiguous localkey segments which are then grouped into a major (`is_minor=False`) and a minor group. + +```{code-cell} ipython3 +root_durations = all_chords[all_chords.root.between(-5,6)].groupby(['root', 'chord_type']).duration_qb.sum() +# sort by stacked bar length: +#root_durations = root_durations.sort_values(key=lambda S: S.index.get_level_values(0).map(S.groupby(level=0).sum()), ascending=False) +bar_data = root_durations.reset_index() +bar_data.root = bar_data.root.map(ms3.fifths2iv) +px.bar(bar_data, x='root', y='duration_qb', color='chord_type') +``` + +```{code-cell} ipython3 +relative_roots = all_chords[['numeral', 'duration_qb', 'relativeroot', 'localkey_is_minor', 'chord_type']].copy() +relative_roots['relativeroot_resolved'] = transform(relative_roots, ms3.resolve_relative_keys, ['relativeroot', 'localkey_is_minor']) +has_rel = relative_roots.relativeroot_resolved.notna() +relative_roots.loc[has_rel, 'localkey_is_minor'] = relative_roots.loc[has_rel, 'relativeroot_resolved'].str.islower() +relative_roots['root'] = transform(relative_roots, roman_numeral2fifths, ['numeral', 'localkey_is_minor']) +chord_type_frequency = all_chords.chord_type.value_counts() +replace_rare = ms3.map_dict({t: 'other' for t in chord_type_frequency[chord_type_frequency < 500].index}) +relative_roots['type_reduced'] = relative_roots.chord_type.map(replace_rare) +#is_special = relative_roots.chord_type.isin(('It', 'Ger', 'Fr')) +#relative_roots.loc[is_special, 'root'] = -4 +``` + +```{code-cell} ipython3 +root_durations = relative_roots.groupby(['root', 'type_reduced']).duration_qb.sum().sort_values(ascending=False) +bar_data = root_durations.reset_index() +bar_data.root = bar_data.root.map(ms3.fifths2iv) +root_order = bar_data.groupby('root').duration_qb.sum().sort_values(ascending=False).index.to_list() +fig = px.bar(bar_data, x='root', y='duration_qb', color='type_reduced', barmode='group', log_y=True, + color_discrete_map=TYPE_COLORS, + category_orders=dict(root=root_order, + type_reduced=relative_roots.type_reduced.value_counts().index.to_list(), + ), + labels=dict(root="intervallic difference between chord root to the local or secondary tonic", + duration_qb="duration in quarter notes", + type_reduced="chord type", + ), + width=1000, + height=400, + ) +fig.update_layout(**STD_LAYOUT, + legend=dict( + orientation='h', + xanchor="right", + x=1, + y=1, + ) + ) +fig.update_yaxes(gridcolor='lightgrey') +fig.show() +``` + +```{code-cell} ipython3 +print(f"Reduced to {len(set(bar_data.iloc[:,:2].itertuples(index=False, name=None)))} types. Paper cites the sum of types in major and types in minor (see below), treating them as distinct.") +``` + +```{code-cell} ipython3 +dim_or_aug = bar_data[bar_data.root.str.startswith("a") | bar_data.root.str.startswith("d")].duration_qb.sum() +complete = bar_data.duration_qb.sum() +print(f"On diminished or augmented scale degrees: {dim_or_aug} / {complete} = {dim_or_aug / complete}") +``` + +```{code-cell} ipython3 +mode_slices = dc.ModeGrouper().process_data(keys_segmented) +``` + +### Whole dataset + +```{code-cell} ipython3 +mode_slices.get_slice_info() +``` + +```{code-cell} ipython3 +unigrams = dc.ChordSymbolUnigrams(once_per_group=True).process_data(mode_slices) +``` + +```{code-cell} ipython3 +unigrams.group2pandas = "group_of_series2series" +``` + +```{code-cell} ipython3 +unigrams.get(as_pandas=True) +``` + +```{code-cell} ipython3 +k = 20 +modes = {True: 'MINOR', False: 'MAJOR'} +for (is_minor,), ugs in unigrams.iter(): + print(f"TOP {k} {modes[is_minor]} UNIGRAMS\n{ugs.shape[0]} types, {ugs.sum()} tokens") + print(ugs.head(k).to_string()) +``` + +```{code-cell} ipython3 +ugs_dict = {modes[is_minor].lower(): (ugs/ugs.sum() * 100).round(2).rename('%').reset_index() for (is_minor,), ugs in unigrams.iter()} +ugs_df = pd.concat(ugs_dict, axis=1) +ugs_df.columns = ['_'.join(map(str, col)) for col in ugs_df.columns] +ugs_df.index = (ugs_df.index + 1).rename('k') +ugs_df.iloc[:50] +``` + +```{code-cell} ipython3 +chords_by_localkey = mode_slices.get_facet('expanded') +chords_by_localkey +``` + +```{code-cell} ipython3 +for is_minor, df in chords_by_localkey.groupby(level=0, group_keys=False): + df = df.droplevel(0) + df = df[df.bass_note.notna()] + sd = ms3.fifths2sd(df.bass_note).rename('sd') + sd.index = df.index + sd_progression = df.groupby(level=[0,1,2], group_keys=False).bass_note.apply(lambda S: S.shift(-1) - S).rename('sd_progression') + if is_minor: + chords_by_localkey_minor = pd.concat([df, sd, sd_progression], axis=1) + else: + chords_by_localkey_major = pd.concat([df, sd, sd_progression], axis=1) +``` + +## Scale degrees + +```{code-cell} ipython3 +chords_by_localkey_minor +``` + +```{code-cell} ipython3 +import plotly.graph_objects as go +from collections import Counter, defaultdict + +def make_sunburst(chords, mode): + in_scale = [] + for sd, sd_prog in chords[['sd', 'sd_progression']].itertuples(index=False): + if len(sd) == 1: + in_scale.append(sd) + label_counts = Counter(in_scale) + labels, values = list(label_counts.keys()), list(label_counts.values()) + #labels, values = zip(*list((sd, label_counts[sd]) for sd in sorted(label_counts))) + parents = [mode] * len(labels) + labels = [mode] + labels + parents = [""] + parents + values = [len(chords)] + values + fig =go.Figure(go.Sunburst( + labels=labels, + parents=parents, + values=values, + branchvalues="total" + )) + fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + return fig + +make_sunburst(chords_by_localkey_minor, 'minor') +``` + +```{code-cell} ipython3 +def make_sunburst(chords, mode): + in_scale = [] + sd2prog = defaultdict(Counter) + for sd, sd_prog in chords[['sd', 'sd_progression']].itertuples(index=False): + if len(sd) == 1: + in_scale.append(sd) + sd2prog[sd].update(["∎"] if pd.isnull(sd_prog) else [str(sd_prog)]) + label_counts = Counter(in_scale) + labels, values = list(label_counts.keys()), list(label_counts.values()) + #labels, values = zip(*list((sd, label_counts[sd]) for sd in sorted(label_counts))) + parents = [mode] * len(labels) + labels = [mode] + labels + parents = [""] + parents + values = [len(chords)] + values + #print(sd2prog) + print(len(labels), len(parents), len(values)) + for scad, prog_counts in sd2prog.items(): + for prog, cnt in prog_counts.most_common(): + labels.append(prog) + parents.append(scad) + values.append(cnt) + if cnt < 3000: + break + print(f"added {prog}, {scad}, {cnt}") + break + + fig =go.Figure(go.Sunburst( + labels=labels, + parents=parents, + values=values, + branchvalues="total" + )) + fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + return fig + +make_sunburst(chords_by_localkey_minor, 'minor') +``` + +```{code-cell} ipython3 +fig =go.Figure(go.Sunburst( + labels=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"], + parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ], + values=[10, 14, 12, 10, 2, 6, 6, 4, 4], +)) +fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + +fig.show() +``` + +```{code-cell} ipython3 +fig =go.Figure(go.Sunburst( + labels=["major", "Cain", "1", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"], + parents=["", "major", "major", "1", "1", "major", "major", "Awan", "major" ], + values=[10, 14, 12, 10, 2, 6, 6, 4, 4], +)) +fig.update_layout(margin = dict(t=0, l=0, r=0, b=0)) + +fig.show() +``` + +```{code-cell} ipython3 +df +``` + +```{code-cell} ipython3 +df = px.data.tips() +fig = px.sunburst(df, path=['sex', 'day', 'time'], values='total_bill', color='time') +fig.show() +``` + +```{code-cell} ipython3 +#localkey_major_no_repeats = ms3.segment_by_adjacency_groups(chords_by_localkey_major, ['sd', 'figbass'], ) +#localkey_major_no_repeats +``` + +```{code-cell} ipython3 +def safe_interval(fifths): + if pd.isnull(fifths): + return "∎" + return ms3.fifths2iv(fifths, smallest=True) +``` + +```{code-cell} ipython3 +def prepare_sunburst_data(chords): + chord_data = chords[chords.sd.str.len() == 1].copy() + chord_data["interval"] = ms3.transform(chord_data.sd_progression, safe_interval).fillna("∎") + chord_data.figbass.fillna('3', inplace=True) + chord_data["following_figbass"] = chord_data.groupby(level=[0,1,2],).figbass.shift(-1).fillna("∎") + return chord_data + +column2name = dict( + sd="scale degree", + figbass="bass figure", + interval="bass progression", + following_figbass="subsequent figure" +) + +def rectangular_sunburst( + chords, + path = ['sd', 'figbass', 'interval'], + height = 1500, + title = "Sunburst", +): + chord_data = prepare_sunburst_data(chords) + title = f"{title} ({' - '.join(column2name[col] for col in path)})" + return px.sunburst( + chord_data, + path=path, + height=height, + title=title, + ) + +rectangular_sunburst(chords_by_localkey_major, title="MAJOR") +``` + +```{code-cell} ipython3 +rectangular_sunburst(chords_by_localkey_major, ['sd', 'interval', 'figbass', 'following_figbass'], title="MAJOR") +``` + +```{code-cell} ipython3 +rectangular_sunburst(chords_by_localkey_minor, title="MINOR") +``` + +```{code-cell} ipython3 +rectangular_sunburst(chords_by_localkey_minor, ['sd', 'interval', 'figbass'], title="MINOR") +``` + +```{code-cell} ipython3 + +``` \ No newline at end of file diff --git a/homepage/notebooks/utils.py b/homepage/notebooks/utils.py new file mode 100644 index 0000000..6e6cd5a --- /dev/null +++ b/homepage/notebooks/utils.py @@ -0,0 +1,122 @@ +import os +from typing import List +from functools import lru_cache +import numpy as np +import colorlover +from git import Repo +import plotly.express as px +import pandas as pd + +STD_LAYOUT = { + 'paper_bgcolor': '#FFFFFF', + 'plot_bgcolor': '#FFFFFF', + 'margin': {'l': 40, 'r': 0, 'b': 0, 't': 40, 'pad': 0}, + 'font': {'size': 15} +} + +CADENCE_COLORS = dict(zip(('HC', 'PAC', 'PC', 'IAC', 'DC', 'EC'), colorlover.scales['6']['qual']['Set1'])) +CORPUS_COLOR_SCALE = px.colors.qualitative.D3 +TYPE_COLORS = dict(zip(('Mm7', 'M', 'o7', 'o', 'mm7', 'm', '%7', 'MM7', 'other'), colorlover.scales['9']['qual']['Paired'])) + +CORPUS_NAMES = dict( + gastoldi_baletti = 'Gastoldi Baletti', + peri_euridice = 'Peri Euridice', + monteverdi_madrigals = 'Monteverdi Madrigals', + sweelinck_keyboard = 'Sweelinck Keyboard', + frescobaldi_fiori_musicali = 'Frescobaldi Fiori Musicali', + kleine_geistliche_konzerte = 'Schütz Kleine Geistliche Konzerte', + corelli = 'Corelli Trio Sonatas', + couperin_clavecin = 'Couperin Clavecin', + handel_keyboard = 'Handel Keyboard', + bach_en_fr_suites = 'Bach Suites', + bach_solo = 'Bach Solo', + couperin_concerts = 'Couperin Concerts Royaux', + pergolesi_stabat_mater = 'Pergolesi Stabat Mater', + scarlatti_sonatas = 'Scarlatti Sonatas', + wf_bach_sonatas = 'WF Bach Sonatas', + jc_bach_sonatas = 'JC Bach Sonatas', + mozart_piano_sonatas = 'Mozart Piano Sonatas', + pleyel_quartets = 'Pleyel Quartets', + beethoven_piano_sonatas = 'Beethoven Sonatas', + kozeluh_sonatas = 'Kozeluh Sonatas', + ABC = 'Beethoven String Quartets', + schubert_dances = 'Schubert Dances', + schubert_winterreise = 'Schubert Winterreise', + mendelssohn_quartets = 'Mendelssohn Quartets', + chopin_mazurkas = 'Chopin Mazurkas', + schumann_kinderszenen = 'R Schumann Kinderszenen', + schumann_liederkreis = 'R Schumann Liederkreis', + c_schumann_lieder = 'C Schumann Lieder', + liszt_pelerinage = 'Liszt Années', + wagner_overtures = 'Wagner Overtures', + tchaikovsky_seasons = 'Tchaikovsky Seasons', + dvorak_silhouettes = 'Dvořák Silhouettes', + grieg_lyric_pieces = 'Grieg Lyric Pieces', + mahler_kindertotenlieder = 'Mahler Kindertotenlieder', + ravel_piano = 'Ravel Piano', + debussy_suite_bergamasque = 'Debussy Suite Bergamasque', + bartok_bagatelles = 'Bartok Bagatelles', + medtner_tales = 'Medtner Tales', + poulenc_mouvements_perpetuels = 'Poulenc Mouvements Perpetuels', + rachmaninoff_piano = 'Rachmaninoff Piano', + schulhoff_suite_dansante_en_jazz = 'Schulhoff Suite Dansante En Jazz', +) + +def color_background(x, color="#ffffb3"): + """Format DataFrame cells with given background color.""" + return np.where(x.notna().to_numpy(), f"background-color: {color};", None) + +def corpus_mean_composition_years(df: pd.DataFrame, + year_column: str = 'composed_end') -> pd.Series: + """Expects a dataframe containing ``year_column`` and computes its means by grouping on the first index level ('corpus' by default). + Returns the result as a series where the index contains corpus names and the values are mean composition years. + """ + return df.groupby(level=0)[year_column].mean().sort_values() + +def chronological_corpus_order(df: pd.DataFrame, + year_column: str = 'composed_end') -> List[str]: + """Expects a dataframe containing ``year_column`` and corpus names in the first index level. + Returns the corpus names in chronological order + """ + mean_composition_years = corpus_mean_composition_years(df=df, year_column=year_column) + return mean_composition_years.index.to_list() + + +@lru_cache() +def get_corpus_display_name(repo_name: str) -> str: + """Looks up a repository name in the CORPUS_NAMES constant. If not present, + the repo name is returned as title case. + """ + name = CORPUS_NAMES.get(repo_name, "") + if name == "": + name = ' '.join(s.title() for s in repo_name.split('_')) + return name + + +def get_repo_name(repo: Repo) -> str: + """Gets the repo name from the origin's URL, or from the local path if there is None.""" + if isinstance(repo, str): + repo = Repo(repo) + if len(repo.remotes) == 0: + return repo.git.rev_parse("--show-toplevel") + remote = repo.remotes[0] + return remote.url.split('.git')[0].split('/')[-1] + +def print_heading(heading: str, underline: chr = '-') -> None: + """Underlines the given heading and prints it.""" + print(f"{heading}\n{underline * len(heading)}\n") + +def resolve_dir(directory: str): + return os.path.realpath(os.path.expanduser(directory)) + + +def value_count_df(S, thing=None, counts='counts'): + """Value counts as DataFrame where the index has the name of the given Series or ``thing`` and where the counts + are given in the column ``counts``. + """ + thing = S.name if thing is None else thing + vc = S.value_counts().rename(counts) + normalized = vc / vc.sum() + df = pd.concat([vc.to_frame(), normalized.rename('%')], axis=1) + df.index.rename(thing, inplace=True) + return df diff --git a/homepage/repo_readme.md b/homepage/repo_readme.md new file mode 100644 index 0000000..2e80648 --- /dev/null +++ b/homepage/repo_readme.md @@ -0,0 +1,56 @@ +![Version](https://img.shields.io/github/v/release/DCMLab/debussy_preludes?display_name=tag) +[![DOI](https://zenodo.org/badge/563841627.svg)](https://zenodo.org/badge/latestdoi/563841627) +![GitHub repo size](https://img.shields.io/github/repo-size/DCMLab/debussy_preludes) +![License](https://img.shields.io/badge/license-CC%20BY--NC--SA%204.0-9cf) + + +This is a README file for a data repository originating from the [DCML corpus initiative](https://github.com/DCMLab/dcml_corpora) +and serves as welcome page for both + +* the GitHub repo [https://github.com/DCMLab/debussy_preludes](https://github.com/DCMLab/debussy_preludes) and the corresponding +* documentation page [https://dcmlab.github.io/debussy_preludes](https://dcmlab.github.io/debussy_preludes) + +For information on how to obtain and use the dataset, please refer to [this documentation page](https://dcmlab.github.io/debussy_preludes/introduction). + +# Claude Debussy – Préludes + +This dataset is part of [The Claude Debussy Solo Piano Corpus](https://github.com/DCMLab/debussy_piano). + +## Cite as + +> Laneve, S., Schaerf, L., Cecchetti, G., Hentschel, J., & Rohrmeier, M. (2023). The diachronic development of Debussy’s musical style: A corpus study with Discrete Fourier Transform. Humanities and Social Sciences Communications, 10(1), 289. https://doi.org/10.1057/s41599-023-01796-7 + +## License + +Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)). + +## Overview +| file_name |measures|labels| +|----------------------------|-------:|-----:| +|l117-01_preludes_danseuses | 31| 0| +|l117-02_preludes_voiles | 64| 0| +|l117-03_preludes_vent | 59| 0| +|l117-04_preludes_sons | 53| 0| +|l117-05_preludes_collines | 96| 0| +|l117-06_preludes_pas | 36| 0| +|l117-07_preludes_ce | 70| 0| +|l117-08_preludes_fille | 39| 0| +|l117-09_preludes_serenade | 137| 0| +|l117-10_preludes_cathedrale | 89| 0| +|l117-11_preludes_danse | 96| 0| +|l117-12_preludes_minstrels | 89| 0| +|l123-01_preludes_brouillards| 52| 0| +|l123-02_preludes_feuilles | 52| 0| +|l123-03_preludes_puerta | 90| 0| +|l123-04_preludes_fees | 127| 0| +|l123-05_preludes_bruyeres | 51| 0| +|l123-06_preludes_general | 109| 0| +|l123-07_preludes_terrasse | 45| 0| +|l123-08_preludes_ondine | 74| 0| +|l123-09_preludes_hommage | 54| 0| +|l123-10_preludes_canope | 33| 0| +|l123-11_preludes_tierces | 165| 0| +|l123-12_preludes_feux | 100| 0| + + +*Overview table automatically updated using [ms3](https://johentsch.github.io/ms3/).* \ No newline at end of file diff --git a/homepage/requirements.txt b/homepage/requirements.txt new file mode 100644 index 0000000..20ad274 --- /dev/null +++ b/homepage/requirements.txt @@ -0,0 +1,5 @@ +sphinx==5.3.0 +pydata-sphinx-theme==0.13.3 +myst-nb==0.17.1 +jupyter_sphinx==0.4.0 +-r notebooks/requirements.txt \ No newline at end of file