Merge pull request #118 from sertansenturk/tomato_wheel

Tomato setup improvements
sertansenturk · Jan 4, 2020 · 698ca68 · 698ca68
2 parents 3d03fc2 + a905815
commit 698ca68
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diff --git a/.dockerignore b/.dockerignore
@@ -7,6 +7,7 @@
 !setup.py
 !requirements.txt
 !MANIFEST.in
+!README.md
 
 # ignore unnecessary files inside the folders
 **/*~

diff --git a/Changelog.md b/Changelog.md
@@ -3,7 +3,7 @@
 ## tomato v0.14.0
 
 - Added dockerfile (Pull request [#107](https://github.com/sertansenturk/tomato/pull/107))
-- Improved `tomato` setup (Pull request [#110](https://github.com/sertansenturk/tomato/pull/110))
+- Improved `tomato` setup (Pull request [#118](https://github.com/sertansenturk/tomato/pull/118))
 - Dropped Python 2 support; users are encouraged to switch to `tomato` docker (Pull request [#110](https://github.com/sertansenturk/tomato/pull/110))
 - Dropped Mac OSX support; users are encouraged to switch to `tomato` docker (Pull request [#108](https://github.com/sertansenturk/tomato/pull/108))
 - Introduced code linting (Pull request [#117](https://github.com/sertansenturk/tomato/pull/117))

diff --git a/Dockerfile b/Dockerfile
@@ -1,12 +1,14 @@
 FROM ubuntu:18.04
 
-# Install Matlab Compiler Runtime 2015a
-# Adopted from a Dockerfile by Stanford Vistalab: 
+# Install pip, LilyPond, and Matlab Compiler Runtime 2015a
+# MCR installation is adopted from a Dockerfile by Stanford Vistalab: 
 # https://raw.githubusercontent.com/vistalab/docker/master/matlab/runtime/2015b/Dockerfile
 RUN apt-get -qq update && \
     apt-get -qq install -y \
         unzip \
-        wget && \
+        wget \
+        python3-pip \
+        lilypond && \
     mkdir /mcr-install && \
     cd /mcr-install && \
     wget --progress=bar:force http://www.mathworks.com/supportfiles/downloads/R2015a/deployment_files/R2015a/installers/glnxa64/MCR_R2015a_glnxa64_installer.zip && \
@@ -21,22 +23,20 @@ RUN apt-get -qq update && \
     cd / && \
     rm -rf mcr-install
 
-# Install dependencies
+# Install Python dependencies from requirements.txt in advance
+# Useful for development since changes in code will not trigger a layer re-build
 COPY requirements.txt /code/
-RUN apt-get -qq install -y \
-        lilypond \
-        python3-pip && \
-    python3 -m pip install --upgrade pip && \
+RUN python3 -m pip install --upgrade pip && \
     pip3 install -r /code/requirements.txt
 
 # Install tomato
 COPY . /code/
 RUN cd /code && \
-    python3 setup.py install && \ 
+    python3 -m pip install . && \
     cd / && \
     rm -rf code
 
-# Set user, workdir etc.
+# Set user & workdir
 RUN useradd --create-home -s /bin/bash tomato_user
 USER tomato_user
 WORKDIR /home/tomato_user/

diff --git a/README.md b/README.md
@@ -71,7 +71,7 @@ ax[0].set_ylim([50, 500])
 plt.show()
 ```
 
-You can refer to the jupyter notebooks in [demos](https://github.com/sertansenturk/tomato/blob/master/demos) folder for detailed, interactive examples.
+You can refer to the Jupyter notebooks in [demos](https://github.com/sertansenturk/tomato/blob/master/demos) folder for detailed, interactive examples.
 
 ## Installation
 
@@ -83,7 +83,7 @@ There are three steps in the installation:
 
 ### Installing tomato
 
-`tomato` may require several packages to be installed depending on your operating system. For example, in *Ubuntu 16.04* using *Python 3.5*, you have to install the _python 3_, _libxml2, libxslt1, freetype_, and _png_ development packages. You can install them by:
+`tomato` may require several packages to be installed, depending on your operating system. For example, in *Ubuntu 16.04* using *Python 3.5*, you have to install the _python 3_, _libxml2, libxslt1, freetype_, and _png_ development packages. You can install them by:
 
 ```bash
 sudo apt-get install python3 python3.5-dev python3-pip libxml2-dev libxslt1-dev libfreetype6-dev libpng12-dev
@@ -101,19 +101,25 @@ Activate the virtual environment:
 source env/bin/activate
 ```
 
-Then change the current directory to the repository folder and install by:
+Then, change the current directory to the repository folder and install by:
 
 ```bash
 cd path/to/tomato
-python setup.py install
+python -m pip install .
 ```
 
-If you want to edit files in the package and have the changes reflected, you can call:
+If you want to edit files in the package and have the changes reflected, instead, you can call:
 
 ```bash
 python -m pip install -e .
 ```
 
+If you want to run the demo Jupyter notebooks and/or make development, you may include the extras to the installation by:
+
+```bash
+python -m pip install -e .[demos,development]
+```
+
 The requirements are installed during the setup. If that step does not work for some reason, you can install the requirements by calling:
 
 ```bash
@@ -130,13 +136,13 @@ We recommend you to install MATLAB Runtime in the default installation path, as
 
 `tomato` uses LilyPond under the hood to convert the music scores to SVG format.
 
-In most Linux distributions, you can install LilyPond from the software repository of your distribution (e.g. `sudo apt install lilypond` in Debian-based distributions).
+In most Linux distributions, you can install LilyPond from the software repository of your distribution (e.g., `sudo apt install lilypond` in Debian-based distributions).
 
-`tomato` requires *LilyPond* version 2.18.2 or above. If your distribution comes with an older version, we recommend you to download the latest stable version from the [LilyPond website](http://lilypond.org/download.html). If you had to install LilyPond this way, you may need to enter the LilyPond binary path to the "custom" field in [tomato/config/lilypond.cfg](https://github.com/sertansenturk/tomato/tree/master/tomato/config) (the default location is ```$HOME/bin/lilypond```).
+`tomato` requires *LilyPond* version 2.18.2 or above. If your distribution comes with an older version, we recommend you to download the latest stable version from the [LilyPond website](http://lilypond.org/download.html). If you had to install LilyPond this way, you might need to enter the LilyPond binary path to the "custom" field in [tomato/config/lilypond.cfg](https://github.com/sertansenturk/tomato/tree/master/tomato/config) (the default location is ```$HOME/bin/lilypond```).
 
 ## Running tomato using docker
 
-For the reproducility and maintability's sake, `tomato` also comes with `docker` support.
+For the reproducibility and maintability's sake, `tomato` also comes with `docker` support.
 
 To build the docker image simply go to the base folder of the repository and run:
 
@@ -168,7 +174,7 @@ Any data (the music scores, extracted features, training models, figures, output
 
 1. **The notes aligned by `JointAnalyzer.align_audio_score(...)` seems shifted. What is the problem?**
 
-    Your audio input is probably a compressed format such as *mp3*. There are typically shifts between different decoders (and even different versions of the same decoder) when they decode the same compressed audio file. In the predominant melody extraction step (`AudioAnalyzer.extract_pitch(...)`), Essentia has to decode the recording for processing. You observe a shift when the application you use another decoder.
+    Your audio input is probably a compressed format, such as *mp3*. There are typically shifts between different decoders (and even different versions of the same decoder) when they decode the same compressed audio file. In the predominant melody extraction step (`AudioAnalyzer.extract_pitch(...)`), Essentia has to decode the recording for processing. You observe a shift when the application you use another decoder.
 
     These shifts are typically small (e.g., 50 samples ~1ms), so they are not very problematic. Nevertheless, there is no guarantee that the shift will be prominent. If you need "perfect" synchronization, you should use an uncompressed format such as *wav* as the audio input.
 
@@ -177,7 +183,7 @@ Any data (the music scores, extracted features, training models, figures, output
 2. **Which operating systems are supported?**
 
     - `tomato` is fully supported **only in Linux**. It is tested against *Ubuntu 16.04* and *18.04*.
-    - We suggest people to use the [tomato docker image](#running-tomato-using-docker) for other operating systems.
+    - We suggest people use the [tomato docker image](#running-tomato-using-docker) for other operating systems.
     - `tomato` was tested on *Mac OSX Sierra* until version [v0.10.1](https://github.com/sertansenturk/tomato/releases/tag/v0.10.1). You can still install `tomato` on *Mac OSX* by referring to the [Linux installation instructions](#installation), but you need to install and configure [Essentia](https://essentia.upf.edu/installing.html#mac-osx) & [MATLAB Compiler Runtime](https://ssd.mathworks.com/supportfiles/downloads/R2015a/deployment_files/R2015a/installers/maci64/MCR_R2015a_maci64_installer.zip) by yourself.
 
 3. **What are the supported Python versions?**
@@ -206,7 +212,7 @@ Any data (the music scores, extracted features, training models, figures, output
 
         Add the path of the LilyPond binary to the "custom" section in the configuration file: `./tomato/config/lilypond.cfg`.
 
-6. Is `tomato` a fruit or vegetable?
+6. Is `tomato` a fruit or a vegetable?
 
     It has a culture-specific answer.
 
@@ -215,52 +221,52 @@ Any data (the music scores, extracted features, training models, figures, output
 Sertan Şentürk
 contact@sertansenturk.com
 
-## Acknowledgements
+## Acknowledgments
 
 We would like to thank [Harold Hagopian](https://en.wikipedia.org/wiki/Harold_Hagopian), the founder of [Traditional Crossroads](http://traditionalcrossroads.com/About-Us), for allowing us to use Tanburi Cemil Bey's performance of [Uşşak Sazsemaisi](http://musicbrainz.org/recording/f970f1e0-0be9-4914-8302-709a0eac088e) in our demos.
 
 ## References
 
 _The toolbox has been realized as part of the thesis:_
 
-[1] Şentürk, S. (2016). *Computational analysis of audio recordings and music scores for the description and discovery of Ottoman-Turkish makam music.* Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Spain.  
+**[1]** Şentürk, S. (2016). *Computational analysis of audio recordings and music scores for the description and discovery of Ottoman-Turkish makam music.* Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Spain.  
 
 _The methods used in the toolbox are described in the papers:_
 
 __Score Phrase Segmentation__  
-[1] Bozkurt, B., Karaosmanoğlu, M. K., Karaçalı, B., and Ünal, E. (2014). *Usul and makam driven automatic melodic segmentation for Turkish music.* Journal of New Music Research. 43(4):375–389.
+**[2]** Bozkurt, B., Karaosmanoğlu, M. K., Karaçalı, B., and Ünal, E. (2014). *Usul and makam driven automatic melodic segmentation for Turkish music.* Journal of New Music Research. 43(4):375–389.
 
 __Score Section Extraction; Semiotic Section and Phrase Analysis__  
-[2] Şentürk S., and Serra X. (2016). *A method for structural analysis of Ottoman-Turkish makam music scores.* In Proceedings of 6th International Workshop on Folk Music Analysis (FMA 2016), pages 39–46, Dublin, Ireland.
+**[3]** Şentürk S., and Serra X. (2016). *A method for structural analysis of Ottoman-Turkish makam music scores.* In Proceedings of 6th International Workshop on Folk Music Analysis (FMA 2016), pages 39–46, Dublin, Ireland.
 
-__Audio Predominant Melody Extraction__
-[3] Atlı, H. S., Uyar, B., Şentürk, S., Bozkurt, B., and Serra, X. (2014). *Audio feature extraction for exploring Turkish makam music.* In Proceedings of 3rd International Conference on Audio Technologies for Music and Media (ATMM 2014), pages 142–153, Ankara, Turkey.
+__Audio Predominant Melody Extraction__  
+**[4]** Atlı, H. S., Uyar, B., Şentürk, S., Bozkurt, B., and Serra, X. (2014). *Audio feature extraction for exploring Turkish makam music.* In Proceedings of 3rd International Conference on Audio Technologies for Music and Media (ATMM 2014), pages 142–153, Ankara, Turkey.
 
 __Audio Pitch Filter__  
-[4] Bozkurt, B. (2008). *An automatic pitch analysis method for Turkish maqam music.* Journal of New Music Research. 37(1):1–13.
+**[5]** Bozkurt, B. (2008). *An automatic pitch analysis method for Turkish maqam music.* Journal of New Music Research. 37(1):1–13.
 
 __Audio Tonic and Transposition Identification, Makam Recognition, Pitch Distribution Computation, Tuning Analysis__  
-[4] Bozkurt, B. (2008). *An automatic pitch analysis method for Turkish maqam music.* Journal of New Music Research. 37(1):1–13.
-[5] Gedik, A. C., and Bozkurt, B. (2010). *Pitch-frequency histogram-based music information retrieval for Turkish music.* Signal Processing. 90(4):1049–1063.  
-[6] Chordia, P. and Şentürk, S. (2013). *Joint recognition of raag and tonic in North Indian music.* Computer Music Journal. 37(3):82–98.  
+**[6]** Bozkurt, B. (2008). *An automatic pitch analysis method for Turkish maqam music.* Journal of New Music Research. 37(1):1–13.  
+**[7]** Gedik, A. C., and Bozkurt, B. (2010). *Pitch-frequency histogram-based music information retrieval for Turkish music.* Signal Processing. 90(4):1049–1063.  
+**[8]** Chordia, P., and Şentürk, S. (2013). *Joint recognition of raag and tonic in North Indian music.* Computer Music Journal. 37(3):82–98.  
 
 __Audio Tonic Identification from the Last Note__  
-[7] Atlı, H. S., Bozkurt, B., and Şentürk, S. (2015). *A method for tonic frequency identification of Turkish makam music recordings.* In Proceedings of 5th International Workshop on Folk Music Analysis (FMA 2015), pages 119–122, Paris, France.
+**[9]** Atlı, H. S., Bozkurt, B., and Şentürk, S. (2015). *A method for tonic frequency identification of Turkish makam music recordings.* In Proceedings of 5th International Workshop on Folk Music Analysis (FMA 2015), pages 119–122, Paris, France.
 
 __Audio Melodic Progression (Seyir) Analysis__  
-[8] Bozkurt B. (2015). *Computational analysis of overall melodic progression for Turkish Makam Music.* In Penser l’improvisation, pages 289–298, Delatour France, Sampzon.
+**[10]** Bozkurt B. (2015). *Computational analysis of overall melodic progression for Turkish Makam Music.* In Penser l’improvisation, pages 289–298, Delatour France, Sampzon.
 
 __Score-Informed Audio Tonic Identification__  
-[9] Şentürk, S., Gulati, S., and Serra, X. (2013). *Score informed tonic identification for makam music of Turkey.* In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR 2013), pages 175–180, Curitiba, Brazil.
+**[11]** Şentürk, S., Gulati, S., and Serra, X. (2013). *Score informed tonic identification for makam music of Turkey.* In Proceedings of 14th International Society for Music Information Retrieval Conference (ISMIR 2013), pages 175–180, Curitiba, Brazil.
 
 __Score-Informed Audio Tempo Estimation__  
-[10] Holzapfel, A., Şimşekli U., Şentürk S., and Cemgil A. T. (2015). *Section-level modeling of musical audio for linking performances to scores in Turkish makam music.* In Proceedings of 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), pages 141–145, Brisbane, Australia.
+**[12]** Holzapfel, A., Şimşekli U., Şentürk S., and Cemgil A. T. (2015). *Section-level modeling of musical audio for linking performances to scores in Turkish makam music.* In Proceedings of 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), pages 141–145, Brisbane, Australia.
 
 __Audio-Score Section Linking__  
-[11] Şentürk, S., Holzapfel, A., and Serra, X. (2014). *Linking scores and audio recordings in makam music of Turkey.* Journal of New Music Research, 43(1):34–52.
+**[13]** Şentürk, S., Holzapfel, A., and Serra, X. (2014). *Linking scores and audio recordings in makam music of Turkey.* Journal of New Music Research, 43(1):34–52.
 
 __Note-Level Audio-Score Aligment__  
-[12] Şentürk, S., Gulati, S., and Serra, X. (2014). *Towards alignment of score and audio recordings of Ottoman-Turkish makam music.* In Proceedings of 4th International Workshop on Folk Music Analysis (FMA 2014), pages 57–60, Istanbul, Turkey.
+**[14]** Şentürk, S., Gulati, S., and Serra, X. (2014). *Towards alignment of score and audio recordings of Ottoman-Turkish makam music.* In Proceedings of 4th International Workshop on Folk Music Analysis (FMA 2014), pages 57–60, Istanbul, Turkey.
 
 __Score-Informed Audio Predominant Melody Correction; Note Modeling__  
-[13] Şentürk, S., Koduri G. K., and Serra X. (2016). *A score-informed computational description of svaras using a statistical model.* In Proceedings of 13th Sound and Music Computing Conference (SMC 2016), pages 427–433, Hamburg, Germany.
+**[15]** Şentürk, S., Koduri G. K., and Serra X. (2016). *A score-informed computational description of svaras using a statistical model.* In Proceedings of 13th Sound and Music Computing Conference (SMC 2016), pages 427–433, Hamburg, Germany.
diff --git a/setup.py b/setup.py
@@ -6,34 +6,26 @@
 from urllib.request import urlopen
 
 from setuptools import find_packages, setup
-from setuptools.command.install import install
 from tomato import __version__
 
+# Get the long description from the README file
+here = os.path.abspath(os.path.dirname(__file__))
+with open(os.path.join(here, 'README.md'), encoding='utf-8') as f:
+    long_description = f.read()
 
-class CustomInstall(install):
-    """Custom installer for tomato: downloads the binaries from relevant
-    git repositories, installs the requirements, and sets up tomato
-
-    Raises:
-        OSError: if the OS is not supported.
-    """
-    def run(self):
-        # download the binaries
-        self.execute(self._setup_binaries, (),
-                     msg="downloading the binaries from tomato_binaries.")
-
-        # install tomato
-        self.do_egg_install()
 
+class BinarySetup:
     @classmethod
-    def _setup_binaries(cls):
-        """
-        Downloads compiled binaries
+    def setup(cls):
+        """Downloads compiled binaries for the OS from the relevant git repos
+
+        Raises:
+            OSError: if the OS is not supported.
         """
         bin_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                                   'tomato', 'bin')
 
-        # find os, linux or macosx
+        # find os
         sys_os = cls._get_os()
 
         # read configuration file
@@ -79,7 +71,12 @@ def _download_binary(fpath, bin_url, sys_os):
 
         # make the binary executable
         subprocess.call(["chmod -R +x " + fpath], shell=True)
-        print("  downloaded %s to %s" % (bin_url, fpath))
+        print("downloaded %s to %s" % (bin_url, fpath))
+
+
+# download binaries in advance so they are detected as package data during
+# instalation
+BinarySetup.setup()
 
 
 setup(name='tomato',
@@ -88,20 +85,10 @@ def _download_binary(fpath, bin_url, sys_os):
       author_email='contact AT sertansenturk DOT com',
       maintainer='Sertan Senturk',
       maintainer_email='contact AT sertansenturk DOT com',
-      url='http://sertansenturk.com',
+      url='https://github.com/sertansenturk/tomato',
       description='Turkish-Ottoman Makam (M)usic Analysis TOolbox',
-      long_description="""
-Turkish-Ottoman Makam (M)usic Analysis TOolbox
-----------------------------------------------
-tomato is a comprehensive and easy-to-use toolbox for the analysis of audio
-recordings and music scores of Turkish-Ottoman makam music.
-The aim of the toolbox is to allow the user to easily analyze large-scale
-audio recording and music score collections of Turkish-Ottoman makam music,
-using the state of the art methodologies specifically designed for the
-necessities of this tradition. The analysis results can then be further used
-for several tasks such as automatic content description, music
-discovery/recommendation and musicological analysis.
-      """,
+      long_description=long_description,
+      long_description_content_type='text/markdown',
       download_url=(
           'https://github.com/sertansenturk/tomato.git'
           if 'dev' in __version__ else
@@ -145,5 +132,7 @@ def _download_binary(fpath, bin_url, sys_os):
           "musicbrainzngs>=0.6",  # metadata crawling from musicbrainz
           "essentia>=2.1b5;platform_system=='Linux'"  # audio signal processing
           ],
-      cmdclass={'install': CustomInstall},
-      )
+      extras_require={
+          "development": ["tox", "pylint", "flake8"],
+          "demos": ["jupyter"]
+      })