Mac Install Anaconda Brew

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Released:

Python module for audio and music processing

Project description

1. Install Python 3 using homebrew (brew install python) or by manually installing the package from Install python3 and python3-pip using the package manager of the Linux Distribution. Install conda using the Anaconda or miniconda installers or the miniforge installers (no administrator permission required for any of those).
2. Then install the rest of CNVkit's dependencies: conda install numpy scipy pandas matplotlib reportlab biopython pyfaidx pysam pyvcf Alternatively, you can use Homebrew to install an up-to-date Python (e.g. Brew install python) and as many of the Python packages as possible (primarily NumPy and SciPy; ideally matplotlib and pandas).

I want to install pip for python 2.7 on my Mac. I think this is the python located in /usr/bin/python. Unfortunately I have already installed Anaconda, which installs python 3.6.3, and changes things so that the command python xxx.py automatically runs xxx.py using python 3.6.3.

A python package for music and audio analysis.

Documentation

See https://librosa.org/doc/ for a complete reference manual and introductory tutorials.

Installation

The latest stable release is available on PyPI, and you can install it by saying

Anaconda users can install using conda-forge:

To build librosa from source, say python setup.py build.Then, to install librosa, say python setup.py install.If all went well, you should be able to execute the demo scripts under examples/(OS X users should follow the installation guide given below).

Alternatively, you can download or clone the repository and use pip to handle dependencies:

or

By calling pip list you should see librosa now as an installed package:

Hints for the Installation

librosa uses soundfile and audioread to load audio files.Note that soundfile does not currently support MP3, which will cause librosa tofall back on the audioread library.

soundfile

If you're using conda to install librosa, then most audio coding dependencies (except MP3) will be handled automatically.

If you're using pip on a Linux environment, you may need to install libsndfilemanually. Please refer to the SoundFile installation documentation for details.

audioread and MP3 support

To fuel audioread with more audio-decoding power (e.g., for reading MP3 files),you may need to install either ffmpeg or GStreamer.

Note that on some platforms, audioread needs at least one of the programs to work properly.

If you are using Anaconda, install ffmpeg by calling

If you are not using Anaconda, here are some common commands for different operating systems: Install traefik docker.

• Linux (apt-get): apt-get install ffmpeg or apt-get install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly
• Linux (yum): yum install ffmpeg or yum install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly
• Mac: brew install ffmpeg or brew install gstreamer
• Windows: download binaries from this website

For GStreamer, you also need to install the Python bindings with

Discussion

Please direct non-development questions and discussion topics to our web forum athttps://groups.google.com/forum/#!forum/librosa

Citing

If you want to cite librosa in a scholarly work, there are two ways to do it.

• If you are using the library for your work, for the sake of reproducibility, please citethe version you used as indexed at Zenodo:

• If you wish to cite librosa for its design, motivation etc., please cite the paperpublished at SciPy 2015:

  McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. 'librosa: Audio and music signal analysis in python.' In Proceedings of the 14th python in science conference, pp. 18-25. 2015.

Release historyRelease notifications | RSS feed

0.8.0

0.7.2

0.7.1

0.7.0

0.7.0rc1 pre-release

0.6.3

0.6.2

0.6.1

0.6.1rc0 pre-release

0.6.0

0.6.0rc1 pre-release

0.6.0rc0 pre-release

0.5.1

0.5.0

0.5.0rc0 pre-release

0.4.3

0.4.3rc0 pre-release

0.4.2

0.4.1

0.4.1rc0 pre-release

0.4.0

0.4.0rc2 pre-release

0.4.0-pre pre-release

0.3.1

0.3.0

0.2.1

0.2.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Hashes for librosa-0.8.0.tar.gz

There are different ways to install scikit-learn:

• Install the latest official release. Thisis the best approach for most users. It will provide a stable versionand pre-built packages are available for most platforms.

• Install the version of scikit-learn provided by youroperating system or Python distribution.This is a quick option for those who have operating systems or Pythondistributions that distribute scikit-learn.It might not provide the latest release version.

• Building the package from source. This is best for users who want thelatest-and-greatest features and aren't afraid of runningbrand-new code. This is also needed for users who wish to contribute to theproject.

Installing the latest release¶

Then run:

In order to check your installation you can use

Note that in order to avoid potential conflicts with other packages it isstrongly recommended to use a virtual environment (venv) or a conda environment.

Using such an isolated environment makes it possible to install a specificversion of scikit-learn with pip or conda and its dependencies independently ofany previously installed Python packages. In particular under Linux is itdiscouraged to install pip packages alongside the packages managed by thepackage manager of the distribution (apt, dnf, pacman…).

Note that you should always remember to activate the environment of your choiceprior to running any Python command whenever you start a new terminal session.

If you have not installed NumPy or SciPy yet, you can also install these usingconda or pip. When using pip, please ensure that binary wheels are used,and NumPy and SciPy are not recompiled from source, which can happen when usingparticular configurations of operating system and hardware (such as Linux ona Raspberry Pi).

Scikit-learn plotting capabilities (i.e., functions start with 'plot_'and classes end with 'Display') require Matplotlib. The examples requireMatplotlib and some examples require scikit-image, pandas, or seaborn. Theminimum version of Scikit-learn dependencies are listed below along with itspurpose.

Warning

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.Scikit-learn 0.21 supported Python 3.5-3.7.Scikit-learn 0.22 supported Python 3.5-3.8.Scikit-learn now requires Python 3.6 or newer.

Note

For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+are required.

Installing on Apple Silicon M1 hardware¶

The recently introduced macos/arm64 platform (sometimes also known asmacos/aarch64) requires the open source community to upgrade the buildconfiguation and automation to properly support it.

At the time of writing (January 2021), the only way to get a workinginstallation of scikit-learn on this hardware is to install scikit-learn and itsdependencies from the conda-forge distribution, for instance using the miniforgeinstallers:

The following issue tracks progress on making it possible to installscikit-learn from PyPI with pip:

Mac Brew Install Anaconda

Third party distributions of scikit-learn¶

Some third-party distributions provide versions ofscikit-learn integrated with their package-management systems.

These can make installation and upgrading much easier for users sincethe integration includes the ability to automatically installdependencies (numpy, scipy) that scikit-learn requires.

The following is an incomplete list of OS and python distributionsthat provide their own version of scikit-learn.

Arch Linux¶

Arch Linux's package is provided through the official repositories aspython-scikit-learn for Python.It can be installed by typing the following command:

Debian/Ubuntu¶

1. Install Python 3 using homebrew (brew install python) or by manually installing the package from Install python3 and python3-pip using the package manager of the Linux Distribution. Install conda using the Anaconda or miniconda installers or the miniforge installers (no administrator permission required for any of those).
2. Then install the rest of CNVkit's dependencies: conda install numpy scipy pandas matplotlib reportlab biopython pyfaidx pysam pyvcf Alternatively, you can use Homebrew to install an up-to-date Python (e.g. Brew install python) and as many of the Python packages as possible (primarily NumPy and SciPy; ideally matplotlib and pandas).

I want to install pip for python 2.7 on my Mac. I think this is the python located in /usr/bin/python. Unfortunately I have already installed Anaconda, which installs python 3.6.3, and changes things so that the command python xxx.py automatically runs xxx.py using python 3.6.3.

A python package for music and audio analysis.

Documentation

See https://librosa.org/doc/ for a complete reference manual and introductory tutorials.

Installation

The latest stable release is available on PyPI, and you can install it by saying

Anaconda users can install using conda-forge:

To build librosa from source, say python setup.py build.Then, to install librosa, say python setup.py install.If all went well, you should be able to execute the demo scripts under examples/(OS X users should follow the installation guide given below).

Alternatively, you can download or clone the repository and use pip to handle dependencies:

or

By calling pip list you should see librosa now as an installed package:

Hints for the Installation

librosa uses soundfile and audioread to load audio files.Note that soundfile does not currently support MP3, which will cause librosa tofall back on the audioread library.

soundfile

If you're using conda to install librosa, then most audio coding dependencies (except MP3) will be handled automatically.

If you're using pip on a Linux environment, you may need to install libsndfilemanually. Please refer to the SoundFile installation documentation for details.

audioread and MP3 support

To fuel audioread with more audio-decoding power (e.g., for reading MP3 files),you may need to install either ffmpeg or GStreamer.

Note that on some platforms, audioread needs at least one of the programs to work properly.

If you are using Anaconda, install ffmpeg by calling

If you are not using Anaconda, here are some common commands for different operating systems: Install traefik docker.

• Linux (apt-get): apt-get install ffmpeg or apt-get install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly
• Linux (yum): yum install ffmpeg or yum install gstreamer1.0-plugins-base gstreamer1.0-plugins-ugly
• Mac: brew install ffmpeg or brew install gstreamer
• Windows: download binaries from this website

For GStreamer, you also need to install the Python bindings with

Discussion

Please direct non-development questions and discussion topics to our web forum athttps://groups.google.com/forum/#!forum/librosa

Citing

If you want to cite librosa in a scholarly work, there are two ways to do it.

• If you are using the library for your work, for the sake of reproducibility, please citethe version you used as indexed at Zenodo:

• If you wish to cite librosa for its design, motivation etc., please cite the paperpublished at SciPy 2015:

  McFee, Brian, Colin Raffel, Dawen Liang, Daniel PW Ellis, Matt McVicar, Eric Battenberg, and Oriol Nieto. 'librosa: Audio and music signal analysis in python.' In Proceedings of the 14th python in science conference, pp. 18-25. 2015.

Release historyRelease notifications | RSS feed

0.8.0

0.7.2

0.7.1

0.7.0

0.7.0rc1 pre-release

0.6.3

0.6.2

0.6.1

0.6.1rc0 pre-release

0.6.0

0.6.0rc1 pre-release

0.6.0rc0 pre-release

0.5.1

0.5.0

0.5.0rc0 pre-release

0.4.3

0.4.3rc0 pre-release

0.4.2

0.4.1

0.4.1rc0 pre-release

0.4.0

0.4.0rc2 pre-release

0.4.0-pre pre-release

0.3.1

0.3.0

0.2.1

0.2.0

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Hashes for librosa-0.8.0.tar.gz

There are different ways to install scikit-learn:

• Install the latest official release. Thisis the best approach for most users. It will provide a stable versionand pre-built packages are available for most platforms.

• Install the version of scikit-learn provided by youroperating system or Python distribution.This is a quick option for those who have operating systems or Pythondistributions that distribute scikit-learn.It might not provide the latest release version.

• Building the package from source. This is best for users who want thelatest-and-greatest features and aren't afraid of runningbrand-new code. This is also needed for users who wish to contribute to theproject.

Installing the latest release¶

Then run:

In order to check your installation you can use

Note that in order to avoid potential conflicts with other packages it isstrongly recommended to use a virtual environment (venv) or a conda environment.

Using such an isolated environment makes it possible to install a specificversion of scikit-learn with pip or conda and its dependencies independently ofany previously installed Python packages. In particular under Linux is itdiscouraged to install pip packages alongside the packages managed by thepackage manager of the distribution (apt, dnf, pacman…).

Note that you should always remember to activate the environment of your choiceprior to running any Python command whenever you start a new terminal session.

If you have not installed NumPy or SciPy yet, you can also install these usingconda or pip. When using pip, please ensure that binary wheels are used,and NumPy and SciPy are not recompiled from source, which can happen when usingparticular configurations of operating system and hardware (such as Linux ona Raspberry Pi).

Scikit-learn plotting capabilities (i.e., functions start with 'plot_'and classes end with 'Display') require Matplotlib. The examples requireMatplotlib and some examples require scikit-image, pandas, or seaborn. Theminimum version of Scikit-learn dependencies are listed below along with itspurpose.

Warning

Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.Scikit-learn 0.21 supported Python 3.5-3.7.Scikit-learn 0.22 supported Python 3.5-3.8.Scikit-learn now requires Python 3.6 or newer.

Note

For installing on PyPy, PyPy3-v5.10+, Numpy 1.14.0+, and scipy 1.1.0+are required.

Installing on Apple Silicon M1 hardware¶

The recently introduced macos/arm64 platform (sometimes also known asmacos/aarch64) requires the open source community to upgrade the buildconfiguation and automation to properly support it.

At the time of writing (January 2021), the only way to get a workinginstallation of scikit-learn on this hardware is to install scikit-learn and itsdependencies from the conda-forge distribution, for instance using the miniforgeinstallers:

The following issue tracks progress on making it possible to installscikit-learn from PyPI with pip:

Mac Brew Install Anaconda

Third party distributions of scikit-learn¶

Some third-party distributions provide versions ofscikit-learn integrated with their package-management systems.

These can make installation and upgrading much easier for users sincethe integration includes the ability to automatically installdependencies (numpy, scipy) that scikit-learn requires.

The following is an incomplete list of OS and python distributionsthat provide their own version of scikit-learn.

Arch Linux¶

Arch Linux's package is provided through the official repositories aspython-scikit-learn for Python.It can be installed by typing the following command:

Debian/Ubuntu¶

The Debian/Ubuntu package is splitted in three different packages calledpython3-sklearn (python modules), python3-sklearn-lib (low-levelimplementations and bindings), python3-sklearn-doc (documentation).Only the Python 3 version is available in the Debian Buster (the more recentDebian distribution).Packages can be installed using apt-get:

Fedora¶

The Fedora package is called python3-scikit-learn for the python 3 version,the only one available in Fedora30.It can be installed using dnf:

Mac Install Anaconda Brew Pub

NetBSD¶

scikit-learn is available via pkgsrc-wip:

Mac Install Anaconda Brew Kit

MacPorts for Mac OSX¶

The MacPorts package is named py-scikits-learn,where XY denotes the Python version.It can be installed by typing the followingcommand:

Anaconda and Enthought Deployment Manager for all supported platforms¶

Anaconda andEnthought Deployment Managerboth ship with scikit-learn in addition to a large set of scientificpython library for Windows, Mac OSX and Linux.

Mac Install Anaconda Brew Reviews

Anaconda offers scikit-learn as part of its free distribution.

Intel conda channel¶

Intel maintains a dedicated conda channel that ships scikit-learn:

This version of scikit-learn comes with alternative solvers for some commonestimators. Those solvers come from the DAAL C++ library and are optimized formulti-core Intel CPUs.

Note that those solvers are not enabled by default, please refer to thedaal4py documentationfor more details.

Compatibility with the standard scikit-learn solvers is checked by running thefull scikit-learn test suite via automated continuous integration as reportedon https://github.com/IntelPython/daal4py.

WinPython for Windows¶

The WinPython project distributesscikit-learn as an additional plugin.

Troubleshooting¶

Error caused by file path length limit on Windows¶

It can happen that pip fails to install packages when reaching the default pathsize limit of Windows if Python is installed in a nested location such as theAppData folder structure under the user home directory, for instance:

In this case it is possible to lift that limit in the Windows registry byusing the regedit tool:

1. Type 'regedit' in the Windows start menu to launch regedit.

2. Go to theComputerHKEY_LOCAL_MACHINESYSTEMCurrentControlSetControlFileSystemkey.

3. Edit the value of the LongPathsEnabled property of that key and setit to 1.

4. Reinstall scikit-learn (ignoring the previous broken installation):