In this section you will download and install Miniconda, which will serve as the Python platform to access the Earth Engine API. As described above, you may use Miniconda or Anaconda, but to minimize impact to your system, this guide will describe a Miniconda installation with no alteration to your system's environmental variables. To run the silent installation of Miniconda for macOS or Linux, specify the -b and -p arguments of the bash installer. The following arguments are supported:-b-Batch mode with no PATH modifications to /.bashrc. Install prerequisite packages; Install AFNI binaries; Install R; Setup Python (opt) Prepare for Bootcamp; Evaluate setup/system; Niceify terminal (optional, but goood) Install extras (optional, but recommended for Bootcamp prep) Keep up-to-date (remember!) 1.1.3. Linux, Ubuntu 16.04 - 17.10. Install prerequisite packages; Install. Miniconda is a bare-essentials version of the Anaconda package; you'll need to install packages like scikit-image yourself. Like Anaconda, it installs Python and provides virtual environments. Conda documentation. Conda-forge, a conda channel maintained with the latest scikit-image package. Curl-O https: // repo. Com / miniconda / Miniconda3-latest-Linux-x8664. Sh sh Miniconda3-latest-Linux-x8664. Sh Follow the instructions in the installer. If you encounter problems, refer to the Miniconda documentation.
How you should install scikit-image depends on your needs and skills:
Simplest solution:scientific Python distribution.
If you can install Python packages and work in virtual environments:
Easy solution but with pitfalls: system package manager (yum, apt, …).
You're looking to contribute to scikit-image.
Supported platforms¶
Windows 64-bit on x86 processors
Mac OS X on x86 processors
Linux 64-bit on x86 processors
For information on other platforms, see other platforms.
Version check¶
To see whether scikit-image is already installed or to check if an install hasworked, run the following in a Python shell or Jupyter notebook:
or, from the command line:
(Try python3 if python is unsuccessful.)
Install Miniconda Mac Brew
You'll see the version number if scikit-image is installed andan error message otherwise.
Scientific Python distributions¶
In a single install these give you Python,scikit-image and libraries it depends on, and other useful scientificpackages. They install into an isolated environment, so they won't conflictwith any existing installed programs.
Drawbacks are that the install can be large and you may not getthe most recent scikit-image.
We recommend one of these distributions:
When using the scikit-imagedocumentation, make sure it's for the version you've installed (seeVersion check above).
Installation via pip and conda¶
These install only scikit-image and its dependencies; pip has an option toinclude related packages.
pip¶
Install Miniconda In Mac
Prerequisites to a pip install: You're able to use your system's command line toinstall packages and are using avirtual environment(any ofseveral).
While it is possible to use pip without a virtual environment, it is not advised:virtual environments create a clean Python environment that does not interferewith any existing system installation, can be easily removed, and contain onlythe package versions your application needs. They help avoid a commonchallenge known asdependency hell.
To install the current scikit-image you'll need at least Python 3.6. Ifyour Python is older, pip will find the most recent compatible version.
To include a selection of other scientific Python packages that expandscikit-image's capabilities to include, e.g., parallel processing, youcan install the package scikit-image[optional]:
Warning
Please do not use the command sudo and pip together as pip mayoverwrite critical system libraries which may require you to reinstall youroperating system.
conda¶
Miniconda is a bare-essentials version of the Anaconda package; you'll need toinstall packages like scikit-image yourself. Like Anaconda, it installsPython and provides virtual environments.
conda-forge, a conda channel maintainedwith the latest
scikit-imagepackage
Once you have your conda environment set up, you can install scikit-imagewith the command:
System package managers¶
Using a package manager (yum, apt-get, etc.) to install scikit-imageor other Python packages is not your best option:
You're likely to get an older version.
You'll probably want to make updates and add new packages outside ofthe package manager, leaving you with the same kind ofdependency conflicts you see when using pip without a virtual environment.
There's an added risk because operating systems use Python, so if youmake system-wide Python changes (installing as root or using sudo),you can break the operating system.
Downloading all demo datasets¶
Some of the data used in our examples is hosted online and is not installedby default by the procedures explained above. Data are downloaded once, at thefirst call, but this requires an internet connection. If you prefer downloadingall the demo datasets to be able to work offline, you can run this command:
or call download_all() in your favourite interactive Python environment(IPython, Jupyter notebook, …).
Other platforms¶
We still support Windows 32-bit on x86 processors but urge switchingto Windows 64-bit.
Unsupported platforms include:
Linux on 32-bit x86 processors.
Linux on 32-bit on ARM processors (Raspberry Pi running Raspbian):
While we do not officially support this distribution, we point users topiwheelsand theirscikit-image's specific page.
You may need to install additional system dependencies listed forimagecodecs.Seeissue 4721.
Linux on 64-bit ARM processors (Nvidia Jetson):
Follow the conversation onissue 4705.
Although these platforms lack official support, many of the coredevelopers have experience with them and can help with questions.
If you want to install on an unsupported platform, trybuilding from source.
Tell us which other platforms you'd like to see scikit-image on!We are very interested in how scikit-image getsused.
If you'd like to package scikit-image for an as-yet-unsupported platform,reach out on GitHub.
Additional help¶
If you still have questions, reach out through
our forum on image.sc
our mailing list
our chat channel
To suggest a change in these instructions,please open an issue on GitHub.
Note:Bioconda supports only 64-bit Linux and Mac OS.
1. Install conda¶
Bioconda requires the conda package manager to be installed. If youhave an Anaconda Python installation, you already have it. Otherwise,the best way to install it is with the Minicondapackage. The Python 3 version is recommended.
On MacOS, run:
On Linux, run:
Follow the instructions in the installer. If you encounter problems,refer to the Miniconda documentation. You can also join ourGitter channel to ask other users for help.
See also
The conda FAQs explain howit's easy to use with existing Python installations.
2. Set up channels¶
After installing conda you will need to add the bioconda channel as well as theother channels bioconda depends on. It is important to add them in thisorder so that the priority is set correctly (that is, conda-forge is highestpriority).
The conda-forge channel contains many general-purpose packages not alreadyfound in the defaults channel.
3. Install packages¶
Browse the packages to see what's available.
Bioconda is now enabled, so any packages on the bioconda channel can be installed into the current conda environment:
Or a new environment can be created:
4. Join the team¶
We invite all parties interested in adding/editing package recipes to join the bioconda team,so that their pull requests don't require merging by the core team or other members. To doso, please fork our recipes have a readthrough the Conda documentation.If you ping @bioconda/core in a pull request we will review it and then add you to the team, if you desire.
5. Spread the word¶
Consider adding a badge to your posters and presentations to promotethat a tool can be easily installed from Bioconda.
