Installation ============ Installing Python ----------------- INDRA is a Python package so the basic requirement for using it is to have Python installed. Python is shipped with most Linux distributions and with OSX. INDRA works with Python 3.8 or higher. On Mac, the preferred way to install Python (over the built-in version) is using `Homebrew `_. .. code-block:: bash brew install python On Windows, we recommend using `Anaconda `_ which contains compiled distributions of the scientific packages that INDRA depends on (numpy, scipy, pandas, etc). Installing INDRA ---------------- Installing via Github ````````````````````` The preferred way to install INDRA is to use pip and point it to either a remote or a local copy of the latest source code from the repository. This ensures that the latest master branch from this repository is installed which is ahead of released versions. To install directly from Github, do: .. code-block:: bash pip install git+https://github.com/gyorilab/indra.git Or first clone the repository to a local folder and use pip to install INDRA from there locally: .. code-block:: bash git clone https://github.com/gyorilab/indra.git cd indra pip install . Cloning the source code from Github ``````````````````````````````````` You may want to simply clone the source code without installing INDRA as a system-wide package. .. code-block:: bash git clone https://github.com/gyorilab/indra.git To be able to use INDRA this way, you need to make sure that all its requirements are installed. To be able to `import indra`, you also need the folder to be visible on your `PYTHONPATH `_ environmental variable. Installing releases with pip ```````````````````````````` Releases of INDRA are also available via `PyPI `_. You can install the latest released version of INDRA as .. code-block:: bash pip install indra INDRA dependencies ------------------ INDRA depends on a few standard Python packages (e.g. rdflib, requests, objectpath). These packages are installed automatically by pip. Below we provide a detailed description of some extra dependencies that may require special steps to install. PySB and BioNetGen `````````````````` INDRA builds on the `PySB `_ framework to assemble rule-based models of biochemical systems. The `pysb` python package is installed by the standard install procedure. However, to be able to generate mathematical model equations and to export to formats such as SBML, the `BioNetGen `_ framework also needs to be installed in a way that is visible to PySB. Detailed instructions are given in the `PySB documentation `_. .. _pyjniussetup: Pyjnius ``````` Pyjnius is currently not required for any of INDRA's features. However, to be able to use INDRA's optional JAR-based offline reading via the REACH and Eidos APIs, `pyjnius `_ is needed to allow using Java/Scala classes from Python. 1. Install JDK from Oracle: ``_. We recommend using Java 8 (INDRA is regularly tested with Java 8), however, Java 11 is also expected to be compatible, with possible extra configuration steps needed that are not described here. 4. Set JAVA\_HOME to your JDK home directory, for instance .. code-block:: bash export JAVA_HOME=/Library/Java/JavaVirtualMachines/jdk-11.0.2.jdk/Contents/Home 3. Install pyjnius (version 1.6.1 or higher). Recent versions install from prebuilt wheels or build cleanly under current Cython, so the separate Cython install previously required is no longer needed. .. code-block:: bash pip install "pyjnius>=1.6.1" On Mac, you may need to `install Legacy Java for OSX `_. If you have trouble installing it, you can try the following as an alternative. Edit .. code-block:: bash /Library/Java/JavaVirtualMachines/jdk-11.0.2.jdk/Contents/Info.plist (the JDK folder name will need to correspond to your local version), and add `JNI` to `JVMCapabilities` as .. code-block:: xml ... JVMCapabilities CommandLine JNI ... Graphviz ```````` Some INDRA modules contain functions that use `Graphviz `_ to visualize graphs. On most systems, doing .. code-block:: bash pip install pygraphviz works. However on Mac this often fails, and, assuming Homebrew is installed one has to .. code-block:: bash brew install graphviz pip install --config-settings="--global-option=build_ext" \ --config-settings="--global-option=-I$(brew --prefix graphviz)/include/" \ --config-settings="--global-option=-L$(brew --prefix graphviz)/lib/" \ pygraphviz where ``brew --prefix graphviz`` resolves the graphviz install location on both Intel and Apple Silicon Macs. Optional additional dependencies ```````````````````````````````` Some dependencies of INDRA are only needed by certain submodules or are only used in specialized use cases. These are not installed by default but are listed as "extra" requirements, and can be installed separately using pip. An extra dependency list (e.g. one called extra_list) can be installed as .. code-block:: bash pip install indra[extra_list] You can also install all extra dependencies by doing .. code-block:: bash pip install indra[all] In all of the above, you may replace `indra` with `.` (if you're in a local copy of the `indra` folder or with the Github URL of the INDRA repo, depending on your installation method. See also the corresponding `pip documentation `_ for more information. The table below provides the name and the description of each "extra" list of dependencies. +-----------------+------------------------------------------------------+ |Extra list name |Purpose | +=================+======================================================+ | bel | BEL input processing and output assembly | +-----------------+------------------------------------------------------+ | trips_offline | Offline reading with local instance of TRIPS system | +-----------------+------------------------------------------------------+ | reach_offline | Offline reading with local instance of REACH system | +-----------------+------------------------------------------------------+ | eidos_offline | Offline reading with local instance of Eidos system | +-----------------+------------------------------------------------------+ | tkg | Reading with textToKnowledgeGraph | +-----------------+------------------------------------------------------+ | geneways | Geneways reader input processing | +-----------------+------------------------------------------------------+ | hypothesis | Processing hypothes.is annotations | +-----------------+------------------------------------------------------+ | isi | ISI reader input processing | +-----------------+------------------------------------------------------+ | sbml | SBML model export through the PySB Assembler | +-----------------+------------------------------------------------------+ | grounding | Packages for re-grounding and disambiguating entities| +-----------------+------------------------------------------------------+ | sklearn_belief | Belief scoring with scikit-learn models | +-----------------+------------------------------------------------------+ | machine | Running a local instance of a "RAS machine" | +-----------------+------------------------------------------------------+ | explanation | Finding explanatory paths in rule-based models | +-----------------+------------------------------------------------------+ | owl | Processing OWL ontologies | +-----------------+------------------------------------------------------+ | api | Running the INDRA REST API service | +-----------------+------------------------------------------------------+ | aws | Accessing AWS compute and storage resources | +-----------------+------------------------------------------------------+ | graph | Assembling and visualizing Graphviz graphs | +-----------------+------------------------------------------------------+ | plot | Creating and displaying plots | +-----------------+------------------------------------------------------+ | tests | Running the INDRA test suite | +-----------------+------------------------------------------------------+ | all | All of the optional dependencies listed above | +-----------------+------------------------------------------------------+ Configuring INDRA ----------------- Various aspects of INDRA, including API keys, dependency locations, and Java memory limits, are parameterized by a configuration file that lives in ~/.config/indra/config.ini. The default configuration file is provided in indra/resources/default_config.ini, and is copied to ~/.config/indra/config.ini when INDRA starts if no configuration already exists. Every value in the configuration can also be set as an environment variable: for a given configuration key, INDRA will first check for an environment variable with that name and if not present, will use the value in the configuration file. In other words, an environment variable, when set, takes precedence over the value set in the config file. Configuration values include: - REACHPATH: The location of the JAR file containing a local instance of the REACH reading system - EIDOSPATH: The location of the JAR file containing a local instance of the Eidos reading system - SPARSERPATH: The location of a local instance of the Sparser reading system (path to a folder) - DRUMPATH: The location of a local installation of the DRUM reading system (path to a folder) - NDEX_USERNAME, NDEX_PASSWORD: Credentials for accessing the NDEx web service - ELSEVIER_API_KEY, ELSEVIER_INST_KEY: Elsevier web service API keys - BIOGRID_API_KEY: API key for BioGRID web service (see http://wiki.thebiogrid.org/doku.php/biogridrest) - INDRA_DEFAULT_JAVA_MEM_LIMIT: Maximum memory limit for Java virtual machines launched by INDRA - SITEMAPPER_CACHE_PATH: Path to an optional cache (a pickle file) for the SiteMapper's automatically obtained mappings.