Getting Started =============== This page describes how to get started with ActivitySim. .. note:: ActivitySim is under active development .. index:: installation Pre-packaged Installer ---------------------- Begining with version 1.2, ActivitySim is now available for Windows via a pre-packaged installer. This installer provides everything you need to run ActivitySim, including Python, all the necessary supporting packages, and ActivitySim itself. You should only choose this installation process if you plan to use ActivitySim but you don't need or want to do other Python development. Note this installer is provided as an "executable" which (of course) installs a variety of things on your system, and it is quite likely to be flagged by Windows, anti-virus, or institutional IT policies as "unusual" software, which may require special treatment to actually install and use. Download the installer from GitHub `here `_. It is strongly recommended to choose the option to install "for me only", as this should not require administrator privileges on your machine. Pay attention to the *complete path* of the installation location. You will need to know that path to run ActivitySim in the future, as the installer does not modify your "PATH" and the location of the `ActivitySim.exe` command line tool will not be available without knowing the path to where the install has happened. Once the install is complete, ActivitySim can be run directly from any command prompt by running `/Scripts/ActivitySim.exe`. Installation ------------ 1. It is recommended that you install and use a *conda* package manager for your system. One easy way to do so is by using `Mambaforge `__. Mamba is a free open source cross-platform package manager that runs on Windows, OS X and Linux and is fully compatible with conda packages. It is also usually substantially faster than conda itself. Alternatively, if you prefer a package installer backed by corporate tech support available (for a fee) as necessary, you can install `Anaconda 64bit Python 3 `__, although you should consult the `terms of service `__ for this product and ensure you qualify since businesses and governments with over 200 employees do not qualify for free usage. If you're using `conda` instead of `mamba`, just replace every call to `mamba` below with `conda`, as they share the same user interface and most command formats. 2. If you access the internet from behind a firewall, then you may need to configure your proxy server. To do so, create a `.condarc` file in your home installation folder, such as: :: proxy_servers: http: http://myproxy.org:8080 https: https://myproxy.org:8080 ssl_verify: false 3. Create a conda environment (basically a Python install just for this project) using conda Prompt (on Windows) or the terminal (macOS or Linux):: mamba create -n asim python=3.9 activitysim -c conda-forge --override-channels This command will create the environment and install all the dependencies required for running ActivitySim. It is only necessary to create the environment once per machine, you do not need to (re)create the environment for each session. If you would also like to install other tools or optional dependencies, it is possible to do so by adding additional libraries to this command. For example:: mamba create -n asim python=3.9 activitysim jupyterlab larch -c conda-forge --override-channels This example installs a specific version of Python, version 3.9. A similar approach can be used to install specific versions of other libraries as well, including ActivitySim, itself. For example:: mamba create -n asim python=3.9 activitysim=1.0.2 -c conda-forge --override-channels Additional libraries can also be installed later. You may want to consider these tools for certain development tasks:: # packages for testing mamba install pytest pytest-cov coveralls black flake8 pytest-regressions -c conda-forge --override-channels -n asim # packages for building documentation mamba install sphinx numpydoc sphinx_rtd_theme==0.5.2 -c conda-forge --override-channels -n asim # packages for estimation integration mamba install larch -c conda-forge --override-channels -n asim # packages for example notebooks mamba install jupyterlab matplotlib geopandas descartes -c conda-forge --override-channels -n asim To create an environment containing all these optional dependencies at once, you can run the shortcut command :: mamba env create activitysim/ASIM -n asim 4. To use the **asim** environment, you need to activate it :: conda activate asim The activation of the correct environment needs to be done every time you start a new session (e.g. opening a new conda Prompt window). Note that the *activate* and *deactivate* commands to start and stop using environments are called as `conda` even if you are otherwise using `mamba`. Alternative Installation Methods ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you prefer to install ActivitySim without conda, it is possible to do so with pip, although you may find it more difficult to get all of the required dependencies installed correctly. If you can use conda for the dependencies, you can get most of the libraries you need from there:: # required packages for running ActivitySim mamba install cytoolz numpy pandas psutil pyarrow numba pytables pyyaml openmatrix requests -c conda-forge # required for ActivitySim version 1.0.1 and earlier pip install zbox And then simply install just activitysim with pip. :: python -m pip install activitysim If you are using a firewall you may need to add ``--trusted-host pypi.python.org --proxy=myproxy.org:8080`` to this command. For development work, can also install ActivitySim directly from source. Clone the ActivitySim repository, and then from within that directory run:: python -m pip install . -e The "-e" will install in editable mode, so any changes you make to the ActivitySim code will also be reflected in your installation. Installing from source is easier if you have all the necessary dependencies already installed in a development conda environment. Developers can create an environment that has all the optional dependencies preinstalled by running:: mamba env create activitysim/ASIM-DEV If you prefer to use a different environment name than `ASIM-DEV`, just append `--name OTHERNAME` to the command. Then all that's left to do is install ActivitySim itself in editable mode as described above. .. note:: ActivitySim is a 64bit Python 3 library that uses a number of packages from the scientific Python ecosystem, most notably `pandas `__ and `numpy `__. As mentioned above, the recommended way to get your own scientific Python installation is to install 64 bit Anaconda, which contains many of the libraries upon which ActivitySim depends + some handy Python installation management tools. Anaconda includes the ``conda`` command line tool, which does a number of useful things, including creating `environments `__ (i.e. stand-alone Python installations/instances/sandboxes) that are the recommended way to work with multiple versions of Python on one machine. Using conda environments keeps multiple Python setups from conflicting with one another. You need to activate the activitysim environment each time you start a new command session. You can remove an environment with ``conda remove -n asim --all`` and check the current active environment with ``conda info -e``. For more information on Anaconda, see Anaconda's `getting started `__ guide. Run the Primary Example ----------------------- ActivitySim includes a :ref:`cli` for creating examples and running the model. To setup and run the primary example (see :ref:`examples`), do the following: * Open a command prompt * If you installed ActivitySim using conda environments, activate the conda environment with ActivitySim installed (i.e. ``conda activate asim``) * Or, if you used the :ref:`pre-packaged installer`, replace all the commands below that call ``activitysim ...`` with the complete path to your installed location, which is probably something like ``c:\programdata\activitysim\scripts\activitysim.exe``. * Type ``activitysim create -e prototype_mtc -d test_prototype_mtc`` to copy the very small prototype_mtc example to a new test_prototype_mtc directory * Change to the test_prototype_mtc directory * Type ``activitysim run -c configs -o output -d data`` to run the example * Review the outputs in the output directory .. note:: Common configuration settings can be overridden at runtime. See ``activitysim -h``, ``activitysim create -h`` and ``activitysim run -h``. ActivitySim model runs can be configured with settings file inheritance to avoid duplicating settings across model configurations. See :ref:`cli` for more information. Additional examples, including the full scale prototype MTC regional demand model, estimation integration examples, multiple zone system examples, and examples for agency partners are available for creation by typing ``activitysim create -l``. To create these examples, ActivitySim downloads the (large) input files from the `ActivitySim resources `__ repository. See :ref:`examples` for more information. Try the Notebooks ----------------- ActivitySim includes a `Jupyter Notebook `__ recipe book with interactive examples. To run a Jupyter notebook, do the following: * Open a conda prompt and activate the conda environment with ActivitySim installed * If needed, ``conda install jupyterlab`` so you can run jupyter notebooks * Type ``jupyter notebook`` to launch the web-based notebook manager * Navigate to the ``examples/prototype_mtc/notebooks`` folder and select a notebook to learn more: * `Getting started `__ * `Summarizing results `__ * `Testing a change in auto ownership `__ * `Adding TNCs `__ * `Memory usage `__ Hardware -------- The computing hardware required to run a model implemented in the ActivitySim framework generally depends on: * The number of households to be simulated for disaggregate model steps * The number of model zones (for each zone system) for aggregate model steps * The number and size of network skims by mode and time-of-day * The number of zone systems, see :ref:`multiple_zone_systems` * The desired runtimes ActivitySim framework models use a significant amount of RAM since they store data in-memory to reduce data access time in order to minimize runtime. For example, the prototype MTC example model has 2.7 million households, 7.5 million people, 1475 zones, 826 network skims and has been run between one hour and one day depending on the amount of RAM and number of processors allocated. See :ref:`multiprocessing` and :ref:`chunk_size` for more information. .. note:: ActivitySim has been run in the cloud, on both Windows and Linux using `Microsoft Azure `__. Example configurations, scripts, and runtimes are in the ``other_resources\example_azure`` folder. .. _mkl_settings : MKL Settings ~~~~~~~~~~~~ Anaconda Python on Windows uses the `Intel Math Kernel Library `__ for many of its computationally intensive low-level C/C++ calculations. By default, MKL threads many of its routines in order to be performant out-of-the-box. However, for ActivitySim multiprocessing, which processes households in parallel since they are largely independent of one another, it can be advantageous to override threading within processes and instead let ActivitySim run each process with one computing core or thread. In order to do so, override the MKL number of threads setting via a system environment variable that is set before running the model. In practice, this means before running the model, first set the MKL number of threads variable via the command line as follows: ``SET MKL_NUM_THREADS=1``