From 72f2c15985357ade5139a958d762187e765dc140 Mon Sep 17 00:00:00 2001 From: Kylen Solvik Date: Wed, 12 Jun 2024 12:14:08 -0600 Subject: [PATCH] Update README with new install instructions --- README.md | 33 +++++++++++++-------------------- 1 file changed, 13 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 0a4fedb..b3c7313 100644 --- a/README.md +++ b/README.md @@ -20,30 +20,22 @@ Neural Networks,Volume 153, 530-552, ISSN 0893-6080, https://doi.org/10.1016/j.n ## Installation +We recommend setting up a virtual environment using either [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) or [virtualenv](https://virtualenv.pypa.io/en/latest/user_guide.html). #### Clone Repo: ```bash git clone git@github.com:StevePny/DataAssimBench.git ``` -#### Set Up Conda Environment - -```bash -cd DataAssimBench -conda env create -f environment.yml -conda activate dab -``` - #### Install dabench ```bash -pip install . +pip install -e ".[full]" ``` -#### Install dependencies (optional) -The user may have to manually install: +Note: this will create a full installation including the ability to access cloud data or interface with other packages such as qgs. For a minimal installation, run: + ```bash -conda install -c conda-forge jax -conda install -c conda-forge pyqg +pip install -e . ``` ## Quick Start @@ -76,7 +68,7 @@ All data objects are customizable. For data-generators (e.g. numerical models such as Lorenz63, Lorenz96, SQGTurb), this means you can change initial conditions, model parameters, timestep size, number of timesteps, etc. -For data-downloaders (e.g. ENSOIDX, AWS, GCP), this means changing which variables you download, the lat/lon bounding box, the time period, etc. +For data-downloaders (e.g. ENSOIDX, GCP), this means changing which variables you download, the lat/lon bounding box, the time period, etc. The recommended way of specifying options is to pass a keyword argument (kwargs) dictionary. The exact options vary between the different types of data objects, so be sure to check the specific documentation for your chosen generator/downloader more info. @@ -91,13 +83,14 @@ l96_obj.generate(n_steps=1000) # Generate Lorenz96 simulation data l96_obj.values # View the output values ``` -- For example, for the Amazon Web Services (AWS) ERA5 data-downloader, we can select our variables and time period like this: +- For example, for the Google Cloud (GCP) ERA5 data-downloader, we can select our variables and time period like this: ```python -aws_options = {'variables': ['air_pressure_at_mean_sea_level', 'sea_surface_temperature'], - 'years': [1984, 1985]} -aws_obj = data.AWS(**aws_options) # Create data generator object -aws_obj.load() # Loads data. Can also use aws_obj.generate() -aws_obj.values # View the output values +gcp_options = {'variables': ['2m_temperature', 'sea_surface_temperature'], + 'date_start': '2020-06-01' + 'date_end': '2020-06-07'} +gcp_obj = data.GCP(**gcp_options) # Create data generator object +gcp_obj.load() # Loads data. Can also use aws_obj.generate() +gcp_obj.values # View the output values ```