awsm


Nameawsm JSON
Version 0.11.2 PyPI version JSON
download
home_pagehttps://github.com/USDA-ARS-NWRC/awsm
SummaryAutomated Water Supply Model
upload_time2020-09-16 20:36:30
maintainer
docs_urlNone
authorUSDA ARS Northwest Watershed Research Center
requires_python>3.5
licenseCC0 1.0
keywords awsm
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            # Automated Water Supply Model

[![GitHub version](https://badge.fury.io/gh/USDA-ARS-NWRC%2Fawsm.svg)](https://badge.fury.io/gh/USDA-ARS-NWRC%2Fawsm)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.898158.svg)](https://doi.org/10.5281/zenodo.898158)
[![DOI](https://readthedocs.org/projects/awsm/badge/)](https://awsm.readthedocs.io)
[![Docker Build Status](https://img.shields.io/docker/build/usdaarsnwrc/awsm.svg)](https://hub.docker.com/r/usdaarsnwrc/awsm/)
[![Docker Automated build](https://img.shields.io/docker/automated/usdaarsnwrc/awsm.svg)](https://hub.docker.com/r/usdaarsnwrc/awsm/)
[![Coverage Status](https://coveralls.io/repos/github/USDA-ARS-NWRC/awsm/badge.svg?branch=HEAD)](https://coveralls.io/github/USDA-ARS-NWRC/awsm?branch=HEAD)
[![Build Status](https://travis-ci.org/USDA-ARS-NWRC/awsm.svg?branch=devel)](https://travis-ci.org/USDA-ARS-NWRC/awsm)


Automated Water Supply Model (AWSM) was developed at the USDA Agricultural
Research Service (ARS) in Boise, ID. AWSM was designed to streamline the work
flow used by the ARS to forecast the water supply of multiple water basins.
AWSM standardizes the steps needed to distribute weather station data with SMRF,
run an energy and mass balance with iSnobal, and process the results, while
maintaining the flexibility of each program.

![image](https://raw.githubusercontent.com/USDA-ARS-NWRC/awsm/master/docs/_static/ModelSystemOverview_new.png)

## Quick Start

The fastest way to get up and running with AWSM is to use the docker images that
are pre-built and can deployed cross platform.

To build AWSM natively from source checkout the install instructions [here].

[here]: https://awsm.readthedocs.io/en/latest/installation.html

### Docker

Docker images are containers that allow us to ship the software to our users
seamlessly and without a headache. It is by far the easiest way to use AWSM. If
you are curious to read more about them, visit [Whats a container] on docker's
website.

[Whats a container]: https://www.docker.com/what-container

Using docker images comes with very minor quirks though, such as requiring you to
mount a volume to access the data when you are done with your run. To mount a
data volume, so that you can share data between the local file system and the
docker, the `-v` option must be used. For a more in depth discussion and
tutorial, read about [docker volumes]. The container has a shared data volume
at `/data` where the container can access the local file system.

[docker volumes]: https://docs.docker.com/storage/volumes/


**NOTE: On the host paths to the volume to mount, you must use full absolute paths!**

### Running the Demo

To simply run the AWSM demo; mount the desired directory as a volume and run
the image, using the following command:

**For Linux:**

```
  docker run -v <path>:/data -it usdaarsnwrc/awsm:develop
```

**For MacOSX:**

```
  docker run -v /Users/<path>:/data -it usdaarsnwrc/awsm:develop
```

**For Windows:**

```
  docker run -v /c/Users/<path>:/data -it usdaarsnwrc/awsm:develop
```

The output netCDF files will be placed in the location you mounted (using the
-v option). We like to use [ncview] to view our netcdf files quickly.

[ncview]: http://meteora.ucsd.edu/~pierce/ncview_home_page.html

### Setting Up Your Run

To use the AWSM docker image to create your own runs, you need to setup a
project folder containing all the files necessary to run the model. Then using
the same command above, mount your project folder and provide a path to the
configuration file. An example of a project folder might like:

```
My_Basin
      ├── air_temp.csv
      ├── cloud_factor.csv
      ├── config.ini
      ├── maxus.nc
      ├── metadata.csv
      ├── output
      ├── precip.csv
      ├── solar.csv
      ├── topo.nc
      ├── vapor_pressure.csv
      ├── wind_direction.csv
      └── wind_speed.csv
```

Then the command would be:

```
docker run -v <path>/My_Basin:/data -it usdaarsnwrc/awsm:develop <path>/My_Basin/config.ini
```



            

Raw data

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    "description": "# Automated Water Supply Model\n\n[![GitHub version](https://badge.fury.io/gh/USDA-ARS-NWRC%2Fawsm.svg)](https://badge.fury.io/gh/USDA-ARS-NWRC%2Fawsm)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.898158.svg)](https://doi.org/10.5281/zenodo.898158)\n[![DOI](https://readthedocs.org/projects/awsm/badge/)](https://awsm.readthedocs.io)\n[![Docker Build Status](https://img.shields.io/docker/build/usdaarsnwrc/awsm.svg)](https://hub.docker.com/r/usdaarsnwrc/awsm/)\n[![Docker Automated build](https://img.shields.io/docker/automated/usdaarsnwrc/awsm.svg)](https://hub.docker.com/r/usdaarsnwrc/awsm/)\n[![Coverage Status](https://coveralls.io/repos/github/USDA-ARS-NWRC/awsm/badge.svg?branch=HEAD)](https://coveralls.io/github/USDA-ARS-NWRC/awsm?branch=HEAD)\n[![Build Status](https://travis-ci.org/USDA-ARS-NWRC/awsm.svg?branch=devel)](https://travis-ci.org/USDA-ARS-NWRC/awsm)\n\n\nAutomated Water Supply Model (AWSM) was developed at the USDA Agricultural\nResearch Service (ARS) in Boise, ID. AWSM was designed to streamline the work\nflow used by the ARS to forecast the water supply of multiple water basins.\nAWSM standardizes the steps needed to distribute weather station data with SMRF,\nrun an energy and mass balance with iSnobal, and process the results, while\nmaintaining the flexibility of each program.\n\n![image](https://raw.githubusercontent.com/USDA-ARS-NWRC/awsm/master/docs/_static/ModelSystemOverview_new.png)\n\n## Quick Start\n\nThe fastest way to get up and running with AWSM is to use the docker images that\nare pre-built and can deployed cross platform.\n\nTo build AWSM natively from source checkout the install instructions [here].\n\n[here]: https://awsm.readthedocs.io/en/latest/installation.html\n\n### Docker\n\nDocker images are containers that allow us to ship the software to our users\nseamlessly and without a headache. It is by far the easiest way to use AWSM. If\nyou are curious to read more about them, visit [Whats a container] on docker's\nwebsite.\n\n[Whats a container]: https://www.docker.com/what-container\n\nUsing docker images comes with very minor quirks though, such as requiring you to\nmount a volume to access the data when you are done with your run. To mount a\ndata volume, so that you can share data between the local file system and the\ndocker, the `-v` option must be used. For a more in depth discussion and\ntutorial, read about [docker volumes]. The container has a shared data volume\nat `/data` where the container can access the local file system.\n\n[docker volumes]: https://docs.docker.com/storage/volumes/\n\n\n**NOTE: On the host paths to the volume to mount, you must use full absolute paths!**\n\n### Running the Demo\n\nTo simply run the AWSM demo; mount the desired directory as a volume and run\nthe image, using the following command:\n\n**For Linux:**\n\n```\n  docker run -v <path>:/data -it usdaarsnwrc/awsm:develop\n```\n\n**For MacOSX:**\n\n```\n  docker run -v /Users/<path>:/data -it usdaarsnwrc/awsm:develop\n```\n\n**For Windows:**\n\n```\n  docker run -v /c/Users/<path>:/data -it usdaarsnwrc/awsm:develop\n```\n\nThe output netCDF files will be placed in the location you mounted (using the\n-v option). We like to use [ncview] to view our netcdf files quickly.\n\n[ncview]: http://meteora.ucsd.edu/~pierce/ncview_home_page.html\n\n### Setting Up Your Run\n\nTo use the AWSM docker image to create your own runs, you need to setup a\nproject folder containing all the files necessary to run the model. Then using\nthe same command above, mount your project folder and provide a path to the\nconfiguration file. An example of a project folder might like:\n\n```\nMy_Basin\n      \u251c\u2500\u2500 air_temp.csv\n      \u251c\u2500\u2500 cloud_factor.csv\n      \u251c\u2500\u2500 config.ini\n      \u251c\u2500\u2500 maxus.nc\n      \u251c\u2500\u2500 metadata.csv\n      \u251c\u2500\u2500 output\n      \u251c\u2500\u2500 precip.csv\n      \u251c\u2500\u2500 solar.csv\n      \u251c\u2500\u2500 topo.nc\n      \u251c\u2500\u2500 vapor_pressure.csv\n      \u251c\u2500\u2500 wind_direction.csv\n      \u2514\u2500\u2500 wind_speed.csv\n```\n\nThen the command would be:\n\n```\ndocker run -v <path>/My_Basin:/data -it usdaarsnwrc/awsm:develop <path>/My_Basin/config.ini\n```\n\n\n",
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