hspf-utils 5.3.2

Command line script and Python library to develop water balance from HSPF models

hspf_utils - Quick Guide

The hspf_utils is a command line script and Python library of utilities to work with the Hydrological Simulation Program - FORTRAN (HSPF). Uses pandas (http://pandas.pydata.org/) or numpy (http://numpy.scipy.org) for any heavy lifting.

Requirements

• tstoolbox - Time-series toolbox; collected and installed by ‘pip’ or ‘easy_install’ command.

• hspfbintoolbox - Utility to extract time-series from HSFP binary output files; collected and installed by ‘pip’ or ‘easy_install’ command.

Installation

Should be as easy as running pip install hspf_utils or easy_install hspf_utils at any command line.

Usage - Command Line

Just run ‘hspf_utils –help’ to get a list of subcommands:

usage: hspf_utils [-h] {about,detailed,summary,mapping,parameters} ...

positional arguments:
  {about,detailed,summary,mapping,parameters}
    about               Display version number and system information.
    detailed            Develops a detailed water balance.
    summary             Develops a summary water balance.
    mapping             Develops a csv file appropriate for joining to a GIS
                        layer.
    parameters          Develops a table of parameter values.

optional arguments:
  -h, --help            show this help message and exit

For the subcommands that output data it is printed to the screen and you can then redirect to a file.

Usage - API

You can use all of the command line subcommands as functions. The function signature is identical to the command line subcommands. The return is always a PANDAS DataFrame. Input can be a CSV or TAB separated file, or a PANDAS DataFrame and is supplied to the function via the ‘input_ts’ keyword.

Simply import hspf_utils:

from hspf_utils import hspf_utils

# Then you could call the functions

# Once you have a PANDAS DataFrame you can use that as input to other
# hspf_utils functions.
ntsd = hspf_utils.aggregate(statistic='mean', agg_interval='daily', input_ts=ntsd)