HydroTools :: Events

This subpackage implements methods to partition hydrometric times series into discrete "events." See the Events Documentation for a complete list and description of the currently available methods. To report bugs or request new methods, submit an issue through the HydroTools Issue Tracker on GitHub.

Installation

In accordance with the python community, we support and advise the usage of virtual environments in any workflow using python. In the following installation guide, we use python's built-in venv module to create a virtual environment in which the tool will be installed. Note this is just personal preference, any python virtual environment manager should work just fine (conda, pipenv, etc. ).

# Create and activate python environment, requires python >= 3.8
$ python3 -m venv venv
$ source venv/bin/activate
$ python3 -m pip install --upgrade pip

# Install events
$ python3 -m pip install hydrotools.events

Usage

The following example demonstrates how one might use hydrotools.events.event_detection to extract hydrological events from a time series of real streamflow measurements. This example requires the hydrotools.nwis_client package.

Code

# Import tools to retrieve data and detect events
from hydrotools.nwis_client.iv import IVDataService
from hydrotools.events.event_detection import decomposition as ev

# Use pandas to resample the data
from pandas import Grouper

# Use a helper function to detect events at multiple sites
def list_events_helper(mi_series, level, halflife, window):
    """Reduce multi-index series before applying event detection."""
    return ev.list_events(mi_series.droplevel(level), halflife, window)

# Retrieve streamflow observations for two sites
observations = IVDataService.get(
    sites='02146750,0214676115,09489000', 
    startDT='2019-10-01', 
    endDT='2020-09-30'
    )

# Drop extra columns to be more efficient
observations = observations[[
    'usgs_site_code', 
    'value_date', 
    'value'
    ]]

# Check for duplicate time series, keep first by default
observations = observations.drop_duplicates(
    subset=['usgs_site_code', 'value_date']
    )

# Resample to hourly, keep first measurement in each 1-hour bin
observations = observations.groupby([
    'usgs_site_code',
    Grouper(key='value_date', freq='H')
    ]).first().ffill()

# Detect events
events = observations['value'].groupby(
    level='usgs_site_code').apply(
        list_events_helper, 
        level='usgs_site_code', 
        halflife='6H', 
        window='7D'
    )

# Print event list    
print(events)

Output

                                start                 end
usgs_site_code                                           
02146750       0  2019-10-13 22:00:00 2019-10-15 04:00:00
               1  2019-10-16 09:00:00 2019-10-18 02:00:00
               2  2019-10-19 03:00:00 2019-10-23 19:00:00
               3  2019-10-27 05:00:00 2019-10-28 18:00:00
               4  2019-10-30 01:00:00 2019-11-02 18:00:00
...                               ...                 ...
09489000       34 2020-09-16 00:00:00 2020-09-16 19:00:00
               35 2020-09-17 00:00:00 2020-09-17 15:00:00
               36 2020-09-18 04:00:00 2020-09-18 10:00:00
               37 2020-09-27 01:00:00 2020-09-27 03:00:00
               38 2020-09-28 05:00:00 2020-09-28 11:00:00