pymt_nwis converts bmi_nwis into a reusable, plug-and-play data component for PyMT modeling framework. pymt_nwis allows the National Water Information System data to be easily coupled with other data or models that expose a Basic Model Interface.

Installation

Install the pymt in a new environment:

$ conda config --add channels conda-forge
$ conda create -n pymt -c conda-forge python=3 pymt
$ conda activate pymt

Install the pymt_nwis using pip:

$ pip install pymt_nwis

or conda

$ conda install -c conda-forge pymt_nwis

Coding Example

You can learn more details about the coding example from the tutorial notebook.

import numpy as np
import cftime
import pandas as pd

from pymt.models import Nwis

# initiate a data component
data_comp = Nwis()
data_comp.initialize('config_file.yaml')

# get variable info
for var_name in data_comp.output_var_names:
    var_unit = data_comp.var_units(var_name)
    var_location = data_comp.var_location(var_name)
    var_type = data_comp.var_type(var_name)
    var_grid = data_comp.var_grid(var_name)
    var_itemsize = data_comp.var_itemsize(var_name)
    var_nbytes = data_comp.var_nbytes(var_name)

    print('variable_name: {} \nvar_unit: {} \nvar_location: {} \nvar_type: {} \nvar_grid: {} \nvar_itemsize: {}'
        '\nvar_nbytes: {} \n'. format(var_name, var_unit, var_location, var_type, var_grid, var_itemsize, var_nbytes))

# get time info
start_time = data_comp.start_time
end_time = data_comp.end_time
time_step = data_comp.time_step
time_units = data_comp.time_units
time_steps = int((end_time - start_time)/time_step) + 1

print('start_time: {} \nend_time: {} \ntime_step: {} \ntime_units: {} \ntime_steps: {} \n'.format(
    start_time, end_time, time_step, time_units, time_steps))

# get variable grid info
grid_type = data_comp.grid_type(var_grid)
grid_rank = data_comp.grid_ndim(var_grid)
grid_node_count = data_comp.grid_node_count(var_grid)
site_lon = data_comp.grid_x(var_grid)[0]
site_lat = data_comp.grid_y(var_grid)[0]

print('grid_type: {} \ngrid_rank: {} \ngrid_node_count: {} \nsite_lon: {} \nsite_lat: {}'.format(
    grid_type, grid_rank, grid_node_count, site_lon, site_lat))

# initiate dataframe to store data
dataset = pd.DataFrame(columns = ['00060','00065','time'])

for i in range(0, time_steps):
    # get values
    stream_flow = data_comp.get_value('Stream flow')
    gage_height = data_comp.get_value('Height')
    time = cftime.num2pydate(data_comp.time, time_units)

    # add new row to dataframe
    dataset.loc[len(dataset)]=[stream_flow[0], gage_height[0], time]

    # update to next time step
    data_comp.update()

# convert time to local time
dataset = dataset.set_index('time').tz_localize(tz='UTC').tz_convert(tz='US/Central')

# plot data
ax = dataset.plot(y=['00060','00065'], subplots=True, figsize=(8,8),
                  xlabel='Time', title = 'Time Series Data at USGS Gage 03339000')
ax[0].set_ylabel('Stream flow (ft3/s)')
ax[1].set_ylabel('Gage height (ft)')

# finalize the data component
data_comp.finalize()