swn.modflow.SwnModflow.get_location_frame_reach_info#

SwnModflow.get_location_frame_reach_info(loc_df, downstream_bias=0.0, geom_loc_df=None)#

Get reach information to location data frame, matched by segnum.

Parameters:
loc_dfpd.DataFrame or geopandas.GeoDataFrame

Location [geo] dataframe, created by SurfaceWaterNetwork.locate_geoms().

downstream_biasfloat, default 0.0

A bias used for spatial location matching that increase the likelihood of finding downstream reaches of a segnum if positive, and upstream reaches if negative. Valid range is -1.0 to 1.0. Default 0.0 is no bias, matching to the closest reach.

geom_loc_dfgeopandas.GeoDataFrame, optional

Optional original geometry used to create loc_df, which is only needed if loc_df is a DataFrame or geometries are non-Point, otherwise there may be some ambiguity matching the best stream reach.

Returns:
pandas.DataFrame

  • reachID (SwnModflow), rno or ifno (SwnMf6)

  • zero-based cell index: k, i, j,

  • one-based reach index: iseg, ireach

  • dist_to_reach

Examples

>>> import flopy
>>> import geopandas
>>> import pandas as pd
>>> import swn
>>> lines = geopandas.GeoSeries.from_wkt([
...    "LINESTRING (60 100, 60  80)",
...    "LINESTRING (40 130, 60 100)",
...    "LINESTRING (70 130, 60 100)"])
>>> lines.index += 100
>>> n = swn.SurfaceWaterNetwork.from_lines(lines)
>>> sim = flopy.mf6.MFSimulation()
>>> _ = flopy.mf6.ModflowTdis(sim, nper=1, time_units="days")
>>> gwf = flopy.mf6.ModflowGwf(sim)
>>> _ = flopy.mf6.ModflowGwfdis(
...     gwf, nrow=3, ncol=2, delr=20.0, delc=20.0, idomain=1,
...     length_units="meters", xorigin=30.0, yorigin=70.0)
>>> nm = swn.SwnMf6.from_swn_flopy(n, gwf)
>>> obs_gs = geopandas.GeoSeries.from_wkt([
...    "POINT (56 103)",
...    "POINT (62 90)"])
>>> obs_gs.index += 10
>>> loc_df = n.locate_geoms(obs_gs)
>>> r_df = nm.get_location_frame_reach_info(loc_df)
>>> r_df
    ifno  k  i  j  iseg  ireach  dist_to_reach
10     3  0  1  1     1       3       1.664101
11     7  0  2  1     3       2       2.000000
>>> loc_reach_df = pd.concat([loc_df, r_df], axis=1)