Dataframe handling¶
We can use DataFrames from pandas together with hydrolib-core.
Note that this functionality can only work on files that are in essence tables
and are represented by a List of objects in hydrolib-core.
Examples of such FileModels with their List fields are:
- ForcingModel.forcing
- CrossDefModel.definition
- CrossLocModel.crosssection
- ExtModel.boundary
- ExtModel.lateral
Say we load in a .bc file and want the resulting forcing in a dataframe.
from hydrolib.core.io.bc.models import ForcingModel
filepath = (
"tests/data/input/e02/f101_1D-boundaries/c01_steady-state-flow/BoundaryConditions.bc"
)
m = ForcingModel(filepath)
m.forcing
[Constant(comments=None, datablock=[[2.5]], name='T1_Dwn_Bnd', function='constant', quantityunitpair=[QuantityUnitPair(quantity='waterlevelbnd', unit='m')], offset=0.0, factor=1.0),
Constant(comments=None, datablock=[[100.0]], name='T1_Up_Bnd', function='constant', quantityunitpair=[QuantityUnitPair(quantity='dischargebnd', unit='m³/s')], offset=0.0, factor=1.0),
Constant(comments=None, datablock=[[2.5]], name='T2_Dwn_Bnd', function='constant', quantityunitpair=[QuantityUnitPair(quantity='waterlevelbnd', unit='m')], offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[0.0, 0.0], [1800.0, 100.0], [4320.0, 100.0]], name='T2_Up_Bnd', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='dischargebnd', unit='m³/s')], timeinterpolation='linear', offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[0.0, 0.0], [1800.0, 2.5], [4320.0, 2.5]], name='T3_Dwn_Bnd', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='waterlevelbnd', unit='m')], timeinterpolation='linear', offset=0.0, factor=1.0),
Constant(comments=None, datablock=[[100.0]], name='T3_Up_Bnd', function='constant', quantityunitpair=[QuantityUnitPair(quantity='dischargebnd', unit='m³/s')], offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[0.0, 0.0], [1800.0, 100.0], [4320.0, 100.0]], name='T4_Up_Bnd', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='dischargebnd', unit='m³/s')], timeinterpolation='linear', offset=0.0, factor=1.0),
QHTable(comments=None, datablock=[[50.0, 1.25], [100.0, 2.5], [150.0, 3.75]], name='T4_Dwn_Bnd', function='qhtable', quantityunitpair=[QuantityUnitPair(quantity='qhbnd discharge', unit='m³/s'), QuantityUnitPair(quantity='qhbnd waterlevel', unit='m')]),
TimeSeries(comments=None, datablock=[[-2629440.0, 0.0]], name='model_wide', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='wind_speed', unit='m/s')], timeinterpolation='linear', offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[-2629440.0, 0.0]], name='model_wide', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='wind_from_direction', unit='degree')], timeinterpolation='linear', offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[-2629440.0, 0.0]], name='model_wide', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='air_temperature', unit='degrees C')], timeinterpolation='linear', offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[-2629440.0, 0.0]], name='model_wide', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='humidity', unit='percentage')], timeinterpolation='linear', offset=0.0, factor=1.0),
TimeSeries(comments=None, datablock=[[-2629440.0, 0.0]], name='model_wide', function='timeseries', quantityunitpair=[QuantityUnitPair(quantity='time', unit='minutes since 2015-01-01 00:00:00'), QuantityUnitPair(quantity='cloudiness', unit='percentage')], timeinterpolation='linear', offset=0.0, factor=1.0)]
We now have a list of forcings that we can convert into a DataFrame.
import pandas as pd
df = pd.DataFrame([f.__dict__ for f in m.forcing])
comments datablock name function ... offset factor _header timeinterpolation
0 None [[2.5]] T1_Dwn_Bnd constant ... 0.0 1.0 forcing linear
1 None [[100.0]] T1_Up_Bnd constant ... 0.0 1.0 forcing linear
2 None [[2.5]] T2_Dwn_Bnd constant ... 0.0 1.0 forcing linear
3 None [[0.0, 0.0], [1800.0, 100.0], [4320.0, 100.0]] T2_Up_Bnd timeseries ... 0.0 1.0 forcing linear
4 None [[0.0, 0.0], [1800.0, 2.5], [4320.0, 2.5]] T3_Dwn_Bnd timeseries ... 0.0 1.0 forcing linear
5 None [[100.0]] T3_Up_Bnd constant ... 0.0 1.0 forcing linear
6 None [[0.0, 0.0], [1800.0, 100.0], [4320.0, 100.0]] T4_Up_Bnd timeseries ... 0.0 1.0 forcing linear
7 None [[50.0, 1.25], [100.0, 2.5], [150.0, 3.75]] T4_Dwn_Bnd qhtable ... NaN NaN forcing NaN
8 None [[-2629440.0, 0.0]] model_wide timeseries ... 0.0 1.0 forcing linear
9 None [[-2629440.0, 0.0]] model_wide timeseries ... 0.0 1.0 forcing linear
10 None [[-2629440.0, 0.0]] model_wide timeseries ... 0.0 1.0 forcing linear
11 None [[-2629440.0, 0.0]] model_wide timeseries ... 0.0 1.0 forcing linear
12 None [[-2629440.0, 0.0]] model_wide timeseries ... 0.0 1.0 forcing linear
[13 rows x 10 columns]
We can also convert this DataFrame back to a ForcingModel.
fm = ForcingModel(forcing=df.to_dict('records'))
fm.forcing == m.forcing
True