conv_v002¶

Inverse distance weighted interpolation.

Version 2 of HydPy-C performs simple inverse distance weighted interpolations between an arbitrary number of models (or data files) providing output and an arbitrary number of models requiring input.

Integration tests¶

Note

When new to HydPy, consider reading section How to understand integration tests? first.

We perform the following examples over a simulation period of 3 days:

>>> from hydpy import pub, Nodes, Element
>>> pub.timegrids = "2000-01-01", "2000-01-04", "1d"

conv_v002 implements no parameter with values depending on the simulation step size, which is why we can pass anything (or nothing) to function parameterstep() without changing the following results:

>>> from hydpy.models.conv_v002 import *
>>> parameterstep()

Due to the following configuration, conv_v002 queries its input from the inlet nodes in1, in2, and in3 and passes the interpolation results to the outlet nodes out1, out2, out3, and out4:

>>> from hydpy import *
>>> in1, in2, in3 = Node("in1"), Node("in2"), Node("in3")
>>> element = Element("conv",
...                   inlets=(in1, in2, in3),
...                   outlets=["out1", "out2", "out3", "out4"])

The following coordinate definitions contain the particular case of outlet node out1, being at the same location as inlet node in1:

>>> inputcoordinates(
...     in1=(0.0, 3.0),
...     in2=(2.0, -1.0),
...     in3=(4.0, 2.0))
>>> outputcoordinates(
...     out1=(0.0, 3.0),
...     out2=(3.0, -2.0),
...     out3=(1.0, 2.0),
...     out4=(1.0, 1.0))

We set the power parameter Power to the standard value of two:

>>> power(2.0)

In the first example, we perform a complete inverse distance weighted interpolation, where we take all input location into account, as long as they provide input data:

>>> maxnmbinputs(3)

conv_v002 does not implement any state or log sequences and thus has no memory at all, making finalising the test setup quite easy. We only need to define time-series for both inlet nodes. Note that we set some numpy nan values to demonstrate how conv_v002 deals with missing values:

>>> element.model = model
>>> from hydpy.core.testtools import IntegrationTest
>>> test = IntegrationTest(element)
>>> test.dateformat = "%Y-%m-%d"
>>> with pub.options.checkseries(False):
...     in1.sequences.sim.series = 1.0, nan, nan
...     in2.sequences.sim.series = 3.0, 2.0, nan
...     in3.sequences.sim.series = 4.0, nan, nan

If available, outlet node in1 receives its value from inlet node in1 due to the same location. At the second time step, all outlet nodes receive the same data as only inlet node in2 provides any data. When no inlet node provides data, the outlet nodes receive nan values:

>>> test()
|       date |           inputs |                 outputs | in1 | in2 | in3 | out1 | out2 | out3 | out4 |
---------------------------------------------------------------------------------------------------------
| 2000-01-01 | 1.0  3.0     4.0 | 1.0  3.0  1.75      2.4 | 1.0 | 3.0 | 4.0 |  1.0 |  3.0 | 1.75 |  2.4 |
| 2000-01-02 | nan  2.0     nan | 2.0  2.0   2.0      2.0 | nan | 2.0 | nan |  2.0 |  2.0 |  2.0 |  2.0 |
| 2000-01-03 | nan  nan     nan | nan  nan   nan      nan | nan | nan | nan |  nan |  nan |  nan |  nan |

We can restrict the number of considered inlet nodes via parameter MaxNmbInputs, which can increase computation speed. However, do not set to low values. Otherwise, you might deteriorate accuracy severely or run into the risk of unhandled nan values:

>>> maxnmbinputs(2)
>>> test()
|       date |           inputs |                          outputs | in1 | in2 | in3 | out1 |     out2 |     out3 | out4 |
--------------------------------------------------------------------------------------------------------------------------
| 2000-01-01 | 1.0  3.0     4.0 | 1.0  3.105263  1.545455      2.0 | 1.0 | 3.0 | 4.0 |  1.0 | 3.105263 | 1.545455 |  2.0 |
| 2000-01-02 | nan  2.0     nan | nan       2.0       nan      2.0 | nan | 2.0 | nan |  nan |      2.0 |      nan |  2.0 |
| 2000-01-03 | nan  nan     nan | nan       nan       nan      nan | nan | nan | nan |  nan |      nan |      nan |  nan |

class hydpy.models.conv_v002.Model[source]¶

Bases: hydpy.models.conv.conv_model.Model

Version 2 of the Conv model.

The following “inlet update methods” are called in the given sequence at the beginning of each simulation step:

Pick_Inputs_V1 Pick the input from all inlet nodes.

The following “run methods” are called in the given sequence during each simulation step:

Calc_Outputs_V2 Perform a simple inverse distance weighted interpolation based on the original data supplied by the input nodes.

The following “outlet update methods” are called in the given sequence at the end of each simulation step:

Pass_Outputs_V1 Pass the output to all outlet nodes.

The following “additional methods” might be called by one or more of the other methods or are meant to be directly called by the user:

Interpolate_InverseDistance_V1 Perform a simple inverse distance weighted interpolation.

class hydpy.models.conv_v002.ControlParameters(master: hydpy.core.parametertools.Parameters, cls_fastaccess: Optional[Type[hydpy.core.parametertools.FastAccessParameter]] = None, cymodel: Optional[hydpy.core.typingtools.CyModelProtocol] = None)¶

Bases: hydpy.core.variabletools.SubVariables[hydpy.core.parametertools.Parameters, Parameter, hydpy.core.parametertools.FastAccessParameter]

Control parameters of model conv_v002.