umami.calculations.residual.kstest module

kstest(model_grid, data_grid, field)[source]

Calculate an Kolmogorov-Smirnov test for a Landlab grid field.

kstest calculates the Kolmogorov-Smirnov test for goodness of fit using the function ks_2samp from scipy.stats.

Parameters

  • model_grid (Landlab model grid) –

  • data_grid (Landlab model grid) –

  • field (str) – An at-node Landlab grid field that is present on both grids.

Returns

out – The KS test statistic

Return type

float

Examples

First an example that only uses the kstest function.

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from umami.calculations import kstest
>>> np.random.seed(42)
>>> model = RasterModelGrid((10, 10))
>>> z_model = model.add_zeros("node", "topographic__elevation")
>>> z_model += model.x_of_node + model.y_of_node
>>> data = RasterModelGrid((10, 10))
>>> z_data = data.add_zeros("node", "topographic__elevation")
>>> z_data +=  data.x_of_node + data.y_of_node
>>> z_data[data.core_nodes] += np.random.random(data.core_nodes.shape)
>>> np.round(kstest(model, data, "topographic__elevation"), decimals=3)
0.125

Next, the same calculations are shown as part of an umami Residual.

>>> from io import StringIO
>>> from umami import Residual
>>> file_like=StringIO('''
... ks:
...     _func: kstest
...     field: topographic__elevation
... ''')
>>> residual = Residual(model, data)
>>> residual.add_from_file(file_like)
>>> residual.names
['ks']
>>> residual.calculate()
>>> residual.values
[0.125]
kstest_watershed(model_grid, data_grid, field, outlet_id)[source]

Calculate an Kolmogorov-Smirnov test for a watershed.

kstest_watershed calculates the Kolmogorov-Smirnov test for goodness of fit using the function ks_2samp from scipy.stats.

Given an outlet_id it identifes a watershed mask for the data_grid. It then uses that mask on both the data_grid and the model_grid.

If the field is “flow__distance”, then this performs a KS test of the width function.

Parameters

  • model_grid (Landlab model grid) –

  • data_grid (Landlab model grid) –

  • field (str) – An at-node Landlab grid field that is present on both grids.

  • outlet_id (int) –

Returns

out – The KS test statistic

Return type

float

Examples

First an example that only uses the kstest function.

>>> import numpy as np
>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from umami.calculations import kstest_watershed
>>> np.random.seed(42)
>>> model = RasterModelGrid((10, 10))
>>> z_model = model.add_zeros("node", "topographic__elevation")
>>> z_model += model.x_of_node + model.y_of_node
>>> data = RasterModelGrid((10, 10))
>>> z_data = data.add_zeros("node", "topographic__elevation")
>>> z_data +=  data.x_of_node + data.y_of_node
>>> z_data[data.core_nodes] += np.random.random(data.core_nodes.shape)
>>> data_fa = FlowAccumulator(data)
>>> data_fa.run_one_step()
>>> model_fa = FlowAccumulator(model)
>>> model_fa.run_one_step()
>>> np.round(
...     kstest_watershed(
...         model,
...         data,
...         "topographic__elevation",
...         outlet_id=1),
...     decimals=3)
0.5

Next, the same calculations are shown as part of an umami Residual.

>>> from io import StringIO
>>> from umami import Residual
>>> file_like=StringIO('''
... ksw:
...     _func: kstest_watershed
...     outlet_id: 1
...     field: topographic__elevation
... ''')
>>> residual = Residual(model, data)
>>> residual.add_from_file(file_like)
>>> residual.names
['ksw']
>>> residual.calculate()
>>> np.round(residual.values, decimals=3)
array([ 0.5])