`RecursionError` for executing `sensitivities_from_surrogate()` function
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FariborzDaneshvar-NOAA commented
Here is the full message for executing sensitivities_from_surrogate() for an ensemble of 5 perturbed tracks for florence 2018:
---------------------------------------------------------------------------
RecursionError Traceback (most recent call last)
Cell In[11], line 2
1 if make_sensitivities_plot:
----> 2 sensitivities = sensitivities_from_surrogate(
3 surrogate_model=surrogate_model,
4 distribution=distribution,
5 variables=perturbations['variable'],
6 nodes=subset,
7 element_table=elements if point_spacing is None else None,
8 filename=sensitivities_filename,
9 )
10 plot_sensitivities(
11 sensitivities=sensitivities,
12 storm=storm_name,
13 output_filename=output_directory / 'sensitivities.png' if save_plots else None,
14 )
16 if make_validation_plot:
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/ensembleperturbation/uncertainty_quantification/surrogate.py:239, in sensitivities_from_surrogate(surrogate_model, distribution, variables, nodes, element_table, filename)
234 if filename is None or not filename.exists():
235 LOGGER.info(f'extracting sensitivities from surrogate model and distribution')
237 sensitivities = [
238 chaospy.Sens_m(surrogate_model, distribution),
--> 239 chaospy.Sens_t(surrogate_model, distribution),
240 ]
242 sensitivities = numpy.stack(sensitivities)
244 sensitivities = xarray.DataArray(
245 sensitivities,
246 coords={
(...)
254 dims=('order', 'variable', 'node'),
255 ).T
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/chaospy/descriptives/sensitivity/total.py:43, in Sens_t(poly, dist, **kws)
41 valids = variance != 0
42 if not numpy.all(valids):
---> 43 out[:, valids] = Sens_t(poly[valids], dist, **kws)
44 return out
46 out[:] = variance
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/chaospy/descriptives/sensitivity/total.py:43, in Sens_t(poly, dist, **kws)
41 valids = variance != 0
42 if not numpy.all(valids):
---> 43 out[:, valids] = Sens_t(poly[valids], dist, **kws)
44 return out
46 out[:] = variance
[... skipping similar frames: Sens_t at line 43 (2956 times)]
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/chaospy/descriptives/sensitivity/total.py:43, in Sens_t(poly, dist, **kws)
41 valids = variance != 0
42 if not numpy.all(valids):
---> 43 out[:, valids] = Sens_t(poly[valids], dist, **kws)
44 return out
46 out[:] = variance
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/chaospy/descriptives/sensitivity/total.py:36, in Sens_t(poly, dist, **kws)
10 """
11 Variance-based decomposition
12 AKA Sobol' indices
(...)
33 [0. , 0. , 1. , 0.57142857]])
34 """
35 dim = len(dist)
---> 36 poly = numpoly.set_dimensions(poly, dim)
38 out = numpy.zeros((dim,) + poly.shape, dtype=float)
39 variance = Var(poly, dist, **kws)
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpoly/poly_function/set_dimensions.py:67, in set_dimensions(poly, dimensions)
64 else:
65 return poly
---> 67 return numpoly.polynomial_from_attributes(
68 exponents=exponents,
69 coefficients=coefficients,
70 names=names,
71 dtype=poly.dtype,
72 allocation=poly.allocation,
73 retain_names=True,
74 )
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpoly/construct/from_attributes.py:72, in polynomial_from_attributes(exponents, coefficients, names, dtype, allocation, retain_coefficients, retain_names)
13 def polynomial_from_attributes(
14 exponents: numpy.typing.ArrayLike,
15 coefficients: Sequence[numpy.typing.ArrayLike],
(...)
20 retain_names: Optional[bool] = None,
21 ) -> ndpoly:
22 """
23 Construct polynomial from polynomial attributes.
24
(...)
70
71 """
---> 72 exponents, coefficients, names = clean.postprocess_attributes(
73 exponents=exponents,
74 coefficients=coefficients,
75 names=names,
76 retain_coefficients=retain_coefficients,
77 retain_names=retain_names,
78 )
79 if coefficients:
80 dtype = coefficients[0].dtype if dtype is None else dtype
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpoly/construct/clean.py:139, in postprocess_attributes(exponents, coefficients, names, retain_coefficients, retain_names)
136 if not retain_names:
137 exponents, names = remove_redundant_names(exponents, names)
--> 139 exponents_, count = numpy.unique(exponents, return_counts=True, axis=0)
140 if numpy.any(count > 1):
141 raise PolynomialConstructionError(
142 f"Duplicate exponent keys found: {exponents_[count > 1][0]}"
143 )
File <__array_function__ internals>:200, in unique(*args, **kwargs)
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpy/lib/arraysetops.py:319, in unique(ar, return_index, return_inverse, return_counts, axis, equal_nan)
315 return uniq
317 output = _unique1d(consolidated, return_index,
318 return_inverse, return_counts, equal_nan=equal_nan)
--> 319 output = (reshape_uniq(output[0]),) + output[1:]
320 return _unpack_tuple(output)
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpy/lib/arraysetops.py:314, in unique.<locals>.reshape_uniq(uniq)
312 uniq = uniq.view(orig_dtype)
313 uniq = uniq.reshape(n, *orig_shape[1:])
--> 314 uniq = np.moveaxis(uniq, 0, axis)
315 return uniq
File <__array_function__ internals>:200, in moveaxis(*args, **kwargs)
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpy/core/numeric.py:1467, in moveaxis(a, source, destination)
1464 a = asarray(a)
1465 transpose = a.transpose
-> 1467 source = normalize_axis_tuple(source, a.ndim, 'source')
1468 destination = normalize_axis_tuple(destination, a.ndim, 'destination')
1469 if len(source) != len(destination):
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpy/core/numeric.py:1398, in normalize_axis_tuple(axis, ndim, argname, allow_duplicate)
1396 pass
1397 # Going via an iterator directly is slower than via list comprehension.
-> 1398 axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])
1399 if not allow_duplicate and len(set(axis)) != len(axis):
1400 if argname:
File ~/miniconda3/envs/perturbation/lib/python3.10/site-packages/numpy/core/numeric.py:1398, in <listcomp>(.0)
1396 pass
1397 # Going via an iterator directly is slower than via list comprehension.
-> 1398 axis = tuple([normalize_axis_index(ax, ndim, argname) for ax in axis])
1399 if not allow_duplicate and len(set(axis)) != len(axis):
1400 if argname:
RecursionError: maximum recursion depth exceeded while calling a Python object