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Hello, I noticed that the same script failed for two different machines, I compared libraries, found out the scipy version was different. Forced the installation of scipy 1.9.1 on the machine where it worked, and it reproduces the same error.
The part of the code that produces the error is
UQ = un.UncertaintyQuantification(model=un_model,
parameters=parameters,
logger_level=u'critical',
logger_filename=unsaveplace+'uncertainpy.log')
undata = UQ.quantify(seed=10,
method='pc',
plot=None,
pc_method='collocation',
logger_level=u'critical',
figure_folder=unsaveplace+'figures',
data_folder=unsaveplace+'data',
single=False)Displaying
File ~/anaconda3/lib/python3.9/site-packages/uncertainpy/uncertainty.py:415, in UncertaintyQuantification.quantify(self, method, pc_method, rosenblatt, uncertain_parameters, polynomial_order, nr_collocation_nodes, quadrature_order, nr_pc_mc_samples, nr_mc_samples, allow_incomplete, seed, single, plot, figure_folder, figureformat, save, data_folder, filename, **custom_kwargs)
397 data = self.polynomial_chaos_single(uncertain_parameters=uncertain_parameters,
398 method=pc_method,
399 rosenblatt=rosenblatt,
(...)
411 filename=filename,
412 **custom_kwargs)
414 else:
--> 415 data = self.polynomial_chaos(uncertain_parameters=uncertain_parameters,
416 method=pc_method,
417 rosenblatt=rosenblatt,
418 polynomial_order=polynomial_order,
419 nr_collocation_nodes=nr_collocation_nodes,
420 quadrature_order=quadrature_order,
421 nr_pc_mc_samples=nr_pc_mc_samples,
422 allow_incomplete=allow_incomplete,
423 seed=seed,
424 plot=plot,
425 figure_folder=figure_folder,
426 figureformat=figureformat,
427 save=save,
428 data_folder=data_folder,
429 filename=filename,
430 **custom_kwargs)
432 elif method.lower() == "mc":
433 if single:
File ~/anaconda3/lib/python3.9/site-packages/uncertainpy/uncertainty.py:702, in UncertaintyQuantification.polynomial_chaos(self, method, rosenblatt, uncertain_parameters, polynomial_order, nr_collocation_nodes, quadrature_order, nr_pc_mc_samples, allow_incomplete, seed, plot, figure_folder, figureformat, save, data_folder, filename, **custom_kwargs)
697 if len(uncertain_parameters) > 20:
698 raise RuntimeWarning("The number of uncertain parameters is high."
699 + "The Monte-Carlo method might be faster.")
--> 702 self.data = self.uncertainty_calculations.polynomial_chaos(
703 method=method,
704 rosenblatt=rosenblatt,
705 uncertain_parameters=uncertain_parameters,
706 polynomial_order=polynomial_order,
707 nr_collocation_nodes=nr_collocation_nodes,
708 quadrature_order=quadrature_order,
709 nr_pc_mc_samples=nr_pc_mc_samples,
710 allow_incomplete=allow_incomplete,
711 seed=seed,
712 **custom_kwargs
713 )
715 self.data.backend = self.backend
717 if filename is None:
File ~/anaconda3/lib/python3.9/site-packages/uncertainpy/core/uncertainty_calculations.py:1383, in UncertaintyCalculations.polynomial_chaos(self, method, rosenblatt, uncertain_parameters, polynomial_order, nr_collocation_nodes, quadrature_order, nr_pc_mc_samples, allow_incomplete, seed, **custom_kwargs)
1374 # TODO add support for more methods here by using
1375 # try:
1376 # getattr(self, method)
(...)
1379
1380 else:
1381 raise ValueError("No polynomial chaos method with name {}".format(method))
-> 1383 data = self.analyse_PCE(U_hat, distribution, data, nr_samples=nr_pc_mc_samples)
1385 data.seed = seed
1387 return data
File ~/anaconda3/lib/python3.9/site-packages/uncertainpy/core/uncertainty_calculations.py:1032, in UncertaintyCalculations.analyse_PCE(self, U_hat, distribution, data, nr_samples)
1030 if feature in U_hat:
1031 data[feature].mean = cp.E(U_hat[feature], distribution)
-> 1032 data[feature].variance = cp.Var(U_hat[feature], distribution)
1034 samples = distribution.sample(nr_samples, "M")
1036 if len(data.uncertain_parameters) > 1:
File ~/anaconda3/lib/python3.9/site-packages/chaospy/descriptives/variance.py:43, in Var(poly, dist, **kws)
41 poly = poly - E(poly, dist, **kws)
42 poly = numpoly.square(poly)
---> 43 return E(poly, dist, **kws)
File ~/anaconda3/lib/python3.9/site-packages/chaospy/descriptives/expected.py:42, in E(poly, dist, **kws)
39 if poly.isconstant():
40 return poly.tonumpy()
---> 42 moments = dist.mom(poly.exponents.T, **kws)
43 if len(dist) == 1:
44 moments = moments[0]
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/distribution.py:668, in Distribution.mom(self, K, allow_approx, **kwargs)
666 K = K.reshape(dim, size)
667 try:
--> 668 out = [self._get_mom(kdata) for kdata in K.T]
669 logger.debug("%s: moment calculated successfully", str(self))
670 except chaospy.UnsupportedFeature:
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/distribution.py:668, in <listcomp>(.0)
666 K = K.reshape(dim, size)
667 try:
--> 668 out = [self._get_mom(kdata) for kdata in K.T]
669 logger.debug("%s: moment calculated successfully", str(self))
670 except chaospy.UnsupportedFeature:
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/distribution.py:693, in Distribution._get_mom(self, kdata)
691 parameters = self.get_parameters(idx=None, cache={}, assert_numerical=False)
692 assert "idx" not in parameters, (self, parameters)
--> 693 ret_val = float(self._mom(kdata, **parameters))
694 assert not isinstance(ret_val, Distribution), (self, ret_val)
695 self._mom_cache[tuple(kdata)] = ret_val
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/operators/joint.py:169, in J._mom(self, kloc, index)
167 kloc = kloc[self._rotation]
168 for unique_idx in numpy.unique(index[self._rotation]):
--> 169 output *= self._owners[unique_idx][1]._get_mom(kloc[index == unique_idx])
170 return output
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/distribution.py:693, in Distribution._get_mom(self, kdata)
691 parameters = self.get_parameters(idx=None, cache={}, assert_numerical=False)
692 assert "idx" not in parameters, (self, parameters)
--> 693 ret_val = float(self._mom(kdata, **parameters))
694 assert not isinstance(ret_val, Distribution), (self, ret_val)
695 self._mom_cache[tuple(kdata)] = ret_val
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/shift_scale.py:109, in ShiftScaleDistribution._mom(self, kloc, dist, shift, scale)
106 poly = numpoly.sum(scale * poly, axis=-1) + shift
107 poly = numpoly.set_dimensions(numpoly.prod(poly**kloc), len(self))
108 out = sum(
--> 109 [
110 dist._get_mom(key) * coeff
111 for key, coeff in zip(poly.exponents, poly.coefficients)
112 ]
113 )
114 return out
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/shift_scale.py:110, in <listcomp>(.0)
106 poly = numpoly.sum(scale * poly, axis=-1) + shift
107 poly = numpoly.set_dimensions(numpoly.prod(poly**kloc), len(self))
108 out = sum(
109 [
--> 110 dist._get_mom(key) * coeff
111 for key, coeff in zip(poly.exponents, poly.coefficients)
112 ]
113 )
114 return out
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/baseclass/distribution.py:693, in Distribution._get_mom(self, kdata)
691 parameters = self.get_parameters(idx=None, cache={}, assert_numerical=False)
692 assert "idx" not in parameters, (self, parameters)
--> 693 ret_val = float(self._mom(kdata, **parameters))
694 assert not isinstance(ret_val, Distribution), (self, ret_val)
695 self._mom_cache[tuple(kdata)] = ret_val
File ~/anaconda3/lib/python3.9/site-packages/chaospy/distributions/collection/trunc_normal.py:50, in trunc_normal._mom(self, n, a, b, mu, sigma)
49 def _mom(self, n, a, b, mu, sigma):
---> 50 return truncnorm.moment(int(n), a, b, loc=mu, scale=sigma)
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_distn_infrastructure.py:1371, in rv_generic.moment(self, order, *args, **kwds)
1369 mu, mu2, g1, g2 = self._stats(*shapes, **mdict)
1370 val = np.empty(loc.shape) # val needs to be indexed by loc
-> 1371 val[...] = _moment_from_stats(n, mu, mu2, g1, g2, self._munp, shapes)
1373 # Convert to transformed X = L + S*Y
1374 # E[X^n] = E[(L+S*Y)^n] = L^n sum(comb(n, k)*(S/L)^k E[Y^k], k=0...n)
1375 result = zeros(i0.shape)
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_distn_infrastructure.py:393, in _moment_from_stats(n, mu, mu2, g1, g2, moment_func, args)
391 val = mu4+4*mu*mu3+6*mu*mu*mu2+mu*mu*mu*mu
392 else:
--> 393 val = moment_func(n, *args)
395 return val
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:8141, in truncnorm_gen._munp(self, n, a, b)
8138 moments.append(mk)
8139 return moments[-1]
-> 8141 return _lazywhere((n >= 0) & (a == a) & (b == b), (n, a, b),
8142 np.vectorize(n_th_moment, otypes=[np.float64]),
8143 np.nan)
File ~/anaconda3/lib/python3.9/site-packages/scipy/_lib/_util.py:69, in _lazywhere(cond, arrays, f, fillvalue, f2)
67 tcode = np.mintypecode([a.dtype.char for a in arrays])
68 out = np.full(np.shape(arrays[0]), fill_value=fillvalue, dtype=tcode)
---> 69 np.place(out, cond, f(*temp))
70 if f2 is not None:
71 temp = tuple(np.extract(~cond, arr) for arr in arrays)
File ~/anaconda3/lib/python3.9/site-packages/numpy/lib/function_base.py:2163, in vectorize.__call__(self, *args, **kwargs)
2160 vargs = [args[_i] for _i in inds]
2161 vargs.extend([kwargs[_n] for _n in names])
-> 2163 return self._vectorize_call(func=func, args=vargs)
File ~/anaconda3/lib/python3.9/site-packages/numpy/lib/function_base.py:2246, in vectorize._vectorize_call(self, func, args)
2243 # Convert args to object arrays first
2244 inputs = [asanyarray(a, dtype=object) for a in args]
-> 2246 outputs = ufunc(*inputs)
2248 if ufunc.nout == 1:
2249 res = asanyarray(outputs, dtype=otypes[0])
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:8127, in truncnorm_gen._munp.<locals>.n_th_moment(n, a, b)
8122 def n_th_moment(n, a, b):
8123 """
8124 Returns n-th moment. Defined only if n >= 0.
8125 Function cannot broadcast due to the loop over n
8126 """
-> 8127 pA, pB = self._pdf([a, b], a, b)
8128 probs = [pA, -pB]
8129 moments = [0, 1]
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:8047, in truncnorm_gen._pdf(self, x, a, b)
8046 def _pdf(self, x, a, b):
-> 8047 return np.exp(self._logpdf(x, a, b))
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:8050, in truncnorm_gen._logpdf(self, x, a, b)
8049 def _logpdf(self, x, a, b):
-> 8050 return _norm_logpdf(x) - _log_gauss_mass(a, b)
File ~/anaconda3/lib/python3.9/site-packages/scipy/stats/_continuous_distns.py:271, in _norm_logpdf(x)
270 def _norm_logpdf(x):
--> 271 return -x**2 / 2.0 - _norm_pdf_logC
TypeError: unsupported operand type(s) for ** or pow(): 'list' and 'int'
Could it be done so it is compatible with newer versions of scipy?
Cheers,
Pablo
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