Merge pull request #193 from SURGroup/Development

Development

docs/code/surrogates/gpr/plot_gpr_custom2D.py

@@ -112,7 +112,7 @@
 y_act = np.array(r2model.qoi_list).reshape(x1g.shape[0], x1g.shape[1])
 
 fig1 = plt.figure()
-ax = fig1.gca(projection='3d')
+ax = fig1.add_subplot(projection='3d')
 surf = ax.plot_surface(x1g, x2g, y_act, cmap=cm.coolwarm, linewidth=0, antialiased=False)
 ax.set_zlim(-1, 15)
 ax.zaxis.set_major_locator(LinearLocator(10))

docs/code/surrogates/pce/plot_pce_camel.py

@@ -76,7 +76,7 @@ def function(x, y):
 f = function(X1_, X2_)
 
 fig = plt.figure(figsize=(10, 6))
-ax = fig.gca(projection='3d')
+ax = fig.add_subplot(projection='3d')
 surf = ax.plot_surface(X1_, X2_, f, rstride=1, cstride=1, cmap='gnuplot2', linewidth=0, antialiased=False)
 ax.set_title('True function')
 ax.set_xlabel('$x_1$', fontsize=15)
@@ -95,7 +95,7 @@ def function(x, y):
 # %%
 
 fig = plt.figure(figsize=(10, 6))
-ax = fig.gca(projection='3d')
+ax = fig.add_subplot(projection='3d')
 ax.scatter(x[:, 0], x[:, 1], y, s=20, c='r')
 
 ax.set_title('Training data')
@@ -168,7 +168,7 @@ def function(x, y):
 # %%
 
 fig = plt.figure(figsize=(10,6))
-ax = fig.gca(projection='3d')
+ax = fig.add_subplot(projection='3d')
 ax.scatter(x_test[:,0], x_test[:,1], y_test, s=1)
 
 ax.set_title('PCE predictor')

docs/code/surrogates/pce/plot_pce_sphere.py

@@ -71,7 +71,7 @@ def function(x,y):
 f = function(X1_, X2_)
 
 fig = plt.figure(figsize=(10,6))
-ax = fig.gca(projection='3d')
+ax = fig.add_subplot(projection='3d')
 surf = ax.plot_surface(X1_, X2_, f, rstride=1, cstride=1, cmap='gnuplot2', linewidth=0, antialiased=False)
 ax.set_title('True function')
 ax.set_xlabel('$x_1$', fontsize=15)
@@ -90,7 +90,7 @@ def function(x,y):
 # %%
 
 fig = plt.figure(figsize=(10,6))
-ax = fig.gca(projection='3d')
+ax = fig.add_subplot(projection='3d')
 ax.scatter(x[:,0], x[:,1], y, s=20, c='r')
 
 ax.set_title('Training data')
@@ -156,7 +156,7 @@ def function(x,y):
 # %%
 
 fig = plt.figure(figsize=(10,6))
-ax = fig.gca(projection='3d')
+ax = fig.add_subplot(projection='3d')
 ax.scatter(x_test[:,0], x_test[:,1], y_test, s=1)
 
 ax.set_title('PCE predictor')

requirements.txt

@@ -7,9 +7,9 @@ pytest == 6.1.2
 coverage == 5.3
 pytest-cov == 2.10.1
 pylint == 2.6.0
-wheel == 0.36.2
+wheel == 0.38.1
 pytest-azurepipelines == 0.8.0
 twine == 3.4.1
 pathlib~=1.0.1
 beartype ==0.9.1
-setuptools~=58.0.4
+setuptools~=65.5.1

src/UQpy/reliability/taylor_series/FORM.py

@@ -312,7 +312,7 @@ def run(self, seed_x: Union[list, np.ndarray] = None,
                 else:
                     k = k + 1
 
-            self.logger.error("Error: %s", error_record[-1])
+            self.logger.info("Error: %s", error_record[-1])
 
             if converged is True or k > self.n_iterations:
                 break

src/UQpy/sampling/SimplexSampling.py

@@ -10,7 +10,7 @@ def __init__(
         self,
         nodes: Union[list, Numpy2DFloatArray] = None,
         nsamples: PositiveInteger = None,
-        random_state: RandomStateType = None,
+        random_state: Union[RandomStateType, np.random.Generator] = None,
     ):
         """
         Generate uniform random samples inside an n-dimensional simplex.

src/UQpy/sampling/adaptive_kriging_functions/baseclass/LearningFunction.py

@@ -4,7 +4,7 @@
 class LearningFunction(ABC):
     def __init(self, ordered_parameters=None, **kwargs):
         self.parameters = kwargs
-        self.ordered_parameters = (ordered_parameters if not None else tuple(kwargs.keys()))
+        self.ordered_parameters = (ordered_parameters if ordered_parameters is not None else tuple(kwargs.keys()))
         if len(self.ordered_parameters) != len(self.parameters):
             raise ValueError("Inconsistent dimensions between order_params tuple and params dictionary.")
 

src/UQpy/sampling/stratified_sampling/RefinedStratifiedSampling.py

@@ -45,7 +45,7 @@ def __init__(
 
         self.random_state = random_state
         if isinstance(self.random_state, int):
-            self.random_state = np.random.RandomState(self.random_state)
+            self.random_state = np.random.default_rng(self.random_state)
         elif not isinstance(self.random_state, (type(None), np.random.RandomState)):
             raise TypeError('UQpy: random_state must be None, an int or an np.random.Generator object.')
         if self.random_state is None:

src/UQpy/sampling/stratified_sampling/refinement/baseclass/Refinement.py

@@ -46,7 +46,7 @@ def finalize(self, samples, samples_per_iteration):
     def identify_bins(strata_metrics, points_to_add, random_state):
         bins2break = np.array([])
         points_left = points_to_add
-        while (np.where(strata_metrics == strata_metrics.max())[0].shape[0] < points_left):
+        while np.where(strata_metrics == strata_metrics.max())[0].shape[0] < points_left:
             bin = np.where(strata_metrics == strata_metrics.max())[0]
             bins2break = np.hstack([bins2break, bin])
             strata_metrics[bin] = 0

src/UQpy/surrogates/__init__.py

@@ -1,6 +1,5 @@
 from UQpy.surrogates.polynomial_chaos import *
 from UQpy.surrogates.stochastic_reduced_order_models import *
-from UQpy.surrogates.kriging import *
 from UQpy.surrogates.gaussian_process import *
 from UQpy.surrogates.baseclass import *