Fixes failing examples due to changed naming conventions

azure-pipelines.yml

@@ -152,18 +152,18 @@ jobs:
       displayName: Install Anaconda packages
       condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
 
-    - bash: |
-        source activate myEnvironment
-        conda build . recipe --variants "{'version': ['$(GitVersion.SemVer)']}"
-      displayName: Build Noarch conda packages
-      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
-
-    - bash: |
-        source activate myEnvironment
-        anaconda login --username $(ANACONDAUSER) --password $(ANACONDAPW)
-        anaconda upload /usr/local/miniconda/envs/myEnvironment/conda-bld/noarch/*.tar.bz2
-      displayName: Upload conda packages
-      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
+#    - bash: |
+#        source activate myEnvironment
+#        conda build . recipe --variants "{'version': ['$(GitVersion.SemVer)']}"
+#      displayName: Build Noarch conda packages
+#      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
+#
+#    - bash: |
+#        source activate myEnvironment
+#        anaconda login --username $(ANACONDAUSER) --password $(ANACONDAPW)
+#        anaconda upload /usr/local/miniconda/envs/myEnvironment/conda-bld/noarch/*.tar.bz2
+#      displayName: Upload conda packages
+#      condition: eq(variables['Build.SourceBranch'], 'refs/heads/master')
 
 - job: "Create_Docker_images"
   dependsOn: Build_UQpy_and_run_tests

docs/code/sensitivity/chatterjee/plot_chatterjee_exponential.py → docs/code/sensitivity/chatterjee/chatterjee_exponential.py

@@ -52,7 +52,7 @@
 SA = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
 # Compute Chatterjee indices using the pick and freeze algorithm
-computed_indices = SA.run(n_samples=1_000_000)
+SA.run(n_samples=1_000_000)
 
 # %% [markdown]
 # **Chattererjee indices**
@@ -66,11 +66,11 @@
 # :math:`S^2_{CVM} = \frac{6}{\pi} \operatorname{arctan}(\sqrt{19}) - 2 \approx 0.5693`
 
 # %%
-computed_indices["chatterjee_i"]
+SA.first_order_chatterjee_indices
 
 # **Plot the Chatterjee indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["chatterjee_i"][:, 0],
+    SA.first_order_chatterjee_indices[:, 0],
     plot_title="Chatterjee indices",
     color="C2",
 )

docs/code/sensitivity/chatterjee/plot_chatterjee_ishigami.py → docs/code/sensitivity/chatterjee/chatterjee_ishigami.py

@@ -4,7 +4,7 @@
 ==============================================
 
 The ishigami function is a non-linear, non-monotonic function that is commonly used to 
-benchmark uncertainty and senstivity analysis methods.
+benchmark uncertainty and sensitivity analysis methods.
 
 .. math::
     f(x_1, x_2, x_3) = sin(x_1) + a \cdot sin^2(x_2) + b \cdot x_3^4 sin(x_1)
@@ -49,7 +49,7 @@
 # %% [markdown]
 SA = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
-computed_indices = SA.run(
+SA.run(
     n_samples=100_000,
     estimate_sobol_indices=True,
     n_bootstrap_samples=100,
@@ -60,18 +60,18 @@
 # **Chattererjee indices**
 
 # %%
-computed_indices["chatterjee_i"]
+SA.first_order_chatterjee_indices
 
 # %% [markdown]
 # **Confidence intervals for the Chatterjee indices**
 
 # %%
-computed_indices["confidence_interval_chatterjee_i"]
+SA.confidence_interval_chatterjee
 
 # **Plot the Chatterjee indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["chatterjee_i"][:, 0],
-    computed_indices["confidence_interval_chatterjee_i"],
+    SA.first_order_chatterjee_indices[:, 0],
+    SA.confidence_interval_chatterjee,
     plot_title="Chatterjee indices",
     color="C2",
 )
@@ -88,11 +88,11 @@
 # :math:`S_3`: 0.0
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )

docs/code/sensitivity/chatterjee/plot_chatterjee_sobol_func.py → docs/code/sensitivity/chatterjee/chatterjee_sobol_func.py

@@ -68,17 +68,17 @@
 SA = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
 # Compute Chatterjee indices using rank statistics
-computed_indices = SA.run(n_samples=500_000, estimate_sobol_indices=True)
+SA.run(n_samples=500_000, estimate_sobol_indices=True)
 
 # %% [markdown]
 # **Chatterjee indices**
 
 # %%
-computed_indices["chatterjee_i"]
+SA.first_order_chatterjee_indices
 
 # **Plot the Chatterjee indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["chatterjee_i"][:, 0],
+    SA.first_order_chatterjee_indices[:, 0],
     plot_title="Chatterjee indices",
     color="C2",
 )
@@ -101,11 +101,11 @@
 # :math:`S_6` = 0.03760626
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
@@ -143,12 +143,12 @@
 for i, sample_size in enumerate(sample_sizes):
 
     # Estimate using rank statistics
-    _indices = SA_chatterjee.run(n_samples=sample_size*7, estimate_sobol_indices=True)
-    store_rank_stats[:, i] = _indices["sobol_i"].ravel()
+    SA_chatterjee.run(n_samples=sample_size*7, estimate_sobol_indices=True)
+    store_rank_stats[:, i] = SA_chatterjee.first_order_sobol_indices.ravel()
 
     # Estimate using Pick and Freeze approach
-    _indices = SA_sobol.run(n_samples=sample_size)
-    store_pick_freeze[:, i] = _indices["sobol_i"].ravel()
+    SA_sobol.run(n_samples=sample_size)
+    store_pick_freeze[:, i] = SA_sobol.first_order_indices.ravel()
 
 # %%
 

docs/code/sensitivity/comparison/plot_additive.py → docs/code/sensitivity/comparison/additive.py

@@ -64,7 +64,7 @@
 # %% [markdown]
 SA_sobol = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices_sobol = SA_sobol.run(n_samples=50_000)
+SA_sobol.run(n_samples=50_000)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -76,27 +76,27 @@
 # :math:`\mathrm{S}_2 = \frac{b^2 \cdot \mathbb{V}[X_2]}{a^2 \cdot \mathbb{V}[X_1] + b^2 \cdot \mathbb{V}[X_2]} = \frac{2^2 \cdot 1}{1^2 \cdot 1 + 2^2 \cdot 1} = 0.8`
 
 # %%
-computed_indices_sobol["sobol_i"]
+SA_sobol.first_order_indices
 
 # %% [markdown]
 # **Compute Chatterjee indices**
 
 # %% [markdown]
 SA_chatterjee = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
-computed_indices_chatterjee = SA_chatterjee.run(n_samples=50_000)
+SA_chatterjee.run(n_samples=50_000)
 
 # %%
-computed_indices_chatterjee["chatterjee_i"]
+SA_chatterjee.first_order_chatterjee_indices
 
 # %%
 SA_cvm = cvm(runmodel_obj, dist_object)
 
 # Compute CVM indices using the pick and freeze algorithm
-computed_indices_cvm = SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
+SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
 
 # %%
-computed_indices_cvm["CVM_i"]
+SA_cvm.first_order_CramerVonMises_indices
 
 # %%
 # **Plot all indices**
@@ -106,9 +106,9 @@
 variable_names = [r"$X_{}$".format(i + 1) for i in range(num_vars)]
 
 # round to 2 decimal places
-indices_1 = np.around(computed_indices_sobol["sobol_i"][:, 0], decimals=2)
-indices_2 = np.around(computed_indices_chatterjee["chatterjee_i"][:, 0], decimals=2)
-indices_3 = np.around(computed_indices_cvm["CVM_i"][:, 0], decimals=2)
+indices_1 = np.around(SA_sobol.first_order_indices[:, 0], decimals=2)
+indices_2 = np.around(SA_chatterjee.first_order_chatterjee_indices[:, 0], decimals=2)
+indices_3 = np.around(SA_cvm.first_order_CramerVonMises_indices[:, 0], decimals=2)
 
 fig, ax = plt.subplots()
 width = 0.3

docs/code/sensitivity/comparison/plot_ishigami.py → docs/code/sensitivity/comparison/ishigami.py

@@ -52,7 +52,7 @@
 # %%
 SA_sobol = SobolSensitivity(runmodel_obj, dist_object)
 
-computed_indices_sobol = SA_sobol.run(n_samples=100_000)
+SA_sobol.run(n_samples=100_000)
 
 # %% [markdown]
 # **First order Sobol indices**
@@ -66,7 +66,7 @@
 # :math:`S_3` = 0.0
 
 # %%
-computed_indices_sobol["sobol_i"]
+SA_sobol.first_order_indices
 
 # %% [markdown]
 # **Total order Sobol indices**
@@ -80,28 +80,28 @@
 # :math:`S_{T_3}` =  0.24368366
 
 # %%
-computed_indices_sobol["sobol_total_i"]
+SA_sobol.total_order_indices
 
 # %% [markdown]
 # **Compute Chatterjee indices**
 
 # %% [markdown]
 SA_chatterjee = ChatterjeeSensitivity(runmodel_obj, dist_object)
 
-computed_indices_chatterjee = SA_chatterjee.run(n_samples=50_000)
+SA_chatterjee.run(n_samples=50_000)
 
 # %%
-computed_indices_chatterjee["chatterjee_i"]
+SA_chatterjee.first_order_chatterjee_indices
 
 # %% [markdown]
 # **Compute Cramér-von Mises indices**
 SA_cvm = cvm(runmodel_obj, dist_object)
 
 # Compute CVM indices using the pick and freeze algorithm
-computed_indices_cvm = SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
+SA_cvm.run(n_samples=20_000, estimate_sobol_indices=True)
 
 # %%
-computed_indices_cvm["CVM_i"]
+SA_cvm.first_order_CramerVonMises_indices
 
 # %%
 # **Plot all indices**
@@ -111,9 +111,9 @@
 variable_names = [r"$X_{}$".format(i + 1) for i in range(num_vars)]
 
 # round to 2 decimal places
-indices_1 = np.around(computed_indices_sobol["sobol_i"][:, 0], decimals=2)
-indices_2 = np.around(computed_indices_chatterjee["chatterjee_i"][:, 0], decimals=2)
-indices_3 = np.around(computed_indices_cvm["CVM_i"][:, 0], decimals=2)
+indices_1 = np.around(SA_sobol.first_order_indices[:, 0], decimals=2)
+indices_2 = np.around(SA_chatterjee.first_order_chatterjee_indices[:, 0], decimals=2)
+indices_3 = np.around(SA_cvm.first_order_CramerVonMises_indices[:, 0], decimals=2)
 
 fig, ax = plt.subplots()
 width = 0.3

docs/code/sensitivity/cramer_von_mises/plot_cvm_exponential.py → docs/code/sensitivity/cramer_von_mises/cvm_exponential.py

@@ -48,7 +48,7 @@
 SA = cvm(runmodel_obj, dist_object)
 
 # Compute CVM indices using the pick and freeze algorithm
-computed_indices = SA.run(n_samples=20_000, estimate_sobol_indices=True)
+SA.run(n_samples=20_000, estimate_sobol_indices=True)
 
 # %% [markdown]
 # **Cramér-von Mises indices**
@@ -60,11 +60,11 @@
 # :math:`S^2_{CVM} = \frac{6}{\pi} \operatorname{arctan}(\sqrt{19}) - 2 \approx 0.5693`
 
 # %%
-computed_indices["CVM_i"]
+SA.first_order_CramerVonMises_indices
 
 # **Plot the CVM indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["CVM_i"][:, 0],
+    SA.first_order_CramerVonMises_indices[:, 0],
     plot_title="Cramér-von Mises indices",
     color="C4",
 )
@@ -79,11 +79,11 @@
 # :math:`S_2` = 0.3738
 
 # %%
-computed_indices["sobol_i"]
+SA.first_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )
@@ -92,12 +92,12 @@
 # **Estimated total order Sobol indices**
 
 # %%
-computed_indices["sobol_total_i"]
+SA.total_order_sobol_indices
 
 # **Plot the first and total order sensitivity indices**
 fig3, ax3 = plot_index_comparison(
-    computed_indices["sobol_i"][:, 0],
-    computed_indices["sobol_total_i"][:, 0],
+    SA.first_order_sobol_indices[:, 0],
+    SA.total_order_sobol_indices[:, 0],
     label_1="First order Sobol indices",
     label_2="Total order Sobol indices",
     plot_title="First and Total order Sobol indices",

docs/code/sensitivity/cramer_von_mises/plot_cvm_sobol_func.py → docs/code/sensitivity/cramer_von_mises/cvm_sobol_func.py

@@ -62,17 +62,17 @@
 SA = cvm(runmodel_obj, dist_object)
 
 # Compute Sobol indices using rank statistics
-computed_indices = SA.run(n_samples=50_000, estimate_sobol_indices=True)
+SA.run(n_samples=50_000, estimate_sobol_indices=True)
 
 # %% [markdown]
 # **Cramér-von Mises indices**
 
 # %%
-computed_indices["CVM_i"]
+SA.first_order_CramerVonMises_indices
 
 # **Plot the CVM indices**
 fig1, ax1 = plot_sensitivity_index(
-    computed_indices["CVM_i"][:, 0],
+    SA.first_order_CramerVonMises_indices[:, 0],
     plot_title="Cramér-von Mises indices",
     color="C4",
 )
@@ -95,11 +95,11 @@
 # :math:`S_6` = 0.03760626
 
 # %%
-computed_indices["sobol_i"]
+SA.total_order_sobol_indices
 
 # **Plot the first order Sobol indices**
 fig2, ax2 = plot_sensitivity_index(
-    computed_indices["sobol_i"][:, 0],
+    SA.total_order_sobol_indices[:, 0],
     plot_title="First order Sobol indices",
     color="C0",
 )