Feature/wisdem upgrade

README.md

@@ -58,7 +58,7 @@ There can be some hardware-software mis-specification issues with WISDEM install
 In the event of issues, WISDEM can be installed manually or using `conda` without issues, then `pip` installation can proceed.
 
 ```shell
-mamba install wisdem=3.18.1 -y
+mamba install wisdem -y
 pip install -e .[dev,docs]
 ```
 

ard/cost/wisdem_wrap.py

@@ -4,7 +4,7 @@
 import openmdao.api as om
 from wisdem.plant_financese.plant_finance import PlantFinance as PlantFinance_orig
 from wisdem.landbosse.landbosse_omdao.landbosse import LandBOSSE as LandBOSSE_orig
-from wisdem.orbit.api.wisdem import Orbit as Orbit_orig
+from wisdem.orbit.orbit_api import Orbit as Orbit_orig
 
 from ard.cost.approximate_turbine_spacing import SpacingApproximations
 

ard/utils/test_utils.py

@@ -1,12 +1,13 @@
-from fileinput import filename
+import sys
 from os import PathLike
 from pathlib import Path
 
 import numpy as np
+import traceback
 
 
 def pyrite_validator(
-    validation_data: dict,
+    data_for_validation: dict,
     filename_pyrite: PathLike,
     rewrite: bool = False,
     rtol_val: float = 1e-6,
@@ -22,7 +23,7 @@ def pyrite_validator(
         # this helper function can write a file to hold pyrite-standard data
 
         # write out a npz file that holds the variables we want to be able to check
-        np.savez(filename_pyrite, **validation_data)
+        np.savez(filename_pyrite, **data_for_validation)
         assert False
     else:
         # ... or it can check to make sure that an existing pyrite file matches the current data
@@ -31,21 +32,30 @@ def pyrite_validator(
         pyrite_data = np.load(filename_pyrite.with_suffix(".npz"))
 
         # for each of the variables in the pyrite-standard data file
+        all_validation_matches = True
         for k, v in pyrite_data.items():
             # count how many of the values in the data match the equivalent validation data
             sum_isclose = np.sum(
-                np.isclose(np.array(v), validation_data[k], rtol=rtol_val)
+                np.isclose(np.array(v), data_for_validation[k], rtol=rtol_val)
             )
-            vd_size = np.array(validation_data[k], dtype=np.float64).size
+            vd_size = np.array(data_for_validation[k], dtype=np.float64).size
             # assert all of the values match
             validation_matches = sum_isclose == vd_size
 
             if not validation_matches:
-                print(f"for variable {k}:")
+                print(f"for variable {k}:", file=sys.stderr)
                 print(
-                    f"\t{sum_isclose} values match of {vd_size} total validation values"
+                    f"\t{sum_isclose} values match of {vd_size} total validation values",
+                    file=sys.stderr,
                 )
-                print(f"\tto a tolerance of {rtol_val:e}")
-                print(f"saved data for {k}: {v}")
-                print(f"computed data for {k}: {validation_data[k]}")
-            assert validation_matches
+                print(f"\tto a tolerance of {rtol_val:e}", file=sys.stderr)
+                print(f"pyrite data for {k}: {v}", file=sys.stderr)
+                print(
+                    f"computed data for {k}: {data_for_validation[k]}", file=sys.stderr
+                )
+                stack = traceback.format_stack(limit=3)
+                print("".join(stack[-2]).replace("  File", "test:"), file=sys.stderr)
+                print(file=sys.stderr)
+
+            all_validation_matches &= validation_matches
+        assert all_validation_matches, "Pyrite validation data must match."

pyproject.toml

@@ -35,7 +35,7 @@ classifiers = [
 dependencies = [
   "numpy<2.3",
   "floris>=4.3",
-  "wisdem==3.18.1",
+  "wisdem>=3.21",
   "NLopt",
   "marmot-agents",
   "openmdao",