Added init files

ROSCO_toolbox/ROSCO_toolbox/sim.py

@@ -10,7 +10,6 @@
 # speROSCO_cific language governing permissions and limitations under the License.
 
 import numpy as np
-from ROSCO_toolbox import turbine as ROSCO_turbine
 import matplotlib.pyplot as plt
 import sys
 

ROSCO_toolbox/ROSCO_toolbox/turbine.py

@@ -19,8 +19,6 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-from ROSCO_toolbox import utilities as ROSCO_utilities
-
 # Some useful constants
 now = datetime.datetime.now()
 pi = np.pi
@@ -205,6 +203,7 @@ def load_from_fast(self, FAST_InputFile,FAST_directory, FAST_ver='OpenFAST',dev_
         if rot_source == 'cc-blade': # Use cc-blade
             self.load_from_ccblade()
         elif rot_source == 'txt':    # Use specified text file
+            from ROSCO_toolbox import utilities as ROSCO_utilities
             file_processing = ROSCO_utilities.FileProcessing()
             self.pitch_initial_rad, self.TSR_initial, self.Cp_table, self.Ct_table, self.Cq_table = file_processing.load_from_txt(
                 txt_filename)

ROSCO_toolbox/__init__.py

@@ -0,0 +1 @@
+from .ROSCO_toolbox import control_interface, controller, sim, turbine, utilities

pCrunch/__init__.py

@@ -0,0 +1 @@
+from .pCrunch import Analysis, CaseGen_Control, pdTools, Processing

pCrunch/pCrunch/Analysis.py

@@ -10,9 +10,7 @@
 import fatpack # 3rd party module used for rainflow counting
 
 from scipy.interpolate import PchipInterpolator
-from ROSCO_toolbox.utilities import FAST_IO
 
-from pCrunch import pdTools
 
 class Loads_Analysis(object):
     '''
@@ -88,6 +86,7 @@ def full_loads_analysis(self, filenames, get_load_ranking=True, return_FastData=
             list or dictionary containing OpenFAST output data
         '''
         # Load openfast data
+        from ROSCO_toolbox.utilities import FAST_IO
         fast_io = FAST_IO()
         fast_data = fast_io.load_FAST_out(filenames, tmin=self.t0, tmax=self.tf, verbose=self.verbose)
 
@@ -244,6 +243,7 @@ def load_ranking(self, stats, names=[], get_df=False):
             pandas DataFrame containing load rankings
         '''
 
+        from pCrunch import pdTools
         # Make sure stats is in pandas df
         if isinstance(stats, dict):
             stats_df = pdTools.dict2df([stats], names=names)
@@ -468,6 +468,7 @@ def AEP(self, stats, windspeeds, U_pwr_curve=[], pwr_curve_vars=[]):
             Annual energy production corresponding to 
         '''
 
+        from pCrunch import pdTools
         # Make sure stats is in pandas df
         if isinstance(stats, dict):
             stats_df = pdTools.dict2df(stats)
@@ -574,6 +575,7 @@ def stat_curve(self, windspeeds, stats, plotvar, plottype, stat_idx=0, names=[])
         ax: axes handle
         '''
 
+        from pCrunch import pdTools
         # Check for valid inputs
         if isinstance(stats, dict):
             stats_df = pdTools.dict2df(stats)
@@ -729,7 +731,8 @@ def plot_load_ranking(self, load_rankings, case_matrix, classifier_type,
         # classifiers = list(set(cmw[classifier_type]))
         # classifier_names = ['ROSCO', 'legacy']
 
-       # Check for valid inputs
+        from pCrunch import pdTools
+        # Check for valid inputs
         if isinstance(load_rankings, dict):
             load_ranking_df = pdTools.dict2df(load_rankings)
         elif isinstance(load_rankings, list):

pCrunch/pCrunch/Processing.py

@@ -15,10 +15,6 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 
-from ROSCO_toolbox.utilities import FAST_IO
-
-from pCrunch import Analysis, pdTools
-
 
 class FAST_Processing(object):
     '''
@@ -185,6 +181,7 @@ def batch_processing(self):
 
         else:
             # Initialize Analysis
+            from pCrunch import Analysis, pdTools
             loads_analysis = Analysis.Loads_Analysis()
             loads_analysis.verbose = self.verbose
             loads_analysis.t0 = self.t0
@@ -279,6 +276,7 @@ def design_comparison(self, filenames):
 
         fnames = np.array(filenames).T.tolist()
         # Setup FAST_Analysis preferences
+        from pCrunch import Analysis, pdTools
         loads_analysis = Analysis.Loads_Analysis()
         loads_analysis.verbose=self.verbose
         loads_analysis.t0 = self.t0

pCrunch/pCrunch/pdTools.py

@@ -2,7 +2,6 @@
 Some tools to ease management of batch analysis data in pandas
 '''
 import pandas as pd
-from pCrunch import Processing
 
 def dict2df(sumstats, names=None):
     '''
@@ -77,6 +76,7 @@ def yaml2df(filename, names=[]):
 
     '''
 
+    from pCrunch import Processing
     data_dict = Processing.load_yaml('test.yaml', package=0)
 
     level = data_dict

setup.py

@@ -123,8 +123,8 @@ def build_extension(self, ext):
     install_requires              = ['openmdao>=3.2','numpy','scipy','pandas','simpy','marmot-agents','nlopt','dill','smt'],
     classifiers                   = [_f for _f in CLASSIFIERS.split('\n') if _f],
     package_dir                   = {'wisdem':'WISDEM/wisdem',
-                                     'ROSCO_toolbox.ROSCO_toolbox':'ROSCO_toolbox',
-                                     'pCrunch.pCrunch':'pCrunch',
+                                     # 'ROSCO_toolbox.ROSCO_toolbox':'ROSCO_toolbox',
+                                     # 'pCrunch.pCrunch':'pCrunch',
                                      }, # weis doesn't need special directions
     packages                      = weis_pkgs + wisdem_pkgs + roscotools_pkgs + pcrunch_pkgs,
     python_requires               = '>=3.6',

weis/multifidelity/methods/trust_region.py

@@ -100,11 +100,12 @@ def find_next_point(self):
         upper_bounds = np.minimum(trust_region_upper_bounds, self.bounds[:, 1])
 
         bounds = list(zip(lower_bounds, upper_bounds))
-        scaled_function = lambda x: self.objective_scaler * self.approximation_functions[
+        scaled_function = lambda x: np.squeeze(self.objective_scaler * self.approximation_functions[
             self.objective
         ](
             x
         )
+        )
         res = minimize(
             scaled_function,
             x0,