self.DOE_A.case_outputs = ['A.f1','A.f2'] self.DOE_A.recorder = DBCaseRecorder(os.path.join(self._tdir,'A.db')) #Iteration Hierarchy self.driver.workflow.add(['DOE_A']) self.DOE_A.workflow.add('A') def cleanup(self): shutil.rmtree(self._tdir, ignore_errors=True) if __name__ == "__main__": #pragma: no cover import sys from openmdao.main.api import set_as_top from openmdao.lib.casehandlers.db import case_db_to_dict analysis = Analysis() set_as_top(analysis) analysis.run() DOE1 = case_db_to_dict(os.path.join(analysis._tdir,'A.db'),['A.x','A.y','A.z','A.f1','A.f2']) DOE2 = case_db_to_dict(os.path.join(analysis._tdir,'A.db'),['A.x','A.y','A.f1','A.f2']) print DOE1 print DOE2 analysis.cleanup()
X_range = arange(-5, 10.2, 0.25) Y_range = arange(0, 15.2, 0.25) X, Y = meshgrid(X_range, Y_range) Z = branin(X, Y) iterator = analysis.branin_meta_model.recorder.get_iterator() plt.contour(X, Y, Z, arange(1, 200, 2), zorder=1) cb = plt.colorbar(shrink=0.45) # plot the initial training data data_train = case_db_to_dict( os.path.join(analysis._tdir, "trainer.db"), ["branin_meta_model.y", "branin_meta_model.x", "branin_meta_model.f_xy"], ) plt.scatter(data_train["branin_meta_model.x"], data_train["branin_meta_model.y"], s=30, c="#572E07", zorder=10) data_EI = case_db_to_dict( os.path.join(analysis._tdir, "retrain.db"), ["branin_meta_model.y", "branin_meta_model.x", "branin_meta_model.f_xy"], ) count = len(data_EI["branin_meta_model.x"]) colors = arange(0, count) / float(count) color_map = get_cmap("spring") plt.scatter(
] #Iteration Hierarchy self.driver.workflow.add(['DOE_A']) self.DOE_A.workflow.add('A') def cleanup(self): shutil.rmtree(self._tdir, ignore_errors=True) if __name__ == "__main__": #pragma: no cover import sys from openmdao.main.api import set_as_top from openmdao.lib.casehandlers.db import case_db_to_dict analysis = Analysis() set_as_top(analysis) analysis.run() DOE1 = case_db_to_dict(os.path.join(analysis._tdir, 'A.db'), ['A.x', 'A.y', 'A.z', 'A.f1', 'A.f2']) DOE2 = case_db_to_dict(os.path.join(analysis._tdir, 'A.db'), ['A.x', 'A.y', 'A.f1', 'A.f2']) print DOE1 print DOE2 analysis.cleanup()
row2 = [] for x,y in zip(x_row,y_row): analysis.spiral_meta_model.x = x analysis.spiral_meta_model.y = y analysis.spiral_meta_model.execute() row1.append(analysis.spiral_meta_model.f1_xy.mu) row2.append(analysis.spiral_meta_model.f2_xy.mu) Z1_pred.append(row1) Z2_pred.append(row2) Z1_pred = array(Z1_pred) Z2_pred = array(Z2_pred) #plot the initial training data data_train = case_db_to_dict(os.path.join(analysis._tdir,'trainer.db'), ['spiral_meta_model.x', 'spiral_meta_model.y', 'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy']) plt.scatter(data_train['spiral_meta_model.x'], data_train['spiral_meta_model.y'],s=30,c='#572E07',zorder=10) data_EI = case_db_to_dict(os.path.join(analysis._tdir,'retrain.db'), ['spiral_meta_model.y', 'spiral_meta_model.x', 'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy']) count = len(data_EI['spiral_meta_model.x']) colors = arange(0,count)/float(count) color_map = get_cmap('spring')
row1 = [] row2 = [] for x, y in zip(x_row, y_row): analysis.spiral_meta_model.x = x analysis.spiral_meta_model.y = y analysis.spiral_meta_model.execute() row1.append(analysis.spiral_meta_model.f1_xy.mu) row2.append(analysis.spiral_meta_model.f2_xy.mu) Z1_pred.append(row1) Z2_pred.append(row2) Z1_pred = array(Z1_pred) Z2_pred = array(Z2_pred) #plot the initial training data data_train = case_db_to_dict(os.path.join(analysis._tdir, 'trainer.db'), [ 'spiral_meta_model.x', 'spiral_meta_model.y', 'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy' ]) plt.scatter(data_train['spiral_meta_model.x'], data_train['spiral_meta_model.y'], s=30, c='#572E07', zorder=10) data_EI = case_db_to_dict(os.path.join(analysis._tdir, 'retrain.db'), [ 'spiral_meta_model.y', 'spiral_meta_model.x', 'spiral_meta_model.f1_xy', 'spiral_meta_model.f2_xy' ]) count = len(data_EI['spiral_meta_model.x']) colors = arange(0, count) / float(count)
X_range = arange(-5,10.2,.25) Y_range = arange(0,15.2,.25) X,Y = meshgrid(X_range,Y_range) Z = branin(X,Y) iterator = analysis.branin_meta_model.recorder.get_iterator() plt.contour(X,Y,Z,arange(1,200,2),zorder=1) cb = plt.colorbar(shrink=.45) #plot the initial training data data_train = case_db_to_dict(os.path.join(analysis._tdir,'trainer.db'), ['branin_meta_model.y', 'branin_meta_model.x', 'branin_meta_model.f_xy']) plt.scatter(data_train['branin_meta_model.x'], data_train['branin_meta_model.y'],s=30,c='#572E07',zorder=10) data_EI = case_db_to_dict(os.path.join(analysis._tdir,'retrain.db'), ['branin_meta_model.y', 'branin_meta_model.x', 'branin_meta_model.f_xy']) count = len(data_EI['branin_meta_model.x']) colors = arange(0,count)/float(count) color_map = get_cmap('spring')
X_range = arange(-5, 10.2, .25) Y_range = arange(0, 15.2, .25) X, Y = meshgrid(X_range, Y_range) Z = branin(X, Y) iterator = analysis.branin_meta_model.recorder.get_iterator() plt.contour(X, Y, Z, arange(1, 200, 2), zorder=1) cb = plt.colorbar(shrink=.45) #plot the initial training data data_train = case_db_to_dict(os.path.join(analysis._tdir, 'trainer.db'), [ 'branin_meta_model.y', 'branin_meta_model.x', 'branin_meta_model.f_xy' ]) plt.scatter(data_train['branin_meta_model.x'], data_train['branin_meta_model.y'], s=30, c='#572E07', zorder=10) data_EI = case_db_to_dict(os.path.join(analysis._tdir, 'retrain.db'), [ 'branin_meta_model.y', 'branin_meta_model.x', 'branin_meta_model.f_xy' ]) count = len(data_EI['branin_meta_model.x']) colors = arange(0, count) / float(count)
row1 = [] row2 = [] for x, y in zip(x_row, y_row): analysis.spiral_meta_model.x = x analysis.spiral_meta_model.y = y analysis.spiral_meta_model.execute() row1.append(analysis.spiral_meta_model.f1_xy.mu) row2.append(analysis.spiral_meta_model.f2_xy.mu) Z1_pred.append(row1) Z2_pred.append(row2) Z1_pred = array(Z1_pred) Z2_pred = array(Z2_pred) # plot the initial training data data_train = case_db_to_dict( os.path.join(analysis._tdir, "trainer.db"), ["spiral_meta_model.x", "spiral_meta_model.y", "spiral_meta_model.f1_xy", "spiral_meta_model.f2_xy"], ) plt.scatter(data_train["spiral_meta_model.x"], data_train["spiral_meta_model.y"], s=30, c="#572E07", zorder=10) data_EI = case_db_to_dict( os.path.join(analysis._tdir, "retrain.db"), ["spiral_meta_model.y", "spiral_meta_model.x", "spiral_meta_model.f1_xy", "spiral_meta_model.f2_xy"], ) count = len(data_EI["spiral_meta_model.x"]) colors = arange(0, count) / float(count) color_map = get_cmap("spring") f1_train = [case.mu for case in data_train["spiral_meta_model.f1_xy"]] f2_train = [case.mu for case in data_train["spiral_meta_model.f2_xy"]]
if __name__ == '__main__': try: os.remove(DOE_OUT_DB) except OSError: pass top_level_analysis = set_as_top(Analysis()) top_level_analysis.run() vars = [ 'adapter.vane_num_out', 'adapter.injector_loc_out', 'adapter.injector_dia_out', 'geometry.vane_num', 'geometry.injector_loc', 'geometry.injector_dia' ] doe_designs = case_db_to_dict(DOE_OUT_DB, vars) print doe_designs for k, v in doe_designs.iteritems(): print k, v print top_level_analysis.doe_driver.recorder data = top_level_analysis.doe_driver.recorder.get_iterator() inputs = [case['adapter.vane_num_out'] for case in data] print inputs exit() # --------------------------------END---------------------------------------- #
self.connect('adapter.injector_loc_out', 'geometry.injector_loc') self.connect('adapter.injector_dia_out', 'geometry.injector_dia') if __name__ == '__main__': try: os.remove(DOE_OUT_DB) except OSError: pass top_level_analysis = set_as_top(Analysis()) top_level_analysis.run() vars = ['adapter.vane_num_out','adapter.injector_loc_out','adapter.injector_dia_out','geometry.vane_num','geometry.injector_loc', 'geometry.injector_dia'] doe_designs = case_db_to_dict(DOE_OUT_DB, vars) print doe_designs for k,v in doe_designs.iteritems(): print k,v print top_level_analysis.doe_driver.recorder data = top_level_analysis.doe_driver.recorder.get_iterator() inputs = [case['adapter.vane_num_out'] for case in data] print inputs exit()