Example #1
0
        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(
Example #3
0
        ]

        #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')
Example #5
0
        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"]]
Example #9
0
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()