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Python Data.inputs方法代码示例

本文整理汇总了Python中SUAVE.Core.Data.inputs方法的典型用法代码示例。如果您正苦于以下问题:Python Data.inputs方法的具体用法?Python Data.inputs怎么用?Python Data.inputs使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在SUAVE.Core.Data的用法示例。


在下文中一共展示了Data.inputs方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: carpet_plot

# 需要导入模块: from SUAVE.Core import Data [as 别名]
# 或者: from SUAVE.Core.Data import inputs [as 别名]
def carpet_plot(problem, number_of_points, plot_obj=1, plot_const=0): 
    #SUAVE.Optimization.carpet_plot(problem, ):
    #takes in an optimization problem and runs a carpet plot of the first 2 variables

    #unpack
    opt_prob        = problem.optimization_problem
    idx0            = 0   #index of variable location
    idx1            = 1
    base_inputs     = opt_prob.inputs
    names           = base_inputs[:,0] # Names
    bnd             = base_inputs[:,2] # Bounds
    scl             = base_inputs[:,3] # Scaling
    base_objective  = opt_prob.objective
    obj_name        = base_objective[0][0] #objective function name (used for scaling)
    obj_scaling     = base_objective[0][1]
    base_constraints= opt_prob.constraints
    constraint_names= base_constraints[:,0]
    constraint_scale= base_constraints[:,3]
   
    #define inputs, output, and constraints for sweep
    inputs          = np.zeros([2,number_of_points])
    obj             = np.zeros([number_of_points,number_of_points])
    constraint_num  = np.shape(base_constraints)[0] # of constraints
    constraint_val  = np.zeros([constraint_num,number_of_points,number_of_points])
    
    
    #create inputs matrix
    inputs[0,:] = np.linspace(bnd[idx0][0], bnd[idx0][1], number_of_points)
    inputs[1,:] = np.linspace(bnd[idx1][0], bnd[idx1][1], number_of_points)

    
    #inputs defined; now run sweep
    for i in range(0, number_of_points):
        for j in range(0,number_of_points):
            #problem.optimization_problem.inputs=base_inputs  #overwrite any previous modification
            opt_prob.inputs[:,1][idx0]= inputs[0,i]
            opt_prob.inputs[:,1][idx1]= inputs[1,j]
   
            obj[j,i]             = problem.objective()*obj_scaling
            constraint_val[:,j,i]= problem.all_constraints().tolist()
  
    if plot_obj==1:
        plt.figure(0)
        CS = plt.contourf(inputs[0,:],inputs[1,:], obj, linewidths=2)
        cbar = plt.colorbar(CS)
        cbar.ax.set_ylabel(obj_name)
        plt.xlabel(names[idx0])
        plt.ylabel(names[idx1])
        
       
    if plot_const==1:
        
        for i in range(0, constraint_num): #constraint_num):
            plt.figure(i+1)
            CS_const=plt.contour(inputs[0,:],inputs[1,:], constraint_val[i,:,:])
            cbar = plt.colorbar(CS_const)
            cbar.ax.set_ylabel(constraint_names[i])
            plt.xlabel(names[idx0])
            plt.ylabel(names[idx1])
    plt.show()      
       
        
    #pack outputs
    outputs= Data()
    outputs.inputs         = inputs
    outputs.objective      = obj
    outputs.constraint_val =constraint_val
    return outputs
    
    
开发者ID:Alexandrovich,项目名称:SUAVE,代码行数:70,代码来源:carpet_plot.py

示例2: setup

# 需要导入模块: from SUAVE.Core import Data [as 别名]
# 或者: from SUAVE.Core.Data import inputs [as 别名]
def setup():

    nexus = Nexus()
    problem = Data()
    nexus.optimization_problem = problem

    # -------------------------------------------------------------------
    # Inputs
    # -------------------------------------------------------------------

    #   [ tag                            , initial, (lb,ub)             , scaling , units ]
    problem.inputs = np.array([
        [ 'wing_area'                    ,  95    , (   90. ,   130.   ) ,   100. , Units.meter**2],
        [ 'cruise_altitude'              ,  11    , (   9   ,    14.   ) ,   10.  , Units.km],
    ])

    # -------------------------------------------------------------------
    # Objective
    # -------------------------------------------------------------------

    # throw an error if the user isn't specific about wildcards
    # [ tag, scaling, units ]
    problem.objective = np.array([
        [ 'fuel_burn', 10000, Units.kg ]
    ])
    
    # -------------------------------------------------------------------
    # Constraints
    # -------------------------------------------------------------------
    
    # [ tag, sense, edge, scaling, units ]
    problem.constraints = np.array([
        [ 'design_range_fuel_margin' , '>', 0., 1E-1, Units.less], #fuel margin defined here as fuel 
    ])
    
    # -------------------------------------------------------------------
    #  Aliases
    # -------------------------------------------------------------------
    
    # [ 'alias' , ['data.path1.name','data.path2.name'] ]

    problem.aliases = [
        [ 'wing_area'                        ,   ['vehicle_configurations.*.wings.main_wing.areas.reference',
                                                  'vehicle_configurations.*.reference_area'                    ]],
        [ 'cruise_altitude'                  , 'missions.base.segments.climb_5.altitude_end'                    ],
        [ 'fuel_burn'                        ,    'summary.base_mission_fuelburn'                               ],
        [ 'design_range_fuel_margin'         ,    'summary.max_zero_fuel_margin'                                ],
    ]    
    
    # -------------------------------------------------------------------
    #  Vehicles
    # -------------------------------------------------------------------
    nexus.vehicle_configurations = Vehicles.setup()
    
    # -------------------------------------------------------------------
    #  Analyses
    # -------------------------------------------------------------------
    nexus.analyses = Analyses.setup(nexus.vehicle_configurations)
    
    # -------------------------------------------------------------------
    #  Missions
    # -------------------------------------------------------------------
    nexus.missions = Missions.setup(nexus.analyses)
    
    # -------------------------------------------------------------------
    #  Procedure
    # -------------------------------------------------------------------    
    nexus.procedure = Procedure.setup()
    
    # -------------------------------------------------------------------
    #  Summary
    # -------------------------------------------------------------------    
    nexus.summary = Data()    
    nexus.total_number_of_iterations = 0
    return nexus
开发者ID:suavecode,项目名称:Tutorials,代码行数:77,代码来源:Optimize.py

示例3: setup

# 需要导入模块: from SUAVE.Core import Data [as 别名]
# 或者: from SUAVE.Core.Data import inputs [as 别名]
def setup():

    nexus = Nexus()
    problem = Data()
    nexus.optimization_problem = problem

    # -------------------------------------------------------------------
    # Inputs
    # -------------------------------------------------------------------

    # [ tag , initial, [lb,ub], scaling, units ]
    problem.inputs = np.array([
        [ 'wing_area'                    ,    124.8 , (    70.    ,   200.   ) ,   124.8 , Units.meter**2],
        [ 'wing_aspect_ratio'            ,     10.18, (     5.    ,    20.   ) ,    10.18,     Units.less],
        [ 'wing_sweep'                   ,    25.   , (     0.    ,    35.   ) ,    25.  ,  Units.degrees],
        [ 'wing_thickness'               ,     0.105 , (     0.07  ,     0.20 ) ,     0.105,     Units.less],
        [ 'design_thrust'                , 52700.   , ( 10000.    , 70000.   ) , 52700.  ,        Units.N],
        [ 'MTOW'                         , 79090.   , ( 20000.    ,100000.   ) , 79090.  ,       Units.kg],
        [ 'MZFW_ratio'                   ,     0.77 , (     0.6   ,     0.99 ) ,    0.77 ,     Units.less],
        [ 'flap_takeoff_angle'           ,    10.   , (     0.    ,    20.   ) ,    10.  ,  Units.degrees],
        [ 'flap_landing_angle'           ,    40.   , (     0.    ,    50.   ) ,    40.  ,  Units.degrees],
        [ 'short_field_TOW'              , 64030.   , ( 20000.    ,100000.   ) , 64030.  ,       Units.kg],
        [ 'design_TOW'                   , 68520.   , ( 20000.    ,100000.   ) , 68520.  ,       Units.kg],
        [ 'noise_takeoff_speed_increase' ,    10.0  , (    10.    ,    20.   ) ,    10.0 ,     Units.knots],
        [ 'noise_cutback_altitude'       ,   304.8  , (   240.    ,   400.   ) ,   304.8 ,    Units.meter],
    ])

    # -------------------------------------------------------------------
    #  Objective
    # -------------------------------------------------------------------

    problem.objective = np.array([

        [ 'noise_cumulative_margin', 17, Units.less ],

    ])


    # -------------------------------------------------------------------
    # Constraints
    # -------------------------------------------------------------------

    # [ tag, sense, edge, scaling, units ]
    problem.constraints = np.array([
        [ 'MZFW consistency' , '>' , 0. , 10 , Units.less],
        [ 'design_range_fuel_margin' , '>', 0., 10, Units.less],
        [ 'short_field_fuel_margin' , '>' , 0. , 10, Units.less],
        [ 'max_range_fuel_margin' , '>' , 0. , 10, Units.less], 
        [ 'wing_span' , '<', 35.9664, 35.9664, Units.less],
        [ 'noise_flyover_margin' , '>', 0. , 10., Units.less],
        [ 'noise_sideline_margin' , '>', 0. , 10. , Units.less],
        [ 'noise_approach_margin' , '>', 0., 10., Units.less],
        [ 'takeoff_field_length' , '<', 1985., 1985., Units.meters],
        [ 'landing_field_length' , '<', 1385., 1385., Units.meters],
        [ '2nd_segment_climb_max_range' , '>', 0.024, 0.024, Units.less],
        [ '2nd_segment_climb_short_field' , '>', 0.024, 0.024, Units.less],
        [ 'max_throttle' , '<', 1., 1., Units.less],
        [ 'short_takeoff_field_length' , '<', 1330., 1330., Units.meters],
        [ 'noise_cumulative_margin' , '>', 10., 10., Units.less],
    ])

    # -------------------------------------------------------------------
    #  Aliases
    # -------------------------------------------------------------------


    problem.aliases = [
        [ 'wing_area'                        ,   ['vehicle_configurations.*.wings.main_wing.areas.reference',
                                                  'vehicle_configurations.*.reference_area'                            ]],
        [ 'wing_aspect_ratio'                ,    'vehicle_configurations.*.wings.main_wing.aspect_ratio'               ],
        [ 'wing_incidence'                   ,    'vehicle_configurations.*.wings.main_wing.twists.root'                ],
        [ 'wing_tip_twist'                   ,    'vehicle_configurations.*.wings.main_wing.twists.tip'                 ],
        [ 'wing_sweep'                       ,    'vehicle_configurations.*.wings.main_wing.sweeps.quarter_chord'        ],
        [ 'wing_thickness'                   ,    'vehicle_configurations.*.wings.main_wing.thickness_to_chord'         ],
        [ 'wing_taper'                       ,    'vehicle_configurations.*.wings.main_wing.taper'                      ],
        [ 'wing_location'                    ,    'vehicle_configurations.*.wings.main_wing.origin[0]'                  ],
        [ 'horizontal_tail_area'             ,    'vehicle_configurations.*.wings.horizontal_stabilizer.areas.reference'],
        [ 'horizontal_tail_aspect_ratio'     ,    'vehicle_configurations.*.wings.horizontal_stabilizer.aspect_ratio'   ],
        [ 'vertical_tail_area'               ,    'vehicle_configurations.*.wings.vertical_stabilizer.areas.reference'  ],
        [ 'vertical_tail_aspect_ratio'       ,    'vehicle_configurations.*.wings.vertical_stabilizer.aspect_ratio'     ],
        [ 'design_thrust'                    ,    'vehicle_configurations.*.propulsors.turbofan.thrust.total_design'   ],
        [ 'MTOW'                             ,   ['vehicle_configurations.*.mass_properties.takeoff'   ,
                                                  'vehicle_configurations.*.mass_properties.max_takeoff'               ]],
        [ 'design_TOW'                       ,    'vehicle_configurations.base.mass_properties.takeoff'                 ],
        [ 'short_field_TOW'                  ,    'vehicle_configurations.short_field_takeoff.mass_properties.takeoff'  ],
        [ 'flap_takeoff_angle'               ,    ['vehicle_configurations.takeoff.wings.main_wing.flaps.angle',
                                                   'vehicle_configurations.short_field_takeoff.wings.main_wing.flaps.angle']],
        [ 'flap_landing_angle'               ,    'vehicle_configurations.landing.wings.main_wing.flaps.angle'          ],
        [ 'slat_takeoff_angle'               ,    ['vehicle_configurations.takeoff.wings.main_wing.slats.angle',
                                               'vehicle_configurations.short_field_takeoff.wings.main_wing.slats.angle']],
        [ 'slat_landing_angle'               ,    'vehicle_configurations.landing.wings.main_wing.slats.angle'          ],
        [ 'wing_span'                        ,    'vehicle_configurations.base.wings.main_wing.spans.projected'         ],
        [ 'noise_approach_margin'            ,    'summary.noise_approach_margin'                                       ],
        [ 'noise_sideline_margin'            ,    'summary.noise_sideline_margin'                                       ],
        [ 'noise_flyover_margin'             ,    'summary.noise_flyover_margin'                                        ],
        [ 'static_stability'                 ,    'summary.static_stability'                                            ],
        [ 'vertical_tail_volume_coefficient' ,    'summary.vertical_tail_volume_coefficient'                            ],
        [ 'horizontal_tail_volume_coefficient',   'summary.horizontal_tail_volume_coefficient'                          ],
        [ 'wing_max_cl_norm'                 ,    'summary.maximum_cl_norm'                                             ],
        [ 'design_range_fuel_margin'         ,    'summary.design_range_fuel_margin'                                    ],
#.........这里部分代码省略.........
开发者ID:michK,项目名称:SUAVE,代码行数:103,代码来源:Noise_Test.py

示例4: carpet_plot

# 需要导入模块: from SUAVE.Core import Data [as 别名]
# 或者: from SUAVE.Core.Data import inputs [as 别名]
def carpet_plot(problem, number_of_points,  plot_obj=1, plot_const=0, sweep_index_0=0, sweep_index_1=1): 
    """ Takes in an optimization problem and runs a carpet plot of the first 2 variables
        sweep_index_0, sweep_index_1 is index of variables you want to run carpet plot (i.e. sweep_index_0=0 means you want to sweep first variable, sweep_index_0 = 4 is the 5th variable)
    
        Assumptions:
        N/A
    
        Source:
        N/A
    
        Inputs:
        problem            [Nexus Class]
        number_of_points   [int]
        plot_obj           [int]
        plot_const         [int]
        sweep_index_0      [int]
        sweep_index_1      [int]
        
        Outputs:
        Beautiful Beautiful Plots!
            Outputs:
                inputs     [array]
                objective  [array]
                constraint [array]
    
        Properties Used:
        N/A
    """         

    #unpack
    idx0            = sweep_index_0 # local name
    idx1            = sweep_index_1
    opt_prob        = problem.optimization_problem
    base_inputs     = opt_prob.inputs
    names           = base_inputs[:,0] # Names
    bnd             = base_inputs[:,2] # Bounds
    scl             = base_inputs[:,3] # Scaling
    base_objective  = opt_prob.objective
    obj_name        = base_objective[0][0] #objective function name (used for scaling)
    obj_scaling     = base_objective[0][1]
    base_constraints= opt_prob.constraints
    constraint_names= base_constraints[:,0]
    constraint_scale= base_constraints[:,3]
   
    #define inputs, output, and constraints for sweep
    inputs          = np.zeros([2,number_of_points])
    obj             = np.zeros([number_of_points,number_of_points])
    constraint_num  = np.shape(base_constraints)[0] # of constraints
    constraint_val  = np.zeros([constraint_num,number_of_points,number_of_points])
    
    
    #create inputs matrix
    inputs[0,:] = np.linspace(bnd[idx0][0], bnd[idx0][1], number_of_points)
    inputs[1,:] = np.linspace(bnd[idx1][0], bnd[idx1][1], number_of_points)

    
    #inputs defined; now run sweep
    for i in range(0, number_of_points):
        for j in range(0,number_of_points):
            #problem.optimization_problem.inputs=base_inputs  #overwrite any previous modification
            opt_prob.inputs[:,1][idx0]= inputs[0,i]
            opt_prob.inputs[:,1][idx1]= inputs[1,j]
   
            obj[j,i]             = problem.objective()*obj_scaling
            constraint_val[:,j,i]= problem.all_constraints().tolist()
  
    if plot_obj==1:
        plt.figure(0)
        CS = plt.contourf(inputs[0,:],inputs[1,:], obj, linewidths=2)
        cbar = plt.colorbar(CS)
        cbar.ax.set_ylabel(obj_name)
        plt.xlabel(names[idx0])
        plt.ylabel(names[idx1])
        
       
    if plot_const==1:
        
        for i in range(0, constraint_num): #constraint_num):
            plt.figure(i+1)
            CS_const=plt.contour(inputs[0,:],inputs[1,:], constraint_val[i,:,:])
            cbar = plt.colorbar(CS_const)
            cbar.ax.set_ylabel(constraint_names[i])
            plt.xlabel(names[idx0])
            plt.ylabel(names[idx1])
    plt.show(block=True)      
       
        
    #pack outputs
    outputs= Data()
    outputs.inputs         = inputs
    outputs.objective      = obj
    outputs.constraint_val = constraint_val
    
    return outputs
    
    
开发者ID:michK,项目名称:SUAVE,代码行数:96,代码来源:carpet_plot.py

示例5: setup

# 需要导入模块: from SUAVE.Core import Data [as 别名]
# 或者: from SUAVE.Core.Data import inputs [as 别名]
def setup():
    nexus = Nexus()
    problem = Data()
    nexus.optimization_problem = problem

    # -------------------------------------------------------------------
    # Inputs
    # -------------------------------------------------------------------

    problem.inputs = np.array([
        # Variable inputs
        ['wing_area', 700., (650., 725.), 500., Units.meter ** 2],
        ['MTOW', 207e3, (207e3, 207e3), 200e3, Units.kg],
        ['alt_outgoing_cruise', 13.14, (9., 14.), 13., Units.km],  # 13.15 #explain the physics behing the optimizer
        ['design_thrust', 110e3, (100e3, 120e3), 100e3, Units.N],
        ['outgoing_cruise_speed', 190., (180., 212.), 200., Units['m/s']],  # 191
        ['spray_cruise_speed', 210., (205., 212.), 200, Units['m/s']],

        ['cruise1_distance', 1050., (1000., 1200.), 1075., Units.km],
        ['cruise2_distance', 1173., (1000., 1300.), 1225., Units.km],
        ['cruise3_distance', 1001., (900., 1200.), 1000., Units.km],

        ['cruise_outgoing_distance', 3393., (3200., 3500.), 3300., Units.km],
        # climb throttle as input?

        # "Set" inputs
        ['AR', 13., (12., 14.), 13., Units.less],  # aerosol released per kg of fuel ratio max?
        ['payload', 35e3, (35e3, 35e3), 30e3, Units.kg],
        # speeds???
    ])

    # -------------------------------------------------------------------
    # Objective
    # -------------------------------------------------------------------

    # throw an error if the user isn't specific about wildcards
    # [ tag, scaling, units ]
    problem.objective = np.array([
        ['fuel_burn', 60000., Units.kg]
    ])

    # -------------------------------------------------------------------
    # Constraints
    # -------------------------------------------------------------------

    # [ tag, sense, edge, scaling, units ]
    problem.constraints = np.array([

        # ['min_throttle', '>', 0., 1e-1, Units.less],
        ['max_throttle', '<', 1., 1., Units.less],
        # ['main_mission_time', '<', 11.1, 10, Units.h],
        ['design_range_fuel_margin', '>', 0.1, 1E-1, Units.less],
        # ['take_off_field_length', '<', 2500., 2500, Units.m],
        # ['landing_field_length', '<', 2500., 2500, Units.m],
        ['clmax', '<', 1.1, 1, Units.less],
        ['non_spraying_range', '>', 3500., 3500., Units.km],
        ['spraying_range', '>', 3500., 3500., Units.km]
        # main mission range

    ])

    # -------------------------------------------------------------------
    #  Aliases
    # -------------------------------------------------------------------

    # [ 'alias' , ['data.path1.name','data.path2.name'] ]

    problem.aliases = [
        ['wing_area', ['vehicle_configurations.*.wings.main_wing.areas.reference',
                       'vehicle_configurations.*.reference_area']],

        ['MTOW', ['vehicle_configurations.*.mass_properties.takeoff',
                  "vehicle_configurations.*.mass_properties.max_takeoff"]],

        ['alt_outgoing_cruise', 'missions.base.segments.final_outgoing.altitude_end'],

        ['design_thrust', 'vehicle_configurations.*.propulsors.turbofan.thrust.total_design'],

        ['spray_cruise_speed', ['missions.base.segments.cruise_1.air_speed',
                                'missions.base.segments.cruise_2.air_speed',
                                'missions.base.segments.cruise_final.air_speed']],

        ['outgoing_cruise_speed', 'missions.base.segments.cruise_outgoing.air_speed'],

        ['AR', 'vehicle_configurations.*.wings.main_wing.aspect_ratio'],

        ['payload', ['vehicle_configurations.*.mass_properties.max_payload',
                     'vehicle_configurations.*.mass_properties.payload']],

        ['fuel_burn', 'summary.base_mission_fuelburn'],

        ['min_throttle', 'summary.min_throttle'],

        ['max_throttle', 'summary.max_throttle'],

        ['main_mission_time', 'summary.main_mission_time'],

        ['mission_range', 'summary.mission_range'],

        ['clmax', 'summary.clmax'],
#.........这里部分代码省略.........
开发者ID:lukekulik,项目名称:saga-one,代码行数:103,代码来源:Optimize.py

示例6: line_plot

# 需要导入模块: from SUAVE.Core import Data [as 别名]
# 或者: from SUAVE.Core.Data import inputs [as 别名]
def line_plot(problem, number_of_points,  plot_obj=1, plot_const=1, sweep_index=0): 
    """
    Takes in an optimization problem and runs a line plot of the first  variable of sweep index
    sweep_index. i.e. sweep_index=0 means you want to sweep the first variable, sweep_index = 4 is the 5th variable)
    
        Assumptions:
        N/A
    
        Source:
        N/A
    
        Inputs:
        problem            [Nexus Class]
        number_of_points   [int]
        plot_obj           [int]
        plot_const         [int]
        sweep_index        [int]

        
        Outputs:
        Beautiful plots!
            Outputs:
                inputs     [array]
                objective  [array]
                constraint [array]
    
        Properties Used:
        N/A
    """         
    
    
    

    idx0            = sweep_index # local name

    opt_prob        = problem.optimization_problem
    base_inputs     = opt_prob.inputs
    names           = base_inputs[:,0] # Names
    bnd             = base_inputs[:,2] # Bounds
    scl             = base_inputs[:,3] # Scaling
    base_objective  = opt_prob.objective
    obj_name        = base_objective[0][0] #objective function name (used for scaling)
    obj_scaling     = base_objective[0][1]
    base_constraints= opt_prob.constraints
    constraint_names= base_constraints[:,0]
    constraint_scale= base_constraints[:,3]
   
    #define inputs, output, and constraints for sweep
    inputs          = np.zeros([2,number_of_points])
    obj             = np.zeros([number_of_points])
    constraint_num  = np.shape(base_constraints)[0] # of constraints
    constraint_val  = np.zeros([constraint_num,number_of_points])
    
    
    #create inputs matrix
    inputs[0,:] = np.linspace(bnd[idx0][0], bnd[idx0][1], number_of_points)
 

    
    #inputs defined; now run sweep
    for i in range(0, number_of_points):
        opt_prob.inputs[:,1][idx0]= inputs[0,i]
   
        obj[i]             = problem.objective()*obj_scaling
        constraint_val[:,i]= problem.all_constraints().tolist()
  
    if plot_obj==1:
        plt.figure(0)
        plt.plot(inputs[0,:], obj, lw = 2)
        plt.xlabel(names[idx0])
        plt.ylabel(obj_name)
        

    if plot_const==1:
        for i in range(0, constraint_num):
            plt.figure(i+1)
            plt.plot(inputs[0,:], constraint_val[i,:], lw = 2)
            plt.xlabel(names[idx0])
            plt.ylabel(constraint_names[i])

       
    plt.show(block=True)      
       
        
    #pack outputs
    outputs= Data()
    outputs.inputs         = inputs
    outputs.objective      = obj
    outputs.constraint_val =constraint_val
    return outputs
    
    
开发者ID:michK,项目名称:SUAVE,代码行数:92,代码来源:line_plot.py


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