Ejemplo n.º 1
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    def get_hatches(self):
        # Fetch data, then convert to objects
        for t in hatch_types:
            # Get data for all hatches of this type, then create Hatch objs for all hatches of
            # that type in the vehicle (eg 2 cargo hatches)

            # TODO: Note [t] indexing and order of looping; we're only querying one hatch type at
            #  a time (due to chamfer/articulation angle difference)
            hatch_type_data = tba.get_data(get_parts_of_interest(t))[t]

            for hatch_name in hatch_type_data:
                self.hatches.append(pinf.Hatch_Info(hatch_type_data[hatch_name], hatch_name))
Ejemplo n.º 2
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    def __init__(self, settings_dict):
        self.settings_dict = settings_dict

        self.desired_params = tba.get_data(get_parts_of_interest())

        self._up_vector = None
        self._tr_mat = None
        self._vehicle_stl = None
        self._litter_stl = None

        self._doors_xyz = None

        self.voxel_data = None
Ejemplo n.º 3
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    def __init__(self):
        self.parts_found = tba.get_data(get_parts_of_interest())

        self.hatches = {}
        self.get_hatches()

        # Determine the connectivity of parts
        self.conn = self.get_connectivity()

        # Load in spring and tbar data & connect each to the appropriate hatch
        self.get_axial_springs()
        self.get_torsion_bars()

        # After assembly initialization, run sanity check
        assert len(self.hatches) > 0, "No hatches were found in this assembly."
Ejemplo n.º 4
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    def __init__(self):
        # get doors, get actuators. Initialize objects and connect.
        # Manually specified vertical distance from hinge center to ground in SETTINGS[
        # 'dist_to_ground'. According to mayavi, heights are 1.76m and 1.07 m, respectively.
        self.parts_found = tba.get_data(get_parts_of_interest())

        # Determine the connectivity of parts
        self.conn = self.get_connectivity()

        self.ramps = {}
        self.get_ramps()

        # Load in actuator data & connect each to the appropriate ramp
        self.get_actuators()

        # After assembly initialization, run sanity check
        assert len(self.ramps) > 0, "No ramps found in this assembly"
Ejemplo n.º 5
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    def __init__(self, settings_f):
        """
        Setup field of fire metrics based on settings read in from file.
        """
        ##pass setting to the test bench api
        settings_dict = tba.load_settings(settings_f)

        ## Optional test bench settings
        self.show_3d = settings_dict.get("show_3d", False)
        horizontal_divs = settings_dict.get("horizontal_divs", 720)
        self.up_direction = settings_dict.get("up_direction", np.array([0, 1.0, 0]))
        self.ground = settings_dict.get("ground", np.array([0, 1.0, 0]))

        ## Set up some constant parameters
        self.hor_sweep = np.linspace(0, 2 * pi, horizontal_divs)

        ## How many rays are considered contiguous - 3degrees of arc
        self.contiguous = int(ceil(3 * horizontal_divs / 360.0))
        self.incr_elev = 2 * pi / 720.0

        ## Get the data for the weapon.
        fof_data = tba.get_data(parts_of_interest)["Weapon_Station_Remote"]

        ## Get a weapon object.  API will enforce there is only one.
        wep_name = fof_data.keys()[0]
        self.weapon = Weapon_Station(wep_name, fof_data)

        ## Specify the classes of objects we want geometry for and load them in.
        class_set = tba.get_all_geom_set() - tba.geom_sets["never_exterior_classes"]
        surface = tba.load_geometry(class_set, single_file=True)

        ## Make a binary space partition to speed up intersection calculations
        self.nodes = np.vstack((surface['x'], surface['y'], surface['z'])).T
        self.tris = surface['tris']
        self.b_tree = BSP_Tree(self.nodes, self.tris)
        self.b_tree.generate_tree()

        ## Set up result storage.
        self.traverse_results = []
Ejemplo n.º 6
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    def __init__(self, settings_f):
        """
        Setup vision metrics based on settings read in from file.
        """
        ##pass setting to the test bench api
        settings_dict = tba.load_settings(settings_f)

        ## Optional test bench settings
        self.show_3d = settings_dict.get("show_3d", False)
        horizontal_divs = settings_dict.get("horizontal_divs", 720)
        self.focal_length_multiplier = settings_dict.get(
            "focal_length_multiplier", 1.5)
        self.far_dist = settings_dict.get("far_dist", 20.0)
        self.up_direction = settings_dict.get("up_direction",
                                              np.array([0, 1.0, 0]))
        self.fore_direction = settings_dict.get("fore_direction",
                                                np.array([0, 0, -1.0]))
        self.ground = settings_dict.get("ground", np.array([0, 1.0, 0]))

        ## Set up some constant parameters
        self.hor_sweep = np.linspace(0, 2 * pi, horizontal_divs)
        self.target_points_horizon = np.zeros(horizontal_divs, np.uint32)

        ## Get a dictionary of parts and properties form the api (periscopes, manikins, screens)
        fov_data = tba.get_data(parts_of_interest)

        ## Get Periscope objects for any that are found
        self.periscopes = [
            Vision_Device(p, fov_data["Periscope"])
            for p in fov_data["Periscope"]
        ]

        ## Get Manikin objects for the vehicle occupants that have vision requirements
        m_data = fov_data["Manikin"]

        req_roles = ["driver", "vehicle_commander", "troop_commander"]

        vis_roles = lambda m: m_data[m]["properties"]["vehicle_role"
                                                      ] in req_roles
        self.manikins = [
            Manikin(m, fov_data["Manikin"]) for m in m_data if vis_roles(m)
        ]

        ## Need exactly one of each
        roles = [m.vehicle_role for m in self.manikins]
        if len(set(roles)) != len(req_roles):
            msg = "Didn't get 1 of each vehicle_role={} instead got={}".format(
                req_roles, roles)
            raise ValueError(msg)

        ## Specify the classes of objects we want geometry for.
        class_set = tba.get_all_geom_set(
        ) - tba.geom_sets["never_exterior_classes"]

        ## And load them all as an effective single file
        surface = tba.load_geometry(class_set, single_file=True)

        ## Make a binary space partition to speed up intersection calculations
        self.nodes = np.vstack((surface['x'], surface['y'], surface['z'])).T
        self.tris = surface['tris']
        self.b_tree = BSP_Tree(self.nodes, self.tris)
        self.b_tree.generate_tree()

        ## TODO this won't cope with unusual orientations
        ## determine orientation of the vehicle given the direction up and forward
        fore = np.dot(self.nodes, self.fore_direction).min()
        fore = np.min(surface["z"])
        self.side_direction = np.cross(self.up_direction, self.fore_direction)
        side_array = np.dot(self.nodes, self.side_direction)

        self.z_ground = np.dot(self.ground, self.up_direction)

        center = (side_array.min() + side_array.max()) / 2.0
        center = (np.min(surface["x"]) + np.max(surface["x"])) * 0.5
        ## Find the vehicle origin (point on ground at front on centerline)
        self.veh_origin = np.array([center, self.z_ground, fore])
        logging.info("Vehicle origin is at {}".format(self.veh_origin))
        self.tran_veh = translate(self.veh_origin)

        self.fore_aft = [None] * len(self.periscopes)
        self.uplook = np.array([-1000.0] * len(self.periscopes))

        self.hit = []

        self.hor_fans = []
Ejemplo n.º 7
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    def __init__(self, settings_f):
        """
        Setup vision metrics based on settings read in from file.
        """
        ##pass setting to the test bench api
        settings_dict = tba.load_settings(settings_f)

        ## Optional test bench settings
        self.show_3d = settings_dict.get("show_3d", False)
        horizontal_divs = settings_dict.get("horizontal_divs", 720)
        self.focal_length_multiplier = settings_dict.get("focal_length_multiplier", 1.5)
        self.far_dist = settings_dict.get("far_dist", 20.0)
        self.up_direction = settings_dict.get("up_direction", np.array([0, 1.0, 0]))
        self.fore_direction = settings_dict.get("fore_direction", np.array([0, 0, -1.0]))
        self.ground = settings_dict.get("ground", np.array([0, 1.0, 0]))

        ## Set up some constant parameters
        self.hor_sweep = np.linspace(0, 2 * pi, horizontal_divs)
        self.target_points_horizon = np.zeros(horizontal_divs, np.uint32)

        ## Get a dictionary of parts and properties form the api (periscopes, manikins, screens)
        fov_data = tba.get_data(parts_of_interest)

        ## Get Periscope objects for any that are found
        self.periscopes = [Vision_Device(p, fov_data["Periscope"]) for p in fov_data["Periscope"]]

        ## Get Manikin objects for the vehicle occupants that have vision requirements
        m_data = fov_data["Manikin"]

        req_roles = ["driver", "vehicle_commander", "troop_commander"]

        vis_roles = lambda m: m_data[m]["properties"]["vehicle_role"] in req_roles
        self.manikins = [Manikin(m, fov_data["Manikin"]) for m in m_data if vis_roles(m)]

        ## Need exactly one of each
        roles = [m.vehicle_role for m in self.manikins]
        if len(set(roles)) != len(req_roles):
            msg = "Didn't get 1 of each vehicle_role={} instead got={}".format(req_roles, roles)
            raise ValueError(msg)

        ## Specify the classes of objects we want geometry for.
        class_set = tba.get_all_geom_set() - tba.geom_sets["never_exterior_classes"]

        ## And load them all as an effective single file
        surface = tba.load_geometry(class_set, single_file=True)

        ## Make a binary space partition to speed up intersection calculations
        self.nodes = np.vstack((surface["x"], surface["y"], surface["z"])).T
        self.tris = surface["tris"]
        self.b_tree = BSP_Tree(self.nodes, self.tris)
        self.b_tree.generate_tree()

        ## TODO this won't cope with unusual orientations
        ## determine orientation of the vehicle given the direction up and forward
        fore = np.dot(self.nodes, self.fore_direction).min()
        fore = np.min(surface["z"])
        self.side_direction = np.cross(self.up_direction, self.fore_direction)
        side_array = np.dot(self.nodes, self.side_direction)

        self.z_ground = np.dot(self.ground, self.up_direction)

        center = (side_array.min() + side_array.max()) / 2.0
        center = (np.min(surface["x"]) + np.max(surface["x"])) * 0.5
        ## Find the vehicle origin (point on ground at front on centerline)
        self.veh_origin = np.array([center, self.z_ground, fore])
        logging.info("Vehicle origin is at {}".format(self.veh_origin))
        self.tran_veh = translate(self.veh_origin)

        self.fore_aft = [None] * len(self.periscopes)
        self.uplook = np.array([-1000.0] * len(self.periscopes))

        self.hit = []

        self.hor_fans = []
Ejemplo n.º 8
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    def __init__(self, settings):
        """
        Setup vehicle metrics based on settings read in from file.
        """
        ## Essential test bench settings
        self.output_json_file = settings["output_json_file"]

        ##testbench specific settings
        transport_dimensions = settings["transport_dimensions"]
        self.up_direction = settings.get("up_direction", np.array([0, 1.0, 0]))
        self.fore_direction = settings.get("fore_direction",
                                           np.array([0, 0, 1.0]))
        self.ground = settings.get("ground", np.array([0, 1.0, 0]))
        self.curb_mass = tba.get_veh_mass()
        self.cent_grav = tba.get_veh_cg()

        try:
            self.up_direction, self.ground_plane, _ = tba.get_up_vector()
        except:
            self.up_direction = settings.get("up_direction",
                                             np.array([0, 1.0, 0]))

        trans_data = tba.get_data(parts_of_interest)

        ## Define dictionaries for each lifting and tie down class
        self.eye_weld = {}
        self.eye_bolt = {}
        self.ring_hoist = {}
        self.d_ring = {}
        self.dbar_weld = {}
        self.dbar_bolt = {}
        self.dbar_eye = {}
        self.pintle = {}
        self.cleat = {}

        self.eye_weld = trans_data[
            "Eye_Welded"]  # TODO: does this crash on empty list?
        self.eye_bolt = trans_data["Eye_Bolted"]
        self.ring_hoist = trans_data["Ring_Hoist"]
        self.d_ring = trans_data["D_Ring_Lashing"]
        self.pintle = trans_data["Pintle_Tow"]
        self.cleat = trans_data["Cleat_Mooring"]
        self.dbar_weld = trans_data["Drawbar_Welded"]
        self.dbar_bolt = trans_data["Drawbar_Bolted"]
        self.dbar_eye = trans_data["Drawbar_Eyebolt"]

        ## Define set of classes to get geometry for
        all_class_set = tba.get_all_geom_set()
        ## Class sets and geometry for approach angles
        class_set = all_class_set - tba.geom_sets["never_exterior_classes"] - \
                    {"Roadwheel",
                     "Trailing_Arm_Hydropneumatic_Rh",
                     "Trailing_Arm_Hydropneumatic_Lh",
                     "Track",
                     "Sprocket_And_Carrier_Drive",
                     "Wheel_Idler"}
        surface = tba.load_geometry(class_set, single_file=True)

        self.surface_coords = np.vstack(
            [surface["x"], surface["y"], surface["z"]]).T

        wheel_set = {"Roadwheel", "Sprocket_And_Carrier_Drive", "Wheel_Idler"}
        wheels = tba.load_geometry(wheel_set, single_file=True)

        track_set = {"Track"}
        track = tba.load_geometry(track_set, single_file=True)

        ## Class set and geometry for container fitting
        trans_class_set = all_class_set
        trans_geom = tba.load_geometry(trans_class_set, single_file=True)

        ## Class sets and geometry for lift eyes and tie_downs
        lift_set = {"Eye_Welded"}
        tie_down_set = {"Ring_Hoist"}

        lift_geom = tba.load_geometry(lift_set, single_file=True)
        tie_down_geom = tba.load_geometry(tie_down_set, single_file=True)

        self.trans_dict = vehicle_fit(surface, self.up_direction,
                                      self.fore_direction,
                                      transport_dimensions)

        self.trans_dict["Lifting_Metrics"] = self.lifting_metrics()
        self.trans_dict["Tie_Down_Metrics"] = self.tie_down_metrics()

        self.trans_dict["Approach_Angles"] = approach_angles(
            surface, wheels, track)

        pintle_count = len(self.pintle)

        cleat_count = len(self.cleat)

        tow_count = len(self.eye_bolt) + len(self.eye_weld) + len(self.dbar_bolt) + \
                    len(self.dbar_eye) + len(self.dbar_weld) + len(self.pintle)
        lift_count = len(self.eye_bolt) + len(self.eye_weld)

        self.trans_dict["Count_for_components_in_towing_class"] = tow_count
        self.trans_dict["Count_for_components_in_lifting_class"] = lift_count
        self.trans_dict["Count_for_components_in_pintle_class"] = pintle_count
        self.trans_dict["Count_for_components_in_mooring_class"] = cleat_count

        plot_vehicle(trans_geom, transport_dimensions)

        plot_lift_tie_down(trans_geom, lift_geom, tie_down_geom)

        tba.write_results(self.trans_dict)
Ejemplo n.º 9
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    def __init__(self, settings):
        """
        Setup vehicle metrics based on settings read in from file.
        """
        ## Essential test bench settings
        self.output_json_file = settings["output_json_file"]

        ##testbench specific settings
        transport_dimensions = settings["transport_dimensions"]
        self.up_direction = settings.get("up_direction", np.array([0, 1.0, 0]))
        self.fore_direction = settings.get("fore_direction", np.array([0, 0, 1.0]))
        self.ground = settings.get("ground", np.array([0, 1.0, 0]))
        self.curb_mass = tba.get_veh_mass()
        self.cent_grav = tba.get_veh_cg()

        try:
            self.up_direction, self.ground_plane, _ = tba.get_up_vector()
        except:
            self.up_direction = settings.get("up_direction", np.array([0, 1.0, 0]))

        trans_data = tba.get_data(parts_of_interest)

        ## Define dictionaries for each lifting and tie down class
        self.eye_weld = {}
        self.eye_bolt = {}
        self.ring_hoist = {}
        self.d_ring = {}
        self.dbar_weld = {}
        self.dbar_bolt = {}
        self.dbar_eye = {}
        self.pintle = {}
        self.cleat = {}

        self.eye_weld = trans_data["Eye_Welded"]  # TODO: does this crash on empty list?
        self.eye_bolt = trans_data["Eye_Bolted"]
        self.ring_hoist = trans_data["Ring_Hoist"]
        self.d_ring = trans_data["D_Ring_Lashing"]
        self.pintle = trans_data["Pintle_Tow"]
        self.cleat = trans_data["Cleat_Mooring"]
        self.dbar_weld = trans_data["Drawbar_Welded"]
        self.dbar_bolt = trans_data["Drawbar_Bolted"]
        self.dbar_eye = trans_data["Drawbar_Eyebolt"]

        ## Define set of classes to get geometry for
        all_class_set = tba.get_all_geom_set()
        ## Class sets and geometry for approach angles
        class_set = all_class_set - tba.geom_sets["never_exterior_classes"] - \
                    {"Roadwheel",
                     "Trailing_Arm_Hydropneumatic_Rh",
                     "Trailing_Arm_Hydropneumatic_Lh",
                     "Track",
                     "Sprocket_And_Carrier_Drive",
                     "Wheel_Idler"}
        surface = tba.load_geometry(class_set, single_file=True)

        self.surface_coords = np.vstack([surface["x"], surface["y"], surface["z"]]).T

        wheel_set = {"Roadwheel", "Sprocket_And_Carrier_Drive", "Wheel_Idler"}
        wheels = tba.load_geometry(wheel_set, single_file=True)

        track_set = {"Track"}
        track = tba.load_geometry(track_set, single_file=True)

        ## Class set and geometry for container fitting
        trans_class_set = all_class_set
        trans_geom = tba.load_geometry(trans_class_set, single_file=True)

        ## Class sets and geometry for lift eyes and tie_downs
        lift_set = {"Eye_Welded"}
        tie_down_set = {"Ring_Hoist"}

        lift_geom = tba.load_geometry(lift_set, single_file=True)
        tie_down_geom = tba.load_geometry(tie_down_set, single_file=True)

        self.trans_dict = vehicle_fit(surface,
                                      self.up_direction,
                                      self.fore_direction,
                                      transport_dimensions)

        self.trans_dict["Lifting_Metrics"] = self.lifting_metrics()
        self.trans_dict["Tie_Down_Metrics"] = self.tie_down_metrics()

        self.trans_dict["Approach_Angles"] = approach_angles(surface, wheels, track)

        pintle_count = len(self.pintle)

        cleat_count = len(self.cleat)

        tow_count = len(self.eye_bolt) + len(self.eye_weld) + len(self.dbar_bolt) + \
                    len(self.dbar_eye) + len(self.dbar_weld) + len(self.pintle)
        lift_count = len(self.eye_bolt) + len(self.eye_weld)

        self.trans_dict["Count_for_components_in_towing_class"] = tow_count
        self.trans_dict["Count_for_components_in_lifting_class"] = lift_count
        self.trans_dict["Count_for_components_in_pintle_class"] = pintle_count
        self.trans_dict["Count_for_components_in_mooring_class"] = cleat_count

        plot_vehicle(trans_geom, transport_dimensions)

        plot_lift_tie_down(trans_geom, lift_geom, tie_down_geom)

        tba.write_results(self.trans_dict)