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How to use selection_order method in hypothesis

Best Python code snippet using hypothesis

tsp_env.py

Source:tsp_env.py

1"""2Travelling Salesman Problem Environment3All x, y axis are normalized to [-1, 1].4L_p distance can be used.5Supports Erdos-Renyi, Barabasi-Albert, and regular graphs.6TODO: Support for other graph types.7"""8import random9from functools import partial10from typing import Any, Dict, List, Literal, Optional, Tuple11import networkx as nx12import numpy as np13class TspEnv:14    """15    Tsp Environment.16    Graph types:17        er: Erdos-Renyi18        ba: Barabasi-Albert19        regular: regular graph20        custom: custom graph21    Returns: state, reward, done, info22        "vertex_occupancy" : np.ndarray23            1 if vertex is visited, 0 otherwise24        "pos" : np.ndarray25            position of each vertex26        "current_idx" : int27            current index of the agent28        "start_idx" : int29            starting index of the agent, by default, 0.30        "edge_index" : np.ndarray31            edge index of the graph32    """33    GRAPH_SET = {34        "er": partial(nx.erdos_renyi_graph, p=0.5),35        "ba": partial(nx.barabasi_albert_graph, m=3),36        "regular": partial(nx.random_regular_graph, d=5),37    }38    def __init__(39        self,40        graph_type: Literal["er", "ba", "regular", "custom"],41        n_nodes: int = 10,42        lp: float = 2.0,43        custom_pos: Optional[List[List[float]]] = None,44        dim: int = 2,45    ):46        self.graph_type = graph_type47        self.n_nodes = n_nodes48        self.lp = lp49        self.dim = dim50        if graph_type == "custom":51            assert custom_pos is not None, "custom_pos must be provided"52            self.n_nodes = len(custom_pos)53            assert all(54                len(pos) == dim for pos in custom_pos55            ), "element of custom_pos must be of length dim"56            self.custom_pos = custom_pos57        self.start_idx: int = 058        self.current_idx: int = 059    def _get_state(self) -> Dict[str, np.ndarray]:60        return {61            "vertex_occupancy": self.vertex_occupancy,62            "edge_index": self.edge_index,63            "pos": self.pos,64            "start_idx": self.start_idx,65            "current_idx": self.current_idx,66        }67    def _reset_graph(self) -> None:68        if self.graph_type == "custom":69            # distance matrirx to nx graph70            self.graph = nx.Graph()71            for i in range(self.n_nodes):72                self.graph.add_node(i)73                for j in range(i + 1, self.n_nodes):74                    self.graph.add_edge(i, j)75            self.pos = np.array(self.custom_pos)76        else:77            self.graph = self.GRAPH_SET[self.graph_type](n=self.n_nodes)78            pos = nx.spring_layout(self.graph, dim=self.dim)79            # put pos into nx graph80            self.pos = np.array([pos[i] for i in range(self.n_nodes)])81            # make it complete82            for i in range(self.n_nodes):83                for j in range(i + 1, self.n_nodes):84                    self.graph.add_edge(i, j)85        # min-max normalize pos86        self.pos = (87            (self.pos - self.pos.min(axis=0))88            / (self.pos.max(axis=0) - self.pos.min(axis=0))89        ) * 2 - 190        for i in self.graph.nodes:91            self.graph.nodes[i]["pos"] = pos[i]92    def reset(93        self, seed: Optional[int] = None, new_graph: bool = False94    ) -> Dict[str, np.ndarray]:95        """96        Reset the environment.97        Args:98            seed: random seed99            new_graph: whether to generate a new graph.100        Returns:101            state: state of the environment102        """103        if seed is not None:104            np.random.seed(seed)105            random.seed(seed)106        if new_graph:107            self._reset_graph()108        self.start_idx = 0109        self.current_idx = 0110        self.vertex_occupancy = np.zeros(self.n_nodes)111        self.vertex_occupancy[self.start_idx] = 1112        self.length = 0113        self.selection_order = [self.start_idx]114        self.edge_index = np.array(self.graph.edges)115        return self._get_state()116    def step(117        self, action: int118    ) -> Tuple[Dict[str, np.ndarray], float, bool, Dict[str, Any]]:119        assert action in range(self.n_nodes), "invalid action, must be in [0, n_nodes)"120        if self.vertex_occupancy[action] > 0:  # already visited121            return self._get_state(), -3, True, {"length": self.length}122        self.vertex_occupancy[action] = 1123        self.current_idx = action124        this_len = np.linalg.norm(125            self.graph.nodes[action]["pos"]126            - self.graph.nodes[self.selection_order[-1]]["pos"],127            ord=self.lp,128        )129        self.selection_order.append(action)130        done = False131        if len(self.selection_order) == self.n_nodes:132            this_len += np.linalg.norm(133                self.graph.nodes[self.selection_order[0]]["pos"]134                - self.graph.nodes[self.selection_order[-1]]["pos"],135                ord=self.lp,136            )137            # just for rendering.138            self.selection_order.append(self.selection_order[0])139            done = True140        self.length += this_len141        return (142            self._get_state(),143            (4 - this_len) / 10,144            done,145            {"length": self.length},146        )147    def render(self, save_path="./img_folder") -> None:148        """149        Save the scene graph to a file.150        Args:151            save_path: path to save the image.152        Returns:153            None154        """155        import os156        if not os.path.exists(save_path):157            os.makedirs(save_path)158        import matplotlib.pyplot as plt159        import matplotlib.lines as mlines160        import matplotlib.patches as mpatches161        fig, ax = plt.subplots()162        ax.set_aspect("equal")163        # set range of x and y164        x_min, x_max = self.pos[:, 0].min(), self.pos[:, 0].max()165        y_min, y_max = self.pos[:, 1].min(), self.pos[:, 1].max()166        ax.set_xlim(x_min, x_max)167        ax.set_ylim(y_min, y_max)168        for i in range(self.n_nodes):169            ax.add_patch(170                mpatches.Circle(171                    self.pos[i],172                    radius=0.05,173                    color="black" if self.vertex_occupancy[i] == 0 else "grey",174                )175            )176        # plot current pos177        ax.add_patch(178            mpatches.Circle(179                self.pos[self.current_idx],180                radius=0.05,181                color="red",182            )183        )184        for i in range(len(self.selection_order) - 1):185            ax.add_line(186                mlines.Line2D(187                    [188                        self.pos[self.selection_order[i]][0],189                        self.pos[self.selection_order[i + 1]][0],190                    ],191                    [192                        self.pos[self.selection_order[i]][1],193                        self.pos[self.selection_order[i + 1]][1],194                    ],195                    color="black",196                )197            )198        # put info on figure199        fig.suptitle(200            f"{self.graph_type} graph, {self.n_nodes} nodes, {self.lp}-norm, Total length: {self.length:.3f} \n \201            current_idx: {self.current_idx}, start_idx: {self.start_idx}",202        )203        plt.savefig(204            os.path.join(save_path, f"tsp_env_{len(self.selection_order) :03d}.png")205        )206        plt.close()207def heuristic(state, eps=0.1):208    """209    Select minimum distance vertex, greedily.210    Simple heuristic to debug / test the enviornment211    Args:212        state :213            "vertex_occupancy" : np.ndarray214                1 if vertex is visited, 0 otherwise215            "pos" : np.ndarray216                position of each vertex217            "current_idx" : int218                current index of the agent219            "start_idx" : int220                starting index of the agent, by default, 0.221            "edge_index" : np.ndarray222                edge index of the graph223        eps : float224            epsilon for greedy selection225    Returns:226        int : index of the vertex to visit227    """228    n_nodes = state["vertex_occupancy"].shape[0]229    # greedy selection230    current_idx = state["current_idx"]231    pos = state["pos"]232    dist = np.linalg.norm(pos - pos[current_idx], ord=2, axis=1)233    dist[state["vertex_occupancy"] == 1] = np.inf234    return np.argmin(dist)235def demo_heuristic():236    """237    Demo the heuristic runner.238    Save the process of the heuristic selection to a gif file.239    """240    env = TspEnv(n_nodes=20, dim=2, graph_type="ba")241    state = env.reset(1)242    env.render("./img_folder/_tmp")243    while True:244        action = heuristic(state)245        state, reward, done, info = env.step(action)246        print(f"action: {action}, reward: {reward}, done: {done}")247        env.render("./img_folder/_tmp")248        if done:249            break250    print(env.length)251    # convert tmp to gif252    import subprocess253    subprocess.call(254        ["convert", "-delay", "100", "./img_folder/_tmp/*.png", "./img_folder/_tmp.gif"]255    )256    # remove tmp folder257    import shutil258    shutil.rmtree("./img_folder/tmp")259if __name__ == "__main__":...

create_mode.py

Source:create_mode.py

...11    players = player_list.split()12    return players13    14# Returns the order of selection from the list of players in snake draft order15def get_selection_order(players: List[str], rounds: int) -> List[str]:16    selection_order = []17    reverse = False18    for i in range(0,rounds):19        if reverse:20            for player in reversed(players):21                selection_order.append(player)22            reverse = False23        else:24            for player in players:25                selection_order.append(player)26            reverse = True27    28    return selection_order29    30def get_word(player: str) -> str:31    word = input(player + ' select a word: ')32    return word33def get_save_path() -> str:34    path = input('File to save game to [Default ' + DEFAULT_SAVE_PATH + ']')35    if path:36        return path37    else:38        return DEFAULT_SAVE_PATH39def get_words(players: List[str], rounds: int) -> Dict[str, List[str]]:40    words = {}41    for player in players:42        words[player] = []43    44    # The list of players names in order of word selection (snake draft)45    selection_order = get_selection_order(players, rounds)46    47    print('Select your words!')48    49    # Loop through and get all the words from input50    for player_selecting in selection_order:51        list_for_player = words[player_selecting]52        word = get_word(player_selecting)53        list_for_player.append(word)54        55    print('Selected words:')56    for player in words:57        print(player + ' ' + str(words[player]))58        59    return words...

track_selection_order.py

Source:track_selection_order.py

1import bpy2class SelectionCallback:3    def __init__(self):4        self.selection_order = []5    6    @staticmethod7    def notify(*args):8        self = args[0]9        layer_objects = bpy.context.view_layer.objects10        if not layer_objects:11            return12        print("\n\n")13        print(layer_objects.active)14        if not layer_objects.active in self.selection_order:15            self.selection_order.append(layer_objects.active)16        if len(layer_objects.selected) > 1:17            selected = set(bpy.context.view_layer.objects.selected)18            selected.remove(bpy.context.view_layer.objects.active)19            for o in selected:20                if not o in self.selection_order:21                    self.selection_order.append(o)22            removed = set(self.selection_order) - selected23            for r in removed:24                self.selection_order.remove(r)25            print(self.selection_order)26        else:27            self.selection_order = []28subscribe_to = bpy.types.LayerObjects, "active"29handle = SelectionCallback()30bpy.msgbus.subscribe_rna(31    key = subscribe_to,32    owner = handle,33    args = (handle,),34    notify = handle.notify,...

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