分析代码: features = load_data.load_json_file(self.all_Line_path)["features"] stop_polygon = [] pedestrian_crossing_polygon = [] for feature in features: if feature["properties"]["subtype"] == 101 or \ feature["properties"]["subtype"] == 105 or \ feature["properties"]["subtype"] == 104: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] stop_polygon.append(tree_polygon) stop_line = LineString(fit_polygon(tree_polygon)) my_polygon.temporary_properties["line_string"] = stop_line self.stop_polygon[tree_polygon.id] = my_polygon elif feature["properties"]["subtype"] == 102: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] pedestrian_crossing_polygon.append(tree_polygon) self.pedestrian_crossing[tree_polygon.id] = my_polygon self.stop_line_tree = STRtree(stop_polygon) self.pedestrian_crossing_tree = STRtree(pedestrian_crossing_polygon)
时间: 2024-04-29 22:20:39 浏览: 136
这段代码的作用是加载一个 JSON 文件,其中包含了一些地图要素,包括停车点和人行横道等。然后,它会根据要素的 subtype 把这些要素分别放入 stop_polygon 和 pedestrian_crossing_polygon 两个列表里。对于每个要素,它首先创建一个 MyPolygon 对象,该对象包含要素的几何坐标和属性等信息。然后,它会根据该要素的 subtype 将 MyPolygon 对象放入相应的列表中。接下来,它会使用 STRtree 对象将停车点和人行横道分别存储在 stop_line_tree 和 pedestrian_crossing_tree 中,以便后续的查询操作。这段代码的主要作用是将地图要素存储在适当的数据结构中,以便进行后续的查询和分析。
相关问题
分析代码: def read_stop_line(self): features = load_data.load_json_file(self.all_Line_path)["features"] stop_polygon = [] pedestrian_crossing_polygon = [] for feature in features: if feature["properties"]["subtype"] == 101 or \ feature["properties"]["subtype"] == 105 or \ feature["properties"]["subtype"] == 104: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] stop_polygon.append(tree_polygon) stop_line = LineString(fit_polygon(tree_polygon)) my_polygon.temporary_properties["line_string"] = stop_line self.stop_polygon[tree_polygon.id] = my_polygon elif feature["properties"]["subtype"] == 102: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] pedestrian_crossing_polygon.append(tree_polygon) self.pedestrian_crossing[tree_polygon.id] = my_polygon self.stop_line_tree = STRtree(stop_polygon) self.pedestrian_crossing_tree = STRtree(pedestrian_crossing_polygon)
这是一个类的方法,用于读取停止线和人行横道线的信息,并构建相应的数据结构(STRtree)以便于后续查询。
在方法中,首先调用load_data模块中的load_json_file函数读取所有的线信息。然后,遍历所有的线特征,如果该特征是停止线、人行横道线或者过街天桥,就将其转换为对应的MyPolygon对象,并将其加入到stop_polygon或pedestrian_crossing_polygon列表中,同时将其对应的LineString对象(如果是停止线或人行横道线)存储到MyPolygon对象的temporary_properties属性中。最后,使用stop_polygon和pedestrian_crossing_polygon构建STRtree对象,并存储到类的self.stop_line_tree和self.pedestrian_crossing_tree属性中。
这样做的好处是,通过构建STRtree,可以在后续的查询中实现高效的空间索引,提高查询效率。
逐行分析以下代码: def read_stop_line(self): features = load_data.load_json_file(self.all_Line_path)["features"] stop_polygon = [] pedestrian_crossing_polygon = [] for feature in features: if feature["properties"]["subtype"] == 101 or \ feature["properties"]["subtype"] == 105 or \ feature["properties"]["subtype"] == 104: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] stop_polygon.append(tree_polygon) stop_line = LineString(fit_polygon(tree_polygon)) my_polygon.temporary_properties["line_string"] = stop_line self.stop_polygon[tree_polygon.id] = my_polygon elif feature["properties"]["subtype"] == 102: my_polygon = MyPolygon(coordinates=feature["geometry"]["coordinates"][0], properties=feature["properties"]) my_polygon.temporary_properties = {} tree_polygon = my_polygon.polygon tree_polygon.id = feature["properties"]["id"] pedestrian_crossing_polygon.append(tree_polygon) self.pedestrian_crossing[tree_polygon.id] = my_polygon self.stop_line_tree = STRtree(stop_polygon) self.pedestrian_crossing_tree = STRtree(pedestrian_crossing_polygon)
这段代码定义了一个名为 "read_stop_line" 的函数,它属于某个类。该函数首先从一个名为 "all_Line_path" 的路径中加载一个 JSON 文件,并获取其中的 "features" 数组。然后,它创建了两个空数组 "stop_polygon" 和 "pedestrian_crossing_polygon"。
接下来,该函数对于 "features" 数组中的每个元素,检查其 "properties" 属性下的 "subtype" 属性是否等于 101、105 或 104。如果是,它将该元素的几何坐标转化为一个多边形,并将该多边形的 ID 和一个包含其适配的线条的 LineString 对象存储在 "stop_polygon" 数组中。此外,该函数还将该多边形存储在字典 "stop_polygon" 中,以多边形 ID 作为键。
如果 "subtype" 属性等于 102,该函数将执行类似的操作,但将多边形存储在 "pedestrian_crossing_polygon" 数组和 "pedestrian_crossing" 字典中。
最后,该函数使用 "stop_polygon" 和 "pedestrian_crossing_polygon" 数组创建两个 STRtree 对象,并将它们存储在类实例对象的 "stop_line_tree" 和 "pedestrian_crossing_tree" 属性中。
阅读全文