轮廓曲线数据（lat，lon，值）在边界内并导出GeoJSON

本文介绍了轮廓曲线数据（lat，lon，值）在边界内并导出GeoJSON的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 29岁程序员，3月因学历无情被辞！ 我尝试在边界内插入数据，并根据纬度，经度，csv文件中的值数据绘制轮廓线（多边形）。 as geojson。 我坚持在csv的基本等高线图上。我真的很感激在这里的帮助。 这是我在这一刻，但不能使它的工作。 import numpy as np import matplotlib.pyplot as plt data = np.genfromtxt（'temp.csv'，delimiter ='，'） x = data [：1] y = data [：2] [x，y] = meshgrid z = data [：3]; plt.contour（x，y，z） plt.show（） b $ b CSV如下所示 2015-10-28T09：25：12.800Z，51.56325,17.529043333， 4.6484375,19.8046875 2015-10-28T09：25：12.900Z，51.56325,17.529041667,4.4921875,19.6875 2015-10-28T09：25：13.000Z，51.563248333,17.529041667,4.453125,19.9609375 2015-10-28T09：25：13.200Z，51.563245,17.529041667,4.140625,19.765625 2015-10-28T09：25：13.300Z，51.563243333,17.529041667,4.609375,19.6484375 2015-10-28T09 ：25：13.500Z，51.563241667,17.529041667,4.609375,19.53125 2015-10-28T09：25：13.600Z，51.56324,17.529041667,4.4921875,19.375 2015-10-28T09：25：13.700Z， 51.563238333,17.529041667,4.4140625,19.765625 2015-10-28T09：25：13.800Z，51.563236667,17.529041667,4.453125,20.234375 2015-10-28T09：25：13.900Z，51.563235,17.529041667,4.3359375， 19.84375 2015-10-28T09：25：14.000Z，51.563233333,17.529041667,4.53125,19.453125 2015-10-28T09：25：14.100Z，51.563231667,17.529041667,4.53125,19.8046875 2015 -10-28T09：25：14.200Z，51.563228333,17.529041667,4.1796875,19.4921875 2015-10-28T09：25：14.300Z，51.563226667,17.529041667,4.2578125,19.453125 2015-10-28T09：25 ：14.400Z，51.563225,17.529041667,4.4921875,19.4921875 2015-10-28T09：25：14.500Z，51.563223333,17.529041667,4.375,19.453125 2015-10-28T09：25：14.600Z，51.563221667， 17.529041667,4.609375,18.90625 2015-10-28T09：25：14.700Z，51.563218333,17.529041667,4.53125,19.6875 2015-10-28T09：25：14.900Z，51.563215,17.529041667,4.140625,18.75 2015-10-28T09：25：15.000Z，51.563213333,17.529041667,4.453125,18.828125 列1 - 纬度列2 - 经度列3 - 值 对于等高线我还需要限制 - 例如（4.1,4.3， 4.6）解决方案我想你的代码有一些错误（根据你的数据，你不应该做 x = data [：1] 但更多 x = data [...，1] ）。 使用你的数据，我将遵循插入 z 值和获取一个输出作为geojson的基本步骤将需要至少 shapely 模块（此处 geopandas 用于方便）。 import numpy as np import matplotlib.pyplot as plt 从geopandas import GeoDataFrame 从matplotlib.mlab import griddata from shapely.geometry import Polygon，MultiPolygon def collec_to_gdf（collec_poly）：将一个`matplotlib.contour.QuadContourSet`转换为一个GeoDataFrame 多边形，colors = []，[] 对于i，多边形在枚举（collec_poly.collections）： mpoly = [] 对于polygon.get_paths try： path.should_simplify = False poly = path.to_polygons（）＃每个多边形都应该包含外环+可能的孔： ，holes = []，[] if len（poly）> 0和len（poly [0]）> 3：＃列表的第一个是外部环： exterior = poly [0] ＃其他是孔：如果len ）> 1： holes = [h for poly in poly [1：] if len（h）> 3 mpoly.append（polygon（exterior，holes）） except： print（'警告：制作多边形时出现几何错误＃{}' .format（i） ） if len（mpoly）> 1： mpoly = MultiPolygon（mpoly） polygons.append（mpoly） colors.append（polygon.get_facecolor（）。tolist（）[0]） elif len mpoly）== 1： polygons.append（mpoly [0]） colors.append（polygon.get_facecolor（）。tolist（）[0]） return GeoDataFrame b geometry = polygons， data = {'RGBA'：colors}， crs = {'init'：'epsg：4326'}） = np.genfromtxt（'temp.csv'，delimiter ='，'）x = data [...，1] y = data [...，2] z = data [ ...，3] xi = np.linspace（x.min（），x.max（），200） yi = np.linspace（y.min ），200） zi = griddata（x，y，z，xi，yi，interp ='linear'）＃你也可以看看scipy.interpolate.griddata nb_class = 15＃设置轮廓创建类的数量＃类也可以通过它们的限制来定义，如[0，122，333] collec_poly = plt.contourf（ xi，yi ，zi，nb_class，vmax = abs（zi）.max（），vmin = -abs（zi）.max（）） gdf ​​= collec_to_gdf（collec_poly） gdf.to_json ）＃以GeoJSON形式输出特征集合：＃'{type：FeatureCollection，features：[{type：Feature，geometry：{ type：Polygon，coordinates：[[[51.563214073747474，＃（...） Edit：您可以通过使用来获取matplotplib使用的颜色值（作为范围0-1中的4个值的数组，表示RGBA值） c $ c> get_facecolor（）方法（然后使用它们填充您的GeoDataFrame的一个（或4）列： colors = [p.get_facecolor（）。tolist（）[0] for p in collec_poly.collections] gdf ​​['RGBA'] = colors 一旦你拥有GeoDataFrame，你就可以很容易地得到它与你的边界相交。使用这些边界来形成多边形并计算与您的结果的交集： mask = Polygon ），（51,18），（52,18），（52,17），（51,17）]） gdf.geometry = gdf.geometry.intersection（mask） 或以GeoDataFrame的形式读取geojson： from shapely.ops import unary_union，polygonize boundary = GeoDataFrame.from_file（'your_geojson'）＃假设你有一个边界作为linestring，将它转换为多边形： mask_geom = unary_union（[i for polygon in polygonize（boundary.geometry）]）＃然后计算交集： gdf.geometry = gdf.geometry.intersection（mask_geom） I'm trying to interpolate data within boundaries and plot contour lines (polygons) based on Latitude, longitude, value data from csv files.Results I want print as geojson.I'm stuck on the basic contour plot from csv. I really appreciate help here.This is what I got in this moment but can't make it work.import numpy as npimport matplotlib.pyplot as pltdata = np.genfromtxt('temp.csv', delimiter=',')x = data[:1]y = data[:2][x,y] = meshgrid(x,y);z = data[:3];plt.contour(x,y,z)plt.show()The CSV looks like this2015-10-28T09:25:12.800Z,51.56325,17.529043333,4.6484375,19.80468752015-10-28T09:25:12.900Z,51.56325,17.529041667,4.4921875,19.68752015-10-28T09:25:13.000Z,51.563248333,17.529041667,4.453125,19.96093752015-10-28T09:25:13.200Z,51.563245,17.529041667,4.140625,19.7656252015-10-28T09:25:13.300Z,51.563243333,17.529041667,4.609375,19.64843752015-10-28T09:25:13.500Z,51.563241667,17.529041667,4.609375,19.531252015-10-28T09:25:13.600Z,51.56324,17.529041667,4.4921875,19.3752015-10-28T09:25:13.700Z,51.563238333,17.529041667,4.4140625,19.7656252015-10-28T09:25:13.800Z,51.563236667,17.529041667,4.453125,20.2343752015-10-28T09:25:13.900Z,51.563235,17.529041667,4.3359375,19.843752015-10-28T09:25:14.000Z,51.563233333,17.529041667,4.53125,19.4531252015-10-28T09:25:14.100Z,51.563231667,17.529041667,4.53125,19.80468752015-10-28T09:25:14.200Z,51.563228333,17.529041667,4.1796875,19.49218752015-10-28T09:25:14.300Z,51.563226667,17.529041667,4.2578125,19.4531252015-10-28T09:25:14.400Z,51.563225,17.529041667,4.4921875,19.49218752015-10-28T09:25:14.500Z,51.563223333,17.529041667,4.375,19.4531252015-10-28T09:25:14.600Z,51.563221667,17.529041667,4.609375,18.906252015-10-28T09:25:14.700Z,51.563218333,17.529041667,4.53125,19.68752015-10-28T09:25:14.900Z,51.563215,17.529041667,4.140625,18.752015-10-28T09:25:15.000Z,51.563213333,17.529041667,4.453125,18.828125Column 1 - LatitudeColumn 2 - LongitudeColumn 3 - ValueFor contour lines I need also limits - for example (4.1,4.3,4.6) 解决方案 I guess there is some mistake in your code (according to your data you shouldn't do x = data[:1] but more x = data[..., 1]).With your of data, the basic steps I will follow to interpolate the z value and fetch an output as a geojson would require at least the shapely module (and here geopandas is used for the convenience).import numpy as npimport matplotlib.pyplot as pltfrom geopandas import GeoDataFramefrom matplotlib.mlab import griddatafrom shapely.geometry import Polygon, MultiPolygondef collec_to_gdf(collec_poly): """Transform a `matplotlib.contour.QuadContourSet` to a GeoDataFrame""" polygons, colors = [], [] for i, polygon in enumerate(collec_poly.collections): mpoly = [] for path in polygon.get_paths(): try: path.should_simplify = False poly = path.to_polygons() # Each polygon should contain an exterior ring + maybe hole(s): exterior, holes = [], [] if len(poly) > 0 and len(poly[0]) > 3: # The first of the list is the exterior ring : exterior = poly[0] # Other(s) are hole(s): if len(poly) > 1: holes = [h for h in poly[1:] if len(h) > 3] mpoly.append(Polygon(exterior, holes)) except: print('Warning: Geometry error when making polygon #{}' .format(i)) if len(mpoly) > 1: mpoly = MultiPolygon(mpoly) polygons.append(mpoly) colors.append(polygon.get_facecolor().tolist()[0]) elif len(mpoly) == 1: polygons.append(mpoly[0]) colors.append(polygon.get_facecolor().tolist()[0]) return GeoDataFrame( geometry=polygons, data={'RGBA': colors}, crs={'init': 'epsg:4326'})data = np.genfromtxt('temp.csv', delimiter=',')x = data[..., 1]y = data[..., 2]z = data[..., 3]xi = np.linspace(x.min(), x.max(), 200)yi = np.linspace(y.min(), y.max(), 200)zi = griddata(x, y, z, xi, yi, interp='linear') # You could also take a look to scipy.interpolate.griddatanb_class = 15 # Set the number of class for contour creation# The class can also be defined by their limits like [0, 122, 333]collec_poly = plt.contourf( xi, yi, zi, nb_class, vmax=abs(zi).max(), vmin=-abs(zi).max())gdf = collec_to_gdf(collec_poly)gdf.to_json()# Output your collection of features as a GeoJSON:# '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[51.563214073747474,# (...)Edit:You can grab the colors values (as an array of 4 values in range 0-1, representing RGBA values) used by matplotplib by fetching them on each item of the collection with the get_facecolor() method (and then use them to populate one (or 4) column of your GeoDataFrame :colors = [p.get_facecolor().tolist()[0] for p in collec_poly.collections]gdf['RGBA'] = colorsOnce you have you GeoDataFrame you can easily get it intersected with your boundaries. Use these boundaries to make a Polygon with shapely and compute the intersection with your result:mask = Polygon([(51,17), (51,18), (52,18), (52,17), (51,17)])gdf.geometry = gdf.geometry.intersection(mask)Or read your geojson as a GeoDataFrame:from shapely.ops import unary_union, polygonizeboundary = GeoDataFrame.from_file('your_geojson')# Assuming you have a boundary as linestring, transform it to a Polygon:mask_geom = unary_union([i for i in polygonize(boundary.geometry)])# Then compute the intersection:gdf.geometry = gdf.geometry.intersection(mask_geom) 这篇关于轮廓曲线数据（lat，lon，值）在边界内并导出GeoJSON的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 上岸，阿里云！