Shapely/03_points_along_line_shapely.ipynb

{"nbformat":4,"nbformat_minor":2,"metadata":{"colab":{"name":"04_points_along_line_shapely.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyPNPOWGGUD2v7/PXWU0kkFD"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":11,"source":["import numpy as np\r\n","import matplotlib.pyplot as plt\r\n","from shapely.geometry import Point, LineString, MultiPoint\r\n","from shapely.ops import substring"],"outputs":[],"metadata":{"id":"WFATIT9Xlhv1","executionInfo":{"status":"ok","timestamp":1632769037870,"user_tz":-120,"elapsed":415,"user":{"displayName":"ivan d'ortenzio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjJkM9KEQgWMc1ULjLaQI2W6lPdYwYiT_UIeDuUmHw=s64","userId":"12592191603011211484"}}}},{"cell_type":"code","execution_count":null,"source":["# Create a line\r\n","line = LineString([(0,0), (1,1), (2,2), (3,3)])\r\n","\r\n","distance = 0.5 # Interpolation distance\r\n","num_points = 10 # Number of points"],"outputs":[],"metadata":{"id":"zyO0GiNvvFQm"}},{"cell_type":"code","execution_count":null,"source":["# Method 1, interpolate one point every n units using interpolate\r\n","interp_points_1 = [line.interpolate(d) for d in np.arange(0, line.length, distance)]\r\n","\r\n","# Add the end point\r\n","interp_points_1.append(Point(line.coords[-1]))"],"outputs":[],"metadata":{"id":"GibQydsOvL00"}},{"cell_type":"code","execution_count":null,"source":["# Method 2, interpolate one point every n units using substring\r\n","interp_points_2 = [Point(substring(line, d, d+distance).coords[:][0]) for d in np.arange(0, line.length, distance)]\r\n","\r\n","# Add the end point\r\n","interp_points_2.append(Point(line.coords[-1]))"],"outputs":[],"metadata":{"id":"u1gnOm1UvNpb"}},{"cell_type":"code","execution_count":null,"source":["# Method 3, Fixed number of points\r\n","interp_points_3 = [line.interpolate(distance) for distance in np.linspace(0, line.length, num_points)]"],"outputs":[],"metadata":{"id":"f48Suul2vPDF"}},{"cell_type":"code","execution_count":64,"source":["# Plot the result of the Method 3\r\n","\r\n","interp_points = np.array([(pt.x, pt.y) for pt in interp_points_3])\r\n","line_points = np.array([(pt[0], pt[1]) for pt in line.coords[:]])\r\n","\r\n","# Creates a figure and one subplot\r\n","fig, ax = plt.subplots()\r\n","\r\n","# Plot the line\r\n","ax.plot(line_points[:,0], line_points[:,1], color='b', marker='o', alpha=0.2, linewidth=1, markersize=10)\r\n","\r\n","# Plot the points\r\n","ax.scatter(line_points[:,0], line_points[:,1], c='r')\r\n","\r\n","# Show the plot\r\n","ax.set_aspect('equal', adjustable='datalim')\r\n","plt.show()"],"outputs":[{"output_type":"display_data","data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":265},"id":"R0GMN6ItmmFB","executionInfo":{"status":"ok","timestamp":1632771103309,"user_tz":-120,"elapsed":465,"user":{"displayName":"ivan d'ortenzio","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjJkM9KEQgWMc1ULjLaQI2W6lPdYwYiT_UIeDuUmHw=s64","userId":"12592191603011211484"}},"outputId":"d7c7bbd1-aa8c-48e0-9911-c40d6a466c04"}}]}