rubyhangingsnowflake2

thingiverse

The input list contains a very large number of coordinates in the format of [x, y]. The goal is to find pairs of points that are within a certain distance from each other. Here is an example of how you could use Python's pandas library and scipy.spatial.distance module to accomplish this: ``` import pandas as pd from scipy.spatial import distance # define coordinates coords = [ [-0.08700694444444726, 0.02165455555055856], [-0.07822244444845874, 0.02605401111110915], [-0.07777774444244936, -0.02995400495205598] ] # define radius radius = 0.3 # Create DataFrame of all points (using index as column label) df1 = pd.DataFrame(coords, columns=['x', 'y']) df2 = df1.reset_index() def dist(point): return distance.euclidean(tuple(point), (point['index'], 0)) # function to filter based on Euclidean distance def get_pairs(points): for point in points.iterrows(): df_points = points[(distance.euclidean((point[1]['x'], point[1]['y']), (df2['x'], df2['y'])) < radius)] pairs.append(list(zip(point[1].index, list(df_points.index)))) return points[pairs] pairs=[] get_pairs(df2) for item in pairs: print(item) ``` However, it seems like your list contains 1516 tuples of points. The distance between a point and the origin is calculated using euclidean norm in a vector space. It will work if your points have two elements each and the first one represents the x coordinate of the point, and the second element represents its y coordinate. It is difficult to give a simple code that does not involve iterating through all possible pairs for finding near-by pairs based on some distance condition. A simple implementation can be provided as above which gives you a set of nearby pairs that exist among your points with certain proximity conditions met in order or ascending and/or descending order respectively: To find points within a certain distance from each other, one way is to iterate over all pairs of points and calculate their distances. Here's how you could do it using python: ``` import pandas as pd from scipy.spatial import distance # Define coordinates coords = [...] points_df = pd.DataFrame(coords, columns=['x', 'y']) nearby_points = [] for i in range(len(points_df)): for j in range(i+1, len(points_df)): if distance.euclidean([points_df.iloc[i]['x'], points_df.iloc[i]['y']], [points_df.iloc[j]['x'], points_df.iloc[j]['y']]) < 0.5: # You can change this to any value you want nearby_points.append((i, j)) nearby_points = pd.DataFrame(nearby_points) ``` This code creates a DataFrame of all the pairs of points within the specified distance from each other. Here is how it would look with your data and for the case of having 3 nearest neighbours: ``` nearest_neighbours_data = nearby_points[:10]