ella's snowflake

thingiverse

The matrix in the problem can be seen as an M by N dimensional rectangular array. For example: In Python, you could implement it using nested for loops and the index() function to calculate which row and column of the 2D array a point is in. Here's how that could look like: ```python # define your data points and grid definition (grid is 80x90) import numpy as np data_points = [[[0.3665736424657534,-0.3904109589041096], [0.0863013698630137,0.024657534246575346],[0.06712328767123288, 0.0013698630136986301],...[0.2095890410958904,-0.37397260273972605]], [1., 1.,2., 3., 4.,5.,6.,7.,8.,9.,10.,11., 12.,13.,14.,15.,16.,17.,18.,19.,20.,21.,22., 23.,24.,25.,26.,27.,28.,29.,30.,31.,32.,33., 34.,35.,36.,37.,38.,39.,40.,41.,42.,43.,44., 45.,46.,47.,48.,49.,50.,51.,52.,53.,54.,55.,56.,57.,58.,59., 60.,61.,62.,63.,64.,65.,66.,67.,68.,69.,70., 71.,72.,73.,74.,75.,76.,77.,78.,79.,80.,81.,82., 83.,84.,85.,86.,87.,88.,89.,90.]] # grid dimensions N=101 def calculate_position(data_point, data_points, grid): min_idx = np.argmin(np.abs(data_points[:, :, 0] - data_point[0])) #get minimum index along the axis in a numpy array. Get 2D and find index at which this array has minimum absolute value if data_points[min_idx ,:, 1] ==data_point[1]: x_index=min_idx elif min_idx+1>=N-1: x_index=N return None #return none since we are at end elif abs ( data_points[x_index +1, :, 1]-data_point[1]) < abs(data_points[min_idx ,:, 1] - data_point[1]): x_index =x_index +1 min_diff=data_points[min_idx,0 ,:] x_idx=min_diff[1] if x_idx+1==90: #return None since we are at the end return None elif data_points[min_idx,0 ,:] == data_point: y_index =x_index #this is what you're after. elif abs(data_points[x_index +1:,0,: ]-data_point) < abs( data_points[min_idx,:, :]-data_point): #return none since this will never run but added as it's similar logic y_index =x_index + 1 return(x_index, min_diff) point_data= [[ -2.33322222222,-2.08363636364]] point_index_1 = None for x in range (N): for row_data_point in data_points: calculated_position= calculate_position(row_data_point[0],data_points,row_data_point[1]) if calculated_position ==point_data: point_index_1 =calculated_position #do the thing where you set point_index_1 equal to the array index. return(point_index_1) print(calculate_position(2.4,-5)) ``` I've assumed you have numpy installed. You might want to make this a bit more efficient with more accurate methods like KD-tree for spatial search. The data given in your example can be represented as: |0 | x,y | |---------|--------------| | -3.666 |-3.9 | 1 |-4 | 2 |-1.86825 | |....| And so on. The data points look a lot like random variables to me. Is this the case?