CODEWITHSUNDEEP
pythonpandaslistcoordinatespoint
energyMax
energyMax

Reputation: 419

Checking if the points fall within circleS

I have a long list of H-points with known coordinates. I have also a list of TP-points. I'd like to know if the H-points fall within any(!) TP-point with certain radius (e.g. r=5).

dfPoints = pd.DataFrame({'H-points' : ['a','b','c','d','e'],
               'Xh' :[10, 35, 52, 78, 9],
               'Yh' : [15,5,11,20,10]})

dfTrafaPostaje = pd.DataFrame({'TP-points' : ['a','b','c','d','e'],
               'Xt' :[15,25,35],
               'Yt' : [15,25,35],
               'M' : [5,2,3]})

def inside_circle(x, y, a, b, r):
    return (x - a)*(x - a) + (y - b)*(y - b) < r*r

I've started but.. it would be much easier to check this for only one TP point. But if I have e.g. 1500 of them and 30.000 H-points, then i need more general solution. Can anyone help?

Upvotes: 0

Views: 622

Answers (2)

Quang Hoang
Quang Hoang

Reputation: 150735

Another option is to use distance_matrix from scipy.spatial:

dist_mat = distance_matrix(dfPoints [['Xh','Yh']], dfTrafaPostaje [['Xt','Yt']])
dfPoints [np.min(dist_mat,axis=1)<5]

Took about 2s for 1500 dfPoints and 30000 dfTrafaPostje.

Update: to get the index of the reference points with highest score:

dist_mat = distance_matrix(dfPoints [['Xh','Yh']], dfTrafaPostaje [['Xt','Yt']])

# get the M scores of those within range
M_mat = pd.DataFrame(np.where(dist_mat <= 5, dfTrafaPosaje['M'].values[None, :], np.nan),
                     index=dfPoints['H-points'] ,
                     columns=dfTrafaPostaje['TP-points'])

# get the points with largest M values
# mask with np.nan for those outside range    
dfPoints['M'] = np.where(M_mat.notnull().any(1), M_mat.idxmax(1), np.nan)

For the included sample data:

  H-points  Xh  Yh   TP
0        a  10  15    a
1        b  35   5  NaN
2        c  52  11  NaN
3        d  78  20  NaN
4        e   9  10  NaN

Upvotes: 2

Dani Mesejo
Dani Mesejo

Reputation: 61910

You could use cdist from scipy to compute the pairwise distances, then create a mask with True where distance is less than radius, and finally filter:

import pandas as pd
from scipy.spatial.distance import cdist

dfPoints = pd.DataFrame({'H-points': ['a', 'b', 'c', 'd', 'e'],
                         'Xh': [10, 35, 52, 78, 9],
                         'Yh': [15, 5, 11, 20, 10]})

dfTrafaPostaje = pd.DataFrame({'TP-points': ['a', 'b', 'c'],
                               'Xt': [15, 25, 35],
                               'Yt': [15, 25, 35]})

radius = 5
distances = cdist(dfPoints[['Xh', 'Yh']].values, dfTrafaPostaje[['Xt', 'Yt']].values, 'sqeuclidean')
mask = (distances <= radius*radius).sum(axis=1) > 0 # create mask

print(dfPoints[mask])

Output

  H-points  Xh  Yh
0        a  10  15

Upvotes: 1

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