python - replace multiple if elif statements and conditions with numpy function such as numpy.where

up vote
-1
down vote

favorite

Radio_index, n_x and n_y are integers
I wrote a if/elif code that can be run.
The purpose is to find x,y position for a radio_index
Am I able to do with np.where

def radio_index2xy(radio_index,n_x,n_y): 



    con1 = radio_index <= n_x

    con2 = (radio_index > n_x) & (radio_index <= n_x+n_y-1)

    con3 = (radio_index > n_x+n_y-1) & (radio_index <= 2*n_x+n_y-2)

    con4 = (radio_index > 2*n_x+n_y-2) & (radio_index <= 2*n_x+2*n_y-4)

    condlist = [[con1],[con2],[con3],[con4]]

    choicelist = [[x_pos = radio_index -1 ,y_pos = 0],

                  [(x_pos = n_x -1),(y_pos = radio_index - n_x)],

                  [(x_pos = (n_x-1)-(radio_index-n_x-n_y+1)),(y_pos = n_y -1)],

                  [（x_pos = 0),(y_pos = 2*n_x+2*n_y-4-radio_index+1)]]

    np.select(condlist,choicelist)



    return x_pos,y_pos 











if radio_index <= n_x:

    x_pos = radio_index -1

    y_pos = 0

elif radio_index > n_x and radio_index <= n_x+n_y-1:

    x_pos = n_x -1

    y_pos = radio_index - n_x

elif radio_index > n_x+n_y-1 and radio_index <= 2*n_x+n_y-2:

    x_pos = (n_x-1)-(radio_index-n_x-n_y+1)

    y_pos = n_y -1

elif radio_index > 2*n_x+n_y-2 and radio_index <= 2*n_x+2*n_y-4:

    x_pos = 0

    y_pos = 2*n_x+2*n_y-4-radio_index+1

edited Nov 11 at 4:40

asked Nov 10 at 21:37

Yci Meow

3

Please tell us what the code is supposed to do, I don't think anybody is going to reverse engineer it.
– timgeb
Nov 10 at 21:40

1

Also there's not a single if/elif in this code.
– timgeb
Nov 10 at 21:41

This is used to find the x(vertical axis) and y(horizontal axis) from the given radio_index
– Yci Meow
Nov 11 at 4:37

The if/elif method can be run.
– Yci Meow
Nov 11 at 4:37

add a comment |

up vote
-1
down vote

favorite

Radio_index, n_x and n_y are integers
I wrote a if/elif code that can be run.
The purpose is to find x,y position for a radio_index
Am I able to do with np.where

def radio_index2xy(radio_index,n_x,n_y): 



    con1 = radio_index <= n_x

    con2 = (radio_index > n_x) & (radio_index <= n_x+n_y-1)

    con3 = (radio_index > n_x+n_y-1) & (radio_index <= 2*n_x+n_y-2)

    con4 = (radio_index > 2*n_x+n_y-2) & (radio_index <= 2*n_x+2*n_y-4)

    condlist = [[con1],[con2],[con3],[con4]]

    choicelist = [[x_pos = radio_index -1 ,y_pos = 0],

                  [(x_pos = n_x -1),(y_pos = radio_index - n_x)],

                  [(x_pos = (n_x-1)-(radio_index-n_x-n_y+1)),(y_pos = n_y -1)],

                  [（x_pos = 0),(y_pos = 2*n_x+2*n_y-4-radio_index+1)]]

    np.select(condlist,choicelist)



    return x_pos,y_pos 











if radio_index <= n_x:

    x_pos = radio_index -1

    y_pos = 0

elif radio_index > n_x and radio_index <= n_x+n_y-1:

    x_pos = n_x -1

    y_pos = radio_index - n_x

elif radio_index > n_x+n_y-1 and radio_index <= 2*n_x+n_y-2:

    x_pos = (n_x-1)-(radio_index-n_x-n_y+1)

    y_pos = n_y -1

elif radio_index > 2*n_x+n_y-2 and radio_index <= 2*n_x+2*n_y-4:

    x_pos = 0

    y_pos = 2*n_x+2*n_y-4-radio_index+1

edited Nov 11 at 4:40

asked Nov 10 at 21:37

Yci Meow

3

Please tell us what the code is supposed to do, I don't think anybody is going to reverse engineer it.
– timgeb
Nov 10 at 21:40

1

Also there's not a single if/elif in this code.
– timgeb
Nov 10 at 21:41

This is used to find the x(vertical axis) and y(horizontal axis) from the given radio_index
– Yci Meow
Nov 11 at 4:37

The if/elif method can be run.
– Yci Meow
Nov 11 at 4:37

add a comment |

up vote
-1
down vote

favorite

Radio_index, n_x and n_y are integers
I wrote a if/elif code that can be run.
The purpose is to find x,y position for a radio_index
Am I able to do with np.where

def radio_index2xy(radio_index,n_x,n_y): 



    con1 = radio_index <= n_x

    con2 = (radio_index > n_x) & (radio_index <= n_x+n_y-1)

    con3 = (radio_index > n_x+n_y-1) & (radio_index <= 2*n_x+n_y-2)

    con4 = (radio_index > 2*n_x+n_y-2) & (radio_index <= 2*n_x+2*n_y-4)

    condlist = [[con1],[con2],[con3],[con4]]

    choicelist = [[x_pos = radio_index -1 ,y_pos = 0],

                  [(x_pos = n_x -1),(y_pos = radio_index - n_x)],

                  [(x_pos = (n_x-1)-(radio_index-n_x-n_y+1)),(y_pos = n_y -1)],

                  [（x_pos = 0),(y_pos = 2*n_x+2*n_y-4-radio_index+1)]]

    np.select(condlist,choicelist)



    return x_pos,y_pos 











if radio_index <= n_x:

    x_pos = radio_index -1

    y_pos = 0

elif radio_index > n_x and radio_index <= n_x+n_y-1:

    x_pos = n_x -1

    y_pos = radio_index - n_x

elif radio_index > n_x+n_y-1 and radio_index <= 2*n_x+n_y-2:

    x_pos = (n_x-1)-(radio_index-n_x-n_y+1)

    y_pos = n_y -1

elif radio_index > 2*n_x+n_y-2 and radio_index <= 2*n_x+2*n_y-4:

    x_pos = 0

    y_pos = 2*n_x+2*n_y-4-radio_index+1

edited Nov 11 at 4:40

asked Nov 10 at 21:37

Yci Meow

Radio_index, n_x and n_y are integers
I wrote a if/elif code that can be run.
The purpose is to find x,y position for a radio_index
Am I able to do with np.where

def radio_index2xy(radio_index,n_x,n_y): 



    con1 = radio_index <= n_x

    con2 = (radio_index > n_x) & (radio_index <= n_x+n_y-1)

    con3 = (radio_index > n_x+n_y-1) & (radio_index <= 2*n_x+n_y-2)

    con4 = (radio_index > 2*n_x+n_y-2) & (radio_index <= 2*n_x+2*n_y-4)

    condlist = [[con1],[con2],[con3],[con4]]

    choicelist = [[x_pos = radio_index -1 ,y_pos = 0],

                  [(x_pos = n_x -1),(y_pos = radio_index - n_x)],

                  [(x_pos = (n_x-1)-(radio_index-n_x-n_y+1)),(y_pos = n_y -1)],

                  [（x_pos = 0),(y_pos = 2*n_x+2*n_y-4-radio_index+1)]]

    np.select(condlist,choicelist)



    return x_pos,y_pos 











if radio_index <= n_x:

    x_pos = radio_index -1

    y_pos = 0

elif radio_index > n_x and radio_index <= n_x+n_y-1:

    x_pos = n_x -1

    y_pos = radio_index - n_x

elif radio_index > n_x+n_y-1 and radio_index <= 2*n_x+n_y-2:

    x_pos = (n_x-1)-(radio_index-n_x-n_y+1)

    y_pos = n_y -1

elif radio_index > 2*n_x+n_y-2 and radio_index <= 2*n_x+2*n_y-4:

    x_pos = 0

    y_pos = 2*n_x+2*n_y-4-radio_index+1

python numpy if-statement

edited Nov 11 at 4:40

asked Nov 10 at 21:37

Yci Meow

edited Nov 11 at 4:40

asked Nov 10 at 21:37

Yci Meow

edited Nov 11 at 4:40

asked Nov 10 at 21:37

Yci Meow

asked Nov 10 at 21:37

Yci Meow

asked Nov 10 at 21:37

Yci Meow

3

Please tell us what the code is supposed to do, I don't think anybody is going to reverse engineer it.
– timgeb
Nov 10 at 21:40

1

Also there's not a single if/elif in this code.
– timgeb
Nov 10 at 21:41

This is used to find the x(vertical axis) and y(horizontal axis) from the given radio_index
– Yci Meow
Nov 11 at 4:37

The if/elif method can be run.
– Yci Meow
Nov 11 at 4:37

add a comment |

3

Please tell us what the code is supposed to do, I don't think anybody is going to reverse engineer it.
– timgeb
Nov 10 at 21:40

1

Also there's not a single if/elif in this code.
– timgeb
Nov 10 at 21:41

This is used to find the x(vertical axis) and y(horizontal axis) from the given radio_index
– Yci Meow
Nov 11 at 4:37

The if/elif method can be run.
– Yci Meow
Nov 11 at 4:37

Please tell us what the code is supposed to do, I don't think anybody is going to reverse engineer it.
– timgeb
Nov 10 at 21:40

Also there's not a single if/elif in this code.
– timgeb
Nov 10 at 21:41

This is used to find the x(vertical axis) and y(horizontal axis) from the given radio_index
– Yci Meow
Nov 11 at 4:37

The if/elif method can be run.
– Yci Meow
Nov 11 at 4:37

add a comment |

1 Answer
1

active

oldest

votes

up vote
0
down vote

accepted

np.searchsorted is useful for this kind of logic:

def radio_index2xy_v(radio_index, n_x, n_y):

    sgn = np.array([0, 1, 1, -1, -1, 0])

    col = np.array([-1, 1, 0, 1, 0, -1])

    coeffs = np.array([[-1, -1],

                       [0, -1],

                       [-n_x, n_x - 1],

                       [n_y - 1, 2*n_x + n_y - 2],

                       [2*n_x + 2*n_y - 3, 0],

                       [-1, -1]])

    cusps = np.cumsum([0, n_x, n_y-1, n_x-1, n_y-2])

    idx = cusps.searchsorted(radio_index)

    out = coeffs[idx]

    out[np.arange(idx.size), col[idx]] += sgn[idx] * radio_index

    return out

Demo:

>>> radio_index2xy_v(np.arange(20), 5, 4)

array([[-1, -1],

       [ 0,  0],

       [ 0,  1],

       [ 0,  2],

       [ 0,  3],

       [ 0,  4],

       [ 1,  4],

       [ 2,  4],

       [ 3,  4],

       [ 3,  3],

       [ 3,  2],

       [ 3,  1],

       [ 3,  0],

       [ 2,  0],

       [ 1,  0],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1]])

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

add a comment |

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1 Answer
1

active

oldest

votes

1 Answer
1

active

oldest

votes

up vote
0
down vote

accepted

np.searchsorted is useful for this kind of logic:

def radio_index2xy_v(radio_index, n_x, n_y):

    sgn = np.array([0, 1, 1, -1, -1, 0])

    col = np.array([-1, 1, 0, 1, 0, -1])

    coeffs = np.array([[-1, -1],

                       [0, -1],

                       [-n_x, n_x - 1],

                       [n_y - 1, 2*n_x + n_y - 2],

                       [2*n_x + 2*n_y - 3, 0],

                       [-1, -1]])

    cusps = np.cumsum([0, n_x, n_y-1, n_x-1, n_y-2])

    idx = cusps.searchsorted(radio_index)

    out = coeffs[idx]

    out[np.arange(idx.size), col[idx]] += sgn[idx] * radio_index

    return out

Demo:

>>> radio_index2xy_v(np.arange(20), 5, 4)

array([[-1, -1],

       [ 0,  0],

       [ 0,  1],

       [ 0,  2],

       [ 0,  3],

       [ 0,  4],

       [ 1,  4],

       [ 2,  4],

       [ 3,  4],

       [ 3,  3],

       [ 3,  2],

       [ 3,  1],

       [ 3,  0],

       [ 2,  0],

       [ 1,  0],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1]])

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

add a comment |

up vote
0
down vote

accepted

np.searchsorted is useful for this kind of logic:

def radio_index2xy_v(radio_index, n_x, n_y):

    sgn = np.array([0, 1, 1, -1, -1, 0])

    col = np.array([-1, 1, 0, 1, 0, -1])

    coeffs = np.array([[-1, -1],

                       [0, -1],

                       [-n_x, n_x - 1],

                       [n_y - 1, 2*n_x + n_y - 2],

                       [2*n_x + 2*n_y - 3, 0],

                       [-1, -1]])

    cusps = np.cumsum([0, n_x, n_y-1, n_x-1, n_y-2])

    idx = cusps.searchsorted(radio_index)

    out = coeffs[idx]

    out[np.arange(idx.size), col[idx]] += sgn[idx] * radio_index

    return out

Demo:

>>> radio_index2xy_v(np.arange(20), 5, 4)

array([[-1, -1],

       [ 0,  0],

       [ 0,  1],

       [ 0,  2],

       [ 0,  3],

       [ 0,  4],

       [ 1,  4],

       [ 2,  4],

       [ 3,  4],

       [ 3,  3],

       [ 3,  2],

       [ 3,  1],

       [ 3,  0],

       [ 2,  0],

       [ 1,  0],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1]])

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

add a comment |

up vote
0
down vote

accepted

np.searchsorted is useful for this kind of logic:

def radio_index2xy_v(radio_index, n_x, n_y):

    sgn = np.array([0, 1, 1, -1, -1, 0])

    col = np.array([-1, 1, 0, 1, 0, -1])

    coeffs = np.array([[-1, -1],

                       [0, -1],

                       [-n_x, n_x - 1],

                       [n_y - 1, 2*n_x + n_y - 2],

                       [2*n_x + 2*n_y - 3, 0],

                       [-1, -1]])

    cusps = np.cumsum([0, n_x, n_y-1, n_x-1, n_y-2])

    idx = cusps.searchsorted(radio_index)

    out = coeffs[idx]

    out[np.arange(idx.size), col[idx]] += sgn[idx] * radio_index

    return out

Demo:

>>> radio_index2xy_v(np.arange(20), 5, 4)

array([[-1, -1],

       [ 0,  0],

       [ 0,  1],

       [ 0,  2],

       [ 0,  3],

       [ 0,  4],

       [ 1,  4],

       [ 2,  4],

       [ 3,  4],

       [ 3,  3],

       [ 3,  2],

       [ 3,  1],

       [ 3,  0],

       [ 2,  0],

       [ 1,  0],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1]])

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

np.searchsorted is useful for this kind of logic:

def radio_index2xy_v(radio_index, n_x, n_y):

    sgn = np.array([0, 1, 1, -1, -1, 0])

    col = np.array([-1, 1, 0, 1, 0, -1])

    coeffs = np.array([[-1, -1],

                       [0, -1],

                       [-n_x, n_x - 1],

                       [n_y - 1, 2*n_x + n_y - 2],

                       [2*n_x + 2*n_y - 3, 0],

                       [-1, -1]])

    cusps = np.cumsum([0, n_x, n_y-1, n_x-1, n_y-2])

    idx = cusps.searchsorted(radio_index)

    out = coeffs[idx]

    out[np.arange(idx.size), col[idx]] += sgn[idx] * radio_index

    return out

Demo:

>>> radio_index2xy_v(np.arange(20), 5, 4)

array([[-1, -1],

       [ 0,  0],

       [ 0,  1],

       [ 0,  2],

       [ 0,  3],

       [ 0,  4],

       [ 1,  4],

       [ 2,  4],

       [ 3,  4],

       [ 3,  3],

       [ 3,  2],

       [ 3,  1],

       [ 3,  0],

       [ 2,  0],

       [ 1,  0],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1],

       [-1, -1]])

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

answered Nov 11 at 8:17

Paul Panzer

28.6k21139

add a comment |

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