Piecewise function in numpy with multiple arguments
I tried to define a function (tent map) as following:
def f(r, x):
return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])
And r, x will be numpy arrays:
no_r = 10001
r = np.linspace(0, 4, no_r)
x = np.random.rand(no_r)
I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
So what should I do to achieve the intended purpose?
python numpy
asked Nov 24 '18 at 1:22
SatoSato
336
add a comment |
I tried to define a function (tent map) as following:
def f(r, x):
return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])
And r, x will be numpy arrays:
no_r = 10001
r = np.linspace(0, 4, no_r)
x = np.random.rand(no_r)
I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
So what should I do to achieve the intended purpose?
python numpy
asked Nov 24 '18 at 1:22
SatoSato
336
add a comment |
I tried to define a function (tent map) as following:
def f(r, x):
return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])
And r, x will be numpy arrays:
no_r = 10001
r = np.linspace(0, 4, no_r)
x = np.random.rand(no_r)
I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
So what should I do to achieve the intended purpose?
python numpy
asked Nov 24 '18 at 1:22
SatoSato
336
I tried to define a function (tent map) as following:
def f(r, x):
return np.piecewise([r, x], [x < 0.5, x >= 0.5], [lambda r, x: 2*r*x, lambda r, x: 2*r*(1-x)])
And r, x will be numpy arrays:
no_r = 10001
r = np.linspace(0, 4, no_r)
x = np.random.rand(no_r)
I would like the result to be a numpy array matching the shapes of r and x, calculated using each pairs of elements of arrays r and x with the same indicies. For example if r = [0, 1, 2, 3] and x = [0.1, 0.7, 0.3, 1], the result should be [0, 0.6, 1.2, 0].
An error occured: "boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 10001"
So what should I do to achieve the intended purpose?
python numpy
python numpy
asked Nov 24 '18 at 1:22
SatoSato
336
asked Nov 24 '18 at 1:22
SatoSato
336
asked Nov 24 '18 at 1:22
SatoSato
336
asked Nov 24 '18 at 1:22
SatoSato
336
asked Nov 24 '18 at 1:22
SatoSato
336
336
add a comment |
add a comment |
1 Answer
1
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oldest
votes
what you want to get as result can be done with np.select such as:
def f(r, x):
return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])
Then with
r = np.array([0, 1, 2, 3])
x = np.array([0.1, 0.7, 0.3, 1])
print (f(r,x))
[0. 0.6 1.2 0. ]
EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:
def f(r,x):
return np.where(x<0.5,2*r*x, 2*r*(1-x))
will give the same result.
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
what you want to get as result can be done with np.select such as:
def f(r, x):
return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])
Then with
r = np.array([0, 1, 2, 3])
x = np.array([0.1, 0.7, 0.3, 1])
print (f(r,x))
[0. 0.6 1.2 0. ]
EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:
def f(r,x):
return np.where(x<0.5,2*r*x, 2*r*(1-x))
will give the same result.
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
add a comment |
what you want to get as result can be done with np.select such as:
def f(r, x):
return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])
Then with
r = np.array([0, 1, 2, 3])
x = np.array([0.1, 0.7, 0.3, 1])
print (f(r,x))
[0. 0.6 1.2 0. ]
EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:
def f(r,x):
return np.where(x<0.5,2*r*x, 2*r*(1-x))
will give the same result.
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
add a comment |
what you want to get as result can be done with np.select such as:
def f(r, x):
return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])
Then with
r = np.array([0, 1, 2, 3])
x = np.array([0.1, 0.7, 0.3, 1])
print (f(r,x))
[0. 0.6 1.2 0. ]
EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:
def f(r,x):
return np.where(x<0.5,2*r*x, 2*r*(1-x))
will give the same result.
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
what you want to get as result can be done with np.select such as:
def f(r, x):
return np.select([x < 0.5,x >= 0.5], [2*r*x, 2*r*(1-x)])
Then with
r = np.array([0, 1, 2, 3])
x = np.array([0.1, 0.7, 0.3, 1])
print (f(r,x))
[0. 0.6 1.2 0. ]
EDIT: in this case, with only 2 conditions that are exclusive, you can also use np.where:
def f(r,x):
return np.where(x<0.5,2*r*x, 2*r*(1-x))
will give the same result.
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
edited Nov 24 '18 at 2:11
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
answered Nov 24 '18 at 1:40
Ben.TBen.T
5,9772523
5,9772523
add a comment |
add a comment |
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