I would like to generate a 2-by-N array in python for use with scipy.optimize.curve_fit.
I have a function of two independent variables stored as 1-D arrays, and the data in a 2-D array. curve_fit requires that the data be flattened, which is easy with data.ravel().
However, this is the hack I'm using to generate the 2xN array of ordinate values:
ordinate = np.array([[l,t] for l in length for t in time]).T
which works, but is slow. What's the (vectorized?) faster way?
If I got the question correctly, you are looking to form a 2D mesh out of the two independent variables stored as 1D arrays. So, for the same, you can use np.meshgrid -
time2D,length2D = np.meshgrid(time,length)
ordinate_vectorized = np.row_stack((length2D.ravel(),time2D.ravel()))
Sample run -
In [149]: time
Out[149]: array([7, 2, 1, 9, 6])
In [150]: length
Out[150]: array([3, 5])
In [151]: ordinate = np.array([[l,t] for l in length for t in time]).T
In [152]: ordinate
Out[152]:
array([[3, 3, 3, 3, 3, 5, 5, 5, 5, 5],
[7, 2, 1, 9, 6, 7, 2, 1, 9, 6]])
In [153]: time2D,length2D = np.meshgrid(time,length)
...: ordinate_vectorized = np.row_stack((length2D.ravel(),time2D.ravel()))
...:
In [154]: ordinate_vectorized
Out[154]:
array([[3, 3, 3, 3, 3, 5, 5, 5, 5, 5],
[7, 2, 1, 9, 6, 7, 2, 1, 9, 6]])
np.meshgrid is the function I was unable to find. Thanks.
ordinate = cartesian([length, time]).