Some ideas for point clouds

[tmelorc]

Some time ago, before I had discovered scikit-tda, I created the following functions to generate some datasets. Feel free to use and improve if you wish. Regards.

def figure_eight_pts(N, a=1):
    theta_list = 2 * np.pi * np.random.sample(N)
    pts = np.zeros((N,2))
    print theta_list
    for i in range(len(theta_list)):
        pts[i,0] = a * np.cos(theta_list[i]) * np.sqrt(2*np.cos(2*theta_list[i]))
        pts[i,1] = a * np.sin(theta_list[i]) * np.sqrt(2*np.cos(2*theta_list[i]))
    return pts

def annulus_pts(N, R=2, r=1):
    theta_list = np.random.random_sample(N)
    radius_list = r + np.random.random_sample(N) * (R-r)
    pts = np.zeros((N,2))
    for i in range(len(theta_list)):
        pts[i,0] = radius_list[i] * np.cos(2*np.pi*theta_list[i])
        pts[i,1] = radius_list[i] * np.sin(2*np.pi*theta_list[i])
    return pts

def cube_pts(N):
    npts = N/6
    faces = {}
    for i in range(3):
        data0 = np.random.random((npts,3))
        data1 = np.random.random((npts,3))
        data0[:,i] = 0
        data1[:,i] = 1
        faces[i]   = data0
        faces[i+3] = data1
    cube = np.concatenate([faces[i] for i in range(6)])
    return cube

[ctralie]

Cool, thanks. Note that you can often do without for loops. For instance,

def figure_eight_pts(N, a=1):
    theta_list = 2 * np.pi * np.random.sample(N)
    pts = np.zeros((N,2))
    pts[:, 0] = a * np.cos(theta_list) * np.sqrt(2*np.cos(2*theta_list))
    pts[:, 1] = a * np.sin(theta_list) * np.sqrt(2*np.cos(2*theta_list))
    return pts

[tmelorc]

Very interesting. This is my difficulty, I have no knowledge how to do better programs. I have only ideas... :-) Thanks.

[ctralie]

Yeah numpy is great, and whenever you can avoid for loops it's usually faster

On Thu, Apr 18, 2019, 12:26 PM Thiago notifications@github.com wrote:

Very interesting. This is my difficulty, I have no knowledge how to do better programs. I have only ideas... :-) Thanks.

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