Numpy

Functions

assert_version()

to(x, dtype=<class 'numpy.float32'>, device=None)

tonumpy(x)

check_device(x, device_name)

backend_type()

sample_transformation(d, n_sample=1, mean=None, cov=None, device='cpu')

identity(d, n_sample=1, epsilon=0, device='cpu')

uniform_meshgrid(xmin, xmax, n_points, device='cpu')

calc_velocity(grid, theta, params)

exp(*args, **kwargs)

linspace(*args, **kwargs)

meshgrid(*args, **kwargs)

matmul(*args, **kwargs)

max(*args, **kwargs)

ones(*args, **kwargs)

pdist(c)

transformer(grid, theta, params, method=None, time=1.0)

gradient(grid, theta, params, method=None, time=1.0)

gradient_space(grid, theta, params, method=None, time=1.0)

interpolate_grid(transformed_grid, params)

Transformer

batch_effect(x, theta)

get_affine(x, theta, params)

precompute_affine(x, theta, params)

right_boundary(c, params)

left_boundary(c, params)

get_cell(x, params)

get_velocity(x, theta, params)

get_psi(x, t, theta, params)

get_hit_time(x, theta, params)

get_phi_numeric(x, t, theta, params)

integrate_numeric(x, theta, params, time=1.0)

integrate_closed_form(x, theta, params, time=1.0)

integrate_closed_form_trace(x, theta, params, time=1.0)

derivative_numeric(x, theta, params, time=1.0, h=0.001)

derivative_closed_form(x, theta, params, time=1.0)

derivative_psi_theta(x, t, theta, k, params)

derivative_phi_time(x, t, theta, k, params)

derivative_thit_theta(x, theta, k, params)

derivative_space_numeric(x, theta, params, time=1.0, h=0.001)

derivative_space_closed_form(x, theta, params, time=1.0)

derivative_thit_x(x, t, theta, params)

derivative_psi_x(x, t, theta, params)

derivative_psi_t(x, t, theta, params)

Interpolation

interpolate(data, grid, outsize)

interpolate_grid(data)