def log_gaussian(x, y, x_sigma, y_sigma, mu):
if x_sigma==<NUM_LIT:0.0> or y_sigma==<NUM_LIT:0.0>:<EOL><INDENT>return x * <NUM_LIT:0.0><EOL><DEDENT>with float_error_ignore():<EOL><INDENT>x_w = np.divide(np.log(x)-mu, x_sigma*x_sigma)<EOL>y_h = np.divide(np.log(y)-mu, y_sigma*y_sigma)<EOL>return np.exp(-<NUM_LIT:0.5>*x_w*x_w + -<NUM_LIT:0.5>*y_h*y_h)<EOL><DEDENT>
Two-dimensional oriented Log Gaussian pattern (i.e., 2D version of a
bell curve with an independent, movable peak). Much like a normal
distribution, but not necessarily placing the peak above the center,
and not necessarily summing to 1.0).