############################################################################### # dB transform # ~~~~~~~~~~~~ # We transform the rainfall data into dB units: 10*log(R) R_, _ = transformation.dB_transform(R, metadata) data.append((R_ - np.mean(R_)) / np.std(R_)) labels.append("dB") skw.append(skew(R_)) ############################################################################### # Square-root transform # ~~~~~~~~~~~~~~~~~~~~~ # Transform the data using the square-root: sqrt(R) R_, _ = transformation.sqrt_transform(R, metadata) data.append((R_ - np.mean(R_)) / np.std(R_)) labels.append("sqrt") skw.append(skew(R_)) ############################################################################### # Box-Cox transform # ~~~~~~~~~~~~~~~~~ # We now apply the Box-Cox transform using the best parameter lambda found above. R_, _ = transformation.boxcox_transform(R, metadata, Lambda) data.append((R_ - np.mean(R_)) / np.std(R_)) labels.append("Box-Cox\n($\lambda=$%.2f)" % Lambda) skw.append(skew(R_)) ###############################################################################
def test_sqrt_transform(R, metadata, inverse, expected): """Test the sqrt_transform.""" assert_array_almost_equal( transformation.sqrt_transform(R, metadata, inverse)[0], expected )