Validation Status of DataLab#

Functional validation#

In DataLab, functional validation is based on a classic test strategy (see Functional validation).

Test coverage is around 90%, with more than 200 tests.

Technical validation#

This paragraph provides the validation status of compute functions in DataLab (this is what we call technical validation, see Technical Validation).

Note

This is a work in progress: the tables below are updated continuously as new functions are validated or test code is adapted (the tables are generated from the test code). Some functions are already validated but do not appear in the list below yet, while others are still in the validation process.

Warning

The validation status must not be confused with the test coverage. The validation status indicates whether the function has been validated against ground-truth data or analytical models. The test coverage indicates the percentage of the code that is executed by the test suite, but it does not necessarily take into account the correctness of the results (DataLab’s test coverage is around 90%).

Validation Statistics#
Category	Signal	Image	Total
Number of compute functions	51	94	145
Number of validated compute functions	44	79	123
Percentage of validated compute functions	86%	84%	84%

Signal Compute Functions#

The table below shows the validation status of signal compute functions in DataLab. It is automatically generated from the source code.

Validation status of signal compute functions#
Compute function	Description	Test function
`compute_abs`	Compute absolute value with `numpy.absolute`	test_signal_abs
`compute_addition`	Add dst and src signals and return dst signal modified in place	test_signal_addition
`compute_addition_constant`	Add dst and a constant value and return a the new result signal object	test_signal_addition_constant
`compute_arithmetic`	Perform arithmetic operation on two signals	test_signal_arithmetic
`compute_astype`	Convert data type with `numpy.astype()`	test_signal_astype
`compute_bandwidth_3db`	Compute bandwidth at -3 dB with `cdl.algorithms.signal.bandwidth()`	test_signal_bandwidth_3db
`compute_calibration`	Compute linear calibration	test_signal_calibration
`compute_clip`	Compute maximum data clipping with `numpy.clip()`	test_signal_clip
`compute_contrast`	Compute contrast with `cdl.algorithms.signal.contrast()`	test_signal_contrast
`compute_convolution`	Compute convolution of two signals	test_signal_convolution
`compute_derivative`	Compute derivative with `numpy.gradient()`	test_signal_derivative
`compute_detrending`	Detrend data with `scipy.signal.detrend()`	N/A
`compute_difference`	Compute difference between two signals	test_signal_difference
`compute_difference_constant`	Subtract a constant value from a signal	test_signal_difference_constant
`compute_division`	Compute division between two signals	test_signal_division
`compute_division_constant`	Divide a signal by a constant value	test_signal_division_constant
`compute_dynamic_parameters`	Compute Dynamic parameters	test_dynamic_parameters
`compute_exp`	Compute exponential with `numpy.exp`	test_signal_exp
`compute_fft`	Compute FFT with `cdl.algorithms.signal.fft1d()`	test_signal_fft
`compute_filter`	Compute frequency filter (low-pass, high-pass, band-pass, band-stop)	N/A
`compute_fw1e2`	Compute FW at 1/e² with `cdl.algorithms.signal.fw1e2()`	test_signal_fw1e2
`compute_fwhm`	Compute FWHM with `cdl.algorithms.signal.fwhm()`	test_signal_fwhm
`compute_gaussian_filter`	Compute gaussian filter with `scipy.ndimage.gaussian_filter()`	test_signal_gaussian_filter
`compute_histogram`	Compute histogram with `numpy.histogram()`	N/A
`compute_ifft`	Compute iFFT with `cdl.algorithms.signal.ifft1d()`	test_signal_ifft
`compute_im`	Compute imaginary part with `numpy.imag()`	test_signal_im
`compute_integral`	Compute integral with `scipy.integrate.cumulative_trapezoid()`	test_signal_integral
`compute_interpolation`	Interpolate data with `cdl.algorithms.signal.interpolate()`	N/A
`compute_log10`	Compute Log10 with `numpy.log10`	test_signal_log10
`compute_magnitude_spectrum`	Compute magnitude spectrum	test_signal_magnitude_spectrum
`compute_moving_average`	Compute moving average with `scipy.ndimage.uniform_filter()`	test_signal_moving_average
`compute_moving_median`	Compute moving median with `scipy.ndimage.median_filter()`	test_signal_moving_median
`compute_normalize`	Normalize data with `cdl.algorithms.signal.normalize()`	test_signal_normalize
`compute_offset_correction`	Correct offset: subtract the mean value of the signal in the specified range	test_signal_offset_correction
`compute_peak_detection`	Peak detection with `cdl.algorithms.signal.peak_indexes()`	N/A
`compute_phase_spectrum`	Compute phase spectrum	test_signal_phase_spectrum
`compute_power`	Compute power with `numpy.power`	test_signal_power
`compute_product`	Multiply dst and src signals and return dst signal modified in place	test_signal_product
`compute_product_constant`	Multiply dst by a constant value and return the new result signal object	test_signal_product_constant
`compute_psd`	Compute power spectral density	test_signal_psd
`compute_quadratic_difference`	Compute quadratic difference between two signals	test_quadratic_difference
`compute_re`	Compute real part with `numpy.real()`	test_signal_re
`compute_resampling`	Resample data with `cdl.algorithms.signal.interpolate()`	N/A
`compute_reverse_x`	Reverse x-axis	test_signal_reverse_x
`compute_sampling_rate_period`	Compute sampling rate and period	test_signal_sampling_rate_period
`compute_sqrt`	Compute square root with `numpy.sqrt`	test_signal_sqrt
`compute_stats`	Compute statistics on a signal	test_signal_stats_unit
`compute_swap_axes`	Swap axes	test_signal_swap_axes
`compute_wiener`	Compute Wiener filter with `scipy.signal.wiener()`	test_signal_wiener
`compute_windowing`	Compute windowing (available methods: hamming, hanning, bartlett, blackman	N/A
`compute_x_at_minmax`	Compute x at min/max	test_signal_x_at_minmax

Image Compute Functions#

The table below shows the validation status of image compute functions in DataLab. It is automatically generated from the source code.

Validation status of image compute functions#
Compute function	Description	Test function
`compute_abs`	Compute absolute value with `numpy.absolute`	test_image_abs
`compute_addition`	Add dst and src images and return dst image modified in place	test_image_addition
`compute_addition_constant`	Add dst and a constant value and return the new result image object	test_image_addition_constant
`compute_arithmetic`	Compute arithmetic operation on two images	test_image_arithmetic
`compute_astype`	Convert image data type with `cdl.algorithms.datatypes.clip_astype()`	test_image_astype
`compute_average_profile`	Compute horizontal or vertical average profile	N/A
`compute_binning`	Binning function on data with `cdl.algorithms.image.binning()`	test_binning
`compute_butterworth`	Compute Butterworth filter with `skimage.filters.butterworth()`	test_butterworth
`compute_calibration`	Compute linear calibration	test_image_calibration
`compute_centroid`	Compute centroid	test_image_centroid
`compute_clip`	Apply clipping with `numpy.clip()`	test_image_clip
`compute_difference`	Compute difference between two images	test_image_difference
`compute_difference_constant`	Subtract a constant value from an image and return the new result image object	test_image_difference_constant
`compute_division`	Compute division between two images	test_image_division
`compute_division_constant`	Divide an image by a constant value and return the new result image object	test_image_division_constant
`compute_enclosing_circle`	Compute minimum enclosing circle	N/A
`compute_exp`	Compute exponential with `numpy.exp`	test_image_exp
`compute_fft`	Compute FFT with `cdl.algorithms.image.fft2d()`	test_image_fft
`compute_flatfield`	Compute flat field correction with `cdl.algorithms.image.flatfield()`	N/A
`compute_fliph`	Flip data horizontally with `numpy.fliplr()`	test_image_fliph
`compute_flipv`	Flip data vertically with `numpy.flipud()`	test_image_flipv
`compute_gaussian_filter`	Compute gaussian filter with `scipy.ndimage.gaussian_filter()`	test_image_gaussian_filter
`compute_histogram`	Compute histogram of the image data, with `numpy.histogram()`	N/A
`compute_hough_circle_peaks`	Compute Hough circles	N/A
`compute_ifft`	Compute inverse FFT with `cdl.algorithms.image.ifft2d()`	test_image_ifft
`compute_im`	Compute imaginary part with `numpy.imag()`	test_image_im
`compute_line_profile`	Compute horizontal or vertical profile	N/A
`compute_log10`	Compute log10 with `numpy.log10`	test_image_log10
`compute_logp1`	Compute log10(z+n) with `numpy.log10`	test_image_logp1
`compute_magnitude_spectrum`	Compute magnitude spectrum	test_image_magnitude_spectrum
`compute_moving_average`	Compute moving average with `scipy.ndimage.uniform_filter()`	test_image_moving_average
`compute_moving_median`	Compute moving median with `scipy.ndimage.median_filter()`	test_image_moving_median
`compute_normalize`		test_image_normalize
`compute_offset_correction`	Apply offset correction	test_image_offset_correction
`compute_phase_spectrum`	Compute phase spectrum	test_image_phase_spectrum
`compute_product`	Multiply dst and src images and return dst image modified in place	test_image_product
`compute_product_constant`	Multiply dst by a constant value and return the new result image object	test_image_product_constant
`compute_psd`	Compute power spectral density	test_image_psd
`compute_quadratic_difference`	Compute quadratic difference between two images	test_quadratic_difference
`compute_radial_profile`	Compute radial profile around the centroid	N/A
`compute_re`	Compute real part with `numpy.real()`	test_image_re
`compute_resize`	Zooming function with `scipy.ndimage.zoom()`	N/A
`compute_rotate`	Rotate data with `scipy.ndimage.rotate()`	test_image_rotate
`compute_rotate270`	Rotate data 270° with `numpy.rot90()`	test_image_rotate270
`compute_rotate90`	Rotate data 90° with `numpy.rot90()`	test_image_rotate90
`compute_segment_profile`	Compute segment profile	N/A
`compute_stats`	Compute statistics on an image	test_image_stats_unit
`compute_swap_axes`	Swap image axes with `numpy.transpose()`	test_image_swap_axes
`compute_wiener`	Compute Wiener filter with `scipy.signal.wiener()`	test_image_wiener
`compute_blob_dog`	Compute blobs using Difference of Gaussian method	N/A
`compute_blob_doh`	Compute blobs using Determinant of Hessian method	N/A
`compute_blob_log`	Compute blobs using Laplacian of Gaussian method	N/A
`compute_blob_opencv`	Compute blobs using OpenCV	N/A
`compute_contour_shape`	Compute contour shape fit	N/A
`compute_peak_detection`	Compute 2D peak detection	N/A
`compute_canny`	Compute Canny filter with `skimage.feature.canny()`	test_canny
`compute_farid`	Compute Farid filter with `skimage.filters.farid()`	test_farid
`compute_farid_h`	Compute horizontal Farid filter with `skimage.filters.farid_h()`	test_farid_h
`compute_farid_v`	Compute vertical Farid filter with `skimage.filters.farid_v()`	test_farid_v
`compute_laplace`	Compute Laplace filter with `skimage.filters.laplace()`	test_laplace
`compute_prewitt`	Compute Prewitt filter with `skimage.filters.prewitt()`	test_prewitt
`compute_prewitt_h`	Compute horizontal Prewitt filter with `skimage.filters.prewitt_h()`	test_prewitt_h
`compute_prewitt_v`	Compute vertical Prewitt filter with `skimage.filters.prewitt_v()`	test_prewitt_v
`compute_roberts`	Compute Roberts filter with `skimage.filters.roberts()`	test_roberts
`compute_scharr`	Compute Scharr filter with `skimage.filters.scharr()`	test_scharr
`compute_scharr_h`	Compute horizontal Scharr filter with `skimage.filters.scharr_h()`	test_scharr_h
`compute_scharr_v`	Compute vertical Scharr filter with `skimage.filters.scharr_v()`	test_scharr_v
`compute_sobel`	Compute Sobel filter with `skimage.filters.sobel()`	test_sobel
`compute_sobel_h`	Compute horizontal Sobel filter with `skimage.filters.sobel_h()`	test_sobel_h
`compute_sobel_v`	Compute vertical Sobel filter with `skimage.filters.sobel_v()`	test_sobel_v
`compute_adjust_gamma`	Gamma correction with `skimage.exposure.adjust_gamma()`	test_adjust_gamma
`compute_adjust_log`	Compute log correction with `skimage.exposure.adjust_log()`	test_adjust_log
`compute_adjust_sigmoid`	Compute sigmoid correction with `skimage.exposure.adjust_sigmoid()`	test_adjust_sigmoid
`compute_equalize_adapthist`	Adaptive histogram equalization	test_equalize_adapthist
`compute_equalize_hist`	Histogram equalization with `skimage.exposure.equalize_hist()`	test_equalize_hist
`compute_rescale_intensity`	Rescale image intensity levels	test_rescale_intensity
`compute_black_tophat`	Compute Black Top-Hat with `skimage.morphology.black_tophat()`	test_black_tophat
`compute_closing`	Compute morphological closing with `skimage.morphology.closing()`	test_closing
`compute_dilation`	Compute Dilation with `skimage.morphology.dilation()`	test_dilation
`compute_erosion`	Compute Erosion with `skimage.morphology.erosion()`	test_erosion
`compute_opening`	Compute morphological opening with `skimage.morphology.opening()`	test_opening
`compute_white_tophat`	Compute White Top-Hat with `skimage.morphology.white_tophat()`	test_white_tophat
`compute_denoise_bilateral`	Compute bilateral filter denoising	test_denoise_bilateral
`compute_denoise_tophat`	Denoise using White Top-Hat	test_denoise_tophat
`compute_denoise_tv`	Compute Total Variation denoising	test_denoise_tv
`compute_denoise_wavelet`	Compute Wavelet denoising	test_denoise_wavelet
`compute_threshold`	Compute the threshold, using one of the available algorithms	test_threshold
`compute_threshold_isodata`	Compute the threshold using the Isodata algorithm with default parameters	test_threshold_isodata
`compute_threshold_li`	Compute the threshold using the Li algorithm with default parameters	test_threshold_li
`compute_threshold_mean`	Compute the threshold using the Mean algorithm	test_threshold_mean
`compute_threshold_minimum`	Compute the threshold using the Minimum algorithm with default parameters	test_threshold_minimum
`compute_threshold_otsu`	Compute the threshold using the Otsu algorithm with default parameters	test_threshold_otsu
`compute_threshold_triangle`	Compute the threshold using the Triangle algorithm with default parameters	test_threshold_triangle
`compute_threshold_yen`	Compute the threshold using the Yen algorithm with default parameters	test_threshold_yen