Etat de validation de DataLab#

Validation fonctionnelle#

Dans DataLab, la validation fonctionnelle est basée sur une stratégie de test classique (voir Validation fonctionnelle).

La couverture de test est d’environ 90%, avec plus de 200 tests.

Validation technique#

Ce paragraphe fournit l’état de validation des fonctions de calcul dans DataLab (c’est ce que nous appelons validation technique, voir Validation technique).

Note

Il s’agit d’un travail en cours : les tableaux ci-dessous sont mis à jour en continu à mesure que de nouvelles fonctions sont validées ou que le code de test est adapté (les tableaux sont générés à partir du code de test). Certaines fonctions sont déjà validées mais n’apparaissent pas encore dans la liste ci-dessous, tandis que d’autres sont encore en cours de validation.

Avertissement

L’état de validation ne doit pas être confondu avec la couverture de test. L’état de validation indique si la fonction a été validée par rapport à des données de référence ou des modèles analytiques. La couverture de test indique le pourcentage du code qui est exécuté par la suite de tests, mais elle ne prend pas nécessairement en compte la justesse des résultats (la couverture de test de DataLab est d’environ 90%).

Statistiques de validation#
Catégorie	Signal	Image	Total
Nombre de fonctions de calcul	62	95	157
Nombre de fonctions de calcul validées	50	83	133
Pourcentage de fonctions de calcul validées	80%	87%	84%

Fonctions de calcul signal#

Le tableau ci-dessous montre l’état de validation des fonctions de calcul signal dans DataLab. Il est généré automatiquement à partir du code source.

Etat de validation de DataLab#
Fonctions de calcul	Description	Fonction de test
`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_allan_deviation`	Compute Allan deviation with `cdl.algorithms.signal.allan_deviation()`	test_signal_allan_deviation
`compute_allan_variance`	Compute Allan variance with `cdl.algorithms.signal.allan_variance()`	test_signal_allan_variance
`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_cartesian2polar`	Convert cartesian coordinates to polar coordinates	test_signal_cartesian2polar
`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_hadamard_variance`	Compute Hadamard variance	N/A
`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_inverse`	Compute inverse with `numpy.invert`	test_signal_inverse
`compute_log10`	Compute Log10 with `numpy.log10`	test_signal_log10
`compute_magnitude_spectrum`	Compute magnitude spectrum	test_signal_magnitude_spectrum
`compute_modified_allan_variance`	Compute Modified Allan variance	N/A
`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_overlapping_allan_variance`	Compute Overlapping Allan variance	N/A
`compute_peak_detection`	Peak detection with `cdl.algorithms.signal.peak_indices()`	N/A
`compute_phase_spectrum`	Compute phase spectrum	test_signal_phase_spectrum
`compute_polar2cartesian`	Convert polar coordinates to cartesian coordinates	test_signal_polar2cartesian
`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_signal_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_time_deviation`	Compute Time Deviation (TDEV)	N/A
`compute_total_variance`	Compute Total variance	N/A
`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`		test_signal_x_at_minmax
`compute_x_at_y`		test_signal_x_at_y

Fonctions de calcul image#

Le tableau ci-dessous montre l’état de validation des fonctions de calcul image dans DataLab. Il est généré automatiquement à partir du code source.

Etat de validation des fonctions de calcul image#
Fonctions de calcul	Description	Fonction de test
`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	test_average_profile
`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_inverse`	Compute the inverse of an image and return the new result image object	test_image_inverse
`compute_line_profile`	Compute horizontal or vertical profile	test_line_profile
`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_image_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	test_segment_profile
`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