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	96	115	211
Number of validated compute functions	96	115	211
Percentage of validated compute functions	100%	100%	100%

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
absolute	Compute absolute value with numpy.absolute	test_signal_absolute
add_gaussian_noise	Add normal noise to the input signal	test_signal_add_gaussian_noise
add_poisson_noise	Add Poisson noise to the input signal	test_signal_add_poisson_noise
add_uniform_noise	Add uniform noise to the input signal	test_signal_add_uniform_noise
addition	Compute the element-wise sum of multiple signals	test_signal_addition
addition_constant	Compute the sum of a signal and a constant value	test_signal_addition_constant
allan_deviation	Compute Allan deviation	test_signal_allan_deviation
allan_variance	Compute Allan variance	test_signal_allan_variance
apply_window	Compute windowing	test_signal_apply_window
arithmetic	Perform an arithmetic operation on two signals	test_signal_arithmetic
astype	Convert data type	test_signal_astype
average	Compute the element-wise average of multiple signals	test_signal_average
bandpass	Compute band-pass filter	test_signal_bandpass
bandstop	Compute band-stop filter	test_signal_bandstop
bandwidth_3db	Compute bandwidth at -3 dB	test_signal_bandwidth_3db
calibration	Compute linear calibration	test_signal_calibration
cdf_fit	Compute CDF fit	test_signal_cdf_fit
clip	Compute maximum data clipping	test_signal_clip
complex_from_magnitude_phase	Combine magnitude and phase signals into a complex signal	test_signal_complex_from_magnitude_phase
complex_from_real_imag	Combine two real signals into a complex signal using real + i * imag	test_signal_complex_from_real_imag
contrast	Compute contrast	test_signal_contrast
convolution	Compute convolution of two signals	test_signal_convolution
deconvolution	Compute deconvolution	test_signal_deconvolution
derivative	Compute derivative	test_signal_derivative
detrending	Detrend data	test_signal_detrending
difference	Compute the element-wise difference between two signals	test_signal_difference
difference_constant	Compute the difference between a signal and a constant value	test_signal_difference_constant
division	Compute the element-wise division between two signals	test_signal_division
division_constant	Compute the division of a signal by a constant value	test_signal_division_constant
dynamic_parameters	Compute Dynamic parameters	test_dynamic_parameters
evaluate_fit	Evaluate fit function from src1 on the x-axis of src2	test_signal_evaluate_fit
exp	Compute exponential with numpy.exp	test_signal_exp
exponential_fit	Compute exponential fit	test_signal_exponential_fit
extract_pulse_features	Extract pulse features	test_signal_extract_pulse_features
extract_roi	Extract single region of interest from data	test_signal_extract_roi
extract_rois	Extract multiple regions of interest from data	test_signal_extract_rois
fft	Compute FFT	test_signal_fft
full_width_at_y		test_signal_full_width_at_y
fw1e2	Compute FW at 1/e²	test_signal_fw1e2
fwhm	Compute FWHM	test_signal_fwhm
gaussian_filter	Compute gaussian filter	test_signal_gaussian_filter
gaussian_fit	Compute Gaussian fit	test_signal_gaussian_fit
hadamard_variance	Compute Hadamard variance	test_signal_hadamard_variance
highpass	Compute high-pass filter	test_signal_highpass
histogram	Compute histogram	test_signal_histogram
ifft	Compute the inverse FFT	test_signal_ifft
imag	Compute imaginary part	test_signal_imag
integral	Compute integral	test_signal_integral
interpolate	Interpolate data	test_signal_interpolate
inverse	Compute the element-wise inverse of a signal	test_signal_inverse
linear_fit	Compute linear fit	test_signal_linear_fit
log10	Compute Log10 with numpy.log10	test_signal_log10
lorentzian_fit	Compute Lorentzian fit	test_signal_lorentzian_fit
lowpass	Compute low-pass filter	test_signal_lowpass
magnitude_spectrum	Compute magnitude spectrum	test_signal_magnitude_spectrum
modified_allan_variance	Compute Modified Allan variance	test_signal_modified_allan_variance
moving_average	Compute moving average	test_signal_moving_average
moving_median	Compute moving median	test_signal_moving_median
normalize	Normalize data	test_signal_normalize
offset_correction	Correct offset: subtract the mean value of the signal in the specified range	test_signal_offset_correction
overlapping_allan_variance	Compute Overlapping Allan variance	test_signal_overlapping_allan_variance
peak_detection	Peak detection	test_signal_peak_detection
phase	Compute the phase (argument) of a complex signal	test_signal_phase
phase_spectrum	Compute phase spectrum	test_signal_phase_spectrum
piecewiseexponential_fit	Compute piecewise exponential fit (raise-decay)	test_signal_piecewiseexponential_fit
planckian_fit	Compute Planckian fit	test_signal_planckian_fit
polynomial_fit	Compute polynomial fit	test_polynomial_fit
power	Compute power with numpy.power	test_signal_power
product	Compute the element-wise product of multiple signals	test_signal_product
product_constant	Compute the product of a signal and a constant value	test_signal_product_constant
psd	Compute power spectral density	test_signal_psd
quadratic_difference	Compute the normalized difference between two signals	test_signal_quadratic_difference
real	Compute real part	test_signal_real
replace_x_by_other_y	Create a new signal using Y from src1 and Y from src2 as X coordinates	test_replace_x_by_other_y
resampling	Resample data	test_signal_resampling
reverse_x	Reverse x-axis	test_signal_reverse_x
sampling_rate_period	Compute sampling rate and period	test_signal_sampling_rate_period
sigmoid_fit	Compute sigmoid fit	test_signal_sigmoid_fit
signals_to_image	Combine multiple signals into an image	test_signal_signals_to_image
sinusoidal_fit	Compute sinusoidal fit	test_sinusoidal_fit
sqrt	Compute square root with numpy.sqrt	test_signal_sqrt
standard_deviation	Compute the element-wise standard deviation of multiple signals	test_signal_standard_deviation
stats	Compute statistics on a signal	test_signal_stats_unit
time_deviation	Compute Time Deviation (TDEV)	test_signal_time_deviation
to_cartesian	Convert polar coordinates to Cartesian coordinates	test_signal_to_cartesian
to_polar	Convert Cartesian coordinates to polar coordinates	test_signal_to_polar
total_variance	Compute Total variance	test_signal_total_variance
transpose	Transpose signal (swap X and Y axes)	test_signal_transpose
twohalfgaussian_fit	Compute two-half-Gaussian fit	test_signal_twohalfgaussian_fit
voigt_fit	Compute Voigt fit	test_signal_voigt_fit
wiener	Compute Wiener filter	test_signal_wiener
x_at_minmax		test_signal_x_at_minmax
x_at_y		test_signal_x_at_y
xy_mode	Simulate the X-Y mode of an oscilloscope	test_signal_xy_mode
y_at_x		test_signal_y_at_x
zero_padding	Compute zero padding	test_signal_zero_padding

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
absolute	Compute absolute value with numpy.absolute	test_image_absolute
add_gaussian_noise	Add Gaussian (normal) noise to the input image	test_image_add_gaussian_noise
add_poisson_noise	Add Poisson noise to the input image	test_image_add_poisson_noise
add_uniform_noise	Add uniform noise to the input image	test_image_add_uniform_noise
addition	Add images in the list and return the result image object	test_image_addition
addition_constant	Add dst and a constant value and return the new result image object	test_image_addition_constant
adjust_gamma	Gamma correction	test_adjust_gamma
adjust_log	Compute log correction	test_adjust_log
adjust_sigmoid	Compute sigmoid correction	test_adjust_sigmoid
arithmetic	Compute arithmetic operation on two images	test_image_arithmetic
astype	Convert image data type	test_image_astype
average	Compute the average of images in the list and return the result image object	test_image_average
average_profile	Compute horizontal or vertical average profile	test_average_profile
binning	Binning: image pixel binning (or aggregation)	test_binning
black_tophat	Compute Black Top-Hat	test_black_tophat
blob_dog	Compute blobs using Difference of Gaussian method	test_image_blob_dog
blob_doh	Compute blobs using Determinant of Hessian method	test_image_blob_doh
blob_log	Compute blobs using Laplacian of Gaussian method	test_image_blob_log
blob_opencv	Compute blobs using OpenCV	test_image_blob_opencv
butterworth	Compute Butterworth filter	test_butterworth
calibration	Compute polynomial calibration	test_image_calibration
canny	Compute Canny filter	test_canny
centroid	Compute centroid	test_image_centroid
clip	Apply clipping	test_image_clip
closing	Compute morphological closing	test_closing
complex_from_magnitude_phase	Combine magnitude and phase images into a complex image	test_image_complex_from_magnitude_phase
complex_from_real_imag	Combine two real images into a complex image using real + i * imag	test_image_complex_from_real_imag
contour_shape	Compute contour shape	test_contour_shape
convolution	Convolve an image with a kernel	test_image_convolution
deconvolution	Deconvolve a kernel from an image using Fast Fourier Transform (FFT)	test_image_deconvolution
denoise_bilateral	Compute bilateral filter denoising	test_denoise_bilateral
denoise_tophat	Denoise using White Top-Hat	test_denoise_tophat
denoise_tv	Compute Total Variation denoising	test_denoise_tv
denoise_wavelet	Compute Wavelet denoising	test_denoise_wavelet
difference	Compute difference between two images	test_image_difference
difference_constant	Subtract a constant value from an image and return the new result image object	test_image_difference_constant
dilation	Compute Dilation	test_dilation
division	Compute division between two images	test_image_division
division_constant	Divide an image by a constant value and return the new result image object	test_image_division_constant
enclosing_circle	Compute minimum enclosing circle	test_image_enclosing_circle
equalize_adapthist	Adaptive histogram equalization	test_equalize_adapthist
equalize_hist	Histogram equalization	test_equalize_hist
erase	Erase an area of the image using the mean value of the image	test_erase
erosion	Compute Erosion	test_erosion
exp	Compute exponential with numpy.exp	test_image_exp
extract_roi	Extract single ROI	test_image_extract_roi
extract_rois	Extract multiple regions of interest from data	test_image_extract_rois
farid	Compute Farid filter	test_farid
farid_h	Compute horizontal Farid filter	test_farid_h
farid_v	Compute vertical Farid filter	test_farid_v
fft	Compute FFT	test_image_fft
flatfield	Compute flat field correction	test_flatfield
fliph	Flip data horizontally	test_image_fliph
flipv	Flip data vertically	test_image_flipv
gaussian_filter	Compute gaussian filter	test_image_gaussian_filter
gaussian_freq_filter	Apply a Gaussian filter in the frequency domain	test_gaussian_freq_filter
histogram	Compute histogram of the image data,	test_image_histogram
horizontal_projection	Compute the sum of pixel intensities along each col. (projection on the x-axis)	test_image_horizontal_projection
hough_circle_peaks	Compute Hough circles	test_image_hough_circle_peaks
ifft	Compute inverse FFT	test_image_ifft
imag	Compute imaginary part	test_image_imag
inverse	Compute the inverse of an image and return the new result image object	test_image_inverse
laplace	Compute Laplace filter	test_laplace
line_profile	Compute horizontal or vertical profile	test_line_profile
log10	Compute log10 with numpy.log10	test_image_log10
log10_z_plus_n	Compute log10(z+n) with numpy.log10	test_image_log10_z_plus_n
magnitude_spectrum	Compute magnitude spectrum	test_image_magnitude_spectrum
moving_average	Compute moving average	test_image_moving_average
moving_median	Compute moving median	test_image_moving_median
normalize		test_image_normalize
offset_correction	Apply offset correction	test_image_offset_correction
opening	Compute morphological opening	test_opening
peak_detection	Compute 2D peak detection	test_image_peak_detection
phase	Compute the phase (argument) of a complex image	test_image_phase
phase_spectrum	Compute phase spectrum	test_image_phase_spectrum
prewitt	Compute Prewitt filter	test_prewitt
prewitt_h	Compute horizontal Prewitt filter	test_prewitt_h
prewitt_v	Compute vertical Prewitt filter	test_prewitt_v
product	Multiply images in the list and return the result image object	test_image_product
product_constant	Multiply dst by a constant value and return the new result image object	test_image_product_constant
psd	Compute power spectral density	test_image_psd
quadratic_difference	Compute quadratic difference between two images	test_image_quadratic_difference
radial_profile	Compute radial profile around the centroid	test_radial_profile
real	Compute real part	test_image_real
resampling	Resample image to new coordinate grid using interpolation	test_image_resampling
rescale_intensity	Rescale image intensity levels	test_rescale_intensity
resize	Zooming function	test_image_resize
roberts	Compute Roberts filter	test_roberts
rotate	Rotate data	test_image_rotate
rotate270	Rotate data 270°	test_image_rotate270
rotate90	Rotate data 90°	test_image_rotate90
scharr	Compute Scharr filter	test_scharr
scharr_h	Compute horizontal Scharr filter	test_scharr_h
scharr_v	Compute vertical Scharr filter	test_scharr_v
segment_profile	Compute segment profile	test_segment_profile
set_uniform_coords	Convert image to uniform coordinate system	test_set_uniform_coords
sobel	Compute Sobel filter	test_sobel
sobel_h	Compute horizontal Sobel filter	test_sobel_h
sobel_v	Compute vertical Sobel filter	test_sobel_v
standard_deviation	Compute the element-wise standard deviation of multiple images	test_image_standard_deviation
stats	Compute statistics on an image	test_image_stats_unit
threshold	Compute the threshold, using one of the available algorithms	test_threshold
threshold_isodata	Compute the threshold using the Isodata algorithm with default parameters	test_threshold_isodata
threshold_li	Compute the threshold using the Li algorithm with default parameters	test_threshold_li
threshold_mean	Compute the threshold using the Mean algorithm	test_threshold_mean
threshold_minimum	Compute the threshold using the Minimum algorithm with default parameters	test_threshold_minimum
threshold_otsu	Compute the threshold using the Otsu algorithm with default parameters	test_threshold_otsu
threshold_triangle	Compute the threshold using the Triangle algorithm with default parameters	test_threshold_triangle
threshold_yen	Compute the threshold using the Yen algorithm with default parameters	test_threshold_yen
translate	Translate data	test_image_translate
transpose	Transpose image	test_image_transpose
vertical_projection	Compute the sum of pixel intensities along each row (projection on the y-axis)	test_image_vertical_projection
white_tophat	Compute White Top-Hat	test_white_tophat
wiener	Compute Wiener filter	test_image_wiener
zero_padding	Zero-padding: add zeros to image borders	test_image_zero_padding