i"`dZddlZddgZd dZd dZd dZd dZd d Zeeeeeed Z dd Z d Z y)z[ The thresholding helper module implements the most popular signal thresholding functions. N thresholdthreshold_firmcVtj|}tj|}tjd5d||z z }|j dd|||z}ddd|dk(rStj ||}tj ||S#1swY=xYw)Nignoredividerminmaxoutnpasarrayabsoluteerrstatecliplesswheredatavalue substitute magnitude thresholdedconds N/var/www/html/BatchJob/venv/lib/python3.12/site-packages/pywt/_thresholding.pysoftrs ::d D D!I H %)5?* QDk:[( ) Qwwy%(xxj+66))s "BB(cbtj|}tj|}tjd5d|dz|dzz z }|j dd|||z}ddd|dk(rStj ||}tj ||S#1swY=xYw)zNon-negative Garrote.rrr rNr rrs r nn_garroter!!s ::d D D!I H %)5!8IqL00 QDk:[( ) Qwwy%(xxj+66))s (B%%B.ctj|}tjtj||}tj|||S)N)rrrrr)rrrrs rhardr#3s< ::d D 772;;t$e ,D 88D*d ++ctj|}tj|r tdtjtj ||||S)Nz,greater thresholding only supports real data)rr iscomplexobj ValueErrorrrrrrs rgreaterr)9sF ::d D tGHH 88BGGD%(*d ;;r$ctj|}tj|r tdtjtj ||||S)Nz)less thresholding only supports real data)rrr&r'rr)r(s rrr@sF ::d D tDEE 88BJJtU+Z >>r$)rr#r)rgarrotegarottec t||||S#t$rNdttjD}t dj dj |wxYw)a Thresholds the input data depending on the mode argument. In ``soft`` thresholding [1]_, data values with absolute value less than `param` are replaced with `substitute`. Data values with absolute value greater or equal to the thresholding value are shrunk toward zero by `value`. In other words, the new value is ``data/np.abs(data) * np.maximum(np.abs(data) - value, 0)``. In ``hard`` thresholding, the data values where their absolute value is less than the value param are replaced with `substitute`. Data values with absolute value greater or equal to the thresholding value stay untouched. ``garrote`` corresponds to the Non-negative garrote threshold [2]_, [3]_. It is intermediate between ``hard`` and ``soft`` thresholding. It behaves like soft thresholding for small data values and approaches hard thresholding for large data values. In ``greater`` thresholding, the data is replaced with `substitute` where data is below the thresholding value. Greater data values pass untouched. In ``less`` thresholding, the data is replaced with `substitute` where data is above the thresholding value. Lesser data values pass untouched. Both ``hard`` and ``soft`` thresholding also support complex-valued data. Parameters ---------- data : array_like Numeric data. value : scalar Thresholding value. mode : {'soft', 'hard', 'garrote', 'greater', 'less'} Decides the type of thresholding to be applied on input data. Default is 'soft'. substitute : float, optional Substitute value (default: 0). Returns ------- output : array Thresholded array. See Also -------- threshold_firm References ---------- .. [1] D.L. Donoho and I.M. Johnstone. Ideal Spatial Adaptation via Wavelet Shrinkage. Biometrika. Vol. 81, No. 3, pp.425-455, 1994. DOI:10.1093/biomet/81.3.425 .. [2] L. Breiman. Better Subset Regression Using the Nonnegative Garrote. Technometrics, Vol. 37, pp. 373-384, 1995. DOI:10.2307/1269730 .. [3] H-Y. Gao. Wavelet Shrinkage Denoising Using the Non-Negative Garrote. Journal of Computational and Graphical Statistics Vol. 7, No. 4, pp.469-488. 1998. DOI:10.1080/10618600.1998.10474789 Examples -------- >>> import numpy as np >>> import pywt >>> data = np.linspace(1, 4, 7) >>> data array([ 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ]) >>> pywt.threshold(data, 2, 'soft') array([ 0. , 0. , 0. , 0.5, 1. , 1.5, 2. ]) >>> pywt.threshold(data, 2, 'hard') array([ 0. , 0. , 2. , 2.5, 3. , 3.5, 4. ]) >>> pywt.threshold(data, 2, 'garrote') array([ 0. , 0. , 0. , 0.9 , 1.66666667, 2.35714286, 3. ]) >>> pywt.threshold(data, 2, 'greater') array([ 0. , 0. , 2. , 2.5, 3. , 3.5, 4. ]) >>> pywt.threshold(data, 2, 'less') array([ 1. , 1.5, 2. , 0. , 0. , 0. , 0. ]) c3(K|] }d|d yw)'N).0keys r zthreshold..s5s!C5 5sz.The mode parameter only takes values from: {}.z, )thresholding_optionsKeyErrorsortedkeysr'formatjoin)rrmoderr7s rrrQskd3#D)$zBB 35+00235I &413 3 3s AA)c|dkr td||kr tdtj|}tj|}tjd5||z }|d||z z z|z }|j dd|||z}dddtj ||kD}tj|dr|||<S#1swYCxYw) a_Firm threshold. The approach is intermediate between soft and hard thresholding [1]_. It behaves the same as soft-thresholding for values below `value_low` and the same as hard-thresholding for values above `thresh_high`. For intermediate values, the thresholded value is in between that corresponding to soft or hard thresholding. Parameters ---------- data : array-like The data to threshold. This can be either real or complex-valued. value_low : float Any values smaller then `value_low` will be set to zero. value_high : float Any values larger than `value_high` will not be modified. Notes ----- This thresholding technique is also known as semi-soft thresholding [2]_. For each value, `x`, in `data`. This function computes:: if np.abs(x) <= value_low: return 0 elif np.abs(x) > value_high: return x elif value_low < np.abs(x) and np.abs(x) <= value_high: return x * value_high * (1 - value_low/x)/(value_high - value_low) ``firm`` is a continuous function (like soft thresholding), but is unbiased for large values (like hard thresholding). If ``value_high == value_low`` this function becomes hard-thresholding. If ``value_high`` is infinity, this function becomes soft-thresholding. Returns ------- val_new : array-like The values after firm thresholding at the specified thresholds. See Also -------- threshold References ---------- .. [1] H.-Y. Gao and A.G. Bruce. Waveshrink with firm shrinkage. Statistica Sinica, Vol. 7, pp. 855-874, 1997. .. [2] A. Bruce and H-Y. Gao. WaveShrink: Shrinkage Functions and Thresholds. Proc. SPIE 2569, Wavelet Applications in Signal and Image Processing III, 1995. DOI:10.1117/12.217582 rzvalue_low must be non-negative.z6value_high must be greater than or equal to value_low.rrr Nr )r'rrrrrrany)r value_low value_highrvdiffr large_valss rrrsp1}:;;I DF F ::d D D!I H %)Y& A )(;$;rDs\   ( )7"7$, <?!% $#* $#-#- Y3xLr$