U g{j< @sddlZddlZddlmZmZejddejddddlmZdd d d d d gZ d/ddZ d0dd Z d1ddZ d2dd Z d3dd Zd4dd Zd5dd Zddd d!d"gdddd#d$d%gddd&ddd#gdd#d$dd'd(ggdd)dd*ddgfd+d,Zd-d.ZdS)6N)UnivariateSplineinterp1dignore)divide)invalid) filter_signal scale_datascale_sections enhance_peaksenhance_ecg_peaksinterpolate_clipping flip_signalcCs:t|t|}t|}||||||}|S)a\scales passed sequence between thresholds Function that scales passed data so that it has specified lower and upper bounds. Parameters ---------- data : 1-d array or list Sequence to be scaled lower : int or float lower threshold for scaling default : 0 upper : int or float upper threshold for scaling default : 1024 Returns ------- out : 1-d array contains scaled data Examples -------- When passing data without further arguments to the function means it scales 0-1024 >>> x = [2, 3, 4, 5] >>> scale_data(x) array([ 0. , 341.33333333, 682.66666667, 1024. ]) Or you can specify a range: >>> scale_data(x, lower = 50, upper = 124) array([ 50. , 74.66666667, 99.33333333, 124. ]) )npmaxmin)datalowerupperrngminimumrT/var/www/html/bwave/python/venv/lib/python3.8/site-packages/heartpy/preprocessing.pyr s& @c Cstdttjdtddtdtt||}t||}d}|}tt|} |t|kr|||} t| d} t | ||} | | ||<||7}||7}qVt | d|S)ascales data using sliding window approach Function that scales the data within the defined sliding window between the defined lower and upper bounds. Parameters ---------- data : 1-d array or list Sequence to be scaled sample_rate : int or float Sample rate of the passed signal windowsize : int or float size of the window within which signal is scaled, in seconds default : 2.5 lower : int or float lower threshold for scaling. Passed to scale_data. default : 0 upper : int or float upper threshold for scaling. Passed to scale_data. default : 1024 Returns ------- out : 1-d array contains scaled data Examples -------- >>> x = [20, 30, 20, 30, 70, 80, 20, 30, 20, 30] >>> scale_sections(x, sample_rate=1, windowsize=2, lower=20, upper=30) array([20., 30., 20., 30., 20., 30., 20., 30., 20., 30.]) alwayszMscale_sections function is deprecated and will be removed in a future release)category stackleveldefaultr) warnings simplefilterDeprecationWarningwarnlenintremptypowerr array) r sample_rateZ windowsizerr total_lengthZwindow_dimensionZ data_startZdata_endoutputZslicedZscaledrrrr @s&'          cCst||k}tt|dkd}g}tdt|D]}|dkrh||dd|d|dfq8|t|dkr||d||d|ddfq8||d||dd|d||fq8|S)amarks clipping sections Function that marks start and end of clipping part it detects the start and end of clipping segments and returns them Parameters ---------- data : 1-d numpy array Sequence to be scaled threshold: int or float the threshold for clipping, recommended to be a few data points below ADC or sensor max value, to compensate for signal noise default : 1020 Returns ------- out : list of tuples the output is a list of tuples. Each tuple marks the start and endpoint of the detected clipping segment Examples -------- Import heartpy and load example data >>> import heartpy as hp >>> data, _ = hp.load_exampledata(example=2) Let's slice a part of the data that I know contains clipping >>> x = data[2000:3000] >>> mark_clipping(x, threshold=970) [(369, 375), (426, 437), (486, 493), (544, 552), (604, 610), (663, 665), (721, 722), (776, 781), (831, 836), (883, 891), (995, 999)] rr)rwherediffranger$append)r thresholdZ clip_binaryZclipping_edgesclipping_segmentsirrr mark_clippings & r5c Cs:t||}td|}g}g}d}|D] }|d|kr:q&||d||d} ||d|d|} t| | f} tddt|d||dDddt|d|d|Df} t|d||d||d|dd|} z6t| | dd }|| }|||d||d|<Wq&Yq&Xq&|S) aEinterpolate peak waveform Function that interpolates peaks between the clipping segments using cubic spline interpolation. It takes the clipping start +/- 100ms to calculate the spline. Parameters ---------- data : 1d list or numpy array data section to be evaluated sample_rate : int or float sample rate with which the data array is sampled threshold : int or float the threshold for clipping, recommended to be a few data points below ADC or sensor max value, to compensate for signal noise default : 1020 Returns ------- out : array the output is an array with clipping segments replaced by interpolated segments Examples -------- First let's load some example data: >>> import heartpy as hp >>> data, _ = hp.load_exampledata(example=2) >>> x = data[2000:3000] >>> x[425:445] array([948, 977, 977, 977, 977, 978, 978, 977, 978, 977, 977, 977, 977, 914, 820, 722, 627, 536, 460, 394]) And interpolate any clipping segments as such: >>> intp = interpolate_clipping(x, sample_rate=117, threshold=970) >>> intp[425:445] array([ 972, 1043, 1098, 1138, 1163, 1174, 1173, 1159, 1134, 1098, 1053, 998, 934, 848, 747, 646, 552, 470, 402, 348]) g?rrcSsg|]}|qSrr.0xrrr sz(interpolate_clipping..cSsg|]}|qSrrr6rrrr9sr)k)r5r%rZ concatenater0linspacer)rr)r2r3Znum_datapointsZnewxZnewyr4segmentZ antecedentZ consequentZ segment_dataZ interpdata_xZx_new interp_funcZ interp_datarrrr s8.    "   FTcCsLt|}t|}t|}|||}|r>> x = [200, 300, 500, 900, 500, 300, 200] We can call the function. If keep_range is False, the signal will be inverted relative to its mean. >>> flip_signal(x, keep_range=False) array([628.57142857, 528.57142857, 328.57142857, -71.42857143, 328.57142857, 528.57142857, 628.57142857]) However, by specifying keep_range, the inverted signal will be put 'back in place' in its original range. >>> flip_signal(x, keep_range=True) array([900., 800., 600., 200., 600., 800., 900.]) It's also possible to use the enhance_peaks function: >>> flip_signal(x, enhancepeaks=True) array([1024. , 621.75746332, 176.85545623, 0. , 176.85545623, 621.75746332, 1024. ]) )rr)rmeanrrr r )rZ enhancepeaksZ keep_rangeZ data_meanZdata_minZdata_maxrrrr s1    rcCs.t|t|D]}t|d}t|}q|S)a enhances peak amplitude relative to rest of signal Function thta attempts to enhance the signal-noise ratio by accentuating the highest peaks. Note: denoise first Parameters ---------- hrdata : 1-d numpy array or list sequence containing heart rate data iterations : int the number of scaling steps to perform default : 2 Returns ------- out : 1-d numpy array array containing enhanced peaks Examples -------- Given an array of data, the peaks can be enhanced using the function >>> x = [200, 300, 500, 900, 500, 300, 200] >>> enhance_peaks(x) array([ 0. , 4.31776016, 76.16528926, 1024. , 76.16528926, 4.31776016, 0. ]) r)r r0rr')hrdata iterationsr4rrrr Ms    r?c Cst|}t|}tt|D]>}t|||}|dkrFtj|dd}q|dkrtj|dd}q|t|dt| }|rt|d|dd}|S) adenhances ECG peaks Function that convolves synthetic QRS templates with the signal, leading to a strong increase signal-to-noise ratio. Function ends with an optional Notch filterstep (default : true) to reduce noise from the iterating convolution steps. Parameters ---------- hrdata : 1-d numpy array or list sequence containing heart rate data sample_rate : int or float sample rate with which the data is sampled iterations : int how many convolutional iterations should be run. More will result in stronger peak enhancement, but over a certain point (usually between 12-16) overtones start appearing in the signal. Only increase this if the peaks aren't amplified enough. default : 4 aggregation : str how the data from the different convolutions should be aggregated. Can be either 'mean' or 'median'. default : mean notch_filter : bool whether to apply a notch filter after the last convolution to get rid of remaining low frequency noise. default : true Returns ------- output : 1d array The array containing the filtered data with enhanced peaks Examples -------- First let's import the module and load the data >>> import heartpy as hp >>> data, timer = hp.load_exampledata(1) >>> sample_rate = hp.get_samplerate_mstimer(timer) After loading the data we call the function like so: >>> filtered_data = enhance_ecg_peaks(data, sample_rate, iterations = 3) By default the module uses the mean to aggregate convolutional outputs. It is also possible to use the median. >>> filtered_data = enhance_ecg_peaks(data, sample_rate, iterations = 3, ... aggregation = 'median', notch_filter = False) In the last example we also disabled the notch filter. r?r)ZaxisZmedianrg?Znotch)Z filtertype) rcopygenerate_ecg_templatesr0r%denoise_convolutionsZnanmeanZ nanmedianr) r@r)rAZ aggregationZ notch_filterr+ templatesr4 convolvedrrrr qs=  2<FPdr:g @g?g@ggc s|g}|D]n}|D]d}|d|}|t|fdd|D}t|d|d|d} t||dd} || | qq|S)zhelper function for enhance_ecg_peaks Helper function that generates synthetic QRS complexes of varying sizes to convolve with the signal. icsg|]}t|qSr)r%r6steprrr9sz*generate_ecg_templates..rr-Zlinear)kind)r$rr<rr1) r)widthsZpresetsZ amplituderFr4jdurationZnew_tZnew_xr>rrOrrDs   rDcCs>g}tt|D]"}tj|||dd}||qt|S)zhelper function for enhance_ecg_peaks Helper function that convolves the generated synthetic QRS templates with the provided signal. Zsame)mode)r0r$rZconvolver1Zasarray)rr)rFZ convolutionsr4rGrrrrEs  rE)rr)rrr)r,)r,)FT)r)rBr?T)r numpyrZscipy.interpolaterrZseterrZ filteringr__all__r r r5r rr r rDrErrrrs:    , A : R @ $ R