def test_basic(self): assert_allclose(windows.bohman(6), [0, 0.1791238937062839, 0.8343114522576858, 0.8343114522576858, 0.1791238937062838, 0]) assert_allclose(windows.bohman(7, sym=True), [0, 0.1089977810442293, 0.6089977810442293, 1.0, 0.6089977810442295, 0.1089977810442293, 0]) assert_allclose(windows.bohman(6, False), [0, 0.1089977810442293, 0.6089977810442293, 1.0, 0.6089977810442295, 0.1089977810442293])
def pre_processing(self): """ Complete various pre-processing steps for encoded protein sequences before doing any of the DSP-related functions or transformations. Zero-pad the sequences, remove any +/- infinity or NAN values, get the approximate protein spectra and window function parameter names. Parameters ---------- :self (PyDSP object): instance of PyDSP class. Returns ------- None """ #zero-pad encoded sequences so they are all the same length self.protein_seqs = zero_padding(self.protein_seqs) #get shape parameters of proteins seqs self.num_seqs = self.protein_seqs.shape[0] self.signal_len = self.protein_seqs.shape[1] #replace any positive or negative infinity or NAN values with 0 self.protein_seqs[self.protein_seqs == -np.inf] = 0 self.protein_seqs[self.protein_seqs == np.inf] = 0 self.protein_seqs[self.protein_seqs == np.nan] = 0 #replace any NAN's with 0's #self.protein_seqs.fillna(0, inplace=True) self.protein_seqs = np.nan_to_num(self.protein_seqs) #initialise zeros array to store all protein spectra self.fft_power = np.zeros((self.num_seqs, self.signal_len)) self.fft_real = np.zeros((self.num_seqs, self.signal_len)) self.fft_imag = np.zeros((self.num_seqs, self.signal_len)) self.fft_abs = np.zeros((self.num_seqs, self.signal_len)) #list of accepted spectra, window functions and filters all_spectra = ['power', 'absolute', 'real', 'imaginary'] all_windows = [ 'hamming', 'blackman', 'blackmanharris', 'gaussian', 'bartlett', 'kaiser', 'barthann', 'bohman', 'chebwin', 'cosine', 'exponential' 'flattop', 'hann', 'boxcar', 'hanning', 'nuttall', 'parzen', 'triang', 'tukey' ] all_filters = [ 'savgol', 'medfilt', 'symiirorder1', 'lfilter', 'hilbert' ] #set required input parameters, raise error if spectrum is none if self.spectrum == None: raise ValueError( 'Invalid input Spectrum type ({}) not available in valid spectra: {}' .format(self.spectrum, all_spectra)) else: #get closest correct spectra from user input, if no close match then raise error spectra_matches = (get_close_matches(self.spectrum, all_spectra, cutoff=0.4)) if spectra_matches == []: raise ValueError( 'Invalid input Spectrum type ({}) not available in valid spectra: {}' .format(self.spectrum, all_spectra)) else: self.spectra = spectra_matches[0] #closest match in array if self.window_type == None: self.window = 1 #window = 1 is the same as applying no window else: #get closest correct window function from user input window_matches = (get_close_matches(self.window, all_windows, cutoff=0.4)) #check if sym=True or sym=False #get window function specified by window input parameter, if no match then window = 1 if window_matches != []: if window_matches[0] == 'hamming': self.window = hamming(self.signal_len, sym=True) self.window_type = "hamming" elif window_matches[0] == "blackman": self.window = blackman(self.signal_len, sym=True) self.window = "blackman" elif window_matches[0] == "blackmanharris": self.window = blackmanharris(self.signal_len, sym=True) #** self.window_type = "blackmanharris" elif window_matches[0] == "bartlett": self.window = bartlett(self.signal_len, sym=True) self.window_type = "bartlett" elif window_matches[0] == "gaussian": self.window = gaussian(self.signal_len, std=7, sym=True) self.window_type = "gaussian" elif window_matches[0] == "kaiser": self.window = kaiser(self.signal_len, beta=14, sym=True) self.window_type = "kaiser" elif window_matches[0] == "hanning": self.window = hanning(self.signal_len, sym=True) self.window_type = "hanning" elif window_matches[0] == "barthann": self.window = barthann(self.signal_len, sym=True) self.window_type = "barthann" elif window_matches[0] == "bohman": self.window = bohman(self.signal_len, sym=True) self.window_type = "bohman" elif window_matches[0] == "chebwin": self.window = chebwin(self.signal_len, sym=True) self.window_type = "chebwin" elif window_matches[0] == "cosine": self.window = cosine(self.signal_len, sym=True) self.window_type = "cosine" elif window_matches[0] == "exponential": self.window = exponential(self.signal_len, sym=True) self.window_type = "exponential" elif window_matches[0] == "flattop": self.window = flattop(self.signal_len, sym=True) self.window_type = "flattop" elif window_matches[0] == "boxcar": self.window = boxcar(self.signal_len, sym=True) self.window_type = "boxcar" elif window_matches[0] == "nuttall": self.window = nuttall(self.signal_len, sym=True) self.window_type = "nuttall" elif window_matches[0] == "parzen": self.window = parzen(self.signal_len, sym=True) self.window_type = "parzen" elif window_matches[0] == "triang": self.window = triang(self.signal_len, sym=True) self.window_type = "triang" elif window_matches[0] == "tukey": self.window = tukey(self.signal_len, sym=True) self.window_type = "tukey" else: self.window = 1 #window = 1 is the same as applying no window #calculate convolution from protein sequences if self.convolution is not None: if self.window is not None: self.convoled_seqs = signal.convolve( self.protein_seqs, self.window, mode='same') / sum( self.window) if self.filter != None: #get closest correct filter from user input filter_matches = (get_close_matches(self.filter, all_filters, cutoff=0.4)) #set filter attribute according to approximate user input if filter_matches != []: if filter_matches[0] == 'savgol': self.filter = savgol_filter(self.signal_len, self.signal_len) elif filter_matches[0] == 'medfilt': self.filter = medfilt(self.signal_len) elif filter_matches[0] == 'symiirorder1': self.filter = symiirorder1(self.signal_len, c0=1, z1=1) elif filter_matches[0] == 'lfilter': self.filter = lfilter(self.signal_len) elif filter_matches[0] == 'hilbert': self.filter = hilbert(self.signal_len) else: self.filter = "" #no filter