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wonambi-python/wonambi
wonambi/ioeeg/ktlx.py
Ktlx.return_dat
def return_dat(self, chan, begsam, endsam): """Read the data based on begsam and endsam. Parameters ---------- chan : list of int list of channel indeces begsam : int index of the first sample endsam : index of the last sample Returns ------- ndarray 2-d matrix with data (might contain NaN) Notes ----- The sample numbering is not based on the samples in the files (i.e. the first sample of the first file is NOT the first sample of the dataset) because it depends on the stamps in the STC file. Usually, the recording starts and after a few millisecond (maybe one second), the actual acquisition starts. STC takes the offset into account. This has the counterintuitive result that if you call read_data, the first few hundreds samples are nan. """ dat = empty((len(chan), endsam - begsam)) dat.fill(NaN) stc, all_stamp = _read_stc(self._filename.with_suffix('.stc')) all_erd = all_stamp['segment_name'].astype('U') # convert to str all_beg = all_stamp['start_stamp'] all_end = all_stamp['end_stamp'] try: begrec = where((all_end >= begsam))[0][0] endrec = where((all_beg < endsam))[0][-1] except IndexError: return dat for rec in range(begrec, endrec + 1): begpos_rec = max(begsam, all_beg[rec]) endpos_rec = min(endsam, all_end[rec] + 1) # check + 1 # this looks weird, but it takes into account whether the values # are outside of the limits of the file d1 = begpos_rec - begsam d2 = endpos_rec - begsam erd_file = (Path(self.filename) / all_erd[rec]).with_suffix('.erd') try: dat_rec = _read_erd(erd_file, begpos_rec, endpos_rec) dat[:, d1:d2] = dat_rec[chan, :] except (FileNotFoundError, PermissionError): lg.warning('{} does not exist'.format(erd_file)) return dat
python
def return_dat(self, chan, begsam, endsam): """Read the data based on begsam and endsam. Parameters ---------- chan : list of int list of channel indeces begsam : int index of the first sample endsam : index of the last sample Returns ------- ndarray 2-d matrix with data (might contain NaN) Notes ----- The sample numbering is not based on the samples in the files (i.e. the first sample of the first file is NOT the first sample of the dataset) because it depends on the stamps in the STC file. Usually, the recording starts and after a few millisecond (maybe one second), the actual acquisition starts. STC takes the offset into account. This has the counterintuitive result that if you call read_data, the first few hundreds samples are nan. """ dat = empty((len(chan), endsam - begsam)) dat.fill(NaN) stc, all_stamp = _read_stc(self._filename.with_suffix('.stc')) all_erd = all_stamp['segment_name'].astype('U') # convert to str all_beg = all_stamp['start_stamp'] all_end = all_stamp['end_stamp'] try: begrec = where((all_end >= begsam))[0][0] endrec = where((all_beg < endsam))[0][-1] except IndexError: return dat for rec in range(begrec, endrec + 1): begpos_rec = max(begsam, all_beg[rec]) endpos_rec = min(endsam, all_end[rec] + 1) # check + 1 # this looks weird, but it takes into account whether the values # are outside of the limits of the file d1 = begpos_rec - begsam d2 = endpos_rec - begsam erd_file = (Path(self.filename) / all_erd[rec]).with_suffix('.erd') try: dat_rec = _read_erd(erd_file, begpos_rec, endpos_rec) dat[:, d1:d2] = dat_rec[chan, :] except (FileNotFoundError, PermissionError): lg.warning('{} does not exist'.format(erd_file)) return dat
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Read the data based on begsam and endsam. Parameters ---------- chan : list of int list of channel indeces begsam : int index of the first sample endsam : index of the last sample Returns ------- ndarray 2-d matrix with data (might contain NaN) Notes ----- The sample numbering is not based on the samples in the files (i.e. the first sample of the first file is NOT the first sample of the dataset) because it depends on the stamps in the STC file. Usually, the recording starts and after a few millisecond (maybe one second), the actual acquisition starts. STC takes the offset into account. This has the counterintuitive result that if you call read_data, the first few hundreds samples are nan.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/ktlx.py#L895-L955
train
23,600
wonambi-python/wonambi
wonambi/ioeeg/ktlx.py
Ktlx.return_markers
def return_markers(self): """Reads the notes of the Ktlx recordings. """ ent_file = self._filename.with_suffix('.ent') if not ent_file.exists(): ent_file = self._filename.with_suffix('.ent.old') try: ent_notes = _read_ent(ent_file) except (FileNotFoundError, PermissionError): markers = [] else: allnote = [] for n in ent_notes: try: n['value'].keys() allnote.append(n['value']) except AttributeError: lg.debug('Note of length {} was not ' 'converted to dict'.format(n['length'])) s_freq = self._hdr['erd']['sample_freq'] pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D' note_time = [] note_name = [] note_note = [] for n in allnote: if n['Text'] == 'Analyzed Data Note': continue if not n['Text']: continue if 'User' not in n['Data'].keys(): continue user1 = n['Data']['User'] == 'Persyst' user2 = False # n['Data']['User'] == 'eeg' user3 = n['Data']['User'] == pcname user4 = n['Data']['User'] == 'XLSpike - Intracranial' user5 = n['Data']['User'] == 'XLEvent - Intracranial' if user1 or user2 or user3 or user4 or user5: continue if len(n['Data']['User']) == 0: note_name.append('-unknown-') else: note_name.append(n['Data']['User'].split()[0]) note_time.append(n['Stamp'] / s_freq) note_note.append(n['Text']) markers = [] for time, name, note in zip(note_time, note_name, note_note): m = {'name': note + ' (' + name + ')', 'start': time, 'end': time, 'chan': None, } markers.append(m) return markers
python
def return_markers(self): """Reads the notes of the Ktlx recordings. """ ent_file = self._filename.with_suffix('.ent') if not ent_file.exists(): ent_file = self._filename.with_suffix('.ent.old') try: ent_notes = _read_ent(ent_file) except (FileNotFoundError, PermissionError): markers = [] else: allnote = [] for n in ent_notes: try: n['value'].keys() allnote.append(n['value']) except AttributeError: lg.debug('Note of length {} was not ' 'converted to dict'.format(n['length'])) s_freq = self._hdr['erd']['sample_freq'] pcname = '0CFEBE72-DA20-4b3a-A8AC-CDD41BFE2F0D' note_time = [] note_name = [] note_note = [] for n in allnote: if n['Text'] == 'Analyzed Data Note': continue if not n['Text']: continue if 'User' not in n['Data'].keys(): continue user1 = n['Data']['User'] == 'Persyst' user2 = False # n['Data']['User'] == 'eeg' user3 = n['Data']['User'] == pcname user4 = n['Data']['User'] == 'XLSpike - Intracranial' user5 = n['Data']['User'] == 'XLEvent - Intracranial' if user1 or user2 or user3 or user4 or user5: continue if len(n['Data']['User']) == 0: note_name.append('-unknown-') else: note_name.append(n['Data']['User'].split()[0]) note_time.append(n['Stamp'] / s_freq) note_note.append(n['Text']) markers = [] for time, name, note in zip(note_time, note_name, note_note): m = {'name': note + ' (' + name + ')', 'start': time, 'end': time, 'chan': None, } markers.append(m) return markers
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Reads the notes of the Ktlx recordings.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/ktlx.py#L1024-L1082
train
23,601
wonambi-python/wonambi
wonambi/ioeeg/blackrock.py
BlackRock.return_markers
def return_markers(self, trigger_bits=8, trigger_zero=True): """We always read triggers as 16bit, but we convert them to 8 here if requested. """ nev_file = splitext(self.filename)[0] + '.nev' markers = _read_neuralev(nev_file, read_markers=True) if trigger_bits == 8: to8 = lambda x: str(int(x) - (256 ** 2 - 256)) for m in markers: m['name'] = to8(m['name']) if trigger_zero: no_zero = (i for i, m in enumerate(markers) if m['name'] != '0') markers_no_zero = [] for i in no_zero: if (i + 1) < len(markers) and markers[i + 1]['name'] == '0': markers[i]['end'] = markers[i + 1]['start'] markers_no_zero.append(markers[i]) return markers_no_zero
python
def return_markers(self, trigger_bits=8, trigger_zero=True): """We always read triggers as 16bit, but we convert them to 8 here if requested. """ nev_file = splitext(self.filename)[0] + '.nev' markers = _read_neuralev(nev_file, read_markers=True) if trigger_bits == 8: to8 = lambda x: str(int(x) - (256 ** 2 - 256)) for m in markers: m['name'] = to8(m['name']) if trigger_zero: no_zero = (i for i, m in enumerate(markers) if m['name'] != '0') markers_no_zero = [] for i in no_zero: if (i + 1) < len(markers) and markers[i + 1]['name'] == '0': markers[i]['end'] = markers[i + 1]['start'] markers_no_zero.append(markers[i]) return markers_no_zero
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/blackrock.py#L144-L166
train
23,602
wonambi-python/wonambi
wonambi/trans/extern/dpss.py
tridi_inverse_iteration
def tridi_inverse_iteration(d, e, w, x0=None, rtol=1e-8): """Perform an inverse iteration to find the eigenvector corresponding to the given eigenvalue in a symmetric tridiagonal system. Parameters ---------- d : ndarray main diagonal of the tridiagonal system e : ndarray offdiagonal stored in e[:-1] w : float eigenvalue of the eigenvector x0 : ndarray initial point to start the iteration rtol : float tolerance for the norm of the difference of iterates Returns ------- e : ndarray The converged eigenvector """ eig_diag = d - w if x0 is None: x0 = np.random.randn(len(d)) x_prev = np.zeros_like(x0) norm_x = np.linalg.norm(x0) # the eigenvector is unique up to sign change, so iterate # until || |x^(n)| - |x^(n-1)| ||^2 < rtol x0 /= norm_x while np.linalg.norm(np.abs(x0) - np.abs(x_prev)) > rtol: x_prev = x0.copy() tridisolve(eig_diag, e, x0) norm_x = np.linalg.norm(x0) x0 /= norm_x return x0
python
def tridi_inverse_iteration(d, e, w, x0=None, rtol=1e-8): """Perform an inverse iteration to find the eigenvector corresponding to the given eigenvalue in a symmetric tridiagonal system. Parameters ---------- d : ndarray main diagonal of the tridiagonal system e : ndarray offdiagonal stored in e[:-1] w : float eigenvalue of the eigenvector x0 : ndarray initial point to start the iteration rtol : float tolerance for the norm of the difference of iterates Returns ------- e : ndarray The converged eigenvector """ eig_diag = d - w if x0 is None: x0 = np.random.randn(len(d)) x_prev = np.zeros_like(x0) norm_x = np.linalg.norm(x0) # the eigenvector is unique up to sign change, so iterate # until || |x^(n)| - |x^(n-1)| ||^2 < rtol x0 /= norm_x while np.linalg.norm(np.abs(x0) - np.abs(x_prev)) > rtol: x_prev = x0.copy() tridisolve(eig_diag, e, x0) norm_x = np.linalg.norm(x0) x0 /= norm_x return x0
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Perform an inverse iteration to find the eigenvector corresponding to the given eigenvalue in a symmetric tridiagonal system. Parameters ---------- d : ndarray main diagonal of the tridiagonal system e : ndarray offdiagonal stored in e[:-1] w : float eigenvalue of the eigenvector x0 : ndarray initial point to start the iteration rtol : float tolerance for the norm of the difference of iterates Returns ------- e : ndarray The converged eigenvector
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/extern/dpss.py#L136-L174
train
23,603
wonambi-python/wonambi
wonambi/trans/extern/dpss.py
tridisolve
def tridisolve(d, e, b, overwrite_b=True): """ Symmetric tridiagonal system solver, from Golub and Van Loan, Matrix Computations pg 157 Parameters ---------- d : ndarray main diagonal stored in d[:] e : ndarray superdiagonal stored in e[:-1] b : ndarray RHS vector Returns ------- x : ndarray Solution to Ax = b (if overwrite_b is False). Otherwise solution is stored in previous RHS vector b """ N = len(b) # work vectors dw = d.copy() ew = e.copy() if overwrite_b: x = b else: x = b.copy() for k in range(1, N): # e^(k-1) = e(k-1) / d(k-1) # d(k) = d(k) - e^(k-1)e(k-1) / d(k-1) t = ew[k - 1] ew[k - 1] = t / dw[k - 1] dw[k] = dw[k] - t * ew[k - 1] for k in range(1, N): x[k] = x[k] - ew[k - 1] * x[k - 1] x[N - 1] = x[N - 1] / dw[N - 1] for k in range(N - 2, -1, -1): x[k] = x[k] / dw[k] - ew[k] * x[k + 1] if not overwrite_b: return x
python
def tridisolve(d, e, b, overwrite_b=True): """ Symmetric tridiagonal system solver, from Golub and Van Loan, Matrix Computations pg 157 Parameters ---------- d : ndarray main diagonal stored in d[:] e : ndarray superdiagonal stored in e[:-1] b : ndarray RHS vector Returns ------- x : ndarray Solution to Ax = b (if overwrite_b is False). Otherwise solution is stored in previous RHS vector b """ N = len(b) # work vectors dw = d.copy() ew = e.copy() if overwrite_b: x = b else: x = b.copy() for k in range(1, N): # e^(k-1) = e(k-1) / d(k-1) # d(k) = d(k) - e^(k-1)e(k-1) / d(k-1) t = ew[k - 1] ew[k - 1] = t / dw[k - 1] dw[k] = dw[k] - t * ew[k - 1] for k in range(1, N): x[k] = x[k] - ew[k - 1] * x[k - 1] x[N - 1] = x[N - 1] / dw[N - 1] for k in range(N - 2, -1, -1): x[k] = x[k] / dw[k] - ew[k] * x[k + 1] if not overwrite_b: return x
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Symmetric tridiagonal system solver, from Golub and Van Loan, Matrix Computations pg 157 Parameters ---------- d : ndarray main diagonal stored in d[:] e : ndarray superdiagonal stored in e[:-1] b : ndarray RHS vector Returns ------- x : ndarray Solution to Ax = b (if overwrite_b is False). Otherwise solution is stored in previous RHS vector b
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/extern/dpss.py#L177-L221
train
23,604
wonambi-python/wonambi
wonambi/trans/extern/dpss.py
autocov
def autocov(x, **kwargs): """Returns the autocovariance of signal s at all lags. Parameters ---------- x : ndarray axis : time axis all_lags : {True/False} whether to return all nonzero lags, or to clip the length of r_xy to be the length of x and y. If False, then the zero lag correlation is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2 Returns ------- cxx : ndarray The autocovariance function Notes ----- Adheres to the definition .. math:: C_{xx}[k]=E\{(X[n+k]-E\{X\})(X[n]-E\{X\})^{*}\} where X is a discrete, stationary (ergodic) random process """ # only remove the mean once, if needed debias = kwargs.pop('debias', True) axis = kwargs.get('axis', -1) if debias: x = remove_bias(x, axis) kwargs['debias'] = False return crosscov(x, x, **kwargs)
python
def autocov(x, **kwargs): """Returns the autocovariance of signal s at all lags. Parameters ---------- x : ndarray axis : time axis all_lags : {True/False} whether to return all nonzero lags, or to clip the length of r_xy to be the length of x and y. If False, then the zero lag correlation is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2 Returns ------- cxx : ndarray The autocovariance function Notes ----- Adheres to the definition .. math:: C_{xx}[k]=E\{(X[n+k]-E\{X\})(X[n]-E\{X\})^{*}\} where X is a discrete, stationary (ergodic) random process """ # only remove the mean once, if needed debias = kwargs.pop('debias', True) axis = kwargs.get('axis', -1) if debias: x = remove_bias(x, axis) kwargs['debias'] = False return crosscov(x, x, **kwargs)
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Returns the autocovariance of signal s at all lags. Parameters ---------- x : ndarray axis : time axis all_lags : {True/False} whether to return all nonzero lags, or to clip the length of r_xy to be the length of x and y. If False, then the zero lag correlation is at index 0. Otherwise, it is found at (len(x) + len(y) - 1)/2 Returns ------- cxx : ndarray The autocovariance function Notes ----- Adheres to the definition .. math:: C_{xx}[k]=E\{(X[n+k]-E\{X\})(X[n]-E\{X\})^{*}\} where X is a discrete, stationary (ergodic) random process
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/extern/dpss.py#L255-L291
train
23,605
wonambi-python/wonambi
wonambi/trans/extern/dpss.py
fftconvolve
def fftconvolve(in1, in2, mode="full", axis=None): """ Convolve two N-dimensional arrays using FFT. See convolve. This is a fix of scipy.signal.fftconvolve, adding an axis argument and importing locally the stuff only needed for this function """ s1 = np.array(in1.shape) s2 = np.array(in2.shape) complex_result = (np.issubdtype(in1.dtype, np.complexfloating) or np.issubdtype(in2.dtype, np.complexfloating)) if axis is None: size = s1 + s2 - 1 fslice = tuple([slice(0, int(sz)) for sz in size]) else: equal_shapes = s1 == s2 # allow equal_shapes[axis] to be False equal_shapes[axis] = True assert equal_shapes.all(), 'Shape mismatch on non-convolving axes' size = s1[axis] + s2[axis] - 1 fslice = [slice(l) for l in s1] fslice[axis] = slice(0, int(size)) fslice = tuple(fslice) # Always use 2**n-sized FFT fsize = 2 ** int(np.ceil(np.log2(size))) if axis is None: IN1 = fftpack.fftn(in1, fsize) IN1 *= fftpack.fftn(in2, fsize) ret = fftpack.ifftn(IN1)[fslice].copy() else: IN1 = fftpack.fft(in1, fsize, axis=axis) IN1 *= fftpack.fft(in2, fsize, axis=axis) ret = fftpack.ifft(IN1, axis=axis)[fslice].copy() del IN1 if not complex_result: ret = ret.real if mode == "full": return ret elif mode == "same": if np.product(s1, axis=0) > np.product(s2, axis=0): osize = s1 else: osize = s2 return signaltools._centered(ret, osize) elif mode == "valid": return signaltools._centered(ret, abs(s2 - s1) + 1)
python
def fftconvolve(in1, in2, mode="full", axis=None): """ Convolve two N-dimensional arrays using FFT. See convolve. This is a fix of scipy.signal.fftconvolve, adding an axis argument and importing locally the stuff only needed for this function """ s1 = np.array(in1.shape) s2 = np.array(in2.shape) complex_result = (np.issubdtype(in1.dtype, np.complexfloating) or np.issubdtype(in2.dtype, np.complexfloating)) if axis is None: size = s1 + s2 - 1 fslice = tuple([slice(0, int(sz)) for sz in size]) else: equal_shapes = s1 == s2 # allow equal_shapes[axis] to be False equal_shapes[axis] = True assert equal_shapes.all(), 'Shape mismatch on non-convolving axes' size = s1[axis] + s2[axis] - 1 fslice = [slice(l) for l in s1] fslice[axis] = slice(0, int(size)) fslice = tuple(fslice) # Always use 2**n-sized FFT fsize = 2 ** int(np.ceil(np.log2(size))) if axis is None: IN1 = fftpack.fftn(in1, fsize) IN1 *= fftpack.fftn(in2, fsize) ret = fftpack.ifftn(IN1)[fslice].copy() else: IN1 = fftpack.fft(in1, fsize, axis=axis) IN1 *= fftpack.fft(in2, fsize, axis=axis) ret = fftpack.ifft(IN1, axis=axis)[fslice].copy() del IN1 if not complex_result: ret = ret.real if mode == "full": return ret elif mode == "same": if np.product(s1, axis=0) > np.product(s2, axis=0): osize = s1 else: osize = s2 return signaltools._centered(ret, osize) elif mode == "valid": return signaltools._centered(ret, abs(s2 - s1) + 1)
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/extern/dpss.py#L352-L399
train
23,606
wonambi-python/wonambi
wonambi/trans/frequency.py
band_power
def band_power(data, freq, scaling='power', n_fft=None, detrend=None, array_out=False): """Compute power or energy acoss a frequency band, and its peak frequency. Power is estimated using the mid-point rectangle rule. Input can be ChanTime or ChanFreq. Parameters ---------- data : instance of ChanTime or ChanFreq data to be analyzed, one trial only freq : tuple of float Frequencies for band of interest. Power will be integrated across this band, inclusively, and peak frequency determined within it. If a value is None, the band is unbounded in that direction. input_type : str 'time' or 'spectrum' scaling : str 'power' or 'energy', only used if data is ChanTime n_fft : int length of FFT. if shorter than input signal, signal is truncated; if longer, signal is zero-padded to length array_out : bool if True, will return two arrays instead of two dict. Returns ------- dict of float, or ndarray keys are channels, values are power or energy dict of float, or ndarray keys are channels, values are respective peak frequency """ if not array_out: power = {} peakf = {} else: power = zeros((data.number_of('chan')[0], 1)) peakf = zeros((data.number_of('chan')[0], 1)) if isinstance(data, ChanFreq): Sxx = data elif isinstance(data, ChanTime): Sxx = frequency(data, scaling=scaling, n_fft=n_fft, detrend=detrend) else: raise ValueError('Invalid data type') if detrend is None: if 'power' == scaling: detrend = 'linear' elif 'energy' == scaling: detrend = None sf = Sxx.axis['freq'][0] f_res = sf[1] - sf[0] # frequency resolution if freq[0] is not None: idx_f1 = asarray([abs(x - freq[0]) for x in sf]).argmin() else: idx_f1 = 0 if freq[1] is not None: idx_f2 = min(asarray([abs(x - freq[1]) for x in sf]).argmin() + 1, len(sf) - 1) # inclusive, to follow convention else: idx_f2 = len(sf) - 1 for i, chan in enumerate(Sxx.axis['chan'][0]): s = Sxx(chan=chan)[0] pw = sum(s[idx_f1:idx_f2]) * f_res idx_peak = s[idx_f1:idx_f2].argmax() pf = sf[idx_f1:idx_f2][idx_peak] if array_out: power[i, 0] = pw peakf[i, 0] = pf else: power[chan] = pw peakf[chan] = pf return power, peakf
python
def band_power(data, freq, scaling='power', n_fft=None, detrend=None, array_out=False): """Compute power or energy acoss a frequency band, and its peak frequency. Power is estimated using the mid-point rectangle rule. Input can be ChanTime or ChanFreq. Parameters ---------- data : instance of ChanTime or ChanFreq data to be analyzed, one trial only freq : tuple of float Frequencies for band of interest. Power will be integrated across this band, inclusively, and peak frequency determined within it. If a value is None, the band is unbounded in that direction. input_type : str 'time' or 'spectrum' scaling : str 'power' or 'energy', only used if data is ChanTime n_fft : int length of FFT. if shorter than input signal, signal is truncated; if longer, signal is zero-padded to length array_out : bool if True, will return two arrays instead of two dict. Returns ------- dict of float, or ndarray keys are channels, values are power or energy dict of float, or ndarray keys are channels, values are respective peak frequency """ if not array_out: power = {} peakf = {} else: power = zeros((data.number_of('chan')[0], 1)) peakf = zeros((data.number_of('chan')[0], 1)) if isinstance(data, ChanFreq): Sxx = data elif isinstance(data, ChanTime): Sxx = frequency(data, scaling=scaling, n_fft=n_fft, detrend=detrend) else: raise ValueError('Invalid data type') if detrend is None: if 'power' == scaling: detrend = 'linear' elif 'energy' == scaling: detrend = None sf = Sxx.axis['freq'][0] f_res = sf[1] - sf[0] # frequency resolution if freq[0] is not None: idx_f1 = asarray([abs(x - freq[0]) for x in sf]).argmin() else: idx_f1 = 0 if freq[1] is not None: idx_f2 = min(asarray([abs(x - freq[1]) for x in sf]).argmin() + 1, len(sf) - 1) # inclusive, to follow convention else: idx_f2 = len(sf) - 1 for i, chan in enumerate(Sxx.axis['chan'][0]): s = Sxx(chan=chan)[0] pw = sum(s[idx_f1:idx_f2]) * f_res idx_peak = s[idx_f1:idx_f2].argmax() pf = sf[idx_f1:idx_f2][idx_peak] if array_out: power[i, 0] = pw peakf[i, 0] = pf else: power[chan] = pw peakf[chan] = pf return power, peakf
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Compute power or energy acoss a frequency band, and its peak frequency. Power is estimated using the mid-point rectangle rule. Input can be ChanTime or ChanFreq. Parameters ---------- data : instance of ChanTime or ChanFreq data to be analyzed, one trial only freq : tuple of float Frequencies for band of interest. Power will be integrated across this band, inclusively, and peak frequency determined within it. If a value is None, the band is unbounded in that direction. input_type : str 'time' or 'spectrum' scaling : str 'power' or 'energy', only used if data is ChanTime n_fft : int length of FFT. if shorter than input signal, signal is truncated; if longer, signal is zero-padded to length array_out : bool if True, will return two arrays instead of two dict. Returns ------- dict of float, or ndarray keys are channels, values are power or energy dict of float, or ndarray keys are channels, values are respective peak frequency
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/frequency.py#L346-L424
train
23,607
wonambi-python/wonambi
wonambi/trans/frequency.py
_create_morlet
def _create_morlet(options, s_freq): """Create morlet wavelets, with scipy.signal doing the actual computation. Parameters ---------- foi : ndarray or list or tuple vector with frequency of interest s_freq : int or float sampling frequency of the data options : dict with 'M_in_s' (duration of the wavelet in seconds) and 'w' (Omega0) Returns ------- ndarray nFreq X nSamples matrix containing the complex Morlet wavelets. """ wavelets = [] foi = options.pop('foi') for f in foi: wavelets.append(morlet(f, s_freq, **options)) return wavelets
python
def _create_morlet(options, s_freq): """Create morlet wavelets, with scipy.signal doing the actual computation. Parameters ---------- foi : ndarray or list or tuple vector with frequency of interest s_freq : int or float sampling frequency of the data options : dict with 'M_in_s' (duration of the wavelet in seconds) and 'w' (Omega0) Returns ------- ndarray nFreq X nSamples matrix containing the complex Morlet wavelets. """ wavelets = [] foi = options.pop('foi') for f in foi: wavelets.append(morlet(f, s_freq, **options)) return wavelets
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Create morlet wavelets, with scipy.signal doing the actual computation. Parameters ---------- foi : ndarray or list or tuple vector with frequency of interest s_freq : int or float sampling frequency of the data options : dict with 'M_in_s' (duration of the wavelet in seconds) and 'w' (Omega0) Returns ------- ndarray nFreq X nSamples matrix containing the complex Morlet wavelets.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/frequency.py#L427-L450
train
23,608
wonambi-python/wonambi
wonambi/trans/frequency.py
morlet
def morlet(freq, s_freq, ratio=5, sigma_f=None, dur_in_sd=4, dur_in_s=None, normalization='peak', zero_mean=False): """Create a Morlet wavelet. Parameters ---------- freq : float central frequency of the wavelet s_freq : int sampling frequency ratio : float ratio for a wavelet family ( = freq / sigma_f) sigma_f : float standard deviation of the wavelet in frequency domain dur_in_sd : float duration of the wavelet, given as number of the standard deviation in the time domain, in one side. dur_in_s : float total duration of the wavelet, two-sided (i.e. from start to finish) normalization : str 'area' means that energy is normalized to 1, 'peak' means that the peak is set at 1, 'max' is a normalization used by nitime which does not change max value of output when you change sigma_f. zero_mean : bool make sure that the wavelet has zero mean (only relevant if ratio < 5) Returns ------- ndarray vector containing the complex Morlet wavelets Notes ----- 'ratio' and 'sigma_f' are mutually exclusive. If you use 'sigma_f', the standard deviation stays the same for all the frequency. It's more common to specify a constant ratio for the wavelet family, so that the frequency resolution changes with the frequency of interest. 'dur_in_sd' and 'dur_in_s' are mutually exclusive. 'dur_in_s' specifies the total duration (from start to finish) of the window. 'dur_in_sd' calculates the total duration as the length in standard deviations in the time domain: dur_in_s = dur_in_sd * 2 * sigma_t, with sigma_t = 1 / (2 * pi * sigma_f) """ if sigma_f is None: sigma_f = freq / ratio else: ratio = freq / sigma_f sigma_t = 1 / (2 * pi * sigma_f) if ratio < 5 and not zero_mean: lg.info('The wavelet won\'t have zero mean, set zero_mean=True to ' 'correct it') if dur_in_s is None: dur_in_s = sigma_t * dur_in_sd * 2 t = arange(-dur_in_s / 2, dur_in_s / 2, 1 / s_freq) w = exp(1j * 2 * pi * freq * t) if zero_mean: w -= exp(-1 / 2 * ratio ** 2) w *= exp(-t ** 2 / (2 * sigma_t ** 2)) if normalization == 'area': w /= sqrt(sqrt(pi) * sigma_t * s_freq) elif normalization == 'max': w /= 2 * sigma_t * sqrt(2 * pi) / s_freq elif normalization == 'peak': pass lg.info('At freq {0: 9.3f}Hz, sigma_f={1: 9.3f}Hz, sigma_t={2: 9.3f}s, ' 'total duration={3: 9.3f}s'.format(freq, sigma_f, sigma_t, dur_in_s)) lg.debug(' Real peak={0: 9.3f}, Mean={1: 12.6f}, ' 'Energy={2: 9.3f}'.format(max(real(w)), mean(w), norm(w) ** 2)) return w
python
def morlet(freq, s_freq, ratio=5, sigma_f=None, dur_in_sd=4, dur_in_s=None, normalization='peak', zero_mean=False): """Create a Morlet wavelet. Parameters ---------- freq : float central frequency of the wavelet s_freq : int sampling frequency ratio : float ratio for a wavelet family ( = freq / sigma_f) sigma_f : float standard deviation of the wavelet in frequency domain dur_in_sd : float duration of the wavelet, given as number of the standard deviation in the time domain, in one side. dur_in_s : float total duration of the wavelet, two-sided (i.e. from start to finish) normalization : str 'area' means that energy is normalized to 1, 'peak' means that the peak is set at 1, 'max' is a normalization used by nitime which does not change max value of output when you change sigma_f. zero_mean : bool make sure that the wavelet has zero mean (only relevant if ratio < 5) Returns ------- ndarray vector containing the complex Morlet wavelets Notes ----- 'ratio' and 'sigma_f' are mutually exclusive. If you use 'sigma_f', the standard deviation stays the same for all the frequency. It's more common to specify a constant ratio for the wavelet family, so that the frequency resolution changes with the frequency of interest. 'dur_in_sd' and 'dur_in_s' are mutually exclusive. 'dur_in_s' specifies the total duration (from start to finish) of the window. 'dur_in_sd' calculates the total duration as the length in standard deviations in the time domain: dur_in_s = dur_in_sd * 2 * sigma_t, with sigma_t = 1 / (2 * pi * sigma_f) """ if sigma_f is None: sigma_f = freq / ratio else: ratio = freq / sigma_f sigma_t = 1 / (2 * pi * sigma_f) if ratio < 5 and not zero_mean: lg.info('The wavelet won\'t have zero mean, set zero_mean=True to ' 'correct it') if dur_in_s is None: dur_in_s = sigma_t * dur_in_sd * 2 t = arange(-dur_in_s / 2, dur_in_s / 2, 1 / s_freq) w = exp(1j * 2 * pi * freq * t) if zero_mean: w -= exp(-1 / 2 * ratio ** 2) w *= exp(-t ** 2 / (2 * sigma_t ** 2)) if normalization == 'area': w /= sqrt(sqrt(pi) * sigma_t * s_freq) elif normalization == 'max': w /= 2 * sigma_t * sqrt(2 * pi) / s_freq elif normalization == 'peak': pass lg.info('At freq {0: 9.3f}Hz, sigma_f={1: 9.3f}Hz, sigma_t={2: 9.3f}s, ' 'total duration={3: 9.3f}s'.format(freq, sigma_f, sigma_t, dur_in_s)) lg.debug(' Real peak={0: 9.3f}, Mean={1: 12.6f}, ' 'Energy={2: 9.3f}'.format(max(real(w)), mean(w), norm(w) ** 2)) return w
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Create a Morlet wavelet. Parameters ---------- freq : float central frequency of the wavelet s_freq : int sampling frequency ratio : float ratio for a wavelet family ( = freq / sigma_f) sigma_f : float standard deviation of the wavelet in frequency domain dur_in_sd : float duration of the wavelet, given as number of the standard deviation in the time domain, in one side. dur_in_s : float total duration of the wavelet, two-sided (i.e. from start to finish) normalization : str 'area' means that energy is normalized to 1, 'peak' means that the peak is set at 1, 'max' is a normalization used by nitime which does not change max value of output when you change sigma_f. zero_mean : bool make sure that the wavelet has zero mean (only relevant if ratio < 5) Returns ------- ndarray vector containing the complex Morlet wavelets Notes ----- 'ratio' and 'sigma_f' are mutually exclusive. If you use 'sigma_f', the standard deviation stays the same for all the frequency. It's more common to specify a constant ratio for the wavelet family, so that the frequency resolution changes with the frequency of interest. 'dur_in_sd' and 'dur_in_s' are mutually exclusive. 'dur_in_s' specifies the total duration (from start to finish) of the window. 'dur_in_sd' calculates the total duration as the length in standard deviations in the time domain: dur_in_s = dur_in_sd * 2 * sigma_t, with sigma_t = 1 / (2 * pi * sigma_f)
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/frequency.py#L453-L530
train
23,609
wonambi-python/wonambi
wonambi/widgets/modal_widgets.py
ChannelDialog.create_widgets
def create_widgets(self): """Build basic components of dialog.""" self.bbox = QDialogButtonBox( QDialogButtonBox.Ok | QDialogButtonBox.Cancel) self.idx_ok = self.bbox.button(QDialogButtonBox.Ok) self.idx_cancel = self.bbox.button(QDialogButtonBox.Cancel) self.idx_group = FormMenu([gr['name'] for gr in self.groups]) chan_box = QListWidget() self.idx_chan = chan_box stage_box = QListWidget() stage_box.addItems(STAGE_NAME) stage_box.setSelectionMode(QAbstractItemView.ExtendedSelection) self.idx_stage = stage_box cycle_box = QListWidget() cycle_box.setSelectionMode(QAbstractItemView.ExtendedSelection) self.idx_cycle = cycle_box
python
def create_widgets(self): """Build basic components of dialog.""" self.bbox = QDialogButtonBox( QDialogButtonBox.Ok | QDialogButtonBox.Cancel) self.idx_ok = self.bbox.button(QDialogButtonBox.Ok) self.idx_cancel = self.bbox.button(QDialogButtonBox.Cancel) self.idx_group = FormMenu([gr['name'] for gr in self.groups]) chan_box = QListWidget() self.idx_chan = chan_box stage_box = QListWidget() stage_box.addItems(STAGE_NAME) stage_box.setSelectionMode(QAbstractItemView.ExtendedSelection) self.idx_stage = stage_box cycle_box = QListWidget() cycle_box.setSelectionMode(QAbstractItemView.ExtendedSelection) self.idx_cycle = cycle_box
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Build basic components of dialog.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/modal_widgets.py#L58-L77
train
23,610
wonambi-python/wonambi
wonambi/widgets/modal_widgets.py
ChannelDialog.update_groups
def update_groups(self): """Update the channel groups list when dialog is opened.""" self.groups = self.parent.channels.groups self.idx_group.clear() for gr in self.groups: self.idx_group.addItem(gr['name']) self.update_channels()
python
def update_groups(self): """Update the channel groups list when dialog is opened.""" self.groups = self.parent.channels.groups self.idx_group.clear() for gr in self.groups: self.idx_group.addItem(gr['name']) self.update_channels()
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Update the channel groups list when dialog is opened.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/modal_widgets.py#L79-L86
train
23,611
wonambi-python/wonambi
wonambi/widgets/modal_widgets.py
ChannelDialog.update_channels
def update_channels(self): """Update the channels list when a new group is selected.""" group_dict = {k['name']: i for i, k in enumerate(self.groups)} group_index = group_dict[self.idx_group.currentText()] self.one_grp = self.groups[group_index] self.idx_chan.clear() self.idx_chan.setSelectionMode(QAbstractItemView.ExtendedSelection) for chan in self.one_grp['chan_to_plot']: name = chan + '—(' + '+'.join(self.one_grp['ref_chan']) + ')' item = QListWidgetItem(name) self.idx_chan.addItem(item)
python
def update_channels(self): """Update the channels list when a new group is selected.""" group_dict = {k['name']: i for i, k in enumerate(self.groups)} group_index = group_dict[self.idx_group.currentText()] self.one_grp = self.groups[group_index] self.idx_chan.clear() self.idx_chan.setSelectionMode(QAbstractItemView.ExtendedSelection) for chan in self.one_grp['chan_to_plot']: name = chan + '—(' + '+'.join(self.one_grp['ref_chan']) + ')' item = QListWidgetItem(name) self.idx_chan.addItem(item)
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Update the channels list when a new group is selected.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/modal_widgets.py#L88-L100
train
23,612
wonambi-python/wonambi
wonambi/widgets/modal_widgets.py
ChannelDialog.update_cycles
def update_cycles(self): """Enable cycles checkbox only if there are cycles marked, with no errors.""" self.idx_cycle.clear() try: self.cycles = self.parent.notes.annot.get_cycles() except ValueError as err: self.idx_cycle.setEnabled(False) msg = 'There is a problem with the cycle markers: ' + str(err) self.parent.statusBar().showMessage(msg) else: if self.cycles is None: self.idx_cycle.setEnabled(False) else: self.idx_cycle.setEnabled(True) for i in range(len(self.cycles)): self.idx_cycle.addItem(str(i+1))
python
def update_cycles(self): """Enable cycles checkbox only if there are cycles marked, with no errors.""" self.idx_cycle.clear() try: self.cycles = self.parent.notes.annot.get_cycles() except ValueError as err: self.idx_cycle.setEnabled(False) msg = 'There is a problem with the cycle markers: ' + str(err) self.parent.statusBar().showMessage(msg) else: if self.cycles is None: self.idx_cycle.setEnabled(False) else: self.idx_cycle.setEnabled(True) for i in range(len(self.cycles)): self.idx_cycle.addItem(str(i+1))
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Enable cycles checkbox only if there are cycles marked, with no errors.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/modal_widgets.py#L102-L121
train
23,613
wonambi-python/wonambi
wonambi/widgets/traces.py
_create_data_to_plot
def _create_data_to_plot(data, chan_groups): """Create data after montage and filtering. Parameters ---------- data : instance of ChanTime the raw data chan_groups : list of dict information about channels to plot, to use as reference and about filtering etc. Returns ------- instance of ChanTime data ready to be plotted. """ # chan_to_plot only gives the number of channels to plot, for prealloc chan_to_plot = [one_chan for one_grp in chan_groups for one_chan in one_grp['chan_to_plot']] output = ChanTime() output.s_freq = data.s_freq output.start_time = data.start_time output.axis['time'] = data.axis['time'] output.axis['chan'] = empty(1, dtype='O') output.data = empty(1, dtype='O') output.data[0] = empty((len(chan_to_plot), data.number_of('time')[0]), dtype='f') all_chan_grp_name = [] i_ch = 0 for one_grp in chan_groups: sel_data = _select_channels(data, one_grp['chan_to_plot'] + one_grp['ref_chan']) data1 = montage(sel_data, ref_chan=one_grp['ref_chan']) data1.data[0] = nan_to_num(data1.data[0]) if one_grp['hp'] is not None: data1 = filter_(data1, low_cut=one_grp['hp']) if one_grp['lp'] is not None: data1 = filter_(data1, high_cut=one_grp['lp']) for chan in one_grp['chan_to_plot']: chan_grp_name = chan + ' (' + one_grp['name'] + ')' all_chan_grp_name.append(chan_grp_name) dat = data1(chan=chan, trial=0) dat = dat - nanmean(dat) output.data[0][i_ch, :] = dat * one_grp['scale'] i_ch += 1 output.axis['chan'][0] = asarray(all_chan_grp_name, dtype='U') return output
python
def _create_data_to_plot(data, chan_groups): """Create data after montage and filtering. Parameters ---------- data : instance of ChanTime the raw data chan_groups : list of dict information about channels to plot, to use as reference and about filtering etc. Returns ------- instance of ChanTime data ready to be plotted. """ # chan_to_plot only gives the number of channels to plot, for prealloc chan_to_plot = [one_chan for one_grp in chan_groups for one_chan in one_grp['chan_to_plot']] output = ChanTime() output.s_freq = data.s_freq output.start_time = data.start_time output.axis['time'] = data.axis['time'] output.axis['chan'] = empty(1, dtype='O') output.data = empty(1, dtype='O') output.data[0] = empty((len(chan_to_plot), data.number_of('time')[0]), dtype='f') all_chan_grp_name = [] i_ch = 0 for one_grp in chan_groups: sel_data = _select_channels(data, one_grp['chan_to_plot'] + one_grp['ref_chan']) data1 = montage(sel_data, ref_chan=one_grp['ref_chan']) data1.data[0] = nan_to_num(data1.data[0]) if one_grp['hp'] is not None: data1 = filter_(data1, low_cut=one_grp['hp']) if one_grp['lp'] is not None: data1 = filter_(data1, high_cut=one_grp['lp']) for chan in one_grp['chan_to_plot']: chan_grp_name = chan + ' (' + one_grp['name'] + ')' all_chan_grp_name.append(chan_grp_name) dat = data1(chan=chan, trial=0) dat = dat - nanmean(dat) output.data[0][i_ch, :] = dat * one_grp['scale'] i_ch += 1 output.axis['chan'][0] = asarray(all_chan_grp_name, dtype='U') return output
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Create data after montage and filtering. Parameters ---------- data : instance of ChanTime the raw data chan_groups : list of dict information about channels to plot, to use as reference and about filtering etc. Returns ------- instance of ChanTime data ready to be plotted.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L1173-L1231
train
23,614
wonambi-python/wonambi
wonambi/widgets/traces.py
_convert_timestr_to_seconds
def _convert_timestr_to_seconds(time_str, rec_start): """Convert input from user about time string to an absolute time for the recordings. Parameters ---------- time_str : str time information as '123' or '22:30' or '22:30:22' rec_start: instance of datetime absolute start time of the recordings. Returns ------- int start time of the window, in s, from the start of the recordings Raises ------ ValueError if it cannot convert the string """ if not CHECK_TIME_STR.match(time_str): raise ValueError('Input can only contain digits and colons') if ':' in time_str: time_split = [int(x) for x in time_str.split(':')] # if it's in 'HH:MM' format, add ':SS' if len(time_split) == 2: time_split.append(0) clock_time = time(*time_split) chosen_start = datetime.combine(rec_start.date(), clock_time) # if the clock time is after start of the recordings, assume it's the next day if clock_time < rec_start.time(): chosen_start += timedelta(days=1) window_start = int((chosen_start - rec_start).total_seconds()) else: window_start = int(time_str) return window_start
python
def _convert_timestr_to_seconds(time_str, rec_start): """Convert input from user about time string to an absolute time for the recordings. Parameters ---------- time_str : str time information as '123' or '22:30' or '22:30:22' rec_start: instance of datetime absolute start time of the recordings. Returns ------- int start time of the window, in s, from the start of the recordings Raises ------ ValueError if it cannot convert the string """ if not CHECK_TIME_STR.match(time_str): raise ValueError('Input can only contain digits and colons') if ':' in time_str: time_split = [int(x) for x in time_str.split(':')] # if it's in 'HH:MM' format, add ':SS' if len(time_split) == 2: time_split.append(0) clock_time = time(*time_split) chosen_start = datetime.combine(rec_start.date(), clock_time) # if the clock time is after start of the recordings, assume it's the next day if clock_time < rec_start.time(): chosen_start += timedelta(days=1) window_start = int((chosen_start - rec_start).total_seconds()) else: window_start = int(time_str) return window_start
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Convert input from user about time string to an absolute time for the recordings. Parameters ---------- time_str : str time information as '123' or '22:30' or '22:30:22' rec_start: instance of datetime absolute start time of the recordings. Returns ------- int start time of the window, in s, from the start of the recordings Raises ------ ValueError if it cannot convert the string
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L1234-L1275
train
23,615
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.read_data
def read_data(self): """Read the data to plot.""" window_start = self.parent.value('window_start') window_end = window_start + self.parent.value('window_length') dataset = self.parent.info.dataset groups = self.parent.channels.groups chan_to_read = [] for one_grp in groups: chan_to_read.extend(one_grp['chan_to_plot'] + one_grp['ref_chan']) if not chan_to_read: return data = dataset.read_data(chan=chan_to_read, begtime=window_start, endtime=window_end) max_s_freq = self.parent.value('max_s_freq') if data.s_freq > max_s_freq: q = int(data.s_freq / max_s_freq) lg.debug('Decimate (no low-pass filter) at ' + str(q)) data.data[0] = data.data[0][:, slice(None, None, q)] data.axis['time'][0] = data.axis['time'][0][slice(None, None, q)] data.s_freq = int(data.s_freq / q) self.data = _create_data_to_plot(data, self.parent.channels.groups)
python
def read_data(self): """Read the data to plot.""" window_start = self.parent.value('window_start') window_end = window_start + self.parent.value('window_length') dataset = self.parent.info.dataset groups = self.parent.channels.groups chan_to_read = [] for one_grp in groups: chan_to_read.extend(one_grp['chan_to_plot'] + one_grp['ref_chan']) if not chan_to_read: return data = dataset.read_data(chan=chan_to_read, begtime=window_start, endtime=window_end) max_s_freq = self.parent.value('max_s_freq') if data.s_freq > max_s_freq: q = int(data.s_freq / max_s_freq) lg.debug('Decimate (no low-pass filter) at ' + str(q)) data.data[0] = data.data[0][:, slice(None, None, q)] data.axis['time'][0] = data.axis['time'][0][slice(None, None, q)] data.s_freq = int(data.s_freq / q) self.data = _create_data_to_plot(data, self.parent.channels.groups)
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Read the data to plot.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L329-L355
train
23,616
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.display
def display(self): """Display the recordings.""" if self.data is None: return if self.scene is not None: self.y_scrollbar_value = self.verticalScrollBar().value() self.scene.clear() self.create_chan_labels() self.create_time_labels() window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') time_height = max([x.boundingRect().height() for x in self.idx_time]) label_width = window_length * self.parent.value('label_ratio') scene_height = (len(self.idx_label) * self.parent.value('y_distance') + time_height) self.scene = QGraphicsScene(window_start - label_width, 0, window_length + label_width, scene_height) self.setScene(self.scene) self.idx_markers = [] self.idx_annot = [] self.idx_annot_labels = [] self.add_chan_labels() self.add_time_labels() self.add_traces() self.display_grid() self.display_markers() self.display_annotations() self.resizeEvent(None) self.verticalScrollBar().setValue(self.y_scrollbar_value) self.parent.info.display_view() self.parent.overview.display_current()
python
def display(self): """Display the recordings.""" if self.data is None: return if self.scene is not None: self.y_scrollbar_value = self.verticalScrollBar().value() self.scene.clear() self.create_chan_labels() self.create_time_labels() window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') time_height = max([x.boundingRect().height() for x in self.idx_time]) label_width = window_length * self.parent.value('label_ratio') scene_height = (len(self.idx_label) * self.parent.value('y_distance') + time_height) self.scene = QGraphicsScene(window_start - label_width, 0, window_length + label_width, scene_height) self.setScene(self.scene) self.idx_markers = [] self.idx_annot = [] self.idx_annot_labels = [] self.add_chan_labels() self.add_time_labels() self.add_traces() self.display_grid() self.display_markers() self.display_annotations() self.resizeEvent(None) self.verticalScrollBar().setValue(self.y_scrollbar_value) self.parent.info.display_view() self.parent.overview.display_current()
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Display the recordings.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L358-L398
train
23,617
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.create_chan_labels
def create_chan_labels(self): """Create the channel labels, but don't plot them yet. Notes ----- It's necessary to have the width of the labels, so that we can adjust the main scene. """ self.idx_label = [] for one_grp in self.parent.channels.groups: for one_label in one_grp['chan_to_plot']: item = QGraphicsSimpleTextItem(one_label) item.setBrush(QBrush(QColor(one_grp['color']))) item.setFlag(QGraphicsItem.ItemIgnoresTransformations) self.idx_label.append(item)
python
def create_chan_labels(self): """Create the channel labels, but don't plot them yet. Notes ----- It's necessary to have the width of the labels, so that we can adjust the main scene. """ self.idx_label = [] for one_grp in self.parent.channels.groups: for one_label in one_grp['chan_to_plot']: item = QGraphicsSimpleTextItem(one_label) item.setBrush(QBrush(QColor(one_grp['color']))) item.setFlag(QGraphicsItem.ItemIgnoresTransformations) self.idx_label.append(item)
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Create the channel labels, but don't plot them yet. Notes ----- It's necessary to have the width of the labels, so that we can adjust the main scene.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L400-L414
train
23,618
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.create_time_labels
def create_time_labels(self): """Create the time labels, but don't plot them yet. Notes ----- It's necessary to have the height of the time labels, so that we can adjust the main scene. Not very robust, because it uses seconds as integers. """ min_time = int(floor(min(self.data.axis['time'][0]))) max_time = int(ceil(max(self.data.axis['time'][0]))) n_time_labels = self.parent.value('n_time_labels') self.idx_time = [] self.time_pos = [] for one_time in linspace(min_time, max_time, n_time_labels): x_label = (self.data.start_time + timedelta(seconds=one_time)).strftime('%H:%M:%S') item = QGraphicsSimpleTextItem(x_label) item.setFlag(QGraphicsItem.ItemIgnoresTransformations) self.idx_time.append(item) self.time_pos.append(QPointF(one_time, len(self.idx_label) * self.parent.value('y_distance')))
python
def create_time_labels(self): """Create the time labels, but don't plot them yet. Notes ----- It's necessary to have the height of the time labels, so that we can adjust the main scene. Not very robust, because it uses seconds as integers. """ min_time = int(floor(min(self.data.axis['time'][0]))) max_time = int(ceil(max(self.data.axis['time'][0]))) n_time_labels = self.parent.value('n_time_labels') self.idx_time = [] self.time_pos = [] for one_time in linspace(min_time, max_time, n_time_labels): x_label = (self.data.start_time + timedelta(seconds=one_time)).strftime('%H:%M:%S') item = QGraphicsSimpleTextItem(x_label) item.setFlag(QGraphicsItem.ItemIgnoresTransformations) self.idx_time.append(item) self.time_pos.append(QPointF(one_time, len(self.idx_label) * self.parent.value('y_distance')))
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Create the time labels, but don't plot them yet. Notes ----- It's necessary to have the height of the time labels, so that we can adjust the main scene. Not very robust, because it uses seconds as integers.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L416-L440
train
23,619
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.add_chan_labels
def add_chan_labels(self): """Add channel labels on the left.""" window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') label_width = window_length * self.parent.value('label_ratio') for row, one_label_item in enumerate(self.idx_label): self.scene.addItem(one_label_item) one_label_item.setPos(window_start - label_width, self.parent.value('y_distance') * row + self.parent.value('y_distance') / 2)
python
def add_chan_labels(self): """Add channel labels on the left.""" window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') label_width = window_length * self.parent.value('label_ratio') for row, one_label_item in enumerate(self.idx_label): self.scene.addItem(one_label_item) one_label_item.setPos(window_start - label_width, self.parent.value('y_distance') * row + self.parent.value('y_distance') / 2)
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Add channel labels on the left.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L442-L452
train
23,620
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.add_time_labels
def add_time_labels(self): """Add time labels at the bottom.""" for text, pos in zip(self.idx_time, self.time_pos): self.scene.addItem(text) text.setPos(pos)
python
def add_time_labels(self): """Add time labels at the bottom.""" for text, pos in zip(self.idx_time, self.time_pos): self.scene.addItem(text) text.setPos(pos)
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Add time labels at the bottom.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L454-L458
train
23,621
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.add_traces
def add_traces(self): """Add traces based on self.data.""" y_distance = self.parent.value('y_distance') self.chan = [] self.chan_pos = [] self.chan_scale = [] row = 0 for one_grp in self.parent.channels.groups: for one_chan in one_grp['chan_to_plot']: # channel name chan_name = one_chan + ' (' + one_grp['name'] + ')' # trace dat = (self.data(trial=0, chan=chan_name) * self.parent.value('y_scale')) dat *= -1 # flip data, upside down path = self.scene.addPath(Path(self.data.axis['time'][0], dat)) path.setPen(QPen(QColor(one_grp['color']), LINE_WIDTH)) # adjust position chan_pos = y_distance * row + y_distance / 2 path.setPos(0, chan_pos) row += 1 self.chan.append(chan_name) self.chan_scale.append(one_grp['scale']) self.chan_pos.append(chan_pos)
python
def add_traces(self): """Add traces based on self.data.""" y_distance = self.parent.value('y_distance') self.chan = [] self.chan_pos = [] self.chan_scale = [] row = 0 for one_grp in self.parent.channels.groups: for one_chan in one_grp['chan_to_plot']: # channel name chan_name = one_chan + ' (' + one_grp['name'] + ')' # trace dat = (self.data(trial=0, chan=chan_name) * self.parent.value('y_scale')) dat *= -1 # flip data, upside down path = self.scene.addPath(Path(self.data.axis['time'][0], dat)) path.setPen(QPen(QColor(one_grp['color']), LINE_WIDTH)) # adjust position chan_pos = y_distance * row + y_distance / 2 path.setPos(0, chan_pos) row += 1 self.chan.append(chan_name) self.chan_scale.append(one_grp['scale']) self.chan_pos.append(chan_pos)
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Add traces based on self.data.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L460-L489
train
23,622
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.display_grid
def display_grid(self): """Display grid on x-axis and y-axis.""" window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') window_end = window_start + window_length if self.parent.value('grid_x'): x_tick = self.parent.value('grid_xtick') x_ticks = arange(window_start, window_end + x_tick, x_tick) for x in x_ticks: x_pos = [x, x] y_pos = [0, self.parent.value('y_distance') * len(self.idx_label)] path = self.scene.addPath(Path(x_pos, y_pos)) path.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine)) if self.parent.value('grid_y'): y_tick = (self.parent.value('grid_ytick') * self.parent.value('y_scale')) for one_label_item in self.idx_label: x_pos = [window_start, window_end] y = one_label_item.y() y_pos_0 = [y, y] path_0 = self.scene.addPath(Path(x_pos, y_pos_0)) path_0.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine)) y_up = one_label_item.y() + y_tick y_pos_up = [y_up, y_up] path_up = self.scene.addPath(Path(x_pos, y_pos_up)) path_up.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine)) y_down = one_label_item.y() - y_tick y_pos_down = [y_down, y_down] path_down = self.scene.addPath(Path(x_pos, y_pos_down)) path_down.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine))
python
def display_grid(self): """Display grid on x-axis and y-axis.""" window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') window_end = window_start + window_length if self.parent.value('grid_x'): x_tick = self.parent.value('grid_xtick') x_ticks = arange(window_start, window_end + x_tick, x_tick) for x in x_ticks: x_pos = [x, x] y_pos = [0, self.parent.value('y_distance') * len(self.idx_label)] path = self.scene.addPath(Path(x_pos, y_pos)) path.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine)) if self.parent.value('grid_y'): y_tick = (self.parent.value('grid_ytick') * self.parent.value('y_scale')) for one_label_item in self.idx_label: x_pos = [window_start, window_end] y = one_label_item.y() y_pos_0 = [y, y] path_0 = self.scene.addPath(Path(x_pos, y_pos_0)) path_0.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine)) y_up = one_label_item.y() + y_tick y_pos_up = [y_up, y_up] path_up = self.scene.addPath(Path(x_pos, y_pos_up)) path_up.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine)) y_down = one_label_item.y() - y_tick y_pos_down = [y_down, y_down] path_down = self.scene.addPath(Path(x_pos, y_pos_down)) path_down.setPen(QPen(QColor(LINE_COLOR), LINE_WIDTH, Qt.DotLine))
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Display grid on x-axis and y-axis.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L491-L530
train
23,623
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.display_markers
def display_markers(self): """Add markers on top of first plot.""" for item in self.idx_markers: self.scene.removeItem(item) self.idx_markers = [] window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') window_end = window_start + window_length y_distance = self.parent.value('y_distance') markers = [] if self.parent.info.markers is not None: if self.parent.value('marker_show'): markers = self.parent.info.markers for mrk in markers: if window_start <= mrk['end'] and window_end >= mrk['start']: mrk_start = max((mrk['start'], window_start)) mrk_end = min((mrk['end'], window_end)) color = QColor(self.parent.value('marker_color')) item = QGraphicsRectItem(mrk_start, 0, mrk_end - mrk_start, len(self.idx_label) * y_distance) item.setPen(color) item.setBrush(color) item.setZValue(-9) self.scene.addItem(item) item = TextItem_with_BG(color.darker(200)) item.setText(mrk['name']) item.setPos(mrk['start'], len(self.idx_label) * self.parent.value('y_distance')) item.setFlag(QGraphicsItem.ItemIgnoresTransformations) item.setRotation(-90) self.scene.addItem(item) self.idx_markers.append(item)
python
def display_markers(self): """Add markers on top of first plot.""" for item in self.idx_markers: self.scene.removeItem(item) self.idx_markers = [] window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') window_end = window_start + window_length y_distance = self.parent.value('y_distance') markers = [] if self.parent.info.markers is not None: if self.parent.value('marker_show'): markers = self.parent.info.markers for mrk in markers: if window_start <= mrk['end'] and window_end >= mrk['start']: mrk_start = max((mrk['start'], window_start)) mrk_end = min((mrk['end'], window_end)) color = QColor(self.parent.value('marker_color')) item = QGraphicsRectItem(mrk_start, 0, mrk_end - mrk_start, len(self.idx_label) * y_distance) item.setPen(color) item.setBrush(color) item.setZValue(-9) self.scene.addItem(item) item = TextItem_with_BG(color.darker(200)) item.setText(mrk['name']) item.setPos(mrk['start'], len(self.idx_label) * self.parent.value('y_distance')) item.setFlag(QGraphicsItem.ItemIgnoresTransformations) item.setRotation(-90) self.scene.addItem(item) self.idx_markers.append(item)
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Add markers on top of first plot.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L532-L571
train
23,624
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.step_prev
def step_prev(self): """Go to the previous step.""" window_start = around(self.parent.value('window_start') - self.parent.value('window_length') / self.parent.value('window_step'), 2) if window_start < 0: return self.parent.overview.update_position(window_start)
python
def step_prev(self): """Go to the previous step.""" window_start = around(self.parent.value('window_start') - self.parent.value('window_length') / self.parent.value('window_step'), 2) if window_start < 0: return self.parent.overview.update_position(window_start)
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Go to the previous step.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L645-L652
train
23,625
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.step_next
def step_next(self): """Go to the next step.""" window_start = around(self.parent.value('window_start') + self.parent.value('window_length') / self.parent.value('window_step'), 2) self.parent.overview.update_position(window_start)
python
def step_next(self): """Go to the next step.""" window_start = around(self.parent.value('window_start') + self.parent.value('window_length') / self.parent.value('window_step'), 2) self.parent.overview.update_position(window_start)
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Go to the next step.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L654-L660
train
23,626
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.page_prev
def page_prev(self): """Go to the previous page.""" window_start = (self.parent.value('window_start') - self.parent.value('window_length')) if window_start < 0: return self.parent.overview.update_position(window_start)
python
def page_prev(self): """Go to the previous page.""" window_start = (self.parent.value('window_start') - self.parent.value('window_length')) if window_start < 0: return self.parent.overview.update_position(window_start)
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Go to the previous page.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L662-L668
train
23,627
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.page_next
def page_next(self): """Go to the next page.""" window_start = (self.parent.value('window_start') + self.parent.value('window_length')) self.parent.overview.update_position(window_start)
python
def page_next(self): """Go to the next page.""" window_start = (self.parent.value('window_start') + self.parent.value('window_length')) self.parent.overview.update_position(window_start)
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Go to the next page.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L670-L674
train
23,628
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.go_to_epoch
def go_to_epoch(self, checked=False, test_text_str=None): """Go to any window""" if test_text_str is not None: time_str = test_text_str ok = True else: time_str, ok = QInputDialog.getText(self, 'Go To Epoch', 'Enter start time of the ' 'epoch,\nin seconds ("1560") ' 'or\nas absolute time ' '("22:30")') if not ok: return try: rec_start_time = self.parent.info.dataset.header['start_time'] window_start = _convert_timestr_to_seconds(time_str, rec_start_time) except ValueError as err: error_dialog = QErrorMessage() error_dialog.setWindowTitle('Error moving to epoch') error_dialog.showMessage(str(err)) if test_text_str is None: error_dialog.exec() self.parent.statusBar().showMessage(str(err)) return self.parent.overview.update_position(window_start)
python
def go_to_epoch(self, checked=False, test_text_str=None): """Go to any window""" if test_text_str is not None: time_str = test_text_str ok = True else: time_str, ok = QInputDialog.getText(self, 'Go To Epoch', 'Enter start time of the ' 'epoch,\nin seconds ("1560") ' 'or\nas absolute time ' '("22:30")') if not ok: return try: rec_start_time = self.parent.info.dataset.header['start_time'] window_start = _convert_timestr_to_seconds(time_str, rec_start_time) except ValueError as err: error_dialog = QErrorMessage() error_dialog.setWindowTitle('Error moving to epoch') error_dialog.showMessage(str(err)) if test_text_str is None: error_dialog.exec() self.parent.statusBar().showMessage(str(err)) return self.parent.overview.update_position(window_start)
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Go to any window
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L676-L704
train
23,629
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.line_up_with_epoch
def line_up_with_epoch(self): """Go to the start of the present epoch.""" if self.parent.notes.annot is None: # TODO: remove if buttons are disabled error_dialog = QErrorMessage() error_dialog.setWindowTitle('Error moving to epoch') error_dialog.showMessage('No score file loaded') error_dialog.exec() return new_window_start = self.parent.notes.annot.get_epoch_start( self.parent.value('window_start')) self.parent.overview.update_position(new_window_start)
python
def line_up_with_epoch(self): """Go to the start of the present epoch.""" if self.parent.notes.annot is None: # TODO: remove if buttons are disabled error_dialog = QErrorMessage() error_dialog.setWindowTitle('Error moving to epoch') error_dialog.showMessage('No score file loaded') error_dialog.exec() return new_window_start = self.parent.notes.annot.get_epoch_start( self.parent.value('window_start')) self.parent.overview.update_position(new_window_start)
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Go to the start of the present epoch.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L706-L718
train
23,630
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.add_time
def add_time(self, extra_time): """Go to the predefined time forward.""" window_start = self.parent.value('window_start') + extra_time self.parent.overview.update_position(window_start)
python
def add_time(self, extra_time): """Go to the predefined time forward.""" window_start = self.parent.value('window_start') + extra_time self.parent.overview.update_position(window_start)
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Go to the predefined time forward.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L720-L723
train
23,631
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.X_more
def X_more(self): """Zoom in on the x-axis.""" if self.parent.value('window_length') < 0.3: return self.parent.value('window_length', self.parent.value('window_length') * 2) self.parent.overview.update_position()
python
def X_more(self): """Zoom in on the x-axis.""" if self.parent.value('window_length') < 0.3: return self.parent.value('window_length', self.parent.value('window_length') * 2) self.parent.overview.update_position()
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Zoom in on the x-axis.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L725-L731
train
23,632
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.X_less
def X_less(self): """Zoom out on the x-axis.""" self.parent.value('window_length', self.parent.value('window_length') / 2) self.parent.overview.update_position()
python
def X_less(self): """Zoom out on the x-axis.""" self.parent.value('window_length', self.parent.value('window_length') / 2) self.parent.overview.update_position()
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Zoom out on the x-axis.
[ "Zoom", "out", "on", "the", "x", "-", "axis", "." ]
1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L733-L737
train
23,633
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.X_length
def X_length(self, new_window_length): """Use presets for length of the window.""" self.parent.value('window_length', new_window_length) self.parent.overview.update_position()
python
def X_length(self, new_window_length): """Use presets for length of the window.""" self.parent.value('window_length', new_window_length) self.parent.overview.update_position()
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Use presets for length of the window.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L739-L742
train
23,634
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.Y_more
def Y_more(self): """Increase the scaling.""" self.parent.value('y_scale', self.parent.value('y_scale') * 2) self.parent.traces.display()
python
def Y_more(self): """Increase the scaling.""" self.parent.value('y_scale', self.parent.value('y_scale') * 2) self.parent.traces.display()
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Increase the scaling.
[ "Increase", "the", "scaling", "." ]
1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L744-L747
train
23,635
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.Y_less
def Y_less(self): """Decrease the scaling.""" self.parent.value('y_scale', self.parent.value('y_scale') / 2) self.parent.traces.display()
python
def Y_less(self): """Decrease the scaling.""" self.parent.value('y_scale', self.parent.value('y_scale') / 2) self.parent.traces.display()
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Decrease the scaling.
[ "Decrease", "the", "scaling", "." ]
1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L749-L752
train
23,636
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.Y_ampl
def Y_ampl(self, new_y_scale): """Make scaling on Y axis using predefined values""" self.parent.value('y_scale', new_y_scale) self.parent.traces.display()
python
def Y_ampl(self, new_y_scale): """Make scaling on Y axis using predefined values""" self.parent.value('y_scale', new_y_scale) self.parent.traces.display()
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Make scaling on Y axis using predefined values
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L754-L757
train
23,637
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.Y_wider
def Y_wider(self): """Increase the distance of the lines.""" self.parent.value('y_distance', self.parent.value('y_distance') * 1.4) self.parent.traces.display()
python
def Y_wider(self): """Increase the distance of the lines.""" self.parent.value('y_distance', self.parent.value('y_distance') * 1.4) self.parent.traces.display()
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Increase the distance of the lines.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L759-L762
train
23,638
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.Y_tighter
def Y_tighter(self): """Decrease the distance of the lines.""" self.parent.value('y_distance', self.parent.value('y_distance') / 1.4) self.parent.traces.display()
python
def Y_tighter(self): """Decrease the distance of the lines.""" self.parent.value('y_distance', self.parent.value('y_distance') / 1.4) self.parent.traces.display()
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Decrease the distance of the lines.
[ "Decrease", "the", "distance", "of", "the", "lines", "." ]
1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L764-L767
train
23,639
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.Y_dist
def Y_dist(self, new_y_distance): """Use preset values for the distance between lines.""" self.parent.value('y_distance', new_y_distance) self.parent.traces.display()
python
def Y_dist(self, new_y_distance): """Use preset values for the distance between lines.""" self.parent.value('y_distance', new_y_distance) self.parent.traces.display()
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Use preset values for the distance between lines.
[ "Use", "preset", "values", "for", "the", "distance", "between", "lines", "." ]
1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L769-L772
train
23,640
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.mousePressEvent
def mousePressEvent(self, event): """Create a marker or start selection Parameters ---------- event : instance of QtCore.QEvent it contains the position that was clicked. """ if not self.scene: return if self.event_sel or self.current_event: self.parent.notes.idx_eventtype.setCurrentText(self.current_etype) self.current_etype = None self.current_event = None self.deselect = True self.event_sel = None self.current_event_row = None self.scene.removeItem(self.highlight) self.highlight = None self.parent.statusBar().showMessage('') return self.ready = False self.event_sel = None xy_scene = self.mapToScene(event.pos()) chan_idx = argmin(abs(asarray(self.chan_pos) - xy_scene.y())) self.sel_chan = chan_idx self.sel_xy = (xy_scene.x(), xy_scene.y()) chk_marker = self.parent.notes.action['new_bookmark'].isChecked() chk_event = self.parent.notes.action['new_event'].isChecked() if not (chk_marker or chk_event): channame = self.chan[self.sel_chan] + ' in selected window' self.parent.spectrum.show_channame(channame) # Make annotations clickable else: for annot in self.idx_annot: if annot.contains(xy_scene): self.highlight_event(annot) if chk_event: row = self.parent.notes.find_row(annot.marker.x(), annot.marker.x() + annot.marker.width()) self.parent.notes.idx_annot_list.setCurrentCell(row, 0) break self.ready = True
python
def mousePressEvent(self, event): """Create a marker or start selection Parameters ---------- event : instance of QtCore.QEvent it contains the position that was clicked. """ if not self.scene: return if self.event_sel or self.current_event: self.parent.notes.idx_eventtype.setCurrentText(self.current_etype) self.current_etype = None self.current_event = None self.deselect = True self.event_sel = None self.current_event_row = None self.scene.removeItem(self.highlight) self.highlight = None self.parent.statusBar().showMessage('') return self.ready = False self.event_sel = None xy_scene = self.mapToScene(event.pos()) chan_idx = argmin(abs(asarray(self.chan_pos) - xy_scene.y())) self.sel_chan = chan_idx self.sel_xy = (xy_scene.x(), xy_scene.y()) chk_marker = self.parent.notes.action['new_bookmark'].isChecked() chk_event = self.parent.notes.action['new_event'].isChecked() if not (chk_marker or chk_event): channame = self.chan[self.sel_chan] + ' in selected window' self.parent.spectrum.show_channame(channame) # Make annotations clickable else: for annot in self.idx_annot: if annot.contains(xy_scene): self.highlight_event(annot) if chk_event: row = self.parent.notes.find_row(annot.marker.x(), annot.marker.x() + annot.marker.width()) self.parent.notes.idx_annot_list.setCurrentCell(row, 0) break self.ready = True
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Create a marker or start selection Parameters ---------- event : instance of QtCore.QEvent it contains the position that was clicked.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L774-L823
train
23,641
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.mouseReleaseEvent
def mouseReleaseEvent(self, event): """Create a new event or marker, or show the previous power spectrum """ if not self.scene: return if self.event_sel: return if self.deselect: self.deselect = False return if not self.ready: return chk_marker = self.parent.notes.action['new_bookmark'].isChecked() chk_event = self.parent.notes.action['new_event'].isChecked() y_distance = self.parent.value('y_distance') if chk_marker or chk_event: x_in_scene = self.mapToScene(event.pos()).x() y_in_scene = self.mapToScene(event.pos()).y() # it can happen that selection is empty (f.e. double-click) if self.sel_xy[0] is not None: # max resolution = sampling frequency # in case there is no data s_freq = self.parent.info.dataset.header['s_freq'] at_s_freq = lambda x: round(x * s_freq) / s_freq start = at_s_freq(self.sel_xy[0]) end = at_s_freq(x_in_scene) if abs(end - start) < self.parent.value('min_marker_dur'): end = start if start <= end: time = (start, end) else: time = (end, start) if chk_marker: self.parent.notes.add_bookmark(time) elif chk_event and start != end: eventtype = self.parent.notes.idx_eventtype.currentText() # if dragged across > 1.5 chan, event is marked on all chan if abs(y_in_scene - self.sel_xy[1]) > 1.5 * y_distance: chan = '' else: chan_idx = int(floor(self.sel_xy[1] / y_distance)) chan = self.chan[chan_idx] self.parent.notes.add_event(eventtype, time, chan) else: # normal selection if self.idx_info in self.scene.items(): self.scene.removeItem(self.idx_info) self.idx_info = None # restore spectrum self.parent.spectrum.update() self.parent.spectrum.display_window() # general garbage collection self.sel_chan = None self.sel_xy = (None, None) if self.idx_sel in self.scene.items(): self.scene.removeItem(self.idx_sel) self.idx_sel = None
python
def mouseReleaseEvent(self, event): """Create a new event or marker, or show the previous power spectrum """ if not self.scene: return if self.event_sel: return if self.deselect: self.deselect = False return if not self.ready: return chk_marker = self.parent.notes.action['new_bookmark'].isChecked() chk_event = self.parent.notes.action['new_event'].isChecked() y_distance = self.parent.value('y_distance') if chk_marker or chk_event: x_in_scene = self.mapToScene(event.pos()).x() y_in_scene = self.mapToScene(event.pos()).y() # it can happen that selection is empty (f.e. double-click) if self.sel_xy[0] is not None: # max resolution = sampling frequency # in case there is no data s_freq = self.parent.info.dataset.header['s_freq'] at_s_freq = lambda x: round(x * s_freq) / s_freq start = at_s_freq(self.sel_xy[0]) end = at_s_freq(x_in_scene) if abs(end - start) < self.parent.value('min_marker_dur'): end = start if start <= end: time = (start, end) else: time = (end, start) if chk_marker: self.parent.notes.add_bookmark(time) elif chk_event and start != end: eventtype = self.parent.notes.idx_eventtype.currentText() # if dragged across > 1.5 chan, event is marked on all chan if abs(y_in_scene - self.sel_xy[1]) > 1.5 * y_distance: chan = '' else: chan_idx = int(floor(self.sel_xy[1] / y_distance)) chan = self.chan[chan_idx] self.parent.notes.add_event(eventtype, time, chan) else: # normal selection if self.idx_info in self.scene.items(): self.scene.removeItem(self.idx_info) self.idx_info = None # restore spectrum self.parent.spectrum.update() self.parent.spectrum.display_window() # general garbage collection self.sel_chan = None self.sel_xy = (None, None) if self.idx_sel in self.scene.items(): self.scene.removeItem(self.idx_sel) self.idx_sel = None
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Create a new event or marker, or show the previous power spectrum
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L907-L980
train
23,642
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.next_event
def next_event(self, delete=False): """Go to next event.""" if delete: msg = "Delete this event? This cannot be undone." msgbox = QMessageBox(QMessageBox.Question, 'Delete event', msg) msgbox.setStandardButtons(QMessageBox.Yes | QMessageBox.No) msgbox.setDefaultButton(QMessageBox.Yes) response = msgbox.exec_() if response == QMessageBox.No: return event_sel = self.event_sel if event_sel is None: return notes = self.parent.notes if not self.current_event_row: row = notes.find_row(event_sel.marker.x(), event_sel.marker.x() + event_sel.marker.width()) else: row = self.current_event_row same_type = self.action['next_of_same_type'].isChecked() if same_type: target = notes.idx_annot_list.item(row, 2).text() if delete: notes.delete_row() msg = 'Deleted event from {} to {}.'.format(event_sel.marker.x(), event_sel.marker.x() + event_sel.marker.width()) self.parent.statusBar().showMessage(msg) row -= 1 if row + 1 == notes.idx_annot_list.rowCount(): return if not same_type: next_row = row + 1 else: next_row = None types = notes.idx_annot_list.property('name')[row + 1:] for i, ty in enumerate(types): if ty == target: next_row = row + 1 + i break if next_row is None: return self.current_event_row = next_row notes.go_to_marker(next_row, 0, 'annot') notes.idx_annot_list.setCurrentCell(next_row, 0)
python
def next_event(self, delete=False): """Go to next event.""" if delete: msg = "Delete this event? This cannot be undone." msgbox = QMessageBox(QMessageBox.Question, 'Delete event', msg) msgbox.setStandardButtons(QMessageBox.Yes | QMessageBox.No) msgbox.setDefaultButton(QMessageBox.Yes) response = msgbox.exec_() if response == QMessageBox.No: return event_sel = self.event_sel if event_sel is None: return notes = self.parent.notes if not self.current_event_row: row = notes.find_row(event_sel.marker.x(), event_sel.marker.x() + event_sel.marker.width()) else: row = self.current_event_row same_type = self.action['next_of_same_type'].isChecked() if same_type: target = notes.idx_annot_list.item(row, 2).text() if delete: notes.delete_row() msg = 'Deleted event from {} to {}.'.format(event_sel.marker.x(), event_sel.marker.x() + event_sel.marker.width()) self.parent.statusBar().showMessage(msg) row -= 1 if row + 1 == notes.idx_annot_list.rowCount(): return if not same_type: next_row = row + 1 else: next_row = None types = notes.idx_annot_list.property('name')[row + 1:] for i, ty in enumerate(types): if ty == target: next_row = row + 1 + i break if next_row is None: return self.current_event_row = next_row notes.go_to_marker(next_row, 0, 'annot') notes.idx_annot_list.setCurrentCell(next_row, 0)
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Go to next event.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L1048-L1101
train
23,643
wonambi-python/wonambi
wonambi/widgets/traces.py
Traces.resizeEvent
def resizeEvent(self, event): """Resize scene so that it fits the whole widget. Parameters ---------- event : instance of QtCore.QEvent not important Notes ----- This function overwrites Qt function, therefore the non-standard name. Argument also depends on Qt. The function is used to change the scale of view, so that the scene fits the whole scene. There are two problems that I could not fix: 1) how to give the width of the label in absolute width, 2) how to strech scene just enough that it doesn't trigger a scrollbar. However, it's pretty good as it is now. """ if self.scene is not None: ratio = self.width() / (self.scene.width() * 1.1) self.resetTransform() self.scale(ratio, 1)
python
def resizeEvent(self, event): """Resize scene so that it fits the whole widget. Parameters ---------- event : instance of QtCore.QEvent not important Notes ----- This function overwrites Qt function, therefore the non-standard name. Argument also depends on Qt. The function is used to change the scale of view, so that the scene fits the whole scene. There are two problems that I could not fix: 1) how to give the width of the label in absolute width, 2) how to strech scene just enough that it doesn't trigger a scrollbar. However, it's pretty good as it is now. """ if self.scene is not None: ratio = self.width() / (self.scene.width() * 1.1) self.resetTransform() self.scale(ratio, 1)
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Resize scene so that it fits the whole widget. Parameters ---------- event : instance of QtCore.QEvent not important Notes ----- This function overwrites Qt function, therefore the non-standard name. Argument also depends on Qt. The function is used to change the scale of view, so that the scene fits the whole scene. There are two problems that I could not fix: 1) how to give the width of the label in absolute width, 2) how to strech scene just enough that it doesn't trigger a scrollbar. However, it's pretty good as it is now.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/traces.py#L1130-L1152
train
23,644
wonambi-python/wonambi
wonambi/ioeeg/edf.py
Edf.return_dat
def return_dat(self, chan, begsam, endsam): """Read data from an EDF file. Reads channel by channel, and adjusts the values by calibration. Parameters ---------- chan : list of int index (indices) of the channels to read begsam : int index of the first sample endsam : int index of the last sample Returns ------- numpy.ndarray A 2d matrix, where the first dimension is the channels and the second dimension are the samples. """ assert begsam < endsam dat = empty((len(chan), endsam - begsam)) dat.fill(NaN) with self.filename.open('rb') as f: for i_dat, blk, i_blk in _select_blocks(self.blocks, begsam, endsam): dat_in_rec = self._read_record(f, blk, chan) dat[:, i_dat[0]:i_dat[1]] = dat_in_rec[:, i_blk[0]:i_blk[1]] # calibration dat = ((dat.astype('float64') - self.dig_min[chan, newaxis]) * self.gain[chan, newaxis] + self.phys_min[chan, newaxis]) return dat
python
def return_dat(self, chan, begsam, endsam): """Read data from an EDF file. Reads channel by channel, and adjusts the values by calibration. Parameters ---------- chan : list of int index (indices) of the channels to read begsam : int index of the first sample endsam : int index of the last sample Returns ------- numpy.ndarray A 2d matrix, where the first dimension is the channels and the second dimension are the samples. """ assert begsam < endsam dat = empty((len(chan), endsam - begsam)) dat.fill(NaN) with self.filename.open('rb') as f: for i_dat, blk, i_blk in _select_blocks(self.blocks, begsam, endsam): dat_in_rec = self._read_record(f, blk, chan) dat[:, i_dat[0]:i_dat[1]] = dat_in_rec[:, i_blk[0]:i_blk[1]] # calibration dat = ((dat.astype('float64') - self.dig_min[chan, newaxis]) * self.gain[chan, newaxis] + self.phys_min[chan, newaxis]) return dat
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Read data from an EDF file. Reads channel by channel, and adjusts the values by calibration. Parameters ---------- chan : list of int index (indices) of the channels to read begsam : int index of the first sample endsam : int index of the last sample Returns ------- numpy.ndarray A 2d matrix, where the first dimension is the channels and the second dimension are the samples.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/edf.py#L177-L212
train
23,645
wonambi-python/wonambi
wonambi/ioeeg/edf.py
Edf._read_record
def _read_record(self, f, blk, chans): """Read raw data from a single EDF channel. Parameters ---------- i_chan : int index of the channel to read begsam : int index of the first sample endsam : int index of the last sample Returns ------- numpy.ndarray A vector with the data as written on file, in 16-bit precision """ dat_in_rec = empty((len(chans), self.max_smp)) i_ch_in_dat = 0 for i_ch in chans: offset, n_smp_per_chan = self._offset(blk, i_ch) f.seek(offset) x = fromfile(f, count=n_smp_per_chan, dtype=EDF_FORMAT) ratio = int(self.max_smp / n_smp_per_chan) dat_in_rec[i_ch_in_dat, :] = repeat(x, ratio) i_ch_in_dat += 1 return dat_in_rec
python
def _read_record(self, f, blk, chans): """Read raw data from a single EDF channel. Parameters ---------- i_chan : int index of the channel to read begsam : int index of the first sample endsam : int index of the last sample Returns ------- numpy.ndarray A vector with the data as written on file, in 16-bit precision """ dat_in_rec = empty((len(chans), self.max_smp)) i_ch_in_dat = 0 for i_ch in chans: offset, n_smp_per_chan = self._offset(blk, i_ch) f.seek(offset) x = fromfile(f, count=n_smp_per_chan, dtype=EDF_FORMAT) ratio = int(self.max_smp / n_smp_per_chan) dat_in_rec[i_ch_in_dat, :] = repeat(x, ratio) i_ch_in_dat += 1 return dat_in_rec
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Read raw data from a single EDF channel. Parameters ---------- i_chan : int index of the channel to read begsam : int index of the first sample endsam : int index of the last sample Returns ------- numpy.ndarray A vector with the data as written on file, in 16-bit precision
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/edf.py#L214-L244
train
23,646
wonambi-python/wonambi
wonambi/ioeeg/brainvision.py
write_brainvision
def write_brainvision(data, filename, markers=None): """Export data in BrainVision format Parameters ---------- data : instance of ChanTime data with only one trial filename : path to file file to export to (use '.vhdr' as extension) """ filename = Path(filename).resolve().with_suffix('.vhdr') if markers is None: markers = [] with filename.open('w') as f: f.write(_write_vhdr(data, filename)) with filename.with_suffix('.vmrk').open('w') as f: f.write(_write_vmrk(data, filename, markers)) _write_eeg(data, filename)
python
def write_brainvision(data, filename, markers=None): """Export data in BrainVision format Parameters ---------- data : instance of ChanTime data with only one trial filename : path to file file to export to (use '.vhdr' as extension) """ filename = Path(filename).resolve().with_suffix('.vhdr') if markers is None: markers = [] with filename.open('w') as f: f.write(_write_vhdr(data, filename)) with filename.with_suffix('.vmrk').open('w') as f: f.write(_write_vmrk(data, filename, markers)) _write_eeg(data, filename)
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Export data in BrainVision format Parameters ---------- data : instance of ChanTime data with only one trial filename : path to file file to export to (use '.vhdr' as extension)
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/brainvision.py#L226-L246
train
23,647
wonambi-python/wonambi
wonambi/source/linear.py
calc_xyz2surf
def calc_xyz2surf(surf, xyz, threshold=20, exponent=None, std=None): """Calculate transformation matrix from xyz values to vertices. Parameters ---------- surf : instance of wonambi.attr.Surf the surface of only one hemisphere. xyz : numpy.ndarray nChan x 3 matrix, with the locations in x, y, z. std : float distance in mm of the Gaussian kernel exponent : int inverse law (1-> direct inverse, 2-> inverse square, 3-> inverse cube) threshold : float distance in mm for a vertex to pick up electrode activity (if distance is above the threshold, one electrode does not affect a vertex). Returns ------- numpy.ndarray nVertices X xyz.shape[0] matrix Notes ----- This function is a helper when plotting onto brain surface, by creating a transformation matrix from the values in space (f.e. at each electrode) to the position of the vertices (used to show the brain surface). There are many ways to move from values to vertices. The crucial parameter is the function at which activity decreases in respect to the distance. You can have an inverse relationship by specifying 'exponent'. If 'exponent' is 2, then the activity will decrease as inverse square of the distance. The function can be a Gaussian. With std, you specify the width of the gaussian kernel in mm. For each vertex, it uses a threshold based on the distance ('threshold' value, in mm). Finally, it normalizes the contribution of all the channels to 1, so that the sum of the coefficients for each vertex is 1. You can also create your own matrix (and skip calc_xyz2surf altogether) and pass it as attribute to the main figure. Because it's a loop over all the vertices, this function is pretty slow, but if you calculate it once, you can reuse it. We take advantage of multiprocessing, which speeds it up considerably. """ if exponent is None and std is None: exponent = 1 if exponent is not None: lg.debug('Vertex values based on inverse-law, with exponent ' + str(exponent)) funct = partial(calc_one_vert_inverse, xyz=xyz, exponent=exponent) elif std is not None: lg.debug('Vertex values based on gaussian, with s.d. ' + str(std)) funct = partial(calc_one_vert_gauss, xyz=xyz, std=std) with Pool() as p: xyz2surf = p.map(funct, surf.vert) xyz2surf = asarray(xyz2surf) if exponent is not None: threshold_value = (1 / (threshold ** exponent)) external_threshold_value = threshold_value elif std is not None: threshold_value = gauss(threshold, std) external_threshold_value = gauss(std, std) # this is around 0.607 lg.debug('Values thresholded at ' + str(threshold_value)) xyz2surf[xyz2surf < threshold_value] = NaN # here we deal with vertices that are within the threshold value but far # from a single electrodes, so those remain empty sumval = nansum(xyz2surf, axis=1) sumval[sumval < external_threshold_value] = NaN # normalize by the number of electrodes xyz2surf /= atleast_2d(sumval).T xyz2surf[isnan(xyz2surf)] = 0 return xyz2surf
python
def calc_xyz2surf(surf, xyz, threshold=20, exponent=None, std=None): """Calculate transformation matrix from xyz values to vertices. Parameters ---------- surf : instance of wonambi.attr.Surf the surface of only one hemisphere. xyz : numpy.ndarray nChan x 3 matrix, with the locations in x, y, z. std : float distance in mm of the Gaussian kernel exponent : int inverse law (1-> direct inverse, 2-> inverse square, 3-> inverse cube) threshold : float distance in mm for a vertex to pick up electrode activity (if distance is above the threshold, one electrode does not affect a vertex). Returns ------- numpy.ndarray nVertices X xyz.shape[0] matrix Notes ----- This function is a helper when plotting onto brain surface, by creating a transformation matrix from the values in space (f.e. at each electrode) to the position of the vertices (used to show the brain surface). There are many ways to move from values to vertices. The crucial parameter is the function at which activity decreases in respect to the distance. You can have an inverse relationship by specifying 'exponent'. If 'exponent' is 2, then the activity will decrease as inverse square of the distance. The function can be a Gaussian. With std, you specify the width of the gaussian kernel in mm. For each vertex, it uses a threshold based on the distance ('threshold' value, in mm). Finally, it normalizes the contribution of all the channels to 1, so that the sum of the coefficients for each vertex is 1. You can also create your own matrix (and skip calc_xyz2surf altogether) and pass it as attribute to the main figure. Because it's a loop over all the vertices, this function is pretty slow, but if you calculate it once, you can reuse it. We take advantage of multiprocessing, which speeds it up considerably. """ if exponent is None and std is None: exponent = 1 if exponent is not None: lg.debug('Vertex values based on inverse-law, with exponent ' + str(exponent)) funct = partial(calc_one_vert_inverse, xyz=xyz, exponent=exponent) elif std is not None: lg.debug('Vertex values based on gaussian, with s.d. ' + str(std)) funct = partial(calc_one_vert_gauss, xyz=xyz, std=std) with Pool() as p: xyz2surf = p.map(funct, surf.vert) xyz2surf = asarray(xyz2surf) if exponent is not None: threshold_value = (1 / (threshold ** exponent)) external_threshold_value = threshold_value elif std is not None: threshold_value = gauss(threshold, std) external_threshold_value = gauss(std, std) # this is around 0.607 lg.debug('Values thresholded at ' + str(threshold_value)) xyz2surf[xyz2surf < threshold_value] = NaN # here we deal with vertices that are within the threshold value but far # from a single electrodes, so those remain empty sumval = nansum(xyz2surf, axis=1) sumval[sumval < external_threshold_value] = NaN # normalize by the number of electrodes xyz2surf /= atleast_2d(sumval).T xyz2surf[isnan(xyz2surf)] = 0 return xyz2surf
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Calculate transformation matrix from xyz values to vertices. Parameters ---------- surf : instance of wonambi.attr.Surf the surface of only one hemisphere. xyz : numpy.ndarray nChan x 3 matrix, with the locations in x, y, z. std : float distance in mm of the Gaussian kernel exponent : int inverse law (1-> direct inverse, 2-> inverse square, 3-> inverse cube) threshold : float distance in mm for a vertex to pick up electrode activity (if distance is above the threshold, one electrode does not affect a vertex). Returns ------- numpy.ndarray nVertices X xyz.shape[0] matrix Notes ----- This function is a helper when plotting onto brain surface, by creating a transformation matrix from the values in space (f.e. at each electrode) to the position of the vertices (used to show the brain surface). There are many ways to move from values to vertices. The crucial parameter is the function at which activity decreases in respect to the distance. You can have an inverse relationship by specifying 'exponent'. If 'exponent' is 2, then the activity will decrease as inverse square of the distance. The function can be a Gaussian. With std, you specify the width of the gaussian kernel in mm. For each vertex, it uses a threshold based on the distance ('threshold' value, in mm). Finally, it normalizes the contribution of all the channels to 1, so that the sum of the coefficients for each vertex is 1. You can also create your own matrix (and skip calc_xyz2surf altogether) and pass it as attribute to the main figure. Because it's a loop over all the vertices, this function is pretty slow, but if you calculate it once, you can reuse it. We take advantage of multiprocessing, which speeds it up considerably.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/source/linear.py#L57-L136
train
23,648
wonambi-python/wonambi
wonambi/source/linear.py
calc_one_vert_inverse
def calc_one_vert_inverse(one_vert, xyz=None, exponent=None): """Calculate how many electrodes influence one vertex, using the inverse function. Parameters ---------- one_vert : ndarray vector of xyz position of a vertex xyz : ndarray nChan X 3 with the position of all the channels exponent : int inverse law (1-> direct inverse, 2-> inverse square, 3-> inverse cube) Returns ------- ndarray one vector with values for one vertex """ trans = empty(xyz.shape[0]) for i, one_xyz in enumerate(xyz): trans[i] = 1 / (norm(one_vert - one_xyz) ** exponent) return trans
python
def calc_one_vert_inverse(one_vert, xyz=None, exponent=None): """Calculate how many electrodes influence one vertex, using the inverse function. Parameters ---------- one_vert : ndarray vector of xyz position of a vertex xyz : ndarray nChan X 3 with the position of all the channels exponent : int inverse law (1-> direct inverse, 2-> inverse square, 3-> inverse cube) Returns ------- ndarray one vector with values for one vertex """ trans = empty(xyz.shape[0]) for i, one_xyz in enumerate(xyz): trans[i] = 1 / (norm(one_vert - one_xyz) ** exponent) return trans
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Calculate how many electrodes influence one vertex, using the inverse function. Parameters ---------- one_vert : ndarray vector of xyz position of a vertex xyz : ndarray nChan X 3 with the position of all the channels exponent : int inverse law (1-> direct inverse, 2-> inverse square, 3-> inverse cube) Returns ------- ndarray one vector with values for one vertex
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/source/linear.py#L139-L160
train
23,649
wonambi-python/wonambi
wonambi/source/linear.py
calc_one_vert_gauss
def calc_one_vert_gauss(one_vert, xyz=None, std=None): """Calculate how many electrodes influence one vertex, using a Gaussian function. Parameters ---------- one_vert : ndarray vector of xyz position of a vertex xyz : ndarray nChan X 3 with the position of all the channels std : float distance in mm of the Gaussian kernel Returns ------- ndarray one vector with values for one vertex """ trans = empty(xyz.shape[0]) for i, one_xyz in enumerate(xyz): trans[i] = gauss(norm(one_vert - one_xyz), std) return trans
python
def calc_one_vert_gauss(one_vert, xyz=None, std=None): """Calculate how many electrodes influence one vertex, using a Gaussian function. Parameters ---------- one_vert : ndarray vector of xyz position of a vertex xyz : ndarray nChan X 3 with the position of all the channels std : float distance in mm of the Gaussian kernel Returns ------- ndarray one vector with values for one vertex """ trans = empty(xyz.shape[0]) for i, one_xyz in enumerate(xyz): trans[i] = gauss(norm(one_vert - one_xyz), std) return trans
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Calculate how many electrodes influence one vertex, using a Gaussian function. Parameters ---------- one_vert : ndarray vector of xyz position of a vertex xyz : ndarray nChan X 3 with the position of all the channels std : float distance in mm of the Gaussian kernel Returns ------- ndarray one vector with values for one vertex
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/source/linear.py#L163-L184
train
23,650
wonambi-python/wonambi
wonambi/ioeeg/micromed.py
_read_history
def _read_history(f, zone): """This matches the Matlab reader from Matlab Exchange but doesn't seem correct. """ pos, length = zone f.seek(pos, SEEK_SET) histories = [] while f.tell() < (pos + length): history = { 'nSample': unpack(MAX_SAMPLE * 'I', f.read(MAX_SAMPLE * 4)), 'lines': unpack('H', f.read(2)), 'sectors': unpack('H', f.read(2)), 'base_time': unpack('H', f.read(2)), 'notch': unpack('H', f.read(2)), 'colour': unpack(MAX_CAN_VIEW * 'B', f.read(MAX_CAN_VIEW)), 'selection': unpack(MAX_CAN_VIEW * 'B', f.read(MAX_CAN_VIEW)), 'description': f.read(64).strip(b'\x01\x00'), 'inputsNonInv': unpack(MAX_CAN_VIEW * 'H', f.read(MAX_CAN_VIEW * 2)), # NonInv : non inverting input 'inputsInv': unpack(MAX_CAN_VIEW * 'H', f.read(MAX_CAN_VIEW * 2)), # Inv : inverting input 'HiPass_Filter': unpack(MAX_CAN_VIEW * 'I', f.read(MAX_CAN_VIEW * 4)), 'LowPass_Filter': unpack(MAX_CAN_VIEW * 'I', f.read(MAX_CAN_VIEW * 4)), 'reference': unpack(MAX_CAN_VIEW * 'I', f.read(MAX_CAN_VIEW * 4)), 'free': f.read(1720).strip(b'\x01\x00'), } histories.append(history) return histories
python
def _read_history(f, zone): """This matches the Matlab reader from Matlab Exchange but doesn't seem correct. """ pos, length = zone f.seek(pos, SEEK_SET) histories = [] while f.tell() < (pos + length): history = { 'nSample': unpack(MAX_SAMPLE * 'I', f.read(MAX_SAMPLE * 4)), 'lines': unpack('H', f.read(2)), 'sectors': unpack('H', f.read(2)), 'base_time': unpack('H', f.read(2)), 'notch': unpack('H', f.read(2)), 'colour': unpack(MAX_CAN_VIEW * 'B', f.read(MAX_CAN_VIEW)), 'selection': unpack(MAX_CAN_VIEW * 'B', f.read(MAX_CAN_VIEW)), 'description': f.read(64).strip(b'\x01\x00'), 'inputsNonInv': unpack(MAX_CAN_VIEW * 'H', f.read(MAX_CAN_VIEW * 2)), # NonInv : non inverting input 'inputsInv': unpack(MAX_CAN_VIEW * 'H', f.read(MAX_CAN_VIEW * 2)), # Inv : inverting input 'HiPass_Filter': unpack(MAX_CAN_VIEW * 'I', f.read(MAX_CAN_VIEW * 4)), 'LowPass_Filter': unpack(MAX_CAN_VIEW * 'I', f.read(MAX_CAN_VIEW * 4)), 'reference': unpack(MAX_CAN_VIEW * 'I', f.read(MAX_CAN_VIEW * 4)), 'free': f.read(1720).strip(b'\x01\x00'), } histories.append(history) return histories
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This matches the Matlab reader from Matlab Exchange but doesn't seem correct.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/micromed.py#L458-L486
train
23,651
wonambi-python/wonambi
wonambi/widgets/video.py
Video.create_video
def create_video(self): """Create video widget.""" self.instance = vlc.Instance() video_widget = QFrame() self.mediaplayer = self.instance.media_player_new() if system() == 'Linux': self.mediaplayer.set_xwindow(video_widget.winId()) elif system() == 'Windows': self.mediaplayer.set_hwnd(video_widget.winId()) elif system() == 'darwin': # to test self.mediaplayer.set_nsobject(video_widget.winId()) else: lg.warning('unsupported system for video widget') return self.medialistplayer = vlc.MediaListPlayer() self.medialistplayer.set_media_player(self.mediaplayer) event_manager = self.medialistplayer.event_manager() event_manager.event_attach(vlc.EventType.MediaListPlayerNextItemSet, self.next_video) self.idx_button = QPushButton() self.idx_button.setText('Start') self.idx_button.clicked.connect(self.start_stop_video) layout = QVBoxLayout() layout.addWidget(video_widget) layout.addWidget(self.idx_button) self.setLayout(layout)
python
def create_video(self): """Create video widget.""" self.instance = vlc.Instance() video_widget = QFrame() self.mediaplayer = self.instance.media_player_new() if system() == 'Linux': self.mediaplayer.set_xwindow(video_widget.winId()) elif system() == 'Windows': self.mediaplayer.set_hwnd(video_widget.winId()) elif system() == 'darwin': # to test self.mediaplayer.set_nsobject(video_widget.winId()) else: lg.warning('unsupported system for video widget') return self.medialistplayer = vlc.MediaListPlayer() self.medialistplayer.set_media_player(self.mediaplayer) event_manager = self.medialistplayer.event_manager() event_manager.event_attach(vlc.EventType.MediaListPlayerNextItemSet, self.next_video) self.idx_button = QPushButton() self.idx_button.setText('Start') self.idx_button.clicked.connect(self.start_stop_video) layout = QVBoxLayout() layout.addWidget(video_widget) layout.addWidget(self.idx_button) self.setLayout(layout)
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Create video widget.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/video.py#L74-L104
train
23,652
wonambi-python/wonambi
wonambi/widgets/video.py
Video.stop_video
def stop_video(self, tick): """Stop video if tick is more than the end, only for last file. Parameters ---------- tick : int time in ms from the beginning of the file useless? """ if self.cnt_video == self.n_video: if tick >= self.end_diff: self.idx_button.setText('Start') self.video.stop()
python
def stop_video(self, tick): """Stop video if tick is more than the end, only for last file. Parameters ---------- tick : int time in ms from the beginning of the file useless? """ if self.cnt_video == self.n_video: if tick >= self.end_diff: self.idx_button.setText('Start') self.video.stop()
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Stop video if tick is more than the end, only for last file. Parameters ---------- tick : int time in ms from the beginning of the file useless?
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/video.py#L106-L119
train
23,653
wonambi-python/wonambi
wonambi/widgets/video.py
Video.next_video
def next_video(self, _): """Also runs when file is loaded, so index starts at 2.""" self.cnt_video += 1 lg.info('Update video to ' + str(self.cnt_video))
python
def next_video(self, _): """Also runs when file is loaded, so index starts at 2.""" self.cnt_video += 1 lg.info('Update video to ' + str(self.cnt_video))
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Also runs when file is loaded, so index starts at 2.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/video.py#L128-L131
train
23,654
wonambi-python/wonambi
wonambi/widgets/video.py
Video.start_stop_video
def start_stop_video(self): """Start and stop the video, and change the button. """ if self.parent.info.dataset is None: self.parent.statusBar().showMessage('No Dataset Loaded') return # & is added automatically by PyQt, it seems if 'Start' in self.idx_button.text().replace('&', ''): try: self.update_video() except IndexError as er: lg.debug(er) self.idx_button.setText('Not Available / Start') return except OSError as er: lg.debug(er) self.idx_button.setText('NO VIDEO for this dataset') return self.idx_button.setText('Stop') elif 'Stop' in self.idx_button.text(): self.idx_button.setText('Start') self.medialistplayer.stop() self.t.stop()
python
def start_stop_video(self): """Start and stop the video, and change the button. """ if self.parent.info.dataset is None: self.parent.statusBar().showMessage('No Dataset Loaded') return # & is added automatically by PyQt, it seems if 'Start' in self.idx_button.text().replace('&', ''): try: self.update_video() except IndexError as er: lg.debug(er) self.idx_button.setText('Not Available / Start') return except OSError as er: lg.debug(er) self.idx_button.setText('NO VIDEO for this dataset') return self.idx_button.setText('Stop') elif 'Stop' in self.idx_button.text(): self.idx_button.setText('Start') self.medialistplayer.stop() self.t.stop()
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Start and stop the video, and change the button.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/video.py#L133-L158
train
23,655
wonambi-python/wonambi
wonambi/widgets/video.py
Video.update_video
def update_video(self): """Read list of files, convert to video time, and add video to queue. """ window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') d = self.parent.info.dataset videos, begsec, endsec = d.read_videos(window_start, window_start + window_length) lg.debug(f'Video: {begsec} - {endsec}') self.endsec = endsec videos = [str(v) for v in videos] # make sure it's a str (not path) medialist = vlc.MediaList(videos) self.medialistplayer.set_media_list(medialist) self.cnt_video = 0 self.n_video = len(videos) self.t = QTimer() self.t.timeout.connect(self.check_if_finished) self.t.start(100) self.medialistplayer.play() self.mediaplayer.set_time(int(begsec * 1000))
python
def update_video(self): """Read list of files, convert to video time, and add video to queue. """ window_start = self.parent.value('window_start') window_length = self.parent.value('window_length') d = self.parent.info.dataset videos, begsec, endsec = d.read_videos(window_start, window_start + window_length) lg.debug(f'Video: {begsec} - {endsec}') self.endsec = endsec videos = [str(v) for v in videos] # make sure it's a str (not path) medialist = vlc.MediaList(videos) self.medialistplayer.set_media_list(medialist) self.cnt_video = 0 self.n_video = len(videos) self.t = QTimer() self.t.timeout.connect(self.check_if_finished) self.t.start(100) self.medialistplayer.play() self.mediaplayer.set_time(int(begsec * 1000))
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Read list of files, convert to video time, and add video to queue.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/video.py#L160-L184
train
23,656
wonambi-python/wonambi
wonambi/trans/montage.py
montage
def montage(data, ref_chan=None, ref_to_avg=False, bipolar=None, method='average'): """Apply linear transformation to the channels. Parameters ---------- data : instance of DataRaw the data to filter ref_chan : list of str list of channels used as reference ref_to_avg : bool if re-reference to average or not bipolar : float distance in mm to consider two channels as neighbors and then compute the bipolar montage between them. method : str 'average' or 'regression'. 'average' takes the average across the channels selected as reference (it can be all) and subtract it from each channel. 'regression' keeps the residuals after regressing out the mean across channels. Returns ------- filtered_data : instance of DataRaw filtered data Notes ----- If you don't change anything, it returns the same instance of data. """ if ref_to_avg and ref_chan is not None: raise TypeError('You cannot specify reference to the average and ' 'the channels to use as reference') if ref_chan is not None: if (not isinstance(ref_chan, (list, tuple)) or not all(isinstance(x, str) for x in ref_chan)): raise TypeError('chan should be a list of strings') if ref_chan is None: ref_chan = [] # TODO: check bool for ref_chan if bipolar: if not data.attr['chan']: raise ValueError('Data should have Chan information in attr') _assert_equal_channels(data.axis['chan']) chan_in_data = data.axis['chan'][0] chan = data.attr['chan'] chan = chan(lambda x: x.label in chan_in_data) chan, trans = create_bipolar_chan(chan, bipolar) data.attr['chan'] = chan if ref_to_avg or ref_chan or bipolar: mdata = data._copy() idx_chan = mdata.index_of('chan') for i in range(mdata.number_of('trial')): if ref_to_avg or ref_chan: if ref_to_avg: ref_chan = data.axis['chan'][i] ref_data = data(trial=i, chan=ref_chan) if method == 'average': mdata.data[i] = (data(trial=i) - mean(ref_data, axis=idx_chan)) elif method == 'regression': mdata.data[i] = compute_average_regress(data(trial=i), idx_chan) elif bipolar: if not data.index_of('chan') == 0: raise ValueError('For matrix multiplication to work, ' 'the first dimension should be chan') mdata.data[i] = dot(trans, data(trial=i)) mdata.axis['chan'][i] = asarray(chan.return_label(), dtype='U') else: mdata = data return mdata
python
def montage(data, ref_chan=None, ref_to_avg=False, bipolar=None, method='average'): """Apply linear transformation to the channels. Parameters ---------- data : instance of DataRaw the data to filter ref_chan : list of str list of channels used as reference ref_to_avg : bool if re-reference to average or not bipolar : float distance in mm to consider two channels as neighbors and then compute the bipolar montage between them. method : str 'average' or 'regression'. 'average' takes the average across the channels selected as reference (it can be all) and subtract it from each channel. 'regression' keeps the residuals after regressing out the mean across channels. Returns ------- filtered_data : instance of DataRaw filtered data Notes ----- If you don't change anything, it returns the same instance of data. """ if ref_to_avg and ref_chan is not None: raise TypeError('You cannot specify reference to the average and ' 'the channels to use as reference') if ref_chan is not None: if (not isinstance(ref_chan, (list, tuple)) or not all(isinstance(x, str) for x in ref_chan)): raise TypeError('chan should be a list of strings') if ref_chan is None: ref_chan = [] # TODO: check bool for ref_chan if bipolar: if not data.attr['chan']: raise ValueError('Data should have Chan information in attr') _assert_equal_channels(data.axis['chan']) chan_in_data = data.axis['chan'][0] chan = data.attr['chan'] chan = chan(lambda x: x.label in chan_in_data) chan, trans = create_bipolar_chan(chan, bipolar) data.attr['chan'] = chan if ref_to_avg or ref_chan or bipolar: mdata = data._copy() idx_chan = mdata.index_of('chan') for i in range(mdata.number_of('trial')): if ref_to_avg or ref_chan: if ref_to_avg: ref_chan = data.axis['chan'][i] ref_data = data(trial=i, chan=ref_chan) if method == 'average': mdata.data[i] = (data(trial=i) - mean(ref_data, axis=idx_chan)) elif method == 'regression': mdata.data[i] = compute_average_regress(data(trial=i), idx_chan) elif bipolar: if not data.index_of('chan') == 0: raise ValueError('For matrix multiplication to work, ' 'the first dimension should be chan') mdata.data[i] = dot(trans, data(trial=i)) mdata.axis['chan'][i] = asarray(chan.return_label(), dtype='U') else: mdata = data return mdata
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Apply linear transformation to the channels. Parameters ---------- data : instance of DataRaw the data to filter ref_chan : list of str list of channels used as reference ref_to_avg : bool if re-reference to average or not bipolar : float distance in mm to consider two channels as neighbors and then compute the bipolar montage between them. method : str 'average' or 'regression'. 'average' takes the average across the channels selected as reference (it can be all) and subtract it from each channel. 'regression' keeps the residuals after regressing out the mean across channels. Returns ------- filtered_data : instance of DataRaw filtered data Notes ----- If you don't change anything, it returns the same instance of data.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/montage.py#L18-L99
train
23,657
wonambi-python/wonambi
wonambi/trans/montage.py
_assert_equal_channels
def _assert_equal_channels(axis): """check that all the trials have the same channels, in the same order. Parameters ---------- axis : ndarray of ndarray one of the data axis Raises ------ """ for i0 in axis: for i1 in axis: if not all(i0 == i1): raise ValueError('The channels for all the trials should have ' 'the same labels, in the same order.')
python
def _assert_equal_channels(axis): """check that all the trials have the same channels, in the same order. Parameters ---------- axis : ndarray of ndarray one of the data axis Raises ------ """ for i0 in axis: for i1 in axis: if not all(i0 == i1): raise ValueError('The channels for all the trials should have ' 'the same labels, in the same order.')
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check that all the trials have the same channels, in the same order. Parameters ---------- axis : ndarray of ndarray one of the data axis Raises ------
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/montage.py#L102-L118
train
23,658
wonambi-python/wonambi
wonambi/trans/montage.py
compute_average_regress
def compute_average_regress(x, idx_chan): """Take the mean across channels and regress out the mean from each channel Parameters ---------- x : ndarray 2d array with channels on one dimension idx_chan: which axis contains channels Returns ------- ndarray same as x, but with the mean being regressed out """ if x.ndim != 2: raise ValueError(f'The number of dimensions must be 2, not {x.ndim}') x = moveaxis(x, idx_chan, 0) # move axis to the front avg = mean(x, axis=0) x_o = [] for i in range(x.shape[0]): r = lstsq(avg[:, None], x[i, :][:, None], rcond=0)[0] x_o.append( x[i, :] - r[0, 0] * avg ) return moveaxis(asarray(x_o), 0, idx_chan)
python
def compute_average_regress(x, idx_chan): """Take the mean across channels and regress out the mean from each channel Parameters ---------- x : ndarray 2d array with channels on one dimension idx_chan: which axis contains channels Returns ------- ndarray same as x, but with the mean being regressed out """ if x.ndim != 2: raise ValueError(f'The number of dimensions must be 2, not {x.ndim}') x = moveaxis(x, idx_chan, 0) # move axis to the front avg = mean(x, axis=0) x_o = [] for i in range(x.shape[0]): r = lstsq(avg[:, None], x[i, :][:, None], rcond=0)[0] x_o.append( x[i, :] - r[0, 0] * avg ) return moveaxis(asarray(x_o), 0, idx_chan)
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Take the mean across channels and regress out the mean from each channel Parameters ---------- x : ndarray 2d array with channels on one dimension idx_chan: which axis contains channels Returns ------- ndarray same as x, but with the mean being regressed out
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/montage.py#L155-L182
train
23,659
wonambi-python/wonambi
wonambi/widgets/utils.py
keep_recent_datasets
def keep_recent_datasets(max_dataset_history, info=None): """Keep track of the most recent recordings. Parameters ---------- max_dataset_history : int maximum number of datasets to remember info : str, optional TODO path to file Returns ------- list of str paths to most recent datasets (only if you don't specify new_dataset) """ history = settings.value('recent_recordings', []) if isinstance(history, str): history = [history] if info is not None and info.filename is not None: new_dataset = info.filename if new_dataset in history: lg.debug(new_dataset + ' already present, will be replaced') history.remove(new_dataset) if len(history) > max_dataset_history: lg.debug('Removing last dataset ' + history[-1]) history.pop() lg.debug('Adding ' + new_dataset + ' to list of recent datasets') history.insert(0, new_dataset) settings.setValue('recent_recordings', history) return None else: return history
python
def keep_recent_datasets(max_dataset_history, info=None): """Keep track of the most recent recordings. Parameters ---------- max_dataset_history : int maximum number of datasets to remember info : str, optional TODO path to file Returns ------- list of str paths to most recent datasets (only if you don't specify new_dataset) """ history = settings.value('recent_recordings', []) if isinstance(history, str): history = [history] if info is not None and info.filename is not None: new_dataset = info.filename if new_dataset in history: lg.debug(new_dataset + ' already present, will be replaced') history.remove(new_dataset) if len(history) > max_dataset_history: lg.debug('Removing last dataset ' + history[-1]) history.pop() lg.debug('Adding ' + new_dataset + ' to list of recent datasets') history.insert(0, new_dataset) settings.setValue('recent_recordings', history) return None else: return history
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Keep track of the most recent recordings. Parameters ---------- max_dataset_history : int maximum number of datasets to remember info : str, optional TODO path to file Returns ------- list of str paths to most recent datasets (only if you don't specify new_dataset)
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L687-L723
train
23,660
wonambi-python/wonambi
wonambi/widgets/utils.py
choose_file_or_dir
def choose_file_or_dir(): """Create a simple message box to see if the user wants to open dir or file Returns ------- str 'dir' or 'file' or 'abort' """ question = QMessageBox(QMessageBox.Information, 'Open Dataset', 'Do you want to open a file or a directory?') dir_button = question.addButton('Directory', QMessageBox.YesRole) file_button = question.addButton('File', QMessageBox.NoRole) question.addButton(QMessageBox.Cancel) question.exec_() response = question.clickedButton() if response == dir_button: return 'dir' elif response == file_button: return 'file' else: return 'abort'
python
def choose_file_or_dir(): """Create a simple message box to see if the user wants to open dir or file Returns ------- str 'dir' or 'file' or 'abort' """ question = QMessageBox(QMessageBox.Information, 'Open Dataset', 'Do you want to open a file or a directory?') dir_button = question.addButton('Directory', QMessageBox.YesRole) file_button = question.addButton('File', QMessageBox.NoRole) question.addButton(QMessageBox.Cancel) question.exec_() response = question.clickedButton() if response == dir_button: return 'dir' elif response == file_button: return 'file' else: return 'abort'
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Create a simple message box to see if the user wants to open dir or file Returns ------- str 'dir' or 'file' or 'abort'
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L726-L748
train
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wonambi-python/wonambi
wonambi/widgets/utils.py
convert_name_to_color
def convert_name_to_color(s): """Convert any string to an RGB color. Parameters ---------- s : str string to convert selection : bool, optional if an event is being selected, it's lighter Returns ------- instance of QColor one of the possible color Notes ----- It takes any string and converts it to RGB color. The same string always returns the same color. The numbers are a bit arbitrary but not completely. h is the baseline color (keep it high to have brighter colors). Make sure that the max module + h is less than 256 (RGB limit). The number you multiply ord for is necessary to differentiate the letters (otherwise 'r' and 's' are too close to each other). """ h = 100 v = [5 * ord(x) for x in s] sum_mod = lambda x: sum(x) % 100 color = QColor(sum_mod(v[::3]) + h, sum_mod(v[1::3]) + h, sum_mod(v[2::3]) + h) return color
python
def convert_name_to_color(s): """Convert any string to an RGB color. Parameters ---------- s : str string to convert selection : bool, optional if an event is being selected, it's lighter Returns ------- instance of QColor one of the possible color Notes ----- It takes any string and converts it to RGB color. The same string always returns the same color. The numbers are a bit arbitrary but not completely. h is the baseline color (keep it high to have brighter colors). Make sure that the max module + h is less than 256 (RGB limit). The number you multiply ord for is necessary to differentiate the letters (otherwise 'r' and 's' are too close to each other). """ h = 100 v = [5 * ord(x) for x in s] sum_mod = lambda x: sum(x) % 100 color = QColor(sum_mod(v[::3]) + h, sum_mod(v[1::3]) + h, sum_mod(v[2::3]) + h) return color
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L760-L790
train
23,662
wonambi-python/wonambi
wonambi/widgets/utils.py
freq_from_str
def freq_from_str(freq_str): """Obtain frequency ranges from input string, either as list or dynamic notation. Parameters ---------- freq_str : str String with frequency ranges, either as a list: e.g. [[1-3], [3-5], [5-8]]; or with a dynamic definition: (start, stop, width, step). Returns ------- list of tuple of float or None Every tuple of float represents a frequency band. If input is invalid, returns None. """ freq = [] as_list = freq_str[1:-1].replace(' ', '').split(',') try: if freq_str[0] == '[' and freq_str[-1] == ']': for i in as_list: one_band = i[1:-1].split('-') one_band = float(one_band[0]), float(one_band[1]) freq.append(one_band) elif freq_str[0] == '(' and freq_str[-1] == ')': if len(as_list) == 4: start = float(as_list[0]) stop = float(as_list[1]) halfwidth = float(as_list[2]) / 2 step = float(as_list[3]) centres = arange(start, stop, step) for i in centres: freq.append((i - halfwidth, i + halfwidth)) else: return None else: return None except: return None return freq
python
def freq_from_str(freq_str): """Obtain frequency ranges from input string, either as list or dynamic notation. Parameters ---------- freq_str : str String with frequency ranges, either as a list: e.g. [[1-3], [3-5], [5-8]]; or with a dynamic definition: (start, stop, width, step). Returns ------- list of tuple of float or None Every tuple of float represents a frequency band. If input is invalid, returns None. """ freq = [] as_list = freq_str[1:-1].replace(' ', '').split(',') try: if freq_str[0] == '[' and freq_str[-1] == ']': for i in as_list: one_band = i[1:-1].split('-') one_band = float(one_band[0]), float(one_band[1]) freq.append(one_band) elif freq_str[0] == '(' and freq_str[-1] == ')': if len(as_list) == 4: start = float(as_list[0]) stop = float(as_list[1]) halfwidth = float(as_list[2]) / 2 step = float(as_list[3]) centres = arange(start, stop, step) for i in centres: freq.append((i - halfwidth, i + halfwidth)) else: return None else: return None except: return None return freq
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L793-L838
train
23,663
wonambi-python/wonambi
wonambi/widgets/utils.py
export_graphics_to_svg
def export_graphics_to_svg(widget, filename): """Export graphics to svg Parameters ---------- widget : instance of QGraphicsView traces or overview filename : str path to save svg """ generator = QSvgGenerator() generator.setFileName(filename) generator.setSize(widget.size()) generator.setViewBox(widget.rect()) painter = QPainter() painter.begin(generator) widget.render(painter) painter.end()
python
def export_graphics_to_svg(widget, filename): """Export graphics to svg Parameters ---------- widget : instance of QGraphicsView traces or overview filename : str path to save svg """ generator = QSvgGenerator() generator.setFileName(filename) generator.setSize(widget.size()) generator.setViewBox(widget.rect()) painter = QPainter() painter.begin(generator) widget.render(painter) painter.end()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L869-L887
train
23,664
wonambi-python/wonambi
wonambi/widgets/utils.py
FormList.get_value
def get_value(self, default=None): """Get int from widget. Parameters ---------- default : list list with widgets Returns ------- list list that might contain int or str or float etc """ if default is None: default = [] try: text = literal_eval(self.text()) if not isinstance(text, list): pass # raise ValueError except ValueError: lg.debug('Cannot convert "' + str(text) + '" to list. ' + 'Using default ' + str(default)) text = default self.set_value(text) return text
python
def get_value(self, default=None): """Get int from widget. Parameters ---------- default : list list with widgets Returns ------- list list that might contain int or str or float etc """ if default is None: default = [] try: text = literal_eval(self.text()) if not isinstance(text, list): pass # raise ValueError except ValueError: lg.debug('Cannot convert "' + str(text) + '" to list. ' + 'Using default ' + str(default)) text = default self.set_value(text) return text
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L390-L419
train
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wonambi-python/wonambi
wonambi/widgets/utils.py
FormDir.connect
def connect(self, funct): """Call funct when the text was changed. Parameters ---------- funct : function function that broadcasts a change. Notes ----- There is something wrong here. When you run this function, it calls for opening a directory three or four times. This is obviously wrong but I don't understand why this happens three times. Traceback did not help. """ def get_directory(): rec = QFileDialog.getExistingDirectory(self, 'Path to Recording' ' Directory') if rec == '': return self.setText(rec) funct() self.clicked.connect(get_directory)
python
def connect(self, funct): """Call funct when the text was changed. Parameters ---------- funct : function function that broadcasts a change. Notes ----- There is something wrong here. When you run this function, it calls for opening a directory three or four times. This is obviously wrong but I don't understand why this happens three times. Traceback did not help. """ def get_directory(): rec = QFileDialog.getExistingDirectory(self, 'Path to Recording' ' Directory') if rec == '': return self.setText(rec) funct() self.clicked.connect(get_directory)
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L528-L554
train
23,666
wonambi-python/wonambi
wonambi/widgets/utils.py
FormMenu.get_value
def get_value(self, default=None): """Get selection from widget. Parameters ---------- default : str str for use by widget Returns ------- str selected item from the combobox """ if default is None: default = '' try: text = self.currentText() except ValueError: lg.debug('Cannot convert "' + str(text) + '" to list. ' + 'Using default ' + str(default)) text = default self.set_value(text) return text
python
def get_value(self, default=None): """Get selection from widget. Parameters ---------- default : str str for use by widget Returns ------- str selected item from the combobox """ if default is None: default = '' try: text = self.currentText() except ValueError: lg.debug('Cannot convert "' + str(text) + '" to list. ' + 'Using default ' + str(default)) text = default self.set_value(text) return text
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Get selection from widget. Parameters ---------- default : str str for use by widget Returns ------- str selected item from the combobox
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/widgets/utils.py#L572-L598
train
23,667
wonambi-python/wonambi
wonambi/ioeeg/bids.py
_write_ieeg_json
def _write_ieeg_json(output_file): """Use only required fields """ dataset_info = { "TaskName": "unknown", "Manufacturer": "n/a", "PowerLineFrequency": 50, "iEEGReference": "n/a", } with output_file.open('w') as f: dump(dataset_info, f, indent=' ')
python
def _write_ieeg_json(output_file): """Use only required fields """ dataset_info = { "TaskName": "unknown", "Manufacturer": "n/a", "PowerLineFrequency": 50, "iEEGReference": "n/a", } with output_file.open('w') as f: dump(dataset_info, f, indent=' ')
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Use only required fields
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/bids.py#L115-L126
train
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wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.value
def value(self, parameter, new_value=None): """This function is a shortcut for any parameter. Instead of calling the widget, its config and its values, you can call directly the parameter. Parameters ---------- parameter : str name of the parameter of interest new_value : str or float, optional new value for the parameter Returns ------- str or float if you didn't specify new_value, it returns the current value. Notes ----- It's important to maintain an organized dict in DEFAULTS which has to correspond to the values in the widgets, also the name of the widget. DEFAULTS is used like a look-up table. """ for widget_name, values in DEFAULTS.items(): if parameter in values.keys(): widget = getattr(self, widget_name) if new_value is None: return widget.config.value[parameter] else: lg.debug('setting value {0} of {1} to {2}' ''.format(parameter, widget_name, new_value)) widget.config.value[parameter] = new_value
python
def value(self, parameter, new_value=None): """This function is a shortcut for any parameter. Instead of calling the widget, its config and its values, you can call directly the parameter. Parameters ---------- parameter : str name of the parameter of interest new_value : str or float, optional new value for the parameter Returns ------- str or float if you didn't specify new_value, it returns the current value. Notes ----- It's important to maintain an organized dict in DEFAULTS which has to correspond to the values in the widgets, also the name of the widget. DEFAULTS is used like a look-up table. """ for widget_name, values in DEFAULTS.items(): if parameter in values.keys(): widget = getattr(self, widget_name) if new_value is None: return widget.config.value[parameter] else: lg.debug('setting value {0} of {1} to {2}' ''.format(parameter, widget_name, new_value)) widget.config.value[parameter] = new_value
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This function is a shortcut for any parameter. Instead of calling the widget, its config and its values, you can call directly the parameter. Parameters ---------- parameter : str name of the parameter of interest new_value : str or float, optional new value for the parameter Returns ------- str or float if you didn't specify new_value, it returns the current value. Notes ----- It's important to maintain an organized dict in DEFAULTS which has to correspond to the values in the widgets, also the name of the widget. DEFAULTS is used like a look-up table.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L88-L119
train
23,669
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.update
def update(self): """Once you open a dataset, it activates all the widgets. """ self.info.display_dataset() self.overview.update() self.labels.update(labels=self.info.dataset.header['chan_name']) self.channels.update() try: self.info.markers = self.info.dataset.read_markers() except FileNotFoundError: lg.info('No notes/markers present in the header of the file') else: self.notes.update_dataset_marker()
python
def update(self): """Once you open a dataset, it activates all the widgets. """ self.info.display_dataset() self.overview.update() self.labels.update(labels=self.info.dataset.header['chan_name']) self.channels.update() try: self.info.markers = self.info.dataset.read_markers() except FileNotFoundError: lg.info('No notes/markers present in the header of the file') else: self.notes.update_dataset_marker()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L121-L134
train
23,670
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.reset
def reset(self): """Remove all the information from previous dataset before loading a new dataset. """ # store current dataset max_dataset_history = self.value('max_dataset_history') keep_recent_datasets(max_dataset_history, self.info) # reset all the widgets self.labels.reset() self.channels.reset() self.info.reset() self.notes.reset() self.overview.reset() self.spectrum.reset() self.traces.reset()
python
def reset(self): """Remove all the information from previous dataset before loading a new dataset. """ # store current dataset max_dataset_history = self.value('max_dataset_history') keep_recent_datasets(max_dataset_history, self.info) # reset all the widgets self.labels.reset() self.channels.reset() self.info.reset() self.notes.reset() self.overview.reset() self.spectrum.reset() self.traces.reset()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L136-L152
train
23,671
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.show_settings
def show_settings(self): """Open the Setting windows, after updating the values in GUI. """ self.notes.config.put_values() self.overview.config.put_values() self.settings.config.put_values() self.spectrum.config.put_values() self.traces.config.put_values() self.video.config.put_values() self.settings.show()
python
def show_settings(self): """Open the Setting windows, after updating the values in GUI. """ self.notes.config.put_values() self.overview.config.put_values() self.settings.config.put_values() self.spectrum.config.put_values() self.traces.config.put_values() self.video.config.put_values() self.settings.show()
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Open the Setting windows, after updating the values in GUI.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L154-L163
train
23,672
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.show_spindle_dialog
def show_spindle_dialog(self): """Create the spindle detection dialog.""" self.spindle_dialog.update_groups() self.spindle_dialog.update_cycles() self.spindle_dialog.show()
python
def show_spindle_dialog(self): """Create the spindle detection dialog.""" self.spindle_dialog.update_groups() self.spindle_dialog.update_cycles() self.spindle_dialog.show()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L180-L184
train
23,673
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.show_slow_wave_dialog
def show_slow_wave_dialog(self): """Create the SW detection dialog.""" self.slow_wave_dialog.update_groups() self.slow_wave_dialog.update_cycles() self.slow_wave_dialog.show()
python
def show_slow_wave_dialog(self): """Create the SW detection dialog.""" self.slow_wave_dialog.update_groups() self.slow_wave_dialog.update_cycles() self.slow_wave_dialog.show()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L186-L190
train
23,674
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.show_event_analysis_dialog
def show_event_analysis_dialog(self): """Create the event analysis dialog.""" self.event_analysis_dialog.update_types() self.event_analysis_dialog.update_groups() self.event_analysis_dialog.update_cycles() self.event_analysis_dialog.show()
python
def show_event_analysis_dialog(self): """Create the event analysis dialog.""" self.event_analysis_dialog.update_types() self.event_analysis_dialog.update_groups() self.event_analysis_dialog.update_cycles() self.event_analysis_dialog.show()
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Create the event analysis dialog.
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L192-L197
train
23,675
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.show_analysis_dialog
def show_analysis_dialog(self): """Create the analysis dialog.""" self.analysis_dialog.update_evt_types() self.analysis_dialog.update_groups() self.analysis_dialog.update_cycles() self.analysis_dialog.show()
python
def show_analysis_dialog(self): """Create the analysis dialog.""" self.analysis_dialog.update_evt_types() self.analysis_dialog.update_groups() self.analysis_dialog.update_cycles() self.analysis_dialog.show()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L199-L204
train
23,676
wonambi-python/wonambi
wonambi/scroll_data.py
MainWindow.closeEvent
def closeEvent(self, event): """save the name of the last open dataset.""" max_dataset_history = self.value('max_dataset_history') keep_recent_datasets(max_dataset_history, self.info) settings.setValue('window/geometry', self.saveGeometry()) settings.setValue('window/state', self.saveState()) event.accept()
python
def closeEvent(self, event): """save the name of the last open dataset.""" max_dataset_history = self.value('max_dataset_history') keep_recent_datasets(max_dataset_history, self.info) settings.setValue('window/geometry', self.saveGeometry()) settings.setValue('window/state', self.saveState()) event.accept()
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/scroll_data.py#L243-L251
train
23,677
wonambi-python/wonambi
wonambi/ioeeg/bci2000.py
_read_header
def _read_header(filename): """It's a pain to parse the header. It might be better to use the cpp code but I would need to include it here. """ header = _read_header_text(filename) first_row = header[0] EXTRA_ROWS = 3 # drop DefaultValue1 LowRange1 HighRange1 hdr = {} for group in finditer('(\w*)= ([\w.]*)', first_row): hdr[group.group(1)] = group.group(2) if first_row.startswith('BCI2000V'): VERSION = hdr['BCI2000V'] else: VERSION = '1' hdr['DataFormat'] = 'int16' for row in header[1:]: if row.startswith('['): # remove '[ ... Definition ]' section = row[2:-14].replace(' ', '') if section == 'StateVector': hdr[section] = [] else: hdr[section] = {} # defaultdict(dict) continue if row == '': continue elif section == 'StateVector': statevector = {key: value for key, value in list(zip(STATEVECTOR, row.split(' ')))} hdr[section].append(statevector) else: group = match('(?P<subsection>[\w:%]*) (?P<format>\w*) (?P<key>\w*)= (?P<value>.*) // ', row) onerow = group.groupdict() values = onerow['value'].split(' ') if len(values) > EXTRA_ROWS: value = ' '.join(onerow['value'].split(' ')[:-EXTRA_ROWS]) else: value = ' '.join(values) hdr[section][onerow['key']] = value # similar to matlab's output return hdr
python
def _read_header(filename): """It's a pain to parse the header. It might be better to use the cpp code but I would need to include it here. """ header = _read_header_text(filename) first_row = header[0] EXTRA_ROWS = 3 # drop DefaultValue1 LowRange1 HighRange1 hdr = {} for group in finditer('(\w*)= ([\w.]*)', first_row): hdr[group.group(1)] = group.group(2) if first_row.startswith('BCI2000V'): VERSION = hdr['BCI2000V'] else: VERSION = '1' hdr['DataFormat'] = 'int16' for row in header[1:]: if row.startswith('['): # remove '[ ... Definition ]' section = row[2:-14].replace(' ', '') if section == 'StateVector': hdr[section] = [] else: hdr[section] = {} # defaultdict(dict) continue if row == '': continue elif section == 'StateVector': statevector = {key: value for key, value in list(zip(STATEVECTOR, row.split(' ')))} hdr[section].append(statevector) else: group = match('(?P<subsection>[\w:%]*) (?P<format>\w*) (?P<key>\w*)= (?P<value>.*) // ', row) onerow = group.groupdict() values = onerow['value'].split(' ') if len(values) > EXTRA_ROWS: value = ' '.join(onerow['value'].split(' ')[:-EXTRA_ROWS]) else: value = ' '.join(values) hdr[section][onerow['key']] = value # similar to matlab's output return hdr
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/ioeeg/bci2000.py#L212-L260
train
23,678
wonambi-python/wonambi
wonambi/trans/reject.py
remove_artf_evts
def remove_artf_evts(times, annot, chan=None, min_dur=0.1): """Correct times to remove events marked 'Artefact'. Parameters ---------- times : list of tuple of float the start and end times of each segment annot : instance of Annotations the annotation file containing events and epochs chan : str, optional full name of channel on which artefacts were marked. Channel format is 'chan_name (group_name)'. If None, artefacts from any channel will be removed. min_dur : float resulting segments, after concatenation, are rejected if shorter than this duration Returns ------- list of tuple of float the new start and end times of each segment, with artefact periods taken out """ new_times = times beg = times[0][0] end = times[-1][-1] chan = (chan, '') if chan else None # '' is for channel-global artefacts artefact = annot.get_events(name='Artefact', time=(beg, end), chan=chan, qual='Good') if artefact: new_times = [] for seg in times: reject = False new_seg = True while new_seg is not False: if type(new_seg) is tuple: seg = new_seg end = seg[1] for artf in artefact: if artf['start'] <= seg[0] and seg[1] <= artf['end']: reject = True new_seg = False break a_starts_in_s = seg[0] <= artf['start'] <= seg[1] a_ends_in_s = seg[0] <= artf['end'] <= seg[1] if a_ends_in_s and not a_starts_in_s: seg = artf['end'], seg[1] elif a_starts_in_s: seg = seg[0], artf['start'] if a_ends_in_s: new_seg = artf['end'], end else: new_seg = False break new_seg = False if reject is False and seg[1] - seg[0] >= min_dur: new_times.append(seg) return new_times
python
def remove_artf_evts(times, annot, chan=None, min_dur=0.1): """Correct times to remove events marked 'Artefact'. Parameters ---------- times : list of tuple of float the start and end times of each segment annot : instance of Annotations the annotation file containing events and epochs chan : str, optional full name of channel on which artefacts were marked. Channel format is 'chan_name (group_name)'. If None, artefacts from any channel will be removed. min_dur : float resulting segments, after concatenation, are rejected if shorter than this duration Returns ------- list of tuple of float the new start and end times of each segment, with artefact periods taken out """ new_times = times beg = times[0][0] end = times[-1][-1] chan = (chan, '') if chan else None # '' is for channel-global artefacts artefact = annot.get_events(name='Artefact', time=(beg, end), chan=chan, qual='Good') if artefact: new_times = [] for seg in times: reject = False new_seg = True while new_seg is not False: if type(new_seg) is tuple: seg = new_seg end = seg[1] for artf in artefact: if artf['start'] <= seg[0] and seg[1] <= artf['end']: reject = True new_seg = False break a_starts_in_s = seg[0] <= artf['start'] <= seg[1] a_ends_in_s = seg[0] <= artf['end'] <= seg[1] if a_ends_in_s and not a_starts_in_s: seg = artf['end'], seg[1] elif a_starts_in_s: seg = seg[0], artf['start'] if a_ends_in_s: new_seg = artf['end'], end else: new_seg = False break new_seg = False if reject is False and seg[1] - seg[0] >= min_dur: new_times.append(seg) return new_times
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1d8e3d7e53df8017c199f703bcab582914676e76
https://github.com/wonambi-python/wonambi/blob/1d8e3d7e53df8017c199f703bcab582914676e76/wonambi/trans/reject.py#L13-L83
train
23,679
SuLab/WikidataIntegrator
wikidataintegrator/wdi_core.py
WDBaseDataType.statement_ref_mode
def statement_ref_mode(self, value): """Set the reference mode for a statement, always overrides the global reference state.""" valid_values = ['STRICT_KEEP', 'STRICT_KEEP_APPEND', 'STRICT_OVERWRITE', 'KEEP_GOOD', 'CUSTOM'] if value not in valid_values: raise ValueError('Not an allowed reference mode, allowed values {}'.format(' '.join(valid_values))) self._statement_ref_mode = value
python
def statement_ref_mode(self, value): """Set the reference mode for a statement, always overrides the global reference state.""" valid_values = ['STRICT_KEEP', 'STRICT_KEEP_APPEND', 'STRICT_OVERWRITE', 'KEEP_GOOD', 'CUSTOM'] if value not in valid_values: raise ValueError('Not an allowed reference mode, allowed values {}'.format(' '.join(valid_values))) self._statement_ref_mode = value
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8ceb2ed1c08fec070ec9edfcf7db7b8691481b62
https://github.com/SuLab/WikidataIntegrator/blob/8ceb2ed1c08fec070ec9edfcf7db7b8691481b62/wikidataintegrator/wdi_core.py#L1641-L1647
train
23,680
SuLab/WikidataIntegrator
wikidataintegrator/wdi_core.py
WDBaseDataType.equals
def equals(self, that, include_ref=False, fref=None): """ Tests for equality of two statements. If comparing references, the order of the arguments matters!!! self is the current statement, the next argument is the new statement. Allows passing in a function to use to compare the references 'fref'. Default is equality. fref accepts two arguments 'oldrefs' and 'newrefs', each of which are a list of references, where each reference is a list of statements """ if not include_ref: # return the result of WDBaseDataType.__eq__, which is testing for equality of value and qualifiers return self == that if include_ref and self != that: return False if include_ref and fref is None: fref = WDBaseDataType.refs_equal return fref(self, that)
python
def equals(self, that, include_ref=False, fref=None): """ Tests for equality of two statements. If comparing references, the order of the arguments matters!!! self is the current statement, the next argument is the new statement. Allows passing in a function to use to compare the references 'fref'. Default is equality. fref accepts two arguments 'oldrefs' and 'newrefs', each of which are a list of references, where each reference is a list of statements """ if not include_ref: # return the result of WDBaseDataType.__eq__, which is testing for equality of value and qualifiers return self == that if include_ref and self != that: return False if include_ref and fref is None: fref = WDBaseDataType.refs_equal return fref(self, that)
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8ceb2ed1c08fec070ec9edfcf7db7b8691481b62
https://github.com/SuLab/WikidataIntegrator/blob/8ceb2ed1c08fec070ec9edfcf7db7b8691481b62/wikidataintegrator/wdi_core.py#L1798-L1814
train
23,681
SuLab/WikidataIntegrator
wikidataintegrator/wdi_core.py
WDBaseDataType.refs_equal
def refs_equal(olditem, newitem): """ tests for exactly identical references """ oldrefs = olditem.references newrefs = newitem.references ref_equal = lambda oldref, newref: True if (len(oldref) == len(newref)) and all( x in oldref for x in newref) else False if len(oldrefs) == len(newrefs) and all( any(ref_equal(oldref, newref) for oldref in oldrefs) for newref in newrefs): return True else: return False
python
def refs_equal(olditem, newitem): """ tests for exactly identical references """ oldrefs = olditem.references newrefs = newitem.references ref_equal = lambda oldref, newref: True if (len(oldref) == len(newref)) and all( x in oldref for x in newref) else False if len(oldrefs) == len(newrefs) and all( any(ref_equal(oldref, newref) for oldref in oldrefs) for newref in newrefs): return True else: return False
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tests for exactly identical references
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8ceb2ed1c08fec070ec9edfcf7db7b8691481b62
https://github.com/SuLab/WikidataIntegrator/blob/8ceb2ed1c08fec070ec9edfcf7db7b8691481b62/wikidataintegrator/wdi_core.py#L1817-L1830
train
23,682
SuLab/WikidataIntegrator
wikidataintegrator/wdi_helpers/__init__.py
try_write
def try_write(wd_item, record_id, record_prop, login, edit_summary='', write=True): """ Write a PBB_core item. Log if item was created, updated, or skipped. Catch and log all errors. :param wd_item: A wikidata item that will be written :type wd_item: PBB_Core.WDItemEngine :param record_id: An external identifier, to be used for logging :type record_id: str :param record_prop: Property of the external identifier :type record_prop: str :param login: PBB_core login instance :type login: PBB_login.WDLogin :param edit_summary: passed directly to wd_item.write :type edit_summary: str :param write: If `False`, do not actually perform write. Action will be logged as if write had occured :type write: bool :return: True if write did not throw an exception, returns the exception otherwise """ if wd_item.require_write: if wd_item.create_new_item: msg = "CREATE" else: msg = "UPDATE" else: msg = "SKIP" try: if write: wd_item.write(login=login, edit_summary=edit_summary) wdi_core.WDItemEngine.log("INFO", format_msg(record_id, record_prop, wd_item.wd_item_id, msg) + ";" + str( wd_item.lastrevid)) except wdi_core.WDApiError as e: print(e) wdi_core.WDItemEngine.log("ERROR", format_msg(record_id, record_prop, wd_item.wd_item_id, json.dumps(e.wd_error_msg), type(e))) return e except Exception as e: print(e) wdi_core.WDItemEngine.log("ERROR", format_msg(record_id, record_prop, wd_item.wd_item_id, str(e), type(e))) return e return True
python
def try_write(wd_item, record_id, record_prop, login, edit_summary='', write=True): """ Write a PBB_core item. Log if item was created, updated, or skipped. Catch and log all errors. :param wd_item: A wikidata item that will be written :type wd_item: PBB_Core.WDItemEngine :param record_id: An external identifier, to be used for logging :type record_id: str :param record_prop: Property of the external identifier :type record_prop: str :param login: PBB_core login instance :type login: PBB_login.WDLogin :param edit_summary: passed directly to wd_item.write :type edit_summary: str :param write: If `False`, do not actually perform write. Action will be logged as if write had occured :type write: bool :return: True if write did not throw an exception, returns the exception otherwise """ if wd_item.require_write: if wd_item.create_new_item: msg = "CREATE" else: msg = "UPDATE" else: msg = "SKIP" try: if write: wd_item.write(login=login, edit_summary=edit_summary) wdi_core.WDItemEngine.log("INFO", format_msg(record_id, record_prop, wd_item.wd_item_id, msg) + ";" + str( wd_item.lastrevid)) except wdi_core.WDApiError as e: print(e) wdi_core.WDItemEngine.log("ERROR", format_msg(record_id, record_prop, wd_item.wd_item_id, json.dumps(e.wd_error_msg), type(e))) return e except Exception as e: print(e) wdi_core.WDItemEngine.log("ERROR", format_msg(record_id, record_prop, wd_item.wd_item_id, str(e), type(e))) return e return True
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8ceb2ed1c08fec070ec9edfcf7db7b8691481b62
https://github.com/SuLab/WikidataIntegrator/blob/8ceb2ed1c08fec070ec9edfcf7db7b8691481b62/wikidataintegrator/wdi_helpers/__init__.py#L58-L101
train
23,683
maykinmedia/django-admin-index
django_admin_index/compat/django18.py
_build_app_dict
def _build_app_dict(site, request, label=None): """ Builds the app dictionary. Takes an optional label parameters to filter models of a specific app. """ app_dict = {} if label: models = { m: m_a for m, m_a in site._registry.items() if m._meta.app_label == label } else: models = site._registry for model, model_admin in models.items(): app_label = model._meta.app_label has_module_perms = model_admin.has_module_permission(request) if not has_module_perms: continue perms = model_admin.get_model_perms(request) # Check whether user has any perm for this module. # If so, add the module to the model_list. if True not in perms.values(): continue info = (app_label, model._meta.model_name) model_dict = { 'name': capfirst(model._meta.verbose_name_plural), 'object_name': model._meta.object_name, 'perms': perms, } if perms.get('change'): try: model_dict['admin_url'] = reverse('admin:%s_%s_changelist' % info, current_app=site.name) except NoReverseMatch: pass if perms.get('add'): try: model_dict['add_url'] = reverse('admin:%s_%s_add' % info, current_app=site.name) except NoReverseMatch: pass if app_label in app_dict: app_dict[app_label]['models'].append(model_dict) else: app_dict[app_label] = { 'name': apps.get_app_config(app_label).verbose_name, 'app_label': app_label, 'app_url': reverse( 'admin:app_list', kwargs={'app_label': app_label}, current_app=site.name, ), 'has_module_perms': has_module_perms, 'models': [model_dict], } if label: return app_dict.get(label) return app_dict
python
def _build_app_dict(site, request, label=None): """ Builds the app dictionary. Takes an optional label parameters to filter models of a specific app. """ app_dict = {} if label: models = { m: m_a for m, m_a in site._registry.items() if m._meta.app_label == label } else: models = site._registry for model, model_admin in models.items(): app_label = model._meta.app_label has_module_perms = model_admin.has_module_permission(request) if not has_module_perms: continue perms = model_admin.get_model_perms(request) # Check whether user has any perm for this module. # If so, add the module to the model_list. if True not in perms.values(): continue info = (app_label, model._meta.model_name) model_dict = { 'name': capfirst(model._meta.verbose_name_plural), 'object_name': model._meta.object_name, 'perms': perms, } if perms.get('change'): try: model_dict['admin_url'] = reverse('admin:%s_%s_changelist' % info, current_app=site.name) except NoReverseMatch: pass if perms.get('add'): try: model_dict['add_url'] = reverse('admin:%s_%s_add' % info, current_app=site.name) except NoReverseMatch: pass if app_label in app_dict: app_dict[app_label]['models'].append(model_dict) else: app_dict[app_label] = { 'name': apps.get_app_config(app_label).verbose_name, 'app_label': app_label, 'app_url': reverse( 'admin:app_list', kwargs={'app_label': app_label}, current_app=site.name, ), 'has_module_perms': has_module_perms, 'models': [model_dict], } if label: return app_dict.get(label) return app_dict
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5bfd90e6945775865d722e0f5058824769f68f04
https://github.com/maykinmedia/django-admin-index/blob/5bfd90e6945775865d722e0f5058824769f68f04/django_admin_index/compat/django18.py#L13-L76
train
23,684
maykinmedia/django-admin-index
django_admin_index/compat/django18.py
get_app_list
def get_app_list(site, request): """ Returns a sorted list of all the installed apps that have been registered in this site. """ app_dict = _build_app_dict(site, request) # Sort the apps alphabetically. app_list = sorted(app_dict.values(), key=lambda x: x['name'].lower()) # Sort the models alphabetically within each app. for app in app_list: app['models'].sort(key=lambda x: x['name']) return app_list
python
def get_app_list(site, request): """ Returns a sorted list of all the installed apps that have been registered in this site. """ app_dict = _build_app_dict(site, request) # Sort the apps alphabetically. app_list = sorted(app_dict.values(), key=lambda x: x['name'].lower()) # Sort the models alphabetically within each app. for app in app_list: app['models'].sort(key=lambda x: x['name']) return app_list
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5bfd90e6945775865d722e0f5058824769f68f04
https://github.com/maykinmedia/django-admin-index/blob/5bfd90e6945775865d722e0f5058824769f68f04/django_admin_index/compat/django18.py#L79-L93
train
23,685
SuLab/WikidataIntegrator
wikidataintegrator/wdi_fastrun.py
FastRunContainer.format_query_results
def format_query_results(self, r, prop_nr): """ `r` is the results of the sparql query in _query_data and is modified in place `prop_nr` is needed to get the property datatype to determine how to format the value `r` is a list of dicts. The keys are: item: the subject. the item this statement is on v: the object. The value for this statement sid: statement ID pq: qualifier property qval: qualifier value ref: reference ID pr: reference property rval: reference value """ prop_dt = self.get_prop_datatype(prop_nr) for i in r: for value in {'item', 'sid', 'pq', 'pr', 'ref'}: if value in i: # these are always URIs for the local wikibase i[value] = i[value]['value'].split('/')[-1] # make sure datetimes are formatted correctly. # the correct format is '+%Y-%m-%dT%H:%M:%SZ', but is sometimes missing the plus?? # some difference between RDF and xsd:dateTime that I don't understand for value in {'v', 'qval', 'rval'}: if value in i: if i[value].get("datatype") == 'http://www.w3.org/2001/XMLSchema#dateTime' and not \ i[value]['value'][0] in '+-': # if it is a dateTime and doesn't start with plus or minus, add a plus i[value]['value'] = '+' + i[value]['value'] # these three ({'v', 'qval', 'rval'}) are values that can be any data type # strip off the URI if they are wikibase-items if 'v' in i: if i['v']['type'] == 'uri' and prop_dt == 'wikibase-item': i['v'] = i['v']['value'].split('/')[-1] else: i['v'] = i['v']['value'] # Note: no-value and some-value don't actually show up in the results here # see for example: select * where { wd:Q7207 p:P40 ?c . ?c ?d ?e } if type(i['v']) is not dict: self.rev_lookup[i['v']].add(i['item']) # handle qualifier value if 'qval' in i: qual_prop_dt = self.get_prop_datatype(prop_nr=i['pq']) if i['qval']['type'] == 'uri' and qual_prop_dt == 'wikibase-item': i['qval'] = i['qval']['value'].split('/')[-1] else: i['qval'] = i['qval']['value'] # handle reference value if 'rval' in i: ref_prop_dt = self.get_prop_datatype(prop_nr=i['pr']) if i['rval']['type'] == 'uri' and ref_prop_dt == 'wikibase-item': i['rval'] = i['rval']['value'].split('/')[-1] else: i['rval'] = i['rval']['value']
python
def format_query_results(self, r, prop_nr): """ `r` is the results of the sparql query in _query_data and is modified in place `prop_nr` is needed to get the property datatype to determine how to format the value `r` is a list of dicts. The keys are: item: the subject. the item this statement is on v: the object. The value for this statement sid: statement ID pq: qualifier property qval: qualifier value ref: reference ID pr: reference property rval: reference value """ prop_dt = self.get_prop_datatype(prop_nr) for i in r: for value in {'item', 'sid', 'pq', 'pr', 'ref'}: if value in i: # these are always URIs for the local wikibase i[value] = i[value]['value'].split('/')[-1] # make sure datetimes are formatted correctly. # the correct format is '+%Y-%m-%dT%H:%M:%SZ', but is sometimes missing the plus?? # some difference between RDF and xsd:dateTime that I don't understand for value in {'v', 'qval', 'rval'}: if value in i: if i[value].get("datatype") == 'http://www.w3.org/2001/XMLSchema#dateTime' and not \ i[value]['value'][0] in '+-': # if it is a dateTime and doesn't start with plus or minus, add a plus i[value]['value'] = '+' + i[value]['value'] # these three ({'v', 'qval', 'rval'}) are values that can be any data type # strip off the URI if they are wikibase-items if 'v' in i: if i['v']['type'] == 'uri' and prop_dt == 'wikibase-item': i['v'] = i['v']['value'].split('/')[-1] else: i['v'] = i['v']['value'] # Note: no-value and some-value don't actually show up in the results here # see for example: select * where { wd:Q7207 p:P40 ?c . ?c ?d ?e } if type(i['v']) is not dict: self.rev_lookup[i['v']].add(i['item']) # handle qualifier value if 'qval' in i: qual_prop_dt = self.get_prop_datatype(prop_nr=i['pq']) if i['qval']['type'] == 'uri' and qual_prop_dt == 'wikibase-item': i['qval'] = i['qval']['value'].split('/')[-1] else: i['qval'] = i['qval']['value'] # handle reference value if 'rval' in i: ref_prop_dt = self.get_prop_datatype(prop_nr=i['pr']) if i['rval']['type'] == 'uri' and ref_prop_dt == 'wikibase-item': i['rval'] = i['rval']['value'].split('/')[-1] else: i['rval'] = i['rval']['value']
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8ceb2ed1c08fec070ec9edfcf7db7b8691481b62
https://github.com/SuLab/WikidataIntegrator/blob/8ceb2ed1c08fec070ec9edfcf7db7b8691481b62/wikidataintegrator/wdi_fastrun.py#L299-L358
train
23,686
SuLab/WikidataIntegrator
wikidataintegrator/wdi_fastrun.py
FastRunContainer.clear
def clear(self): """ convinience function to empty this fastrun container """ self.prop_dt_map = dict() self.prop_data = dict() self.rev_lookup = defaultdict(set)
python
def clear(self): """ convinience function to empty this fastrun container """ self.prop_dt_map = dict() self.prop_data = dict() self.rev_lookup = defaultdict(set)
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8ceb2ed1c08fec070ec9edfcf7db7b8691481b62
https://github.com/SuLab/WikidataIntegrator/blob/8ceb2ed1c08fec070ec9edfcf7db7b8691481b62/wikidataintegrator/wdi_fastrun.py#L504-L510
train
23,687
sighingnow/parsec.py
src/parsec/__init__.py
times
def times(p, mint, maxt=None): '''Repeat a parser between `mint` and `maxt` times. DO AS MUCH MATCH AS IT CAN. Return a list of values.''' maxt = maxt if maxt else mint @Parser def times_parser(text, index): cnt, values, res = 0, Value.success(index, []), None while cnt < maxt: res = p(text, index) if res.status: values = values.aggregate( Value.success(res.index, [res.value])) index, cnt = res.index, cnt + 1 else: if cnt >= mint: break else: return res # failed, throw exception. if cnt >= maxt: # finish. break # If we don't have any remaining text to start next loop, we need break. # # We cannot put the `index < len(text)` in where because some parser can # success even when we have no any text. We also need to detect if the # parser consume no text. # # See: #28 if index >= len(text): if cnt >= mint: break # we already have decent result to return else: r = p(text, index) if index != r.index: # report error when the parser cannot success with no text return Value.failure(index, "already meets the end, no enough text") return values return times_parser
python
def times(p, mint, maxt=None): '''Repeat a parser between `mint` and `maxt` times. DO AS MUCH MATCH AS IT CAN. Return a list of values.''' maxt = maxt if maxt else mint @Parser def times_parser(text, index): cnt, values, res = 0, Value.success(index, []), None while cnt < maxt: res = p(text, index) if res.status: values = values.aggregate( Value.success(res.index, [res.value])) index, cnt = res.index, cnt + 1 else: if cnt >= mint: break else: return res # failed, throw exception. if cnt >= maxt: # finish. break # If we don't have any remaining text to start next loop, we need break. # # We cannot put the `index < len(text)` in where because some parser can # success even when we have no any text. We also need to detect if the # parser consume no text. # # See: #28 if index >= len(text): if cnt >= mint: break # we already have decent result to return else: r = p(text, index) if index != r.index: # report error when the parser cannot success with no text return Value.failure(index, "already meets the end, no enough text") return values return times_parser
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L405-L441
train
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sighingnow/parsec.py
src/parsec/__init__.py
optional
def optional(p, default_value=None): '''`Make a parser as optional. If success, return the result, otherwise return default_value silently, without raising any exception. If default_value is not provided None is returned instead. ''' @Parser def optional_parser(text, index): res = p(text, index) if res.status: return Value.success(res.index, res.value) else: # Return the maybe existing default value without doing anything. return Value.success(res.index, default_value) return optional_parser
python
def optional(p, default_value=None): '''`Make a parser as optional. If success, return the result, otherwise return default_value silently, without raising any exception. If default_value is not provided None is returned instead. ''' @Parser def optional_parser(text, index): res = p(text, index) if res.status: return Value.success(res.index, res.value) else: # Return the maybe existing default value without doing anything. return Value.success(res.index, default_value) return optional_parser
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L450-L463
train
23,689
sighingnow/parsec.py
src/parsec/__init__.py
separated
def separated(p, sep, mint, maxt=None, end=None): '''Repeat a parser `p` separated by `s` between `mint` and `maxt` times. When `end` is None, a trailing separator is optional. When `end` is True, a trailing separator is required. When `end` is False, a trailing separator is not allowed. MATCHES AS MUCH AS POSSIBLE. Return list of values returned by `p`.''' maxt = maxt if maxt else mint @Parser def sep_parser(text, index): cnt, values, res = 0, Value.success(index, []), None while cnt < maxt: if end in [False, None] and cnt > 0: res = sep(text, index) if res.status: # `sep` found, consume it (advance index) index, values = res.index, Value.success( res.index, values.value) elif cnt < mint: return res # error: need more elemnts, but no `sep` found. else: break res = p(text, index) if res.status: values = values.aggregate( Value.success(res.index, [res.value])) index, cnt = res.index, cnt + 1 elif cnt >= mint: break else: return res # error: need more elements, but no `p` found. if end is True: res = sep(text, index) if res.status: index, values = res.index, Value.success( res.index, values.value) else: return res # error: trailing `sep` not found if cnt >= maxt: break return values return sep_parser
python
def separated(p, sep, mint, maxt=None, end=None): '''Repeat a parser `p` separated by `s` between `mint` and `maxt` times. When `end` is None, a trailing separator is optional. When `end` is True, a trailing separator is required. When `end` is False, a trailing separator is not allowed. MATCHES AS MUCH AS POSSIBLE. Return list of values returned by `p`.''' maxt = maxt if maxt else mint @Parser def sep_parser(text, index): cnt, values, res = 0, Value.success(index, []), None while cnt < maxt: if end in [False, None] and cnt > 0: res = sep(text, index) if res.status: # `sep` found, consume it (advance index) index, values = res.index, Value.success( res.index, values.value) elif cnt < mint: return res # error: need more elemnts, but no `sep` found. else: break res = p(text, index) if res.status: values = values.aggregate( Value.success(res.index, [res.value])) index, cnt = res.index, cnt + 1 elif cnt >= mint: break else: return res # error: need more elements, but no `p` found. if end is True: res = sep(text, index) if res.status: index, values = res.index, Value.success( res.index, values.value) else: return res # error: trailing `sep` not found if cnt >= maxt: break return values return sep_parser
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Repeat a parser `p` separated by `s` between `mint` and `maxt` times. When `end` is None, a trailing separator is optional. When `end` is True, a trailing separator is required. When `end` is False, a trailing separator is not allowed. MATCHES AS MUCH AS POSSIBLE. Return list of values returned by `p`.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L478-L522
train
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sighingnow/parsec.py
src/parsec/__init__.py
one_of
def one_of(s): '''Parser a char from specified string.''' @Parser def one_of_parser(text, index=0): if index < len(text) and text[index] in s: return Value.success(index + 1, text[index]) else: return Value.failure(index, 'one of {}'.format(s)) return one_of_parser
python
def one_of(s): '''Parser a char from specified string.''' @Parser def one_of_parser(text, index=0): if index < len(text) and text[index] in s: return Value.success(index + 1, text[index]) else: return Value.failure(index, 'one of {}'.format(s)) return one_of_parser
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Parser a char from specified string.
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ed50e1e259142757470b925f8d20dfe5ad223af0
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train
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sighingnow/parsec.py
src/parsec/__init__.py
none_of
def none_of(s): '''Parser a char NOT from specified string.''' @Parser def none_of_parser(text, index=0): if index < len(text) and text[index] not in s: return Value.success(index + 1, text[index]) else: return Value.failure(index, 'none of {}'.format(s)) return none_of_parser
python
def none_of(s): '''Parser a char NOT from specified string.''' @Parser def none_of_parser(text, index=0): if index < len(text) and text[index] not in s: return Value.success(index + 1, text[index]) else: return Value.failure(index, 'none of {}'.format(s)) return none_of_parser
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Parser a char NOT from specified string.
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ed50e1e259142757470b925f8d20dfe5ad223af0
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train
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sighingnow/parsec.py
src/parsec/__init__.py
space
def space(): '''Parser a whitespace character.''' @Parser def space_parser(text, index=0): if index < len(text) and text[index].isspace(): return Value.success(index + 1, text[index]) else: return Value.failure(index, 'one space') return space_parser
python
def space(): '''Parser a whitespace character.''' @Parser def space_parser(text, index=0): if index < len(text) and text[index].isspace(): return Value.success(index + 1, text[index]) else: return Value.failure(index, 'one space') return space_parser
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Parser a whitespace character.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L589-L597
train
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sighingnow/parsec.py
src/parsec/__init__.py
letter
def letter(): '''Parse a letter in alphabet.''' @Parser def letter_parser(text, index=0): if index < len(text) and text[index].isalpha(): return Value.success(index + 1, text[index]) else: return Value.failure(index, 'a letter') return letter_parser
python
def letter(): '''Parse a letter in alphabet.''' @Parser def letter_parser(text, index=0): if index < len(text) and text[index].isalpha(): return Value.success(index + 1, text[index]) else: return Value.failure(index, 'a letter') return letter_parser
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L605-L613
train
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sighingnow/parsec.py
src/parsec/__init__.py
digit
def digit(): '''Parse a digit character.''' @Parser def digit_parser(text, index=0): if index < len(text) and text[index].isdigit(): return Value.success(index + 1, text[index]) else: return Value.failure(index, 'a digit') return digit_parser
python
def digit(): '''Parse a digit character.''' @Parser def digit_parser(text, index=0): if index < len(text) and text[index].isdigit(): return Value.success(index + 1, text[index]) else: return Value.failure(index, 'a digit') return digit_parser
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Parse a digit character.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L616-L624
train
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sighingnow/parsec.py
src/parsec/__init__.py
eof
def eof(): '''Parser EOF flag of a string.''' @Parser def eof_parser(text, index=0): if index >= len(text): return Value.success(index, None) else: return Value.failure(index, 'EOF') return eof_parser
python
def eof(): '''Parser EOF flag of a string.''' @Parser def eof_parser(text, index=0): if index >= len(text): return Value.success(index, None) else: return Value.failure(index, 'EOF') return eof_parser
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Parser EOF flag of a string.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L627-L635
train
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sighingnow/parsec.py
src/parsec/__init__.py
string
def string(s): '''Parser a string.''' @Parser def string_parser(text, index=0): slen, tlen = len(s), len(text) if text[index:index + slen] == s: return Value.success(index + slen, s) else: matched = 0 while matched < slen and index + matched < tlen and text[index + matched] == s[matched]: matched = matched + 1 return Value.failure(index + matched, s) return string_parser
python
def string(s): '''Parser a string.''' @Parser def string_parser(text, index=0): slen, tlen = len(s), len(text) if text[index:index + slen] == s: return Value.success(index + slen, s) else: matched = 0 while matched < slen and index + matched < tlen and text[index + matched] == s[matched]: matched = matched + 1 return Value.failure(index + matched, s) return string_parser
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Parser a string.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L638-L650
train
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sighingnow/parsec.py
src/parsec/__init__.py
ParseError.loc_info
def loc_info(text, index): '''Location of `index` in source code `text`.''' if index > len(text): raise ValueError('Invalid index.') line, last_ln = text.count('\n', 0, index), text.rfind('\n', 0, index) col = index - (last_ln + 1) return (line, col)
python
def loc_info(text, index): '''Location of `index` in source code `text`.''' if index > len(text): raise ValueError('Invalid index.') line, last_ln = text.count('\n', 0, index), text.rfind('\n', 0, index) col = index - (last_ln + 1) return (line, col)
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Location of `index` in source code `text`.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L29-L35
train
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sighingnow/parsec.py
src/parsec/__init__.py
ParseError.loc
def loc(self): '''Locate the error position in the source code text.''' try: return '{}:{}'.format(*ParseError.loc_info(self.text, self.index)) except ValueError: return '<out of bounds index {!r}>'.format(self.index)
python
def loc(self): '''Locate the error position in the source code text.''' try: return '{}:{}'.format(*ParseError.loc_info(self.text, self.index)) except ValueError: return '<out of bounds index {!r}>'.format(self.index)
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Locate the error position in the source code text.
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ed50e1e259142757470b925f8d20dfe5ad223af0
https://github.com/sighingnow/parsec.py/blob/ed50e1e259142757470b925f8d20dfe5ad223af0/src/parsec/__init__.py#L37-L42
train
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