def test_merge(loc): from neurochat.nc_lfp import NLfp lfp = NLfp() lfp.set_filename(loc) lfp.load() print(lfp.get_total_samples()) print(lfp.get_duration())
class NData(): """ The NData object is composed of data objects (NSpike(), NSpatial(), NLfp(), and Nhdf()) and is built upon the composite structural object pattern. This data class is the main data element in NeuroChaT which delegates the analysis and other operations to respective objects. """ def __init__(self): """ Attributes ---------- spatial : NSpatial Spatial data object spike : NSpike Spike data object lfp : Nlfp LFP data object hdf : NHdf Object for manipulating HDF5 file data_format : str Recording system or format of the data file """ super().__init__() self.spike = NSpike(name='C0') self.spatial = NSpatial(name='S0') self.lfp = NLfp(name='L0') self.data_format = 'Axona' self._results = oDict() self.hdf = Nhdf() self.__type = 'data' def subsample(self, sample_range): """ Split up a data object in the collection into parts. Parameters ---------- sample_range: tuple times in seconds to extract Returns ------- NData subsampled version of initial ndata object """ ndata = NData() if self.lfp.get_duration() != 0: ndata.lfp = self.lfp.subsample(sample_range) if self.spike.get_duration() != 0: ndata.spike = self.spike.subsample(sample_range) if self.spatial.get_duration() != 0: ndata.spatial = self.spatial.subsample(sample_range) return ndata def get_type(self): """ Returns the type of object. For NData, this is always `data` type Parameters ---------- None Returns ------- str """ return self.__type def get_results(self, spaces_to_underscores=False): """ Returns the parametric results of the analyses Parameters ---------- None Returns ------- OrderedDict """ if spaces_to_underscores: results = { x.replace(' ', '_'): v for x, v in self._results.items() } return results return self._results def update_results(self, results): """ Adds new parametric results of the analyses Parameters ---------- results : OrderedDict New analyses results (parametric) Returns ------- None """ self._results.update(results) def reset_results(self): """ Reset the NData results to an empty OrderedDict Parameters ---------- None Returns ------- None """ self._results = oDict() # self.spike.reset_results() # self.spatial.reset_results() # self.lfp.reset_results() def get_data_format(self): """ Returns the recording system or data format Parameters ---------- None Returns ------- str """ return self.data_format def set_data_format(self, data_format=None): """ Returns the parametric results of the analyses Parameters ---------- data_format : str Recording system or format of the data Returns ------- None """ if data_format is None: data_format = self.get_data_format() self.data_format = data_format self.spike.set_system(data_format) self.spatial.set_system(data_format) self.lfp.set_system(data_format) def load(self): """ Loads the data from the filenames in each constituing objects, i.e, spatial, spike and LFP Parameters ---------- None Returns ------- None """ self.load_spike() self.load_spatial() self.load_lfp() def save_to_hdf5(self): """ Stores the spatial, spike and LFP datasets to HDF5 file Parameters ---------- None Returns ------- None """ try: self.hdf.save_object(obj=self.spike) except: logging.warning( 'Error in exporting NSpike data from NData object to the hdf5 file!' ) try: self.hdf.save_object(obj=self.spatial) except: logging.warning( 'Error in exporting NSpatial data from NData object to the hdf5 file!' ) try: self.hdf.save_object(obj=self.lfp) except: logging.warning( 'Error in exporting NLfp data from NData object to the hdf5 file!' ) def set_unit_no(self, unit_no): """ Sets the unit number of the spike dataset to analyse Parameters ---------- unit_no : int Unit or cell number to analyse Returns ------- None """ self.spike.set_unit_no(unit_no) def set_spike_name(self, name='C0'): """ Sets the name of the spike dataset Parameters ---------- name : str Name of the spike dataset Returns ------- None """ self.spike.set_name(name) def set_spike_file(self, filename): """ Sets the filename of the spike dataset Parameters ---------- filename : str Full file directory of the spike dataset Returns ------- None """ self.spike.set_filename(filename) def get_spike_file(self): """ Gets the filename of the spike dataset Parameters ---------- None Returns ------- str Filename of the spike dataset """ return self.spike.get_filename() def load_spike(self): """ Loads spike dataset from the file to NSpike() object Parameters ---------- None Returns ------- None """ self.spike.load() def set_spatial_file(self, filename): """ Sets the filename of the spatial dataset Parameters ---------- filename : str Full file directory of the spike dataset Returns ------- None """ self.spatial.set_filename(filename) def get_spatial_file(self): """ Gets the filename of the spatial dataset Parameters ---------- None Returns ------- str Filename of the spatial dataset """ return self.spatial.get_filename() def set_spatial_name(self, name): """ Sets the name of the spatial dataset Parameters ---------- name : str Name of the spatial dataset Returns ------- None """ self.spatial.set_name(name) def load_spatial(self): """ Loads spatial dataset from the file to NSpatial() object Parameters ---------- filename : str Full file directory of the spike dataset Returns ------- None """ self.spatial.load() def set_lfp_file(self, filename): """ Sets the filename of the LFP dataset Parameters ---------- filename : str Full file directory of the spike dataset Returns ------- None """ self.lfp.set_filename(filename) def get_lfp_file(self): """ Gets the filename of the LFP dataset Parameters ---------- None Returns ------- str Filename of the LFP dataset """ return self.lfp.get_filename() def set_lfp_name(self, name): """ Sets the name of the NLfp() object Parameters ---------- name : str Name of the LFP dataset Returns ------- None """ self.lfp.set_name(name) def load_lfp(self): """ Loads LFP dataset to NLfp() object Parameters ---------- None Returns ------- None """ self.lfp.load() # Forwarding to analysis def wave_property(self): """ Analysis of wavefor characteristics of the spikes of a unit Delegates to NSpike().wave_property() Parameters ---------- None Returns ------- dict Graphical data of the analysis See also -------- nc_spike.NSpike().wave_property """ gdata = self.spike.wave_property() self.update_results(self.spike.get_results()) return gdata def isi(self, bins='auto', bound=None, density=False, refractory_threshold=2): """ Analysis of ISI histogram Delegates to NSpike().isi() Parameters ---------- bins : str or int Number of ISI histogram bins. If 'auto', NumPy default is used bound : int Length of the ISI histogram in msec density : bool If true, normalized historagm is calcultaed refractory_threshold : int Length of the refractory period in msec Returns ------- dict Graphical data of the analysis See also -------- nc_spike.NSpike().isi """ gdata = self.spike.isi(bins, bound, density, refractory_threshold) self.update_results(self.spike.get_results()) return gdata def isi_auto_corr(self, spike=None, **kwargs): """ Analysis of ISI autocrrelation histogram Delegates to NSpike().isi_corr() Parameters ---------- spike : NSpike() If specified, it calulates cross-correlation. **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spike.NSpike().isi_corr, nc_spike.NSpike().psth """ gdata = self.spike.isi_corr(spike, **kwargs) return gdata def burst(self, burst_thresh=5, ibi_thresh=50): """ Burst analysis of spik-train Delegates to NSpike().burst() Parameters ---------- burst_thresh : int Minimum ISI between consecutive spikes in a burst ibi_thresh : int Minimum inter-burst interval between two bursting groups of spikes Returns ------- None See also -------- nc_spike.NSpike().burst """ self.spike.burst(burst_thresh, ibi_thresh=ibi_thresh) self.update_results(self.spike.get_results()) def theta_index(self, **kwargs): """ Calculates theta-modulation of spike-train ISI autocorrelation histogram. Delegates to NSpike().theta_index() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spike.NSpike().theta_index() """ gdata = self.spike.theta_index(**kwargs) self.update_results(self.spike.get_results()) return gdata def theta_skip_index(self, **kwargs): """ Calculates theta-skipping of spike-train ISI autocorrelation histogram. Delegates to NSpike().theta_skip_index() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spike.NSpike().theta_skip_index() """ gdata = self.spike.theta_skip_index(**kwargs) self.update_results(self.spike.get_results()) return gdata def bandpower_ratio(self, first_band, second_band, win_sec, **kwargs): """ Calculate the ratio in power between two bandpass filtered signals. Delegates to NLfp.bandpower_ratio() Suggested [5, 11] and [1.5, 4 bands] Parameters ---------- first_band, second_band, win_sec, **kwargs See also -------- nc_lfp.NLfp.bandpower_ratio() """ self.lfp.bandpower_ratio(first_band, second_band, win_sec, **kwargs) self.update_results(self.lfp.get_results()) def spectrum(self, **kwargs): """ Analyses frequency spectrum of the LFP signal Delegates to NLfp().spectrum() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_lfp.NLfp().spectrum() """ gdata = self.lfp.spectrum(**kwargs) return gdata def phase_dist(self, **kwargs): """ Analysis of spike to LFP phase distribution Delegates to NLfp().phase_dist() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_lfp.NLfp().phase_dist() """ gdata = self.lfp.phase_dist(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.lfp.get_results()) return gdata def phase_at_spikes(self, **kwargs): """ Analysis of spike to LFP phase distribution Delegates to NLfp().phase_dist() Parameters ---------- **kwargs Keyword arguments Returns ------- phases, times, positions See also -------- nc_lfp.NLfp().phase_at_events() """ key = "keep_zero_idx" out_data = {} if not key in kwargs.keys(): kwargs[key] = True should_filter = kwargs.get("should_filter", True) ftimes = self.spike.get_unit_stamp() phases = self.lfp.phase_at_events(ftimes, **kwargs) _, positions, directions = self.get_event_loc(ftimes, **kwargs) if should_filter: place_data = self.place(**kwargs) boundary = place_data["placeBoundary"] co_ords = place_data["indicesInPlaceField"] largest_group = place_data["largestPlaceGroup"] out_data["good_place"] = (largest_group != 0) out_data["phases"] = phases[co_ords] out_data["times"] = ftimes[co_ords] out_data["directions"] = directions[co_ords] out_data["positions"] = [ positions[0][co_ords], positions[1][co_ords] ] out_data["boundary"] = boundary else: out_data["phases"] = phases out_data["times"] = ftimes out_data["positions"] = positions out_data["directions"] = directions self.update_results(self.get_results()) return out_data def plv(self, **kwargs): """ Calculates phase-locking value of the spike train to underlying LFP signal. Delegates to NLfp().plv() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_lfp.NLfp().plv() """ gdata = self.lfp.plv(self.spike.get_unit_stamp(), **kwargs) return gdata # def sfc(self, **kwargs): # """ # Calculates spike-field coherence of spike train with underlying LFP signal. # Delegates to NLfp().sfc() # Parameters # ---------- # **kwargs # Keyword arguments # Returns # ------- # dict # Graphical data of the analysis # See also # -------- # nc_lfp.NLfp().sfc() # """ # gdata = self.lfp.plv(self.spike.get_unit_stamp(), **kwargs) # return gdata def event_trig_average(self, **kwargs): """ Averaging event-triggered LFP signals Delegates to NLfp().event_trig_average() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_lfp.NLfp().event_trig_average() """ gdata = self.lfp.event_trig_average(self.spike.get_unit_stamp(), **kwargs) return gdata def spike_lfp_causality(self, **kwargs): """ Analyses spike to underlying LFP causality Delegates to NLfp().spike_lfp_causality() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_lfp.NLfp().spike_lfp_causality() """ gdata = self.lfp.spike_lfp_causality(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.lfp.get_results()) return gdata def speed(self, **kwargs): """ Analysis of unit correlation with running speed Delegates to NSpatial().speed() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().speed() """ gdata = self.spatial.speed(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def angular_velocity(self, **kwargs): """ Analysis of unit correlation to angular head velocity (AHV) of the animal Delegates to NSpatial().angular_velocity() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().angular_velocity() """ gdata = self.spatial.angular_velocity(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def place(self, **kwargs): """ Analysis of place cell firing characteristics Delegates to NSpatial().place() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().place() """ gdata = self.spatial.place(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata # Created by Sean Martin: 13/02/2019 def place_field_centroid_zscore(self, **kwargs): """ Calculates a very simple centroid of place field Delegates to NSpatial().place_field() Parameters ---------- **kwargs Keyword arguments Returns ------- ndarray Centroid of the place field See also -------- nc_spatial.NSpatial().place_field() """ gdata = self.spatial.place_field_centroid_zscore( self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def loc_time_lapse(self, **kwargs): """ Time-lapse firing proeprties of the unit with respect to location Delegates to NSpatial().loc_time_lapse() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().loc_time_lapse() """ gdata = self.spatial.loc_time_lapse(self.spike.get_unit_stamp(), **kwargs) return gdata def loc_shuffle(self, **kwargs): """ Shuffling analysis of the unit to see if the locational firing specifity is by chance or actually correlated to the location of the animal Delegates to NSpatial().loc_shuffle() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().loc_shuffle() """ gdata = self.spatial.loc_shuffle(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def loc_shift(self, shift_ind=np.arange(-10, 11), **kwargs): """ Analysis of firing specificity of the unit with respect to animal's location to oberve whether it represents past location of the animal or anicipates a future location. Delegates to NSpatial().loc_shift() Parameters ---------- shift_ind : ndarray Index of spatial resolution shift for the spike event time. Shift -1 implies shift to the past by 1 spatial time resolution, and +2 implies shift to the future by 2 spatial time resoultion. **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().loc_shift() """ gdata = self.spatial.loc_shift(self.spike.get_unit_stamp(), shift_ind=shift_ind, **kwargs) self.update_results(self.spatial.get_results()) return gdata def loc_auto_corr(self, **kwargs): """ Calculates the two-dimensional correlation of firing map which is the map of the firing rate of the animal with respect to its location Delegates to NSpatial().loc_auto_corr() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().loc_auto_corr() """ gdata = self.spatial.loc_auto_corr(self.spike.get_unit_stamp(), **kwargs) return gdata def loc_rot_corr(self, **kwargs): """ Calculates the rotational correlation of the locational firing rate of the animal with respect to location, also called firing map Delegates to NSpatial().loc_rot_corr() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().loc_rot_corr() """ gdata = self.spatial.loc_rot_corr(self.spike.get_unit_stamp(), **kwargs) return gdata def hd_rate(self, **kwargs): """ Analysis of the firing characteristics of a unit with respect to animal's head-direction Delegates to NSpatial().hd_rate() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().hd_rate() """ gdata = self.spatial.hd_rate(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def hd_rate_ccw(self, **kwargs): """ Analysis of the firing characteristics of a unit with respect to animal's head-direction split into clockwise and counterclockwised directions Delegates to NSpatial().hd_rate_ccw() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().hd_rate_ccw() """ gdata = self.spatial.hd_rate_ccw(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def hd_time_lapse(self): """ Time-lapse firing proeprties of the unit with respect to the head-direction of the animal Delegates to NSpatial().hd_time_lapse() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().hd_time_lapse() """ gdata = self.spatial.hd_time_lapse(self.spike.get_unit_stamp()) return gdata def hd_shuffle(self, **kwargs): """ Shuffling analysis of the unit to see if the head-directional firing specifity is by chance or actually correlated to the head-direction of the animal Delegates to NSpatial().hd_shuffle() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().hd_shuffle() """ gdata = self.spatial.hd_shuffle(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def hd_shift(self, shift_ind=np.arange(-10, 11), **kwargs): """ Analysis of firing specificity of the unit with respect to animal's head direction to oberve whether it represents past direction or anicipates a future direction. Delegates to NSpatial().hd_shift() Parameters ---------- shift_ind : ndarray Index of spatial resolution shift for the spike event time. Shift -1 implies shift to the past by 1 spatial time resolution, and +2 implies shift to the future by 2 spatial time resoultion. **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().speed() """ gdata = self.spatial.hd_shift(self.spike.get_unit_stamp(), shift_ind=shift_ind) self.update_results(self.spatial.get_results()) return gdata def border(self, **kwargs): """ Analysis of the firing characteristic of a unit with respect to the environmental border Delegates to NSpatial().border() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().border() """ gdata = self.spatial.border(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def gradient(self, **kwargs): """ Analysis of gradient cell, a unit whose firing rate gradually increases as the animal traverses from the border to the cneter of the environment Delegates to NSpatial().gradient() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().gradient() """ gdata = self.spatial.gradient(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def grid(self, **kwargs): """ Analysis of Grid cells characterised by formation of grid-like pattern of high activity in the firing-rate map Delegates to NSpatial().grid() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().grid() """ gdata = self.spatial.grid(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def multiple_regression(self, **kwargs): """ Multiple-rgression analysis where firing rate for each variable, namely location, head-direction, speed, AHV, and distance from border, are used to regress the instantaneous firing rate of the unit. Delegates to NSpatial().multiple_regression() Parameters ---------- **kwargs Keyword arguments Returns ------- dict Graphical data of the analysis See also -------- nc_spatial.NSpatial().multiple-regression() """ gdata = self.spatial.multiple_regression(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) return gdata def interdependence(self, **kwargs): """ Interdependence analysis where firing rate of each variable is predicted from another variable and the distributive ratio is measured between the predicted firing rate and the caclulated firing rate. Delegates to NSpatial().interdependence() Parameters ---------- **kwargs Keyword arguments Returns ------- None See also -------- nc_spatial.NSpatial().interdependence() """ self.spatial.interdependence(self.spike.get_unit_stamp(), **kwargs) self.update_results(self.spatial.get_results()) def __getattr__(self, arg): """ Sets precedence for delegation with NSpike() > NLfp() > NSpatial() Parameters ---------- arg : str Name of the function ot attributes to look for """ if hasattr(self.spike, arg): return getattr(self.spike, arg) elif hasattr(self.lfp, arg): return getattr(self.lfp, arg) elif hasattr(self.spatial, arg): return getattr(self.spatial, arg) else: logging.warning( 'No ' + arg + ' method or attribute in NeuroData or in composing data class')