Python TimeSeries.filtered方法代码示例

# 需要导入模块: from timeseries import TimeSeries [as 别名]
# 或者: from timeseries.TimeSeries import filtered [as 别名]

#.........这里部分代码省略.........
            to avoid edge effects when filtering (in time unit of the
            data).
        resampled_rate: {float},optional
            New samplerate to resample the data to after loading.
        filt_freq: {array_like},optional
            The range of frequencies to filter (depends on the filter
            type.)
        filt_type = {scipy.signal.band_dict.keys()},optional
            Filter type.
        filt_order = {int},optional
            The order of the filter.
        keep_buffer: {boolean},optional
            Whether to keep the buffer when returning the data.
        """

        # translate back to dur and offset
        dur = end_time - start_time
        offset = start_time
        buf = buffer_time
        
        # Sanity checks:
        if(dur<0):
            raise ValueError('Duration must not be negative! '+
                             'Specified duration: '+str(dur))
        if(np.min(event_offsets)<0):
            raise ValueError('Event offsets must not be negative!')

        # make sure the events are an actual array
        event_offsets = np.asarray(event_offsets)
        
        # set event durations from rate
        # get the samplesize
        samplesize = 1./self.samplerate

        # get the number of buffer samples
        buf_samp = int(np.ceil(buf/samplesize))

        # calculate the offset samples that contains the desired offset
        offset_samp = int(np.ceil((np.abs(offset)-samplesize*.5)/samplesize)*
                          np.sign(offset))

        # finally get the duration necessary to cover the desired span
        #dur_samp = int(np.ceil((dur - samplesize*.5)/samplesize))
        dur_samp = (int(np.ceil((dur+offset - samplesize*.5)/samplesize)) -
                    offset_samp + 1)
        
        # add in the buffer
        dur_samp += 2*buf_samp
        offset_samp -= buf_samp

        # check that we have all the data we need before every event:
        if(np.min(event_offsets+offset_samp)<0):
            bad_evs = ((event_offsets+offset_samp)<0)
            raise ValueError('The specified values for offset and buffer '+
                             'require more data than is available before '+
                             str(np.sum(bad_evs))+' of all '+
                             str(len(bad_evs))+' events.')

        # process the channels
        if channels is None or len(np.atleast_1d(channels))==0:
            channels = np.arange(self.nchannels)
        channels = np.atleast_1d(channels)

        # load the timeseries (this must be implemented by subclasses)
        eventdata = self._load_data(channels,event_offsets,dur_samp,offset_samp)

        # calc the time range
        # get the samplesize
        samp_start = offset_samp*samplesize
        samp_end = samp_start + (dur_samp-1)*samplesize
        time_range = np.linspace(samp_start,samp_end,dur_samp)

	# make it a timeseries
        dims = [Dim(channels,'channels'),
                Dim(event_offsets,'event_offsets'),
                Dim(time_range,'time')]
        eventdata = TimeSeries(np.asarray(eventdata),
                               'time',
                               self.samplerate,dims=dims)

	# filter if desired
	if not(filt_freq is None):
	    # filter that data
            eventdata = eventdata.filtered(filt_freq,
                                           filt_type=filt_type,
                                           order=filt_order)

	# resample if desired
	if (not(resampled_rate is None) and
            not(resampled_rate == eventdata.samplerate)):
	    # resample the data
            eventdata = eventdata.resampled(resampled_rate)

        # remove the buffer and set the time range
	if buf > 0 and not(keep_buffer):
	    # remove the buffer
            eventdata = eventdata.remove_buffer(buf)

        # return the timeseries
        return eventdata

# 需要导入模块: from timeseries import TimeSeries [as 别名]
# 或者: from timeseries.TimeSeries import filtered [as 别名]

#.........这里部分代码省略.........
            event_offsets = events[eoffset]
        else:
            raise ValueError(eoffset+' must be a valid fieldname '+
                             'specifying the offset for the data.')
        
        # Sanity checks:
        if(dur<0):
            raise ValueError('Duration must not be negative! '+
                             'Specified duration: '+str(dur))
        if(np.min(event_offsets)<0):
            raise ValueError('Event offsets must not be negative!')

        # make sure the events are an actual array:
        event_offsets = np.asarray(event_offsets)
        if eoffset_in_time:
            # convert to samples
            event_offsets = np.atleast_1d(np.int64(
                np.round(event_offsets*self.samplerate)))
        
        # set event durations from rate
        # get the samplesize
        samplesize = 1./self.samplerate

        # get the number of buffer samples
        buf_samp = int(np.ceil(buf/samplesize))

        # calculate the offset samples that contains the desired offset
        offset_samp = int(np.ceil((np.abs(offset)-samplesize*.5)/samplesize)*
                          np.sign(offset))

        # finally get the duration necessary to cover the desired span
        #dur_samp = int(np.ceil((dur - samplesize*.5)/samplesize))
        dur_samp = (int(np.ceil((dur+offset - samplesize*.5)/samplesize)) -
                    offset_samp + 1)
        
        # add in the buffer
        dur_samp += 2*buf_samp
        offset_samp -= buf_samp

        # check that we have all the data we need before every event:
        if(np.min(event_offsets+offset_samp)<0):
            bad_evs = ((event_offsets+offset_samp)<0)
            raise ValueError('The specified values for offset and buffer '+
                             'require more data than is available before '+
                             str(np.sum(bad_evs))+' of all '+
                             str(len(bad_evs))+' events.')

        # process the channels
        if isinstance(channels, dict):
            # turn into indices
            ch_info = self.channels
            key = channels.keys()[0]
            channels = [np.nonzero(ch_info[key]==c)[0][0] for c in channels[key]]
        elif isinstance(channels, str):
            # find that channel by name
            channels = np.nonzero(self.channels['name']==channels)[0][0]
        if channels is None or len(np.atleast_1d(channels))==0:
            channels = np.arange(self.nchannels)
        channels = np.atleast_1d(channels)
        channels.sort()

        # load the timeseries (this must be implemented by subclasses)
        eventdata = self._load_data(channels,event_offsets,dur_samp,offset_samp)

        # calc the time range
        # get the samplesize
        samp_start = offset_samp*samplesize
        samp_end = samp_start + (dur_samp-1)*samplesize
        time_range = np.linspace(samp_start,samp_end,dur_samp)

        # make it a timeseries
        dims = [Dim(self.channels[channels],'channels'),  # can index into channels
                Dim(events,'events'),
                Dim(time_range,'time')]
        eventdata = TimeSeries(np.asarray(eventdata),
                               'time',
                               self.samplerate,dims=dims)

	# filter if desired
	if not(filt_freq is None):
	    # filter that data
            eventdata = eventdata.filtered(filt_freq,
                                           filt_type=filt_type,
                                           order=filt_order)

	# resample if desired
	if (not(resampled_rate is None) and
            not(resampled_rate == eventdata.samplerate)):
	    # resample the data
            eventdata = eventdata.resampled(resampled_rate,
                                            loop_axis=loop_axis,
                                            num_mp_procs=num_mp_procs)

        # remove the buffer and set the time range
	if buf > 0 and not(keep_buffer):
	    # remove the buffer
            eventdata = eventdata.remove_buffer(buf)

        # return the timeseries
        return eventdata