本文整理汇总了Python中xarray.DataArray.attrs['samplerate']方法的典型用法代码示例。如果您正苦于以下问题：Python DataArray.attrs['samplerate']方法的具体用法？Python DataArray.attrs['samplerate']怎么用？Python DataArray.attrs['samplerate']使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类xarray.DataArray的用法示例。
在下文中一共展示了DataArray.attrs['samplerate']方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_event_data_xray
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import attrs['samplerate'] [as 别名]
#.........这里部分代码省略.........
(hasattr(events, 'dtype') and events.dtype.names is None)):
# they just passed in a list
event_offsets = events
elif ((hasattr(events, 'dtype') and (eoffset in events.dtype.names)) or
(hasattr(events, 'columns') and (eoffset in events.columns))):
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 = list(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)
# when channels is and array of channels labels i.e. strings like '002','003',...
# we need to use xray arrays to do fancy indexing
self.channels_xray = DataArray(self.channels.number, coords=[self.channels.name], dims=['name'])
if channels.dtype.char == 'S':
self.channels_xray = self.channels_xray.loc[channels]
else:
self.channels_xray = self.channels_xray[channels]
# ORIGINAL CODE
# self.channels_xray = np.rec.fromarrays([self.channels_xray.values, self.channels_xray.coords['name'].values],
# names='number,name')
self.channels_xray = self.channels_xray.coords['name'].values
eventdata = DataArray(eventdata, coords=[self.channels_xray, events, time_range],
dims=['channels', 'events', 'time'])
eventdata.attrs['samplerate'] = self.samplerate
return eventdata示例2: get_event_data_xray_simple
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import attrs['samplerate'] [as 别名]
def get_event_data_xray_simple(self,channels,events,start_offset,end_offset,buffer=0):
# process the channels
if isinstance(channels, dict):
# turn into indices
ch_info = self.channels
key = list(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)
event_offsets = np.array(start_offset,ndmin=1)
dur_samp = end_offset-start_offset + 2*buffer
offset_samp = -buffer
eventdata = self._load_data(channels,event_offsets,dur_samp,offset_samp)
# eventdata = self._load_all_data(channels,start_offset-buffer)
# calc the time range
# get the samplesize
samplesize = 1.0/self.samplerate
samp_start = (start_offset-buffer)*samplesize
samp_end = (end_offset+buffer)*samplesize
time_range = np.linspace(samp_start,samp_end,dur_samp)
eegoffset = np.arange(start_offset-buffer, end_offset+buffer)
time_axis = np.rec.fromarrays([time_range,eegoffset],names='time,eegoffset')
# when channels is and array of channels labels i.e. strings like '002','003',...
# we need to use xray arrays to do fancy indexing
self.channels_xray = DataArray(self.channels.number,coords=[self.channels.name],dims=['name'])
if channels.dtype.char=='S':
self.channels_xray = self.channels_xray.loc[channels]
else:
self.channels_xray = self.channels_xray[channels]
self.channels_xray=np.rec.fromarrays([self.channels_xray.values,self.channels_xray.coords['name'].values],names='number,name')
# eventdata = DataArray(eventdata,coords=[self.channels_xray,np.arange(len(events)),time_range],dims=['channels','events','time'])
eventdata = DataArray(eventdata,coords=[self.channels_xray,np.arange(len(events)),time_axis],dims=['channels','events','time'])
eventdata.attrs['samplerate'] = self.samplerate
return eventdata