def testReadChannelGainNIDQ(self):
for meta_data_file in self.meta_files:
if meta_data_file.name.split('.')[-2] not in ['nidq']:
continue
md = spikeglx.read_meta_data(meta_data_file)
nc = spikeglx._get_nchannels_from_meta(md)
cg = spikeglx._conversion_sample2v_from_meta(md)
i2v = md.get('niAiRangeMax') / 32768
self.assertTrue(np.all(cg['nidq'][slice(0, int(np.sum(md.acqMnMaXaDw[:3])))] == i2v))
self.assertTrue(np.all(cg['nidq'][slice(int(np.sum(md.acqMnMaXaDw[-1])), None)] == 1.))
self.assertTrue(len(cg['nidq']) == nc)
def testReadChannelGainAPLF(self):
for meta_data_file in self.meta_files:
if meta_data_file.name.split('.')[-2] not in ['lf', 'ap']:
continue
md = spikeglx.read_meta_data(meta_data_file)
cg = spikeglx._conversion_sample2mv_from_meta(md)
i2v = md.get('imAiRangeMax') / 512
self.assertTrue(np.all(cg['lf'][0:-1] == i2v / 250))
self.assertTrue(np.all(cg['ap'][0:-1] == i2v / 500))
# also test consistent dimension with nchannels
nc = spikeglx._get_nchannels_from_meta(md)
self.assertTrue(len(cg['ap']) == len(cg['lf']) == nc)
def phy_model_from_ks2_path(ks2_path):
params_file = ks2_path.joinpath('params.py')
if params_file.exists():
m = model.load_model(params_file)
else:
meta_file = next(ks2_path.rglob('*.ap.meta'), None)
if meta_file and meta_file.exists():
meta = spikeglx.read_meta_data(meta_file)
fs = spikeglx._get_fs_from_meta(meta)
nch = (spikeglx._get_nchannels_from_meta(meta) -
len(spikeglx._get_sync_trace_indices_from_meta(meta)))
else:
fs = 30000
nch = 384
m = model.TemplateModel(dir_path=ks2_path,
dat_path=[],
sample_rate=fs,
n_channels_dat=nch)
return m
def phy_model_from_ks2_path(ks2_path, bin_path, bin_file=None):
if not bin_file:
bin_file = next(bin_path.rglob('*.ap.*bin'), None)
meta_file = next(bin_path.rglob('*.ap.meta'), None)
if meta_file and meta_file.exists():
meta = spikeglx.read_meta_data(meta_file)
fs = spikeglx._get_fs_from_meta(meta)
nch = (spikeglx._get_nchannels_from_meta(meta) -
len(spikeglx._get_sync_trace_indices_from_meta(meta)))
else:
fs = 30000
nch = 384
m = model.TemplateModel(dir_path=ks2_path,
dat_path=bin_file, # this assumes the raw data is in the same folder
sample_rate=fs,
n_channels_dat=nch,
n_closest_channels=NCH_WAVEFORMS)
m.depths = m.get_depths()
return m
