neuralib.imaging.suite2p.signals

neuralib.imaging.suite2p.signals.get_neuron_signal(s2p, n=None, *, signal_type='df_f', normalize=True, dff=True, correct_neuropil=True, method='maximin')[source]

Select neuronal signals for analysis. For single cell (F,) OR multiple cells (N, F)

Parameters:

s2p (Suite2PResult) – suite 2p result
n (int | ndarray | list[int] | None) – neuron index (int) or index arraylike (Array[int, N]). If None, then use all neurons
signal_type (Literal['df_f', 'spks']) – signal type. SIGNAL_TYPE {‘df_f’, ‘spks’}
normalize (bool) – 01 normalization for each neuron
dff (bool) – normalize to the baseline fluorescence changed (dF/F)
correct_neuropil (bool) – do the neuropil correction
method (Literal['maximin', 'constant', 'constant_prctile']) – baseline calculation method {‘maximin’, ‘constant’, ‘constant_prctile’}

Returns:

tuple with (signal, baseline signal). Array[float, F|[N,F]]

Return type:

tuple[ndarray, ndarray]

neuralib.imaging.suite2p.signals.normalize_signal(s, axis=-1)[source]

do 0 1 normalization

Parameters:

s (ndarray) – signal
axis (int) – axis

Returns:

normalized signal

Return type:

ndarray

neuralib.imaging.suite2p.signals.normalize_signal_factor(s, axis=-1)[source]

Parameters:

s (ndarray)
axis (int)

Return type:

tuple[ndarray, ndarray]

class neuralib.imaging.suite2p.signals.DFFSignal[source]

Container for dF/F signal processing.

Dimension parameters:

N = number of neurons

F = number of frame

For single cell (F,) OR multiple cells (N, F)

s2p: Suite2PResult: Suite2PResult

f: ndarray: Fluorescence intensity. Array[float, F | [N,F]]

fneu: ndarray: Neuropil intensity. Array[float, F | [N,F]]

fcorr: ndarray: Fluorescence corrected by fneu that used for dff calculation. Array[float, F | [N,F]]

f0: ndarray: Background Fluorescence. Array[float, F | [N,F]]

dff: ndarray: dff after f0 normalization. Array[float, F | [N,F]]

static __new__(_cls, s2p, f, fneu, fcorr, f0, dff)

Create new instance of DFFSignal(s2p, f, fneu, fcorr, f0, dff)

Parameters:

s2p (Suite2PResult)
f (ndarray)
fneu (ndarray)
fcorr (ndarray)
f0 (ndarray)
dff (ndarray)

property dff_baseline: ndarray: baseline of dff, supposed to be 0

property baseline_fluctuation: ndarray

get the fluctuation of the fneu signal. Array[float, F | [N,F]]

Perhaps not fully corrected with physiological reason. used to get the baseline std (i.e., trial reliability metric)

neuralib.imaging.suite2p.signals.dff_signal(f, fneu, s2p, correct_neuropil=True, method='maximin')[source]

df_f signal normalization container

Parameters:

f (ndarray) – Fluorescence intensity. Array[float, F | [N,F]]
fneu (ndarray) – Neuropil intensity. Array[float, F | [N,F]]
s2p (Suite2PResult) – Suite2PResult
correct_neuropil (bool) – whether do the subtraction using neuropil
method (Literal['maximin', 'constant', 'constant_prctile']) – {‘maximin’, ‘constant’, ‘constant_prctile’}

Returns:

DFFSignal

Return type:

DFFSignal

neuralib.imaging.suite2p.signals.calc_signal_baseline(signal, s2p, method='maximin')[source]

f0 calculation