Trace Processing
Calculating Δf/f0
CalSciPy supports a variety of methods for calculating the fold fluorescence over baseline through a single function.
from CalScipy.traces import calculate_dfof
dFoF = calculate_dfof(traces, method="mean")
Supported baseline calculation methods
“low-pass”: x-th percentile of the :func:` low-pass <CalSciPy.traces.baseline.low_pass_baseline>` filtered trace
“mean”:
mean
of the trace“median”:
median
of the trace“moving_mean”:
moving mean
of the trace using a specified window length“percentile”:
x-th percentile
of the trace“sliding_mean”:
sliding mean
calculated using a sliding window of specified length“sliding_median:
sliding median
calculated using a sliding window of specified length“sliding_percentile:
sliding percentile
calculated using a sliding window of specified length
Baseline Corrections
CalSciPy currently provides a function for polynomial detrending to correct for a drifting baseline due to time-dependent degradation of signal-to-noise
from CalScipy.traces import detrend_polynomial
detrended_traces = detrend_polynomial(traces, frame_rate=frame_rate)
Assessing Trace Quality
CalSciPy supports assessment of trace quality using a standardized noise metric first defined in the publication associated with the spike-inference software package CASCADE.
from CalScipy.traces import calculate_standardized_noise
# acquisition frequency
frame_rate = 30
standardized_noise = calculate_standardized_noise(dFoF, frame_rate=frame_rate)