samuroi.plugins package

Functions

samuroi.plugins.andor.load_andor(prefix)

todo fixme

return the data of the tif file as numpy array it will have the shape (X,Y,N) where - X denotes the coordinate from left to right - Y denotes the coordinate from top to bottom - N denotes the frames return tuple of data, parsed dictionary

samuroi.plugins.andor.parse_txt(prefix)

Read the andor text file

class samuroi.plugins.swc.SWCFile(filename=None)

Subclass of numpy.recarray for swc files.

branches

Returns:A generator object that allows to iterate over all branches.

nbranches

Returns:The number of branches in the file.

samuroi.plugins.baseline.bandstop(data, fs, start, stop, order=3)

Apply bandstop filter on data.

Parameters:

• data – 3D video data
• fs – sampling frequency
• order – the order of butter filter used.
• start – lower frequency where band starts
• stop – higher frequency where band ends

Returns:

the filtered 3d data set.

samuroi.plugins.baseline.linbleeched_F0(data)

Calculate a linear fit (\(y(t)=m t+y_0)\) for each pixel, which is assumed to correct for bleeching effects.

Parameters:data – he video data of shape (M,N,T). Returns:tuple (m,y0) with two images each with shape (M,N).

samuroi.plugins.baseline.linbleeched_deltaF(data, F0=None)

Assumes that the fluorescence F0 follows linear bleeching (see samuroi.plugins.baseline.linbleeched_F0()). Determines the linear fit parameters m,y0 for \(F_0(t) = m f(t)+y_0\). Then uses \(F_0(t)\) to calculate \((F(t)-F_0(t))/F_0(t)\).

Parameters:

• data – The video data of shape (M,N,T).
• F0 –

Returns:

deltaF/F0 for bleech corrected \(F_0(t)\).

samuroi.plugins.baseline.median_F0(data)

Calculate a time dependent F0, which does not depend on spatial coordinates. The definition is as follows: \(F_0(t)\) = median(\(F(x,y,t)\) for all x,y)

Parameters:data – The video data of shape (M,N,T). Returns:F0 array of shape (T,).

samuroi.plugins.baseline.median_deltaF(data)

Apply the deltaF/F transformation with \(F_0\) defined as in samuroi.plugins.baseline.median_F0().

Parameters:data – The video data of shape (M,N,T). Returns:deltaF/F0 for median \(F_0(t)\).

samuroi.plugins.baseline.power_spectrum(data, fs)

Calculate the power spectrum for each pixel and then average over all pixels.

Parameters:

• data – the 3D video data.
• fs – sampling frequency.

Returns:

tuple(df,avgpower) where df is a 1d array of frequencies and avgpower is a 1D array with the respective average power.

samuroi.plugins.baseline.stdv_F0(data, windows=None)

Calculate the baseline for each pixel of data. Subdivides data in blocks of B frames and calculate the standard deviation for each block. Then takes the block with minimum standard deviation and calculate the mean of that block. The above is done on a per pixel basis. I.e. different pixels can have the mean calculated for different blocks.

Parameters:

• data – NxMxF array, where F is number of frames and NxM is image shape.
• windows – The number of windows to use. Default: split the data in blocks of 100 frames. If data.shape[2] mod 100 != 0 drop the frames that are remaining.

Returns:

NxM array with baseline for each pixel.

samuroi.plugins.baseline.stdv_deltaF(data, F0=None, windows=None)

Calculate the fraction dF/F0 for each pixel. F0 is assumed to not depend on time, but on spatial coordinates. for the definition of F0 see samuroi.plugins.baseline.stdv_F0().

Parameters:

• data – The video data, shape M,N,T
• F0 – precalculated F0 or None(default calculate F0 internally)
• windows – The number of windows, forwarded to stdv_F0

Returns:

numpy.array with shape M,N,T with values \((F(x,y,t)-F0(x,y))/F0(x,y)\)