sair package

Module contents

class saibr.SaibrCalibrate(gfp: Optional[list] = None, af: Optional[list] = None, rfp: Optional[list] = None, roi: Optional[list] = None, paths: Optional[list] = None, gfp_regex: Optional[str] = '*488 SP 535-50*', af_regex: Optional[str] = '*488 SP 630-75*', rfp_regex: Optional[str] = None, roi_regex: Optional[str] = '*ROI*', sigma: float = 2.0, intercept0: bool = False, expand: float = 10.0, method: str = 'OLS')

Bases: object

Class for performing SAIBR calibration

Two methods of specifying data - input the images directly as a list for each channel along with a list of ROIs - specify a list of paths and regular expressions for image files and ROIs (easier method if files are set up appropriately)

If rfp channel is not specified, will perform the normal two channel calibration

Parameters

  • gfp – list of GFP channel images

  • af – list off AF channel images

  • rfp – list of RFP channel images

  • roi – list of ROIs

  • paths – list of paths containing n2 images

  • gfp_regex – regular expression found in gfp channel image files

  • af_regex – regular expression found in af channel image files

  • rfp_regex – regular expression found in rfp channel files (optional)

  • roi_regex – regular expression found in ROI files

  • sigma – gaussian blur to apply to images prior to regression

  • intercept0 – if True, force intercept of regression to go through zero. Not recommended

  • expand – expand ROIs by this many pixels (useful to include a portion of background)

  • method – fitting method, either ‘OLS’ for ordinary least squares or ‘ODR’ for orthogonal distance regression

To run regression, initialise class and run() SAIBR parameters will then be found at self.params

plot_correlation(s: Optional[float] = None)
plot_prediction(s: float = 0.001)
plot_residuals(s: float = 0.001)
run()
saibr.direcslist(dest: str, levels: int = 0, exclude: Optional[tuple] = ('!',), exclusive: Optional[tuple] = None) → list

Gives a list of directories within a given directory (full path) Todo: os.walk

Parameters

  • dest – path of parent directory

  • levels – number of levels to go down. E.g. if 0, only return folders within the parent folder; if 1, return

  • folder (folders within folders within the parent) –

  • exclude – exclude directories containing any strings within this tuple

  • exclusive – exclude directories that don’t contain all the strings within this tuple

Returns

list of directories

saibr.load_image(filename: str) → ndarray

Given the filename of a TIFF, creates numpy array with pixel intensities

Parameters

filename – full path to the file to import (including extension)

Returns

A numpy array of the image

saibr.make_mask(shape: tuple, roi: ndarray) → ndarray
saibr.offset_coordinates(roi: ndarray, offsets: Union[ndarray, float], periodic: bool = True) → ndarray

Reads in coordinates, adjusts according to offsets

Parameters

  • roi – two column array containing x and y coordinates. e.g. coors = np.loadtxt(filename)

  • offsets – array the same length as coors. Direction?

  • periodic –

Returns

array in same format as coors containing new coordinates. To save this in a fiji readable format: np.savetxt(filename, newcoors, fmt=’%.4f’, delimiter=’ ‘)

saibr.saibr_correct(ch1: ndarray, ch2: ndarray, m: float, c: float) → ndarray

Subtract ch2 from ch1 ch2 is first adjusted to m * ch2 + c

Parameters

  • ch1 – numpy array of channel 1

  • ch2 – numpy array of channel 2

  • m – m parameter obtained from n2 data

  • c – c parameter obtained from n2 data

Returns

numpy array of af corrected image

saibr.saibr_correct_3channel(ch1: ndarray, ch2: ndarray, ch3: ndarray, m1: float, m2: float, c: float) → ndarray

Subtract ch2 and ch3 from ch1 ch2 and ch3 are first adjusted to m1 * ch2 + m2 * ch3 + c

Parameters

  • ch1 – numpy array of channel 1

  • ch2 – numpy array of channel 2

  • ch3 – numpy array of channel 3

  • m1 – m1 parameter obtained from n2 data

  • m2 – m2 parameter obtained from n2 data

  • c – c parameter obtained from n2 data

Returns

numpy array of af corrected image

saibr.saibr_correlation(img1: ndarray, img2: ndarray, mask: ndarray = None, intercept0: bool = False, method: str = 'OLS') → Tuple[list, ndarray, ndarray]

Calculates pixel-by-pixel correlation between two channels Takes 3d image stacks shape [n, 512, 512]

Parameters

  • img1 – gfp channel

  • img2 – af channel

  • mask – mask specifying region of images to use for regression, from make_mask function

  • intercept0 – if True, force the intercept to go through zero. Not recommended

  • method – fitting method, either ‘OLS’ for ordinary least squares or ‘ODR’ for orthogonal distance regression

Returns

parameters from regression [m, c] numpy array of all af channel pixels used for regression numpy array of all gfp channel pixels used for regression

saibr.saibr_correlation_3channel(img1: ndarray, img2: ndarray, img3: ndarray, mask: Optional[ndarray] = None, intercept0: bool = False, method: str = 'OLS') → Tuple[list, ndarray, ndarray, ndarray]

Calculates pixel-by-pixel correlation between three channels Takes 3d image stacks shape [n, 512, 512]

Parameters

  • img1 – gfp channel

  • img2 – af channel

  • img3 – third channel

  • mask – mask specifying region of images to use for regression, from make_mask function

  • intercept0 – if True, force the intercept to go through zero. Not recommended

  • method – fitting method, either ‘OLS’ for ordinary least squares or ‘ODR’ for orthogonal distance regression

Returns

parameters from regression [m, c] numpy array of all af channel pixels used for regression numpy array of all third channel pixels used for regression numpy array of all gfp channel pixels used for regression

saibr.save_img(img: ndarray, direc: str)

Saves 2D array as .tif file

Parameters

  • img – numpy array of the image to save

  • direc – file path to save to (including ‘.tif’ extension)

saibr.save_img_jpeg(img: ndarray, direc: str, cmin: Optional[float] = None, cmax: Optional[float] = None, cmap: str = 'gray')

Saves 2D array as jpeg, according to min and max pixel intensities

Parameters

  • img – numpy array of the image to save

  • direc – file path to save to (including ‘.jpeg’ extension)

  • cmin – optional, sets intensity scaling (along with cmax)

  • cmax – optional, sets intensity scaling (along with cmin)

  • cmap – colour map (use string corresponding to matplotlib colormap)