PYME.Analysis.points.twoColour module

class PYME.Analysis.points.twoColour.ShiftModel(*args, **kwargs)

Bases: object

To recreate shiftmap from dictionary of fit results, call the model with a keyword argument ‘dict’, i.e. linModel(dict=shiftmap.__dict__)

classmethod from_md_entry(mdentry)

to_JSON()

PYME.Analysis.points.twoColour.calcCorrections(filenames)

PYME.Analysis.points.twoColour.dispRatio(res_g, res_r)

PYME.Analysis.points.twoColour.findWonkyVectors(x, y, dx, dy, tol=100)

PYME.Analysis.points.twoColour.fitIndep(g, r, ofindThresh)

PYME.Analysis.points.twoColour.genRGBImage(g, r, gsat=1, rsat=1)

PYME.Analysis.points.twoColour.genShiftVectorField(nx, ny, nsx, nsy)

interpolates shift vectors using radial basis functions

PYME.Analysis.points.twoColour.genShiftVectorFieldLinear(x, y, dx, dy, err_sx, err_sy)

interpolates shift vectors using smoothing splines

PYME.Analysis.points.twoColour.genShiftVectorFieldMC(nx, ny, nsx, nsy, p, Nsamp)

interpolates shift vectors using several monte-carlo subsampled sets of the vectors and averages to give a smooth field

PYME.Analysis.points.twoColour.genShiftVectorFieldQ(nx, ny, nsx, nsy, err_sx, err_sy, bbox=None)

interpolates shift vectors using smoothing splines

PYME.Analysis.points.twoColour.genShiftVectorFieldQuad(x, y, dx, dy, err_sx, err_sy)

interpolates shift vectors using smoothing splines

PYME.Analysis.points.twoColour.genShiftVectorFieldQuadz(x, y, z, dx, dy, err_sx, err_sy)

interpolates shift vectors using smoothing splines

PYME.Analysis.points.twoColour.genShiftVectorFieldQz(nx, ny, nz, nsx, nsy, err_sx, err_sy, bbox=None)

interpolates shift vectors using smoothing splines

PYME.Analysis.points.twoColour.genShiftVectorFieldSpline(nx, ny, nsx, nsy, err_sx, err_sy, bbox=None)

interpolates shift vectors using smoothing splines

PYME.Analysis.points.twoColour.genShiftVectors(res_g, res_r)

PYME.Analysis.points.twoColour.getCorrection(x, y, x_sv, y_sv)

looks up correction in calculated vector fields

class PYME.Analysis.points.twoColour.lin2Model(*args, **kwargs)

Bases: ShiftModel

To recreate shiftmap from dictionary of fit results, call the model with a keyword argument ‘dict’, i.e. linModel(dict=shiftmap.__dict__)

ev(x, y)

Mimic a bivariate spline object. Since we’re assuming it is linear along one axis, we use the axis that was defined when fitting the model

fit(x, y, dx, var=1)

class PYME.Analysis.points.twoColour.lin3Model(*args, **kwargs)

Bases: ShiftModel

To recreate shiftmap from dictionary of fit results, call the model with a keyword argument ‘dict’, i.e. linModel(dict=shiftmap.__dict__)

ev(x, y)

Mimic a bivariate spline object. Since we’re assuming it is linear along one axis, we use the axis that was defined when fitting the model

fit(x, y, dx, var=1)

sc = 5.555555555555556e-05

class PYME.Analysis.points.twoColour.lin3zModel(*args, **kwargs)

Bases: ShiftModel

To recreate shiftmap from dictionary of fit results, call the model with a keyword argument ‘dict’, i.e. linModel(dict=shiftmap.__dict__)

ZDEPSHIFT = True

ev(x, y, z=0)

Mimic a bivariate spline object. Since we’re assuming it is linear along one axis, we use the axis that was defined when fitting the model

fit(x, y, z, dx, var=1)

sc = 5.555555555555556e-05

class PYME.Analysis.points.twoColour.linModel(*args, **kwargs)

Bases: ShiftModel

To recreate shiftmap from dictionary of fit results, call the model with a keyword argument ‘dict’, i.e. linModel(dict=shiftmap.__dict__)

ev(x, y)

Mimic a bivariate spline object. Since we’re assuming it is linear along one axis, we use the axis that was defined when fitting the model

fit(x, dx, var, axis)

PYME.Analysis.points.twoColour.read_bead_data(filename)

PYME.Analysis.points.twoColour.read_h5f_cols(h5f, slice)

extracts colours from a h5 slice - file should be open!

PYME.Analysis.points.twoColour.robustLin2Lhood(p, x, y, dx, var=1)

p is parameter vector, x and y as expected, and var the variance of the y value. We use a t-distribution as our likelihood as it’s long tails will not overly weight outliers.

PYME.Analysis.points.twoColour.robustLin3Lhood(p, x, y, dx, var=1)

p is parameter vector, x and y as expected, and var the variance of the y value. We use a t-distribution as our likelihood as it’s long tails will not overly weight outliers.

PYME.Analysis.points.twoColour.robustLin3zLhood(p, x, y, z, dx, var=1)

p is parameter vector, x and y as expected, and var the variance of the y value. We use a t-distribution as our likelihood as it’s long tails will not overly weight outliers.

PYME.Analysis.points.twoColour.robustLinLhood(p, x, y, var=1)

p is parameter vector, x and y as expected, and var the variance of the y value. We use a t-distribution as our likelihood as it’s long tails will not overly weight outliers.

class PYME.Analysis.points.twoColour.sffake(*args, **kwargs)

Bases: ShiftModel

To recreate shiftmap from dictionary of fit results, call the model with a keyword argument ‘dict’, i.e. linModel(dict=shiftmap.__dict__)

ev(x, y)

fit(val)

PYME.Analysis.points.twoColour.shift_and_rot_model(p, x, y, dx, dy)

PYME.Analysis.points.twoColour.shift_and_rot_model_eval(p, x, y)

PYME.Analysis.points.twoColour.warpCorrectRedImage(r, dx, dy)