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)¶