pyfrp.modules package¶

Submodules¶

pyfrp.modules.pyfrp_IO_module module¶

Input/Output module for PyFRAP toolbox.

Handles saving/loading PyFRAP projects into pickled files and the memory handling that comes with it.

pyfrp.modules.pyfrp_IO_module.cleanUp()¶: Calls garbage collector to clean up.

pyfrp.modules.pyfrp_IO_module.copyAndRenameFile(fn, fnNew, debug=False)¶

Copies file fn into same directory as fn and renames it fnNew.

Note

If copying fails, then function will return old filename.

Keyword Arguments:
Parameters:	fn (str) – Filepath of original file. fnNew (str) – New filename.
	debug (bool) – Print out debugging messages.
Returns:	Path to new file.
Return type:	str

pyfrp.modules.pyfrp_IO_module.copyMeshFiles(fn, fnGeo, fnMsh, debug=False)¶

Copies meshfiles to new location.

If fn does not end on meshfiles, will create a folder meshfiles where to dump new files.

Note

If fnGeo is a merged file, will try to copy all used .geo and .msh files and also update the merged file such that it refers to the new mesh files.

Keyword Arguments:
Parameters:	fn (str) – Filepath or parent directory where to put meshfiles. fnGeo (str) – Filepath of geo file. fnMsh (str) – Filepath of msh file.
	debug (bool) – Print out debugging messages.
Returns:	Tuple containing: fnGeoNew (str): New geo file location. fnMshNew (str): New msh file location.
Return type:	tuple

pyfrp.modules.pyfrp_IO_module.loadEmbryo(fn, update=True)¶

Loads embryo object from pickle file and brings it up-to-date.

Keyword Arguments:
Parameters:	fn (str) – Filename.
	update (bool) – Update to current version.
Returns:	Embryo file.
Return type:	pyfrp.subclasses.pyfrp_embryo

pyfrp.modules.pyfrp_IO_module.loadFromPickle(fn)¶

Loads obj from pickled format.

Parameters:	fn (str) – Filename.
Returns:	Output filename.
Return type:	str

pyfrp.modules.pyfrp_IO_module.loadMolecule(fn, update=True)¶

Loads molecule object from pickle file and brings it up-to-date.

Keyword Arguments:
Parameters:	fn (str) – Filename.
	update (bool) – Update to current version.
Returns:	Molecule file.
Return type:	pyfrp.subclasses.pyfrp_molecule

pyfrp.modules.pyfrp_IO_module.saveToPickle(obj, fn=None)¶

Saves obj into pickled format.

Note

If fn==Non, will try to save to obj.name, otherwise unnamed.pk

Keyword Arguments:
	fn (str) – Output file name.
Returns:	Output filename.
Return type:	str

pyfrp.modules.pyfrp_IO_module.writeTableToCSV(l, header, fn, col=False)¶

Writes table to csv file.

If col=True, columns are given via l, otherwise rows are given.

Parameters:

l (list) – List of rows or columns.
header (list) – List of headers.
col (bool) – Flag on how rows/columns are given.

Returns:

Tuple containing:

header (list): Header of table.

table (list): Table as a list of rows.

Return type:

tuple

pyfrp.modules.pyfrp_fit_module module¶

Parameter fitting module for PyFRAP toolbox. Contains functions for the following two tasks:

Fitting a PyFRAP simulation to FRAP data analyzed by PyFRAP and saving its results in a pyfrp.subclasses.pyfrp_fit.fit` instance.
Pinning PyFRAP simulation and data between 0 and 1.
Likelihood profiling functions.

pyfrp.modules.pyfrp_fit_module.FRAPFitting(fit, debug=False, ax=None)¶

Main fitting function.

Fits simulation result to analyzed data.

Keyword Arguments:
Parameters:	fit (pyfrp.subclasses.pyfrp_fit) – Fit object containing all important information needed.
	debug (bool) – Display debugging output and plots. ax (matplotlib.axes) – Axes to display plots in.
Returns:	Performed fit.
Return type:	pyfrp.subclasses.pyfrp_fit

pyfrp.modules.pyfrp_fit_module.FRAPObjFunc(x, fit, debug, ax, returnFit)¶

Objective function for fitting FRAP experiments.

Does the following.

Checks input using pyfrp.modules.pyfrp_fit_module.checkInput() .

Assigns input variables using pyfrp.modules.pyfrp_fit_module.assignInputVariables() .

Adjust kinetic scales using pyfrp.modules.pyfrp_fit_module.downscaleKinetics() .

Scales simulation vector using pyfrp.modules.pyfrp_fit_module.scaleROIs() .

Adds reaction kinetics using using pyfrp.modules.pyfrp_fit_module.addKineticsToSolution() .

Equalizes simulation vector using pyfrp.modules.pyfrp_fit_module.equalize() .

Computes and returns SSD.

Parameters:	x (list) – Input vector, consiting of [D,(prod),(degr)]. fit (pyfrp.subclasses.pyfrp_fit) – Fit object. debug (bool) – Display debugging output and plots. ax (matplotlib.axes) – Axes to display plots in. returnFit (bool) – Return fit instead of SSD.
Returns:	SSD of fit. Except `returnFit==True`, then will return fit itself.
Return type:	float

pyfrp.modules.pyfrp_fit_module.addKineticsToSolution(scaledSimVecs, tvec, prod, degr)¶

Adds reaction kinetics to simulation solution.

Parameters:	scaledSimVecs (list) – List of scaled simulation vectors by ROI. tvec (numpy.ndarray) – Data time vector. prod (float) – Production rate. degr (float) – Degredation rate.
Returns:	List of rescaled simulation vectors by ROI.
Return type:	(list)

pyfrp.modules.pyfrp_fit_module.assignInputVariables(x, fit)¶

Decodes array given to objective function to suit fitting options.

If fit.fitProd or fit.fitDegr are selected, will use x[1] or x[2] as input values for degradation and production rate.

If fit.equOn=True, then will use last entry of x0 for equalization factors, otherwise will return empty list for equFacts.

Parameters:

x (list) – Input vector of objective function.
fit (pyfrp.subclasses.pyfrp_fit) – Fit object.

Returns:

Tuple containing:

Dnew (float): Diffusion rate.

prod (float): Production rate.

degr (float): Degredation rate.

equFacts (list): Equalization factors.

Return type:

tuple

pyfrp.modules.pyfrp_fit_module.checkInput(x, iteration, fit)¶

Checks if input vector x is non-negative.

Parameters:	x (list) – Input vector of objective function. iteration (int) – Number of iteration. fit (pyfrp.subclasses.pyfrp_fit) – Fit object.
Returns:	`True` if everythings positive, `False` else.
Return type:	bool

pyfrp.modules.pyfrp_fit_module.computeBkgd(vec, useMin=False, debug=False)¶

Computes background value of vector.

Keyword Arguments:
Parameters:	vec (numpy.ndarray) – Vector to be pinned.
	useMin (bool) – Use minimum value for background computation. debug (bool) – Print debugging messages.
Returns:	Background value.
Return type:	float

pyfrp.modules.pyfrp_fit_module.computeEquFactors(dataVec, simVec)¶

Computes equalization factors per ROI.

The purpose of equalization factors is to account for immobile fractions, adjusting the level of the simulation vector to the data vector.

Check requirements: Checks if 0.1<=dataVec[i]/simVec[i]<=3, if not, sets = 1. In practical terms, this means that the volume fraction is not allowed to increase by more than 3fold (>3), and that the immobile fraction cannot be more than 90% (< 0.1)

Parameters:	dataVec (numpy.ndarray) – Data vector of ROI. simVec (numpy.ndarray) – Scaled simulation vector of ROI.
Returns:	Array of equalization factors.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_fit_module.computeFitLikelihoodProfiles(fit, epsPerc=0.1, steps=100, debug=False)¶

Computes likelihood profile of all parameters fitted in fit.

Warning

Since we don’t yet fit the loglikelihood function, we only compute the SSD. Even though the SSD is proportional to the loglikelihood, it should be used carefully.

See also pyfrp.modules.pyfrp_fit_module.computeLikehoodProfile().

Keyword Arguments:
Parameters:	fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object.
	epsPerc (float) – Percentage of variation. steps (int) – Number of values around optimal parameter value. debug (bool) – Show debugging messages
Returns:	Tuple containing: names (list): List of parameter names. xvaryVec (List): List of parameter variation arrays. SSDsVec (list): List of corresponding SSDs.
Return type:	tuple

pyfrp.modules.pyfrp_fit_module.computeLikelihoodProfile(xOpt, fit, idx, steps=100, epsPerc=0.1, debug=False)¶

Computes likelihood profile of parameter with index idx of fit.

Warning

Since we don’t yet fit the loglikelihood function, we only compute the SSD. Even though the SSD is proportional to the loglikelihood, it should be used carefully.

Keyword Arguments:
Parameters:	xOpt (list) – Vector with optimal parameters. fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object. idx (int) – Index of parameter in xOpt of which profile is calculated.
	epsPerc (float) – Percentage of variation. steps (int) – Number of values around optimal parameter value. debug (bool) – Show debugging messages
Returns:	Tuple containing: xvary (numpy.ndarray): Array with varied parameter. SSDs (list): Corresponding SSDs.
Return type:	tuple

pyfrp.modules.pyfrp_fit_module.computeNorm(vec, useMax=False, debug=False)¶

Computes norming value of vector.

Keyword Arguments:
Parameters:	vec (numpy.ndarray) – Vector to be pinned.
	useMax (bool) – Use maximum value for norm value computation. debug (bool) – Print debugging messages.
Returns:	Norming value.
Return type:	float

pyfrp.modules.pyfrp_fit_module.computePinVals(vec, useMin=False, useMax=False, bkgdVal=None, debug=False)¶

Computes pinning values of vector.

Keyword Arguments:
Parameters:	vec (numpy.ndarray) – Vector to be pinned.
	useMin (bool) – Use minimum value for background computation. useMax (bool) – Use maximum value for norm value computation. bkgdVal (float) – Use this background value instead of newly computing it. debug (bool) – Print debugging messages.
Returns:	Tuple containing: bkgdVal (float): Background value. normVal (float): Norming value.
Return type:	tuple

pyfrp.modules.pyfrp_fit_module.cutTvec(tvec, tCutIndex)¶

Cuts time vector as index.

Parameters:	tvec (numpy.ndarray) – Time vector. tCutIndex (int) – Index to cut at.
Returns:	Cut time vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_fit_module.downscaleKinetics(prod, degr, rate)¶

Adjusts time-scale of kinetics so prod/degr will have same weight as diffusion rate.

Parameters:

prod (float) – Production rate.
degr (float) – Degredation rate.
rate (float) – Scaling rate.

Returns:

Tuple containing:

prod (float): Scaled production rate.

degr (float): Scaled degredation rate.

Return type:

tuple

pyfrp.modules.pyfrp_fit_module.equalize(dataVecs, simVecs, equFacts, fromVecs=False)¶

Equalizes all simulation vectors of all ROIs defined in fit.ROIsFitted.

If fromVecs=True, will use findMinEquFacts() to compute the equalization factors that minimize SSD from a set of equalization factors given by the ratio between simulation and data vector.

Keyword Arguments:
Parameters:	simVecs (list) – List of scaled simulation vectors by ROI. dataVecs (list) – List of data vectors by ROI. equFacts (list) – List of equalization factors.
	fromVecs (bool) – Compute equalization factors from data/simulation ratio.
Returns:	Tuple containing: equSimVecs (list): List of equalized simulation vectors. equFacts (list): List of (optimal) equalization factors by ROI.
Return type:	tuple

pyfrp.modules.pyfrp_fit_module.findMinEquFacts(dataVecs, simVecs)¶

Computes list of equalization factors per ROI in fit.ROIsFitted and then finds the one that minimizes SSD.

Does this by:

Computing equalization factors per ROI via computeEquFactors().

Computing SSD for each equalization factor per ROI.

Selecting equalization factor per ROI that minimizes SSD.

Parameters:

simVecs (list) – List of scaled simulation vectors by ROI.
dataVecs (list) – List of data vectors by ROI.
equFacts (list) – List of equalization factors.

Returns:

Tuple containing:

equSimVecs (list): List of equalized simulation vectors.

equFactsFinal (list): List of optimal equalization factors by ROI.

Return type:

tuple

pyfrp.modules.pyfrp_fit_module.getTvecCutIndex(tvec, tCut)¶

Finds last index of time vector before time vector exceeds tCut.

Parameters:	tvec (numpy.ndarray) – Time vector. tCut (float) – Time to cut at.
Returns:	Last index.
Return type:	int

pyfrp.modules.pyfrp_fit_module.interpolateSolution(tvecData, tvecScaled, yvec)¶

Interpolates scaled simulation vector onto data time vector.

Parameters:	tvecData (numpy.ndarray) – Data time vector. tvecScaled (numpy.ndarray) – Scaled simulation time vector. yvec (numpy.ndarray) – Simulation values.
Returns:	Scaled simulation vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_fit_module.pinConc(vec, bkgdVal, normVal, axes=None, debug=False, tvec=None, color='b')¶

Substract background and normalize: Pin concentrations between 0 and 1.

Keyword Arguments:
Parameters:	vec (numpy.ndarray) – Vector to be pinned. bkgdVal (float) – Background value. normVal (float) – Norming value.
	axes (list) – List of matplotlib.axes for debugging plots. tvec (numpy.ndarray) – Time vector (only necessay when plotting). debug (bool) – Print debugging messages. color (str) – Color of plots.
Returns:	Pinned vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_fit_module.plotFitLikehoodProfiles(fit, epsPerc=0.1, steps=100, debug=False, axes=None)¶

Computes and plots likelihood profile of all parameters fitted in fit.

Warning

Since we don’t yet fit the loglikelihood function, we only plot the SSD. Even though the SSD is proportional to the loglikelihood, it should be used carefully.

See also pyfrp.modules.pyfrp_fit_module.computeFitLikehoodProfiles().

Keyword Arguments:
Parameters:	xOpt (list) – Vector with optimal parameters. fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object. idx (int) – Index of parameter in xOpt of which profile is calculated.
	epsPerc (float) – Percentage of variation. steps (int) – Number of values around optimal parameter value. debug (bool) – Show debugging messages
Returns:	List of matplotlib.axes objects used for plotting.
Return type:	list

pyfrp.modules.pyfrp_fit_module.scaleROIs(fit, Dnew)¶

Scales all simulation vectors of all ROIs defined in fit.ROIsFitted.

Parameters:

fit (pyfrp.subclasses.pyfrp_fit) – Fit object.
Dnew (float) – Scaling diffusion rate.

Returns:

Tuple containing:

fit (pyfrp.subclasses.pyfrp_fit): Updated fit object.

tvecScaled (numpy.ndarray): Scaled time vector.

tvecData (numpy.ndarray): Data time vector.

scaledSimVecs (list): List of scaled simulation vectors by ROI.

dataVecs (list): List of data vectors by ROI.

Return type:

tuple

pyfrp.modules.pyfrp_fit_module.scaleTime(tvec, D, Dnew)¶

Scales time vector with D/Dnew.

Parameters:	tvec (numpy.ndarray) – Time vector. D (float) – Relative diffusion rate. Dnew (float) – Scaling diffusion rate.
Returns:	Scaled time vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_geometry_module module¶

PyFRAP module for simple geometric operations, such as

Angle computation.
Normal computation.

pyfrp.modules.pyfrp_geometry_module.checkColinear(vec1, vec2)¶

Returns True if two vectors are colinear.

Parameters:	vec1 (numpy.ndarray) – Vector 1. vec2 (numpy.ndarray) – Vector 2.
Returns:	True if colinear.
Return type:	bool

pyfrp.modules.pyfrp_geometry_module.computeNormal(vertices, method='cross')¶

Computes normal.

Vertices should be given as

>>> vertices=[[x1,y1,z1],[x2,y2,z2],...]

Currently there are two methods available:

cross, see also normalByCross().

newells, see also newells().

If method is unknown, will fall back to cross.

Keyword Arguments:
Parameters:	vertices (list) – List of vertex coordinates.
	method (str) – Method of normal computation.
Returns:	Normal vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_geometry_module.decodeEuclideanBase(d)¶

Decodes a euclidean base vector given as a literal.

Example:

>>> decodeEuclideanBase('z')
>>> array([ 0.,  0.,  1.])

Parameters:	d (str) – Direction (“x”/”y”/”z”)
Returns:	Base vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_geometry_module.flipCoordinate(x, destAxis, origAxis='x', debug=False)¶

Transforms coodinate from one axis to another by rolling the coordinates, e.g. clockwise turning the point.

destAxis and origAxis are given as one of x,y,z.

Keyword Arguments:
Parameters:	x (numpy.ndarray) – Coordinate to turn. destAxis (str) – Destination axis.
	origAxis (str) – Original axis. debug (bool) – Print debugging output.
Returns:	Transformed coordinate.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_geometry_module.getAngle(vec1, vec2)¶

Returns angle between two vectors in radians.

Angle is calculated via

\[\phi = \frac{v_1 \dot v_2}{|v_1| |v_2|}\]

Note

Checks for numerical errors and corrects them if necessary.

Parameters:	vec1 (numpy.ndarray) – Vector 1. vec2 (numpy.ndarray) – Vector 2.
Returns:	Angle.
Return type:	float

pyfrp.modules.pyfrp_geometry_module.getRotMatrix(n1, n2)¶

Builds rotation matrix for the rotation of a vector n2 onto n1.

Taken from http://stackoverflow.com/questions/9423621/3d-rotations-of-a-plane .

Parameters:	n1 (numpy.ndarray) – Vector to be rotated to. n2 (numpy.ndarray) – Vector to rotate.
Returns:	Rotation matrix
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_geometry_module.newellsMethod(vertices)¶

Computes normal using Newell’s method.

Adapted from http://stackoverflow.com/questions/39001642/calculating-surface-normal-in-python-using-newells-method.

Vertices should be given as

>>> vertices=[[x1,y1,z1],[x2,y2,z2],...]

Parameters:	vertices (list) – List of vertex coordinates.
Returns:	Normal vector to surface.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_geometry_module.normalByCross(vec1, vec2)¶

Returns normalised normal vectors of plane spanned by two vectors.

Normal vector is computed by:

\[\mathbf{n} = \frac{\mathbf{v_1} \times \mathbf{v_2}}{|\mathbf{v_1} \times \mathbf{v_2}|}\]

Note

Will return zero vector if vec1 and vec2 are colinear.

Parameters:	vec1 (numpy.ndarray) – Vector 1. vec2 (numpy.ndarray) – Vector 2.
Returns:	Normal vector.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_gmsh_IO_module module¶

PyFRAP module for reading/writing gmsh .geo files. Module mainly has the following features:

Read .geo files.

Translate geometric entities and variables defined in .geo files.

Construct pyfrp.pyfrp_gmsh_geometry.domain object describing complete geometry.

Update parameters in .geo files.

Add/Remove some geometric entities.

Add/update box fields to allow refinement of certain ROIs in mesh.

This module together with pyfrp.pyfrp_gmsh_geometry and pyfrp.pyfrp_gmsh_module works partially as a python gmsh wrapper, however is incomplete. If you want to know more about gmsh, go to http://gmsh.info/doc/texinfo/gmsh.html .

pyfrp.modules.pyfrp_gmsh_IO_module.addBoxField(fn, volSizeIn, volSizeOut, rangeX, rangeY, rangeZ, comment='', fnOut='', overwrite=True, sameComment=True)¶

Adds box field to .geo file by doing the following:

Copies file into temp file for backup using copyIntoTempFile().

Finds all IDs of previous Field entities using getAllIDsOfType().

Finds current background field using getBkgdFieldID() .

If previous fields exist, removes them from file using removeElementFromFile() .

Finds last non-empty line using getLastNonEmptyLine() .

Removes empty lines at end of file using removeTailingLines() .

Writes comment using writeComment() .

Writes box field using writeBoxField() .

Writes background field using writeBackgroundField() .

Note

Comment is useful to describe in .geo file what the the box field actually does.

Note

Generally, background field will use volSizeIn as background mesh volume size.

Note

Unit for parameter is pixels.

Note

If fnOut is not specified, will overwrite input file.

Note

Will always remove previous background fields. If overwrite=True, will remove all fields. If additionally sameComment=True, will look for the field that has the same comment as comment and only remove this particular one.

Parameters:

fn (str) – Filename of .geo file.
volSizeIn (float) – Mesh element volume inside box.
volSizeOut (float) – Mesh element volume outside box.
rangeX (list) – Range of box field in x-direction given as [minVal,maxVal].
rangeY (list) – Range of box field in y-direction given as [minVal,maxVal].
rangeZ (list) – Range of box field in z-direction given as [minVal,maxVal].

Keyword Arguments:

comment (str) – Comment to be added before box field.
fnOut (str) – Filepath for output.
overwrite (bool) – Overwrite previously exisiting box fields.
sameComment (bool) – Only remove box field with particular comment.

pyfrp.modules.pyfrp_gmsh_IO_module.applyParmDic(val, parmDic)¶

Applies parameter dictionary to variable value.

Example:

>>> parmDic={'radius',3}
>>> applyParmDic('radius',parmDic)
>>> 3

And also applies mathemtical expressions:

>>> parmDic={'radius',3}
>>> applyParmDic('radius^2-radius',parmDic)
>>> 6

Parameters:	val (str) – Value string of geometric variable. parmDic (dict) – Parameter dictionary.
Returns:	Evaluated value.
Return type:	val (float)

pyfrp.modules.pyfrp_gmsh_IO_module.convertMathExpr(val)¶: Converts math expressions from .geo syntax into python syntax.

Note

Not all translations have been implemented yet. You can simply add here expressions by adding a translation to the translations list (translations.append([CExpression,PythonExpression])).

pyfrp.modules.pyfrp_gmsh_IO_module.copyIntoTempFile(fn, close=True)¶

Copies file into tempfile.

Note

Will create temporary file using tempfile.mkstemp(). You should have read/write access to whereever mkstemp is putting files.

Note

If close==True, will return fh=None and tempFile=None.

Keyword Arguments:
Parameters:	fn (str) – Filename of file.
	close (bool) – Close files after copying.
Returns:	Tuple containing: tempFile (file): File handle to temp file. fh (file): File handle to original file. tempPath (tempPath): Path to temp file.
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.findComment(fn, comment)¶

Finds a specific comment in .geo file and returns line in which it appears, otherwise -1.

Note

Will only look for an exact match.

Parameters:	fn (str) – Filename of .geo file. comment (str) – Comment to look for.
Returns:	Line number of appearance.
Return type:	int

pyfrp.modules.pyfrp_gmsh_IO_module.genMergeGeoFile(meshFiles, fnGeo)¶

Generates merged .geo file.

Parameters:	meshFiles (list) – List of meshfiles that will be included. fnGeo (str) – Output geometry file.
Returns:	True if no error/warning occured.
Return type:	bool

pyfrp.modules.pyfrp_gmsh_IO_module.getAllIDsOfType(fn, elementType)¶

Finds all IDs of a specific .geo element type in a .geo file.

Parameters:	fn (str) – Filename of .geo file. elementType (str) – Type of parameter, for example `"Point"`.
Returns:	List of IDs.
Return type:	list

pyfrp.modules.pyfrp_gmsh_IO_module.getBkgdFieldID(fn)¶

Finds ID of background field in .geo file.

Note

Will return None if .geo file has no background field specified.

Parameters:	fn (str) – Filename of .geo file.
Returns:	ID of background field.
Return type:	int

pyfrp.modules.pyfrp_gmsh_IO_module.getCorrespondingGeoFile(fn, meshFileExt='.msh')¶

Returns the corresponding geometry file to a mesh file.

Assumes that meshfile has same name and lives in the same folder.

Keyword Arguments:
Parameters:	fn (str) – Path to mesh file.
	meshFileExt (str) – Extension of meshfile.
Returns:	Tuple containing: exists (bool): Flag if corresponding file exits. fnGeo (str): Path to geometry file.
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.getFieldByComment(fn, comment, lineDiff=3)¶

Returns field that is preceeded by comment comment.

Note

Will only look for an exact match.

Keyword Arguments:
Parameters:	fn (str) – Filename of .geo file. comment (str) – Comment to look for.
	lineDiff (int) – Maximum allowed difference between line of comment and field.
Returns:	Line number of appearance.
Return type:	int

pyfrp.modules.pyfrp_gmsh_IO_module.getId(var, delimOpen='(', delimClose=')')¶

Returns ID of object that is given between the delimiters delimOpen and delimClose.

Example:

>>> getId("Point(3)")
>>> ("Point",3)

Keyword Arguments:
Parameters:	var (str) – String describing geoFile variable name.
	delimOpen (str) – Openening delimiter of ID. delimClose (str) – Closing delimiter of ID.
Returns:	Tuple containing: typ (str): Type of geometric variable. Id (str): Id of geometric variable.
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.getLargestIDOfType(fn, elementType)¶

Finds largest ID of a specific .geo element type in a .geo file.

Parameters:	fn (str) – Filename of .geo file. elementType (str) – Type of parameter, for example `"Point"`.
Returns:	Largest ID.
Return type:	int

pyfrp.modules.pyfrp_gmsh_IO_module.getLastNonEmptyLine(fn)¶

Finds index of last non-empty line in .geo file.

Parameters:	fn (str) – Filename of .geo file.
Returns:	Index of last non-empty line.
Return type:	int

pyfrp.modules.pyfrp_gmsh_IO_module.getLinesByID(fn, elementId, elementType='')¶

Finds all lines in .geo file that contain geometric entitity with ID elementId.

Note

IDs in geometric files can be given per entitity type. That is, one can have for example a point with ID=1 (Point(1)) aswell as a line with ID=1 (Line(1)). Thus one may want to use elementType to restrict the search for a specific element type.

Keyword Arguments:
Parameters:	fn (str) – Filename of .geo file. elementId (int) – ID to look for.
	elementType (str) – Type of element to restrict search on.
Returns:	Line numbers at which element appears.
Return type:	list

pyfrp.modules.pyfrp_gmsh_IO_module.getVals(val, parmDic, openDelim='{', closeDelim='}', sep=', ')¶

Translates value of parameter into list of floats.

Uses parameter dictionary to translate predefined variables into floats.

Example:

>>> getVals("{10,3,5}")
>>> [10.,3.,5.]

Keyword Arguments:
Parameters:	val (str) – Value string of geometric variable. parmDic (dict) – Parameter dictionary.
	openDelim (str) – Opening delimiter. closeDelim (str) – Closing delimiter. sep (str) – Seperator between values.
Returns:	List of translated values.
Return type:	rList (list)

pyfrp.modules.pyfrp_gmsh_IO_module.initBkgdField(line, domain)¶

Initiates background field when reading a .geo file.

Parameters:	line (str) – Line in geo file. domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – Domain object.
Returns:	Updated domain.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

pyfrp.modules.pyfrp_gmsh_IO_module.initField(val, domain, Id)¶

Adds the right type of field object to domain.

Parameters:	val (str) – Value string. domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – Domain object. Id (int) – Id of new field.
Returns:	Updated domain.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

pyfrp.modules.pyfrp_gmsh_IO_module.isMergeFile(fn)¶

Checks if geo file is a merge file and returns and returns the meshes used inside the merge file.

Parameters:	fn (str) – Path to geo file.
Returns:	Tuple containing: isMerge (bool): Flag if corresponding file is merge file. mergedFiles (list): List of mesh files used in merge file.
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.mergeMeshes(meshFiles, fn, run=True, debug=False, redirect=False, fnStout=None, fnSterr=None, volSizeMax=None)¶

Generates meshfile merging all meshes in meshFiles.

If one of the files that is supposed to be merged is already a merged file, then this function will try to find the original mesh files to write a complete merged file. See also isMergeFile() and genMergeGeoFile().

If run==True is selected, then gmsh will be run via pyfrp.modules.pyfrp_gmsh_module.runGmsh() and generate the corresponding .msh file of the merged .geo file.

Keyword Arguments:
Parameters:	meshFiles (list) – List of path to mesh files. fn (str) – Name of output .geo file.
	run (bool) – Run gmsh on merged .geo file. debug (bool) – Print debugging messages. redirect (bool) – Redirect gmsh stout/sterr into seperate files. fnStout (str) – File for gmsh stout. fnSterr (str) – File for gmsh sterr. volSizeMax (float) – Maximum allowed mesh element size.
Returns:	Tuple containing: fn (str): Path to generated .geo file. fnOut (str): Path to generated .msh file.
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.readFieldLine(line, domain, parmDic)¶

Reads line that belongs to field definition in .geo file.

If line defines new field, will create new field using initField().

Otherwise will try to find field in domain and set new property value.

Parameters:	line (str) – Line to read. domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – Domain object. parmDic (dict) – Parameter dictionary.
Returns:	Updated domain.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

pyfrp.modules.pyfrp_gmsh_IO_module.readGeoFile(fn)¶

Reads in .geo file and tries to extract geometry defined in .geo file into a pyfrp.modules.pyfrp_gmsh_geometry.domain.

Parameters:	fn (str) – Filename of .geo file.
Returns:	Tuple containing: parmDic (dict): Updated parameter dictionary. domain (pyfrp.modules.pyfrp_gmsh_geometry.domain): Domain object.
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.readGeoLine(line, parmDic, domain)¶

Reads in line from .geo file.

Tries to extract type of geometric object and its parameters and uses this to append a geomtric entity to domain.

If line describes a parameter, stores parameter name and its value in parmDic.

Parameters:

line (str) – Line to be splitted.
parmDic (dict) – Parameter dictionary.
domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – Domain object, storing all geometric entities.

Returns:

Tuple containing:

parmDic (dict): Updated parameter dictionary.

typ (str): Object type type.

Id (int): ID of object.

vals (list): Values of object.

domain (pyfrp.modules.pyfrp_gmsh_geometry.domain): Updated domain object.

Return type:

tuple

pyfrp.modules.pyfrp_gmsh_IO_module.readParameter(line, parmDic)¶

Reads in parameter from line and translates values using parmDic.

Parameters:

line (str) – Line to be splitted.
parmDic (dict) – Parameter dictionary.

Returns:

Tuple containing:

var (str): Name of variable.

val (float): Value of variable

Return type:

tuple

pyfrp.modules.pyfrp_gmsh_IO_module.readStlFile(fn, domain=None, volSizePx=20.0)¶

Reads stl file to domain.

Note

Uses numpy-stl package. You may need to install via pip install numpy-stl

If no domain is given, will create new one

Keyword Arguments:
Parameters:	fn (str) – Path to stl file.
	volSizePx (float) – Mesh density assigned at vertices. domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – A domain object.
Returns:	A domain object.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

pyfrp.modules.pyfrp_gmsh_IO_module.removeCommentFromFile(fn, comment)¶

Removes comment comment from .geo file.

Note

Will remove all appearances of comment.

Note

Will also remove comments that only start with comment.

Parameters:	fn (str) – Filename of .geo file. comment (str) – Comment to remove

pyfrp.modules.pyfrp_gmsh_IO_module.removeElementFromFile(fn, elementType, elementId, delimOpen='(', delimClose=')')¶

Removes element with type elementType and ID elementID from .geo file.

Keyword Arguments:
Parameters:	fn (str) – Filename of .geo file. elementId (int) – ID of element to remove. elementType (str) – Type of element to remove.
	delimOpen (str) – Openening delimiter of ID. delimClose (str) – Closing delimiter of ID.

pyfrp.modules.pyfrp_gmsh_IO_module.removeTailingLines(filePath, idx)¶

Removes all empty lines at the end of a .geo file.

Note

Will create temporary file using tempfile.mkstemp(). You should have read/write access to whereever mkstemp is putting files.

Parameters:	filePath (str) – Filename of .geo file. idx (int) – Index of last non-empty line

pyfrp.modules.pyfrp_gmsh_IO_module.repairDefaultGeoFiles(debug=False)¶

Copies default geometry files from backup folder to meshfile folder. Useful if geometry files got somehow overwritten or corrupted.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	`True` if success, `False` otherwise.
Return type:	bool

pyfrp.modules.pyfrp_gmsh_IO_module.sortKeysByLength(dic)¶: Sorts dictionary by length of keys.

pyfrp.modules.pyfrp_gmsh_IO_module.splitLine(line, delim='=', closer=';')¶

Splits line at delim, trimming closer.

Example:

>>> splitLine("Point(3)={1,3,1};")
>>> ("Point(3)","{1,3,1}")

Keyword Arguments:
Parameters:	line (str) – Line to be splitted.
	delim (str) – Delimiter at which to be splitted. closer (str) – Closing character to be trimmed.
Returns:	Tuple containing: var (str): Name of variable. val (str): Value of variable
Return type:	tuple

pyfrp.modules.pyfrp_gmsh_IO_module.updateParmGeoFile(fn, name, val)¶

Updates parameter in .geo file.

Note

Will create temporary file using tempfile.mkstemp(). You should have read/write access to whereever mkstemp is putting files.

Parameters:	fn (str) – Filename of .geo file. name (str) – Name of parameter. val (float) – Value of parameter.

pyfrp.modules.pyfrp_gmsh_IO_module.writeAttractorField(f, fieldID, NodesList)¶

Writes attractor field into into file.

See also: http://gmsh.info/doc/texinfo/gmsh.html#Specifying-mesh-element-sizes and :pyfrp.modules.pyfrp_gmsh_geometry.attractorField.

Parameters:	f (file) – Filehandle. fieldID (int) – ID of new box field. NodesList (list) – List of vertex IDs at which attractor is placed.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeBackgroundField(f, fieldID)¶

Writes background field into into file.

Note

Will take finest mesh for background field. See also: http://gmsh.info/doc/texinfo/gmsh.html#Specifying-mesh-element-sizes .

Parameters:	f (file) – Filehandle. fieldID (int) – ID of new background field.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeBoundaryLayerField(f, fieldID, elements, fieldOpts)¶: Writes boundary layer mesh.

pyfrp.modules.pyfrp_gmsh_IO_module.writeBoxField(f, fieldID, volSizeIn, volSizeOut, rangeX, rangeY, rangeZ)¶

Writes box field into into file.

Parameters:	f (file) – Filehandle. fieldID (int) – ID of new box field. volSizeIn (float) – Mesh element volume inside box. volSizeOut (float) – Mesh element volume outside box. rangeX (list) – Range of box field in x-direction given as `[minVal,maxVal]`. rangeY (list) – Range of box field in y-direction given as `[minVal,maxVal]`. rangeZ (list) – Range of box field in z-direction given as `[minVal,maxVal]`.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeComment(f, comment)¶

Writes comment line into file.

Parameters:	f (file) – Filehandle. comment (str) – Comment to be written.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeFieldProp(f, fieldID, prop, val)¶

Writes field property to file.

Parameters:	f (file) – File to write to. fieldID (int) – ID of field. prop (str) – Name of property to write. val (str) – Value to write.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeFieldPropByDict(f, fieldID, dic)¶

Writes dictionary of field properties to file.

Keyword Arguments:
Parameters:	f (file) – File to write to. fieldID (int) – ID of field.
	dic (dict) – Keyword Arguments.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeMergeLine(f, fnMsh)¶

Adds merge line to geo file.

Parameters:	f (file) – File handle. fnMsh (str) – Mesh file to be added.
Returns:	File handle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeMinField(f, fieldID, ids, charExtendFromBoundary=True)¶

Writes minimum field into into file.

Note

Useful to determine background mesh. It’s often reasonable to take the finest mesh for background field. See also: http://gmsh.info/doc/texinfo/gmsh.html#Specifying-mesh-element-sizes .

Parameters:	f (file) – Filehandle. fieldID (int) – ID of new background field. ids (list) – List of field IDs used for background mesh computation.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_IO_module.writeThresholdField(f, fieldID, IField, LcMin, LcMax, DistMin, DistMax)¶

Writes threshold field into into file.

See also: http://gmsh.info/doc/texinfo/gmsh.html#Specifying-mesh-element-sizes . and :pyfrp.modules.pyfrp_gmsh_geometry.thresholdField.

Parameters:	f (file) – Filehandle. fieldID (int) – ID of new box field. IField (int) – ID of vertex that is center to threshold field. LcMin (float) – Minimum volSize of threshold field. LcMax (float) – Maximum volSize of threshold field. DistMin (float) – Minimun density of field. DistMax (float) – Maximum density of field.
Returns:	Filehandle.
Return type:	file

pyfrp.modules.pyfrp_gmsh_geometry module¶

PyFRAP module for creating/extracting gmsh geometries for PyFRAP toolbox. Module mainly has the following classes:

A domain class, acting as a canvas.

A vertex class, substituting gmsh’s Point.

A edge class, parenting all different kind of edges.

A line class, substituting gmsh’s Line.

A arc class, substituting gmsh’s Circle.

A bSpline class, substituting gmsh’s bSpline.

A lineLoop class, substituting gmsh’s Line Loop.

A ruledSurface class, substituting gmsh’s Ruled Surface.

A surfaceLoop class, substituting gmsh’s Surface Loop.

A volume class, substituting gmsh’s Volume.

A field class, parenting all different kind of fields.

A attractorField class, substituting gmsh’s Attractor field.

A boundaryLayerField class, substituting gmsh’s Boundary Layer field.

A thresholdField class, substituting gmsh’s Threshold field.

A minField class, substituting gmsh’s Min field.

This module together with pyfrp.pyfrp_gmsh_IO_module and pyfrp.pyfrp_gmsh_module works partially as a python gmsh wrapper, however is incomplete. If you want to know more about gmsh, go to http://gmsh.info/doc/texinfo/gmsh.html .

class pyfrp.modules.pyfrp_gmsh_geometry.arc(domain, vstart, vcenter, vend, Id)¶

Bases: pyfrp.modules.pyfrp_gmsh_geometry.edge

Arc class storing information from gmsh .geo cicle.

Will compute angleOffset, angle and pOffset on creation.

Parameters:	domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – Domain arc belongs to. vstart (pyfrp.modules.pyfrp_gmsh_geometry.vertex) – Start vertex. vcenter (pyfrp.modules.pyfrp_gmsh_geometry.vertex) – Center vertex. vend (pyfrp.modules.pyfrp_gmsh_geometry.vertex) – End vertex. Id (int) – ID of arc.

computeAngle()¶: Computes and returns angle of arc.

computeAngleOffset()¶: Computes and returns offset angle of arc.

computePOffset()¶: Computes and returns offset point of arc.

computeRadius()¶

Computes and returns radius of arc.

Returns:	Radius of arc.
Return type:	float

draw(ax=None, color=None, ann=None, backend='mpl', render=False, drawVertices=True)¶

Draws arc.

There are two different backends for drawing, namely

Matplotlib (backend='mpl')

VTK (backend='vtk')

Matplotlib is easier to handle, but slower. VTK is faster for complex geometries.

Note

If backend=mpl, ax should be a matplotlib.axes, if backend='vtk', ax should be a vtk.vtkRenderer object.

Note

If no axes is given, will create new one, see also pyfrp.modules.pyfrp_plot_module.makeGeometryPlot() or pyfrp.modules.pyfrp_vtk_module.makeVTKCanvas().

Warning

Annotations are not properly working with backend='vtk'.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes to be plotted in. color (str) – Color of line. ann (bool) – Show annotations. render (bool) – Render in the end (only in vtk mode). drawVertices (bool) – Also draw vertices.
Returns:	Updated axes.
Return type:	matplotlib.axes

drawMPL(ax=None, color=None, ann=None, render=False)¶

Draws arc into matplotlib axes.

Note

If ann=None, will set ann=False.

Note

If no axes is given, will create new one, see also pyfrp.modules.pyfrp_plot_module.makeGeometryPlot().

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes to be plotted in. color (str) – Color of line. ann (bool) – Show annotations.
Returns:	Axes.
Return type:	matplotlib.axes

drawVTK(ax=None, color=None, ann=None, render=False)¶

Draws arc into VTK renderer.

Note

If ann=None, will set ann=False.

Note

If no axes is given, will create new vtkRenderer, see also pyfrp.modules.pyfrp_vtk_module.makeVTKCanvas().

Keyword Arguments:
	ax (vtk.vtkRenderer) – Renderer to draw in. color (str) – Color of line. ann (bool) – Show annotations. render (bool) – Render in the end.
Returns:	Updated renderer.
Return type:	vtk.vtkRenderer

getAngle()¶: Returns angle of arc.

getAngleOffset()¶: Returns offset angle of arc.

getFirstVertex(orientation)¶

Returns first vertex of arc given a orientation.

Orientation can be either forward (1), or reverse (-1).

Parameters:	orientation (int) – Orientation of arc.
Returns:	Vertex.
Return type:	pyfrp.pyfrp_gmsh_geometry.vertex

getLastVertex(orientation)¶

Returns last vertex of arc given a orientation.

Orientation can be either forward (1), or reverse (-1).

Parameters:	orientation (int) – Orientation of arc.
Returns:	Vertex.
Return type:	pyfrp.pyfrp_gmsh_geometry.vertex

getNormVec()¶

Computes and returns vectors normal to arc.

Returns:	Tuple containing: v1n (numpy.ndarray): Normal vector to `vstart-vcenter`. v2n (numpy.ndarray): Normal vector to `vend-vcenter`.
Return type:	tuple

getPlotVec()¶

Returns vectors for plotting arc.

Returns:	Tuple containing: x (numpy.ndarray): x-array. y (numpy.ndarray): y-array. z (numpy.ndarray): z-array.
Return type:	tuple

getPointOnArc(a)¶

Returns point on arc at angle a.

Returns:	Tuple containing: x (float): x-coordinate. y (float): y-coordinate. z (float): z-coordinate.
Return type:	tuple

getRadius()¶: Returns radius of arc.

getSubElements()¶

Returns all elements that define this element.

Returns:	List of elements.
Return type:	list

getVcenter()¶: Returns center vertex of arc.

getVend()¶: Returns end vertex of arc.

getVstart()¶: Returns start vertex of arc.

getXcenter()¶: Returns center coordinate of arc.

getXend()¶: Returns end coordinate of arc.

getXstart()¶: Returns start coordinate of arc.

inArc(x, debug=False)¶

Tells if coordinate x is on arc or not.

Returns:	`True` if on arc, `False` otherwise.
Return type:	bool

writeToFile(f)¶

Writes arc to file.

Parameters:	f (file) – File to write to.
Returns:	File.
Return type:	file

class pyfrp.modules.pyfrp_gmsh_geometry.attractorField(domain, Id, NodesList=[])¶

Bases: pyfrp.modules.pyfrp_gmsh_geometry.field

Attractor field class storing information from gmsh .geo.

Subclasses from field.

Keyword Arguments:
Parameters:	domain (pyfrp.modules.pyfrp_gmsh_geometry.domain) – Domain surface belongs to. Id (int) – ID of field.
	NodesList (list) – List of IDs of the Nodes that attractor field centers around.

addNodeByID(ID)¶

Adds vertex object to NodesList given the ID of the vertex.

Parameters:	ID (int) – ID of vertex to be added.
Returns:	Updated NodesList.
Return type:	list

getSubElements()¶

Returns all elements that define this element.

Returns:	List of elements.
Return type:	list

includedInThresholdField()¶

Returns all the threshholdFields where attractorField is included in.

Returns:	List of threshholdField objects.
Return type:	list

initNodesList(NodesList)¶

Adds a list of vertices to NodesList.

pyfrp.modules.pyfrp_gmsh_module module¶

PyFRAP module for running Gmsh on .geo files. Module mainly has the following features:

Functions for updating parameters in standard .geo files.

Mesh refinement.

Running Gmsh

This module together with pyfrp.pyfrp_gmsh_geometry and pyfrp.pyfrp_gmsh_IO_module works partially as a python gmsh wrapper, however is incomplete. If you want to know more about gmsh, go to http://gmsh.info/doc/texinfo/gmsh.html .

pyfrp.modules.pyfrp_gmsh_module.getGmshBin(fnPath=None)¶: Returns path to Gmsh binary defined in path file.

pyfrp.modules.pyfrp_gmsh_module.refineMsh(fn, debug=False)¶

Refines mesh by splitting elements.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Keyword Arguments:
Parameters:	fn (str) – Filepath.
	debug (bool) – Print debugging messages.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_gmsh_module.runGmsh(fn, fnOut=None, debug=False, redirect=False, fnStout=None, fnSterr=None, volSizeMax=None)¶

Runs Gmsh generating mesh from .geo file.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Note

If redirect=True, but fnStout or fnSterr is not specified, will dump stout/sterr into meshfiles/gmshLogs/.

Note

Gmsh is run with the following settings (if all flags are activated): gmsh -v -3 -optimize -algo3d -clmax volSizeMax -o fnOut fn This requires that Gmsh was compiled with TetGen algorithm. PyFRAP can be installed with Gmsh + TetGen included by choosing the --gmsh flag. See also http://pyfrap.readthedocs.org/en/latest/setup.html#pyfrap-setup-py-api .

Keyword Arguments:
Parameters:	fn (str) – Filepath.
	fnOut (str) – Output filepath. debug (bool) – Print debugging messages. redirect (bool) – Redirect gmsh stout/sterr into seperate files. fnStout (str) – File for gmsh stout. fnSterr (str) – File for gmsh sterr. volSizeMax (float) – Maximum allowed mesh element size.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_gmsh_module.updateBallGeo(fn, radius, center, run=True, debug=False)¶

Upates parameters in default ball.geo file.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Note

For function to work, parameters in geo file need to be defined as follows:

radius -> radius
center -> center_x , center_y

Keyword Arguments:
Parameters:	fn (str) – Filepath. radius (float) – New ball radius. center (list) – New ball center.
	run (bool) – Run gmsh on updated file. debug (bool) – Print debugging messages.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_gmsh_module.updateConeGeo(fn, upperRadius, lowerRadius, height, center, run=True, debug=False)¶

Upates parameters in default cone.geo file.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Note

For function to work, parameters in geo file need to be defined as follows:

upperRadius -> upper_radius
lowerRadius -> lower_radius
slice_height -> slice_height
center -> center_x , center_y

Keyword Arguments:
Parameters:	fn (str) – Filepath. upperRadius (float) – New upper cone radius. lowerRadius (float) – New lower cone radius. height (float) – New cone height. center (list) – New cone center.
	run (bool) – Run gmsh on updated file. debug (bool) – Print debugging messages.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_gmsh_module.updateCylinderGeo(fn, radius, height, center, run=True, debug=False)¶

Upates parameters in default cylinder.geo file.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Note

For function to work, parameters in geo file need to be defined as follows:

radius -> radius
height -> height
center -> center_x , center_y

Keyword Arguments:
Parameters:	fn (str) – Filepath. radius (float) – New cylinder radius. height (float) – New cylinder height. center (list) – New cylinder center.
	run (bool) – Run gmsh on updated file. debug (bool) – Print debugging messages.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_gmsh_module.updateDomeGeo(fn, radius, slice_height, center, run=False, debug=False)¶

Upates parameters in default dome.geo file.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Note

The way that dome.geo is written, gmsh will automatically compute the dome geometry from slice_height and radius.

Note

For function to work, parameters in geo file need to be defined as follows:

radius -> radius
slice_height -> slice_height
center -> center_x , center_y

Keyword Arguments:
Parameters:	fn (str) – Filepath. radius (float) – New dome imaging radius. slice_height (float) – Height of imaging slice. center (list) – New dome center.
	run (bool) – Run gmsh on updated file. debug (bool) – Print debugging messages.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_gmsh_module.updateVolSizeGeo(fn, volSize_px, run=False, debug=False)¶

Upates parameter that defines mesh element volume in .geo file.

Note

Debug will also activate full debugging output of gmsh. See also http://gmsh.info/doc/texinfo/gmsh.html#Command_002dline-options .

Note

For function to work, parameters in geo file need to be defined as follows:

volSize_px -> volSize_px

Keyword Arguments:
Parameters:	fn (str) – Filepath. volSize_px (float) – New mesh element size.
	run (bool) – Run gmsh on updated file. debug (bool) – Print debugging messages.
Returns:	Path to mesh file.
Return type:	str

pyfrp.modules.pyfrp_idx_module module¶

Indexing module for PyFRAP toolbox. Mainly contains functions that help finding either

image indices

mesh indices

extended indices

for all types of ROIs, such as

circular pyfrp.subclasses.pyfrp_ROI.radialROI

rectangular pyfrp.subclasses.pyfrp_ROI.rectangleROI

quadratic pyfrp.subclasses.pyfrp_ROI.squarelROI

polygonial pyfrp.subclasses.pyfrp_ROI.polyROI

Also provides functions to handle indices in case of quadrant reduction and a powerful suite of check functions that help to figure out if a list of coordinates is inside a ROI., using numpy.where.

pyfrp.modules.pyfrp_idx_module.checkInsideCircle(x, y, center, radius)¶

Checks if coordinate (x,y) is in circle with given radius and center.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Parameters:	x (numpy.ndarray) – Array of x-coordinates. y (numpy.ndarray) – Array of y-coordinates. center (numpy.ndarray) – Center of circle. radius (float) – Radius of circle.
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkInsideImg(x, y, res, offset=[0, 0])¶

Checks if coordinate (x,y) is inside image.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Parameters:	x (numpy.ndarray) – Array of x-coordinates. y (numpy.ndarray) – Array of y-coordinates. res (int) – Resolution of image (e.g. 512).
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkInsidePoly(x, y, poly)¶

Checks if coordinate (x,y) is inside polyogn.

Adapted from http://www.ariel.com.au/a/python-point-int-poly.html.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Parameters:	poly (list) – List of (x,y)-coordinates of corners. x (float) – x-coordinate. y (float) – y-coordinate.
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkInsidePolyVec(x, y, poly)¶

Checks if coordinate (x,y) is inside polyogn, checks first if vector or just value.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Parameters:	poly (list) – List of (x,y)-coordinates of corners. x (numpy.ndarray) – Array of x-coordinates. y (numpy.ndarray) – Array of y-coordinates.
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkInsideRectangle(x, y, offset, sidelengthX, sidelengthY)¶

Checks if coordinate (x,y) is in rectangle with given offset and sidelength.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Note

Offset is set to be bottom left corner.

Parameters:	x (numpy.ndarray) – Array of x-coordinates. y (numpy.ndarray) – Array of y-coordinates. offset (numpy.ndarray) – Offset of rectangle. sidelengthX (float) – Sidelength in x-direction. sidelengthY (float) – Sidelength in y-direction.
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkInsideSquare(x, y, offset, sidelength)¶

Checks if coordinate (x,y) is in square with given offset and sidelength.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Note

Offset is set to be bottom left corner.

Parameters:	x (numpy.ndarray) – Array of x-coordinates. y (numpy.ndarray) – Array of y-coordinates. offset (numpy.ndarray) – Offset of square. sidelength (float) – Sidelength.
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkQuad(x, y, res)¶

Checks if coordinate (x,y) is inside first quadrant.

Note

If x and y are float, will return bool, otherwise numpy.ndarray of booleans.

Parameters:	x (float) – x-coordinate. y (float) – y-coordinate. res (int) – Resolution of image (e.g. 512).
Returns:	True if inside, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkSquCentered(offset, sidelength, res)¶

Checks if square is centered in image.

Note

Need a correction by .5 because there is a difference between pixels and coordinates.

Parameters:	offset (numpy.ndarray) – Offset of square. sidelength (float) – Sidelength. res (int) – Resolution of image (e.g. 512).
Returns:	True if centered, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkSquareCenteredFromInd(ind_sq_x, ind_sq_y, res)¶

Checks if square described by indices is cenntered in image.

Parameters:	ind_sq_x (list) – Image indices in x-direction. ind_sq_y (list) – Image indices y x-direction. res (int) – Resolution of image (e.g. 512).
Returns:	True if centered, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.checkSquareSize(ind_sq_x, ind_sq_y, sidelength)¶

Checks if square described by indices has right size.

Parameters:	ind_sq_x (list) – Image indices in x-direction. ind_sq_y (list) – Image indices y x-direction. sidelength (float) – Sidelength of square.
Returns:	True if equal size, otherwise False.
Return type:	bool

pyfrp.modules.pyfrp_idx_module.getAllIdxImg(res, debug=False)¶

Returns all indices of image.

Keyword Arguments:
Parameters:	res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of indices inside image in x-direction. indY (list): List of indices inside image in y-direction.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getCenterOfMass(xs, axis=0, masses=None)¶

Computes center of mass of a given set of points.

Note

If masses==None, then all points are assigned \(m_i=1\).

Center of mass is computed by:

\[C=\frac{1}{M}\sum\limits_i m_i (x_i)^T\]

where

\[M = \sum\limits_i m_i\]

Keyword Arguments:
Parameters:	xs (numpy.ndarray) – Coordinates.
	masses (numpy.ndarray) – List of masses.
Returns:	Center of mass.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_idx_module.getCircleIdxImg(center, radius, res, debug=False)¶

Returns all indices of image that lie within given circle.

Keyword Arguments:
Parameters:	center (numpy.ndarray) – Center of circle. radius (float) – Radius of circle. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: ind_circ_x (list): List of indices inside circle in x-direction. ind_circ_y (list): List of indices inside circle in y-direction.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getCircleIdxMesh(center, radius, mesh, zmin='-inf', zmax='inf', debug=False)¶

Returns all indices of mesh that lie within given circle and between zmin and zmax.

Keyword Arguments:
Parameters:	center (numpy.ndarray) – Center of circle. radius (float) – Radius of circle. mesh (fipy.Gmsh3DImporter) – Mesh.
	zmin (float) – Minimal z-coordinate. zmax (float) – Maximal z-coordinate. debug (bool) – Print debugging messages.
Returns:	List of mesh indices inside circle.
Return type:	list

pyfrp.modules.pyfrp_idx_module.getCommonExtendedPixels(ROIs, res, debug=False, procedures=None)¶

Finds theoretical pixels that could be filled up with rim concentration for a list of pyfrp.subclasses.pyfrp_ROI.ROI ROIs.

The procedures input is only necessary if ROI is a pyfrp.subclasses.pyfrp_ROI.customROI, combining multiple ROIs via addition/substraction.

Keyword Arguments:
Parameters:	ROIs (list) – List of ROIs. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages. procedures (list) – List of addition/substraction procedures.
Returns:	Tuple containing: indX (list): List of x-indices. indY (list): List of y-indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getCommonXYExtend(ROIs, debug=False)¶

Finds common x-y-extend of a list of pyfrp.subclasses.pyfrp_ROI.ROI ROIs..

Parameters:	ROIs (list) – List of ROIs.
Returns:	Tuple containing: xExtend (list): `[minx,maxx]`. yExtend (list): `[miny,maxy]`.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getExtendedPixelsCircle(center, radius, res, debug=False)¶

Finds theoretical pixels that could be filled up with rim concentration for a circle.

Keyword Arguments:
Parameters:	center (numpy.ndarray) – Center of circle. radius (float) – Radius of circle. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of x-indices. indY (list): List of y-indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getExtendedPixelsPolygon(corners, res, debug=False)¶

Finds theoretical pixels that could be filled up with rim concentration for a polygon.

Keyword Arguments:
Parameters:	corners (list) – List of (x,y)-coordinates of corners. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of x-indices. indY (list): List of y-indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getExtendedPixelsRectangle(offset, sidelengthX, sidelengthY, res, debug=False)¶

Finds theoretical pixels that could be filled up with rim concentration for a rectangle.

Keyword Arguments:
Parameters:	offset (numpy.ndarray) – Offset of rectangle. sidelengthX (float) – Sidelength in x-direction. sidelengthY (float) – Sidelength in y-direction. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of x-indices. indY (list): List of y-indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getExtendedPixelsSquare(offset, sidelength, res, debug=False)¶

Finds theoretical pixels that could be filled up with rim concentration for a square.

Keyword Arguments:
Parameters:	offset (numpy.ndarray) – Offset of square. sidelength (float) – Sidelength. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of x-indices. indY (list): List of y-indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getPolyIdxImg(corners, res, debug=False)¶

Returns all indices of image that lie within given polygon.

Keyword Arguments:
Parameters:	corners (list) – List of (x,y)-coordinates of corners. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of indices inside polygon in x-direction. indY (list): List of indices inside polygon in y-direction.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getPolyIdxMesh(corners, mesh, zmin='-inf', zmax='inf', debug=False)¶

Returns all indices of mesh that lie within given polygon and between zmin and zmax.

Keyword Arguments:
Parameters:	corners (list) – List of (x,y)-coordinates of corners. mesh (fipy.Gmsh3DImporter) – Mesh.
	zmin (float) – Minimal z-coordinate. zmax (float) – Maximal z-coordinate. debug (bool) – Print debugging messages.
Returns:	List of mesh indices inside polygon.
Return type:	list

pyfrp.modules.pyfrp_idx_module.getRectangleIdxImg(offset, sidelengthX, sidelengthY, res, debug=False)¶

Returns all indices of image that lie within given rectangle.

Note

Offset is set to be bottom left corner.

Keyword Arguments:
Parameters:	offset (numpy.ndarray) – Offset of rectangle. sidelengthX (float) – Sidelength in x-directiion. sidelengthY (float) – Sidelength in y-directiion. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of indices inside rectangle in x-direction. indY (list): List of indices inside rectangle in y-direction.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getRectangleIdxMesh(sidelengthX, sidelengthY, offset, mesh, zmin='-inf', zmax='inf', debug=False)¶

Returns all indices of mesh that lie within given rectangle and between zmin and zmax.

Note

Offset is set to be bottom left corner.

Keyword Arguments:
Parameters:	offset (numpy.ndarray) – Offset of rectangle. sidelengthX (float) – Sidelength in x-directiion. sidelengthY (float) – Sidelength in y-directiion. mesh (fipy.Gmsh3DImporter) – Mesh.
	zmin (float) – Minimal z-coordinate. zmax (float) – Maximal z-coordinate. debug (bool) – Print debugging messages.
Returns:	List of mesh indices inside rectangle.
Return type:	list

pyfrp.modules.pyfrp_idx_module.getSliceIdxMesh(z, zmin, zmax, debug=False)¶

Returns all indices of mesh that lie within given slice between zmin and zmax.

Keyword Arguments:
Parameters:	z (float) – z-coordinates of mesh. zmin (float) – Minimal z-coordinate. zmax (float) – Maximal z-coordinate.
	debug (bool) – Print debugging messages.
Returns:	List of mesh indices inside slice.
Return type:	list

pyfrp.modules.pyfrp_idx_module.getSquareIdxImg(offset, sidelength, res, debug=False)¶

Returns all indices of image that lie within given square.

Note

Offset is set to be bottom left corner.

Keyword Arguments:
Parameters:	offset (numpy.ndarray) – Offset of square. sidelengtX (float) – Sidelength. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indX (list): List of indices inside square in x-direction. indY (list): List of indices inside square in y-direction.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.getSquareIdxMesh(sidelength, offset, mesh, zmin='-inf', zmax='inf', debug=False)¶

Returns all indices of mesh that lie within given square and between zmin and zmax.

Note

Offset is set to be bottom left corner.

Keyword Arguments:
Parameters:	offset (numpy.ndarray) – Offset of square. sidelength (float) – Sidelength. mesh (fipy.Gmsh3DImporter) – Mesh.
	zmin (float) – Minimal z-coordinate. zmax (float) – Maximal z-coordinate. debug (bool) – Print debugging messages.
Returns:	List of mesh indices inside square.
Return type:	list

pyfrp.modules.pyfrp_idx_module.idx2QuadImg(indX, indY, res, debug=False)¶

Reduces indices found for whole domain to first quadrant.

Note

Need a correction by .5 because there is a difference between pixels and coordinates.

Keyword Arguments:
Parameters:	indX (list) – List of indices in x-direction. indY (list) – List of indices in y-direction. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: indXQuad (list): List of reduced indices. indYQuad (list): List of reduced indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.ind2mask(vals, ind_x, ind_y, val)¶

Converts indices lists into mask.

Parameters:	vals (numpy.ndarray) – Array on which to be masked. ind_x (list) – Indices in x-direction. ind_y (list) – Indices in y-direction. val (float) – Value that is assigned to pixels in indices-lists.
Returns:	Masked array.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_idx_module.mask2ind(mask, res)¶

Converts mask into indices list.

Parameters:

mask (numpy.ndarray) – Mask array.
res (int) – Resolution of image (e.g. 512).

Returns:

Tuple containing:

indX_new (list): List of x-indices.

indY_new (list): List of y-indices.

Return type:

tuple

pyfrp.modules.pyfrp_idx_module.maskMeshByDistance(x, y, d, grid)¶

Filters all (x,y) coordinates that are more than d in meshgrid given some actual coordinates (x,y).

Parameters:	x (numpy.ndarray) – x-coordinates. y (numpy.ndarray) – y-coordinates. d (float) – Maximum distance. grid (numpy.ndarray) – Numpy meshgrid.
Returns:	List of booleans.
Return type:	idxs (list)

pyfrp.modules.pyfrp_idx_module.nearestNeighbour3D(xi, yi, zi, x, y, z, k=1, minD=None)¶

Finds k nearest neighbour to points.

Uses http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.query.html#scipy.spatial.cKDTree.query .

Example:

>>> from pyfrp.modules import pyfrp_idx_module
>>> import numpy as np
>>> import matplotlib.pyplot as plt

>>> N=50

>>> x=np.random.random(N)
>>> y=np.random.random(N)
>>> z=np.random.random(N)

>>> xi=[0.,1.]
>>> yi=[0.,1.]
>>> zi=[0.,1.]

>>> idx,dist=pyfrp_idx_module.nearestNeighbour3D(xi,yi,zi,x,y,z,k=1)

>>> fig=plt.figure()
>>> ax=fig.add_subplot(111,projection='3d')
>>> ax.scatter(x,y,z,color='k')

>>> ax.scatter(x[idx[0]],y[idx[0]],z[idx[0]],color='b',s=50)
>>> ax.scatter(x[idx[1]],y[idx[1]],z[idx[1]],color='r',s=50)
>>> ax.scatter(xi,yi,zi,color='g',s=50)

>>> plt.show()

Note

To avoid that the a point is nearest neighbour to itself, one can choose minD=0 to avoid that points with distances 0 are returned. Note, if this could be the possibility, one might have at least k=2 to avoid an empty return.

Keyword Arguments:
Parameters:	xi (numpy.ndarray) – x-coordinates of points to find neighbours to. yi (numpy.ndarray) – x-coordinates of points to find neighbours to. zi (numpy.ndarray) – x-coordinates of points to find neighbours to. x (numpy.ndarray) – x-coordinates of possible neighbours. y (numpy.ndarray) – x-coordinates of possible neighbours. z (numpy.ndarray) – x-coordinates of possible neighbours.
	k (int) – Number of neighbours to find per point. minD (float) – Minimum distance nearest neighbour must be away.
Returns:	Tuple containing: indexes (list): Indices of k -closest neighbours per point. dists (list): Distances of k -closest neighbours per point.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.regions2quad(inds, res, debug=False)¶

Reduces indices of regions specified in inds found for whole domain to first quadrant.

Keyword Arguments:
Parameters:	inds (list) – List of indices-list doubles. res (int) – Resolution of image (e.g. 512).
	debug (bool) – Print debugging messages.
Returns:	List of reduced indices-list doubles.
Return type:	list

pyfrp.modules.pyfrp_idx_module.remRepeatedImgIdxs(idxX, idxY, debug=False)¶

Remove repeated indices tupels from index lists for images.

Keyword Arguments:
Parameters:	idxX (list) – List of x-indices. idxY (list) – List of y-indices.
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: idxX (list): Filtred list of x-indices. idxY (list): Filtered list of y-indices.
Return type:	tuple

pyfrp.modules.pyfrp_idx_module.triangulatePoly(coords, addPoints=False, iterations=2, debug=False)¶

Triangulates a polygon with given coords using a Delaunay triangulation.

Uses http://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.Delaunay.html#scipy.spatial.Delaunay to calculate triangulation, then filters the triangles actually lying within the polygon.

Keyword Arguments:
Parameters:	coords (list) – List of (x,y)-coordinates of corners.
	addPoints (bool) – Allow incremental addition of points. iterations (int) – Number of iterations of additional point adding. debug (boo) – Print debugging messages.
Returns:	Tuple containing: triFinal (list): List of found triangles. coordsTri (list): List of vertex coordinates.
Return type:	tuple

pyfrp.modules.pyfrp_img_module module¶

Image analysis module for PyFRAP toolbox. This is one of the key modules of PyFRAP, gathering all necessary image manipulation and reading functions used by PyFRAP. Mainly focuses on:

Analyzing complete FRAP datasets as specified in a pyfrp.subclasses.pyfrp_analysis.analysis instance.

Reading concentrations from images over specific regions as specified in pyfrp.subclasses.pyfrp_ROI.ROI instances.

Handling and saving rim concentrations.

Image manipulation through filters/quadrant reduction.

Image plotting and histograms.

Fiji wrapping.

pyfrp.modules.pyfrp_img_module.analyzeDataset(analysis, signal=None, embCount=None, debug=False, debugAll=False, showProgress=True)¶

Main dataset analysis function doing the following steps.

Reset all data arrays for all ROIs.
Computes flattening/norm/background mask if necessary.
Loops through images, processing images and computing mean concentraions per ROI.
Showing final debugging plots if selected.

Keyword Arguments:
Parameters:	analysis (pyfrp.subclasses.pyfrp_analysis) – Object containing all necessary information for analysis.
	signal (PyQt4.QtCore.pyqtSignal) – PyQT signal to send progress to GUI. embCount (int) – Counter of counter process if multiple datasets are analyzed. debug (bool) – Print final debugging messages and show debugging plots. debugAll (bool) – Print debugging messages and show debugging plots of each step. showProgress (bool) – Print out progress.
Returns:	Performed analysis.
Return type:	pyfrp.subclasses.pyfrp_analysis

pyfrp.modules.pyfrp_img_module.computeFlatMask(img, dataOffset)¶

Computes flattening mask from image.

Parameters:	img (numpy.ndarray) – Image to be used. dataOffset (int) – Offset used.
Returns:	Flattening mask.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.computeMeanImg(fnFolder, fileList, dataEnc, median=False)¶

Computes Mean Image from a list of files.

Keyword Arguments:
Parameters:	fnFolder (str) – Path to folder containing files. fileList (list) – List of file names in fnFolder. dataEnc (str) – Encoding of images, e.g. uint16.
	median (bool) – Apply median filter per image.
Returns:	Mean image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.computeRadialProfile(img, center)¶

Computes radial profile of image from center.

Parameters:

img (numpy.ndarray) – Image to be profiled
center (list) – Center of image.

Returns:

Tuple containing:

r (numpy.ndarray): Array of radii.

v (numpy.ndarray): Array of corresponding image values.

Return type:

tuple

pyfrp.modules.pyfrp_img_module.convSkio2NP(img)¶

Returns mean concentration over given indices.

Keyword Arguments:
Parameters:	idxX (list) – x-indices of pixels used. idxY (list) – y-indices of pixels used. vals (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages.
Returns:	Mean concentration.
Return type:	float

pyfrp.modules.pyfrp_img_module.dist(p1, p2)¶: Computes euclidean distance between two points p1 and p2.

pyfrp.modules.pyfrp_img_module.extractCZI(folder, fijiBin=None, macroPath=None, debug=False, batch=True)¶

Converts all czi files in folder to tif files using Fiji.

Keyword Arguments:
Parameters:	folder (str) – Path to folder containing czi files
	fijiBin (str) – Path to fiji binary macroPath (str) – Path to fiji macro debug (bool) – Print out debugging messages batch (bool) – Execute in batch mode.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_img_module.extractLSM(folder, fijiBin=None, macroPath=None, debug=False, batch=True)¶

Converts all lsm files in folder to tif files using Fiji.

Keyword Arguments:
Parameters:	folder (str) – Path to folder containing lsm files
	fijiBin (str) – Path to fiji binary macroPath (str) – Path to fiji macro debug (bool) – Print out debugging messages batch (bool) – Execute in batch mode.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_img_module.extractMicroscope(folder, ftype, fijiBin=None, macroPath=None, debug=False, batch=True)¶

Converts all microscopy files of type ftype in folder to files using Fiji.

Keyword Arguments:
Parameters:	folder (str) – Path to folder containing czi files ftype (str) – Type of microscopy file, such as lsm or czi
	fijiBin (str) – Path to fiji binary macroPath (str) – Path to fiji macro debug (bool) – Print out debugging messages batch (bool) – Execute in batch mode.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_img_module.findMinOffset(fnFolder, fileList, dataEnc, oldOffset=None, defaultAdd=1.0, debug=False)¶

Simple function that loops through all images in file list and returns minimum integer that needs to be added such that all pixels are positiv.

Keyword Arguments:
Parameters:	fnFolder (str) – Path to folder containing files. fileList (list) – List of file names in fnFolder. dataEnc (str) – Encoding of images, e.g. uint16.
	oldOffset (int) – Take some other offset into account. defaultAdd (int) – Default value added to minimal offset. debug (bool) – Show debugging outputs.
Returns:	Minimal Offset
Return type:	int

pyfrp.modules.pyfrp_img_module.findProblematicNormingPixels(img, imgPre, dataOffset, axes=None, debug=False)¶

Checks which pixels are problematic for norming.

Note that function will temporarily set >>> np.errstate(divide=’raise’) so numpy RuntimeWarning actually gets raised instead of just printed.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to be normed. imgPre (numpy.ndarray) – Image to be normed. dataOffset (int) – Offset used for norming.
	axes (list) – List with matplotlib axes used for plotting. If not specified, generate new ones. (will) – debug (bool) – Show debugging outputs and plots.
Returns:	Binary mask of problematic pixels.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.fixedThresh(img, thresh, smaller=False, fill=<Mock name='mock.nan' id='140635021242256'>)¶

Apply fixed threshold to image and fill pixels with values greater than thresh with fill value .

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image for thresholding. thresh (float) – Threshold used.
	fill (float) – Fill values for pixels that are smaller or greater than thresh. smaller (bool) – Apply fill to pixels smaller or greater than thresh.
Returns:	Tuple containing: img (numpy.ndarray): Output image. indX (numpy.ndarray): x-indices of pixels that where thresholded. indY (numpy.ndarray): y-indices of pixels that where thresholded.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.flattenImg(img, mask)¶: Flattens image with flattening mask.

pyfrp.modules.pyfrp_img_module.flipQuad(img, debug=False, testimg=False)¶

Flip image into quaddrant.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages and show debugging plots. testimg (bool) – Check with test image if flip works properly
Returns:	Flipped image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.gaussianFilter(img, sigma=2.0, debug=False, axes=None)¶

Applies gaussian filter to image.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	sigma (float) – Standard deviation of gaussian kernel applied. axes (list) – List of matplotlib axes used for plotting. If not specified, will generate new ones. debug (bool) – Print debugging messages and show debugging plots.
Returns:	Processed image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.genFakeIC(res, valIn, valOut, offset, sidelength, radius, center, fill=0.0, debug=False)¶

Create fake image consisting of circular ROI with pixel value valOut and a square ROI centered inside circle with pixel value valIn.

Note

If fill=np.nan, then pyfrp.modules.pyfrp_sim_module.applyInterpolatedICs() will not work.

Keyword Arguments:
Parameters:	res (int) – Resolution of desired image. valIn (float) – Value inside bleached region. valOut (float) – Value outside of bleached region. offset (numpy.ndarray) – Offset of bleached square. sidelength (float) – Sidelength of bleached square. radius (float) – Radius of circular ROI. center (list) – Center of circular ROI.
	fill (float) – Fill value for everything outside of circular domain. debug (bool) – Print debugging messages.
Returns:	Generated image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.genFakeSigmoidIC(res, valIn, valOut, rJump, rate, radius, center, fill=0.0, debug=False)¶

Create fake image consisting of circular ROI with pixel value valOut that decays with a sigmoid function towards center.

Note

If fill=np.nan, then pyfrp.modules.pyfrp_sim_module.applyInterpolatedICs() will not work.

Keyword Arguments:
Parameters:	res (int) – Resolution of desired image. valIn (float) – Value inside bleached region. valOut (float) – Value outside of bleached region. radius (float) – Radius of circular ROI. center (list) – Center of circular ROI. rate (float) – Decay rate of sigmoid. rJump (float) – Radius at which decay happens.
	fill (float) – Fill value for everything outside of circular domain. debug (bool) – Print debugging messages.
Returns:	Generated image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.getCommonRange(imgs)¶

Finds common range of list of images.

Parameters:	imgs (list) – List of images.
Returns:	Tuple containing: minVal (float): Lowest value over all images. maxVal (float): Largest value over all images.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.getImgSmoothness(arr)¶

Returns smoothness of img.

Smoothness \(s\) is computed as:

\[s=\frac{d_{\mathrm{max}}}{\bar{d}}\]

where \(d_{\mathrm{max}}\) is the maximum derivation from the nearest neighbour over the whole array, and \(\bar{d}\) the average derivation.

Parameters:	arr (numpy.ndarray) – Some image.
Returns:	Tuple containing: s (float): Smoothmess coefficient. dmax(float): Maximum diff.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.getIntRangeDtype(dtype)¶: Returns range of int based dtype.

pyfrp.modules.pyfrp_img_module.getMaxRangeChannel(img, debug=False)¶

Loops through all channels of image and returns channel of image with maximal range.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: img (numpy.ndarray): Monochromatic image. ind_max (int): Index of channel chosen.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.getMeanIntensitiesImgs(fnFolder, fileList, dataEnc)¶

Reads all images in folder, returns mean intensity vector.

Parameters:	fnFolder (str) – Path to folder containing files. fileList (list) – List of file names in fnFolder. dataEnc (str) – Encoding of images, e.g. uint16.
Returns:	Array of mean intensities per image.
Return type:	list

pyfrp.modules.pyfrp_img_module.getRimConc(ROIs, img, debug=False)¶

Computes mean rim concentration from ROIs that have useForRim flag on.

Keyword Arguments:
Parameters:	ROIs (list) – List of pyfrp.subclasses.pyfrp_ROI objects. img (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages and show debugging plots.
Returns:	Rim concentration.
Return type:	float

pyfrp.modules.pyfrp_img_module.imgHist(img, binMin=0, binMax=65535, nbins=256, binSize=1, binsFit=True, fixSize=False, density=False)¶

Creates histogram for image with some good default settings.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	nbins (int) – Number of bins of histogram. binSize (int) – Size of bins. binMin (float) – Minimum value of bins. binMax (float) – Maximum value of bins. binsFit (bool) – Fits bin range properly around range of image. fixSize (bool) – Use binSize for fixed bin size. density (bool) – Normalize histogram as probability density function.
Returns:	Tuple containing: bins (float): Bin array of histogram. hist (float): Histogram array. w (float): Width of bins.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.loadImg(fn, enc, dtype='float')¶

Loads image from filename fn with encoding enc and returns it as with given dtype.

Keyword Arguments:
Parameters:	fn (str) – File path. enc (str) – Image encoding, e.g. ‘uint16’.
	dtype (str) – Datatype of pixels of returned image.
Returns:	Loaded image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.meanConc(idxX, idxY, vals, debug=False)¶

Returns mean concentration over given indices.

Keyword Arguments:
Parameters:	idxX (list) – x-indices of pixels used. idxY (list) – y-indices of pixels used. vals (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages.
Returns:	Mean concentration.
Return type:	float

pyfrp.modules.pyfrp_img_module.meanExtConc(idxX, idxY, img, concRim, numExt, addRimImg, debug=False)¶

Returns mean concentration, taking potential pixels outside of image into account.

Keyword Arguments:
Parameters:	idxX (list) – x-indices of pixels used. idxY (list) – y-indices of pixels used. img (numpy.ndarray) – Input image. concRim (float) – Rim concentration applied to pixels outside of image. numExt (int) – Number of pixels outside of image. addRimImg (bool) – Add rim concentraion.
	debug (bool) – Print debugging messages.
Returns:	Mean extendended concentration.
Return type:	float

pyfrp.modules.pyfrp_img_module.medianFilter(img, radius=1, debug=False, dtype='uint16', scaleImg=False, method='scipy', axes=None)¶

Applies median filter to image.

Note

Skimage algorithm requires images to be scaled up into 16bit range.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	radius (int) – Odd integer defining median kernel size. axes (list) – List of matplotlib axes used for plotting. If not specified, will generate new ones. debug (bool) – Print debugging messages and show debugging plots. dtype (str) – Optimal image dtype necessary for scaling. scaleImg (bool) – Scale image to full range of dtype. method (str) – Median algorithm used (scipy/skimage).
Returns:	Processed image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.normImg(img, imgPre, dataOffset=1.0, debug=False)¶

Norms image by preimage.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image. imgPre (numpy.ndarray) – Norming mask.
	dataOffset (float) – Offset used for norming. debug (bool) – Print debugging messages and show debugging plots.
Returns:	Processed image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.otsuImageJ(img, maxVal, minVal, debug=False)¶

Python implementation of Fiji’s Otsu algorithm.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image as 2D-array. maxVal (int) – Value assigned to pixels above threshold. minVal (int) – Value assigned to pixels below threshold.
	debug (bool) – Show debugging outputs and plots.
Returns:	Tuple containing: kStar (int): Optimal threshold binImg (np.ndarray): Binary image
Return type:	tuple

pyfrp.modules.pyfrp_img_module.plotRadialHist(img, center, nbins=10, byMean=True, axes=None, color='r', linestyle='-', fullOutput=False, maxR=None, plotBinSize=False, label='', legend=False, linewidth=1.0)¶

Plots radial histogram of image from center.

Example:

>>> axes=pyfrp_img_module.plotRadialHist(emb.simulation.ICimg,emb.geometry.getCenter(),nbins=100)

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to be profiled center (list) – Center of image.
	nbins (int) – Number of bins of histogram. byMean (bool) – Norm bins by number of items in bin. maxR (float) – Maximum radius considered. plotBinSize (bool) – Include number of items in bin on secondary axis. axes (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linestyle (str) – Linestyle of plot. fullOutput (bool) – Also return result arrays from radialImgHist ? linewidth (float) – Linewidth of plot.
Returns:	Tuple containing: ax (matplotlib.axes): Matplotlib axes. ax2 (matplotlib.axes): Matplotlib axes for secondary axes. None if plotBinSize==False bins (numpy.ndarray): Bin vector. binsMid (numpy.ndarray): Array of midpoints of bins. histY (numpy.ndarray): Array of number of items per bin. binY (numpy.ndarray): Array of average value per bin.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.plotRadialProfile(img, center, ax=None, color='r', linestyle='-', fullOutput=False, linewidth=1.0)¶

Plots radial profile of image from center.

Example:

>>> ax=pyfrp_img_module.plotRadialProfile(emb.simulation.ICimg,emb.geometry.getCenter(),color='g')

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to be profiled center (list) – Center of image.
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linestyle (str) – Linestyle of plot. fullOutput (bool) – Also return result arrays from computeRadialProfile ? linewidth (float) – Linewidth of plot.
Returns:	Tuple containing: ax (matplotlib.axes): Matplotlib axes. r (numpy.ndarray): Array of radii. v (numpy.ndarray): Array of corresponding image values.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.processImg(img, processDic, flatteningMask, bkgdMask, preMask, dataOffset=1.0, axes=None, debug=False)¶

Main image processing function containing the following steps:

Quadrant reduction.
Median filter.
Gaussian filter.
Norming.
Background substraction.
Flattening.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image. processDic (dict) – Dictionary defining what to do. See also pyfrp.subclasses.pyfrp_analysis . flatteningMask (np.ndarray) – Flattening mask that will be used if flattening is selected. bkgdMask (numpy.ndarray) – Background mask that will be used if background substraction is selected. preMask (numpy.ndarray) – Preimage mask that will be used if norming is selected.
	dataOffset (float) – Offset used for norming. axes (list) – List of matplotlib axes used for plotting. If not specified, will generate new ones. debug (bool) – Print debugging messages and show debugging plots.
Returns:	Processed image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.radialImgHist(img, center, nbins=10, byMean=True, maxR=None)¶

Computes radial histogram of image from center.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to be profiled center (list) – Center of image.
	nbins (int) – Number of bins of histogram. byMean (bool) – Norm bins by number of items in bin. maxR (float) – Maximum radius considered.
Returns:	Tuple containing: bins (numpy.ndarray): Bin vector. binsMid (numpy.ndarray): Array of midpoints of bins. histY (numpy.ndarray): Array of number of items per bin. binY (numpy.ndarray): Array of average value per bin.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.runFijiMacro(macroPath, macroArgs, fijiBin=None, debug=False, batch=True)¶

Runs Fiji Macro.

Keyword Arguments:
Parameters:	macroPath (str) – Path to fiji macro macroArgs (str) – Arguments being passed to Fiji macro
	fijiBin (str) – Path to fiji binary debug (bool) – Print out debugging messages batch (bool) – Execute in batch mode.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_img_module.saveImg(img, fn, enc='uint16', scale=True, maxVal=None)¶

Saves image as tif file.

scale triggers the image to be scaled to either the maximum range of encoding or maxVal. See also scaleToEnc().

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to save. fn (str) – Filename.
	enc (str) – Encoding of image. scale (bool) – Scale image. maxVal (int) – Maximum value to which image is scaled.
Returns:	Filename.
Return type:	str

pyfrp.modules.pyfrp_img_module.scaleToEnc(img, enc, maxVal=None)¶

Scales image to either the maximum range of encoding or maxVal.

Possible encodings:

4bit

8bit

16bit

32bit

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to save. enc (str) – Encoding of image.
	maxVal (int) – Maximum value to which image is scaled.
Returns:	Scaled image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.showImgAndHist(img, axes=None, sup='', title=None, color='b', vmin=None, vmax=None, binMin=0, binMax=65535, nbins=256, binSize=1, binsFit=True, fixSize=False, density=False)¶

Creates plot of image and corresponding histogram.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Image to be plotted.
	nbins (int) – Number of bins of histogram. binSize (int) – Size of bins. binMin (float) – Minimum value of bins. binMax (float) – Maximum value of bins. binsFit (bool) – Fits bin range properly around range of image. fixSize (bool) – Use binSize for fixed bin size. density (bool) – Normalize histogram as probability density function. vmin (float) – Minimum display value of image. vmax (float) – Maximum display value of image. title (list) – List of titles of plots. sup (str) – Supporting title of figure. axes (list) – List of matplotlib axes used for plotting. If not specified, will generate new ones. color (str) – Color of histogram plot.
Returns:	Tuple containing: bins (float): Bin array of histogram. hist (float): Histogram array. w (float): Width of bins.
Return type:	tuple

pyfrp.modules.pyfrp_img_module.substractBkgd(img, bkgd, substractMean=True, nonNeg=True)¶

Substracts background from image.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image. bkgd (numpy.ndarray) – Background mask that will be used.
	substractMean (bool) – Substract mean background. nonNeg (bool) – Fill negative pixels with 1. to avoid further problems.
Returns:	Processed image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_img_module.symmetryTest(img, debug=False)¶

Checks if images is LR and UD symmetirc. If so, returns True.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages and show debugging plots.
Returns:	True if image is both LR and UD symmetric
Return type:	bool

pyfrp.modules.pyfrp_img_module.unflipQuad(img, debug=False, testimg=False)¶

Unflip image from quaddrant into normal picture.

Keyword Arguments:
Parameters:	img (numpy.ndarray) – Input image.
	debug (bool) – Print debugging messages and show debugging plots. testimg (bool) – Check with test image if unflip works properly
Returns:	Unflipped image.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_integration_module module¶

Integration module for PyFRAP toolbox.

Warning

Might get merged with simulation module at some point.

pyfrp.modules.pyfrp_integration_module.calcTetSidelengths(point0, point1, point2, point3)¶: Calculates sidelengths of tetrahedron given by 4 points.

Note

Taking point0 as base point.

pyfrp.modules.pyfrp_integration_module.getAvgConc(val, cvs, ind)¶

Integrates simulation result over specific set of indices.

Parameters:	val (fipy.CellVariable) – PDE solution variable. cvs (numpy.ndarray) – Array containing cell volumes. ind (list) – List of indices.
Returns:	Integration result.
Return type:	float

pyfrp.modules.pyfrp_misc_module module¶

Miscellaneous module for PyFRAP toolbox. Contains functions handling:

Filepath management (Windowns/Unix conversion etc.)
String searching/replacing
List matching/comparison
Dictionary to object and back conversion/extraction.
Settings path management.
Embryo wizard.
etc.

pyfrp.modules.pyfrp_misc_module.addPathToWinPATHs(path)¶

Adds a path to Windows’ PATH list.

Note

Only adds path if file exits.

Note

You will need to restart the terminal to be sure that the change has any effect.

Parameters:	path (str) – Path to be added.
Returns:	True if successful.
Return type:	bool

pyfrp.modules.pyfrp_misc_module.appDtype(l, s, dtype='int')¶

Appends string to list and convert to right dtype.

Note

Will use s.strip() before conversion to avoid unnecessary spaces.

Note

Will remove quotes from strings using removeQuoteSignsFromString().

Keyword Arguments:
Parameters:	l (list) – A list. s (str) – String to append.
	dtype (str) – Data type (float,int,str)
Returns:	List with appended value.
Return type:	list

pyfrp.modules.pyfrp_misc_module.assignIfVal(var, val, valCheck)¶

Assigns val to var if var==valCheck.

Parameters:	var (var) – Variable val (any) – Value to be assigned valCheck (any) – Value to be checked for

pyfrp.modules.pyfrp_misc_module.buildEmbryoWizard(fn, ftype, name, nChannel=1, fnDest=None, createEmbryo=True, recoverIdent=['recover', 'post'], bleachIdent=['bleach'], preIdent=['pre'], colorPrefix='_c00', cleanUp=True)¶

Creates embryo object ready for analysis from microscope data.

Extracts microscope data into .tif files
Builds folder structure
Moves image files in proper folders
Creates embryo object and automatically sets filepaths properly

Keyword Arguments:
Parameters:	fn (str) – Path to embryo folder ftype (str) – Type of microscopy file, such as lsm or czi name (str) – Name of embryo
	nChannel (int) – Defines which channel of the images contains relevant data fnDest (str) – Path of embryo data structure createEmbryo (boo) – Flag if embryo object should be created recoverIdent (list) – List of identifiers for recovery data bleachIdent (list) – List of identifiers for bleach data preIdent (list) – List of identifiers for pre-bleach data colorPrefix (str) – Defines how to detect if multichannel or not cleanUp (bool) – Clean up .tif files from other channels afterwards.
Returns:	Created Embryo in case of success, otherwise -1
Return type:	pyfrp.subclasses.pyfrp_embryo.embryo

pyfrp.modules.pyfrp_misc_module.checkDataMultiChannel(fn, recoverIdent=['recover', 'post'], bleachIdent=['bleach'], preIdent=['pre'], colorPrefix='_c00')¶

Checks if extracted bleach/pre/recover microscopy data are multichannel.

Keyword Arguments:
Parameters:	fn (str) – Path to folder containing images
	recoverIdent (list) – List of identifiers for recovery data bleachIdent (list) – List of identifiers for bleach data preIdent (list) – List of identifiers for pre-bleach data colorPrefix (str) – Defines how to detect if multichannel or not
Returns:	Tuple containing: recoverMulti (bool): True if recover is multichannel preMulti (bool): True if pre is multichannel bleachMulti (bool): True if bleach is multichannel
Return type:	tuple

pyfrp.modules.pyfrp_misc_module.checkIfGmshBin(fn)¶

pyfrp.modules.pyfrp_misc_module.checkMultiChannel(fn, ident, colorPrefix='_c00')¶

Checks if extracted microscopy with identifier are multichannel.

Keyword Arguments:
Parameters:	fn (str) – Path to folder containing images ident (list) – List of identifiers, for example [“recover”,”post”]
	colorPrefix (str) – Defines how to detect if multichannel or not
Returns:	True if multichannel, False if not
Return type:	bool

pyfrp.modules.pyfrp_misc_module.checkPaths(fnPath=None)¶

Checks if all paths in paths file exist.

If fnPath is not given, will use the return of getPathFile.

Keyword Arguments:
	fnPath (str) – Path to path file.

pyfrp.modules.pyfrp_misc_module.cleanUpImageFiles(fn, ftype, ident=None, debug=False, colorPrefix='_c00', nChannel=None)¶

Removes all image files fullfilling *ident*colorPrefix*ftype from fn.

If nChannel=None, will remove all files of ftype.

Keyword Arguments:
Parameters:	fn (str) – Path to folder containing images. ftype (str) – Type of file, for example “tif”.
	debug (bool) – Debugging flag colorPrefix (str) – String prefix before channel number. nChannel (int) – Defines which channel to delete ident (list) – List of identifiers, for example [“recover”,”post”].
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_misc_module.compareArrays(arr1, arr2)¶

Converts two lists/arrays into numpy arrays and compares then elementwise.

Parameters:	arr1 (numpy.ndarray) – Some array. arr2 (numpy.ndarray) – Other arrayy.
Returns:	True if elemet are the same element wise.
Return type:	bool

pyfrp.modules.pyfrp_misc_module.compareObjAttr(obj1, obj2)¶

Compare the values of two objects.

Parameters:

obj1 (object) – First object.
object2 (object) – Second object.

Returns:

Tuple containing:

same (dict): Dictionary of attributes with same values

different (dict): Dictionary of attributes with different values

notInBoth (dict): Dictionary of attributes that are not in both objects

Return type:

tuple

pyfrp.modules.pyfrp_misc_module.compareROIs(emb1, emb2, byName=True)¶

Compares the list of ROIs between to pyfrp.subclasses.pyfrp_embryo.embryo` objects.

Parameters:

emb1 (pyfrp.subclasses.pyfrp_embryo.embryo) – First embryo.
emb2 (pyfrp.subclasses.pyfrp_embryo.embryo) – Second embryo.

Returns:

Tuple containing:

sameAll (list): List of same output of compareObjAttr(ROI,ROI2) per ROI, see also compareObjAttr().

differentAll (list): List of different output of compareObjAttr(ROI,ROI2) per ROI, see also compareObjAttr().

notInBothAll (list): List of notInBoth output of compareObjAttr(ROI,ROI2) per ROI, see also compareObjAttr().

notFound (dict): Dictionary of ROI names that are in emb1 but not emb2.

Return type:

tuple

pyfrp.modules.pyfrp_misc_module.compareVectors(x, y)¶

Compares two vectors.

Parameters:	x (numpy.ndarray) – Vector 1. y (numpy.ndarray) – Vector 2.
Returns:	True if vectors are identical
Return type:	bool

pyfrp.modules.pyfrp_misc_module.complValsFast(l1, l2)¶

Returns complimentary values of two lists, faster version.

Parameters:	l1 (list) – A list. l2 (list) – Another list.
Returns:	List with complimentary values.
Return type:	list

pyfrp.modules.pyfrp_misc_module.complValsSimple(l1, l2)¶

Returns complimentary values of two lists.

Parameters:	l1 (list) – A list. l2 (list) – Another list.
Returns:	List with complimentary values.
Return type:	list

pyfrp.modules.pyfrp_misc_module.copyListOfFiles(l, dest)¶

Copies list of files using shutil.copy.

Parameters:	l (list) – List of files. dest (str) – Destination of files.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_misc_module.dict2string(dic, sep='=', newline=False)¶

Build string with variable name and its value from dict.

Keyword Arguments:
Parameters:	dic (dict) – Dictionary.
	sep (list) – Seperator between variable and value. newline (bool) – Start newline after each variable.
Returns:	Built string.
Return type:	str

pyfrp.modules.pyfrp_misc_module.enumeratedName(baseName, listOfNames, sep='_')¶

Generates a new name given a list of names.

Example:

>>> baseName=embryo
>>> listOfNames=[embryo_1,embryo_5]
>>> enumeratedName(baseNamem,listOfNames)
"embryo_6"

Keyword Arguments:
Parameters:	baseName (str) – basename used for naming listOfNames (list) – List of names
	sep (str) – Seperator between basename and number
Returns:	New name that has not been used yet
Return type:	str

pyfrp.modules.pyfrp_misc_module.findDateString(s, sep='', lendate=8, yearreq='', monthreq='', debug=False)¶

Finds date in string.

Useful for example to find date a filename.

Keyword Arguments:
Parameters:	s (str) – String
	sep (str) – Separator between date parts. lendate (int) – Length of dates in characters. yearreq (str) – Date must contain year, for example (“16”). monthreq (str) – Date must cotain month, for example (“03”).
Returns:	Date found, or empty string if date was not found.
Return type:	str

pyfrp.modules.pyfrp_misc_module.findFn(fn, base, lvlsUp=3, folder=False, debug=False)¶

Finds filename within folder structure

Keyword Arguments:
Parameters:	fn (str) – File name to look for base (str) – Base path to look in
	lvlsUp (int) – How many levels to go up debug (bool) – Debugging flag folder (bool) – Look for folder
Returns:	Path to preimage
Return type:	str
Raises:	`OSError` – If file cannot be found

pyfrp.modules.pyfrp_misc_module.findIntString(s, idxvec=[], debug=False)¶

Finds integers in string.

Keyword Arguments:
Parameters:	s (str) – String
	idxvec (list) – List of already found indices. debug (bool) – Show debugging output.
Returns:	List of indices if integers in string.
Return type:	list

pyfrp.modules.pyfrp_misc_module.findPreimage(key, base, lvlsUp=1, fType='tif', debug=False)¶

Finds preimage automatically

Keyword Arguments:
Parameters:	key (str) – Key pattern to look for, e.g. “_pre” base (str) – Base path to look in
	lvlsUp (int) – How many levels to go up. fType (str) – Filetype of preimage debug (bool) – Debugging flag
Returns:	Path to preimage
Return type:	str
Raises:	`OSError` – If preimage cannot be found

pyfrp.modules.pyfrp_misc_module.fixPath(path)¶

Fixes path by expanding user and making sure that path is according to OS definitions.

Parameters:	path (str) – Path to fix.
Returns:	Fixed path.
Return type:	str

pyfrp.modules.pyfrp_misc_module.getAllObjWithAttrVal(listOfObjects, AttributeName, AttributeValue)¶

Filters all objects from a list that have a given attribute value.

Parameters:	listOfObjects (list) – List of objects that all possess the same attribute. AttributeName (str) – Name of attribute. AttributeValue (str) – Value of attribute.
Returns:	List of objects that fulfill requirement.
Return type:	list

pyfrp.modules.pyfrp_misc_module.getConfDir()¶

pyfrp.modules.pyfrp_misc_module.getFijiBin(fnPath=None)¶

pyfrp.modules.pyfrp_misc_module.getGUIDir()¶

pyfrp.modules.pyfrp_misc_module.getIdxOfNLargest(x, N)¶

Returns indices of N largest values in array/list.

Parameters:

x (numpy.nparray) – An array.
N (int) – Number of values.

Returns:

Tuple containing:

list: List containing N largest numbers.

list: List containing indices of N largest numbers.

Return type:

tuple

pyfrp.modules.pyfrp_misc_module.getMacroDir()¶

pyfrp.modules.pyfrp_misc_module.getMeshfilesDir()¶

pyfrp.modules.pyfrp_misc_module.getModulesDir()¶

pyfrp.modules.pyfrp_misc_module.getNLargest(x, N)¶

Returns N largest values in array/list.

Parameters:	x (numpy.nparray) – An array. N (int) – Number of values.
Returns:	List containing N largest numbers.
Return type:	list

pyfrp.modules.pyfrp_misc_module.getOpenscadBin(fnPath=None)¶

pyfrp.modules.pyfrp_misc_module.getPath(identifier, fnPath=None, defaultOutput='')¶

Extracts path with identifier from path definition file.

If not defined diferently, will first look in configurations/paths, then configurations/paths.default.

Keyword Arguments:
Parameters:	identifier (str) – Identifier of path
	fnPath (str) – Path to path definition file
Returns:	Path
Return type:	str

pyfrp.modules.pyfrp_misc_module.getPathFile()¶

pyfrp.modules.pyfrp_misc_module.getSortedFileList(fnFolder, fType)¶

Gets sorted file list from folder for files of type fType

Parameters:	fnFolder (str) – Folder path. fType (str) – File type.
Returns:	list of filenames.
Return type:	list

pyfrp.modules.pyfrp_misc_module.getSubclassesDir()¶

pyfrp.modules.pyfrp_misc_module.leastCommonSubstring(S, T)¶

Find longest common substring.

Taken from http://www.bogotobogo.com/python/python_longest_common_substring_lcs_algorithm_generalized_suffix_tree.php

Parameters:	S (str) – String to find substring in. T (str) – Substring.
Returns:	Least common substring.
Return type:	str

pyfrp.modules.pyfrp_misc_module.lenRange(l)¶

Return the range of values in list

Parameters:	l (list) – List
Returns:	Range of values of list
Return type:	float

pyfrp.modules.pyfrp_misc_module.lin2winPath(p)¶

Converts Linux Path to Win path (/ -> )

Parameters:	p (str) – Path.
Returns:	Converted path.
Return type:	str

pyfrp.modules.pyfrp_misc_module.makeEmbryoFolderStruct(fn)¶

Creates default folder structure for embryo object.

fn

|–recover

|–pre

|–bleach

|–lsm

|–meshfiles

Parameters:	fn (str) – Path to embryo folder
Returns:	0
Return type:	int

pyfrp.modules.pyfrp_misc_module.matchVals(l1, l2)¶

Returns matching values of two lists.

Parameters:	l1 (list) – A list. l2 (list) – Another list.
Returns:	List of matching values.
Return type:	list

pyfrp.modules.pyfrp_misc_module.mkdir(fn)¶

Tries to make folder if not already existent.

Parameters:	fn (str) – Path of folder to create.
Returns:	True if success, False otherwise.
Return type:	bool

pyfrp.modules.pyfrp_misc_module.modIdx(i, l)¶

Returns index of list when input is larger than list by returning the modulo of the length of the list.

Useful if lists refer to loop etc.

Parameters:	i (int) – Index. l (list) – Some list.
Returns:	New index.
Return type:	int

pyfrp.modules.pyfrp_misc_module.moveImageFiles(fn, fnTarget, ftype, ident, isMulti, fnDest=None, debug=False, colorPrefix='_c00', nChannel=1)¶

Moves all image files fullfilling ident*(isMulti*colorPrefix) to fnDest+fnTarget.

Keyword Arguments:
Parameters:	fn (str) – Path to folder containing images. fnTarget (str) – Name of folder files should go in, for example “recover”. ftype (str) – Type of file, for example “tif”. ident (list) – List of identifiers, for example [“recover”,”post”]. isMulti (bool) – Flag if images are multichannel or not.
	fnDest (str) – Path containing fnTarget debug (bool) – Debugging flag colorPrefix (str) – Defines how to detect if multichannel or not nChannel (int) – Defines which channel of the images contains relevant data
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_misc_module.moveListOfFiles(l, dest)¶

Moves list of files using shutil.move.

Parameters:	l (list) – List of files. dest (str) – Destination of files.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_misc_module.objAttr2Dict(obj, attr=[])¶

Writes all object attributes with names defined in list in the form attributeName = attributeValue into dictionary.

If attr=[], all attributes are written into dictionary, otherwise only the ones specified in attr.

Keyword Arguments:
Parameters:	obj (object) – Object to be printed.
	maxL (int) – Maximum length threshold.

pyfrp.modules.pyfrp_misc_module.objAttrToList(listOfObjects, AttributeName)¶

Extracts a single object attribute from a list of objects and saves it into list.

Parameters:	listOfObjects (list) – List of objects that all possess the same attribute AttributeName (str) – Name of attribute to be appended
Returns:	List containing value of attribute of all objects
Return type:	list

pyfrp.modules.pyfrp_misc_module.popRange(l, idxStart, idxEnd)¶

Basically list.pop() for range of indices.

Parameters:

l (list) – A list
idxStart (int) – Start index of range.
idxEnd (int) – End index of range.

Returns:

Tuple containing:

popped (list): Popped items.

l (list): Resulting list.

Return type:

tuple

pyfrp.modules.pyfrp_misc_module.printPaths(fnPath=None)¶

Prints out path file.

If fnPath is not given, will use the return of getPathFile.

Keyword Arguments:
	fnPath (str) – Path to path file.

pyfrp.modules.pyfrp_misc_module.rangeLists(ls)¶

Return the range of values in list of lists

Parameters:	ls (list) – List of lists
Returns:	Tuple containing: minL (float): Minimum value over all lists maxL (float): Maximum value over all lists
Return type:	tuple

pyfrp.modules.pyfrp_misc_module.remRepeatsList(l)¶

Removes repeated entries from list.

Similar to numpy.unique.

Parameters:	l (list) – List
Returns:	Filtered list.
Return type:	list

pyfrp.modules.pyfrp_misc_module.removeAllOccFromList(l, val)¶

Removes all occurences of value in list.

Parameters:	l (list) – A list. val (value) – Value to remove.
Returns:	List without removed values.
Return type:	list

pyfrp.modules.pyfrp_misc_module.removeListOfFiles(l)¶

Removes list of files using os.remove.

Parameters:	l (list) – List of files.
Returns:	Returns 0 if success, -1 if error
Return type:	int

pyfrp.modules.pyfrp_misc_module.removeQuoteSignsFromString(s)¶

Removes Quote signs from string.

Example: >>> a=‘“Test(‘Test’)”’ >>> removeQuoteSignsFromString() >>> ‘Test(Test)’

Parameters:	s (str) – A string.
Returns:	String without quote signs.
Return type:	str

pyfrp.modules.pyfrp_misc_module.setPath(identifier, val, fnPath=None)¶

Sets path in path file.

If fnPath is not given, will use the return of getPathFile.

Keyword Arguments:
Parameters:	identifier (str) – Identifier of path. val (str) – Value of path.
	fnPath (str) – Path to path file.

pyfrp.modules.pyfrp_misc_module.simpleHist(x, y, bins)¶

Performs a simple histogram onto x-array.

Parameters:

x (numpy.ndarray) – x coordinates of data
y (numpy.ndarray) – y coordinates of data
bins (int) – number of bins

Returns:

Tuple containing:

xBin (numpy.ndarray): Center of bins

yBin (numpy.ndarray): Bin Values

Return type:

tuple

pyfrp.modules.pyfrp_misc_module.slashToFn(fn)¶

Append / or to filepath if necessary.

Parameters:	fn (str) – Filepath
Returns:	Filepath
Return type:	str

pyfrp.modules.pyfrp_misc_module.sortImageFiles(fn, fnDest, ftype, recoverIdent=['recover', 'post'], bleachIdent=['bleach'], preIdent=['pre'], nChannel=1, debug=False, colorPrefix='_c00', cleanUp=True)¶

Sorts all image data in fn into the respective folders of embryo project.

Keyword Arguments:
Parameters:	fn (str) – Path to folder containing images fnDest (str) – Path of embryo project. ftype (str) – Type of microscopy file, such as lsm or czi
	recoverIdent (list) – List of identifiers for recovery data bleachIdent (list) – List of identifiers for bleach data preIdent (list) – List of identifiers for pre-bleach data nChannel (int) – Defines which channel of the images contains relevant data debug (bool) – Debugging flag colorPrefix (str) – Defines how to detect if multichannel or not cleanUp (bool) – Clean up .tif files from other channels afterwards.
Returns:	0
Return type:	int

pyfrp.modules.pyfrp_misc_module.sortListsWithKey(l, keyList)¶

Sorts two lists according to key list.

Example:

>>> l=[1,3,5]
>>> keyList=[2,5,1]

would result in

>>> [5,1,3],[1,2,5]

Parameters:

l (list) – list to be sorted.
keyList (list) – list after which is being sorted.

Returns:

Tuple containing:

sortedList (list): Sorted list

sortedKeys (list): Sorted keys

Return type:

tuple

pyfrp.modules.pyfrp_misc_module.str2list(l, dtype='int', openDelim='[', closeDelim=']', sep=', ')¶

String with lists/sublists to list filled with integers.

Example:

>>> l="[1,2,[3,4]]"
>>> str2list(l)
>>> [1,2,[3,4]]

Keyword Arguments:
Parameters:	l (str) – A list or tuple as a string.
	dtype (str) – Data type (float,int,str) openDelim (str) – Opening delimiter. closeDelim (str) – Closing delimiter. sep (str) – Seperator between values.
Returns:	Tuple containing: lnew (list): Converted string. i (int): Last character visited.
Return type:	tuple

pyfrp.modules.pyfrp_misc_module.translateNPFloat(x)¶

Translates string into numpy float.

If string==+/-‘inf’, will return +/- numpy.inf, otherwise will leave x unchanged.

This function is necessary to prevent Sphinx from not being able to import modules because np.inf is an default value for an keyword arg, but numpy is mocked.

Parameters:	x (float) – Input number/string.
Returns:	Translated float.
Return type:	float

pyfrp.modules.pyfrp_misc_module.txtLineReplace(filePath, pattern, subst)¶

Replaces line in file that starts with pattern and substitutes it with subst.

Note

Will create temporary file using tempfile.mkstemp(). You should have read/write access to whereever mkstemp is putting files.

Parameters:	filePath (str) – Filename. pattern (str) – Pattern to be looked for. subst (str) – String used as a replacement.

pyfrp.modules.pyfrp_misc_module.unzipLists(l)¶

Unzips two zipped lists into seperate lists.

Parameters:	l (list) – Zipped lists
Returns:	Tuple containing: l1 (list): Unzipped list 1. l2 (list): Unzipped list 2.
Return type:	tuple

pyfrp.modules.pyfrp_misc_module.updateObj(objBlank, obj, debug=False)¶

Updates object with respect to blank object.

If object does not have attribute, will add attribute from objBlank with value of objBlank.

Keyword Arguments:
Parameters:	objBlank (object) – Object Template obj (object) – Object to be updated
	debug (bool) – Print debugging output.
Returns:	Updated object.
Return type:	object

pyfrp.modules.pyfrp_misc_module.vars2dict(var, loc, filt=[])¶

Builds dict of list of variables (only works if vars are in locals()).

Parameters:	var (list) – List of variable names. loc (dict) – Handle to locals().
Returns:	Built dictionary.
Return type:	dict

pyfrp.modules.pyfrp_misc_module.win2linPath(p)¶

Converts Win Path to Linux path (-> /)

Parameters:	p (str) – Path.
Returns:	Converted path.
Return type:	str

pyfrp.modules.pyfrp_openscad_module module¶

PyFRAP module for handling building geometries via openscad.

pyfrp.modules.pyfrp_openscad_module.runOpenscad(fn, fnOut=None)¶

Runs openscad to convert scad file to stl file.

If fnOut=None, then the output file will have the same filename as the input file.

Keyword Arguments:
Parameters:	fn (str) – Path to scad file.
	fnOut (str) – Output filename.
Returns:	Output filename.
Return type:	str

pyfrp.modules.pyfrp_optimization_module module¶

Optimization module for PyFRAP toolbox.

Currently contains all functions necessary to transform a constrained FRAP optimization problem into a unconstrained one, making it suitable to Nelder-Mead optimization algorithm.

pyfrp.modules.pyfrp_optimization_module.buildBoundLists(fit)¶

Builds list of lower bounds and upper bounds.

Parameters:	fit (pyfrp.subclasses.pyfrp_fit) – Fit object.
Returns:	Tuple containing: LBs (list): List of lower bounds. UBs (list): List of upper bounds.
Return type:	tuple

pyfrp.modules.pyfrp_optimization_module.constrObjFunc(x, fit, debug, ax, returnFit)¶

Objective function when using Constrained Nelder-Mead.

Calls pyfrp.modules.pyfrp_optimization_module.xTransform() to transform x into constrained version, then uses pyfrp.modules.pyfrp_fit_module.FRAPObjFunc() to find SSD.

Parameters:	x (list) – Input vector, consiting of [D,(prod),(degr)]. fit (pyfrp.subclasses.pyfrp_fit) – Fit object. debug (bool) – Display debugging output and plots. ax (matplotlib.axes) – Axes to display plots in. returnFit (bool) – Return fit instead of SSD.
Returns:	SSD of fit. Except `returnFit==True`, then will return fit itself.
Return type:	float

pyfrp.modules.pyfrp_optimization_module.transformX0(x0, LB, UB)¶

Transforms x0 into constrained form, obeying upper bounds UB and lower bounds LB.

Idea taken from http://www.mathworks.com/matlabcentral/fileexchange/8277-fminsearchbnd–fminsearchcon

Parameters:	x0 (list) – Input initial vector, consiting of [D,(prod),(degr)]. LB (list) – List of lower bounds for `D,prod,degr`. UB (list) – List of upper bounds for `D,prod,degr`.
Returns:	Transformed x-values.
Return type:	list

pyfrp.modules.pyfrp_optimization_module.xTransform(x, LB, UB)¶

Transforms x into constrained form, obeying upper bounds UB and lower bounds LB.

Note

Will add tiny offset to LB(D), to avoid singularities.

Idea taken from http://www.mathworks.com/matlabcentral/fileexchange/8277-fminsearchbnd–fminsearchcon

Parameters:	x (list) – Input vector, consiting of [D,(prod),(degr)]. LB (list) – List of lower bounds for `D,prod,degr`. UB (list) – List of upper bounds for `D,prod,degr`.
Returns:	Transformed x-values.
Return type:	list

pyfrp.modules.pyfrp_plot_module module¶

Plotting module for PyFRAP toolbox.

Contains functions and classes that are often used by PyFRAP toolbox and simplify plot creation and management.

class pyfrp.modules.pyfrp_plot_module.FRAPBoundarySelector(embryo=None, fn=None)¶

Simple GUI widget to select circular FRAP boundaries.

Has useful center marker that is activatable that helps finding the center of the image.

Mouse Input:

Left: Set center.

Right: Set Radius.

Middle: Activate center marker.

Keyboard Input:

Left Arrow: Move center to the left.

Right Arrow: Move center to the right.

Up Arrow: Move center upwards.

Down Arrow: Move center downwards.

Control + Up Arrow: Increase circle radius.

Control + Down Arrow: Decrease circle radius.

Note

If embryo is given at initiation, will use first image specified in embryo’s fileList as background image.

Example Usage:

>>> sel=FRAPBoundarySelector(fn="path/to/img/file")

Use mouse/keyboard to define circular boundary.

>>> center,radius=sel.getResults()

Keyword Arguments:
	embryo (pyfrp.subclasses.pyfrp_embryo.embryo) – PyFRAP embryo instance. fn (str) – Filepath to image file taken for boundary selection.

checkInput()¶

Checks if at least one of the two keyword arguments, embryo or fn, is given.

If not, prints error message and closes down widget.

clearCenter()¶: Removes center marker from canvas.

clearCenterMarker()¶: Removes center maker from canvas.

clearRadius()¶: Removes circle from canvas.

closeFigure(event)¶: Returns center and radius at close event.

computeRadiusFromCoordinate(x, y)¶: Computes radius from given cordinate (x,y).

createCanvas()¶: Creates figure and canvas used for plotting.

decreaseRadius()¶: Decreases radius by 1 px.

drawCenter()¶: Draws a red marker at selected center on canvas.

drawCenterMarker()¶: Draws a yellow marker in center of the image, making it easier to find image center when selecting center of boundary.

drawRadius()¶: Draws a red circle around selected center with selected radius on canvas.

getEmbryo()¶: Returns embryo object if given.``

getMouseInput(event)¶: Directs mouse input to the right actions.

getResults()¶: Returns center and radius.

increaseRadius()¶: Increases radius by 1 px.

keyPressed(event)¶: Directs all key press events to the respective functions.

moveDown()¶: Moves center 1 px down.

moveLeft()¶: Moves center 1 px to the left.

moveRight()¶: Moves center 1 px to the right.

moveUp()¶: Moves center 1 px up.

redraw()¶: Redraws both center and radius if available.

showFirstDataImg()¶: Shows either first data image defined in embryo.fileList or image specified by fn.

Note

If both are given, will use the embryo option.

showImg(img)¶

Shows image on canvas.

Parameters:	img (numpy.ndarray) – Image to be shown.

pyfrp.modules.pyfrp_plot_module.adjustImshowRange(axes, vmin=None, vmax=None)¶

Adjust display range of matplotlib.pyplot.imshow plots in list of axes.

Finds first image artist in each axes in axes list and then sets display range to [vmin,vmax].

Keyword Arguments:
Parameters:	axes (list) – List of matplotlib axes.
	vmin (float) – Minimum value of display range. vmax (float) – Maximum value of display range.
Returns:	Updated list of matplotlib axes.
Return type:	list

pyfrp.modules.pyfrp_plot_module.closerLabels(ax, padx=10, pady=10)¶: Moves x/y labels closer to axis.

pyfrp.modules.pyfrp_plot_module.findArtist(ax, key)¶

Finds matplotlib.artist which name contains key.

Note

Will stop searching after first artist is found.

Will return None if no artist can be found.

Parameters:	ax (matplotlib.axes) – Matplotlib axes. key (str) – Key used for search.
Returns:	Matplotlib artist.
Return type:	matplotlib.artist

pyfrp.modules.pyfrp_plot_module.getPubParms()¶

Returns dictionary with good parameters for nice publication figures.

Resulting dict can be loaded via plt.rcParams.update().

Note

Use this if you want to include LaTeX symbols in the figure.

Returns:	Parameter dictionary.
Return type:	dict

pyfrp.modules.pyfrp_plot_module.getRandomColor()¶: Returns triplet defining a random color.

pyfrp.modules.pyfrp_plot_module.is3DAxes(ax)¶

Returns if an axes is a 3D axis.

Parameters:	ax (matplotlib.axes) – A matplotlib axes.
Returns:	True if 3d axis.
Return type:	bool

pyfrp.modules.pyfrp_plot_module.makeFittingLevels(vmin, vmax, val, nlevels, buff=0.01)¶

Generates array with fitting levels for contour plots.

Note

If vmin=None or vmax=None, will pick the minimum/maximum value of val.

Keyword Arguments:
Parameters:	val (numpy.ndarray) – Array to be plotted. vmin (float) – Minimum value displayed in contour plot. vmax (float) – Maximum value displayed in contour plot. nlevels (int) – Number of contour levels.
	buff (float) – Percentage buffer to be added to both sides of the array.
Returns:	Tuple containing: vmin (float): Minimum value displayed in contour plot. vmax (float): Maximum value displayed in contour plot. levels (numpy.ndarray): Level array to be handed to contour function.
Return type:	tuple

pyfrp.modules.pyfrp_plot_module.makeGeometryPlot(titles=None, tight=False, sup=None, fig=None, show=True, unit='px')¶

Generates matplotlib figure and single axes optimized for geometry plots.

Keyword Arguments:

titles (list) – List of axes titles.
tight (bool) – Use tight layout.
sup (str) – Figure title.
fig (matplotlib.figure) – Figure used for axes.
show (bool) – Show figure right away.
unit (str) – Unit displayed in axes label.

Returns:

Tuple containing:

fig (matplotlib.figure): Figure.

axes (list): List of Matplotlib axes.

Return type:

tuple

pyfrp.modules.pyfrp_plot_module.makeSubplot(size, titles=None, tight=False, sup=None, proj=None, fig=None, show=True)¶

Generates matplotlib figure with (x,y) subplots.

Note

List of titles needs to be same size as desired number of axes. Otherwise will turn off titles.

Note

List of proj needs to be same size as desired number of axes. Otherwise will turn off projections.

Example:

>>> makeSubplot([2,2],titles=["Axes 1", "Axes 2", "Axes 3", "Axes 4"],proj=[None,None,'3d',None])

will result in

Keyword Arguments:
Parameters:	size (list) – Size of subplot arrangement.
	titles (list) – List of axes titles. tight (bool) – Use tight layout. sup (str) – Figure title. proj (list) – List of projections. fig (matplotlib.figure) – Figure used for axes. show (bool) – Show figure right away.
Returns:	Tuple containing: fig (matplotlib.figure): Figure. axes (list): List of Matplotlib axes.
Return type:	tuple

pyfrp.modules.pyfrp_plot_module.plotSolutionVariable(x, y, val, ax=None, vmin=None, vmax=None, nlevels=25, colorbar=True, plane='xy', zs=None, zdir=None, title='Solution Variable', sup='', dThresh=None, nPts=1000, mode='normal', typ='contour')¶

Plots simulation solution variable as 2D contour plot or 3D surface plot.

Note

If no ax is given, will create new one.

Note

x and y do not necessarily have to be coordinates in x/y-direction, but rather correspond to the two directions defined in plane.

plane variable controls in which plane the solution variable is supposed to be plotted. Acceptable input variables are "xy","xz","yz". See also pyfrp.subclasses.pyfrp_ROI.ROI.getMaxExtendPlane().

mode controls which contour/surface function is used:

normal: Will create rectangular grid and use scipy.interpolate.gridddata and interpolate solution onto it, then plot using matplotlib.pyplot.contourf, see also http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.contourf and http://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.interpolate.griddata.html#scipy.interpolate.griddata .

tri: Will plot irregular grid using matplotlib.pyplot.tricontourf. See also http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.tricontourf .

typ controls which plot type is used:

contour: Produces contour plots using either matplotlib.pyplot.contourf or matplotlib.pyplot.tricontourf.

surface: Produces contour plots using either matplotlib.Axed3D.plot_surface or matplotlib.pyplot.plot_trisurf.

Warning

matplotlib.pyplot.tricontourf has problems when val only is in a single level of contour plot. To avoid this, we currently add some noise in this case just to make it plottable. This is not the most elegant solution. (only in case of 3D plotting)

Keyword Arguments:
Parameters:	x (numpy.ndarray) – x-coordinates. y (numpy.ndarray) – y-coordinates. val (numpy.ndarray) – Solution variable values.
	ax (matplotlib.axes) – Axes used for plotting. vmin (float) – Minimum value displayed in contour plot. vmax (float) – Maximum value displayed in contour plot. nlevels (int) – Number of contour levels. colorbar (bool) – Display color bar. plane (str) – Plane in which solution variable is supposed to be plotted. zs (float) – In case of a 3D plot, height in direction zdir where to put contour. zdir (str) – Orthogonal direction to plane. nPts (int) – Number of points used for interpolating (only if `mode=normal`). mode (str) – Which contour function to use. title (str) – Title of plot. typ (str) – Type of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

pyfrp.modules.pyfrp_plot_module.plotTS(xvec, yvec, label='', title='', sup='', ax=None, color=None, linewidth=1, legend=True, linestyle='-', show=True, alpha=1.0, legLoc=-1)¶

Plot timeseries all-in-one function.

Keyword Arguments:
Parameters:	xvec (numpy.ndarray) – x-data to be plotted. yvec (numpy.ndarray) – y-data to be plotted.
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linestyle (str) – Linestyle of plot. linewidth (float) – Linewidth of plot. legend (bool) – Show legend. sup (str) – Figure title. title (str) – Axes title. label (str) – Label for legend. show (bool) – Show figure. alpha (float) – Transparency of line. legLoc (int) – Location of legend.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

pyfrp.modules.pyfrp_plot_module.redraw(ax)¶

Redraws axes’s figure’s canvas.

Makes sure that current axes content is visible.

Parameters:	ax (matplotlib.axes) – Matplotlib axes.
Returns:	Matplotlib axes
Return type:	matplotlib.axes

pyfrp.modules.pyfrp_plot_module.set3DAxesEqual(ax)¶

Make axes of 3D plot have equal scale.

This is one possible solution to Matplotlib’s ax.set_aspect(‘equal’) and ax.axis(‘equal’) not working for 3D.

Modified from http://stackoverflow.com/questions/13685386/matplotlib-equal-unit-length-with-equal-aspect-ratio-z-axis-is-not-equal-to .

Parameters:	ax (matplotlib.axes) – A matplotlib axes.
Returns:	Modified matplotlib axes.
Return type:	matplotlib.axes

pyfrp.modules.pyfrp_plot_module.setPubAxis(ax)¶

Gets rid of top and right axis.

Parameters:	ax (matplotlib.axes) – A matplotlib axes.
Returns:	Modified matplotlib axes.
Return type:	matplotlib.axes

pyfrp.modules.pyfrp_plot_module.setPubFigSize(fig, figWidthPt=180.4, figHeightPt=None, ptPerInches=72.27)¶

Adjusts figure size/aspect.

If figHeightPt is not given, will use golden ratio to compute it.

Keyword Arguments:
	figWidthPt (float) – Width of the figure in pt. figHeightPt (float) – Height of the figure in pt. ptPerInches (float) – Resolution in pt/inches.
Returns:	Adjust figure.
Return type:	matplotlib.figure

pyfrp.modules.pyfrp_plot_module.turnAxesForPub(ax, adjustFigSize=True, figWidthPt=180.4, figHeightPt=None, ptPerInches=72.27)¶

Turns axes nice for publication.

If adjustFigSize=True, will also adjust the size the figure.

Keyword Arguments:
Parameters:	ax (matplotlib.axes) – A matplotlib axes.
	adjustFigSize (bool) – Adjust the size of the figure. figWidthPt (float) – Width of the figure in pt. figHeightPt (float) – Height of the figure in pt. ptPerInches (float) – Resolution in pt/inches.
Returns:	Modified matplotlib axes.
Return type:	matplotlib.axes

pyfrp.modules.pyfrp_sim_module module¶

Simulaton module for PyFRAP toolbox. Handles simulating FRAP experiments and all necessary functions to do so, such as

Handling initial conditions.
Mimicing bleaching effects.
Experiment simulation.

pyfrp.modules.pyfrp_sim_module.applyIdealICs(phi, simulation, bleachedROI=None, valOut=None)¶

Applies ideal initial conditions.

That is, everything falling inside the bleached ROI in x-y-direction will be set its initial dataVec value, everything else will be set equal to valOut.

Note

The bleachedROI and valOut are often stored inside the simulation object. If those two cannot be found, will try to find a ROI called Bleached Square for the bleached ROI and set valOut to concRim. If this again fails, will return error.

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – PDE solution variable. simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object.
	bleachedROI (pyfrp.subclasses.pyfrp_ROI.ROI) – Bleached ROI. valOut (float) – Value to be assigned outside of bleached ROI.
Returns:	Updated solution variable.
Return type:	fipy.CellVariable

pyfrp.modules.pyfrp_sim_module.applyImperfectICs(phi, simulation, center, rJump, sliceHeight, maxVal=1.0, maxMinValPerc=0.25, minMinValPerc=None, rate=0.1, matchWithMaster=True, debug=False)¶: Mimic imperfect bleaching through cone approximation, return phi.

Warning

Not working in current version. Will be integrated in further versions again.

pyfrp.modules.pyfrp_sim_module.applyInterpolatedICs(phi, simulation, matchWithMaster=True, debug=False, fixNeg=True, fillICWithConcRim=True)¶

Interpolates initial conditions onto mesh.

Uses a bivarariate spline interpolation (http://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.interpolate.RectBivariateSpline.html) to generate an interpolation function of the IC image. Then applies interpolated values to solution variable phi if mesh nodes are inside image and masterROI. If not, will apply rim concentration.

Note

If no rim concentration has been calculated (for example through running the data analysis) applyInterpolatedICs will try to compute concRim by itself. For this it will take the mean concentration outside of bleached square but inside masterROI.

Note

The bleached square used here is not defined as a ROI object here, but rather through the properties embryo.sideLengthBleachedPx and embryo.offsetBleachedPx. This might change in future versions.

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – PDE solution variable. simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object.
	matchWithMaster (bool) – Match interpolation indices with `masterROI` indices. debug (bool) – Print debugging messages.
Returns:	Updated solution variable.
Return type:	fipy.CellVariable

pyfrp.modules.pyfrp_sim_module.applyROIBasedICs(phi, simulation)¶

Applies ROI-based initial conditions.

First sets concentration on all mesh nodes equal to simulation.embryo.analysis.concRim. Afterwards, mesh nodes get assigned the value of the first entry dataVec of the ROI covering them. Note: If a mesh node is covered by two ROIs, will assign the value of the ROI that is last in embryo’s ROIs list. See also pyfrp.subclasses.pyfrp_simulation.setICMode().

Parameters:	phi (fipy.CellVariable) – PDE solution variable. simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object.
Returns:	Updated solution variable.
Return type:	fipy.CellVariable

pyfrp.modules.pyfrp_sim_module.applyRadialICs(phi, simulation, radSteps=15, debug=False)¶

Applies radially averaged image data to solution variable as IC.

Note

Will use embryo.geometry.center as center circle and the maximum distant pixel from the center as maximum radius.

Parameters:	phi (fipy.CellVariable) – PDE solution variable. simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object. radSteps (int) – Number of radial levels. debug (bool) – Print debugging messages.
Returns:	Updated solution variable.
Return type:	fipy.CellVariable

pyfrp.modules.pyfrp_sim_module.fixNegValues(phi, minVal=None)¶

Fixes negative values in solution variable.

Interpolation sometimes returns negative values if gradients are really steep. Will apply minVal to such nodes.

If minVal==None, will take smallest non-negative value of solution value.

pyfrp.modules.pyfrp_sim_module.rerunReactDiff(simulation, signal=None, embCount=None, showProgress=True, debug=False)¶

Reruns simulation by extracting values from simulation.vals.

Performs the following steps:

Resets simVecs of all ROIs.

Extracts values per ROI from simulation.vals.

Note

Only works if simulation has been run before with saveSim enabled.

Keyword Arguments:
Parameters:	simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object.
	signal (PyQt4.QtCore.pyqtSignal) – PyQT signal to send progress to GUI. embCount (int) – Counter of counter process if multiple datasets are analyzed. debug (bool) – Print final debugging messages and show debugging plots. showProgress (bool) – Show simulation progress.
Returns:	Updated simulation object.
Return type:	pyfrp.subclasses.pyfrp_simulation.simulation

pyfrp.modules.pyfrp_sim_module.sigmoidBleachingFct(x, y, z, center, rJump, sliceHeight, maxVal=1.0, maxMinValPerc=0.25, minMinValPerc=0.25, rate=0.1)¶

Generates sigmoid scaling function for imperfect bleaching at coordinates x/y/z.

The idea behind the sigmoid function is:

Through scattering and other effects, the bleached window becomes blurry in larger depths, resulting in a radial sigmoid scaling function around center.

Similarly, bleaching intensity increases with depth. Thus, a linear term controls the values close to center of the sigmoid function. Bleaching closer to the laser than the imaged height will be rendered stronger, while bleaching effects below will be decreased by bumping up the bleached window. However, only until some threshold is reached.

The sigmoid function is given by:

\[s(r,z) = v_{\mathrm{min}}(z)+(v_{\mathrm{max}}-v_{\mathrm{min}}(z))\frac{1}{1+\exp(-\rho(r-r_\mathrm{Jump}))},\]

where \(\rho\) is the sigmoid slope given by rate, \(r_\mathrm{Jump}\) is the radius from center at which sigmoid function has its jump, given by rJump and \(r\) the radius of coordinate [x,y] from center.

\(v_{\mathrm{min}}(z)\) is a linear function describing how strong the bleaching is dependent on the depth \(z\) given by

\[v_{\mathrm{min}}(z) = \frac{v_{\mathrm{max}} - v_{\mathrm{max. bleach}}}{h_s} z + v_{\mathrm{max. bleach}},\]

where \(v_{\mathrm{max}}\) is the value of the sigmoid function far from center, \(v_{\mathrm{max. bleach}}\) is the strongest expected bleaching value (mostly at \(z=0\)) and \(h_s\) is the height of the imaging slice, given by sliceHeight. The maximum rate of bleaching \(v_{\mathrm{max. bleach}}\) is computed by:

\[v_{\mathrm{max. bleach}} = (1-p_{\mathrm{max. bleach}})v_{\mathrm{max}},\]

where \(p_{\mathrm{max. bleach}}\) is the percentage of maximum expected bleaching compared to the values in the imaging height, given by maxMinValPerc. That is, by how much has the laser power already decreased on its way from entry point of the sample to the imaging height.

For sample depths deeper than the imaging height, bleaching is expected to be decrease in intensity, thus the bleached area is getting bumped up. To avoid bumping the bleached area by too much, eventually even resulting in the bleached area having a higher concentration than the area outside, the sigmoid function has a cut-off: If values of \(s(r,z)\) pass

\[v_{\mathrm{min. bleach}} = (1+p_{\mathrm{min. bleach}})v_{\mathrm{max}},\]

where \(p_{\mathrm{min. bleach}}\) is the percentage of bleaching to cut-off, then we set

\[s(r,z) = v_{\mathrm{min. bleach}},\]

ultimately resulting in a scaling function given by

\[\begin{split}s(r,z) = \left\{\begin{array}{cc} v_{\mathrm{min}}(z)+(v_{\mathrm{max}}-v_{\mathrm{min}}(z))\frac{1}{1+\exp(-\rho(r-r_\mathrm{Jump}))} & \mbox{ if } s(r,z) <= v_{\mathrm{min. bleach}} , \\ v_{\mathrm{min. bleach}} & \mbox{ else } \end{array} \right.\end{split}\]

Keyword Arguments:
Parameters:	x (numpy.ndarray) – x-coordinates. y (numpy.ndarray) – y-coordinates. z (numpy.ndarray) – z-coordinates. center (list) – Center of bleaching. rJump (float) – Radius from center where sigmoid jump is expected. sliceHeight (float) – Height at which dataset was recorded.
	maxVal (float) – Value of sigmoid function outside of bleached region. maxMinValPerc (float) – Percentage of maximum bleaching intensity. minMinValPerc (float) – Percentage of minimum bleaching intensity. rate (float) – Rate at which sigmoid increases.
Returns:
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_sim_module.simulateReactDiff(simulation, signal=None, embCount=None, showProgress=True, debug=False)¶

Simulates reaction diffusion equation goverining FRAP experiment.

Performs the following steps:

Resets simVecs of all ROIs.

If not generated yet, generates mesh (should never be the case!)

Initializes PDE with Neumann boundary conditions resulting in the problem:

\[\partial_t c = D \nabla^2 c - k_1 c + k_2,\]

where \(k_1\) is the degradation rate and \(k_2\) the production rate.

Applies initial conditions defined in simulation.ICmode.

Simulates FRAP experimment.

Keyword Arguments:
Parameters:	simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object.
	signal (PyQt4.QtCore.pyqtSignal) – PyQT signal to send progress to GUI. embCount (int) – Counter of counter process if multiple datasets are analyzed. debug (bool) – Print final debugging messages and show debugging plots. showProgress (bool) – Show simulation progress.
Returns:	Updated simulation object.
Return type:	pyfrp.subclasses.pyfrp_simulation.simulation

pyfrp.modules.pyfrp_stats_module module¶

Statistics module for PyFRAP toolbox, mainly used to evaluate goodness of fit, but also providing functions to assess overall measurement statistics.

pyfrp.modules.pyfrp_stats_module.Rsq(data, x)¶

Computes R-squared values for fit series to data series.

R-squared value is being computed as

\[R^2 = 1 - \frac{\sum\limits_i (x_i - d_i)^2}{\sum\limits_i (d_i - \bar{d} )^2}\]

Parameters:	x (numpy.ndarray) – data (numpy.ndarray) – Data series.
Returns:	R-squared value.
Return type:	float

pyfrp.modules.pyfrp_stats_module.compareFitsByAIC(fits, ROIs=None, sigma=1, fromSSD=True, thresh=None)¶

Computes AIC and Akaike weights for all fits in a list and returning best models given certain criteria.

For the computation of the AIC see computeAIC() and the computation of the Akaike weights computeAkaikeWeights().

If threshold thresh=None, then will select model with maximum Akaike weight as best model, that is, the model with the highest likelihood of being the best model. If thresh is given, will return list of acceptable models based on

\[w_{\mathrm{max}}-w_i<thresh,\]

that is, all models that are within a given range of probability of the most likely one.

Keyword Arguments:
Parameters:	fits (list) – List of fit objects.
	ROIs (list) – List of ROIs to be considered for computation. sigma (float) – Standard deviation of normal distribution if known. fromSSD (bool) – Simply use SSD as maximum likelihood. thresh (float) – Probability range for model selection.
Returns:	Tuple containing: AICs (list): List of AIC values of the respective fits. deltaAICs (numpy.ndarray): List of Akaike difference values of the respective fits. weights (numpy.ndarray): List of Akaike difference weights of the respective fits. acc (list): List of acceptable fits by model selection. ks (list): List of number of parameters fitted of the respective fits. ns (list): List of number of datapoints fitted of the respective fits.
Return type:	tuple

pyfrp.modules.pyfrp_stats_module.compareFitsByCorrAIC(fits, ROIs=None, sigma=1, fromSSD=True, thresh=None)¶

Computes AICc and Akaike weights for all fits in a list and returning best models given certain criteria.

For the computation of the corrected AIC see computeCorrAIC() and the computation of the Akaike weights computeAkaikeWeights().

If threshold thresh=None, then will select model with maximum Akaike weight as best model, that is, the model with the highest likelihood of being the best model. If thresh is given, will return list of acceptable models based on

\[w_{\mathrm{max}}-w_i<thresh,\]

that is, all models that are within a given range of probability of the most likely one.

Keyword Arguments:
Parameters:	fits (list) – List of fit objects.
	ROIs (list) – List of ROIs to be considered for computation. sigma (float) – Standard deviation of normal distribution if known. fromSSD (bool) – Simply use SSD as maximum likelihood. thresh (float) – Probability range for model selection.
Returns:	Tuple containing: AICs (list): List of AICc values of the respective fits. deltaAICs (numpy.ndarray): List of Akaike difference values of the respective fits. weights (numpy.ndarray): List of Akaike difference weights of the respective fits. acc (list): List of acceptable fits by model selection. ks (list): List of number of parameters fitted of the respective fits. ns (list): List of number of datapoints fitted of the respective fits.
Return type:	tuple

pyfrp.modules.pyfrp_stats_module.computeAIC(fit, ROIs=None, sigma=1, fromSSD=True)¶

Computes Akaike Information Criterion of fit.

The AIC is defined by

\[AIC= 2k - 2\log(L),\]

\(k\) is the number of free parameters of the model used in fit and \(L\) is the maximum likelihood function of the model, see also computeLogLikelihood().

Since generally the AIC is only used for model comparison and a normal distribution of the data around the model is assumed, one simply can use the SSD as a log-likelihood function. fromSSD controls if just the SSD is used. This is also useful, since \(sigma\) is not necessarily known if the objective function of the fit was not a maximum likelihood function.

Note

If ROIs=None, will use all ROIs defined in fit.ROIsFitted. If ROIs are specified, but not in fit.ROIsFitted, will simply skip them.

If the AIC or the AICc should be used can be determined using useAIC().

Keyword Arguments:
Parameters:	fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object.
	ROIs (list) – List of ROIs to be considered for computation. sigma (float) – Standard deviation of normal distribution if known. fromSSD (bool) – Simply use SSD as maximum likelihood.
Returns:	AIC of fit.
Return type:	float

pyfrp.modules.pyfrp_stats_module.computeAkaikeWeights(AICs)¶

Computes Akaike weights for a list of AIC values.

Akaike weightsare given by:

\[w_i = \frac{\exp\left(\frac{-\Delta_{i}}{2}\right)}{\sum\limits_{i=1}^{N} \exp\left(\frac{- \Delta_{i}}{2}\right)},\]

where \(\Delta_{i}\) is the Akaike difference of model \(i\) computed by

\[\Delta_{i} = AIC_i - AIC_{\mathrm{min}},\]

and \(N\) is the total number of models considered. Note that

\[\sum\limits_{i=1}^{N} \exp\left(\frac{- \Delta_{i}}{2}\right) = 1.\]

Parameters:	AICs (list) – List of AIC values.
Returns:	Corresponding Akaike weights.
Return type:	np.ndarray

pyfrp.modules.pyfrp_stats_module.computeCorrAIC(fit, ROIs=None, sigma=1, fromSSD=True)¶

Computes corrected Akaike Information Criterion of fit.

The AICc is defined by

\[AICc= AIC + \frac{2k(k+1)}{n-k-1},\]

where \(n\) is the number of datapoints used for the fit and \(k\) is the number of free parameters. For the computation of the AIC, please refer to the documentation of computeAIC().

Note

If ROIs=None, will use all ROIs defined in fit.ROIsFitted. If ROIs are specified, but not in fit.ROIsFitted, will simply skip them.

If the AIC or the AICc should be used can be determined using useAIC().

Keyword Arguments:
Parameters:	fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object.
	ROIs (list) – List of ROIs to be considered for computation. sigma (float) – Standard deviation of normal distribution if known. fromSSD (bool) – Simply use SSD as maximum likelihood.
Returns:	AICc of fit.
Return type:	float

pyfrp.modules.pyfrp_stats_module.computeFitRsq(fit)¶

Computes R-squared values for fit object.

R-squared values contain:

Mean R-squared value over all ROIs included in fit, stored in fit.MeanRsq.

Product of R-squared value over all ROIs included in fit, stored in fit.Rsq.

R-squared value for each ROIs included in fit, stored in fit.RsqBuROI.

Parameters:	fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object.
Returns:	Updated fit object.
Return type:	pyfrp.subclasses.pyfrp_fit.fit

pyfrp.modules.pyfrp_stats_module.computeLogLikelihood(fit, ROIs=None, neg=True, sigma=1)¶

Computes log-likelihood of fit assuming normal distribution of data around fit.

Generally we assume that the data is distributed normally around the fitted model, that is

\[(m-d) \sim \mathcal{N}(0,\sigma)\]

Assuming that all measurements are independent, this results in a likelihood function of

\[\prod\limits_{j=1}^{n} \frac{1}{\sigma \sqrt{2\pi}} \exp\left(-\frac{(m_j-d_j)^2}{2\sigma^2}\right)\]

resulting in a log-likelihood function of

\[- \frac{n}{2} \log (2\pi) - \frac{\sum\limits_{j=1}^{n} (m_j-d_j)^2 }{2\sigma^2}\]

Function allows to compute both negative and positive log-likelihood by neg flag.

If the standard deviation \(sigma\) is unknown, but the log-likelihood is only needed for model comparison reasons over the same dataset, all sigma dependent terms can be ignored. Thus this function returns both SSD and log-likelihood.

Note

If ROIs=None, will use all ROIs defined in fit.ROIsFitted. If ROIs are specified, but not in fit.ROIsFitted, will simply skip them.

Keyword Arguments:
Parameters:	fit (pyfrp.subclasses.pyfrp_fit.fit) – Fit object.
	ROIs (list) – List of ROIs to be considered for computation. neg (bool) – Compute negative log-likehood. sigma (float) – Standard deviation of normal distribution if known.
Returns:	Tuple containing: sign (float): Sign of likelihood function (-1/1). SSD (float): SSD of fit over all ROIs specified in ROIs. logLL (float): Log-likehood function at `sigma=sigma`
Return type:	tuple

pyfrp.modules.pyfrp_stats_module.computeSSD(data, x)¶

Computes sum of squared differences (SSD) of fit series to data series.

The SSD is computed by

\[SSD = \sum\limits_i (x_i - d_i)^2\]

Parameters:	x (numpy.ndarray) – data (numpy.ndarray) – Data series.
Returns:	SSD.
Return type:	float

pyfrp.modules.pyfrp_stats_module.overlapSubSampleError(d, n, k)¶

Computes error between overlapping subsamples.

Error is calculated by

\[\left|\frac{\bar{d_i}+\epsilon}{\bar{d_j}+\epsilon}\right|,\]

where \(i,j \in {1,..,\frac{N}{n-k}}\) and \(\epsilon\) is some offset to avoid singularties.

Note

The resulting error matrix is symmetric.

Parameters:	d (numpy.ndarray) – Data vector. n (int) – Size of subsamples. k (int) – Overlap.
Returns:	Error matrix.
Return type:	numpy.ndarray

pyfrp.modules.pyfrp_stats_module.overlapSubSampleSelect(d, n, k)¶

Takes subsamples of size n that overlap on both sides by k points out of d.

Algorithm collects snippets of d from \(j(n-k)\) to \((j+1)n-jk\), where \(j\) is the counter for the subsamples.

Parameters:	d (numpy.ndarray) – Data vector. n (int) – Size of subsamples. k (int) – Overlap.
Returns:	List of `numpy.ndarray` of overlapping subsamples.
Return type:	list

pyfrp.modules.pyfrp_stats_module.parameterStats(x)¶

Returns mean, standard deviation and standard error of array.

Note that standard error is computed as

\[\sigma_n = \frac{\sigma}{\sqrt{n}}\]

where \(n\) is the number of samples in x and :math: \(\sigma\) is the standard deviation over x.

Parameters:	x (numpy.ndarray) – List of values.
Returns:	Tuple containing: xMean (float): Mean of x. xStd (float): Standard deviation of x. xSterr (float): Standard error of x.
Return type:	tuple

pyfrp.modules.pyfrp_stats_module.selectDataByOverlapSubSample(d, n, k, thresh, debug=False)¶

Selects data vector based on overlapping subsampling and simple thresholding.

This algorithm combines local derivatives with global changes and filters both datasets that have large local changes as well as large global changes. However, taking means over subsamples prevents neglecting data sets that have short peaks over only 1 or 2 time points, such as bubbles etc.

Keyword Arguments:
Parameters:	d (numpy.ndarray) – Data vector. n (int) – Size of subsamples. k (int) – Overlap. thresh (float) – Selecting threshold.
	debug (bool) – Print debugging messages.
Returns:	True if data set should be neglected.
Return type:	bool

pyfrp.modules.pyfrp_stats_module.useAIC(n, k)¶

Returns if corrected or not corrected version of the AIC should be used.

Rule of thumb is that AIC should be used if

\[\frac{n}{k}>40,\]

where \(n\) is the number of datapoints used for the fit and \(k\) is the number of free parameters.

Returns:	`True`, use AIC, `False` use AICc.
Return type:	bool

pyfrp.modules.pyfrp_term_module module¶

Terminal module for PyFRAP toolbox. Provides extra functions for a nicer custom output inside a Python/bash terminal.

pyfrp.modules.pyfrp_term_module.getArrayDetailsString(l)¶

Returns string saying “Array of shape x”, where x is the shape of the array.

Parameters:	l (numpy.ndarray) – Some array.
Returns:	Printout of type and shape.
Return type:	str

pyfrp.modules.pyfrp_term_module.getFunctionCall(idx=1)¶

Returns the name of function or method that was called idx frames outwards.

Note

idx=0 will of course return getFunctionCall.

Parameters:	idx (int) – Steps outwards.
Returns:	Name of function or method.
Return type:	str

pyfrp.modules.pyfrp_term_module.getListDetailsString(l)¶

Returns string saying “List of length x”, where x is the length of the list.

Parameters:	l (list) – Some list.
Returns:	Printout of type and length.
Return type:	str

pyfrp.modules.pyfrp_term_module.printAllObjAttr(obj, maxL=5)¶

Prints all object attributes in the form attributeName = attributeValue.

If attributes are of type list or numpy.ndarray, will check if the size exceeds threshold. If so, will only print type and dimension of attribute.

Keyword Arguments:
Parameters:	obj (object) – Object to be printed.
	maxL (int) – Maximum length threshold.

pyfrp.modules.pyfrp_term_module.printAttr(name, attr, maxL=5)¶

Prints single attribute in the form attributeName = attributeValue.

If attributes are of type list or numpy.ndarray, will check if the size exceeds threshold. If so, will only print type and dimension of attribute.

Keyword Arguments:
Parameters:	name (str) – Name of attribute. attr (any) – Attribute value.
	maxL (int) – Maximum length threshold.

pyfrp.modules.pyfrp_term_module.printDict(dic, maxL=5)¶

Prints all dictionary entries in the form key = value.

If attributes are of type list or numpy.ndarray, will check if the size exceeds threshold. If so, will only print type and dimension of attribute.

Parameters:	dic (dict) – Dictionary to be printed.
Returns:	True
Return type:	bool

pyfrp.modules.pyfrp_term_module.printError(txt, showCall=True, idx=2)¶

Prints Error of the form “ERROR: txt”, while error is rendered red.

Keyword Arguments:
Parameters:	txt (str) – Text to be printed:
	showCall (bool) – Show function in which print function was called. idx (int) – Traceback index that is given to `pyfrp.modules.pyfrp_term_module.getFunctionCall()`.

pyfrp.modules.pyfrp_term_module.printNote(txt, showCall=True, idx=2)¶

Prints note of the form “NOTE: txt”, while note is rendered green.

Keyword Arguments:
Parameters:	txt (str) – Text to be printed:
	showCall (bool) – Show function in which print function was called. idx (int) – Traceback index that is given to `pyfrp.modules.pyfrp_term_module.getFunctionCall()`.

pyfrp.modules.pyfrp_term_module.printObjAttr(var, obj)¶

Prints single object attribute in the form attributeName = attributeValue. :param var: Name of attribute. :type var: str :param obj: Object to be printed. :type obj: object

Returns:	Name of attribute.
Return type:	str

pyfrp.modules.pyfrp_term_module.printTable(l, header, col=False)¶

Prints table using tabulate package.

If col=True, columns are given via l, otherwise rows are given.

Parameters:

l (list) – List of rows or columns.
header (list) – List of headers.
col (bool) – Flag on how rows/columns are given.

Returns:

Tuple containing:

header (list): Header of table.

table (list): Table as a list of rows.

Return type:

tuple

pyfrp.modules.pyfrp_term_module.printWarning(txt, showCall=True, idx=2)¶

Prints Warning of the form “WARNING: txt”, while warning is rendered yellow.

Keyword Arguments:
Parameters:	txt (str) – Text to be printed:
	showCall (bool) – Show function in which print function was called. idx (int) – Traceback index that is given to `pyfrp.modules.pyfrp_term_module.getFunctionCall()`.

pyfrp.modules.pyfrp_vtk_module module¶

VTK module for PyFRAP toolbox.

Contains functions that allow drawing/plotting via VTK. For more information about VTK, go to www.vtk.org and http://www.vtk.org/Wiki/VTK/Examples/Python .

pyfrp.modules.pyfrp_vtk_module.drawVTKArc(pStart, pCenter, pEnd, color=[0, 0, 0], renderer=None, res=32)¶

Draws VTK arc object defined through 3 points into renderer.

If renderer=None, will create actor but not add it to renderer.

Keyword Arguments:
Parameters:	pStart (numpy.ndarray) – Coordinate of start point. pCenter (numpy.ndarray) – Coordinate of center point. pEnd (numpy.ndarray) – Coordinate of end point.
	color (list) – Color of sphere in normed RGB values. renderer (vtk.vtkRenderer) – Renderer to draw in. res (int) – Resolution of arc.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.drawVTKLine(p1, p2, color=[0, 0, 0], renderer=None)¶

Draws VTK line object going from point 1 to point 2 into renderer.

If renderer=None, will create actor but not add it to renderer.

Keyword Arguments:
Parameters:	p1 (numpy.ndarray) – Coordinate of point 1. p2 (numpy.ndarray) – Coordinate of point 2.
	color (list) – Color of sphere in normed RGB values. renderer (vtk.vtkRenderer) – Renderer to draw in.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.drawVTKPoint(p, asSphere=True, color=[0, 0, 0], size=10, renderer=None)¶

Draws point into renderer.

If renderer=None, will create actor but not add it to renderer.

Keyword Arguments:
Parameters:	p (numpy.ndarray) – Position of point.
	color (list) – Color of sphere in normed RGB values. size (float) – Size of vertex/radius of sphere. asSphere (bool) – Draw point as sphere. renderer (vtk.vtkRenderer) – Renderer to draw in.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.drawVTKPolyLine(pts, closed=False, color=[0, 0, 0], renderer=None)¶

Draws VTK polyLine object defined through points into renderer.

If renderer=None, will create actor but not add it to renderer.

Keyword Arguments:
Parameters:	pts (list) – Coordinates of points.
	color (list) – Color of sphere in normed RGB values. renderer (vtk.vtkRenderer) – Renderer to draw in. closed (bool) – Close line.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.drawVTKSphere(center, radius, color=[0, 0, 0], renderer=None)¶

Draws vtk source sphere with given center and radius into renderer.

If renderer=None, will create actor but not add it to renderer.

Keyword Arguments:
Parameters:	center (numpy.ndarray) – Center of sphere. radius (float) – Radius of sphere.
	color (list) – Color of sphere in normed RGB values. renderer (vtk.vtkRenderer) – Renderer to draw in.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.drawVTKText(text, position, fontSize=18, color=[0, 0, 0], renderer=None)¶

Draws text in renderer.

Keyword Arguments:
Parameters:	text (str) – Text. position (numpy.ndarray) – Position where to draw it.
	fontSize (int) – Font Size. color (list) – Color of text in normed RGB values. renderer (vtk.vtkRenderer) – Renderer to draw in.
Returns:	Text actor
Return type:	vtk.vtkTextActor

pyfrp.modules.pyfrp_vtk_module.drawVTKVertex(p, color=[0, 0, 0], size=20, renderer=None)¶

Draws vtk vertex given its position into renderer.

If renderer=None, will create actor but not add it to renderer.

Keyword Arguments:
Parameters:	p (numpy.ndarray) – Position of vertex.
	color (list) – Color of sphere in normed RGB values. size (float) – Size of vertex. renderer (vtk.vtkRenderer) – Renderer to draw in.
Returns:	VTK actor.
Return type:	vtk.vtkSphereSource

pyfrp.modules.pyfrp_vtk_module.getVTKActor(color)¶

Returns VTK actor object and colors it.

Note

Colors can also be given as matplotlib string colors.

Parameters:	color (list) – Color of sphere in normed RGB values.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.getVTKArc(pStart, pCenter, pEnd, res=32)¶

Returns VTK arc object defined through 3 points.

Keyword Arguments:
Parameters:	pStart (numpy.ndarray) – Coordinate of start point. pCenter (numpy.ndarray) – Coordinate of center point. pEnd (numpy.ndarray) – Coordinate of end point.
	res (int) – Resolution of arc.
Returns:	VTK actor.
Return type:	vtk.vtkActor

pyfrp.modules.pyfrp_vtk_module.getVTKLine(p1, p2)¶

Returns VTK line object going from point 1 to point 2.

Parameters:	p1 (numpy.ndarray) – Coordinate of point 1. p2 (numpy.ndarray) – Coordinate of point 2.
Returns:	VTK line object.
Return type:	vtk.vtkLine

pyfrp.modules.pyfrp_vtk_module.getVTKOutput(obj)¶

Returns the fitting vtk output.

Fixes versioning problems between vtk versions 5 and older.

Parameters:	obj (vtk.vtkObject) – A VTK object.
Returns:	VTK poly data.
Return type:	vtk.vtkPolyData

pyfrp.modules.pyfrp_vtk_module.getVTKPolyDataMapper(polyData)¶

Returns a VTK poly data mapper.

Fixes versioning problems between vtk versions 5 and older.

Parameters:	obj (vtk.vtkPolyData) – A VTK poly data object.
Returns:	VTK poly data mapper.
Return type:	vtk.vtkPolyDataMapper

pyfrp.modules.pyfrp_vtk_module.getVTKPolyLine(pts, closed=False)¶

Returns VTK polyLine object defined through points.

Keyword Arguments:
Parameters:	pts (list) – Coordinates of points.
	closed (bool) – Close line.
Returns:	VTK polyData.
Return type:	vtk.vtkPolyData

pyfrp.modules.pyfrp_vtk_module.getVTKSphere(center, radius)¶

Returns vtk source sphere object with given center and radius.

Parameters:	center (numpy.ndarray) – Center of sphere. radius (float) – Radius of sphere.
Returns:	VTK sphere.
Return type:	vtk.vtkSphereSource

pyfrp.modules.pyfrp_vtk_module.makeVTKCanvas(offScreen=False, bkgd=[1, 1, 1], renderer=None)¶

Creates a vtk renderer and includes it into a renderer window.

Warning

offScreen=True is still in development.

If renderer is given, will just create the render Window and interactor around it.

Keyword Arguments:

offScreen (bool) – Don’t show Window.
bkgd (list) – Background color of renderer in normed RGB values.
renderer (vtk.vtkRenderer) – Renderer.

Returns:

Tuple containing:

renderer (vtk.renderer): Renderer.

renderWindow (vtk.renderWindow): Render Window.

renderWindowInteractor (vtk.renderWindowInteractor): Render Window Interactor.

Return type:

tuple

pyfrp.modules.pyfrp_vtk_module.renderVTK(renderer)¶

Renders everything contained in renderWindow and starts Interactor.

Parameters:	renderer (vtk.vtkRenderer) – A renderer.
Returns:	Renderer.
Return type:	vtk.vtkRenderer

pyfrp.modules.pyfrp_zstack_module module¶

Z-stack module for PyFRAP toolbox. Used for reading z-stack datasets and converting them into meshes that then can be used for a FRAP simulation.

Warning

This module is still experimental and unfinished and should not be used. Will be added as stable in further versions.

pyfrp.modules.pyfrp_zstack_module.fillEndStacks(n, debug=False)¶

pyfrp.modules.pyfrp_zstack_module.getContours(img, kernel=(10, 10))¶

pyfrp.modules.pyfrp_zstack_module.interpolateContourGaps(contours)¶

pyfrp.modules.pyfrp_zstack_module.point_inside_polygon(x, y, poly)¶

Module contents¶

PyFRAP: A Python based FRAP analysis tool box. Basic modules.