Welcome to PyFRAP’s API!¶

PyFRAP is a extensive Python based FRAP (Fluorescence Recovery After Photobleaching) analysis software, featuring various tools that help analyzing FRAP datasets, such as

Import FRAP datasets from timelapse experiments and analyze image data with various options such as
- various filters
- background substraction
- illumination correction
Simulate the FRAP experiment with exact interpolated initial conditions
Fit simulated experiment to analyzed data and extract diffusion coefficient
Statistical analysis of fitting results
Hierarchical data structure making data exchange/sharing easy
Comprehensive GUI, making almost all PyFRAP tools available

I have tried to keep the API short but clear. If it is unclear, don’t hesitate to mail.

Installation (setup.py)¶

Installing PyFRAP via setup.py¶

PyFRAP can conveniently be installed via:

python setup.py install

Since PyFRAP requires on I/O of data files that come with it we recommend using:

python setup.py install --user

setuptools comes with multiple installation options, to check them out, type:

python setup.py install --help

PyFRAP has some additional installation options:

Option	Effect
`--fiji`	Download and install Fiji during PyFRAP installation. Link Fiji with PyFRAP
`--gmsh`	Download and install Gmsh during PyFRAP installation. Link Gmsh with PyFRAP
`--silent`	Print out less log messages.

For --fiji and --gmsh to work, you need to install wget. To install wget, type:

pip install wget

or if you use the Ananconda distribution:

conda install pywget

PyFRAP setup.py API¶

getOptions()¶

Checks options given to script:

If --fiji is in sys.argv, will set dFiji=1. n

If --gmsh is in sys.argv, will set dGmsh=1.

If --silent is in sys.argv, will set silent=1.

Note

Makes dGmsh and dFiji global: Not nice but seems easiest way to get options into OverrideInstall.

getOpt(optStr)¶: Checks if optStr is in sys.argv. If this is the case, returns 1 and removes it form sys.argv so setup.py will not crash, otherwise returns 0.

class OverrideInstall(install)¶

Override class subclassing install class from setuptools.

The Main purpose of this class is to give more possibilities when installing PyFRAP, such as:

Download Gmsh and enter it automatically into path spec file

Download Fiji and enter it automatically into path spec file

Set ownership of data files so that even PyFRAP gets installed as superuser, users will be able to use its full capacities.

Idea taken from here (thanks a bunch!)

initOptions()¶: Parses options into override class.

run()¶: Runs install.

addData()¶

Adds Datafiles to PyFRAP installation.

Makes sure that $USER has proper read/write/execute rights. Note that for Windows it will change rights, since it is not necesary. n Also makes sure that gmsh/Fiji bin ins properly linked.

cleanUpExe(fnDL,folderFn,filesBefore,exePath):

Moves it to executables directory and cleans up afterwards.

Parameters:	fnDL (str) – Filename of downloaded file. folderFn (str) – Filename of folder containing extracted files. filesBefore (list) – Snapshot of `cwd`. exePath (str) – Path where executables go.

downloadGmsh()¶: Downloads Gmsh, moves it to executables directory and cleans up afterwards.

Note

Only works if wget is installed.

downloadGmshWin(arch, gmshVersion)¶

Downloads Gmsh from Gmsh website for Windows

Parameters:	arch (str) – System architecture, e.g. 64/32. gmshVersion (str) – gmshVersion String, e.g. 2.12.0 .
Returns:	(Donwload filename, Filename of extracted download files)
Return type:	(str ,str)

downloadGmshOSX(arch, gmshVersion)¶

Downloads Gmsh from Gmsh website for OSX.

Parameters:	arch (str) – System architecture, e.g. 64/32. gmshVersion (str) – gmshVersion String, e.g. 2.12.0 .
Returns:	(Donwload filename, Filename of extracted download files)
Return type:	(str ,str)

downloadGmshLinux(arch, gmshVersion)¶

Downloads Gmsh from Gmsh website for Linux.

Parameters:	arch (str) – System architecture, e.g. 64/32. gmshVersion (str) – gmshVersion String, e.g. 2.12.0 .
Returns:	(Donwload filename, Filename of extracted download files)
Return type:	(str ,str)

makeExeFolder()¶: Make executables folder if it doesn’t exist yet

downloadFiji()¶: Downloads Gmsh, moves it to executables directory and cleans up afterwards.

Note

Only works if wget is installed.

downloadFijiLinux(arch)¶

Downloads Fiji from Fiji website for Linux.

Parameters:	arch (str) – System architecture, e.g. 64/32.
Returns:	(Donwload filename, Filename of extracted download files)
Return type:	(str ,str)

downloadFijiWin(arch)¶

Downloads Fiji from Fiji website for Windows.

Parameters:	arch (str) – System architecture, e.g. 64/32.
Returns:	(Donwload filename, Filename of extracted download files)
Return type:	(str ,str)

downloadFijiOSX()¶

Downloads Fiji from Fiji website for OSX.

Returns:	(Donwload filename, Filename of extracted download files)
Return type:	(str ,str)

setExePath(fn, identifier, exePath)¶

Enters executable path into path spec file.

Parameters:	fn (str) – Path to gmsh executable. identifier (str) – Identifier in spec file. exePath (str) – Path to exe file

setGmshPath(fn)¶

Enters gmsh executable path into path spec file.

Parameters:	fn (str) – Path to gmsh executable.

setFijiPath(fn)¶

Enters fiji executable path into path spec file.

Parameters:	fn (str) – Path to fiji executable.

changePermissions(filepath, uid, gid, mode)¶

Sets File Permissions.

Parameters:	filepath (str) – Path to file. uid (int) – user ID. gid (int) – group ID. mode (int) – Permission mode.
Returns:	True if success
Return type:	bool

makeAdditionalDataFolders(folder, fn, uid, gid, mode)¶

Tries to generate additional data folders.

Parameters:	folder (str) – Path to containing folder. fn (str) – New folder name uid (int) – user ID. gid (int) – group ID. mode (int) – Permission mode.
Returns:	True if success
Return type:	bool

The modules package¶

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='140079914734032'>)¶

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.

The subclasses package¶

pyfrp.subclasses package¶

Submodules¶

pyfrp.subclasses.pyfrp_ROI module¶

class pyfrp.subclasses.pyfrp_ROI.ROI(embryo, name, Id, zmin='-inf', zmax='inf', color='b')¶

Bases: object

adaptRefineInMeshByField(nNodesReq, factor=3.0, addZ=15.0, zIncrement=1.0, fIncrement=1.0, nNodesMax='inf', debug=False, ROIReq=None, fnOut=None)¶

Refines mesh inside ROI adaptively until a given number of nodes inside ROI is reached.

Does this by:

Refining through refineInMeshByField().

Computing mesh indices via computeMeshIdx().

If number of nodes did not change, increase addZ, else increase factor.

Check if desired number of nodes is reached or not, if not, repeat.

Note

If the new number of nodes in the ROI exceeds nNodesMax, will revert the last step and perform the other operation, e.g. increasing addZ instead of factor and vice versa.

Note

If ROIReq is given, will try to refine in self such that ROIReq has at least nNodesReq mesh nodes. If it is not given, nNodesReq refers to the nodes in self.

Keyword Arguments:
Parameters:	nNodesReq (int) – Desired number of nodes inside ROI.
	factor (float) – Refinement factor. addZ (float) – Number of pixels added above and below ROI for box field. zIncrement (float) – Number of pixels addZ is increased per adaptive step. fIncrement (float) – Stepsize of refinement factor. nNodesMax (float) – Maximum number of nodes allowed in ROI. debug (bool) – Print debugging messages. ROIReq (pyfrp.subclasses.pyfrp_ROI.ROI) – The ROI object that is referred to with nNodesReq. fnOut (str) – Path to output geo file.
Returns:	Final number of nodes in ROI.
Return type:	int

addBoundaryLayerAtSurfaces(fn=None, segments=48)¶

Adds boundary layer around ROI to the mesh.

Does this by:

Generating a stl file describing ROI, see also pyfrp.subclasses.pyfrp_ROI.ROI.render2StlInGeometry().

Read in stl file as new pyfrp.modules.pyfrp_gmsh_geometry.domain via pyfrp.modules.pyfrp_gmsh_IO_module.readStlFile().

Simplify new geometry via pyfrp.modules.pyfrp_gmsh_geometry.domain.simplifySurfaces().

Extracting selected surfaces via pyfrp.modules.pyfrp_gmsh_geometry.gmshElement.extract().

If selected, surface boundaries are approximated into splines via pyfrp.modules.pyfrp_gmsh_geometry.gmshElement.extract().

Reading in geometry’s .geo file via pyfrp.sublcasses.pyfrp_geometry.geometry.readGeoFile().

Merging ROI geometry into main geometry via pyfrp.modules.pyfrp_gmsh_geometry.domain.merge().

Adding a boundary layer mesh via pyfrp.modules.pyfrp_gmsh_geometry.domain.addBoundaryLayerField().

Adding all surfaces of ROI’s domain to boundary layer, see pyfrp.modules.pyfrp_gmsh_geometry.boundaryLayerField.addFaceListByID().

Writing new .geo file.

Setting new .geo file as fnGeo.

Running genMesh().

Clean up .stl and .scad files that are not needed anymore.

Note

volSizeLayer only allows a single definition of mesh size in layer. Note that the pyfrp.modules.pyfrp_gmsh_geometry.boundaryLayerField class allows different mesh sizes normal and along surfaces. For more information, see its documentation.

Note

If no fnOut is given, will save a new .geo file in same folder as original fnGeo with subfix: fnGeo_roiName_BL.geo.

Note

pyfrp.modules.pyfrp_gmsh_geometry.domain.simplifySurfaces() is not a simple procedure, we recommend reading its documentation.

If volSizePx is given, will overwrite mesh’s volSizePx and set it globally at all nodes.

Keyword Arguments:
Parameters:	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – An ROI.
	fnOut (str) – Path to new .geo file. segments (int) – Number of segments used for convex hull of surface. simplify (bool) – Simplify surfaces of stl file. iterations (int) – Number of iterations used for simplification. triangIterations (int) – Number of iterations used for subdivision of surfaces. addPoints (bool) – Allow adding points inside surface triangles. fixSurfaces (bool) – Allow fixing of surfaces, making sure they are coherent with Gmsh requirements. debug (bool) – Print debugging messages. volSizePx (float) – Global mesh density. volSizeLayer (float) – Boundary layer mesh size. thickness (float) – Thickness of boundary layer. cleanUp (bool) – Clean up temporary files when finished. approxBySpline (bool) – Approximate curvatures by spline. angleThresh (float) – Threshold angle under which loops are summarized. faces (list) – List of faces. onlyAbs (bool) – Take absolute value of faces into account.
Returns:	Path to new .geo file.
Return type:	str

checkSymmetry(debug=False)¶

checkZInside(z)¶

computeExtIdx(debug=False)¶

Computes indices of external pixels.

Does this by comparing extended pixels of self with the one of the master ROI.

Keyword Arguments:
	debug (bool) – Print out debugging messages.
Returns:	Tuple containing: extImgIdxX (list): External image indices in x direction. extImgIdxY (list): External image indices in y direction.
Return type:	tuple

computeExtMask()¶

Computes mask of extended pixels of ROI.

Mask is a 2D array with the value 1 for pixels inside ROI and 0 elsewhere.

Note

Returns None,None,None if there are no extended pixels.

Also returns coordinate arrays, since offset of extended mask is not [0,0]. See also http://docs.scipy.org/doc/numpy/reference/generated/numpy.meshgrid.html .

Returns:	Tuple containing: mX (numpy.ndarray): Coordinate array corresponding to pixels of extended mask. mY (numpy.ndarray): Coordinate array corresponding to pixels of extended mask. extMask (numpy.ndarray): Extended mask.
Return type:	tuple

computeIdxs(matchMesh=False, debug=False)¶

Computes image and mesh indices of ROI.

Will do this by:

Compute image indices.

Match image indices with master ROI.

Compute external indices.

Compute mesh indices.

Match mesh indices with the ones of master ROI.

Note

If no master ROI is defined, will not do anything.

Note

If master ROI has not been indexed yet, will first index it, then continue.

Note

Will skip mesh index computation if there is no mesh generated yet.

Keyword Arguments:

matchMesh (bool) – Match mesh indices with master ROI.
debug (bool) – Print out debugging messages.

Returns:

Tuple containing:

imgIdxX (list): Image indices in x direction.

imgIdxY (list): Image indices in y direction.

meshIdx (list): Mesh indices.

Return type:

tuple

computeImgMask()¶

Computes image mask of ROI.

Image mask is a dataResPx * dataResPx array with the value 1 for pixels inside ROI and 0 elsewhere.

Returns:	Image mask.
Return type:	numpy.ndarray

computeNumExt()¶

Computes number of extended pixels of ROI.

Returns:	Number of extended pixels.
Return type:	int

copyIdxs(r)¶

Copies indices of other ROI and inserts them into ROI.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI to take indices from.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction. meshIdx (list): Mesh indices.
Return type:	tuple

emptyIdxs()¶

Flushes all indices, inserting empty lists for all of them.

Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction. meshIdx (list): Mesh indices.
Return type:	tuple

findIncluded()¶

Returns list of pyfrp.subclasses.pyfrp_ROI.customROI objects in which ROI is included.

Returns:	List of customROIs.
Return type:	list

genAsOpenscadInGeometry()¶

Generates intersection between ROI and geometry as solid python object.

See also pyfrp.subclasses.pyfrp_geometry.geometry.genAsOpenscad() and pyfrp.subclasses.pyfrp_ROI.ROI.genAsOpenscad().

Returns:	Solid python object.
Return type:	solid.solidpython.cylinder

genGmshDomain(volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Translates ROI into gmsh domain object.

This object can then be used to write ROIs to .geo files.

Note

If minID==None, will grab maximum ID via pyfrp.subclasses.pyfrp_geometry.geometry.getMaxGeoID() and add 1.

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Domain object.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

genMeshFile(fn=None, volSizePx=20.0, debug=False, minID=None)¶

Writes ROI to geo file.

Note

If fn is not given, will save .msh file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.msh .

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Path to mesh file.
Return type:	str

getAllIdxs()¶

Returns all index arrays of ROI.

Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction. meshIdx (list): Mesh indices.
Return type:	tuple

getArea()¶

Returns area of ROI.

Area is computed as area covered by imgMask + extMask

Returns:	Area of ROI.
Return type:	float

getColor()¶: Returns color of ROI.

getCopy()¶

Returns deepcopy of ROI object.

Uses copy.copy to generate copy of object, see also https://docs.python.org/2/library/copy.html .

copy.deepcopy also generates copies of other objects, including ROI.embryo.

getDataVec()¶

Returns current data vector of ROI.

Returns:	Current data vector.
Return type:	numpy.ndarray

getEncapsulatingBox()¶

Returns encapsulating box of ROI.

That is, a box defined by [xmin,xmax],[ymin,ymax],[zmin,zmax] in which ROI lies fully within.

Returns:	Tuple containing: xExtend (list): List describing extend in x-direction (`[xmin,xmax]`). yExtend (list): List describing extend in y-direction (`[ymin,ymax]`). zExtend (list): List describing extend in z-direction (`[zmin,zmax]`).
Return type:	tuple

getExtImgIdx()¶

Returns extended image indices of ROI.

Returns:	Tuple containing: extImgIdxX (list): Extended image indices in x-direction. extImgIdxY (list): Extended image indices in y-direction.
Return type:	tuple

getExtMask()¶

Returns extended mask of ROI.

Returns:	Extended mask.
Return type:	numpy.ndarray

getExtend()¶

Returns x-/y-/z-extend of ROI.

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate. zmin (float): Minimum z-coordinate. zmax (float): Maximum z-coordinate.
Return type:	tuple

getFittedVec(fit)¶

Returns fitted simulation vector of ROI of given fit.

Note

To avoid crashes, function returns empty list if ROI is in ROIsFItted but has not been fitted yet. Also inserts an empty list at the respective index.

Parameters:	fit (pyfrp.subclasses.pyfrp_fit) – Fit object.
Returns:	Fitted simulation vector.
Return type:	numpy.ndarray

getId()¶

Returns Id of ROI.

Returns:	Id.
Return type:	int

getImgIdx()¶

Returns image indices of ROI.

Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

getImgMask()¶

Returns image mask of ROI.

Returns:	Image mask.
Return type:	numpy.ndarray

getInterpolationError()¶

Prints out interpolation error for the volume of this ROI.

Interpolation error is defined as:

dataVec[0]/simVec[0],

That is, by how much does the first simulation value defer from first data value.

Returns:	Interpolation error.
Return type:	float

getMaxExtendPlane()¶

Returns in which plane (“xy”,”xz”,”yz”) the ROI has the biggest extend.

Returns:	Plane with largest extend.
Return type:	str

getMaxNodeDistance()¶

Returns maximum node distance in x/y/z direction for all nodes in ROI.

Returns:	Tuple containing: dmaxX (float): Maximum distance in x-direction dmaxY (float): Maximum distance in y-direction dmaxZ (float): Maximum distance in z-direction
Return type:	tuple

getMeshDensity()¶

Returns average mesh density inside ROI.

Mesh density is defined by

\[\rho=N/V,\]

where $N$ is the number of mesh nodes inside ROI and $V$ is the volume of ROI, see also getVolume().

Returns:	Mesh density.
Return type:	float

getMeshIdx()¶

Returns mesh indices of ROI.

Returns:	Mesh indices of ROI.
Return type:	list

getMeshIdxExtend()¶

Returns extend of ROI’s meshIdx.

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

getMeshIdxXExtend()¶

Returns extend of ROI’s meshIdx in x-coordinate.

Returns:	Tuple containing: (float): Minimum x-coordinate. (float): Maximum x-coordinate.
Return type:	tuple

getMeshIdxYExtend()¶

Returns extend of ROI’s meshIdx in y-coordinate.

Returns:	Tuple containing: (float): Minimum y-coordinate. (float): Maximum y-coordinate.
Return type:	tuple

getMeshIdxZExtend()¶

Returns extend of ROI’s meshIdx in z-coordinate.

Returns:	Tuple containing: (float): Minimum z-coordinate. (float): Maximum z-coordinate.
Return type:	tuple

getNImgPxs()¶

Returns number of image pixels inside ROI.

Returns:	Number of indices.
Return type:	int

getNMeshNodes()¶

Returns number of mesh indices inside ROI.

Returns:	Number of nodes.
Return type:	int

getName()¶

Returns name of ROI.

Returns:	Current name.
Return type:	str

getNumExt()¶

Returns number of extended pixels of ROI.

Returns:	Number of extended pixels.
Return type:	int

getOpenscadZExtend()¶

Returns extend in z-direction suitable for rendering the ROI via openscad.

If either zmin or zmax is infinity, then uses :py:func:getRealZExend to return more meaningful extend.

Returns:	Z-extend given by `[zmin,zmax]`.
Return type:	list

getOrthogonal2Plane()¶

Returns orthogonal direction to plane of maximum extension.

Returns:	Direction.
Return type:	str

getPlaneMidCoordinate()¶

Returns midpoint of extend orthogonal to plane of maximum extension.

Returns:	Midpoint.
Return type:	float

getROIHeight()¶

Returns height of ROI.

Returns:	Height of ROI.
Return type:	float

getRealZExend()¶

Returns real extend in z-direction.

Real extend returns

\[z_{\mathrm{min}}=\mathrm{max} (z_{\mathrm{min,ROI}},z_{\mathrm{min,geometry}})\]

and

\[z_{\mathrm{max}}=\mathrm{min} (z_{\mathrm{max,ROI}},z_{\mathrm{max,geometry}})\]

Returns:	Z-extend given by `[zmin,zmax]`.
Return type:	list

getSimConc(phi, append=True)¶

Computes the simulation concentration over ROI.

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – Solution variable.
	append (bool) – Append result to simulation vector.
Returns:	Simulation concentration over ROI.
Return type:	float

getSimVec()¶

Returns current simulation vector of ROI.

Returns:	Current simulation vector.
Return type:	numpy.ndarray

getType()¶

Returns type of ROI, splitting off all module names etc. .

Returns:	Type of ROI.
Return type:	str

getUseForRim()¶

Returns if the ROI is used for rim calculation.

Returns:	Current flag value.
Return type:	bool

getVolume()¶

Returns volume of ROI.

Since ROIs only behave linearly in z-direction, volume is given by

\[V = A * h,\]

where $h$ is ROI height (see getROIHeight()) and $A$ is ROI area (see getArea()).

Returns:	ROI volume.
Return type:	float

getZExtend()¶

Returns extend in z-direction.

Returns:	Z-extend given by `[zmin,zmax]`.
Return type:	list

getdataVecFitted(fit)¶

Returns fitted data vector of ROI of given fit.

Note

To avoid crashes, function returns empty list if ROI is in ROIsFItted but has not been fitted yet. Also inserts an empty list at the respective index.

Parameters:	fit (pyfrp.subclasses.pyfrp_fit) – Fit object.
Returns:	Fitted data vector.
Return type:	numpy.ndarray

idxs2Full()¶

idxs2Quad(debug=False)¶

isAnalyzed()¶

Checks if ROI has been analyzed.

Returns:	True if ROI has been analyzed.
Return type:	bool

isFitted()¶

Checks if ROI has been fitted in ALL fits of embryo.

Returns:	True if ROI has been fitted.
Return type:	bool

isMaster()¶

Returns if ROI is masterROI.

Returns:	True if masterROI.
Return type:	bool

isSimulated()¶

Checks if ROI has been simulated.

Returns:	True if ROI has been simulated.
Return type:	bool

matchImgIdx(r)¶

Matches image indices of self with the ones of ROI r.

Does this by generating masks of both ROIs and multiplicating them.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI to match with.
Returns:	Tuple containing: imgIdxX (list): Matched image indices in x direction. imgIdxY (list): Matched image indices in y direction.
Return type:	tuple

matchMeshIdx(r, matchZ=False)¶

pinAllTS(bkgdVal=None, normVal=None, bkgdValSim=None, normValSim=None, debug=False)¶

Pins both data and simulation timeseries of ROI.

Keyword Arguments:

bkgdVal (float) – Use this background value instead of newly computing it.
normVal (float) – Use this norming value instead of newly computing it.
bkgdValSim (float) – Use this background value for simulation timeseries instead of newly computing it.
normValSim (float) – Use this norming value for simulation timeseries instead of newly computing it.
debug (bool) – Print debugging messages.

Returns:

Tuple containing:

dataVecPinned (numpy.ndarray): Pinned data vector.

simVecPinned (numpy.ndarray): Pinned simulation vector.

Return type:

tuple

pinDataTS(bkgdVal=None, normVal=None, debug=False)¶

Pins data timeseries of ROI.

Keyword Arguments:
	bkgdVal (float) – Use this background value instead of newly computing it. normVal (float) – Use this norming value instead of newly computing it. debug (bool) – Print debugging messages.
Returns:	Pinned data vector.
Return type:	numpy.ndarray

pinSimTS(bkgdVal=None, normVal=None, debug=False)¶

Pins simulation timeseries of ROI.

Keyword Arguments:
	bkgdVal (float) – Use this background value instead of newly computing it. normVal (float) – Use this norming value instead of newly computing it. debug (bool) – Print debugging messages.
Returns:	Pinned simulation vector.
Return type:	numpy.ndarray

plotData(ax=None, color=None, linewidth=1, legend=True, linestyle='-', label=None, legLoc=-1)¶

Plot data vector of ROI.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	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. legLoc (int) – Location of legend.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotDataPinned(ax=None, color=None, linewidth=1, legend=True, linestyle='-', label=None, legLoc=-1)¶

Plot pinned data vector of ROI.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	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. legLoc (int) – Location of legend.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotFit(fit, ax=None, color=None, linewidth=1, legend=True, title=None, linestyles=['-', '-.'], show=True)¶

Plot fit for ROI.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linestyles (list) – Linestyles of data and simulation. linewidth (float) – Linewidth of plot. legend (bool) – Show legend. show (bool) – Show figure.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotSim(ax=None, color=None, linewidth=1, legend=True, linestyle='--', label=None, legLoc=-1)¶

Plot simulation vector of ROI.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	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. legLoc (int) – Location of legend.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotSimConcProfile(phi, ax=None, direction='x', mode='normal', nbins=20, color=None, label=None, legend=False)¶

Plots concentration profile of solution variable in single direction.

mode can be either "normal" or "hist". If mode="hist", will plot a histogram with nbins bins using pyfrp.modules.pyfrp_misc_module.simpleHist().

Note

direction sets in which direction the profile should be plotted. if direction="r", then function will plot a radial profile and uses self.embryo.geometry.center as center if ROI does not have a center, else uses center of ROI.

Note

Will create axes if not given via ax.

Example:

Grab ROI:

>>> sl=emb.getROIByName("Slice")

Make some plot:

>>> fig,axes=pyfrp_plot_module.makeSubplot([2,2])

Plot some concentration profiles:

>>> ax=sl.plotSimConcProfile(emb.simulation.IC,mode='hist',color='g',label='direction = x',nbins=100,ax=axes[0],legend=False)
>>> ax=sl.plotSimConcProfile(emb.simulation.IC,mode='hist',direction='y',color='r',label='direction = y',nbins=100,ax=axes[1],legend=False)
>>> ax=sl.plotSimConcProfile(emb.simulation.IC,mode='hist',direction='r',color='b',nbins=100,label='direction = r',ax=axes[2],legend=False)
>>> ax=sl.plotSimConcProfile(emb.simulation.IC,mode='normal',direction='r',color='b',label='direction = r',ax=axes[3],legend=False)

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – Solution variable
	ax (matplotlib.axes) – Axes to be plotted in. direction (str) – Direction to be plotted (x/y/z/r). color (str) – Color of plot. legend (bool) – Show legend. label (str) – Label of plot. nbins (int) – Number of bins of histogram. mode (str) – Either `normal` or `hist`.
Returns:	Matplotlib axes used for plotting.
Return type:	matplotlib.axes

plotSimPinned(ax=None, color=None, linewidth=1, legend=True, linestyle='--', label=None, legLoc=-1)¶

Plot pinned simulation vector of ROI.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	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. legLoc (int) – Location of legend.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotSolutionVariable(phi, ax=None, vmin=None, vmax=None, nlevels=25, colorbar=True, plane='xy', zs=None, zdir=None, mask=True, nPts=1000, mode='normal', title='Solution Variable', typ='contour')¶

Plots simulation solution variable over all indices of ROI as 2D contour plot.

Note

If no ax is given, will create new one.

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().

Warning

matplotlib.pyplot.tricontourf has problems when phi 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.

You can find a more detailed explanation in the documentation of pyfrp.modules.pyfrp_plot_module.plotSolutionVariable().

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – Solution variable.
	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

printDetails()¶: Prints out all attributes of ROI object.

refineInMeshByField(factor=3.0, addZ=15.0, findIdxs=True, debug=False, run=True, fnOut=None)¶

Refines mesh inside ROI by adding box field to mesh file.

The mesh size inside the box is computed by mesh.volSizePx/factor. To ensure that there are enough original nodes inside ROI that then allow refinement from, addZ pixels is added in z-direction both below and above the ROI.

Keyword Arguments:
	factor (float) – Refinement factor. addZ (float) – Number of pixels added above and below ROI for box field. findIdxs (bool) – Find mesh indices of ROI after refinement. run (bool) – Run Gmsh to generate new mesh after refinement. debug (bool) – Print debugging messages. fnOut (str) – Path to output geo file.
Returns:	Path to new .geo file.
Return type:	str

render2Openscad(fn=None, segments=48)¶

Generates .scad file for the ROI.

Note

If fn is not given, will save .scad file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.scad.

Keyword Arguments:
	fn (str) – Output filename. segments (int) – Number of segments used for convex hull of surface.
Returns:	Output filename.
Return type:	str

render2OpenscadInGeometry(fn=None, segments=48)¶

Generates .scad file for the intersection between ROI and geometry.

Note

If fn is not given, will save .scad file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.scad.

Keyword Arguments:
	fn (str) – Output filename. segments (int) – Number of segments used for convex hull of surface.
Returns:	Output filename.
Return type:	str

render2Stl(fn=None, segments=48)¶

Generates .stl file for the ROI.

Will do this by:

Generating openscad object via genAsOpenscad().

Rendering this to scad file via render2Openscad().

Calling pyfrp.modules.pyfrp_openscad_module.runOpenscad().

Note

If fn is not given, will save .stl file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.stl.

Keyword Arguments:
	fn (str) – Output filename. segments (int) – Number of segments used for convex hull of surface.
Returns:	Output filename.
Return type:	str

render2StlInGeometry(fn=None, segments=48)¶

Generates .stl file for the intersection between ROI and geometry.

Will do this by:

Generating openscad object via genAsOpenscadInGeometry().

Rendering this to scad file via render2OpenscadInGeometry().

Calling pyfrp.modules.pyfrp_openscad_module.runOpenscad().

Note

If fn is not given, will save .stl file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.stl.

Keyword Arguments:
	fn (str) – Output filename. segments (int) – Number of segments used for convex hull of surface.
Returns:	Output filename.
Return type:	str

resetDataVec()¶: Resets data vector to an empty list

resetSimVec()¶: Resets simulation vector to an empty list

setColor(color)¶

Sets color of ROI.

Color can be either str, float or tuple. See also: http://matplotlib.org/api/colors_api.html

Parameters:	color (str) – New color.
Returns:	New color.
Return type:	str

setDataVec(vec)¶

Sets data vector of ROI.

Parameters:	vec (numpy.ndarray) – New data vector.
Returns:	New data vector.
Return type:	numpy.ndarray

setId(Id)¶

Sets Id of ROI.

Parameters:	Id (int) – New Id.
Returns:	New Id.
Return type:	int

setName(n)¶

Sets name of ROI.

Parameters:	n (str) – New name.
Returns:	New name.
Return type:	str

setSimVec(vec)¶

Sets simulation vector of ROI.

Parameters:	vec (numpy.ndarray) – New simulation vector.
Returns:	New simulation vector.
Return type:	numpy.ndarray

setUseForRim(b)¶

Marks the ROI to be used for rim calculation.

Parameters:	b (bool) – True if ROI should be used, False else.
Returns:	Current flag value.
Return type:	bool

setZExtend(zmin, zmax)¶

Sets extend in z-direction.

Parameters:	zmin (float) – Minimum z-coordinate. zmax (float) – Maximum z-coordinate.
Returns:	New z-extend given by `[zmin,zmax]`.
Return type:	list

showExtImgIdx(ax=None)¶

showIdxs(axes=None)¶

showImgIdx(ax=None)¶

showMeshIdx(ax=None)¶

showMeshIdx2D(ax=None)¶

writeToGeoFile(fn=None, volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Writes ROI to geo file.

Note

If fn is not given, will save .geo file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.geo .

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Path to geo file.
Return type:	str

class pyfrp.subclasses.pyfrp_ROI.customROI(embryo, name, Id, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.ROI

addROI(r, p)¶

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Does this by looping through all ROIs specified in ROIsIncluded and checking if x/y is supposed to lie inside or outside of the respective ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Returns:	Solid python object.
Return type:	solid.solidpython.openscad_object

getROIsIncluded()¶

mergeROIs(r)¶

removeROI(r)¶

roiIncluded(r)¶

Returns if a ROI is included in customROI.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – A ROI.
Returns:	`True` if included, `False` else.
Return type:	bool

setROIsIncluded(l)¶

showBoundary(color=None, linewidth=3, ax=None)¶

Shows ROI in a 2D plot by plotting all included ROIs.

If no color is specified, will use color specified in ROI.color. If color=="each", will plot each included ROI in its respective color.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linewidth (float) – Linewidth of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

substractROIs(r)¶

updateIdxs()¶

class pyfrp.subclasses.pyfrp_ROI.polyROI(embryo, name, Id, corners, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.ROI

addCorner(c, pos=-1)¶

appendCorner(c)¶

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Returns:	Solid python object.
Return type:	solid.solidpython.linear_extrude

getCenterOfMass()¶

Computes center of mass of ROI.

The center of mass is computed by

\[c = \frac{1}{N} \sum\limits_{i=1}{N} x_i ,\]

where $c$ is the center of mass, $N$ the number of corners and $x_i$ is the coordinate of corner $i$ .

Returns:	Center of mass.
Return type:	numpy.ndarray

getCorners()¶

moveCorner(idx, x, y)¶

Moves corner to new postion.

Parameters:	idx (int) – Index of corner to be moved. x (float) – New x-coordinate. y (float) – New y-coordinate.

Results:: list: Updated corners list.

removeCorner(pos)¶

setCorners(corners)¶

showBoundary(color=None, linewidth=3, ax=None)¶

Shows ROI in a 2D plot.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linewidth (float) – Linewidth of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

class pyfrp.subclasses.pyfrp_ROI.polySliceROI(embryo, name, Id, corners, height, width, sliceBottom, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.polyROI, pyfrp.subclasses.pyfrp_ROI.sliceROI

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Returns:	Solid python object.
Return type:	solid.solidpython.linear_extrude

genGmshDomain(volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Translates ROI into gmsh domain object.

This object can then be used to write ROIs to .geo files.

Note

If minID==None, will grab maximum ID via pyfrp.subclasses.pyfrp_geometry.geometry.getMaxGeoID() and add 1.

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Domain object.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

writeToGeoFile(fn=None, volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Writes ROI to geo file.

Note

If fn is not given, will save .geo file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.geo .

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Path to geo file.
Return type:	str

class pyfrp.subclasses.pyfrp_ROI.radialROI(embryo, name, Id, center, radius, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.ROI

Radial ROI class.

Inherits from ROI.

Main attributes are:

radius: Radius of ROI.

center: Center of ROI.

center2Mid()¶

checkCentered()¶

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Note

Will grab extent of geometry to find bounds in z-direction.

Returns:	Solid python object.
Return type:	solid.solidpython.cylinder

getCenter()¶

Returns current center of ROI.

Returns:	Current center.
Return type:	list

getCenterOfMass()¶

Returns center of mass of ROI.

For a radial ROI, this is equivalent to the center.

getRadius()¶

Returns current radius of ROI.

Returns:	Current radius.
Return type:	float

makeReducable(auto=False, debug=False)¶

setCenter(c)¶

Sets radius of ROI.

Parameters:	c (list) – New center.
Returns:	New center.
Return type:	list

setRadius(r)¶

Sets radius of ROI.

Parameters:	r (float) – New radius
Returns:	New radius.
Return type:	float

showBoundary(color=None, linewidth=3, ax=None)¶

Shows ROI in a 2D plot.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linewidth (float) – Linewidth of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

class pyfrp.subclasses.pyfrp_ROI.radialSliceROI(embryo, name, Id, center, radius, height, width, sliceBottom, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.sliceROI, pyfrp.subclasses.pyfrp_ROI.radialROI

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad(allowInf=False)¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Keyword Arguments:
	allowInf (bool) – Allow infinity in bounds of z-direction.
Returns:	Solid python object.
Return type:	solid.solidpython.cylinder

genGmshDomain(volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Translates ROI into gmsh domain object.

This object can then be used to write ROIs to .geo files.

Note

If minID==None, will grab maximum ID via pyfrp.subclasses.pyfrp_geometry.geometry.getMaxGeoID() and add 1.

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Domain object.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

writeToGeoFile(fn=None, volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Writes ROI to geo file.

Note

If fn is not given, will save .geo file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.geo .

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Path to geo file.
Return type:	str

class pyfrp.subclasses.pyfrp_ROI.rectangleROI(embryo, name, Id, offset, sidelengthX, sidelengthY, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.ROI

centerOffset()¶

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Note

Will grab extent of geometry to find bounds in z-direction.

Returns:	Solid python object.
Return type:	solid.solidpython.cube

getCenterOfMass()¶

Computes center of mass of ROI.

The center of mass is computed by

\[c = \frac{1}{N} \sum\limits_{i=1}{N} x_i ,\]

where $c$ is the center of mass, $N$ the number of corners and $x_i$ is the coordinate of corner $i$ .

Returns:	Center of mass.
Return type:	numpy.ndarray

getCorners()¶

Returns corners of rectangle in counter-clockwise order, starting with offset.

Returns:	List of 2D coordinates of corners.
Return type:	list

getOffset()¶

getSideLengthX()¶

getSideLengthY()¶

makeReducable(atuo=False, debug=False)¶

setOffset(c)¶

setSideLengthX(s)¶

setSideLengthY(s)¶

showBoundary(color=None, linewidth=3, ax=None)¶

Shows ROI in a 2D plot.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linewidth (float) – Linewidth of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

class pyfrp.subclasses.pyfrp_ROI.rectangleSliceROI(embryo, name, Id, offset, sidelengthX, sidelengthY, height, width, sliceBottom, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.rectangleROI, pyfrp.subclasses.pyfrp_ROI.sliceROI

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Returns:	Solid python object.
Return type:	solid.solidpython.cube

genGmshDomain(volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Translates ROI into gmsh domain object.

This object can then be used to write ROIs to .geo files.

Note

If minID==None, will grab maximum ID via pyfrp.subclasses.pyfrp_geometry.geometry.getMaxGeoID() and add 1.

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Domain object.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

writeToGeoFile(fn=None, volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Writes ROI to geo file.

Note

If fn is not given, will save .geo file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.geo .

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Path to geo file.
Return type:	str

class pyfrp.subclasses.pyfrp_ROI.sliceROI(embryo, name, Id, height, width, sliceBottom, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.ROI

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Only returns True, since sliceROI is not limited in x/y-direction.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y], all `True`.
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Note

Since sliceROI theoretically is not having any limits in x/y-direction, function returns limits given by input image, that is, [0,embryo.dataResPx].

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

computeZExtend()¶

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Note

Will grab extent of geometry to find bounds in x/y-direction.

Returns:	Solid python object.
Return type:	solid.solidpython.cube

getHeight()¶

getSliceBottom()¶

getWidth()¶

setHeight(h)¶

setSliceBottom(s)¶

setWidth(w)¶

class pyfrp.subclasses.pyfrp_ROI.squareROI(embryo, name, Id, offset, sidelength, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.ROI

centerOffset()¶

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Note

Will grab extent of geometry to find bounds in z-direction.

Returns:	Solid python object.
Return type:	solid.solidpython.cube

getCenterOfMass()¶

Computes center of mass of ROI.

The center of mass is computed by

\[c = \frac{1}{N} \sum\limits_{i=1}{N} x_i ,\]

where $c$ is the center of mass, $N$ the number of corners and $x_i$ is the coordinate of corner $i$ .

Returns:	Center of mass.
Return type:	numpy.ndarray

getCorners()¶

Returns corners of square in counter-clockwise order, starting with offset.

Returns:	List of 2D coordinates of corners.
Return type:	list

getOffset()¶

getSideLength()¶

makeReducable(auto=False, debug=False)¶

setOffset(c)¶

setSideLength(s)¶

showBoundary(color=None, linewidth=3, ax=None)¶

Shows ROI in a 2D plot.

If no color is specified, will use color specified in ROI.color.

Keyword Arguments:
	ax (matplotlib.axes) – Matplotlib axes used for plotting. If not specified, will generate new one. color (str) – Color of plot. linewidth (float) – Linewidth of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

class pyfrp.subclasses.pyfrp_ROI.squareSliceROI(embryo, name, Id, offset, sidelength, height, width, sliceBottom, color='b')¶

Bases: pyfrp.subclasses.pyfrp_ROI.squareROI, pyfrp.subclasses.pyfrp_ROI.sliceROI

checkXYInside(x, y)¶

Checks if coordinates are inside ROI.

Parameters:	x (np.ndarray) – Array of x-coordinates. y (np.ndarray) – Array of y-coordinates.
Returns:	Array of booleans with corresponding to [x,y].
Return type:	np.ndarray

computeImgIdx(debug=False)¶

Computes image indices of ROI.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: imgIdxX (list): Image indices in x-direction. imgIdxY (list): Image indices in y-direction.
Return type:	tuple

computeMeshIdx(mesh)¶

Computes mesh indices of ROI.

Parameters:	mesh (fipy.GmshImporter3D) – Fipy mesh object.
Returns:	Newly computed mesh indices.
Return type:	list

computeXYExtend()¶

Computes extend of ROI in x/y direction.

Returns:	Tuple containing: xExtend (list): List containing minimum/maximum x-coordinate (`[xmin,xmax]`). yExtend (list): List containing minimum/maximum y-coordinate (`[ymin,ymax]`).
Return type:	tuple

genAsOpenscad()¶

Generates ROI as solid python object.

Useful if ROI is used to be passed to openscad.

Returns:	Solid python object.
Return type:	solid.solidpython.cube

genGmshDomain(volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Translates ROI into gmsh domain object.

This object can then be used to write ROIs to .geo files.

Note

If minID==None, will grab maximum ID via pyfrp.subclasses.pyfrp_geometry.geometry.getMaxGeoID() and add 1.

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume.
Returns:	Domain object.
Return type:	pyfrp.modules.pyfrp_gmsh_geometry.domain

writeToGeoFile(fn=None, volSizePx=20.0, genLoops=True, genSurfaces=True, genVol=True, minID=None)¶

Writes ROI to geo file.

Note

If fn is not given, will save .geo file of ROI in same folder as the geometry file of the embryo with the following path: path/to/embryos/geo/file/nameOfEmbryo_nameOfROI.geo .

Keyword Arguments:
	volSizePx (float) – Mesh size of vertices. genLoops (bool) – Generate line loops. genSurfaces (bool) – Generate surfaces. genVol (bool) – Generate surface loop and corresponding volume. minID (int) – Id at which geo IDs should start.
Returns:	Path to geo file.
Return type:	str

pyfrp.subclasses.pyfrp_analysis module¶

Essential PyFRAP module containing analysis class.

class pyfrp.subclasses.pyfrp_analysis.analysis(embryo)¶

PyFRAP analysis class storing information about analysis options and some analysis results.

Analysis options are:

gaussian: Apply gaussian filter to images. Default kernel size is gaussianSigma=2.

median: Apply gaussian filter to images. Default kernel size is medianRadius=5.

flatten: Apply flattening mask.

norm: Norm by pre image.

bkgd: Substract background.

quad: Perform reduction to first quadrant by flipping.

flipBeforeProcess: Flip into quadrant before other processing options are applied.

Analysis options are stored in process dictionary. If analysis finds option in process.keys, it will perform option. Analysis options can be turned on/off using the respective functions, such as

pyfrp.subclasses.pyfrp_analysis.medianOn()

pyfrp.subclasses.pyfrp_analysis.flattenOn()

etc.

Processing parameters are stored in process.values.

The default processing options are process={}, meaning that no image modification is applied before concentration readout, see also genDefaultProcess().

Warning

Quadrant reduction is still experimental.

Three other important attributes are:

dataOffset: The offset of the data that is for example used for norming, see also getOptimalOffset().

addRimImg: Flag that controls if rim concentrations are added to ROI concentratrtion profiles, see also setAddRimImg().

concRim: The rim concentration of the first post-bleaching image used later by the simulation for nodes that are outside of original image boundaries.

Note

addRimImg=True by default. This is generally good, since the simulation value in ROIs is getting evaluated over over mesh nodes both inside the actual image and outside of it.

Parameters:	embryo (pyfrp.subclasses.pyfrp_embryo.embryo) – PyFRAP embryo instance.

bkgdOn()¶

Returns current state of this option.

Returns:	`True` if switched on, `False` else.
Return type:	bool

computeBkgdMask(flatteningMask, applyProcess=True, applyFlatten=False)¶

Computes background mask.

Takes first nBkgd images in fnBkgd and computes mean image of these images. Then, if applyProcess is selected, applies the selected process options defined in process dictionary to it.

Note

Will not apply process options norm and bkgd to mean background image.

Keyword Arguments:
Parameters:	flatteningMask (numpy.ndarray) – Flattening mask.
	applyProcess (bool) – Apply processing options to background mask. applyFlatten (bool) – Apply flattening to background mask.
Returns:	Background mask.
Return type:	numpy.ndarray

computeFlatteningMask(applyProcess=True)¶

Computes flattening mask.

Takes first nFlatten images in fnFlatten and computes mean image of these images. Then, if applyProcess is selected, applies the selected process options defined in process dictionary to it.

Note

Will not apply process options norm, flatten and bkgd to mean flattening image.

Keyword Arguments:
	applyProcess (bool) – Apply processing options to flattening mask.
Returns:	Flattening mask.
Return type:	numpy.ndarray

computePreMask(flatteningMask, bkgdMask, applyProcess=True)¶

Computes norming mask.

Takes first nPre images in fnPreimage and computes mean image of these images. Then, if applyProcess is selected, applies the selected process options defined in process dictionary to it.

Note

Will not apply process option norm to mean background image.

Keyword Arguments:
Parameters:	flatteningMask (numpy.ndarray) – Flattening mask. bkgdMask (numpy.ndarray) – Background mask.
	applyProcess (bool) – Apply processing options to background mask.
Returns:	Norming mask.
Return type:	numpy.ndarray

flattenOn()¶

Returns current state of this option.

Returns:	`True` if switched on, `False` else.
Return type:	bool

flipBeforeProcessOn()¶

Returns current state of this option.

Returns:	`True` if switched on, `False` else.
Return type:	bool

gaussianOn()¶

Returns current state of this option.

Returns:	`True` if switched on, `False` else.
Return type:	bool

genDefaultProcess()¶

Sets process dictionary to default options.

Default options are:

gaussian=False

median=False

quad=False

flatten=False

bkgd=False

norm=False

flipBeforeProcess=True

Returns:	Updated `process` dictionary.
Return type:	dict

getAddRimImg()¶

Returns the addRimImg flag.

pyfrp.subclasses.pyfrp_conf module¶

class pyfrp.subclasses.pyfrp_conf.configuration¶

addRecentFile(fn)¶

backupPathFile()¶

copyPathFileToDefaultLocation()¶

getBackup2File(h)¶

getBackup2Memory(h)¶

getPathFile()¶

getPlotHidden(h)¶

getPropHidden(h)¶

getRecentFiles(r)¶

getTermHidden(h)¶

printConfiguration()¶

save(fn=None)¶

setBackup2File(h)¶

setBackup2Memory(h)¶

setPathFile(fn)¶

setPlotHidden(h)¶

setPropHidden(h)¶

setRecentFiles(r)¶

setTermHidden(h)¶

updateVersion()¶

pyfrp.subclasses.pyfrp_embryo module¶

Essential PyFRAP module containing embryo class.

class pyfrp.subclasses.pyfrp_embryo.embryo(name)¶

Main PyFRAP class, gathering all the data and parameters of FRAP experiment.

The embryo class basically stores:

A minimum set of basic FRAP parameters.

A list of pyfrp.subclasses.pyfrp_ROI.ROI classes, describing all ROIs used for for evaluating simulation and analysis results.

A pyfrp.subclasses.pyfrp_geometry.geometry class describing the 3-dimensional geometry of the experiment.

A pyfrp.subclasses.pyfrp_analysis.analysis class describing how the dataset is going to be analyzed.

A pyfrp.subclasses.pyfrp_simulation.simulation class describing how the dataset is going to be simulated.

A list of pyfrp.subclasses.pyfrp_fit.fit classes, storing different fitting options and results.

The embryo class comes with a comprehensive set of methods aimed at making it as powerful as possible, while still keeping it simple. The hierarchical structure should make it easy to navigate through a FRAP dataset.

ROIs2Full()¶

Sets ROIs to full mode.

Returns:	Updated list of ROIs.
Return type:	list

ROIs2Quad()¶

Reduces ROIs to quadrant reduced mode, mapping all their indices in first quadrant.

Note

Use :py:func:setEmbryo2Quad to make sure that both geometry and ROIs are reduced.

Warning

Quadrant reduction is still experimental.

Returns:	Updated list of ROIs.
Return type:	list

addFit(fit)¶

Appends fit object to list of fits

Parameters:	fit (pyfrp.subclasses.pyfrp_fit.fit) – fit object.
Returns:	Updated `fits` list.
Return type:	list

addROI(roi)¶

Adds ROI to ROIs list.

Parameters:	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – A PyFRAP ROI.
Returns:	Updated ROIs list.
Return type:	list

checkQuadReducable(tryFix=False, auto=False, debug=False)¶

Checks if embryo is reducable to quadrant by checking if all ROIs are either point or axis symmetric around geometry center.

Note

You want to call :py:meth:showAllROIBoundaries and :py:meth:computeROIIdxs afterwards to make sure everything went properly.

Warning

Quadrant reduction is still experimental.

Keyword Arguments:
	tryFix (bool) – Tries to readjust ROIs into reducable form. auto (bool) – Readjust ROIs automatically. debug (bool) – Print debugging messages.
Returns:	True if embryo is reducable.
Return type:	bool

checkROIIdxs(debug=False)¶

Checks if all ROIs have their mesh and image indices computed.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Tuple containing: img (bool): True if all ROIs have up-to-date image indices. mesh (bool): True if all ROIs have up-to-date mesh indices.
Return type:	tuple

clearAllAttributes()¶

Replaces all attribute values of embryo object with None, except name.

Useful if embryos are seperated and molecule file needs to be compressed.

Returns:	True if success, False else.
Return type:	bool

compareFitsByAIC(ROIs=None, sigma=1, fromSSD=True, thresh=None, printOut=True)¶

Compares all fits of embryo using the Akaike information criterion (AIC).

For a detailed explanation of the model selection procedure, please refer to pyfrp.modules.pyfrp_stats_module.compareFitsByAIC().

If printOut is selected, will print a list of selected fits and the underlying Akaike values in a readable table.

If the AIC or the AICc should be used can be determined using pyfrp.modules.pyfrp_stats_module.useAIC().

Keyword Arguments:

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

compareFitsByCorrAIC(ROIs=None, sigma=1, fromSSD=True, thresh=None, printOut=True)¶

Compares all fits of embryo using the corrected Akaike information criterion (AICc).

For a detailed explanation of the model selection procedure, please refer to pyfrp.modules.pyfrp_stats_module.compareFitsByCorrAIC().

If printOut is selected, will print a list of selected fits and the underlying Akaike values in a readable table.

If the AIC or the AICc should be used can be determined using pyfrp.modules.pyfrp_stats_module.useAIC().

Keyword Arguments:

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

computeBkgd(useMin=False, fromTS='both', debug=False)¶

Computes background value over all ROIs.

If useMin==False, will use value at first index of data/simulation vectors as norming value.

Note

Use fromTS='both' to use values from both simulation and data for background computation. If fromTS='data', will only use data, of fromTS='sim' will only use simulation vectors.

Keyword Arguments:
	useMin (bool) – Use minimum value for background computation. fromTS (bool) – Which time series to use for background computation. debug (bool) – Print debugging messages.
Returns:	Background value.
Return type:	float

computeConvFact(updateDim=True)¶

Computes conversion factor between um to px (unit=um/px).

If updateDimensions is selected, will update all dimensions of embryo object data rely on convFact.

Keyword Arguments:
	updateDim (bool) – Automatically updated all convFact relevant attributes.
Returns:	New conversion factor.
Return type:	float

computeIdealFRAPPinVals(bkgdName='Bleached Square', normName='Slice', debug=False, useMin=False, useMax=False, sepSim=True, switchThresh=0.95)¶

Computes background and norming value using optimized settings.

Idea: Instead using values from all ROIs to compute pinning values, select two ROIs that should lead to optimal pinning values. In the default case, this is the ROI describing the bleached region (here we expect the lowest intensities), and the slice (here we expect the overall end concentration the experiment is converging to).

If useMin==False, will use value at first index of data/simulation vectors as norming value.

If useMax==False, will use value at last index of data/simulation vectors as norming value.

Note

If bkgdName and normName are not set differently, will look for ROIs with these names for background and norming computation, respectively. If they don’t exist, will return None. genDefaultROIs() will make sure that both those ROIs exist.

Warning

Not all ROIs are suitable for pinning value computation. Generally ROIs that have the least extended volume proof most suitable.

Note

switchThresh checks if recovery is complete. This is important if recovery curves are very slow and bleached region does not reach full recovery. If this happens, we divide by a number that is smaller than 1 and hence boost all timeseries way above one instead of limiting it below one

Warning

sepSim==True makes sure that we never get negative intensities in the pinned simulation vectors. Since interpolation is never perfect, this can happen if $bkgdValue(data)>bkgdValue(sim)$.

Keyword Arguments:

bkgdName (str) – Name of ROI used for background computation.
normName (str) – Name of ROI used for norming computation.
useMin (bool) – Use minimum value for background computation.
useMax (bool) – Use maximum value for norm value computation.
fromTS (bool) – Which time series to use for background computation.
debug (bool) – Print debugging messages.
sepSim (bool) – Use seperate pinning values for simulation vectors.

Returns:

Tuple containing:

bkgdVal (float): Background value for data vectors.

normVal (float): Norming value for data vectors.

bkgdValSim (float): Background value for simulation vectors.

normValSim (float): Norming value for simulation vectors.

Return type:

tuple

computeNorm(bkgdVal, useMax=True, fromTS='both', debug=False)¶

Computes background value over all ROIs.

If useMax==False, will use value at last index of data/simulation vectors as norming value.

Note

Use fromTS='both' to use values from both simulation and data for background computation. If fromTS='data', will only use data, of fromTS='sim' will only use simulation vectors.

Keyword Arguments:
Parameters:	bkgdVal (float) – Use this background value instead of newly computing it.
	useMax (bool) – Use maximum value for norm value computation. fromTS (bool) – Which time series to use for background computation. debug (bool) – Print debugging messages.
Returns:	Norming value.
Return type:	float

computePinVals(useMin=True, useMax=True, bkgdVal=None, debug=False)¶

Compute overall pinning values over all ROIs.

Keyword Arguments:

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

computeROIIdxs(signal=None, debug=True)¶

Computes image, extended and mesh indices of all ROIs in embryo’s ROIs list.

Keyword Arguments:
	signal (PyQt4.QtCore.pyqtSignal) – PyQT signal to send progress to GUI. debug (bool) – Print final debugging messages and show debugging plots.
Returns:	Updated list of ROIs.
Return type:	list

copy()¶

Copies embryo, preserving all attributes and methods.

Returns:	Embryo copy.
Return type:	pyfrp.subclasses.pyfrp_embryo.embryo

deleteFit(i)¶

Deletes fit with index i from fits list.

Parameters:	i (int) – Index of fit to be deleted.
Returns:	Updated `fits` list.
Return type:	list

fixFilePaths()¶

Fixes paths to geometry/meshfiles.

Returns:	True if success for all paths.
Return type:	bool

genDefaultROIs(center, radius, rimFactor=0.66, masterROI=None, bleachedROI=None, rimROI=None, sliceHeightPx=None, clean=True)¶

Creates a standard set of ROI objects and adds them to ROIs list.

The set of ROIs covers the generally most useful ROIs used for FRAP experiments, providing already all the settings that PyFRAP uses for rim computation etc.

ROIs contain:

All: ROI covering all pixels and mesh nodes. pyfrp.subclasses.pyfrp_ROI.sliceROI

All Square: ROI covering all pixels and mesh nodes inside bleached region. pyfrp.subclasses.pyfrp_ROI.squareROI

All Out: ROI covering all pixels and mesh nodes outside bleached region. pyfrp.subclasses.pyfrp_ROI.customROI

Slice: ROI covering all pixels and mesh nodes inside recorded field of view. pyfrp.subclasses.pyfrp_ROI.radialSliceROI

Slice rim: ROI covering all pixels that are not used for rim concentration computation. pyfrp.subclasses.pyfrp_ROI.radialSliceROI

Rim: ROI covering all pixels that are used for rim concentration computation. pyfrp.subclasses.pyfrp_ROI.customROI

Bleached Square: ROI covering all pixels and mesh nodes inside bleached region and imaging slice. pyfrp.subclasses.pyfrp_ROI.squareSliceROI

Out: ROI covering all pixels and mesh nodes outside bleached region but inside imaging slice. pyfrp.subclasses.pyfrp_ROI.customROI

Note

Will automatically set Slice as masterROI.

Note

If masterROI is given, will use this ROI instead of Slice as master ROI via setMasterROIIdx(). Will not create Slice at all. If masterROI is not in ROIs list yet, it will be automatically added to the list.

Note

If bleachedROI is given, will use this ROI instead of Bleached Square. Will generate copy of bleachedROI to generate All Square type ROI.

Note

If rimROI is given, Slice rim is not created, and rimROI is used instead.

Note

If sliceHeightPx is given, will create ROIs Slice, Out, Bleached Square and Rim at this height, otherwise will use value stored in embryo.sliceHeightPx.

Keyword Arguments:
Parameters:	center (list) – Center of circle defining imaging slice. radius (float) – Radius of circle defining imaging slice.
	rimFactor (float) – Factor describing percentage of imaging slice excluded from rim computation. sliceHeightPx (float) – Height of slice ROI in px. clean (bool) – Will remove all ROIs from embryo object before creating new ones. masterROI (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI that is supposed to be used as a masterROI. bleachedROI (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI that is supposed to be used to indicate the bleached region. rimROI (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI that is substracted from Slice. Should lie withing Slice.
Returns:	Updated list of ROIs.
Return type:	list

geometry2Full()¶

If current geometry was in quadrant reduced version, converts it to full version, keeping essential parameters the same.

Warning

Quadrant reduction is still experimental.

Note

Will set fnGeo back to default value in meshfiles folder.

Returns:	Updated geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.geometry

geometry2Quad()¶

Converts current geometry to quadrant reduced version if available, keeping essential parameters the same.

If geometry is already reduced or there is no quadrant version of the geometry, will do nothing and return unchanged geometry.

Warning

Quadrant reduction is still experimental.

Note

Will set fnGeo back to default value in meshfiles folder.

Returns:	Updated geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.geometry

getDataEnc()¶: Returns current data encoding.

getDataFT()¶: Returns current data filetype.

getDataFolder()¶: Returns folder containing recovery data files.

getDataResMu()¶: Returns resolution of data in $\um m$ .

getDataResPx()¶: Returns resolution of data in px.

getFileList()¶: Returns list of recovery data files.

getFitByName(name)¶

Returns fit in fits list with name name.

If it doesn’t exists, returns None.

getFrameInterval()¶: Returns current frame interval.

getFreeROIId()¶: Returns first free ID of ROIs.

getGeometry()¶

Returns embryo’s geometry object.

Returns:	PyFRAP geometry object.
Return type:	pyfrp.subclasses.pyfrp_geometry.geometry

getInterpolationError()¶: Prints out interpolation error by ROI.

getMasterROI()¶: Returns master ROI.

getMasterROIIdx()¶: Returns index of master ROI

getNFrames()¶: Returns current number of frames.

getName()¶: Returns embryo name.

getOptimalAllROI(name='All', makeNew=False)¶: Readjusts ROI with name All (if existent) to cover whole geometry.

getROIById(Id)¶

Returns ROI in ROIs list with specified ID.

If ROI with Id does not exist, returns None.

getROIByName(name)¶

Returns ROI in ROIs list with specified name.

If ROI with name does not exist, returns None.

getROIIdx(r)¶

Returns index of ROI in ROIs list.

Returns -1 if ROI is not in list.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – Some ROI.
Returns:	Index of ROI.
Return type:	int

getROIs()¶: Returns ROIs list.

getRimFactorByPx(radius, px)¶

Returns the correct rimFactor if a rim of with px in a ROI of radius radius is desired.

Rim factor $f$ is then given by:

\[f=1-\frac{p}{r},\]

where $p$ is px and $r$ is radius.

Parameters:	radius (float) – Radius of ROI. px (float) – Number of pixels that rim should be wide.
Returns:	Calculated rim factor.
Return type:	float

getTEnd()¶: Returns current exerpiment end time.

getTStart()¶: Returns current exerpiment start time.

getTvecData()¶: Returns current data time vector.

isAnalyzed()¶: Returns True if all ROIs have been analyzed.

isFitted()¶: Returns True if all fits are fitted.

isSimulated()¶: Returns True if all ROIs have been simulated.

listROIs()¶: Prints out all ROIs and their respective type.

loadDataImg(idx)¶

Loads data image in fnDatafolder of index idx.

Parameters:	idx (int) – Index of data image to be loaded.
Returns:	Loaded image.
Return type:	numpy.ndarray

makeQuadReducable(auto=False, debug=False)¶

Makes embryo quadrant reducable by:

Checks if embryo is reducable to quadrant by checking if all ROIs are either point or axis symmetric around geometry center.

Tries to readjust non-symmetric ROIs to make them quad-reducable.

If ROIs are reducable, will center geometry at center of data image.

Note

You want to call :py:meth:showAllROIBoundaries and :py:meth:computeROIIdxs afterwards to make sure everything went properly.

Warning

Quadrant reduction is still experimental.

Keyword Arguments:
	tryFix (bool) – Tries to readjust ROIs into reducable form. auto (bool) – Readjust ROIs automatically. debug (bool) – Print debugging messages.
Returns:	True if embryo is reducable.
Return type:	bool

newAnalysis()¶

Creates new pyfrp.subclasses.pyfrp_analysis.analysis object and sets it as embryos’s analysis.

Returns:	New analysis object.
Return type:	pyfrp.subclasses.pyfrp_analysis.analysis

newCustomROI(name, Id, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.customROI object and adds it to the ROIs list of embryo object.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.customROI

newFit(name)¶

Creates new fit object and appends it to list of fits.

Parameters:	name (str) – Name of fit object.
Returns:	Newly created fit.
Return type:	pyfrp.subclasses.pyfrp_fit.fit

newPolyROI(name, Id, corners, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.polyROI object and adds it to the ROIs list of embryo object.

Each corner given in corners list must be a list of form [x,y].

Note

Polygon is automatically closed.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. corners (list) – List of `[x,y]` coordinates describing corners.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.polyROI

newPolySliceROI(name, Id, corners, height, width, sliceBottom, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.polySliceROI object and adds it to the ROIs list of embryo object.

Each corner given in corners list must be a list of form [x,y].

Note

Polygon is automatically closed.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Note

If sliceBottom==True, slice ranges from height<=z<=height+width, otherwise from height-width/2<=z<=height+width/2.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. center (list) – Center of radial ROI. corners (list) – List of `[x,y]` coordinates describing corners. width (float) – width in z-direction of slice. sliceBottom (bool) – Put origin of slice at botton of slice.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.polySliceROI

newROI(name, Id, zmin='-inf', zmax='inf', color='b', asMaster=False)¶

Creates new simple pyfrp.subclasses.pyfrp_ROI.ROI object and adds it to the ROIs list of embryo object.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI.
	zmin (float) – Lower boundary in z-direction. zmax (float) – upper boundary in z-direction. color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.ROI

newRadialROI(name, Id, center, radius, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.radialROI object and adds it to the ROIs list of embryo object.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. center (list) – Center of radial ROI. radius (float) – Radius of radial ROI.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.radialROI

newRadialSliceROI(name, Id, center, radius, height, width, sliceBottom, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.radialSliceROI object and adds it to the ROIs list of embryo object.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Note

If sliceBottom==True, slice ranges from height<=z<=height+width, otherwise from height-width/2<=z<=height+width/2.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. center (list) – Center of radial ROI. radius (float) – Radius of radial ROI. height (float) – z-coordinate of slice. width (float) – width in z-direction of slice. sliceBottom (bool) – Put origin of slice at botton of slice.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.radialSliceROI

newRectangleROI(name, Id, offset, sidelengthX, sidelengthY, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.rectangleROI object and adds it to the ROIs list of embryo object.

Note

Offset is set to be left-bottom corner of rectangle.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. offset (list) – Offset of of square. sidelengthX (float) – Sidelength of rectangle in x-direction. sidelengthY (float) – Sidelength of rectangle in y-direction.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.rectangleROI

newRectangleSliceROI(name, Id, offset, sidelengthX, sidelengthY, height, width, sliceBottom, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.rectangleSliceROI object and adds it to the ROIs list of embryo object.

Note

Offset is set to be left-bottom corner of rectangle.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Note

If sliceBottom==True, slice ranges from height<=z<=height+width, otherwise from height-width/2<=z<=height+width/2.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. center (list) – Center of radial ROI. offset (list) – Offset of of rectangle. sidelengthX (float) – Sidelength of rectangle in x-direction. sidelengthY (float) – Sidelength of rectangle in y-direction. width (float) – width in z-direction of slice. sliceBottom (bool) – Put origin of slice at botton of slice.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.rectangleSliceROI

newSimulation()¶

Creates new pyfrp.subclasses.pyfrp_simulation.simulation object and sets it as embryos’s simulation.

Returns:	New simulation object.
Return type:	pyfrp.subclasses.pyfrp_simulation.simulation

newSliceROI(name, Id, height, width, sliceBottom, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.sliceROI object and adds it to the ROIs list of embryo object.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Note

If sliceBottom==True, slice ranges from height<=z<=height+width, otherwise from height-width/2<=z<=height+width/2.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. height (float) – z-coordinate of slice. width (float) – width in z-direction of slice. sliceBottom (bool) – Put origin of slice at botton of slice.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.sliceROI

newSquareROI(name, Id, offset, sidelength, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.squareROI object and adds it to the ROIs list of embryo object.

Note

Offset is set to be left-bottom corner of square.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. offset (list) – Offset of of square. sidelength (float) – Sidelength of square.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.squareROI

newSquareSliceROI(name, Id, offset, sidelength, height, width, sliceBottom, color='b', asMaster=False)¶

Creates new pyfrp.subclasses.pyfrp_ROI.squareSliceROI object and adds it to the ROIs list of embryo object.

Note

Offset is set to be left-bottom corner of square.

Note

You can use getFreeROIId() to find a unused ID for the newROI.

Note

If sliceBottom==True, slice ranges from height<=z<=height+width, otherwise from height-width/2<=z<=height+width/2.

Keyword Arguments:
Parameters:	name (str) – Name of new ROI. Id (int) – ID of new ROI. center (list) – Center of radial ROI. offset (list) – Offset of of square. sidelength (float) – Sidelength of square. width (float) – width in z-direction of slice. sliceBottom (bool) – Put origin of slice at botton of slice.
	color (str) – Color of ROI. asMaster (bool) – Set ROI as master ROI?
Returns:	Newly created ROI object.
Return type:	pyfrp.subclasses.pyfrp_ROI.squareSliceROI

pinAllROIs(bkgdVal=None, normVal=None, bkgdValSim=None, normValSim=None, useMin=False, useMax=False, debug=False)¶

Pins both simulation and data vectors of all ROIs.

Note

If no bkgdVal or normVal is giving, will try to compute it using computePinVals(). Only then input of useMax and useMin are relevant.

Keyword Arguments:
	bkgdVal (float) – Background value used for data pinning. normVal (float) – Norming value used for data pinning. bkgdValSim (float) – Background value used for simulation pinning. normValSim (float) – Norming value used for simulation pinning. useMin (bool) – Use minimum value for background computation. useMax (bool) – Use maximum value for norm value computation. debug (bool) – Print debugging messages.
Returns:	Updated list of ROIs.
Return type:	list

plotAllData(ax=None, legend=True)¶

Plots all data timeseries for all ROIs in ROIs list.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	ax (matplotlib.axes) – Axes to be plotted in. legend (bool) – Show legend in plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotAllDataPinned(ax=None, legend=True)¶

Plots all pinned data timeseries for all ROIs in ROIs list.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	ax (matplotlib.axes) – Axes to be plotted in. legend (bool) – Show legend in plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotAllSim(ax=None, legend=True)¶

Plots all simulation timeseries for all ROIs in ROIs list.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	ax (matplotlib.axes) – Axes to be plotted in. legend (bool) – Show legend in plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotAllSimPinned(ax=None, legend=True)¶

Plots all pinned simulation timeseries for all ROIs in ROIs list.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	ax (matplotlib.axes) – Axes to be plotted in. legend (bool) – Show legend in plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

printAllAttr(full=False)¶: Prints out all attributes of embryo object.

quickAnalysis(maxDExpPx=None, timeScale='log')¶

Performs complete FRAP analysis of embryo object including:

Finding ROI indices by calling computeROIIdxs() .

Running image analysis through pyfrp.subclasses.pyfrp_analysis.analysis.run() .

Generating optimal time simulation vector through pyfrp.subclasses.pyfrp_simulation.simulation.getOptTvecSim()

Running simulation through pyfrp.subclasses.pyfrp_simulation.simulation.run() .

Pin simulation and data timeseries using computeIdealFRAPPinVals() and pinAllROIs().

Run all fits in fits list by calling pyfrp.subclasses.pyfrp_fit.fit.run()

Keyword Arguments:
	maxDExpPx (float) – Maximum expected diffusion coefficient. timeScale (str) – Linear (`'lin'`) or logarithmic (`'log'`) time scaling.

removeROI(i)¶: Removes ROI of index i.

renameMeshFiles(fn=None, debug=False)¶

Renames meshfiles associated with embryo fo fn.

If fn=None will rename to self.name.ext, where ext is either .geo or .msh.

Example:

>>> emb.geometry.getFnGeo()
>>> path/to/meshfiles/dome.geo
>>> emb.getName()
>>> myEmbryo
>>> emb.renameMeshFiles()
>>> emb.geometry.getFnGeo()
>>> path/to/meshfiles/myEmbryo.geo

Note

Will automatically update geometry.fnGeo and simulation.mesh.fnMesh properties.

Keyword Arguments:

fn (str) – Desired filename.
debug (bool) – Print out debugging messages.

Returns:

Tuple containing:

fnGeoNew (str): New path to geometry file.

fnMeshNew (str): New path to mesh file.

Return type:

tuple

save(fn=None, copyMeshFiles=True, debug=False)¶

Saves embryo object to pickle file.

If fn=None will save to self.name.emb.

Keyword Arguments:
	fn (str) – Output filename. copyMeshFiles (bool) – Copy meshfiles to embryo file destination. debug (bool) – Print out debugging messages.
Returns:	Output filename.
Return type:	str

setDataEnc(e)¶: Sets data encoding of datasets, for example uint16 .

setDataFT(f)¶: Sets data filetype of datasets, for example .tif .

setDataFolder(fn)¶

Set folder containing recovery data files.

Will automatically try to update fileList by calling updateFileList().

Parameters:	fn (str) – Path to folder containing data files.
Returns:	New data folder path.
Return type:	str

setDataResMu(res)¶: Sets resolution of data in $\mu m$.

setDataResPx(res)¶: Sets resolution of data in px.

setEmbryo2Full()¶

Sets both geometry and ROIs to full mode.

Returns:	Tuple Containing: `self.geometry` (pyfrp.subclasses.pyfrp_geometry.geometry): Updated geometry. `self.ROIs` (list): Updated list of ROIs.
Return type:	tuple

setEmbryo2Quad()¶

Reduces both geometry and ROIs of embryo to quadrant, such that embryo object is fully quadrant reduced.

Returns:	Tuple Containing: `self.geometry` (pyfrp.subclasses.pyfrp_geometry.geometry): Updated geometry. `self.ROIs` (list): Updated list of ROIs.
Return type:	tuple

setFileList(l)¶: Sets file list to l.

setFrameInterval(dt)¶

Sets interval between imaging frames, then updates all time vectors.

Parameters:	dt (float) – New frame interval in seconds.
Returns:	Set frame interval.
Return type:	float

setGeometry2Ball(center, imagingRadius)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.ball.

Parameters:	center (list) – Center of geometry. imagingRadius (float) – Radius of embryo in imaging slice.
Returns:	New ball geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.ball

setGeometry2BallQuad(center, imagingRadius)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.ballQuad.

Warning

Quadrant reduction is still experimental.

Parameters:	center (list) – Center of geometry. imagingRadius (float) – Radius of embryo in imaging slice.
Returns:	New ball quadrant geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.ball

setGeometry2Cone(center, upperRadius, lowerRadius, height)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.cone.

Parameters:	center (list) – Center of geometry. upperRadius (float) – Radius at upper end of cone. lowerRadius (float) – Radius at lower end of cone. height (float) – Height of cylinder.
Returns:	New cone geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.cone

setGeometry2Custom(center, fnGeo='')¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.custom.

Parameters:	center (list) – Center of geometry. fnGeo (str) – Path to geometry file.
Returns:	New custom geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.custom

setGeometry2Cylinder(center, radius, height)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.cylinder.

Parameters:	center (list) – Center of geometry. radius (float) – Radius of cylinder. height (float) – Height of cylinder.
Returns:	New cylinder geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.cylinder

setGeometry2CylinderQuad(center, radius, height)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.cylinderQuad.

Warning

Quadrant reduction is still experimental.

Parameters:	center (list) – Center of geometry. radius (float) – Radius of cylinder. height (float) – Height of cylinder.
Returns:	New cylinder quadrant geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.cylinder

setGeometry2ZebraFishDomeStage(center, imagingRadius, radiusScale=1.1)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStage.

Keyword Arguments:
Parameters:	center (list) – Center of geometry. imagingRadius (float) – Radius of embryo at imaging slice.
	radiusScale (float) – Scaling factor defining how much bigger outer radius is to inner radius.
Returns:	New zebrafish geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStage

setGeometry2ZebraFishDomeStageQuad(center, imagingRadius, radiusScale=1.1)¶

Sets embryo’s geometry to pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStageQuad.

Warning

Quadrant reduction is still experimental.

Keyword Arguments:
Parameters:	center (list) – Center of geometry. imagingRadius (float) – Radius of embryo at imaging slice.
	radiusScale (float) – Scaling factor defining how much bigger outer radius is to inner radius.
Returns:	New zebrafish quadrant geometry.
Return type:	pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStageQuad

setMasterROIIdx(idx)¶: Define ROI with index idx as master ROI.

setNFrames(n)¶: Sets number of frames, then updates all time vectors.

setName(n)¶: Sets embryo name.

setSideLengthBleachedMu(s)¶: Sets sidelength of bleached square in $\mu m$, will then update bleached region parameters by calling updateBleachedRegion().

Warning

Attributes sideLengthBleachedMu and offsetBleachedPx will deprecated in further versions. Bleached region definition will then solely rely on ROI definitions.

setSliceDepthMu(d, updateGeometry=True)¶

Sets resolution of data in $\mu m$ and then updates all dimensions of embryo object data rely on sliceDepthMu.

If updateGeometry is selected, will automatically update geometry.

Note

Not every geometry depends on slice depth. If the geometry object has a method restoreDefault, this will be called. Otherwise geometry is not updated.

Keyword Arguments:
Parameters:	d (float) – New slice depth in $\mu m$.
	updateGeometry (bool) – Update geometry.
Returns:	New slice depth.
Return type:	float

setTEnd(t)¶

Sets end time of experiment, then updates all time vectors.

Parameters:	t (float) – End time in seconds.
Returns:	Set end time.
Return type:	float

setTStart(t)¶

Sets start time of experiment, then updates all time vectors.

Parameters:	t (float) – Start time in seconds.
Returns:	Set start time.
Return type:	float

showAllROIBoundaries(ax=None, withImg=False, idx=0)¶

Shows boundaries of all ROIs in ROIs list.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	ax (matplotlib.axes) – Axes to be plotted in. withImg (bool) – Shows data image inside same axes. idx (int) – Index of data image to be shown.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

showAllROIIdxs(axes=None)¶

Shows image, extended and mesh indices of all ROIs in ROIs list.

If no axes are given via axes, will create new list of matplotlib axes. If axes are given, they should have len(axes)=3*len(ROIs).

Note

If the mesh has a large number of nodes, this can take up a lot of memory due to every node being plotted in a matplotlib scatter plot.

Keyword Arguments:
	axes (list) – List of matplotlib.axes to be plotted in.
Returns:	List of axes used for plotting.
Return type:	matplotlib.axes

showDataImg(ax=None, idx=0)¶

Shows data image in fnDatafolder of index idx.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	idx (int) – Index of data image to be shown.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

sliceEmbryo(nSlices, direction='z')¶

Slices embryo in z-direction in nSlices.

Creates nSlices new pyfrp.subclasses.pyfrp_ROI.sliceROI ROIs and appends them to ROIs list.

Note

Slice ROIs will be created with sliceBottom=False.

Parameters:	nSlices (int) – Number of slices embryo is supposed to get cut.
Returns:	Updates `ROIs` list.
Return type:	list

updateBleachedRegion()¶

Updates sidelength and offset of bleached square in px.

Bleached region is defined around center of image. That is, the offset is set to

\[x_{\mathrm{offset}} = \frac{1}{2}(r_{\mathrm{px}},r_{\mathrm{px}})^T - (s_{\mathrm{bleached,px}},s_{\mathrm{bleached,px}})^T\]

where $r_{\mathrm{px}}$ is the resolution of the data in pixel, and $s_{\mathrm{bleached,px}$ is the sidelength of the bleached square in pixel.

Warning

Attributes sideLengthBleachedMu and offsetBleachedPx will deprecated in further versions. Bleached region definition will then solely rely on ROI definitions.

updateFileList()¶

Updates file list containing all names of recovery images.

If new fileList is not empty, will update number of frames to match number of files in fileList by calling updateNFrames().

Returns:	Updated file list.
Return type:	list

updateNFrames()¶

Updates number of frames, then updates all time vectors.

Note

Gets number of frames by reading number of files of type dataFT in fnDatafolder. So you should make sure that there are no extra files in fnDatafolder.

Returns:	Updated number of frames.
Return type:	int

updatePxDimensions()¶

Updates all all convFact relevant attributes.

Returns:	Tuple containing: sliceWidthPx (float): Updated slice width in px. sliceDepthPx (float): Updated slice depth in px. sliceHeightPx (float): Updated slice height in px. sideLengthBleachedPx (float): Updated side length of bleached square in px. offsetBleachedPx (float): Updated offset if bleached square in px.
Return type:	tuple

updateTimeDimensions(simUpdate=True)¶

Updates time dimensions using information in frameInterval, nFrames and tStart.

Note

If embryo object already possesses simulation object, will update simulation time dimensions using pyfrp.subclasses.pyfrp_simulation.simulation.toDefaultTvec() . If you have different settings in simulation vector that you want to preserve, select simUpdate=False.

Keyword Arguments:
	simUpdate (bool) – Update simulation time dimensions.
Returns:	Updated data time vector.
Return type:	numpy.ndarray

updateVersion()¶

Updates embryo object to current version, making sure that it possesses all attributes.

Creates a new embryo object and compares self with the new embryo object. If the new embryo object has a attribute that self does not have, will add attribute with default value from the new embryo object.

Returns:	`self`
Return type:	pyfrp.subclasses.pyfrp_embryo.embryo

pyfrp.subclasses.pyfrp_fit module¶

Essential PyFRAP module containing pyfrp.subclasses.pyfrp_fit.fit class.

class pyfrp.subclasses.pyfrp_fit.fit(embryo, name)¶

Main fit class of PyFRAP.

The purpose of the fit class is to save all attributes used for fitting PyFRAP simulation results to data analysis results. The main attributes are:

Fitting algorithm specifics:

Fitting algorithm, see also setOptMeth().

Stopping criteria, see also setMaxfun() and setOptTol().

Initial guess, see also getX0().

Boundaries, see also getBounds().

Fitting options:

fitProd, see also getFitProd().

fitDegr, see also getFitDegr().

fitPinned, see also getFitPinned().

equOn, see also getEqu().

fitCutOffT, see also getFitCutOffT().

The ROIs to be fitted, see also getROIsFitted().

Fitting results, see also printResults().

Fitted vectors.

The most important methods are:

run(): Runs fitting.

addROIByName(): Adds ROI to be used for fitting.

getX0(): Builds and returns current initial guess.

getBounds(): Builds and returns current bounds.

computeStats(): Compares post-fitting statistics.

The fit uses simulation and data vectors stored in all pyfrp.subclasses.pyfrp_ROI.ROI objects defined in ROIsFitted list to compute the optimal values for DOptMu (prodOpt or degrOpt if fitProd or fitDegr is selected, respectively).

After calling run(), will automatically compute proper x0 via getX0() and getBounds().

Parameters:	embryo (pyfrp.subclasses.pyfrp_embryo.embryo) – Embryo object that fit belongs to. name (str) – Name of fit.

addROI(r)¶

Adds ROI to the list of fitted ROIs.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI to be used for fitting.
Returns:	Updated list of ROIs used for fitting.
Return type:	list

addROIById(Id)¶

Adds ROI to the list of fitted ROIs, given a specific ROI Id.

Parameters:	Id (int) – Id of ROI to be used for fitting.
Returns:	Updated list of ROIs used for fitting.
Return type:	list

addROIByName(name)¶

Adds ROI to the list of fitted ROIs, given a specific name.

Parameters:	name (str) – Name of ROI to be used for fitting.
Returns:	Updated list of ROIs used for fitting.
Return type:	list

assignOptParms(res)¶

Assigns optimal parameters found by optimization algorithm to attributes in fit object depending on fit options chosen.

Parameters:	res (list) – Result array from optimization algorithm.
Returns:	Tuple containing: DOptPx (float): Optimal diffusion coefficient in $\frac{\mathrm{px}^2}{s}}$. prod (float): Optimal production rate in $\frac{\[c\]}{s}}$. degr (float): Optimal degradation rate in $\frac{1}{s}}$. DOptMu (float): Optimal diffusion coefficient in $\frac{\mu\mathrm{m}^2}{s}}$.
Return type:	tuple

checkPinned()¶

Checks if all ROIs in ROIsFitted have been pinned.

Returns:	`True` if all ROIs have been pinned, `False` else.
Return type:	bool

checkSimulated()¶

Checks if all ROIs in ROIsFitted have been simulated.

Returns:	`True` if all ROIs have been simulated, `False` else.
Return type:	bool

computeStats()¶

Computes stastics for fit.

Statistics include:

MeanRsq

Rsq

RsqByROI

getBounds()¶

Generates tuple of boundary tuples, limiting parameters varied during SSD minimization.

Will generate exactly the boundary tuple that is currently useful to the optimization algorithm, meaning that only values that are needed since they are turned on via fitProd or fitDegr will be included into tuple.

Will use values that are stored in LBx and UBx, where x is D, Prod, or Degr for the creation of the tuples.

Will also add a tuple of bounds defined via LBEqu and UBEqu for each ROI in ROIsFitted.

Note

Always gets executed at the start of run.

Returns:	Boundary value tuple.
Return type:	tuple

getBruteInitDArray(steps=5)¶

Generates array of different possibilities to be used as initial guess for D.

If LBD and UBD is given, will simply divide the range between the two in 4 equidistant values. Otherwise will vary around x0 in 2 orders of magnitude.

Keyword Arguments:
	steps (int) – How many initial guesses to generate.
Returns:	Array with possible initial guesses for D.
Return type:	list

getCutOffT()¶

Returns timepoint at which timeseries are cut if fitCutOffT is turned on.

Warning

This option is currently VERY experimental. Fitting might crash.

Returns:	Timepoint.
Return type:	float

getEqu()¶

Returns equalization flag.

Returns:	Current flag value.
Return type:	bool

getFitCutOffT()¶

Returns flag controlling if only the first cutOffT timesteps are supposed to be fitted.

Warning

This option is currently VERY experimental. Fitting might crash.

Returns:	Current flag value.
Return type:	bool

getFitDegr()¶

Returns flag controlling if a degredation term is supposed to be used for fitting.

Returns:	Current flag value.
Return type:	bool

getFitPinned()¶

Returns flag controlling if pinned timeseries are supposed to be used for fitting.

Returns:	Current flag value.
Return type:	bool

getFitProd()¶

Returns flag controlling if a production term is supposed to be used for fitting.

Returns:	Current flag value.
Return type:	bool

getFittedParameterNames()¶

Returns names of parameters that are selected for fitting.

Returns:	Names of parameters fitted.
Return type:	list

getKineticTimeScale()¶

Returns the kinetic time scale factor used for fitting.

Returns:	Current kinetic time scale factor.
Return type:	float

getLBD()¶

Returns the lower bound for the diffusion rate.

Returns:	Current lower bound for diffusion rate.
Return type:	float

getLBDegr()¶

Returns the lower bound for the degradation rate.

Returns:	Current lower bound for degradation rate.
Return type:	float

getLBProd()¶

Returns the lower bound for the production rate.

Returns:	Current lower bound for production rate.
Return type:	float

getMaxfun()¶

Returns maximum number of function evaluations at which optimization algorithm stops.

Returns:	Current maximum number of function evaluations.
Return type:	int

getNParmsFitted(inclEqu=True)¶

Returns the number of parameters fitted in this fit.

Note

If equlalization is turned on, each ROI in ROIsFitted counts as an extra parameter.

Example: We fit production and equalization for 2 ROIs, then we have fitted

D

degradation

equalization ROI 1

equalization ROI 2

leading to in total 4 fitted parameters.

Keyword Arguments:
	inclEqu (bool) – Include equalization as additional fitted parameter.
Returns:	Number of parameters fitted.
Return type:	int

getName()¶

Returns name of fit.

Returns:	Name of fit.
Return type:	str

getOptMeth()¶

Returns the currently used optimization algorithm.

Returns:	Optimization algorithm.
Return type:	str

getOptTol()¶

Returns tolerance level at which optimization algorithm stops.

Returns:	Current tolerance level.
Return type:	float

getROIsFitted()¶

Returns list of ROIs used for fitting.

Returns:	list of ROIs used for fitting.
Return type:	list

getSaveTrack()¶

Returns flag controlling if whole fitting process is supposed to be saved in fit object.

Returns:	Current flag value.
Return type:	bool

getUBD()¶

Returns the upper bound for the diffusion rate.

Returns:	Current upper bound for diffusion rate.
Return type:	float

getUBDegr()¶

Returns the upper bound for the degradation rate.

Returns:	Current upper bound for degradation rate.
Return type:	float

getUBProd()¶

Returns the upper bound for the production rate.

Returns:	Current upper bound for production rate.
Return type:	float

getX0()¶

Returns initial guess of fit in the form that is useful for the call of the optimization algorithm.

Copies x0 into local variable to pass to solver, pop entries that are currently not needed since they are turned off via fitProd or fitDegr.

Always appends initial guess for equalization factors, even though they might not been used.

Note

Always gets executed at the start of run.

Returns:	Currently used x0.
Return type:	list

getX0D()¶

Returns the initial guess for the diffusion rate.

Returns:	Initial guess for diffusion rate.
Return type:	float

getX0Degr()¶

Returns the initial guess for the degradation rate.

Returns:	Initial guess for degration rate.
Return type:	float

getX0Equ(x)¶

Returns the initial guess for the equalization factor for all ROIs fitted.

Returns:	Initial guess for equalization factor.
Return type:	list

getX0Prod()¶

Returns the initial guess for the production rate.

Returns:	Initial guess for production rate.
Return type:	float

isFitted()¶

Checks if fit already has been run and succeeded.

Returns:	`True` if success.
Return type:	bool

plotFit(ax=None, legend=True, title=None, show=True)¶

Plots fit, showing the result for all fitted ROIs.

Note

If no ax is given, will create new one.

Keyword Arguments:
	ax (matplotlib.axes) – Axes used for plotting. legend (bool) – Show legend. title (str) – Title of plot. show (bool) – Show plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotLikehoodProfiles(epsPerc=0.1, steps=100, debug=False)¶

Plots likelihood profiles for all fitted parameters.

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.

Keyword Arguments:
	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

printAllAttr()¶: Prints out all attributes of fit object.

printResults()¶: Prints out main results of fit.

printRsqByROI()¶: Prints out Rsq value per ROI.

removeROI(r)¶

Removes ROI from the list of fitted ROIs.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI to be removed.
Returns:	Updated list of ROIs used for fitting.
Return type:	list

reset2DefaultX0()¶

Resets initial guess x0 to its default form.

The default form of x0 is

>>> [10., 0. ,0. , 1.,1.,1.]

The last entries are the initial guess of equlalization factors and is set to be list of of ones of the same length of ROIsFitted.

Returns:	New initial guess x0.
Return type:	list

resultsToDict()¶: Extracts all important results into dictionary, making it easier for printout or csv extraction.

resultsToVec()¶

Puts results back in vector as optimization algorithm would return it.

Returns:	Result vector.
Return type:	list

run(debug=False, ax=None)¶

Runs fit.

Fitting is done by passing fit object to pyfrp.modules.pyfrp_fit_module.FRAPFitting(). This function then calls all necessary methods of fit to prepare it for optimization and then passes it to optimization algorithm.

Note

If bruteInitD is turned on, will execute runBruteInit() instead.

Keyword Arguments:
	debug (bool) – Print debugging messages. ax (matplotlib.axes) – Axes to show debugging plots in.
Returns:	`self`.
Return type:	pyfrp.subclasses.pyfrp_fit.fit

runBruteInit(debug=False, ax=None, steps=5, x0Ds=[])¶

Runs fit for different initial guesses of the diffusion constant D, then selects the one that actually yielded the minimal SSD.

Initially guesses are generated with getBruteInitDArray() if no array x0Ds is given.

Fitting is done by passing fit object to pyfrp.modules.pyfrp_fit_module.FRAPFitting(). This function then calls all necessary methods of fit to prepare it for optimization and then passes it to optimization algorithm.

Will select the initial guess that yielded the minimal SSD and then rerun with this x0 again, making sure that everything is updated in fit object.

Keyword Arguments:
	debug (bool) – Print debugging messages. ax (matplotlib.axes) – Axes to show debugging plots in. steps (int) – How many initial guesses to generate. x0Ds (list) – Array with possible initial guesses for D.
Returns:	`self`.
Return type:	pyfrp.subclasses.pyfrp_fit.fit

setBruteInitD(b)¶

Turns on/off if the initial guess of for the diffusion rate D should be bruteforced.

Parameters:	b (bool) – Flag value.
Returns:	Current flag value.
Return type:	bool

setCutOffT(t)¶

setEqu(b)¶

Turns on/off equalization.

Parameters:	b (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setFitCutOffT(b)¶

Turns on/off if only a certain fraction of the timeseries is supposed to be fitted.

Warning

This option is currently VERY experimental. Fitting might crash.

Parameters:	b (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setFitDegr(b)¶

Turns on/off if degradation is supposed to be considered in fit.

Parameters:	b (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setFitPinned(b)¶

Turns on/off if pinned series are supposed to be fitted.

Parameters:	b (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setFitProd(b)¶

Turns on/off if production is supposed to be considered in fit.

Parameters:	b (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setKineticTimeScale(s)¶

Sets the kinetic time scale factor used for fitting.

Parameters:	s (float) – New kinetic time scale factor.

setLBD(b)¶

Sets the lower bound for the diffusion rate.

Parameters:	b (float) – New lower bound for diffusion rate.

setLBDegr(b)¶

Sets the lower bound for the degradation rate.

Parameters:	b (float) – New lower bound for degradation rate.

setLBProd(b)¶

Sets the lower bound for the production rate.

Parameters:	b (float) – New lower bound for production rate.

setMaxfun(m)¶

Sets maximum number of function evaluations at which optimization algorithm stops.

Parameters:	m (int) – New maximum number of function evaluations.

setName(s)¶

Sets name of fit.

Parameters:	s (str) – New name of fit.

setOptMeth(m)¶

Sets optimization method.

Available optimization methods are:

Constrained Nelder-Mead

Nelder-Mead

TNC

L-BFGS-B

SLSQP

brute

BFGS

CG

You can find out more about the constrained Nelder-Mead algorithm in the documentation of pyfrp.modules.pyfrp_optimization_module.constrObjFunc().

Parameters:	m (str) – New method.

setOptTol(m)¶

Sets tolerance level at which optimization algorithm stops.

Parameters:	m (float) – New tolerance level.

setSaveTrack(b)¶

Turns on/off if fitting process is supposed to be stored.

This then can then be used to following the convergence of the optimization algorithm and possibly to identify local minima.

Parameters:	b (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setUBD(b)¶

Sets the upper bound for the diffusion rate.

Parameters:	b (float) – New upper bound for diffusion rate.

setUBDegr(b)¶

Sets the upper bound for the degradation rate.

Parameters:	b (float) – New upper bound for degradation rate.

setUBProd(b)¶

Sets the upper bound for the production rate.

Parameters:	b (float) – New upper bound for production rate.

setX0(x)¶

Sets the initial guess x0.

Argument x needs to have length 3, otherwise it is being rejected.

Note

If fitProd or fitDegr are not chosen, the values in x0 are going to be used as static parameters.

Parameters:	x (list) – New desired initial guess.
Returns:	New initial guess.
Return type:	list

setX0D(x)¶

Sets the initial guess for the diffusion rate.

Parameters:	x (float) – Initial guess for diffusion rate.

setX0Degr(x)¶

Sets the initial guess for the degradation rate.

Parameters:	x (float) – Initial guess for degradation rate.

setX0Equ(x)¶

Sets the initial guess for the equalization factor.

Note

Does this for all ROIs in ROIsFitted.

Parameters:	x (float) – Initial guess for equalization factor.

setX0Prod(x)¶

Sets the initial guess for the production rate.

Parameters:	x (float) – Initial guess for production rate.

updateVersion()¶

Updates fit object to current version, making sure that it possesses all attributes.

Creates a new fit object and compares self with the new fit object. If the new fit object has a attribute that self does not have, will add attribute with default value from the new fit object.

Returns:	`self`
Return type:	pyfrp.subclasses.pyfrp_fit.fit

pyfrp.subclasses.pyfrp_geometry module¶

PyFRAP module containing geometry classes. The geometry class is a simple geometry class providing basic parameters and methods, parenting different more specific geometries such as:

zebrafishDomeStage: Describing a zebrafish embryo in dome stage.

cylinder: A simple cylinder.

xenopusBall: A simple ball geometry (looking like a xenopus embryo).

cone: A (truncated) cone.

For most of the geometries, this module also provides quadrant reduced versions of the geometry, reducing the geometry to the first quadrant around the center.

Note

Rules for adding new geometries:

Always subclass from geometry
Always center geometry around geometry.center. That includes having defining the center in the .geo file by center_x and center_y.
Unit is pixels.
Be careful with method overwrites. Use them wisely.
Include the geometries into the GUI.
Make them accessable by sharing them.

class pyfrp.subclasses.pyfrp_geometry.cone(embryo, center, upperRadius, lowerRadius, height)¶

Bases: pyfrp.subclasses.pyfrp_geometry.geometry

Geometry describing a cut-off cone.

The crucial geometrical parameters are:

upperRadius: Upper radius of cone.

lowerRadius: Lower radius of cone.

height: Height of cone.

Note

Can also be extended to a real cone by setting lowerRadius=0.

computeRadiusFromSliceHeight(height)¶

Returns the slice radius given a slice height.

Slice radius is computed by

\[r(s) = \frac{l-u}{h} s +u\]

where $l,u$ are lower and upper radius respectively and $h$ is cone height.

Parameters:	height (float) – Slice height.
Returns:	Slice radius.
Return type:	float

computeSliceHeightFromRadius(radius)¶

Returns the slice height given a slice radius.

Slice height is computed by

\[s(r) = \frac{h}{l-u} (r-u)\]

where $l,u$ are lower and upper radius respectively and $h$ is cone height.

Parameters:	radius (float) – Slice radius.
Returns:	Slice height.
Return type:	float

genAsOpenscad()¶

Generates cone geometry as solid python object.

Useful if geometry is used to be passed to openscad.

Returns:

getHeight()¶: Returns cone radius.

getLowerRadius()¶: Returns lower radius of cone.

getUpperRadius()¶: Returns upper radius of cone.

getXYExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getXYExtend().

By default, cone geometry is set to range from center[i]-max([self.upperRadius,self.lowerRadius]) to center[i]+max([self.upperRadius,self.lowerRadius]).

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate.
Return type:	tuple

getZExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getZExtend().

By default, cone geometry is set to range from -height to 0.

optimalAllROI(name='', Id=0, color='b', asMaster=False, roi=None)¶

Sets optimal ROI to a pyfrp.subclasses.pyfrp_ROI.radialSliceROI with radius upperRadius, center center, covering the whole z-range of geometry.

Keyword Arguments:
	name (str) – Name of new ROI. Id (int) – ID of new ROI. color (str) – Color that ROI is going to be associated with. asMaster (bool) – Make new ROI masterROI.

setHeight(h)¶

Sets cone height.

Parameters:	h (float) – New height.
Returns:	New height.
Return type:	float

setLowerRadius(r)¶

Sets cone lower radius.

Parameters:	h (float) – New lower radius.
Returns:	New lower radius.
Return type:	float

setUpperRadius(r)¶

Sets cone upper radius.

Parameters:	h (float) – New upper radius.
Returns:	New upper radius.
Return type:	float

updateGeoFile(debug=False)¶

Updates .geo file of geometry.

Keyword Arguments:
	debug (bool) – Print debugging messages.

class pyfrp.subclasses.pyfrp_geometry.custom(embryo, center, fnGeo)¶

Bases: pyfrp.subclasses.pyfrp_geometry.geometry

Custom geometry class for custom geometry configurations.

class pyfrp.subclasses.pyfrp_geometry.cylinder(embryo, center, radius, height)¶

Bases: pyfrp.subclasses.pyfrp_geometry.geometry

Geometry describing a cylinder.

The crucial geometrical parameters are:

radius: Radius of the cylinder.

height: Height of the cylinder.

genAsOpenscad()¶

Generates cylinder geometry as solid python object.

Useful if geometry is used to be passed to openscad.

Returns:

getHeight()¶

Returns cylinder height.

Returns:	Height.
Return type:	float

getRadius()¶

Returns cylinder radius.

Returns:	Radius.
Return type:	float

getXYExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getXYExtend().

By default, cylinder geometry is set to range from center[i]-radius to center[i]+radius.

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate.
Return type:	tuple

getZExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getZExtend().

By default, cylinder geometry is set to range from -height to 0.

optimalAllROI(name='', Id=0, color='b', asMaster=False, roi=None)¶

Sets optimal ROI to a pyfrp.subclasses.pyfrp_ROI.radialSliceROI with radius radius, center center, covering the whole z-range of geometry.

Keyword Arguments:
	name (str) – Name of new ROI. Id (int) – ID of new ROI. color (str) – Color that ROI is going to be associated with. asMaster (bool) – Make new ROI masterROI.

setHeight(h)¶

Sets cylinder height.

Parameters:	h (float) – New height.
Returns:	New height.
Return type:	float

setRadius(r)¶

Sets cylinder radius.

Parameters:	h (float) – New radius.
Returns:	New radius.
Return type:	float

updateGeoFile(debug=False)¶

Updates .geo file of geometry.

Keyword Arguments:
	debug (bool) – Print debugging messages.

class pyfrp.subclasses.pyfrp_geometry.cylinderQuad(embryo, center, radius, height)¶

Bases: pyfrp.subclasses.pyfrp_geometry.cylinder

Geometry describing a cylinder, reduced to first quadrant.

Inherits from cylinder. Please refer to its documentation for further details.

class pyfrp.subclasses.pyfrp_geometry.geometry(embryo, typ, fnGeo, center)¶

Bases: object

Basic PyFRAP geometry class.

Stores all the necessary information to describe a geometry. Comes with helpful methods for

Centering (see centerInImg())

Defining optimal ROIs (see optimalAllROI())

Geometry file parsing (see readGeoFile())

Geometry plotting (see plotGeometry())

Parameters:	embryo (pyfrp.subclasses.pyfrp_emrbyo.embryo) – Embryo class that geometry belongs to. typ (str) – Type of geometry. fnGeo (str) – Path to gmsh .geo file describing the geometry. center (numpy.ndarray) – Center of geometry.

center2Mid(updateInFile=True)¶

Sets geometry center to center of image.

Uses embryo.dataResPx to calculate image center.

Note

The geometry center attribute is then also set in .geo file if updateInFile is selected.

Keyword Arguments:
	updateInFile (bool) – Update center in .geo file.
Returns:	New center.
Return type:	numpy.ndarray

centerInImg()¶

Sets geometry center to center of image, updates .geo file and if avaialable remeshes.

Uses embryo.dataResPx to calculate image center.

Note

The geometry center attribute is then also set in .geo file.

Returns:	New center.
Return type:	numpy.ndarray

getCenter()¶

Returns geometry center.

Returns:	New center.
Return type:	numpy.ndarray

getEmbryo()¶: Returns pyfrp.subclasses.pyfrp_embryo.embryo instance that geometry belongs to.

getExtend()¶

Returns extend in x/y/z-direction.

Will call getXYExtend() and getZExtend() for it.

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate. zmin (float): Minimum z-coordinate. zmax (float): Maximum z-coordinate.
Return type:	tuple

getFnGeo()¶

Returns path to .geo file.

Returns:	Path to file.
Return type:	str

getMaxGeoID()¶

Returns maximum ID over all elements in .geo file.

Sell also readGeoFile() and pyfrp.modules.pyfrp_gmsh_geometry.domain.getAllMaxID().

Returns:	Maximum ID.
Return type:	int

getTyp()¶

Returns type of geometry.

Returns:	Type of geometry.
Return type:	str

getXYExtend()¶

Returns extend in x/y-direction by reading out vertices from .geo file and returning maximum and minimum x/y-coordinates.

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate.
Return type:	tuple

getZExtend()¶

Returns extend in z-direction by reading out vertices from .geo file and returning maximum and minimum z-coordinates.

Returns:	Tuple containing: zmin (float): Minimum z-coordinate. zmax (float): Maximum z-coordinate.
Return type:	tuple

moveGeoFile(fn)¶

Moves geometry file to different directory.

Note

This function actually copies the file so that files in pyfrp/meshfiles/ will not be removed.

Will update geometry.fnGeo to the new file location.

Note

If existent, will also copy the corresponding mesh file.

Parameters:	fn (str) – Path of folder where geo file is supposed to go.
Returns:	New file location.
Return type:	str

plotGeometry(ax=None, color='k', ann=False)¶

Plots geometry in 3D.

Reads the .geo file and parses it into a pyfrp.modules.pyfrp_gmsh_geometry.domain instance. Then draws the domain.

If no axes are given via ax, will create new matplotlib axes.

Keyword Arguments:
	ax (matplotlib.axes) – Axes to draw in. color (str) – Color of plot. ann (bool) – Show annotations.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

printDetails()¶: Prints out all details of geometry object.

readGeoFile()¶

Reads the .geo file and parses it into a pyfrp.modules.pyfrp_gmsh_geometry.domain instance.

Returns:	Domain containing geometry.
Return type:	`pyfrp.modules.pyfrp_gmsh_geometry.domain`

render2Openscad(fn=None, segments=48)¶

Generates .scad file for the geometry.

Note

If fn=None, then will use the same filename and path as .geo file.

Keyword Arguments:
	fn (str) – Output filename. segments (int) – Number of segments used for convex hull of surface.

render2Stl(fn=None, segments=48)¶

Generates .stl file for the geometry.

Note

If fn=None, then will use the same filename and path as .geo file.

Keyword Arguments:
	fn (str) – Output filename. segments (int) – Number of segments used for convex hull of surface.

setAllROI(name='All', makeNew=False, updateIdxs=False)¶

Tries to set the optimal All ROI for a specific geometry.

Keyword Arguments:
	name (str) – Name of All ROI to look for. makeNew (bool) – Generate a new All ROI. updateIdxs (bool) – Update indices of new ROI.
Returns:	New ROI instance.
Return type:	pyfrp.subclasses.pyfrp_ROI.ROI

setCenter(c, updateInFile=True)¶

Sets geometry center.

Note

The geometry center attribute is then also set in .geo file if updateInFile is selected.

Keyword Arguments:
Parameters:	c (numpy.ndarray) – New center `[x,y]`.
	updateInFile (bool) – Update center in .geo file.
Returns:	New center.
Return type:	numpy.ndarray

setFnGeo(fn)¶

Sets path to .geo file.

Parameters:	fn (str) – Path to file.
Returns:	Path to file.
Return type:	str

class pyfrp.subclasses.pyfrp_geometry.xenopusBall(embryo, center, imagingRadius)¶

Bases: pyfrp.subclasses.pyfrp_geometry.geometry

Geometry describing a ball.

This geometry is similar to a xenopus in stage 7-10, see http://www.xenbase.org/anatomy/alldev.do.

The crucial geometrical parameters are:

radius: Radius of the ball.

PyFRAP automatically computes these parameters given

imagingRadius: Radius of embryo at imaging depth.

imagingHeight: Imaging depth.

For details of this computations, see computeBall().

computeBall()¶

Computes ball geometry from imagingRadius and imagingHeight.

Computes ball geometry as follows:

\[r=\frac{r_{\mathrm{imaging}}^2+h_{\mathrm{imaging}}^2}{-2 h_{\mathrm{imaging}}},\]

where $r$ is the radius, $h_{\mathrm{imaging}}$ is the imagingHeight and $r_{\mathrm{imaging}}$ is the imagingRadius.

The center of the ball is set to [center[0],center[1],-radius] (Only in .geo file).

getImagingHeight(h)¶: Returns imaging height.

getImagingRadius(r)¶: Returns imaging radius.

getRadius()¶: Returns ball radius.

getXYExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getXYExtend().

By default, ball geometry is set to range from center[i]-radius to center[i]+radius.

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate.
Return type:	tuple

getZExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getZExtend().

By default, ball geometry is set to range from -imagingRadius to 0.

optimalAllROI(name='', Id=0, color='b', asMaster=False, roi=None)¶

Sets optimal ROI to a pyfrp.subclasses.pyfrp_ROI.radialSliceROI with radius radius, center center, covering the whole z-range of geometry.

Keyword Arguments:
	name (str) – Name of new ROI. Id (int) – ID of new ROI. color (str) – Color that ROI is going to be associated with. asMaster (bool) – Make new ROI masterROI.

restoreDefault()¶

Restores default values.

Only default value of ball geometry is that imagingHeight is set to be equal to sliceHeightPx of embryo.

setImagingHeight(h)¶

Sets imaging height and updates ball geometry.

Parameters:	h (float) – New height.
Returns:	New height.
Return type:	float

setImagingRadius(r)¶

Sets imaging radius and updates ball geometry.

Parameters:	h (float) – New radius.
Returns:	New radius.
Return type:	float

updateGeoFile(debug=False)¶

Updates .geo file of geometry.

Keyword Arguments:
	debug (bool) – Print debugging messages.

class pyfrp.subclasses.pyfrp_geometry.xenopusBallQuad(embryo, center, imagingRadius)¶

Bases: pyfrp.subclasses.pyfrp_geometry.xenopusBall

Geometry describing a ball, reduced to first quadrant.

Inherits from xenopusBall. Please refer to its documentation for further details.

Warning

meshfiles/quad_ball.geo does not exist yet.

class pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStage(embryo, center, imagingRadius, radiusScale=1.1)¶

Bases: pyfrp.subclasses.pyfrp_geometry.geometry

Geometry describing a zebrafish embryo in dome stage.

For information about zebrafish stages, see http://onlinelibrary.wiley.com/doi/10.1002/aja.1002030302/abstract;jsessionid=1EAD19FE5563DAA94E3C22C5D5BEEC85.f01t03.

The zebrafish geometry is basically described by two half-balls with different radii piled on top of each other. The crucial geometrical parameters are:

outerRadius: Radius of the outer ball.

innerRadius: Radius of the inner ball.

centerDist: Distance between the centers of the two balls.

PyFRAP automatically computes these parameters given

imagingRadius: Radius of embryo at imaging depth.

imagingHeight: Imaging depth.

radiusScale: Scaling factor between radii.

For details of this computations, see computeDome().

computeDome()¶

Updates zebrafish geometry.

Computes zebrafish geometry as follows:

\[\begin{split}r_{\mathrm{outer}}=\frac{r_{\mathrm{imaging}}^2+h_{\mathrm{imaging}}^2}{-2 h_{\mathrm{imaging}}},\\ r_{\mathrm{inner}}=s_{\mathrm{radius}}r_{\mathrm{outer}},\\ d_{\mathrm{center}}=\sqrt{r_{\mathrm{inner}}^2-r_{\mathrm{outer}}^2},\end{split}\]

where $r_{\mathrm{outer}}$ is the outerRadius, $r_{\mathrm{inner}}$ is the innerRadius, $h_{\mathrm{imaging}}$ is the imagingHeight and $r_{\mathrm{imaging}}$ is the imagingRadius.

Returns:	Tuple containing: innerRadius (float): New inner radius. centerDist (float): New distance between centers.
Return type:	tuple

genAsOpenscad()¶

Generates zebrafish geometry as solid python object.

Useful if geometry is used to be passed to openscad.

Returns:

getImagingHeight()¶: Returns imaging height.

getImagingRadius()¶: Returns imaging radius.

getInnerRadius()¶: Returns inner radius.

getOuterRadius()¶: Returns outer radius.

getRadiusScale()¶: Returns radius scaling factor.

getVolume()¶

Returns volume of geometry.

Volume is computed by:

\[V_{dome}=\frac{\pi}{6}(4 r_{\mathrm{outer}}^3 - (d_{\mathrm{center}}-r_{\mathrm{inner}})(3r_{\mathrm{outer}}^2+(d_{\mathrm{center}}-r_{\mathrm{inner}})^2))\]

Returns:	Volume of zebrafish dome.
Return type:	float

getXYExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getXYExtend().

By default, zebrafish geometry is set to range from center[i]-outerRadius to center[i]+outerRadius.

Returns:	Tuple containing: xmin (float): Minimum x-coordinate. xmax (float): Maximum x-coordinate. ymin (float): Minimum y-coordinate. ymax (float): Maximum y-coordinate.
Return type:	tuple

getZExtend()¶

Overwrites pyfrp.subclasses.pyfrp_geometry.geometry.getZExtend().

By default, zebrafish geometry is set to range from -outerRadius to 0.

optimalAllROI(name='', Id=0, color='b', asMaster=False)¶

Sets optimal ROI to a pyfrp.subclasses.pyfrp_ROI.radialSliceROI with radius outerRadius, center center, covering the whole z-range of geometry.

Keyword Arguments:
	name (str) – Name of new ROI. Id (int) – ID of new ROI. color (str) – Color that ROI is going to be associated with. asMaster (bool) – Make new ROI masterROI.

restoreDefault()¶

Restores default values.

Only default value of zebrafish geometry is that imagingHeight is set to be equal to sliceHeightPx of embryo.

setImagingHeight(h)¶

Sets imaging height and updates zebrafish geometry.

Parameters:	h (float) – New height.
Returns:	New height.
Return type:	float

setImagingRadius(r)¶

Sets imaging radius and updates zebrafish geometry.

Parameters:	r (float) – New radius.
Returns:	New radius.
Return type:	float

setOuterRadius(r)¶

Sets outer radius and updates zebrafish geometry.

Parameters:	r (float) – New radius.
Returns:	New radius.
Return type:	float

setRadiusScale(s)¶

Sets scaling factor between outer and inner radius and updates zebrafish geometry.

Parameters:	s (float) – New scaling factor.
Returns:	New scaling factor.
Return type:	float

updateGeoFile(debug=False)¶

Updates .geo file of geometry.

Keyword Arguments:
	debug (bool) – Print debugging messages.

class pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStageQuad(embryo, center, imagingRadius, radiusScale=1.1)¶

Bases: pyfrp.subclasses.pyfrp_geometry.zebrafishDomeStage

Geometry describing a zebrafish embryo in dome stage, reduced to first quadrant.

Inherits from zebrafishDomeStage. Please refer to its documentation for further details.

pyfrp.subclasses.pyfrp_mesh module¶

Essential PyFRAP module containing mesh class.

class pyfrp.subclasses.pyfrp_mesh.mesh(simulation)¶

Bases: object

Mesh class for PyFRAP.

The mesh class stores all information about location and creation of the mesh used for a simulation. It is directly associated with the pyfrp.subclasses.pyfrp_simulation.simulation object that uses it.

Meshes can either be created via running Gmsh onto the .geo file of the pyfrp.subclasses.pyfrp_geometry.geometry, or by running Gmsh internally from FiPy using some predefined functions (limited geometry support). See also genMesh().

The most important attributes are:

mesh: The actual mesh as a fipy.GmshImporter3D object.

fromFile: Flag that controls if mesh should be created from .geo file or not.

volSizePx: Mesh element size in px.

Besides mesh storage and creation, the mesh class contains useful functions such as:

Mesh refinement, see refine(), addBoxField() and forceMinMeshDensityInROI().

Information output, see printStats().

Plotting, see plotMesh() and plotDensity().

Parameters:	simulation (pyfrp.subclasses.pyfrp_simulation.simulation) – Simulation object.

addBoundaryLayerAroundROI(roi, fnOut=None, segments=48, simplify=True, iterations=3, triangIterations=2, fixSurfaces=True, debug=False, volSizePx=None, volSizeLayer=10, thickness=15.0, cleanUp=True, approxBySpline=True, angleThresh=0.95, faces='all', onlyAbs=True)¶

Adds boundary layer around ROI to the mesh.

Does this by:

Generating a stl file describing ROI, see also pyfrp.subclasses.pyfrp_ROI.ROI.render2StlInGeometry().

Read in stl file as new pyfrp.modules.pyfrp_gmsh_geometry.domain via pyfrp.modules.pyfrp_gmsh_IO_module.readStlFile().

Simplify new geometry via pyfrp.modules.pyfrp_gmsh_geometry.domain.simplifySurfaces().

Extracting selected surfaces via pyfrp.modules.pyfrp_gmsh_geometry.gmshElement.extract().

If selected, surface boundaries are approximated into splines via pyfrp.modules.pyfrp_gmsh_geometry.gmshElement.extract().

Reading in geometry’s .geo file via pyfrp.sublcasses.pyfrp_geometry.geometry.readGeoFile().

Merging ROI geometry into main geometry via pyfrp.modules.pyfrp_gmsh_geometry.domain.merge().

Adding a boundary layer mesh via pyfrp.modules.pyfrp_gmsh_geometry.domain.addBoundaryLayerField().

Adding all surfaces of ROI’s domain to boundary layer, see pyfrp.modules.pyfrp_gmsh_geometry.boundaryLayerField.addFaceListByID().

Writing new .geo file.

Setting new .geo file as fnGeo.

Running genMesh().

Clean up .stl and .scad files that are not needed anymore.

Note

volSizeLayer only allows a single definition of mesh size in layer. Note that the pyfrp.modules.pyfrp_gmsh_geometry.boundaryLayerField class allows different mesh sizes normal and along surfaces. For more information, see its documentation.

Note

If no fnOut is given, will save a new .geo file in same folder as original fnGeo with subfix: fnGeo_roiName_BL.geo.

Note

pyfrp.modules.pyfrp_gmsh_geometry.domain.simplifySurfaces() is not a simple procedure, we recommend reading its documentation.

If volSizePx is given, will overwrite mesh’s volSizePx and set it globally at all nodes.

Keyword Arguments:
Parameters:	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – An ROI.
	fnOut (str) – Path to new .geo file. segments (int) – Number of segments used for convex hull of surface. simplify (bool) – Simplify surfaces of stl file. iterations (int) – Number of iterations used for simplification. triangIterations (int) – Number of iterations used for subdivision of surfaces. addPoints (bool) – Allow adding points inside surface triangles. fixSurfaces (bool) – Allow fixing of surfaces, making sure they are coherent with Gmsh requirements. debug (bool) – Print debugging messages. volSizePx (float) – Global mesh density. volSizeLayer (float) – Boundary layer mesh size. thickness (float) – Thickness of boundary layer. cleanUp (bool) – Clean up temporary files when finished. approxBySpline (bool) – Approximate curvatures by spline. angleThresh (float) – Threshold angle under which loops are summarized. faces (list) – List of faces. onlyAbs (bool) – Take absolute value of faces into account.
Returns:	Path to new .geo file.
Return type:	str

addBoxField(volSizeIn, rangeX, rangeY, rangeZ, newFile=True, fnAppendix='_box', comment='newField', run=False, fnOut=None)¶

Adds box field to mesh.

Box fields allow to refine certain areas of the mesh, see also http://gmsh.info/doc/texinfo/gmsh.html#Specifying-mesh-element-sizes .

Note

Will keep volSizePx as volSize outside of the box.

Note

If fnOut is not specified, will do the following:

If newFile=True, will create new file with path fnGeo+/field/custom/fnGeo+fnAppendix.geo
Else writes into fnGeo.

Parameters:	volSizeIn (float) – volSize in px inside the 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]`. newFile (bool) – Write new mesh into a new .geo file. fnAppendix (str) – Append this to new file name. comment (str) – Comment in .geo file before definition of box field. run (bool) – Run Gmsh on new .geo file afterwards. fnOut (str) – Path to output geo file.
Returns:	Path to new .geo file.
Return type:	str

calcAllTetSidelenghts()¶

Calculates sidelengths of all tetrahedra.

Returns:	List of all sidelengths.
Return type:	list

forceMinMeshDensityInROI(ROI, density, stepPercentage=0.1, debug=False, findIdxs=True, method='refine', maxCells=100000)¶

Forces global mensh density such that a certain density is reached in a given ROI.

Tries to achive a mesh density density in ROI by globally refining mesh either through decreasing volSizePx by stepPercentage percent (method=volSize), or by using Gmsh’s -refine option (method=refine). If maximum number of cells is exceeded, will use the last mesh that did not exceed maxCells.

Keyword Arguments:
Parameters:	ROI (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI object. density (float) – Desired density.
	stepPercentage (float) – If method is `volSize`, percentage of `volSize` decrease. method (str) – Refinement method (`refine`/`volSize`). maxCells (int) – Total maximum number of mesh cells allowed. findIdxs (bool) – Find ROI indices after refinement. debug (bool) – Print debugging messages.
Returns:	New `volSizePx`
Return type:	float

genMesh(fnOut=None, debug=False)¶

Main mesh generation function.

Note

If fnOut=None, will use geometry.fnGeo.

Note

If fromFile=True, will generate from .geo file running gmsh directly on the file. If not, will try to run hard coded FiPy version for mesh generation via runFiPyMeshGenerator() .

Keyword Arguments:
	fnOut (str) – Output filepath for meshfile. debug (bool) – Print debugging messages.
Returns:	Gmsh mesh object.
Return type:	fipy.GmshImporter3D

getFnMesh()¶: Returns the filepath of meshfile.

getMaxNodeDistance()¶

Returns maximum node distance in x/y/z direction.

Returns:	Tuple containing: dmaxX (float): Maximum distance in x-direction dmaxY (float): Maximum distance in y-direction dmaxZ (float): Maximum distance in z-direction
Return type:	tuple

getMesh()¶

Returns mesh that is used for simulation.

Returns:	Gmsh mesh object.
Return type:	fipy.GmshImporter3D

getNNodes()¶

Returns number of nodes in mesh.

If no mesh has been generated yet, will return 0.

Returns:	Number of nodes.
Return type:	int

getSimulation()¶

Returns pyfrp.subclasses.pyfrp_simulation that mesh belongs to.

Returns:	Simulation object.
Return type:	pyfrp.subclasses.pyfrp_simulation

getVolSizePx()¶

Returns mesh volSize in px.

Returns:	VolSize.
Return type:	float

importMeshFromFile(fn)¶

Imports mesh from a Gmsh .msh file.

Parameters:	fn (str) – Filepath to meshfile.
Returns:	Gmsh mesh object.
Return type:	fipy.GmshImporter3D

importVTKFile(fnVTK='', sub=False)¶

Imports a .vtk file into a vtk renderer.

If fnVTK is not given, will generate .vtk file from meshfile stored in fnMesh using writeVTKFile().

If sub==True, will start a seperate subprocess and submit pyfrp_meshIO_script.py to it. This can be sometimes useful, since PyFRAP sometimes tends to crash otherwise.

Note

This function imports vtk. vtk is only necessary in a few functions, hence only imported when needed. This should make PyFRAP more portable.

Keyword Arguments:
	fnVTK (str) – Path to input vtk file. sub (bool) – Subprocess flag.
Returns:	Renderer object.
Return type:	vtk.vtkRenderer

plotCellCenters(ax=None, proj=None, color='k', indicateHeight=False, s=5.0, roi=None)¶

Plots location of cell centers of mesh.

Note

If no ax are given will create new ones.

If proj=[3d], will create 3D scatter plot, otherwise project cell centers in 2D.

Example:

Create figure

>>> fig,axes = pyfrp_plot_module.makeSubplot([2,2],titles=['2D','2D indicate','3D','3D indicate'],proj=[None,None,'3d','3d'])

Plot in 4 different ways

>>> mesh.plotCellCenters(ax=axes[0],s=1.)
>>> mesh.plotCellCenters(ax=axes[1],indicateHeight=True,s=5.)
>>> mesh.plotCellCenters(ax=axes[2],s=3.)
>>> mesh.plotCellCenters(ax=axes[3],indicateHeight=True,s=3.)

Keyword Arguments:
	ax (matplotlib.axes) – Axes to plot in. proj (list) – List of projections. color (str) – Color of mesh nodes. indicateHeight (bool) – Indicate height by color. s (float) – Size of marker. roi (pyfrp.subclasses.pyfrp_ROI) – ROI.
Returns:	Matplotlib axes.
Return type:	matplotlib.axes

plotDensity(axes=None, hist=True, bins=100, color='b')¶

Plots the mesh density in x/y/z-direction.

hist=True is recommended, since otherwise plots generally appear fairly noisy.

Note

If no axes are given or they do not have the necessary size, will create new ones.

Keyword Arguments:
	axes (list) – List of `matplotlib.axes`. hist (bool) – Summarize densities in bins. bins (int) – Number of bins used for hist. color (str) – Color of plot.
Returns:	List of `matplotlib.axes`.
Return type:	list

plotMesh(fnVTK='')¶

Plots the mesh using VTK.

If fnVTK is not given, will generate .vtk file from meshfile stored in fnMesh using writeVTKFile().

Note

This function imports vtk. vtk is only necessary in a few functions, hence only imported when needed. This should make PyFRAP more portable.

Keyword Arguments:
	fnVTK (str) – Path to input vtk file.
Returns:	RenderWindow object.
Return type:	vtk.vtkRenderWindow

printAllAttr()¶: Prints out all attributes of mesh object.

printStats(tetLenghts=False)¶

Prints out statistics of mesh.

Also calculates all tetraheder lengths if tetLenghts is selected. This might take some time depending on mesh size.

Keyword Arguments:
	tetLenghts (bool) – Also calculate and print out tetrahedra sidelengths.

refine(debug=False)¶

Refines mesh by splitting.

Keyword Arguments:
	debug (bool) – Print debugging messages.

restoreDefaults()¶

Restores default parameters of mesh.

Default parameters are:

mesh.geometry=mesh.simulation.embryo.geometry

mesh.fromFile=True

mesh.volSizePx=20

mesh.fnMesh=""

runFiPyMeshGenerator(typ)¶

Runs gmsh on the via FiPy internally defined meshes.

Available meshes:

cylinder

zebrafishDomeStage

xenopusBall

Note

Any refinement method will not work if mesh is created this way.

Parameters:	typ (str) – Type of mesh to be created (see list above).
Returns:	Gmsh mesh object.
Return type:	fipy.GmshImporter3D

saveMeshToImg(fnOut, fnVTK='', renderer=None, magnification=10, show=True)¶

Saves mesh to image file.

Supported extensions are:

‘.ps’ (PostScript)

‘.eps’ (Encapsualted PostScript)

‘.pdf’ (Portable Document Format)

‘.jpg’ (Joint Photographic Experts Group)

‘.png’ (Portable Network Graphics)

‘.pnm’ (Portable Any Map)

‘.tif’ (Tagged Image File Format)

‘.bmp’ (Bitmap Image)

If fnVTK is not given, will generate .vtk file from meshfile stored in fnMesh using writeVTKFile().

If no renderer is given, will create one using plotMesh().

Note

This function imports vtk. vtk is only necessary in a few functions, hence only imported when needed. This should make PyFRAP more portable.

Some code taken from http://www.programcreek.com/python/example/23102/vtk.vtkGL2PSExporter .

Keyword Arguments:
Parameters:	fnOut (str) – Path to output file.
	fnVTK (str) – Path to input vtk file. renderer (vtk.vtkOpenGLRenderer) – Renderer. magnification (int) – Degree of magnification. show (bool) – Show vtk render window.
Returns:	Exporter object.
Return type:	vtk.vtkExporter

saveMeshToPS(fnOut, fnVTK='', renderer=None)¶

Saves mesh to postscript file.

Supported extensions are:

‘.ps’ (PostScript)

‘.eps’ (Encapsualted PostScript)

‘.pdf’ (Portable Document Format)

‘.tex’ (LaTeX)

‘.svg’ (Scalable Vector Graphics)

If fnVTK is not given, will generate .vtk file from meshfile stored in fnMesh using writeVTKFile().

If no renderer is given, will create one using plotMesh().

Note

This function imports vtk. vtk is only necessary in a few functions, hence only imported when needed. This should make PyFRAP more portable.

Some code taken from http://www.programcreek.com/python/example/23102/vtk.vtkGL2PSExporter .

Keyword Arguments:
Parameters:	fnOut (str) – Path to output file.
	fnVTK (str) – Path to input vtk file. renderer (vtk.vtkOpenGLRenderer) – Renderer. magnification (int) – Degree of magnification.
Returns:	Exporter object.
Return type:	vtk.vtkGL2PSExporter

setFnMesh(fn)¶

Sets the filepath of meshfile.

Imports the new mesh right away using importMeshFromFile().

setFromFile(v)¶

Sets flag if mesh is supposed to be created from file (recommended) or from internally defined mesh creation method.

Parameters:	v (bool) – New flag value.
Returns:	New flag value.
Return type:	bool

setMesh(m)¶

Sets mesh attribute to a new mesh.

Parameters:	m (fipy.GmshImporter3D) – New mesh.
Returns:	Gmsh mesh object.
Return type:	fipy.GmshImporter3D

setVolSizePx(v, remesh=True, fnOut=None)¶

Sets volSize of mesh in px.

Note

If fnOut=None, then either fnMesh will be used, or, if fnMesh is not set yet, will use geometry.fnGeo.

Keyword Arguments:
Parameters:	v (float) – New volSize.
	remesh (bool) – Generate mesh with new volSize. fnOut (str) – Output filepath for meshfile.
Returns:	New volSize.
Return type:	float

updateGeoFile(debug=False)¶

Updates geometry file by writing new volSizePx into embryo.geometry.fnGeo.

Keyword Arguments:
	debug (bool) – Print debugging messages.
Returns:	Path to ouput meshfile.
Return type:	str

writeVTKFile(fn='', sub=False)¶

Writes mesh into vtk file.

Uses meshIO (https://github.com/nschloe/meshio), to convert the mesh saved in fnMesh to a .vtk file.

If sub==True, will start a seperate subprocess and submit pyfrp_meshIO_script.py to it. This can be sometimes useful, since PyFRAP sometimes tends to crash otherwise.

If no output path is given via fn, will use same path as fnMesh.

Note

meshIO only gets imported inside this function, making PyFRAP running even without the package installed. However, this feature will only run with meshIO.

Keyword Arguments:
	fn (str) – Optional output path. sub (bool) – Subprocess flag.
Returns:	Used output path.
Return type:	str

pyfrp.subclasses.pyfrp_molecule module¶

class pyfrp.subclasses.pyfrp_molecule.molecule(name)¶

Molecule class, collecting information about a series of FRAP experiments.

The main purpose of the molecule class is to gather and summarize multiple FRAP experiments. Embryo objects are stored in a embryos list. From those embryo objects, fit objects can be added to the selFits list to then be summarized to calculate measurement statistics. Fits can be forced to overlap in a set of parameters defined in crucialParameters.

addEmbryo(embryo)¶

Appends embryo object to embryos list.

Parameters:	embryo (pyfrp.subclasses.pyfrp_embryo) – Embryo to append.
Returns:	Updated pyfrp.subclasses.pyfrp_molecule.embryos list
Return type:	list

checkEmbryoNames()¶

Check if all embryos in embryos list have different names.

Returns:	True if all different, False else.
Return type:	bool

clearAllEmbryos()¶

Replaces all attribute values of each embryo in embryos list with None, except name.

Useful if embryos are seperated and molecule file needs to be compressed.

Note

Embryos should have all different names, so there will not be any missassignment when reimporting embryo files.

Returns:	True if success, False else.
Return type:	bool

extractEmbryos2Files(fn='', copyMeshFiles=True, debug=False)¶

Extracts embryos in embryos list into seperate pickled files.

Note

Will create folder fn if non-existent. If fn is not specified, will assume fn='embryoFiles/' .

Keyword Arguments:
	fn (str) – Path of folder where to save embryo files. copyMeshFiles (bool) – Copy meshfiles to embryo file destination. debug (bool) – Print out debugging messages.
Returns:	True if success, False else.
Return type:	bool

getEmbryoByName(s)¶: Returns embryo with name s from embryos list, otherwise False.

getName()¶

newEmbryo(name)¶

Creates new embryo and appends it to embryos list.

Parameters:	name (str) – Name of new embryo.
Returns:	New embryo object.
Return type:	pyfrp.subclasses.pyfrp_embryo

printResults()¶: Prints results summarized in pyfrp.subclasses.pyfrp_molecule.sumUpResults().

removeEmbryo(embryo)¶

Removes embryo object from embryos list.

Parameters:	embryo (pyfrp.subclasses.pyfrp_embryo) – Embryo object.
Returns:	Updated pyfrp.subclasses.pyfrp_molecule.embryos list
Return type:	list

replaceEmbryo(embryo, name='')¶

Replaces embryo with same name that embryo in molecule’s embryos list.

If no embryo with the same name exists, will simply add embryo. If name is given, will try to replace embryo with name name in embryos list.

Keyword Arguments:
Parameters:	embryo (pyfrp.subclasses.pyfrp_embryo) – New embryo to be inserted.
	name (str) – Optional name of embryo embryo to be replaced.
Returns:	True if anything was replaced, False if anything was appended.
Return type:	bool

save(fn=None)¶

Saves molecule to pickle file.

Note

If fn is not specified, will assume fn=self.name.

Keyword Arguments:
	fn (str) – Molecule file name.
Returns:	Filename of molecule file.
Return type:	str

saveExtract(fn=None, copyMeshFiles=True, debug=False)¶

Saves molecule to pickle file in compressed version by doing:

Extracts embryos in embryos list into seperate pickled files.

Clears all attributes of embryo objects

Saves molecule file

This function is really useful if molecule file size gets out-of-hand.

Note

Embryo files will be saved in path/to/moculefile/moleculename/ .

Note

If fn is not specified, will assume fn=self.name.

Keyword Arguments:
	fn (str) – Molecule file name. copyMeshFiles (bool) – Copy meshfiles to embryo file destination. debug (bool) – Print out debugging messages.
Returns:	True if success, False else.
Return type:	bool

setName(n)¶

sumUpResults(sameSettings=False)¶

Sums up results from all fits in selFits list.

Keyword Arguments:
	sameSettings (bool) – Fits must overlap in parameters defined in `crucialParameters`.
Returns:	True if success, False else.
Return type:	bool

updateVersion()¶

Updates molecule file to current version, making sure that it possesses all attributes.

Creates a new molecule object and compares self with the new molecule file. If the new molecule object has a attribute that self does not have, will add attribute with default value from the new molecle file.

Note

Will also update all subobject, making sure that embryo and fit objects are up-to-date.

Returns:	`self`
Return type:	pyfrp.subclasses.pyfrp_molecule

pyfrp.subclasses.pyfrp_simulation module¶

class pyfrp.subclasses.pyfrp_simulation.simulation(embryo)¶

Bases: object

PyFRAP simulation class.

Stores all important properties about how FRAP simulation is performed, such as:

compareICInterpolation(axes=None, roi=None)¶

Shows initial image, its interpolation, the resulting initial condition and its interpolation back onto an image.

Will create new axes if necessary.

Warning

Some images might be flipped due to plotting functions. Will be fixed in future version.

Keyword Arguments:
	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – A PyFRAP ROI. axes (matplotlib.axes) – List of axes of length 4.
Returns:	List of axes.
Return type:	list

computeInterpolatedICImg(roi=None)¶

Interpolates ICs back onto 2D image.

Uses scipy.interpolate.griddata, see also http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.interpolate.griddata.html

If roi is specified, will only interpolate nodes of this ROI.

Keyword Arguments:
	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – A PyFRAP ROI.
Returns:	Tuple containing: X (numpy.ndarray): Meshgrid x-coordinates. Y (numpy.ndarray): Meshgrid y-coordinates. interpIC (numpy.ndarray): Interpolated ICs.
Return type:	tuple

computeInterpolatedSolutionToImg(vals, roi=None, method='linear')¶

Interpolates solution back onto 2D image.

Uses scipy.interpolate.griddata, see also http://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.interpolate.griddata.html

If roi is specified, will only interpolate nodes of this ROI.

For more details about interpolation methods, check out https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.interpolate.griddata.html .

Keyword Arguments:

vals (numpy.ndarray) – Solution to be interpolated.
roi (pyfrp.subclasses.pyfrp_ROI.ROI) – A PyFRAP ROI.
method (str) – Interpolation method.
fillVal (float) – Value applied outside of ROI.

Returns:

Tuple containing:

X (numpy.ndarray): Meshgrid x-coordinates.

Y (numpy.ndarray): Meshgrid y-coordinates.

interpIC (numpy.ndarray): Interpolated solution.

Return type:

tuple

getD()¶

Returns current diffusion coefficient used for simulation.

Returns:	Current diffusion coefficient in $\mathrm{px}^2/s$.
Return type:	float

getDegr()¶

Returns degradation rate used for simulation.

Returns:	Current degradation rate in $1/[c]s$.
Return type:	float

getICImgSmoothness()¶

Returns smoothness of initial condition.

See also pyfrp.modules.pyfrp_img_module.getICImgSmoothness().

Returns:	Tuple containing: s (float): Smoothmess coefficient. dmax(float): Maximum diff.
Return type:	tuple

getICSmoothness(roi=None)¶

Returns smoothness of initial condition.

Note

If roi!=None, will only evaluate smoothness over this specific ROI.

Keyword Arguments:
	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – PyFRAP ROI.
Returns:	Tuple containing: s (float): Smoothmess coefficient. dmax(float): Maximum diff.
Return type:	tuple

getICimg()¶

Returns image for initial condition interpolation.

Returns:	Current ICimg.
Return type:	numpy.ndarray

getIterations()¶

Returns current iterations.

Returns:	Current solver
Return type:	str

getOptTvecSim(maxDExpectedPx)¶

Generates time vector that is optimal to fit experiments with expected diffusion coefficients up to maxDExpectedPx.

Basically computes how long a simulation needs to run in seconds to capture the dynamics of an experiment with diffusion coefficient of maxDExpectedPx. Does this by setting end time point to

\[t_{\mathrm{end,sim}} = \frac{D_{\mathrm{max. exp.}}}{D_{\mathrm{sim}}} t_{\mathrm{end,data}}\]

Note

Keeps time scaling.

Parameters:	maxDExpectedPx (float) – Maximum expected diffusion coefficient.
Returns:	New simulation time vector.
Return type:	numpy.ndarray

getProd()¶

Returns production rate used for simulation.

Returns:	Current production rate in $1/s$.
Return type:	float

getSaveSim()¶

Returns flag if simulation should be saved.

Returns:	Current flag value.
Return type:	bool

getSolutionVariableSmoothness(vals, roi=None)¶

Returns smoothness of solution variable.

Smoothness $s$ is computed as:

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

where $d_{\mathrm{max}}$ is the maximum derivative from the nearest neighbour over the whole array, and $\bar{d}$ the average derivation. Derivative from nearest neighbour is computed by

\[d=\frac{c-c_\mathrm{nearest}}{||\textbf{x}-\textbf{x}_\mathrm{nearest}||_2}\]

Note

If roi!=None, will only evaluate smoothness over this specific ROI.

Warning

Nearest neighbour finding algorithm is slow. Should be changed to ckdTree at some point.

Keyword Arguments:
	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – PyFRAP ROI.
Returns:	Tuple containing: s (float): Smoothmess coefficient. dmax(float): Maximum diff.
Return type:	tuple

getSolver()¶

Returns current solver.

Returns:	Current solver
Return type:	str

getTolerance()¶

Returns current tolerance.

Returns:	Current solver
Return type:	str

isLogTimeScale()¶

Returns if time spacing of simulation is logarithmic.

Returns:	Time spacing is logarithmic.
Return type:	bool

mapOntoImgs(tvec=None, roi=None, fnOut='', showProgress=True, method='linear', fillVal=0.0, scale=True, enc='uint16')¶

Maps simulation solution back onto images.

Note

Only works if simulation has been run before and saved via saveSim.

For more details about interpolation methods, check out https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.interpolate.griddata.html .

Keyword Arguments:
	tvec (numpy.ndarray) – Timepoints at which solution is saved to image. roi (pyfrp.subclasses.pyfrp_ROI.ROI) – PyFRAP ROI. fnOut (str) – Path where images should be saved. showProgress (bool) – Show progress of output. method (str) – Interpolation method. fillVal (float) – Value applied outside of ROI.
Returns:	True if everything ran through, False else.
Return type:	bool

plotICStack(ROIs, withGeometry=True, vmin=None, vmax=None, ax=None, colorbar=False)¶

Plots a stack of the initial conditions in a given list of ROIs.

Will automatically compute the direction in which ROI lies in the 3D space and reduce the ROI into this plane for contour plot.

If vmin=None or vmax=None, will compute overall maximum and minimum values over all ROIs.

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – Simulation solution variable (or numpy array). ROIs (list) – List of `pyfrp.subclasses.pyfrp_ROI.ROI` objects.
	withGeometry (bool) – Show geometry inside plot. vmin (float) – Overall minimum value to be displayed in plot. vmax (float) – Overall maximum value to be displayed in plot. ax (matplotlib.axes) – Axes used for plotting. colorbar (bool) – Display color bar.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

plotSolStack(phi, ROIs, withGeometry=True, vmin=None, vmax=None, ax=None, colorbar=False)¶

Plots a stack of the solution variable in a given list of ROIs.

Will automatically compute the direction in which ROI lies in the 3D space and reduce the ROI into this plane for contour plot.

If vmin=None or vmax=None, will compute overall maximum and minimum values over all ROIs.

Keyword Arguments:
Parameters:	phi (fipy.CellVariable) – Simulation solution variable (or numpy array). ROIs (list) – List of `pyfrp.subclasses.pyfrp_ROI.ROI` objects.
	withGeometry (bool) – Show geometry inside plot. vmin (float) – Overall minimum value to be displayed in plot. vmax (float) – Overall maximum value to be displayed in plot. ax (matplotlib.axes) – Axes used for plotting. colorbar (bool) – Display color bar.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

printAllAttr()¶: Prints out all attributes of embryo object.

rerun(signal=None, embCount=None, showProgress=True, debug=False)¶

Reruns simulation.

Note

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

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

restoreDefaults()¶: Restores default parameters for simulations.

run(signal=None, embCount=None, showProgress=True, debug=False)¶

Runs simulation.

Checks if ROI indices are computed, if not, computes them. Then passes simulation object to pyfrp.modules.pyfrp_sim_module.simulateReactDiff().

Keyword Arguments:
	signal (PyQt4.QtCore.pyqtSignal) – PyQT signal to send progress to GUI. embCount (int) – Counter of counter process if multiple datasets are simulated. debug (bool) – Print debugging messages and show debugging plots. showProgress (bool) – Print out progress.
Returns:	True if success, False otherwise.
Return type:	bool

setBleachedROI(r)¶

Sets bleached ROI that is used when ideal ICs (ICmode=4) is selected.

Parameters:	r (pyfrp.subclasses.pyfrp_ROI.ROI) – ROI to be set bleached ROI.

setD(D)¶

Sets diffusion coefficient used for simulation.

Parameters:	D (float) – New diffusion coefficient in $\mu\mathrm{m}^2/s$.
Returns:	New diffusion coefficient in $\mathrm{px}^2/s$.
Return type:	float

setDegr(degr)¶

Sets degradation rate used for simulation.

Parameters:	prod (float) – New degradation rate in $1/[c]s$.
Returns:	New degradation rate in $1/[c]s$.
Return type:	float

setICMode(m)¶

Sets the mode of initial conditions.

Initial condition modes are defined as:

0: ROI-based: 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.modules.pyfrp_sim_module.applyROIBasedICs().

1: Radial: Concentrations get radially approximated around some center. See also pyfrp.modules.pyfrp_sim_module.applyRadialICs().

2: Imperfect: Interpolates ICimg onto mesh, fills nodes that are not covered by image with embryo.analysis.concRim and then mimics imperfect bleaching via sigmoid function in z-direction. See also pyfrp.modules.pyfrp_sim_module.applyImperfectICs().

3: Inpterpolated: Interpolates ICimg onto mesh, fills nodes that are not covered by image with embryo.analysis.concRim. See also pyfrp.modules.pyfrp_sim_module.applyInterpolatedICs().

4: Ideal: Ideal ICs, that is, single value inside and single value outside of bleached region. See also pyfrp.modules.pyfrp_sim_module.applyIdealICs(), setValOut() and setBleachedROI().

Note

The default mode is Interpolated (m=3) and is highly recommended to obtain most realistic results.

Parameters:	m (int) – Which mode to be used.
Returns:	Current initial condition mode used.
Return type:	int

setICimg(img)¶

Sets image for initial condition interpolation.

Parameters:	img (numpy.ndarray) – A 2D image.
Returns:	New ICimg.
Return type:	numpy.ndarray

setICimgByFn(fn)¶

Sets image for initial condition interpolation given a filepath.

Parameters:	fn (str) – Path to file.
Returns:	New ICimg.
Return type:	numpy.ndarray

setIterations(tol)¶

Sets iterations of solver.

Parameters:	tol (float) – New iterations.
Returns:	Current iterations.
Return type:	float

setMesh(m)¶

Sets mesh to a new mesh object.

Parameters:	m (pyfrp.subclasses.pyfrp_mesh.mesh) – PyFRAP mesh object.
Returns:	Updated mesh instance.
Return type:	pyfrp.subclasses.pyfrp_mesh.mesh

setProd(prod)¶

Sets production rate used for simulation.

Parameters:	prod (float) – New production rate in $1/s$.
Returns:	New production rate in $1/s$.
Return type:	float

setSaveSim(b)¶

Sets flag if simulation should be saved.

Parameters:	b (bool) – New flag value.
Returns:	Updated flag value.
Return type:	bool

setSolver(solver)¶

Sets solver to use.

Implemented solvers are:

PCG

LU

Parameters:	solver (str) – Solver to use.
Returns:	Current solver
Return type:	str

setTEnd(T)¶

Updates timevector of simulation to end at new time point.

Note

Keeps scaling.

Parameters:	T (float) – New end timepoint.
Returns:	New simulation time vector.
Return type:	numpy.ndarray

setTimesteps(n)¶

Sets number of simulation time steps and updates time vector.

Parameters:	n (int) – New number of time steps.
Returns:	New number of time steps.
Return type:	int

setTolerance(tol)¶

Sets tolerance of solver.

Parameters:	tol (float) – New tolerance.
Returns:	Current tolerance.
Return type:	float

setValOut(v)¶

Sets valOut that is used when ideal ICs (ICmode=4) is selected.

Parameters:	v (float) – Value that is to assigned outside of bleachedROI.

showIC(ax=None, roi=None, nlevels=25, vmin=None, vmax=None, typ='contour')¶

Plots initial conditions applied to mesh in 2D or 3D.

If roi is given, will only plot initial conditions for nodes inside ROI, else will plot initial condition for all nodes in mesh.

Note

Simulation needs to be run first before this plotting function can be used.

Example:

>>> simulation.plotIC(typ='contour')

will produce the following:

See also pyfrp.modules.pyfrp_plot_module.plotSolutionVariable() and pyfrp.subclasses.pyfrp_ROI.plotSolutionVariable().

Keyword Arguments:
	roi (pyfrp.subclasses.pyfrp_ROI.ROI) – A PyFRAP ROI object. vmin (float) – Overall minimum value to be displayed in plot. vmax (float) – Overall maximum value to be displayed in plot. ax (matplotlib.axes) – Axes used for plotting. nlevels (int) – Number of contour levels to display. typ (str) – Typ of plot.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

showICimg(ax=None, typ='contour', colorbar=True)¶

Plots image used for initial 2D.

Keyword Arguments:
	ax (matplotlib.axes) – Axes used for plotting.
Returns:	Axes used for plotting.
Return type:	matplotlib.axes

showInterpolatedIC(ax=None, roi=None)¶

Shows ICs interpolated back onto 2D image.

If roi is specified, will only interpolate nodes of this ROI.

Module contents¶

PyFRAP: A Python based FRAP analysis tool box. Subclass module.

Welcome to PyFRAP’s API!¶

Installation (setup.py)¶

Installing PyFRAP via setup.py¶

PyFRAP setup.py API¶

The modules package¶

pyfrp.modules package¶

Submodules¶

pyfrp.modules.pyfrp_IO_module module¶

pyfrp.modules.pyfrp_fit_module module¶

pyfrp.modules.pyfrp_geometry_module module¶

pyfrp.modules.pyfrp_gmsh_IO_module module¶

pyfrp.modules.pyfrp_gmsh_geometry module¶

pyfrp.modules.pyfrp_gmsh_module module¶

pyfrp.modules.pyfrp_idx_module module¶

pyfrp.modules.pyfrp_img_module module¶

pyfrp.modules.pyfrp_integration_module module¶

pyfrp.modules.pyfrp_misc_module module¶

pyfrp.modules.pyfrp_openscad_module module¶

pyfrp.modules.pyfrp_optimization_module module¶

pyfrp.modules.pyfrp_plot_module module¶

pyfrp.modules.pyfrp_sim_module module¶

pyfrp.modules.pyfrp_stats_module module¶

pyfrp.modules.pyfrp_term_module module¶

pyfrp.modules.pyfrp_vtk_module module¶

pyfrp.modules.pyfrp_zstack_module module¶

Module contents¶

The subclasses package¶

pyfrp.subclasses package¶

Submodules¶

pyfrp.subclasses.pyfrp_ROI module¶

pyfrp.subclasses.pyfrp_analysis module¶

pyfrp.subclasses.pyfrp_conf module¶

pyfrp.subclasses.pyfrp_embryo module¶

pyfrp.subclasses.pyfrp_fit module¶

pyfrp.subclasses.pyfrp_geometry module¶

pyfrp.subclasses.pyfrp_mesh module¶

pyfrp.subclasses.pyfrp_molecule module¶

pyfrp.subclasses.pyfrp_simulation module¶

Module contents¶

Indices and tables¶