fluidimage.works.piv.multipass#

Multipass PIV (`fluidimage.works.piv.multipass`)#

class fluidimage.works.piv.multipass.WorkPIV(params=None)[source]#

Main work for PIV with multipass.

Parameters:

paramsfluiddyn.util.paramcontainer.ParamContainer: The default parameters are obtained from the class method WorkPIV.create_default_params().

Notes

Steps for a computation:

first estimation of the PIV field with a work fluidimage.works.piv.singlepass.FirstWorkPIV
fix (remove “false vectors”) with a work fluidimage.works.piv.fix.WorkFIX
depending of the value of params.multipass.number, iteration to compute PIV displacements with the works fluidimage.works.piv.singlepass.WorkPIVFromDisplacement and fluidimage.works.piv.fix.WorkFIX.

classmethod _complete_params_with_default(params)[source]#: Complete the default parameters (class method).

calcul(couple)[source]#: Compute a PIV field (multipass) from a couple of image.

_prepare_with_image(im=None, imshape=None)[source]#: Prepare the works PIV with an image.

Parameters indicating the input series of images.

path : str, {‘’}

String indicating the input images (can be a full path towards an image file or a string given to glob).
str_subset : ‘pairs’

String indicating as a Python slicing how couples of images are formed. There is one couple per value of i. The values of i are set with the other parameters ind_start, ind_step and ind_stop approximately with the function range (range(ind_start, ind_stop, ind_step)).

Python slicing is a very powerful notation to define subset from a (possibly multidimensional) set of images. For a user, an alternative is to understand how Python slicing works. See for example this page: http://stackoverflow.com/questions/509211/explain-pythons-slice-notation.

Another possibility is to follow simple examples:

For single-frame images (im0, im1, im2, im3, …), we keep the default value ‘i:i+2’ to form the couples (im0, im1), (im1, im2), …

To see what it gives, one can use IPython and range:
```
>>> i = 0
>>> list(range(10))[i:i+2]
[0, 1]
```
```
>>> list(range(10))[i:i+4:2]
[0, 2]
```
We see that we can also use the value ‘i:i+4:2’ to form the couples (im0, im2), (im1, im3), …

For double-frame images (im1a, im1b, im2a, im2b, …) you can write
```
>>> params.series.str_subset = 'i, 0:2'
```
In this case, the first couple will be (im1a, im1b).

To get the first couple (im1a, im1a), we would have to write
```
>>> params.series.str_subset = 'i:i+2, 0'
```
ind_start : int, {‘first’}
ind_step : int, {1}
int_stop : None

Parameters describing one PIV step.

shape_crop_im0 : int (48)

Shape of the cropped images 0 from which are computed the correlation.
shape_crop_im1 : int or None

Shape of the cropped images 0 (has to be None for correl based on fft).
displacement_max : None

Displacement maximum used in correlation classes. The exact effect depends on the correlation method. For fft based correlation, it can also be of the form ‘50%’ and then the maximum displacement is computed for each pass as a pourcentage of max(shape_crop_im0).
displacement_mean : None

Displacement averaged over space (NotImplemented).
method_correl : str, default ‘fftw’

Can be in [‘pythran’, ‘pycuda’, ‘scipy.signal’, ‘scipy.ndimage’, ‘np.fft’, ‘fftw’, ‘cufft’, ‘skcufft’]
method_subpix : str, default ‘2d_gaussian2’

Can be in [‘2d_gaussian’, ‘2d_gaussian2’, ‘centroid’, ‘no_subpix’]
nsubpix : None

Integer used in the subpix finder to compute the shape of the correlation crop ((1+2*nsubpix,)*2). It is related to the typical size of the particles. It has to be increased in case of peak locking (plot the histograms of the displacements).
nb_peaks_to_search : 1, int

Number of peaks to search. Secondary peaks can be used during the fix step.
particle_radius : 3, int

Typical radius of a particle (or more precisely of a correlation peak). Used only if nb_peaks_to_search is larger than one.

Parameters describing the grid.

overlap : float (0.5)

Number smaller than 1 defining the overlap between interrogation windows.
from : str {‘overlap’}

Keyword for the method from which is computed the grid.

Parameters describing how images are masked.

strcrop : None, str

Two-dimensional slice (for example ‘100:600, :’). If None, the whole image is used.

Parameters indicating how are detected and processed false vectors.

correl_min : 0.2

Vectors associated with correlation smaller than correl_min are considered as false vectors.
threshold_diff_neighbour : 10

Vectors for which the difference with the average vectors is larger than threshold_diff_neighbour are considered as false vectors.
displacement_max : None

Vectors larger than displacement_max are considered as false vectors.

Multipass PIV parameters:

number : int (default 1)

Number of PIV passes.
coeff_zoom : integer or iterable of size number - 1.

Reduction coefficient defining the size of the interrogation windows for the passes 1 (second pass) to number - 1 (last pass) (always defined comparing the passes i-1).
use_tps : bool or ‘last’

If it is True, the interpolation is done using the Thin Plate Spline method (computationally heavy but sometimes interesting). If it is ‘last’, the TPS method is used only for the last pass.
subdom_size : int

Number of vectors in the subdomains used for the TPS method.
smoothing_coef : float

Coefficient used for the TPS method. The result is smoother for larger smoothing_coef. 2 is often reasonable. Can typically be between 0 to 40.
threshold_tps : float

Allowed difference of displacement (in pixels) between smoothed and input field for TPS filter used in an iterative filtering method. Vectors too far from the corresponding interpolated vector are removed.

Classes

WorkPIV([params])

Main work for PIV with multipass.