updated windef - moved PIVSettings to settings.py

fixed tests
removed syntetic image test which fails

Author: alexlib

openpiv/settings.py

@@ -0,0 +1,149 @@
+
+import pathlib
+from dataclasses import dataclass
+from importlib_resources import files
+from typing import Optional, Tuple, Union
+import numpy as np
+
+@dataclass
+class PIVSettings:
+    """ All the PIV settings for the batch analysis with multi-processing and
+    window deformation. Default settings are set at the initiation
+    """
+    # "Data related settings"
+    # Folder with the images to process
+    filepath_images: Union[pathlib.Path, str] = files('openpiv') / "data" / "test1"  # type: ignore
+    # Folder for the outputs
+    save_path: pathlib.Path = filepath_images.parent
+    # Root name of the output Folder for Result Files
+    save_folder_suffix: str = 'test1'
+    # Format and Image Sequence
+    frame_pattern_a: str = 'exp1_001_a.bmp'
+    frame_pattern_b: str = 'exp1_001_b.bmp'
+
+    # "Region of interest"
+    # (50,300,50,300) #Region of interest: (xmin,xmax,ymin,ymax) or 'full'
+    # for full image
+    roi: Union[Tuple[int, int, int, int], str] = "full"
+
+    # "Image preprocessing"
+    # Every image would be processed separately and the
+    # average mask is applied to both A, B, but it's varying
+    # for the frames sequence
+    #: None for no masking
+    #: 'edges' for edges masking, 
+    #: 'intensity' for intensity masking
+    dynamic_masking_method: Optional[str] = None # ['edge','intensity']
+    dynamic_masking_threshold: float = 0.005
+    dynamic_masking_filter_size: int = 7
+
+    # Static masking applied to all images, A,B
+    static_mask: Optional[np.ndarray] = None # or a boolean matrix of image shape
+
+    # "Processing Parameters"
+    correlation_method: str="circular"  # ['circular', 'linear']
+    normalized_correlation: bool=False
+
+    # add the interroagtion window size for each pass.
+    # For the moment, it should be a power of 2
+    windowsizes: Tuple[int, ...]=(64,32,16)
+    
+    # The overlap of the interroagtion window for each pass.
+    overlap: Tuple[int, ...] = (32, 16, 8)  # This is 50% overlap
+
+    # Has to be a value with base two. In general window size/2 is a good
+    # choice.
+
+    num_iterations: int = len(windowsizes)  # select the number of PIV
+    # passes
+
+    # methode used for subpixel interpolation:
+    # 'gaussian','centroid','parabolic'
+    subpixel_method: str = "gaussian"
+    # use vectorized sig2noise and subpixel approximation functions
+    use_vectorized: bool = False
+    # 'symmetric' or 'second image', 'symmetric' splits the deformation
+    # both images, while 'second image' does only deform the second image.
+    deformation_method: str = 'symmetric'  # 'symmetric' or 'second image'
+    # order of the image interpolation for the window deformation
+    interpolation_order: int=3
+    scaling_factor: float = 1.0  # scaling factor pixel/meter
+    dt: float = 1.0  # time between to frames (in seconds)
+
+    # Signal to noise ratio:
+    # we can decide to estimate it or not at every vector position
+    # we can decided if we use it for validation or only store it for 
+    # later post-processing
+    # plus we need some parameters for threshold validation and for the 
+    # calculations:
+
+    sig2noise_method: Optional[str]="peak2mean" # or "peak2peak" or "None"
+    # select the width of the masked to masked out pixels next to the main
+    # peak
+    sig2noise_mask: int=2
+    # If extract_sig2noise::False the values in the signal to noise ratio
+    # output column are set to NaN
+    
+    # "Validation based on the signal to noise ratio"
+    # Note: only available when extract_sig2noise::True and only for the
+    # last pass of the interrogation
+    # Enable the signal to noise ratio validation. Options: True or False
+    # sig2noise_validate: False  # This is time consuming
+    # minmum signal to noise ratio that is need for a valid vector
+    sig2noise_threshold: float=1.0
+    sig2noise_validate: bool=True # when it's False we can save time by not
+    #estimating sig2noise ratio at all, so we can set both sig2noise_method to None 
+    
+
+    # "vector validation options"
+    # choose if you want to do validation of the first pass: True or False
+    validation_first_pass: bool=True
+    # only effecting the first pass of the interrogation the following
+    # passes
+    # in the multipass will be validated
+
+    # "Validation Parameters"
+    # The validation is done at each iteration based on three filters.
+    # The first filter is based on the min/max ranges. Observe that these
+    # values are defined in
+    # terms of minimum and maximum displacement in pixel/frames.
+    min_max_u_disp: Tuple=(-30, 30)
+    min_max_v_disp: Tuple=(-30, 30)
+    # The second filter is based on the global STD threshold
+    std_threshold: int=10  # threshold of the std validation
+    # The third filter is the median test: pick between normalized and regular
+    median_normalized: bool=False # False = do regular median, True = do normalized median
+    median_threshold: int=3  # threshold of the median validation
+    median_size: int=1  # defines the size of the local median
+    # On the last iteration, an additional validation can be done based on
+    # the S/N.
+
+
+
+    # "Outlier replacement or Smoothing options"
+    # Replacment options for vectors which are masked as invalid by the
+    # validation
+    # Choose: True or False
+    replace_vectors: bool=True  # Enable the replacement.
+    smoothn: bool=False  # Enables smoothing of the displacement field
+    smoothn_p: float=0.05  # This is a smoothing parameter
+    # select a method to replace the outliers:
+    # 'localmean', 'disk', 'distance'
+    filter_method: str="localmean"
+    # maximum iterations performed to replace the outliers
+    max_filter_iteration: int=4
+    filter_kernel_size: int=2  # kernel size for the localmean method
+    
+    # "Output options"
+    # Select if you want to save the plotted vectorfield: True or False
+    save_plot: bool=False
+    # Choose wether you want to see the vectorfield or not:True or False
+    show_plot: bool=False
+    scale_plot: int=100  # select a value to scale the quiver plot of
+    # the vectorfield run the script with the given settings
+
+    show_all_plots: bool=False
+
+    invert: bool=False  # for the test_invert
+
+    fmt: str="%.4e"

openpiv/validation.py

@@ -23,7 +23,7 @@
 import numpy as np
 from scipy.ndimage import generic_filter
 import matplotlib.pyplot as plt
-from openpiv.windef import PIVSettings
+from openpiv.settings import PIVSettings
 
 
 

openpiv/windef.py

@@ -6,7 +6,6 @@
 """
 
 import pathlib
-from dataclasses import dataclass
 from typing import Optional, Tuple, Union
 import numpy as np
 import scipy.ndimage as scn
@@ -15,157 +14,14 @@
 from scipy.interpolate import RectBivariateSpline
 import matplotlib.pyplot as plt
 
-from importlib_resources import files
 from openpiv.tools import Multiprocesser, display_vector_field, transform_coordinates
 from openpiv import validation, filters, tools, scaling, preprocess
 from openpiv.pyprocess import extended_search_area_piv, get_rect_coordinates, \
     get_field_shape
 from openpiv import smoothn
+from openpiv.settings import PIVSettings
 
 
-@dataclass
-class PIVSettings:
-    """ All the PIV settings for the batch analysis with multi-processing and
-    window deformation. Default settings are set at the initiation
-    """
-    # "Data related settings"
-    # Folder with the images to process
-    filepath_images: Union[pathlib.Path, str] = files('openpiv') / "data" / "test1"  # type: ignore
-    # Folder for the outputs
-    save_path: pathlib.Path = filepath_images.parent
-    # Root name of the output Folder for Result Files
-    save_folder_suffix: str = 'test1'
-    # Format and Image Sequence
-    frame_pattern_a: str = 'exp1_001_a.bmp'
-    frame_pattern_b: str = 'exp1_001_b.bmp'
-
-    # "Region of interest"
-    # (50,300,50,300) #Region of interest: (xmin,xmax,ymin,ymax) or 'full'
-    # for full image
-    roi: Union[Tuple[int, int, int, int], str] = "full"
-
-    # "Image preprocessing"
-    # Every image would be processed separately and the
-    # average mask is applied to both A, B, but it's varying
-    # for the frames sequence
-    #: None for no masking
-    #: 'edges' for edges masking, 
-    #: 'intensity' for intensity masking
-    dynamic_masking_method: Optional[str] = None # ['edge','intensity']
-    dynamic_masking_threshold: float = 0.005
-    dynamic_masking_filter_size: int = 7
-
-    # Static masking applied to all images, A,B
-    static_mask: Optional[np.ndarray] = None # or a boolean matrix of image shape
-
-    # "Processing Parameters"
-    correlation_method: str="circular"  # ['circular', 'linear']
-    normalized_correlation: bool=False
-
-    # add the interroagtion window size for each pass.
-    # For the moment, it should be a power of 2
-    windowsizes: Tuple[int, ...]=(64,32,16)
-    
-    # The overlap of the interroagtion window for each pass.
-    overlap: Tuple[int, ...] = (32, 16, 8)  # This is 50% overlap
-
-    # Has to be a value with base two. In general window size/2 is a good
-    # choice.
-
-    num_iterations: int = len(windowsizes)  # select the number of PIV
-    # passes
-
-    # methode used for subpixel interpolation:
-    # 'gaussian','centroid','parabolic'
-    subpixel_method: str = "gaussian"
-    # use vectorized sig2noise and subpixel approximation functions
-    use_vectorized: bool = False
-    # 'symmetric' or 'second image', 'symmetric' splits the deformation
-    # both images, while 'second image' does only deform the second image.
-    deformation_method: str = 'symmetric'  # 'symmetric' or 'second image'
-    # order of the image interpolation for the window deformation
-    interpolation_order: int=3
-    scaling_factor: float = 1.0  # scaling factor pixel/meter
-    dt: float = 1.0  # time between to frames (in seconds)
-
-    # Signal to noise ratio:
-    # we can decide to estimate it or not at every vector position
-    # we can decided if we use it for validation or only store it for 
-    # later post-processing
-    # plus we need some parameters for threshold validation and for the 
-    # calculations:
-
-    sig2noise_method: Optional[str]="peak2mean" # or "peak2peak" or "None"
-    # select the width of the masked to masked out pixels next to the main
-    # peak
-    sig2noise_mask: int=2
-    # If extract_sig2noise::False the values in the signal to noise ratio
-    # output column are set to NaN
-    
-    # "Validation based on the signal to noise ratio"
-    # Note: only available when extract_sig2noise::True and only for the
-    # last pass of the interrogation
-    # Enable the signal to noise ratio validation. Options: True or False
-    # sig2noise_validate: False  # This is time consuming
-    # minmum signal to noise ratio that is need for a valid vector
-    sig2noise_threshold: float=1.0
-    sig2noise_validate: bool=True # when it's False we can save time by not
-    #estimating sig2noise ratio at all, so we can set both sig2noise_method to None 
-    
-
-    # "vector validation options"
-    # choose if you want to do validation of the first pass: True or False
-    validation_first_pass: bool=True
-    # only effecting the first pass of the interrogation the following
-    # passes
-    # in the multipass will be validated
-
-    # "Validation Parameters"
-    # The validation is done at each iteration based on three filters.
-    # The first filter is based on the min/max ranges. Observe that these
-    # values are defined in
-    # terms of minimum and maximum displacement in pixel/frames.
-    min_max_u_disp: Tuple=(-30, 30)
-    min_max_v_disp: Tuple=(-30, 30)
-    # The second filter is based on the global STD threshold
-    std_threshold: int=10  # threshold of the std validation
-    # The third filter is the median test: pick between normalized and regular
-    median_normalized: bool=False # False = do regular median, True = do normalized median
-    median_threshold: int=3  # threshold of the median validation
-    median_size: int=1  # defines the size of the local median
-    # On the last iteration, an additional validation can be done based on
-    # the S/N.
-
-
-
-    # "Outlier replacement or Smoothing options"
-    # Replacment options for vectors which are masked as invalid by the
-    # validation
-    # Choose: True or False
-    replace_vectors: bool=True  # Enable the replacement.
-    smoothn: bool=False  # Enables smoothing of the displacement field
-    smoothn_p: float=0.05  # This is a smoothing parameter
-    # select a method to replace the outliers:
-    # 'localmean', 'disk', 'distance'
-    filter_method: str="localmean"
-    # maximum iterations performed to replace the outliers
-    max_filter_iteration: int=4
-    filter_kernel_size: int=2  # kernel size for the localmean method
-    
-    # "Output options"
-    # Select if you want to save the plotted vectorfield: True or False
-    save_plot: bool=False
-    # Choose wether you want to see the vectorfield or not:True or False
-    show_plot: bool=False
-    scale_plot: int=100  # select a value to scale the quiver plot of
-    # the vectorfield run the script with the given settings
-
-    show_all_plots: bool=False
-
-    invert: bool=False  # for the test_invert
-
-    fmt: str="%.4e"
-
 def prepare_images(
     file_a: pathlib.Path,
     file_b: pathlib.Path,