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old_windef.py
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old_windef.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Oct 4 14:04:04 2019
@author: Theo
"""
import os
import numpy as np
from scipy.fft import rfft2, irfft2, fftshift
import scipy.ndimage as scn
from scipy.interpolate import RectBivariateSpline
from openpiv import pyprocess, validation, filters, tools, preprocess,scaling
from openpiv import smoothn
from openpiv.pyprocess import sig2noise_ratio as sig2noise_ratio_function
from openpiv.pyprocess import get_field_shape, get_coordinates
from openpiv.tools import display_vector_field, save
from openpiv.pyprocess import normalize_intensity
from openpiv.pyprocess import find_subpixel_peak_position
from openpiv.pyprocess import fft_correlate_strided_images as correlation_func
from openpiv.windef import create_deformation_field as frame_interpolation
import matplotlib.pyplot as plt
def piv(settings):
# '''the func fuction is the "frame" in which the PIV evaluation is done'''
def func(args):
"""A function to process each image pair."""
# this line is REQUIRED for multiprocessing to work
# always use it in your custom function
file_a, file_b, counter = args
# counter2=str(counter2)
#####################
# Here goes you code
#####################
' read images into numpy arrays'
frame_a = tools.imread(os.path.join(settings.filepath_images, file_a))
frame_b = tools.imread(os.path.join(settings.filepath_images, file_b))
## Miguel: I just had a quick look, and I do not understand the reason for this step.
# I propose to remove it.
#frame_a = (frame_a*1024).astype(np.int32)
#frame_b = (frame_b*1024).astype(np.int32)
' crop to ROI'
if settings.ROI=='full':
frame_a=frame_a
frame_b=frame_b
else:
frame_a = frame_a[settings.ROI[0]:settings.ROI[1],settings.ROI[2]:settings.ROI[3]]
frame_b = frame_b[settings.ROI[0]:settings.ROI[1],settings.ROI[2]:settings.ROI[3]]
if settings.dynamic_masking_method=='edge' or 'intensity':
frame_a = preprocess.dynamic_masking(frame_a,method=settings.dynamic_masking_method,filter_size=settings.dynamic_masking_filter_size,threshold=settings.dynamic_masking_threshold)
frame_b = preprocess.dynamic_masking(frame_b,method=settings.dynamic_masking_method,filter_size=settings.dynamic_masking_filter_size,threshold=settings.dynamic_masking_threshold)
'''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'''
'first pass'
x, y, u, v, sig2noise_ratio = first_pass(frame_a,frame_b,settings.windowsizes[0], settings.overlap[0],settings.iterations,
correlation_method=settings.correlation_method, subpixel_method=settings.subpixel_method, do_sig2noise=settings.extract_sig2noise,
sig2noise_method=settings.sig2noise_method, sig2noise_mask=settings.sig2noise_mask,)
'validation using gloabl limits and std and local median'
'''MinMaxU : two elements tuple
sets the limits of the u displacment component
Used for validation.
MinMaxV : two elements tuple
sets the limits of the v displacment component
Used for validation.
std_threshold : float
sets the threshold for the std validation
median_threshold : float
sets the threshold for the median validation
filter_method : string
the method used to replace the non-valid vectors
Methods:
'localmean',
'disk',
'distance',
max_filter_iteration : int
maximum of filter iterations to replace nans
filter_kernel_size : int
size of the kernel used for the filtering'''
mask=np.full_like(x,False)
if settings.validation_first_pass==True:
u, v, mask_g = validation.global_val( u, v, settings.MinMax_U_disp, settings.MinMax_V_disp)
u,v, mask_s = validation.global_std( u, v, std_threshold = settings.std_threshold )
u, v, mask_m = validation.local_median_val( u, v, u_threshold=settings.median_threshold, v_threshold=settings.median_threshold, size=settings.median_size )
if settings.extract_sig2noise==True and settings.iterations==1 and settings.do_sig2noise_validation==True:
u,v, mask_s2n = validation.sig2noise_val( u, v, sig2noise_ratio, threshold = settings.sig2noise_threshold)
mask=mask+mask_g+mask_m+mask_s+mask_s2n
else:
mask=mask+mask_g+mask_m+mask_s
'filter to replace the values that where marked by the validation'
if settings.iterations>1:
u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size)
'adding masks to add the effect of all the validations'
if settings.smoothn==True:
u,dummy_u1,dummy_u2,dummy_u3=smoothn.smoothn(u,s=settings.smoothn_p)
v,dummy_v1,dummy_v2,dummy_v3=smoothn.smoothn(v,s=settings.smoothn_p)
elif settings.iterations==1 and settings.replace_vectors==True:
u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size)
'adding masks to add the effect of all the validations'
if settings.smoothn==True:
u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size)
u,dummy_u1,dummy_u2,dummy_u3=smoothn.smoothn(u,s=settings.smoothn_p)
v,dummy_v1,dummy_v2,dummy_v3=smoothn.smoothn(v,s=settings.smoothn_p)
i = 1
'all the following passes'
for i in range(2, settings.iterations+1):
x, y, u, v, sig2noise_ratio, mask = multipass_img_deform(frame_a, frame_b, settings.windowsizes[i-1], settings.overlap[i-1],settings.iterations,i,
x, y, u, v, correlation_method=settings.correlation_method,
subpixel_method=settings.subpixel_method, do_sig2noise=settings.extract_sig2noise,
sig2noise_method=settings.sig2noise_method, sig2noise_mask=settings.sig2noise_mask,
MinMaxU=settings.MinMax_U_disp,
MinMaxV=settings.MinMax_V_disp,std_threshold=settings.std_threshold,
median_threshold=settings.median_threshold,median_size=settings.median_size,filter_method=settings.filter_method,
max_filter_iteration=settings.max_filter_iteration, filter_kernel_size=settings.filter_kernel_size,
interpolation_order=settings.interpolation_order)
# If the smoothing is active, we do it at each pass
if settings.smoothn==True:
u,dummy_u1,dummy_u2,dummy_u3= smoothn.smoothn(u,s=settings.smoothn_p)
v,dummy_v1,dummy_v2,dummy_v3= smoothn.smoothn(v,s=settings.smoothn_p)
'''%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%'''
if settings.extract_sig2noise==True and i==settings.iterations and settings.iterations!=1 and settings.do_sig2noise_validation==True:
u,v, mask_s2n = validation.sig2noise_val( u, v, sig2noise_ratio, threshold = settings.sig2noise_threshold)
mask=mask+mask_s2n
if settings.replace_vectors==True:
u, v = filters.replace_outliers( u, v, method=settings.filter_method, max_iter=settings.max_filter_iteration, kernel_size=settings.filter_kernel_size)
'pixel/frame->pixel/sec'
u=u/settings.dt
v=v/settings.dt
'scales the results pixel-> meter'
x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = settings.scaling_factor )
'save to a file'
save(x, y, u, v,sig2noise_ratio, mask ,os.path.join(save_path,'field_A%03d.txt' % counter), delimiter='\t')
'some messages to check if it is still alive'
'some other stuff that one might want to use'
if settings.show_plot==True or settings.save_plot==True:
plt.close('all')
plt.ioff()
Name = os.path.join(save_path, 'Image_A%03d.png' % counter)
display_vector_field(os.path.join(save_path, 'field_A%03d.txt' % counter), scale=settings.scale_plot)
if settings.save_plot==True:
plt.savefig(Name)
if settings.show_plot==True:
plt.show()
print('Image Pair ' + str(counter+1))
'Below is code to read files and create a folder to store the results'
save_path=os.path.join(settings.save_path,'Open_PIV_results_'+str(settings.windowsizes[settings.iterations-1])+'_'+settings.save_folder_suffix)
if not os.path.exists(save_path):
os.makedirs(save_path)
task = tools.Multiprocesser(
data_dir=settings.filepath_images, pattern_a=settings.frame_pattern_a, pattern_b=settings.frame_pattern_b)
task.run(func=func, n_cpus=1)
# def correlation_func(cor_win_1, cor_win_2,
# correlation_method='circular',
# normalized_correlation=False):
# '''This function is doing the cross-correlation. Right now circular cross-correlation
# That means no zero-padding is done
# the .real is to cut off possible imaginary parts that remains due to finite numerical accuarcy
# '''
# if normalize:
# cor_win_1 = normalize_intensity(cor_win_1)
# cor_win_2 = normalize_intensity(cor_win_2)
# if correlation_method=='linear':
# # cor_win_1 = cor_win_1-cor_win_1.mean(axis=(1,2)).reshape(cor_win_1.shape[0],1,1)
# # cor_win_2 = cor_win_2-cor_win_2.mean(axis=(1,2)).reshape(cor_win_1.shape[0],1,1)
# # cor_win_1[cor_win_1<0]=0
# # cor_win_2[cor_win_2<0]=0
# window_size = np.array(cor_win_2.shape[1]) # 0 is the number of IW, 2 should be equal to 1
# corr = fftshift(irfft2(np.conj(rfft2(cor_win_1,s=(2*window_size,2*window_size))) *
# rfft2(cor_win_2,s=(2*window_size,2*window_size))), axes=(-2, -1))
# corr=corr[:,window_size//2:3*window_size//2,window_size//2:3*window_size//2]
# else:
# corr = fftshift(irfft2(np.conj(rfft2(cor_win_1)) *
# rfft2(cor_win_2)), axes=(-2, -1))
# return corr
# def frame_interpolation(frame, x, y, u, v, interpolation_order=1):
# '''This one is doing the image deformation also known as window deformation
# Therefore, the pixel values of the old image are interpolated on a new grid that is defined
# by the grid of the previous pass and the displacment evaluated by the previous pass
# '''
# '''
# The interpolation function dont like meshgrids as input. Hence, the the edges
# must be extracted to provide the sufficient input, also the y coordinates need
# to be inverted since the image origin is in the upper left corner and the
# y-axis goes downwards. The x-axis goes to the right.
# '''
# frame=frame.astype(np.float32)
# y1 = y[:, 0] # extract first coloumn from meshgrid
# # y1 = y1[::-1] #flip
# x1 = x[0, :] #extract first row from meshgrid
# side_x = np.arange(0, np.size(frame[0, :]), 1) #extract the image grid
# side_y = np.arange(0, np.size(frame[:, 0]), 1)
# ip = RectBivariateSpline(y1, x1, u) #interpolate the diplacement on the image grid
# ut = ip(side_y, side_x)# the way how to use the interpolation functions differs
# #from matlab
# ip2 = RectBivariateSpline(y1, x1, v)
# vt = ip2(side_y, side_x)
# '''This lines are interpolating the displacement from the interrogation window
# grid onto the image grid. The result is displacment meshgrid with the size of the image.
# '''
# x, y = np.meshgrid(side_x, side_y)#create a meshgrid
# frame_def = scn.map_coordinates(
# frame, ((y+vt, x+ut,)), order=interpolation_order,mode='nearest')
# #deform the image by using the map coordinates function
# '''This spline interpolation is doing the image deformation. This one likes meshgrids
# new grid is defined by the old grid + the displacement.
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%5
# This function returns the deformed image.
# '''
# #print('stop')
# return frame_def
def first_pass(frame_a, frame_b, window_size, overlap,iterations,correlation_method='circular', subpixel_method='gaussian',do_sig2noise=False, sig2noise_method='peak2peak', sig2noise_mask=2):
"""
First pass of the PIV evaluation.
This function does the PIV evaluation of the first pass. It returns
the coordinates of the interrogation window centres, the displacment
u and v for each interrogation window as well as the mask which indicates
wether the displacement vector was interpolated or not.
Parameters
----------
frame_a : 2d np.ndarray
the first image
frame_b : 2d np.ndarray
the second image
window_size : int
the size of the interrogation window
overlap : int
the overlap of the interrogation window normal for example window_size/2
subpixel_method: string
the method used for the subpixel interpolation.
one of the following methods to estimate subpixel location of the peak:
'centroid' [replaces default if correlation map is negative],
'gaussian' [default if correlation map is positive],
'parabolic'
Returns
-------
x : 2d np.array
array containg the x coordinates of the interrogation window centres
y : 2d np.array
array containg the y coordinates of the interrogation window centres
u : 2d np.array
array containing the u displacement for every interrogation window
u : 2d np.array
array containing the u displacement for every interrogation window
"""
cor_win_1 = pyprocess.moving_window_array(frame_a, window_size, overlap)
cor_win_2 = pyprocess.moving_window_array(frame_b, window_size, overlap)
'''Filling the interrogation window. They windows are arranged
in a 3d array with number of interrogation window *window_size*window_size
this way is much faster then using a loop'''
correlation = correlation_func(cor_win_1, cor_win_2,
correlation_method=correlation_method,
normalized_correlation=False)
'do the correlation'
disp = np.zeros((np.size(correlation, 0), 2))#create a dummy for the loop to fill
for i in range(0, np.size(correlation, 0)):
''' determine the displacment on subpixel level '''
disp[i, :] = find_subpixel_peak_position(
correlation[i, :, :], subpixel_method=subpixel_method)
'this loop is doing the displacment evaluation for each window '
disp = np.array(disp) - np.floor(np.array(correlation[0,:,:].shape)/2)
shapes = np.array(pyprocess.get_field_shape(
frame_a.shape, window_size, overlap))
u = disp[:, 1].reshape(shapes)
v = -disp[:, 0].reshape(shapes)
'reshaping the interrogation window to vector field shape'
x, y = get_coordinates(frame_a.shape, window_size, overlap)
'get coordinates for to map the displacement'
if do_sig2noise==True and iterations==1:
sig2noise_ratio = sig2noise_ratio_function(correlation, sig2noise_method=sig2noise_method, width=sig2noise_mask)
sig2noise_ratio = sig2noise_ratio.reshape(shapes)
else:
sig2noise_ratio=np.full_like(u,np.nan)
return x, y, u, v, sig2noise_ratio
def multipass_img_deform(frame_a, frame_b, window_size, overlap,iterations,current_iteration, x_old, y_old, u_old, v_old,correlation_method='circular',
subpixel_method='gaussian', do_sig2noise=False, sig2noise_method='peak2peak', sig2noise_mask=2,
MinMaxU=(-100, 50), MinMaxV=(-50, 50), std_threshold=5, median_threshold=2,median_size=1, filter_method='localmean',
max_filter_iteration=10, filter_kernel_size=2, interpolation_order=3):
"""
First pass of the PIV evaluation.
This function does the PIV evaluation of the first pass. It returns
the coordinates of the interrogation window centres, the displacment
u and v for each interrogation window as well as the mask which indicates
wether the displacement vector was interpolated or not.
Parameters
----------
frame_a : 2d np.ndarray
the first image
frame_b : 2d np.ndarray
the second image
window_size : tuple of ints
the size of the interrogation window
overlap : tuple of ints
the overlap of the interrogation window normal for example window_size/2
x_old : 2d np.ndarray
the x coordinates of the vector field of the previous pass
y_old : 2d np.ndarray
the y coordinates of the vector field of the previous pass
u_old : 2d np.ndarray
the u displacement of the vector field of the previous pass
v_old : 2d np.ndarray
the v displacement of the vector field of the previous pass
subpixel_method: string
the method used for the subpixel interpolation.
one of the following methods to estimate subpixel location of the peak:
'centroid' [replaces default if correlation map is negative],
'gaussian' [default if correlation map is positive],
'parabolic'
MinMaxU : two elements tuple
sets the limits of the u displacment component
Used for validation.
MinMaxV : two elements tuple
sets the limits of the v displacment component
Used for validation.
std_threshold : float
sets the threshold for the std validation
median_threshold : float
sets the threshold for the median validation
filter_method : string
the method used to replace the non-valid vectors
Methods:
'localmean',
'disk',
'distance',
max_filter_iteration : int
maximum of filter iterations to replace nans
filter_kernel_size : int
size of the kernel used for the filtering
interpolation_order : int
the order of the spline interpolation used for the image deformation
Returns
-------
x : 2d np.array
array containg the x coordinates of the interrogation window centres
y : 2d np.array
array containg the y coordinates of the interrogation window centres
u : 2d np.array
array containing the u displacement for every interrogation window
u : 2d np.array
array containing the u displacement for every interrogation window
mask : 2d np.array
array containg the mask values (bool) which contains information if
the vector was filtered
"""
x, y = get_coordinates(np.shape(frame_a), window_size, overlap)
'calculate the y and y coordinates of the interrogation window centres'
y_old = y_old[:, 0]
# y_old = y_old[::-1]
x_old = x_old[0, :]
y_int = y[:, 0]
# y_int = y_int[::-1]
x_int = x[0, :]
'''The interpolation function dont like meshgrids as input. Hence, the the edges
must be extracted to provide the sufficient input. x_old and y_old are the
are the coordinates of the old grid. x_int and y_int are the coordiantes
of the new grid'''
ip = RectBivariateSpline(y_old, x_old, u_old)
u_pre = ip(y_int, x_int)
ip2 = RectBivariateSpline(y_old, x_old, v_old)
v_pre = ip2(y_int, x_int)
''' interpolating the displacements from the old grid onto the new grid
y befor x because of numpy works row major
'''
frame_b_deform = frame_interpolation(
frame_b, x, y, u_pre, -v_pre, interpolation_order=interpolation_order)
'''this one is doing the image deformation (see above)'''
cor_win_1 = pyprocess.moving_window_array(frame_a, window_size, overlap)
cor_win_2 = pyprocess.moving_window_array(
frame_b_deform, window_size, overlap)
'''Filling the interrogation window. They windows are arranged
in a 3d array with number of interrogation window *window_size*window_size
this way is much faster then using a loop'''
correlation = correlation_func(cor_win_1, cor_win_2,
correlation_method=correlation_method,
normalized_correlation=False)
'do the correlation'
disp = np.zeros((np.size(correlation, 0), 2))
for i in range(0, np.size(correlation, 0)):
''' determine the displacment on subpixel level '''
disp[i, :] = find_subpixel_peak_position(
correlation[i, :, :], subpixel_method=subpixel_method)
'this loop is doing the displacment evaluation for each window '
disp = np.array(disp) - np.floor(np.array(correlation[0,:,:].shape)/2)
'reshaping the interrogation window to vector field shape'
shapes = np.array(pyprocess.get_field_shape(
np.shape(frame_a), window_size, overlap))
u = disp[:, 1].reshape(shapes)
v = -disp[:, 0].reshape(shapes)
'adding the recent displacment on to the displacment of the previous pass'
u = u+u_pre
v = v+v_pre
'validation using gloabl limits and local median'
u, v, mask_g = validation.global_val(u, v, MinMaxU, MinMaxV)
u, v, mask_s = validation.global_std(u, v, std_threshold=std_threshold)
u, v, mask_m = validation.local_median_val(u, v, u_threshold=median_threshold, v_threshold=median_threshold, size=median_size)
mask = mask_g+mask_m+mask_s
'adding masks to add the effect of alle the validations'
#mask=np.zeros_like(u)
'filter to replace the values that where marked by the validation'
if current_iteration != iterations:
'filter to replace the values that where marked by the validation'
u, v = filters.replace_outliers(
u, v, method=filter_method, max_iter=max_filter_iteration, kernel_size=filter_kernel_size)
if do_sig2noise==True and current_iteration==iterations and iterations!=1:
sig2noise_ratio=sig2noise_ratio_function(correlation, sig2noise_method=sig2noise_method, width=sig2noise_mask)
sig2noise_ratio = sig2noise_ratio.reshape(shapes)
else:
sig2noise_ratio=np.full_like(u,np.nan)
return x, y, u, v,sig2noise_ratio, mask
class Settings(object):
pass
if __name__ == "__main__":
""" Run windef.py as a script:
python windef.py
"""
settings = Settings()
'Data related settings'
# Folder with the images to process
settings.filepath_images = './examples/test1/'
# Folder for the outputs
settings.save_path = './examples/test1/'
# Root name of the output Folder for Result Files
settings.save_folder_suffix = 'Test_4'
# Format and Image Sequence
settings.frame_pattern_a = 'exp1_001_a.bmp'
settings.frame_pattern_b = 'exp1_001_b.bmp'
'Region of interest'
# (50,300,50,300) #Region of interest: (xmin,xmax,ymin,ymax) or 'full' for full image
settings.ROI = 'full'
'Image preprocessing'
# 'None' for no masking, 'edges' for edges masking, 'intensity' for intensity masking
# WARNING: This part is under development so better not to use MASKS
settings.dynamic_masking_method = 'None'
settings.dynamic_masking_threshold = 0.005
settings.dynamic_masking_filter_size = 7
'Processing Parameters'
settings.correlation_method='circular' # 'circular' or 'linear'
settings.iterations =1 # select the number of PIV passes
# add the interroagtion window size for each pass.
# For the moment, it should be a power of 2
settings.windowsizes = (128, 64, 32) # if longer than n iteration the rest is ignored
# The overlap of the interroagtion window for each pass.
settings.overlap = (64, 32, 16) # This is 50% overlap
# Has to be a value with base two. In general window size/2 is a good choice.
# methode used for subpixel interpolation: 'gaussian','centroid','parabolic'
settings.subpixel_method = 'gaussian'
# order of the image interpolation for the window deformation
settings.interpolation_order = 3
settings.scaling_factor = 1 # scaling factor pixel/meter
settings.dt = 1 # time between to frames (in seconds)
'Signal to noise ratio options (only for the last pass)'
# It is possible to decide if the S/N should be computed (for the last pass) or not
settings.extract_sig2noise = True # 'True' or 'False' (only for the last pass)
# method used to calculate the signal to noise ratio 'peak2peak' or 'peak2mean'
settings.sig2noise_method = 'peak2peak'
# select the width of the masked to masked out pixels next to the main peak
settings.sig2noise_mask = 2
# If extract_sig2noise==False the values in the signal to noise ratio
# output column are set to NaN
'vector validation options'
# choose if you want to do validation of the first pass: True or False
settings.validation_first_pass = 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.
settings.MinMax_U_disp = (-30, 30)
settings.MinMax_V_disp = (-30, 30)
# The second filter is based on the global STD threshold
settings.std_threshold = 10 # threshold of the std validation
# The third filter is the median test (not normalized at the moment)
settings.median_threshold = 3 # threshold of the median validation
# On the last iteration, an additional validation can be done based on the S/N.
settings.median_size=1 #defines the size of the local median
'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
settings.do_sig2noise_validation = False # This is time consuming
# minmum signal to noise ratio that is need for a valid vector
settings.sig2noise_threshold = 1.2
'Outlier replacement or Smoothing options'
# Replacment options for vectors which are masked as invalid by the validation
settings.replace_vectors = True # Enable the replacment. Chosse: True or False
settings.smoothn=True #Enables smoothing of the displacemenet field
settings.smoothn_p=0.5 # This is a smoothing parameter
# select a method to replace the outliers: 'localmean', 'disk', 'distance'
settings.filter_method = 'localmean'
# maximum iterations performed to replace the outliers
settings.max_filter_iteration = 4
settings.filter_kernel_size = 2 # kernel size for the localmean method
'Output options'
# Select if you want to save the plotted vectorfield: True or False
settings.save_plot = True
# Choose wether you want to see the vectorfield or not :True or False
settings.show_plot = False
settings.scale_plot = 100 # select a value to scale the quiver plot of the vectorfield
# run the script with the given settings
piv(settings)