def minfuncVecField_V3_10r(params, U, V, x, y):
import numpy as np
import PIVutils
from scipy.integrate import simps
assert U.shape[0] == U.shape[1], 'Data must be a square matrix.'
assert U.shape == V.shape, 'U and V fields must be the same size'
[U2, V2] = PIVutils.genHairpinField_V3(int((U.shape[0] - 1) / 2),
*params,
x=x,
y=y)
BoxSize = int((U.shape[0] - 1) / 2)
W = np.zeros([2 * BoxSize + 1, 2 * BoxSize + 1])
X, Y = np.meshgrid(x, y)
R = np.hypot(X - params[2], Y - params[2])
W[R <= params[1]] = 1
W[R >= params[1]] = params[1] / R[R >= params[1]]
#a = W*((U-U2)**2 + (V-V2)**2)
#print(a.shape)
#Determine integral of weighting function
Area = simps(simps(W, y), x)
#Area = 1
obj = W * ((U - U2)**2 + (V - V2)**2)
#Area_lite = np.mean(W)*(x[-1]-x[0])*(y[-1]-y[0])
#return np.sum(obj)/np.mean(W) #okay but not acceptable
#return np.sum(obj)/np.mean(obj) #doesn't converge at all
return np.sum(obj) / Area
def minfuncVecField_V3_og(params, U, V, x, y):
import numpy as np
import PIVutils
from scipy.integrate import simps
assert U.shape[0] == U.shape[1], 'Data must be a square matrix.'
assert U.shape == V.shape, 'U and V fields must be the same size'
[U2, V2] = PIVutils.genHairpinField_V3(int((U.shape[0] - 1) / 2),
*params,
x=x,
y=y)
BoxSize = int((U.shape[0] - 1) / 2)
W = np.zeros([2 * BoxSize + 1, 2 * BoxSize + 1])
X, Y = np.meshgrid(x, y)
R = np.hypot(X - params[2], Y - params[2])
W[R <= params[1]] = 1
W[R >= params[1]] = params[1] / R[R >= params[1]]
#a = W*((U-U2)**2 + (V-V2)**2)
#print(a.shape)
#Determine integral of weighting function
Area = simps(simps(W, y), x)
#Area = 1
return np.sum(W * ((U - U2)**2 + (V - V2)**2)) / Area
def minfuncVecField_V2(params, U, V, x, y):
import numpy as np
import PIVutils
assert U.shape[0] == U.shape[1], 'Data must be a square matrix.'
assert U.shape == V.shape, 'U and V fields must be the same size'
[U2, V2] = PIVutils.genHairpinField_V2(int((U.shape[0] - 1) / 2),
*params,
x=x,
y=y)
return np.sum(((U - U2)**2 + (V - V2)**2))
def minfuncVecField_V5(params, U, V, x, y):
#weighting function
# w (R<r) = 1
# w (R>r) = abr/(x^2 + a^2)
#serpentine with a radius a = (R)
import numpy as np
import PIVutils
from scipy.integrate import simps
import math
assert U.shape[0] == U.shape[1], 'Data must be a square matrix.'
assert U.shape == V.shape, 'U and V fields must be the same size'
[U2, V2] = PIVutils.genHairpinField_V3(int((U.shape[0] - 1) / 2),
*params,
x=x,
y=y)
BoxSize = int((U.shape[0] - 1) / 2)
W = np.zeros([2 * BoxSize + 1, 2 * BoxSize + 1])
X, Y = np.meshgrid(x, y)
R = np.hypot(X - params[2], Y - params[2])
k = 1
r = k * params[1]
R = np.array(R)
W[R <= r] = 1
W[R >= r] = np.sqrt(R[R >= r]**2 +
r**2) + r * R[R >= r] / (R[R >= r]**2 + r**2)
#serpentine + circle
#a = W*((U-U2)**2 + (V-V2)**2)
#print(a.shape)
#Determine integral of weighting function
Area = simps(simps(W, y), x)
return np.sum(W * ((U - U2)**2 + (V - V2)**2)) / Area
def minfuncVecField10r_rect(params, U, V, x, y):
import numpy as np
import PIVutils
from scipy.integrate import simps
big_ = max(U.shape)
a, b = U.shape
if len(x) > len(y): x1 = y1 = x
else: x1 = y1 = y
[U2_, V2_] = PIVutils.genHairpinField_V3(int((big_ - 1) / 2),
*params,
x=x1,
y=y1)
[U2, V2] = Reshaped(U2_, V2_, U.shape)
W = np.zeros(U.shape)
X, Y = np.meshgrid(x, y)
R = np.hypot(X - params[2], Y - params[2])
#print(R.shape, W.shape, 'RW')
#print(x.shape, y.shape, 'xy')
W[R <= params[1]] = 1
W[R >= params[1]] = params[1] / R[R >= params[1]]
#Determine integral of weighting function
#Area = simps(simps(W, y), x)
Area = simps(simps(W, x), y)
#Area = 1
obj = W * ((U - U2)**2 + (V - V2)**2)
return np.sum(obj) / Area
def psf(G):
import PIVutils
PIVutils.plotScalarField(G)
return
def init_data(mat_path = "C:/Users/Kommalapati sahil/Desktop/owen"):
"""
used to load initial data to begin the simulations.
returns X, Y, U, V, Swirl, Cond,Prof
"""
import matplotlib.pyplot as plt
import numpy as np
import h5py
from importlib import reload
from scipy import interpolate
import PIVutils
import PODutils
import os
import sys
noEdge = True
interpVecs = True
print("current path :", sys.executable)
X, Y, U, V, Swirl, Cond, Prof = PIVutils.loadDataset(mat_path + "/RNV45-RI2.mat",\
['X','Y','U','V','Swirl'],['Cond','Prof'],matlabData = True)
#print("Uinf", Cond["Uinf"][0][0])
X = X/Cond["delta"]
Y = Y/Cond["delta"]
NanLocs = np.isnan(Swirl)
uSize = Swirl.shape
scale = (X[1,-1]-X[1,1])/(uSize[1]-1)
#interpolate
missVecs = np.zeros(U.shape)
missVecs[np.isnan(U)] = 1
PercentMissing = np.zeros(U.shape[2])
for i in range(U.shape[2]):
PercentMissing[i] = missVecs[:,:,i].sum()/(U.shape[0]*U.shape[1])*100
if interpVecs:
for i in range(uSize[2]):
#print(i)
f = interpolate.interp2d(X[0,:], Y[:,0], U[:,:,i], kind='linear')
U[:,:,i] = f(X[0,:],Y[:,0])
f = interpolate.interp2d(X[0,:], Y[:,0], V[:,:,i], kind='linear')
V[:,:,i] = f(X[0,:],Y[:,0])
f = interpolate.interp2d(X[0,:], Y[:,0], Swirl[:,:,i], kind='linear')
Swirl[:,:,i] = f(X[0,:],Y[:,0])
#remove background noise
Noise = np.std(Swirl,axis=(2,1))
Noise = np.std(Noise[-5:])
print(Noise)
SwirlFilt = Swirl.copy() #think this should completely copy the list, allowing me to try things
NoiseFilt = 20 # Filter at 20 times rms of freestream swirl
#Swirl must be above a certain background value or it is zeroed
SwirlFilt[np.absolute(Swirl)<NoiseFilt*Noise] = 0
#noramlize with std
SwirlStd = np.std(Swirl,axis=(2,1))
#print(SwirlStd)
#Normalize field by the std of Swirl
SwirlFilt = SwirlFilt/SwirlStd.reshape(uSize[0],1,1) #match the SwirlStd length (123) with the correct index in Swirl (also 123)
SwirlFiltBackup = SwirlFilt.copy()
# Create thresholded field
SwirlFilt = SwirlFiltBackup.copy() #think this should completely copy the list, allowing me to try things
#Then only keep those locations where swirls is greater than Thresh*SwirlStd
#Unified V 5 major change! *here*
ThreshSTD = 0.6
SwirlFilt[np.absolute(SwirlFilt)<ThreshSTD] = 0
SwirlFiltPro = SwirlFilt.copy()
SwirlFiltPro[SwirlFiltPro>0] = 0
SwirlFiltRet = SwirlFilt.copy()
SwirlFiltRet[SwirlFiltRet<0] = 0
return X, Y, U, V,Swirl, Cond,Prof, SwirlFiltPro, SwirlFiltRet, SwirlFilt