Test script to find all locations with large swirl

Aim is to take a velocity field, find all locations with large swirl, and then identify distinct blobs of swirl.

This script makes use of the Source Extraction and Photometry (SEP) library



In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import h5py
from importlib import reload
import PIVutils



In [2]:

    
#PIVutils = reload(PIVutils)
X, Y, Swirl, Cond, Prof = PIVutils.importMatlabPIVdata2D('/Users/Owen/Dropbox/Data/ABL/SBL PIV data/RNV45-RI2.mat',['X','Y','Swirl'],['Cond','Prof'])



In [3]:

    
NanLocs = np.isnan(Swirl)
uSize = Swirl.shape
scale = (X[1,-1]-X[1,1])/(uSize[1]-1)



In [4]:

    
PIVutils = reload(PIVutils)
PIVutils.plotScalarField(X,Y,Swirl[:,:,1],50)









    Out[4]:





[<matplotlib.figure.Figure at 0x1046ba4a8>,
 <matplotlib.axes._subplots.AxesSubplot at 0x10b4f3c18>]

Normalize the swirl for distance from the wall (if wanted)



In [5]:

    
#Find profile of swirl std
SwirlStd = np.std(np.nanmean(Swirl,axis=2),axis = 1)
plt.plot(SwirlStd,Y[:,1])
plt.ylabel('y(m)')
plt.xlabel('Swirl rms')









    Out[5]:





<matplotlib.text.Text at 0x11c3080f0>



In [6]:

    
#Normalize field by the std of Swirl
SwirlNorm = Swirl/SwirlStd.reshape(uSize[0],1,1) #match the SwirlStd length (123) with the correct index in Swirl (also 123)

Plot after normalization



In [7]:

    
PIVutils.plotScalarField(X,Y,Swirl[:,:,1],30)









    Out[7]:





[<matplotlib.figure.Figure at 0x10b3d1cf8>,
 <matplotlib.axes._subplots.AxesSubplot at 0x11c324278>]



In [8]:

    
Swirl[NanLocs] = 0   #Get rid of nans for now

Create thresholded field



In [9]:

    
import copy

SwirlFilt = copy.copy(Swirl)    #think this should completely copy the list, allowing me to try things

#Swirl must be above a certain background value or it is zeroed
SwirlFilt[np.absolute(SwirlFilt)<7] = 0

#Then only keep those locations where swirls is greater than Thresh*SwirlStd
Thresh = 60
SwirlFilt[np.absolute(SwirlNorm)<Thresh] = 0

Find all blobs that agree with the thresholds



In [137]:

    
PIVutils = reload(PIVutils)
[num_features,features_per_frame, labeled_array, cent] = PIVutils.findBlobs(SwirlFilt)









    



There are  55342  features identified



In [138]:

    
[f, ax] = PIVutils.plotScalarField(X,Y,SwirlFilt[:,:,1],10)

for i in range(features_per_frame[1]):
    plt.plot(cent[1][i][1]*scale+X[1,1],cent[1][i][0]*scale+Y[1,1],'oy',markersize=4,markeredgecolor=None)

Find all blobs and filter for size



In [139]:

    
PIVutils = reload(PIVutils)
Thresh = 50
[num_features,features_per_frame, labeled_array, cent] = PIVutils.findBlobs(SwirlFilt,50)









    



There are  55342  features identified
A total of  2638  are larger than the threshold size



In [140]:

    
[f, ax] = PIVutils.plotScalarField(X,Y,SwirlFilt[:,:,1],10)

for i in range(features_per_frame[1]):
    plt.plot(cent[1][i][1]*scale+X[1,1],cent[1][i][0]*scale+Y[1,1],'oy',markersize=4,markeredgecolor=None)



In [ ]: