In [1]:

    

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

saveFolder = '/Users/Owen/Dropbox/Python Codes/ASIIT/Data/'
saveFile = 'RNV45-thumbs.hdf5'
imgFolder = saveFolder[:-5] + 'RNV45-PODthumbs'

import os
if not os.path.exists(imgFolder):
    os.makedirs(imgFolder)


    

In [2]:

    

import sys
sys.executable


    

    
Out[2]:





'/Users/Owen/anaconda/bin/python'

In [3]:

    

#PIVutils = reload(PIVutils)
#X, Y, U, V, Swirl = PIVutils.loadDataset('/Users/Owen/Dropbox/Data/ABL/Heat Flux Data/Processed Results/N/Neutral45_2.mat',['X','Y','U','V','Swirl'],[])
#X, Y, U, V, Swirl, Cond, Prof = PIVutils.loadDataset('/Users/Owen/Dropbox/Data/ABL/Heat Flux Data/Processed Results/N/Neutral45.mat',['X','Y','U','V','Swirl'],['Cond','Prof'])
#X, Y, U, V, Swirl, Cond, Prof = PIVutils.loadDataset('/Users/Owen/Dropbox/Data/ABL/SBL PIV data/RNV45-RI2.mat',\
#                                                     ['X','Y','U','V','Swirl'],['Cond','Prof'])


    

In [57]:

    

X,Y,x,y,U,V,S,missVecs_Pro,Yind_Pro,Ypos_Pro,Ur,Vr,Sr,missVecs_Ret,Yind_Ret,Ypos_Ret,Yvec,Cond,Prof,ThumbParams = \
                        PIVutils.loadDataset(saveFolder + saveFile,
                         ['X','Y','x','y','U','V','S','missVecs','Yind_Pro','Ypos_Pro','Ur','Vr','Sr','missVecs_Ret','Yind_Ret','Ypos_Ret','Yvec'], 
                         ['Cond','Prof','ThumbParams'])
BoxSize = ThumbParams['BoxSize']
uSize = U.shape


    

    




['Cond', 'Prof', 'S', 'Sr', 'ThumbParams', 'U', 'Ur', 'V', 'Vr', 'X', 'Y', 'Yind_Pro', 'Yind_Ret', 'Ypos_Pro', 'Ypos_Ret', 'Yvec', 'missVecs', 'missVecs_Ret', 'x', 'y']

In [5]:

    

#Calculate mean velocity profiles
Umean = Prof['U']

#Now get the thumbnails
thumb = 0
for i in range(uSize[2]):
    ind = Yind_Pro[i]
    #U[:,:,thumb] = U[:,:,thumb]-np.transpose(np.tile(Umean[ind-BoxSize:ind+BoxSize+1],(1,uSize[0])),(1,0))
    U[:,:,thumb] = U[:,:,thumb]-np.tile(Umean[ind-BoxSize:ind+BoxSize+1],(1,uSize[0]))#Now get the thumbnails
    thumb+=1
#del Umean


    

    




/Users/Owen/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:10: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future

In [6]:

    

#Set all NaNs to zero
U[np.isnan(U)] = 0
V[np.isnan(V)] = 0

uSize = U.shape


    

In [7]:

    

uAll = np.concatenate((U.reshape(uSize[0]*uSize[1],uSize[2]), V.reshape(uSize[0]*uSize[1],uSize[2])), axis = 0)
#uAll.shape


    

In [8]:

    

num_modes = 50;
modes, eig_vals = mr.compute_POD_matrices_snaps_method(uAll, list(range(num_modes)))


    

In [9]:

    

menergy = eig_vals/np.sum(eig_vals)
menergy_sum = np.zeros(len(menergy))
for i in range(len(menergy)):
    menergy_sum[i] = np.sum(menergy[:i]);


    

In [10]:

    

reload(PODutils)
Umodes, Vmodes= PODutils.reconstructPODmodes(modes,uSize,num_modes,2)


    

In [11]:

    

#Calculate the mode coefficients
C = modes.transpose()*uAll
C = np.asarray(C)


    

In [12]:

    

ind = np.arange(num_modes)  # the x locations for the groups
width = 1       # the width of the bars

f = plt.figure()
ax = plt.gca()
ax2 = plt.twinx()
rect = ax.bar(ind,menergy[:num_modes], width, color='gray')
line = ax2.plot(ind,menergy_sum[:num_modes],'--r')

ax.set_xlabel("Mode Number",fontsize=14)
ax.set_ylabel("Scaled Mode Energy",fontsize=14)
ax2.set_ylabel("Integrated Energy",fontsize=14,color='red')

f.savefig(saveFolder + '/ModeEnergy-Pro-10.pdf', transparent=True, bbox_inches='tight', pad_inches=0)


    

In [13]:

    

import PODutils
PODutils.plotPODmodes2D(X[:2*BoxSize+1,:2*BoxSize+1],Y[:2*BoxSize+1,:2*BoxSize+1],Umodes,Vmodes,list(range(25)),saveFolder)


    

    




/Users/Owen/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  from ipykernel import kernelapp as app
/Users/Owen/anaconda/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)