In [172]:
# %loadpy tutorial-part1.py
In [173]:
import sys
sys.path.append('/Users/alex/Documents/OpenPIV/openpiv-python')

import openpiv.tools
import openpiv.process
import openpiv.scaling
In [174]:
frame_a  = openpiv.tools.imread( 'exp1_001_a.bmp' )
frame_b  = openpiv.tools.imread( 'exp1_001_b.bmp' )
In [175]:
winsize = 24 # pixels
searchsize = 64  # pixels, search in image B
overlap = 12 # pixels
dt = 0.02 # sec


u0, v0, sig2noise = openpiv.process.extended_search_area_piv( frame_a, frame_b, window_size=winsize, overlap=overlap, dt=dt, search_area_size=searchsize, sig2noise_method='peak2peak' )
In [176]:
x, y = openpiv.process.get_coordinates( image_size=frame_a.shape, window_size=winsize, overlap=overlap )
In [177]:
u1, v1, mask = openpiv.validation.sig2noise_val( u0, v0, sig2noise, threshold = 1.3 )
print nansum((u1 - u0)**2)
0.0
In [178]:
u2, v2 = openpiv.filters.replace_outliers( u1, v1, method='localmean', max_iter=10, kernel_size=2)
print nansum((u2 - u1)**2)
0.0
In [179]:
x, y, u3, v3 = openpiv.scaling.uniform(x, y, u2, v2, scaling_factor = 96.52 )
print nansum((u3 - u2)**2)
836665.088096
In [180]:
openpiv.tools.save(x, y, u3, v3, mask, 'exp1_001.txt' )
In [181]:
openpiv.tools.display_vector_field('exp1_001.txt', scale=100, width=0.0025)
In [182]:
# Small demonstration
x = np.array([1,2,3,4])
y = x # would preserve changes of x
z = x.copy() # would remain [1,2,3,4]

print 'x'; print x
print 'y'; print y
print 'z'; print z

# change x once:
x[0] = 10
print 'x'; print x
print 'y'; print y
print 'z'; print z
x
[1 2 3 4]
y
[1 2 3 4]
z
[1 2 3 4]
x
[10  2  3  4]
y
[10  2  3  4]
z
[1 2 3 4]
In [183]:
# returns a view of the modified array
def test_change(x):
    x[0] = 0
    return x

# returns a copy of the modified array
def test_change_with_copy(x):
    x[-1] = 25
    return x.copy()
In [184]:
x_new_copy = test_change_with_copy(x)
x_new = test_change(x)

print 'x'; print x
print 'y'; print y
print 'z'; print z
print 'x_new'; print(x_new)
print 'x_copy'; print(x_new_copy)
x
[ 0  2  3 25]
y
[ 0  2  3 25]
z
[1 2 3 4]
x_new
[ 0  2  3 25]
x_copy
[10  2  3 25]