Profile class¶

New class implementation (2020/06/25)¶

Test new implementation of class from profile methods to objects, in analogy to what is done with pySurf. Here we test and document.

The main class is Profile, representing a set of x,y data with related information and operations.

In [7]:
from pyProfile.profile_class import Profile

Can be defined in the most trivial way from x and y:

P = Profile(x, y, units=['mm','nm'], name='profile_1')

It is generally easy to write a routine to read its own format and return a Profile object.

Helper function make_signal (see Appendix or make_signal? for details) can be used to generate a (sinusoid-based) test profile.

I can use Python introspection to get info on each function:

In [9]:
make_signal?
In [10]:
# use helper function to create x and y:
x,y = make_signal(amp=10.,L=30.,N=21,nwaves=2.8,ystartend=(0,0),noise=0)

# plot them with usual matplotlib commands:
plt.plot(x,y)
Out[10]:
[<matplotlib.lines.Line2D at 0x2f9e4cdf0b8>]

This is how a Profile object can be defined:

In [11]:
P = Profile(x,y,units=['mm','nm'],name='profile_1')
In [12]:
P.std()
Out[12]:
7.044127837632114

As well, x and y can be retrieved either as P.x and P.y, or with x,y = P()

In [13]:
P()
Out[13]:
(array([ 0. ,  1.5,  3. ,  4.5,  6. ,  7.5,  9. , 10.5, 12. , 13.5, 15. ,
        16.5, 18. , 19.5, 21. , 22.5, 24. , 25.5, 27. , 28.5, 30. ]),
 array([10.        ,  6.3742399 , -1.87381315, -8.7630668 , -9.29776486,
        -3.09016994,  5.35826795,  9.92114701,  7.28968627, -0.6279052 ,
        -8.09016994, -9.68583161, -4.25779292,  4.25779292,  9.68583161,
         8.09016994,  0.6279052 , -7.28968627, -9.92114701, -5.35826795,
         3.09016994]))
In [14]:
P.x
Out[14]:
array([ 0. ,  1.5,  3. ,  4.5,  6. ,  7.5,  9. , 10.5, 12. , 13.5, 15. ,
       16.5, 18. , 19.5, 21. , 22.5, 24. , 25.5, 27. , 28.5, 30. ])

Plotting is standard python plotting (matplotlib), accept same arguments and manipulation.

In [15]:
P.plot()
plt.title('first signal test')
Out[15]:
Text(0.5, 1.0, 'first signal test')
In [16]:
#TODO: test remove_nan_ends.
#TODO: test register_profile.

Profile methods and functions¶

Algebric operations¶

We build different test profiles.

Create two similar quadratic profiles a and b with different x values:

BEWARE: units in algebraic operations are not verified, usually the ones from first term are used for result, this may change in future.

In [17]:
# Make different test profiles:

x0 = np.arange(5)

a = Profile(x0,x0**2)
a.plot(marker='o',ls='-',label = 'a')

b =  Profile(x0+0.5,(x0+0.5)**2)
b.plot(marker='x',ls='-',label = 'b')

plt.legend(loc=0)
Out[17]:
<matplotlib.legend.Legend at 0x2f9e5502b00>

Algebraic operations can be performed on Profile objects.

Resampling can be directly accessed by resample method, but there is usually no need to perform, because it is automatically handled by algebraic operations (resample on first by default, ):

In [18]:
c = a.resample(b)
In [19]:
# plot interpolation

a.plot(marker='+',ls='-',label = 'a')
b.plot(marker='x',ls='-',label = 'b')
c.plot(marker='o',ls='',label='a resampled on b')

plt.legend(loc=0)
Out[19]:
<matplotlib.legend.Legend at 0x2f9e557cfd0>

Here some examples of algebraic operations on different x:

In [20]:
a.plot(marker='o',ls='-', label = 'a')
b.plot(marker='x',ls='-', label = 'b')
(a+b).plot(label = 'a + b',marker='o')
((a+b)/2).plot(label = '(a + b)/2',marker='d',ls='')
(3*a-b).plot(label = '(3*a - b)',marker='x',ls='-')
plt.grid()
plt.legend(loc=0)
Out[20]:
<matplotlib.legend.Legend at 0x2f9e5615748>
In [21]:
#(a/b).plot(label='a/b')
#(b/a).plot(label='b/a')
(c/b).plot()
plt.title('ratio between interpolated and original')
Out[21]:
Text(0.5, 1.0, 'ratio between interpolated and original')

Leveling¶

Outliers filtering¶

TBD

In [24]:
a=0
In [25]:
a = 1
In [26]:
print(a)

1
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