Analog Filter Design
Analog Filter Design
Part. 2: Scipy (Python) Signals Tools
P. Bruschi - Analog Filter Design
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Python - Scipy
Modules:
Standard Library Optional modules
................ ................ Scientific Python .................
numpy: functions, array, solvers etc. scipy: scientific and engineering modules matplotlib: 2D, 3D plotting functions
.................
Anaconda distribution: includes all scientific modules and takes care of all possible
dependences
Suggested Editor: Idle ( \Lib \ Idlelib \Idle.bat) Suggested .py files open method: place a link to Idle.bat into windows "send to" folder (lo locate the "send to" folder, execute the command "shell:sendto" with the Windows "Run" dialog box, which can be called with the keystrokes: Win + R)
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Array ? like structures in python
Base Python: Lists: e.g. >a=[1,3,5,"aaa",[2,3]] # (non homogeneous data types) Tuple: e.g. > a=(1,3,5,"aaa",[2,3]) # (non homogeneous data types) ......... Numpy Arrays (class ndarray)
> import numpy as np > a=np.array([1.0,3.67,2.9]) (homogeneous numerical data) Many numpy functions accept both lists (or tuples) and arrays as arguments, but convert everything to array
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Array generation functions
Creation of 1D array a=np.arange(start,stop,step) a=np.linspace(start, stop, num_points) a=np.logspace(first_dec,last_dec, num_point)
Array importing and saving (from / to text files) A=np.loadtxt("nome_file") np.savetxt(A) It is possible to specify a data delimiter through an optional argument: delimiter=" ". Default is space.
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Array indexing and manipulation
Example: 2D array: a[k,h] (this notation does not work with lists)
Application of functions to arrays: element by element Example np.sin(a) -> Returns an array by applying the sin() function
to all the elements of a
Array stacking. Example: A=np.array([[1,4],[-2.5,6]]); b=np.array([22,11])
A
1 2.5
64
b 1212
np.row_stack([A,b]) np.column_stack([A,b])
1 4
2.5 6
22
11
1 2.5
4 6
22 11
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