Functions operating on waveforms

pycircuit.post.functions

This module contains functions that operates on wave objects or scalars

pycircuit.post.functions.IIP2(output, input, fund1, fund2, fund0=None)

Calculate input referred second order intermodulation intercept point

The intermodulation product is evaluated at fund1 + fund2

pycircuit.post.functions.IIP3(output, input, fund1, fund2, fund0=None)

Calculate input referred third order intermodulation intercept point

The intermodulation product is evaluated at fund1 + 2 * fund2

pycircuit.post.functions.IM2(w, fund1, fund2, fund0=None)

Return input referred third order intermodulation tone

The intermodulation product is evaluated at fund1 + fund2

pycircuit.post.functions.IM3(w, fund1, fund2, fund0=None)

Return input referred third order intermodulation tone

The intermodulation product is evaluated at fund1 + 2 * fund2

pycircuit.post.functions.average(w, axis=-1)

Calculate average

Example:

>>> w1=Waveform([range(2), range(2)],array([[1.0, 3.0], [0.0, 5.0]]))
>>> average(w1)
Waveform(array([0, 1]), array([ 2. ,  2.5]))

>>> w1=Waveform([range(2), range(2)],array([[1.0, 3.0], [0.0, 5.0]]),                     xlabels=['row','col'])
>>> average(w1, axis='row')
Waveform(array([0, 1]), array([ 0.5,  4. ]))

pycircuit.post.functions.bandwidth(w, db=3.0, type='low')

Calculate bandwidth of transfer as function of frequency

Example

>>> w = 2 * pi * np.logspace(3,8)
>>> w1 = -1e6
>>> H = Waveform(w, 1 / (1 - 1j*w / w1))
>>> bandwidth(H)
1000896.9666087811

pycircuit.post.functions.calc_extrapolation_line(w_db, slope, extrapolation_point=None, axis=-1, plot=False, plotargs={})

Return linear extrapolation line and optionally plot it

pycircuit.post.functions.clip(w, xfrom, xto=None)

pycircuit.post.functions.compression_plot(w_db, extrapolation_point=None, compression=1, axis=-1)

Plot of compression point of a quantity in dB

Both x and y axis should be in dB units

pycircuit.post.functions.compression_point(w_db, slope=1, compression=1, extrapolation_point=None, axis=-1)

Return input referred compression point

pycircuit.post.functions.cross(w, crossval=0.0, n=0, crosstype=3, axis=-1)

Calculates the x-axis value where a particular crossing with the specified edge type occurs

Examples

1-d waveform

>>> phi = arange(0, 4*pi, pi/10)-pi/4
>>> y = Waveform(phi, sin(phi))
>>> cross(y)
0.0
>>> cross(y, crosstype=falling)
3.1415926535897931

2-d waveform

>>> x1 = [pi/4,pi/2]
>>> x2 = arange(0, 4*pi, pi/10)-pi/4
>>> phi = vstack([x2 for p in x1])
>>> y = Waveform([x1,x2], sin(phi))
>>> cross(y)
Waveform(array([ 0.78539816,  1.57079633]), array([ 0.,  0.]))

No crossing

>>> cross(Waveform([[0,1,2,3]], array([1,1,1,1])))
nan

Todo

handle case where x-values are exactly at the crossing

pycircuit.post.functions.db10(w)

Return x in dB where x is assumed to be a non-power quantity

>>> w1=Waveform(array([1,2,3]),array([complex(-1,0),complex(0,1),2]))
>>> db10(w1)
Waveform(array([1, 2, 3]), array([ 0.        ,  0.        ,  3.01029996]))

pycircuit.post.functions.db20(w)

Return x in dB where x is assumed to be a non-power quantity

>>> w1=Waveform(array([1,2,3]),array([complex(-1,0),complex(0,1),               complex(1,-1)]), ylabel='x')
>>> db20(w1)
Waveform(array([1, 2, 3]), array([ 0.        ,  0.        ,  3.01029996]))
>>> db20(w1).ylabel
'db20(x)'

pycircuit.post.functions.deriv(w)

Calculate derivative of a waveform with respect to the inner x-axis

pycircuit.post.functions.dft(w)

Calculates the discrete Fourier transform of the input waveform

pycircuit.post.functions.phase(w)

Return argument in degrees of complex values

Example:

>>> phase(1)
0.0
>>> phase(complex(0,1))
90.0
>>> phase(Waveform((range(3),), array([1, complex(1,1), complex(0,-1)])))
Waveform(array([0, 1, 2]), array([  0.,  45., -90.]))

pycircuit.post.functions.phase_margin(g)

Calculate phase margin of a loop gain vs frequency waveform

>>> w = 2 * pi * np.logspace(3,8,41)
>>> w1 = -1e6
>>> H = Waveform(w, 1.5 * (1 / (1 - 1j*w / w1))**2)
>>> '%0.4g'%phase_margin(H)
'110.4'

pycircuit.post.functions.rms(w, axis=-1)

Calculate root-mean-square

pycircuit.post.functions.stddev(w, axis=-1)

Calculate the standard deviation

Returns the standard deviation over the highest dimension, a measure of the spread of a distribution.

Example

>>> w1=Waveform([range(2), range(4)], array([[1,2,3,4],[1,1,1,1]]))
>>> stddev(w1)
Waveform(array([0, 1]), array([ 1.11803399,  0.        ]))

pycircuit.post.functions.unityGainFrequency(g)

Calculate the frequency where the gain is unity