SLiCAPmath.py

SLiCAP module with math functions.

PdBm2V(p, r)

Returns the RMS value of the voltage that generates p dBm power in a resistor with resistance r.

Parameters:

  • p (sympy.Symbol, sympy.Expression, int, or float) – Power in dBm

  • r (sympy.Symbol, sympy.Expression, int, or float) – Resistance

Returns:

voltage

Return type:

sympy.Expression

assumePosParams(expr, params='all')

Returns the sympy expression ‘expr’ in which variables, except the Laplace variable, have been redefined as real.

Parameters:

  • expr (sympy.Expr, sympy.Symbol) – Sympy expression

  • params (list, str) – List with variable names (str), or ‘all’ or a variable name (str).

Returns:

Expression with redefined variables.

Return type:

sympy.Expr, sympy.Symbol

assumeRealParams(expr, params='all')

Returns the sympy expression ‘expr’ in which variables, except the Laplace variable, have been redefined as real.

Parameters:

  • expr (sympy.Expr, sympy.Symbol) – Sympy expression

  • params (list, str) – List with variable names (str), or ‘all’ or a variable name (str).

Returns:

Expression with redefined variables.

Return type:

sympy.Expr, sympy.Symbol

besselPoly(n)

Returns a normalized Bessel polynomial of the n-th order of the Laplace variable.

Parameters:

n (int) – order

Returns:

Bessel polynomial of the n-th order of the Laplace variable

Return type:

sympy.Expression

butterworthPoly(n)

Returns a narmalized Butterworth polynomial of the n-th order of the Laplace variable.

Parameters:

n (int) – order

Returns:

Butterworth polynomial of the n-th order of the Laplace variable

Return type:

sympy.Expression

clearAssumptions(expr, params='all')

Returns the sympy expression ‘expr’ in which the assumtions ‘Real’ and ‘Positive’ have been deleted.

Parameters:

  • expr (sympy.Expr, sympy.Symbol) – Sympy expression

  • params (list, str) – List with variable names (str), or ‘all’ or a variable name (str).

Returns:

Expression with redefined variables.

Return type:

sympy.Expr, sympy.Symbol

coeffsTransfer(rational, var=s, method='lowest')

Returns a nested list with the coefficients of the variable of the numerator and of the denominator of ‘rational’.

The coefficients are in ascending order.

Parameters:

  • rational (sympy.Expr) – Rational function of the variable.

  • variable (sympy.Symbol) – Variable of the rational function

  • method (str) –

    Normalization method:

    • ”highest”: the coefficients of the highest order of <variable> of the denominator will be noramalized to unity.

    • ”lowest”: the coefficients of the lowest order of <variable> of the denominator will be noramalized to unity.

Returns:

Tuple with gain and two lists: [gain, numerCoeffs, denomCoeffs]

  1. gain (sympy.Expr): ratio of the nonzero coefficient of the lowest order of the numerator and the coefficient of the nonzero coefficient of the lowest order of the denominator.

  2. numerCoeffs (list): List with all coeffcients of the numerator in ascending order.

  3. denomCoeffs (list): List with all coeffcients of the denominator in ascending order.

Return type:

tuple

det(M, method='ME')

Returns the determinant of a square matrix ‘M’ calculated using recursive minor expansion (Laplace expansion). For large matrices with symbolic entries, this is faster than the built-in sympy.Matrix.det() method.

Parameters:

  • M (sympy.Matrix) – Sympy matrix

  • method –

    Method used:

    • ME: SLiCAP Minor expansion

    • BS: SLiCAP Bareis fraction-free expansion

    • FC: SLiCAP Frequency Constants method

    • LU: Sympy built-in LU method

    • bareis: Sympy built-in Bareis method

Returns:

Determinant of ‘M’

Return type:

sympy.Expr

doCDS(noiseResult, tau)

Returns noiseResult after multiplying it with (2*sin(pi*ini.frequency*tau))^2

Parameters:

  • noiseResult (sympy.Expr, sympy.Symbol, int or float) – sympy expression of a noise density spectrum in V^2/Hz or A^2/Hz

  • tau (sympy.Expr, sympy.Symbol, int or float) – Time between two samples

Returns:

noiseResult*(2*sin(pi*ini.frequency*tau))^2

Return type:

sympy.Expr, sympy.Symbol, int or float

doCDSint(noiseResult, tau, f_min, f_max)

Returns the integral from ini.frequency = f_min to ini.frequency = f_max, of a noise spectrum after multiplying it with (2*sin(pi*ini.frequency*tau))^2

Parameters:

  • noiseResult (sympy.Expr, sympy.Symbol, int or float) – sympy expression of a noise density spectrum in V^2/Hz or A^2/Hz

  • tau (sympy.Expr, sympy.Symbol, int or float) – Time between two samples

  • f_min (sympy.Expr, sympy.Symbol, int or float) – Lower limit of the integral

  • f_max (sympy.Expr, sympy.Symbol, int or float) – Upper limit of the integral

Returns:

integral of the spectrum from f_min to f_max after corelated double sampling

Return type:

sympy.Expr, sympy.Symbol, int or float

equateCoeffs(protoType, transfer, noSolve=[], numeric=True)

Returns the solutions of the equation transferFunction = protoTypeFunction.

Both transfer and prototype should be Laplace rational functions. Their numerators should be polynomials of the Laplace variable of equal order and their denominators should be polynomials of the Laplace variable of equal order.

Parameters:

  • protoType (sympy.Expr) – Prototype rational expression of the Laplace variable

  • transfer

Transfer fucntion of which the parameters need to be solved. The numerator and the denominator of this rational expression should be of the same order as those of the prototype.

Parameters:

  • noSolve (list) – List with variables (str, sympy.core.symbol.Symbol) that do not need to be solved. These parameters will remain symbolic in the solutions.

  • numeric (bool) – True will convert numeric results with floats instead of rationals

Returns:

Dictionary with key-value pairs:

  • key: name of the parameter (sympy.core.symbol.Symbol)

  • value: solution of this parameter: (sympy.Expr, int, float)

Return type:

dict

findServoBandwidth(loopgainRational)

Determines the intersection points of the asymptotes of the magnitude of the loopgain with unity.

Parameters:

loopgainRational – Rational function of the Laplace variable, that represents the loop gain of a circuit.

Returns:

Dictionary with key-value pairs:

  • hpf: frequency of high-pass intersection

  • hpo: order at high-pass intersection

  • lpf: frequency of low-pass intersection

  • lpo: order at low-pass intersection

  • mbv: mid-band value of the loopgain (highest value at order = zero)

  • mbf: lowest freqency of mbv

Return type:

dict

float2rational(expr)

Converts floats in expr into rational numbers.

Parameters:

expr (sympy.Expression) – Sympy expression in which floats need to be converterd into rational numbers.

Returns:

expression in which floats have been replaced with rational numbers.

Return type:

sympy.Expression

fullSubs(valExpr, parDefs)

Returns ‘valExpr’ after all parameters of ‘parDefs’ have been substituted into it recursively until no changes occur or until the maximum number of substitutions is achieved.

The maximum number opf recursive substitutions is set by ini.maxRexSubst.

Parameters:

  • valExpr (sympy.Expr, sympy.Symbol, int, float) – Eympy expression in which the parameters should be substituted.

  • parDefs –

    Dictionary with key-value pairs:

    • key (sympy.Symbol): parameter name

    • value (sympy object, int, float): value of the parameter

Returns:

Expression or value obtained from recursive substitutions of parameter definitions into ‘valExpr’.

Return type:

sympy object, int, float

ilt(expr, s, t, integrate=False)

Returns the Inverse Laplace Transform f(t) of an expression F(s) for t > 0.

Parameters:

  • expr (Sympy expression, integer, float, or str.) – Function of the Laplace variable F(s).

  • s (sympy.Symbol) – Laplace variable

  • t (sympy.Symbol) – time variable

  • integrate (Bool) – True multiplies expr with 1/s, defaults to False

return: Inverse Laplace Transform f(t) :rtype: sympy.Expr

nonPolyCoeffs(expr, var)

Returns a dictionary with coefficients of negative and positive powers of var.

Parameters:

var (sympy.Symbol) – Variable of which the coefficients will be returned

Returns:

Dict with key-value pairs:

  • key: order of the variable

  • value: coefficient of that order

normalizeRational(rational, var=s, method='lowest')

Normalizes a rational expression to:

\[F(s) = gain\,s^{\ell} \frac{1+b_1s + ... + b_ms^m}{1+a_1s + ... + a_ns^n}\]
Parameters:

  • Rational (sympy.Expr) – Rational function of the variable.

  • var (sympy.Symbol) – Variable of the rational function

  • method (str) –

    Normalization method:

    • ”highest”: the coefficients of the highest order of <variable> of the denominator will be noramalized to unity.

    • ”lowest”: the coefficients of the lowest order of <variable> of the denominator will be noramalized to unity.

Returns:

Normalized rational function of the variable.

Return type:

sympy.Expr

phaseMargin(LaplaceExpr)

Calculates the phase margin assuming a loop gain definition according to the asymptotic gain model.

This function uses scipy.newton() for determination of the the unity-gain frequency. It uses the function SLiCAPmath.findServoBandwidth() for the initial guess, and ini.disp for the relative accuracy.

if ini.hz == True, the units will be degrees and Hz, else radians and radians per seconds.

Parameters:

LaplaceExpr (sympy.Expr, list) – Univariate function (sympy.Expr*) or list with univariate functions (sympy.Expr*) of the Laplace variable.

Returns:

Tuple with phase margin (float) and unity-gain frequency (float), or Tuple with lists with phase margins (float) and unity-gain frequencies (float).

Return type:

tuple

rational2float(expr)

Converts rational numbers in expr into floats.

Parameters:

expr (sympy.Expression) – Sympy expression in which rational numbers need to be converterd into floats.

Returns:

expression in which rational numbers have been replaced with floats.

Return type:

sympy.Expression

rmsNoise(noiseResult, noise, fmin, fmax, source=None, CDS=False, tau=None)

Calculates the RMS source-referred noise or detector-referred noise, or the contribution of a specific noise source or a collection od sources to it.

Parameters:

  • noiseResult (SLiCAPprotos.allResults) – Results of the execution of an instruction with data type ‘noise’.

  • noise – ‘inoise’ or ‘onoise’ for source-referred noise or detector- referred noise, respectively.

  • fmin (str, int, float, sp.Symbol) – Lower limit of the frequency range in Hz.

  • fmax (str, int, float, sp.Symbol) – Upper limit of the frequency range in Hz.

  • source (str, list) – refDes (ID) or list with IDs of noise sources of which the contribution to the RMS noise needs to be evaluated. Only IDs of current of voltage sources with a nonzero value for ‘noise’ are accepted.

  • CDS (Bool) – True if correlated double sampling is required, defaults to False If True parameter ‘tau’ must be given a nonzero finite value (can be symbolic)

  • tau (str, int, float, sp.Symbol) – CDS delay time

Returns:

RMS noise over the frequency interval.

  • An expression or value if parameter stepping of the instruction is disabled.

  • A list with expressions or values if parameter stepping of the instruction is enabled.

Return type:

int, float, sympy.Expr

roundN(expr, numeric=False)

Rounds all float and rational numbers in an expression to ini.disp digits, and converts integers into floats if their number of digits exceeds ini.disp

Parameters:

  • expr (sympy.Expr) – Input expression

  • numeric (Bool) – True if numeric evaluation (pi = 3.14…) must be done

Returns:

modified expression

Return type:

sympy.Expr

routh(charPoly, eps=epsilon)

Returns the Routh array of a polynomial of the Laplace variable (ini.laplace).

Parameters:

  • charPoly (sympy.Expr) – Expression that can be written as a polynomial of the Laplace variable (ini.laplace).

  • eps (sympy.Symbol) – Symbolic variable used to indicate marginal stability. Use a symbol that is not present in charPoly.

Returns:

Routh array

Return type:

sympy.Matrix

Example:

>>> # ini.laplace = sp.Symbol('s')
>>> s, eps = sp.symbols('s, epsilon')
>>> charPoly = s**4+2*s**3+(3+k)*s**2+(1+k)*s+(1+k)
>>> M = routh(charPoly, eps)
>>> print(M.col(0)) # Number of roots in the right half plane is equal to
>>>                 # the number of sign changes in the first column of the
>>>                 # Routh array
Matrix([[1], [2], [k/2 + 5/2], [(k**2 + 2*k + 1)/(k + 5)], [k + 1]])
step2PeriodicPulse(ft, t_pulse, t_period, n_periods)

Converts a step response in a periodic pulse response. Works with symbolic and numeric time functions.

For evaluation of numeric values, use the SLiCAP function: _makeNumData().

Parameters:

  • ft (sympy.Expr) – Time function f(t)

  • t_pulse (int, float) – Pulse width

  • t_period (int, float) – Pulse period

  • n_periods – Number of pulses

Typen_periods:

int, float

Returns:

modified time function

Return type:

sympy.Expr