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In mathematics, an elementary function is a function of a single variable (typically real or complex) that is defined as taking sums, products, roots and compositions of finitely many polynomial, rational, trigonometric, hyperbolic, and exponential functions, including possibly their inverse functions (e.g., arcsin, log, or x1/n). All elementary functions are continuous on their domains. Elementary functions were introduced by Joseph Liouville in a series of papers from 1833 to 1841. An algebraic treatment of elementary functions was started by Joseph Fels Ritt in the 1930s. Elementary functions of a single variable x include: Constant functions: etc. Rational powers of x: etc. Exponential functions: Logarithms: Trigonometric functions: etc. Inverse trigonometric functions: etc. Hyperbolic functions: etc. Inverse hyperbolic functions: etc. All functions obtained by adding, subtracting, multiplying or dividing a finite number of any of the previous functions All functions obtained by root extraction of a polynomial with coefficients in elementary functions All functions obtained by composing a finite number of any of the previously listed functions Certain elementary functions of a single complex variable z, such as and , may be multivalued. Additionally, certain classes of functions may be obtained by others using the final two rules. For example, the exponential function composed with addition, subtraction, and division provides the hyperbolic functions, while initial composition with instead provides the trigonometric functions. Examples of elementary functions include: Addition, e.g. (x+1) Multiplication, e.g. (2x) Polynomial functions The last function is equal to , the inverse cosine, in the entire complex plane. All monomials, polynomials, rational functions and algebraic functions are elementary. The absolute value function, for real , is also elementary as it can be expressed as the composition of a power and root of : .
[2]Paolo Prandoni, [3]LCAV, EPFL
In [1]:
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as sp
import seaborn as sns
sns.set()
In [2]:
plt.rcParams["figure.figsize"] = (14,4)
The basic reproduction number[4]¶
The propagation mechanism of an epidemic such as the one caused by
SARS-CoV-2 virus can be roughly modeled, at least in its inital phase,
as a process in which each infected individ [2]Paolo Prandoni, [3]LCAV, EPFL
The Parks-McClellan (PM) algorithm is a clever application of advanced
polynomial fitting techniques to the problem of FIR filter design. In
this notebook we will explore the key ideas behind the method by
considering the design of a simple Type-I lowpass filter.
NOTE: This notebook uses interactive widgets. If the interactive
controls do not work or show up, please fo