dirac function

[abhayku2002]

here is the `from sympy import * from sympy.plotting import plot from sympy.abc import x, k,t,f,i,j

func2 = exp(2piift) inverse_fourier_transform(func2, f, t)`

[abhayku2002]

from sympy import * from sympy.plotting import plot from sympy.abc import x, k,t,f,i,j

func2 = exp(2piift) inverse_fourier_transform(func2, f, t)

`from sympy import * from sympy.plotting import plot from sympy.abc import x, k,t,f,i,j

func2 = exp(2piift) inverse_fourier_transform(func2, f, t)`

[abhayku2002]

from sympy.plotting import plot
from sympy.abc import x, k,t,f,i,j

func2 = exp(2*pi*i*f*t)
inverse_fourier_transform(func2, f, t)```

[abhayku2002]

from sympy.plotting import plot
from sympy.abc import x, k,t,f,i,j

func2 = exp(2*pi*i*f*t)
inverse_fourier_transform(func2, f, t)

[abhayku2002]

from sympy.plotting import plot
from sympy.abc import x, k,t,f,i,j

func2 = exp(2*pi*i*f*t)
inverse_fourier_transform(func2, f, t)

[abhayku2002]

\displaystyle e^{- 2 i \pi t}

[abhayku2002]

`\displaystyle e^{- 2 i \pi t}

`

[abhayku2002]

[abhayku2002]

\displaystyle e^{- 2 i \pi t}

[abhayku2002]

$\displaystyle e^{- 2 i \pi t}$

[abhayku2002]

In the Inverse Fourier Transform, omega and t represent the frequency and time variables

from sympy import * 
from sympy.plotting import plot
from sympy.abc import x, k,t,omega,i,j

delta = DiracDelta(omega - 1)
inverse_fourier_transform(delta, omega, t)

Output: $\displaystyle e^{2 i \pi t}$