I would approximate the filter using an finite impulse design where the wrights of the FIR filter are computed based on the spectrum that the filter passes. You can do this by choosing samples of the desired frequency response (from the graphs above) and taking the inverse discrete Fourier transform of the samples. The result will be the impulse response of the desired filter. The filter is then realized as a weighted sum using the impulse response sample values as the weights. So your filter will be Ao + A1Z^-1 + A2Z^-2... Where Ao is the first value of the impulse response, A1 is the second and so on. Z^-n represents a sample delay of n (coming from a Z domain representation of the filter structure). This approach is sometimes referred to as the "frequency sampling" approach to FIR filter design. I have used this approach to create time sequences that contain specific spectral content. Google FIR filter design, frequency sampling.

I would approximate the filter using an finite impulse design where the wrights of the FIR filter are computed based on the spectrum that the filter passes. You can do this by choosing samples of the desired frequency response (from the graphs above) and taking the inverse discrete Fourier transform of the samples. The result will be the impulse response of the desired filter. The filter is then realized as a weighted sum using the impulse response sample values as the weights. So your filter will be $A_0 + A_1Z^{-1} + A_2Z^{-2}...$ Where $A_0$ is the first value of the impulse response, $A_1$ is the second and so on. $Z^{-n}$ represents a sample delay of n (coming from a Z domain representation of the filter structure). This approach is sometimes referred to as the "frequency sampling" approach to FIR filter design. I have used this approach to create time sequences that contain specific spectral content. Google FIR filter design, frequency sampling.