Hello, I am a full time student and I need help with my homework. I have the pyt

Hello,
I am a full time student and I need help with my homework. I have the python code for part 1 but I am getting an error ” could not find hash sign (#) indicating the start of the block”. I need help with someone who understand this type of homework. You can re-use my code and fix whatever you need to fix to complete my homework. The python code worked before with the same machine called Vector Network Analyzer (VNA). The code I am providing is for part 1 of the homework. please, use my code and figure out how to fix part 1. Then, provide the code for part 2.
Let me explain the homework.
The specific goals of this project are as follows:
Part-1. Characterize the RF pulse in the time domain – How close does it approach a gaussian pulse (provide estimates of ????t)? Does its shape better fit any other well-known function such as the sinc2 function? Use the minimum mean square estimate to explain your analysis.
Part-2. Characterize the RF pulse in the frequency domain – use your knowledge of RF spectrum analysis to characterize the pulse in the frequency. Capture the RF spectrum from the spectrum analyzer and use the point-wise frequency data for further processing steps. Using the FFT function (Python, MATLAB etc.) rebuild the time-domain profile of the pulse (in different VNA modes). Compare the time-domain shape of the rebuilt pulse with that captured from the VNA using MMSE methodology.
The instructor gave us this webiste to find information about the code:
Then use a function such as:
https://www.mathworks.com/help/curvefit/fit.html
to characterize the shape of the pulse.
Does it fit a Gaussian model (calc mean squared error) or does it fit a sinc^2 model (calc MSE)? Explain.
Here is the python code for part-1:
#Homework part-1
#
import visa
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit
from scipy.fft import ifft
# Initialize and configure VNA
def initialize_vna():
rm = visa.ResourceManager()
inst = rm.open_resource(‘USB0::0x0957::0x1309::MY49203280::0::INSTR ‘)
inst.timeout = 30000
return inst
def configure_vna_for_s11(inst):
inst.write(‘:TRIG:POIN ON’)
inst.write(‘:TRIG:SOUR INT’)
inst.write(‘:INIT1:CONT ON’)
inst.write(‘:FORM:DATA REAL,64’)
inst.write(‘:FORM:BORD SWAP’)
inst.write(‘:CALC1:PAR:COUN 1’) # Only S11
inst.write(‘:CALC1:PAR1:DEF S11’)
def acquire_s11_data(inst):
inst.write(‘:CALC1:PAR1:SEL’)
data = inst.query_binary_values(‘:CALC1:DATA:SDAT?’, datatype=’d’)
return data
def get_frequency_data(inst):
return inst.query_binary_values(‘:SENS1:FREQ:DATA?’, datatype=’d’)
def convert_to_time_domain(frequency, s11_data):
# Assuming data is interleaved as real and imaginary parts
real = s11_data[0::2]
imaginary = s11_data[1::2]
complex_data = np.array(real) + 1j * np.array(imaginary)
time_data = ifft(complex_data)
return time_data
def gaussian_function(t, a, t0, sigma):
return a * np.exp(-(t – t0)**2 / (2 * sigma**2))
def fit_gaussian(time_data, pulse_data):
popt, _ = curve_fit(gaussian_function, time_data, pulse_data, p0=[1, 0, 1])
return popt
def plot_data(time, data, fitted_data):
plt.plot(time, data, label=’Measured Data’)
plt.plot(time, fitted_data, label=’Fitted Gaussian’, linestyle=’–‘)
plt.title(‘Time Domain Data and Gaussian Fit’)
plt.xlabel(‘Time’)
plt.ylabel(‘Amplitude’)
plt.legend()
plt.grid(True)
plt.show()
def main():
inst = initialize_vna()
configure_vna_for_s11(inst)
frequency = get_frequency_data(inst)
s11_data = acquire_s11_data(inst)
time_data = convert_to_time_domain(frequency, s11_data)
time = np.linspace(0, len(time_data)/max(frequency), len(time_data)) # Time vector
popt = fit_gaussian(time, np.abs(time_data))
fitted_data = gaussian_function(time, *popt)
plot_data(time, np.abs(time_data), fitted_data)
print(f’Gaussian Parameters: Amplitude={popt[0]}, Center={popt[1]}, Sigma={popt[2]}’)
inst.close()
if __name__ == “__main__”:
main()
please see attached file