this is my code. Python creating weird plots. My question is why at first plot the measured data (that is in rage from 0 to 140) is squished into a line, not the curve

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U lightbulb.py X RadioactiveDecay.py X 1 import numpy as np NAFA979987~~*~~~NINGFFFFFF4799: 2 import matplotlib.pyplot as plt 3 5 6 10 11 16 12 data_plate = np.loadtxt("C:/Users/Trota/OneDrive/Pa60 m/UT/PHY224 LAB/radioactive decay/20: 13 (sample_plate, counts_plate)= np.loadtxt("C:/Users/Trota/OneDrive/Pa m/UT/PHY224 LAB/rad 14 sample_plate = np.array([float(i) for i in sample_plate]) 15 counts_plate = np.array( [float(i) for i in counts_plate]) 17 18 19 # Define the model functions 20 def linear_function(x, a, b): return a * x + b 21 25 22 23 def exponential_function(x, a, b): return b* np.exp(a * x) 24 26 27 28 29 delta_t = 5.0 30 Ns= counts_plate - counts_back 34 31 rates = Ns/delta_t # Calculate rates (divide by At) 35 32 u_Ns = np.full(len(counts_plate),np.sqrt(len(counts_plate) + len(counts_back))) # Uncertainty in 33 u_rates = np.sqrt(u_Ns/delta_t) # Calculate rates (divide by At) 36 from scipy.optimize import curve_fit 37 data_background = np.loadtxt("2023_10_06_AM_background.txt", delimiter='\t', skiprows=2) (sample_back, counts_back)= np.loadtxt("2023_10_06_AM_background.txt", delimiter='\t', skiprows=2, sample_back = np.array([float(i) for i in sample_back]) counts_back = np.array([float(i) for i in counts_back]) 42 43 38 39 # Perform linear regression using curve_fit 40 params_linear, covariance_linear = curve_fit(linear_function, sample_plate, np.log(rates), sigma=np. a_linear, b_linear = params_linear 41 44 # Calculate the uncertainty for each data point 45 46 # Perform nonlinear regression using curve_fit params_nonlinear, covariance_nonlinear = curve_fit (exponential_function, sample_plate, rates, sigm: a_nonlinear, b_nonlinear = params_nonlinear 16 14 12 10 0.8 0.6 0.4 0.2 0.0 le12 -Linear regression Non-linear regression Measured data 10 20 30 Time, [s] 83 % Help Variable Explorer Plots Files 50 Console 1/A X Half-life value for nonlinear regression: -0.693 + 2.402374682648461e-19 minutes Reduced Chi-Squared (Linear Fit): 111.606 Reduced Chi-Squared (Nonlinear Fit): 437834544345145671680.00 In [189]: runfile('C:/Users/Trota/OneDrive/Pa60 m/UT/PHY224 LAB/ radioactive decay/RadioactiveDecay.py', wdir='C:/Users/Trota/OneDrive/Pa60u mo/UoT/PHY224 LAB/radioactive decay') Half-life value for linear regression: 2128.2 -40652980.466 seconds Half-life value for nonlinear regression: -0.693 2.402374682648461e-19 seconds Reduced Chi-Squared (Linear Fit): 111.606 Reduced Chi-Squared (Nonlinear Fit): 437834544345145671680.00 In [190]: U lightbulb.py X RadioactiveDecay.py X 1 import numpy as np NAFA979987~~*~~~NINGFFFFFF4799: 2 import matplotlib.pyplot as plt 3 5 6 10 11 16 12 data_plate = np.loadtxt("C:/Users/Trota/OneDrive/Pa60 m/UT/PHY224 LAB/radioactive decay/20: 13 (sample_plate, counts_plate)= np.loadtxt("C:/Users/Trota/OneDrive/Pa m/UT/PHY224 LAB/rad 14 sample_plate = np.array([float(i) for i in sample_plate]) 15 counts_plate = np.array( [float(i) for i in counts_plate]) 17 18 19 # Define the model functions 20 def linear_function(x, a, b): return a * x + b 21 25 22 23 def exponential_function(x, a, b): return b* np.exp(a * x) 24 26 27 28 29 delta_t = 5.0 30 Ns= counts_plate - counts_back 34 31 rates = Ns/delta_t # Calculate rates (divide by At) 35 32 u_Ns = np.full(len(counts_plate),np.sqrt(len(counts_plate) + len(counts_back))) # Uncertainty in 33 u_rates = np.sqrt(u_Ns/delta_t) # Calculate rates (divide by At) 36 from scipy.optimize import curve_fit 37 data_background = np.loadtxt("2023_10_06_AM_background.txt", delimiter='\t', skiprows=2) (sample_back, counts_back)= np.loadtxt("2023_10_06_AM_background.txt", delimiter='\t', skiprows=2, sample_back = np.array([float(i) for i in sample_back]) counts_back = np.array([float(i) for i in counts_back]) 42 43 38 39 # Perform linear regression using curve_fit 40 params_linear, covariance_linear = curve_fit(linear_function, sample_plate, np.log(rates), sigma=np. a_linear, b_linear = params_linear 41 44 # Calculate the uncertainty for each data point 45 46 # Perform nonlinear regression using curve_fit params_nonlinear, covariance_nonlinear = curve_fit (exponential_function, sample_plate, rates, sigm: a_nonlinear, b_nonlinear = params_nonlinear 16 14 12 10 0.8 0.6 0.4 0.2 0.0 le12 -Linear regression Non-linear regression Measured data 10 20 30 Time, [s] 83 % Help Variable Explorer Plots Files 50 Console 1/A X Half-life value for nonlinear regression: -0.693 + 2.402374682648461e-19 minutes Reduced Chi-Squared (Linear Fit): 111.606 Reduced Chi-Squared (Nonlinear Fit): 437834544345145671680.00 In [189]: runfile('C:/Users/Trota/OneDrive/Pa60 m/UT/PHY224 LAB/ radioactive decay/RadioactiveDecay.py', wdir='C:/Users/Trota/OneDrive/Pa60u mo/UoT/PHY224 LAB/radioactive decay') Half-life value for linear regression: 2128.2 -40652980.466 seconds Half-life value for nonlinear regression: -0.693 2.402374682648461e-19 seconds Reduced Chi-Squared (Linear Fit): 111.606 Reduced Chi-Squared (Nonlinear Fit): 437834544345145671680.00 In [190]: