Modeling global warming with a sinusoidal fit in Python

I would like to model global warming from temperature records recorded daily from June 1920 to October 2019 in Montélimar on Python. To do this, I would first like to model these seasonal variations by a sinusoidal fit.

Here is the sinusoidal model that I have reproduced: $$T(t) = A \sin(\omega t + \phi) + B$$ where the parameters A (amplitude), 𝜙 (phase) and B (average temperature) are fitted to the data with $$\omega = \frac{2 \pi}{365}$$

However, such a model fitted to the whole data set does not give any increase in average temperature. I therefore try to apply a sinusoidal fit for each decade.

I first plotted the data in the data file like this:

# Import of modulesimport numpy as npimport matplotlib.pyplot as pltimport pandas as pdfrom scipy.optimize import curve_fit# Read the montelimar_temperature containing the dataDate, Temperature = np.loadtxt('montelimar_temperature.dat', unpack = True)Date = Date + 2400000.5 # Conversion of dates from Modified Julian Days to Julian Daysdate_new = pd.to_datetime(Date, unit = 'D', origin = "julian") # Convert Julian Days dates to datetime64 format# Size of the graphplt.figure(figsize = (25, 9))# Plot of the curveplt.plot(date_new, Temperature)# Title and othersplt.title("Evolution of daily recorded temperatures in Montélimar between 1920 and 2020", fontsize = 30) # Titleplt.xlabel("Year", fontsize = 20) # Title of the absissaplt.ylabel("Temperature (°C)", fontsize = 20) # Title of the ordinatesplt.grid()plt.show()

Then I created a time variable so I could do my decadal average. I applied the sine fit to all the decades in the data file, then plotted the entire graph with the fit. You will find my code below. It works flawlessly, but I feel like I'm repeating myself a lot with the code. I feel like I could do this without so much repetition, but every time I try I get errors and my graphs don't plot correctly. That's why I would like to know if there is a way to rewrite this in a cleaner way.

Here is the code I would like to simplify:

# Definition of the function for sinusoidal adjustmentdef sinLaw(t, A, phi, B):    omega = (2 * np.pi) / 365    return A * np.sin(omega * t + phi) + Bimport datetimecurrent_decade = np.datetime64(date_new[0], 'Y')time_for_B = np.linspace(1930, 2020, 10)#print(time_for_B)count_time = np.array([]) # Variable to store the temperatures of a decadecount_date = np.array([])B_list = np.array([])n = 1for i in range(0, len(date_new)): # Browse the date_new indexes to simply access the date_new value and the corresponding temperature    if np.datetime64(date_new[i], 'Y') >= current_decade + np.timedelta64(10, 'Y'): # Arrived at a new decade        current_decade = current_decade + np.timedelta64(10, 'Y') # Change in the current decade        n = n + 1        plt.figure(n)        plt.plot(count_date, count_time)        N = len(count_date)        time_model = np.linspace(0, N, N)        # Fit of the linear model        solution = curve_fit(sinLaw, time_model, count_time)        # Identification of the parameters        A, phi, B = solution[0]        # Display the result        #print('A = {:4.2f} amplitude'.format(A))        #print('B = {:4.2f} °C'.format(B))        #print('phi = {:4.2f} radians'.format(phi))        # Display the sine fit        y = sinLaw(time_model, A, phi, B)        B_list = np.append(B_list, B)        plt.plot(count_date, y)        errors = 5. * np.ones(y.shape)        # Fit of the linear model        solution, pcov = curve_fit(sinLaw, time_model, y, sigma = errors, absolute_sigma = True)        # Identification of the model parameters        A, phi, B = solution        # Calculation of the uncertainty on the fitted parameters        perr = np.sqrt(np.diag(pcov))        # Display        print('B = {:5.7f} ± {:5.3f} °C'.format(B, perr[0]))        count_time = np.array([])        count_date = np.array([])    count_time = np.append(count_time, Temperature[i])    count_date = np.append(count_date, date_new[i])n = n + 1plt.figure(n)plt.plot(count_date, count_time, '.')N = len(count_date)time_model = np.linspace(0, N, N)# Fit of the linear modelsolution = curve_fit(sinLaw, time_model, count_time)# Identification of the parametersA, phi, B = solution[0]# Display the result#print('A = {:4.2f} amplitude'.format(A))#print('B = {:4.2f} °C'.format(B))#print('phi = {:4.2} radians'.format(phi))# Display the sine fity = sinLaw(time_model, A, phi, B)B_list = np.append(B_list, B)plt.plot(count_date, y)#print('B =', B_list)plt.grid()plt.figure(n + 1)plt.plot(time_for_B, B_list)plt.grid()# Definition of the table of measurement errorserrors = 0.117 * np.ones(B_list.shape)solution, pcov = curve_fit(sinLaw, time_model, count_time)# Identification of the model parametersA, phi, B = solutionperr = np.sqrt(np.diag(pcov))# Displayprint('B = {:5.7f} ± {:5.3f} °C'.format(B, perr[0]))# Graphical representation of the data with the error barsplt.errorbar(time_for_B, B_list, yerr = errors, marker = '+', linestyle = '')# Graph optionplt.xlabel('Date [year]')plt.ylabel('B [°C]')plt.show()

Here is the result I get with this code and which corresponds to what I expect:

I can't put all the data of my file here because it contains 36296 lines and I can't attach a file but you will find below a sample of the first 100 lines of my file which has the name "montelimar_temperature.dat":

The columns have no names. The first column corresponds to dates in Modified Julian Days and the second column corresponds to temperatures in degrees Celsius.