Using scipy.optimize.curve_fit with more than one input data and p0= two variables

Question

I am using scipy.optimize.curve_fit to fit measured (test in code) data to theoretical (run in code) data. Attached are two codes. In the first one I have one measured and theoretical data. When I use scipy.optimize.curve_fit I get approximately the correct temperature. The problem comes when I need to extend scipy.optimize.curve_fit to more one measured and theoretical data. The second code is my progress so far. How do I deal with two input data, i.e, what do I replace x-and y-data with. For example do I a need to combine the data in some manner. I have tried a few ways to non-success. Any help would be appreciated.

import pandas as pd
import numpy as np
from scipy import interpolate

# mr data: wave, counts, temp

run_1 = pd.read_excel("run_1.xlsx")
run_1_temp = np.array(run_1['temp'])
run_1_counts = np.array(run_1['count'])

# test data: wave, counts, temp = 30

test_1 = pd.read_excel("test_1.xlsx")
xdata = test_1['wave']
ydata = test_1['counts']

# Interpolate

inter_run_1 = interpolate.interp1d(run_1_temp,run_1_counts, kind='linear', fill_value='extrapolation')
run_1_temp_new = np.linspace(20,50,0.1)
run_1_count_new = inter_run_1(run_1_temp_new)

# Curve-fit

def f(wave, temp):
    signal = inter_run_1(temp)
    return signal
popt, pcov = scipy.optimize.curve_fit(f,xdata,ydata,p0=[30])
print(popt, pcov)

import pandas as pd
import numpy as np
from scipy import interpolate

# mr data: wave, counts, temp

run_1 = pd.read_excel("run_1.xlsx")
run_2 = pd.read_excel("run_2.xlsx")

# test data 1: wave, counts, temp = 30

test_1 = pd.read_excel("test_1.xlsx")
xdata = test_1['wave']
ydata = test_1['counts']

test data 1: wave, counts, temp = 40 

test_2 = pd.read_excel("test_2.xlsx")
x1data = test_2['wave']
y1data = test_2['counts']

run_1_temp = np.array(run_1['temp'])
run_1_counts = np.array(run_1['count'])

run_2_temp = np.array(run_2['temp'])
run_2_counts = np.array(run_2['count'])

# Interpolate

inter_run_1 = interpolate.interp1d(run_1_temp,run_1_counts, kind='linear', fill_value='extrapolation')
run_1_temp_new = np.linspace(20,50,0.1)
run_1_count_new = inter_run_1(run_1_temp_new)

inter_run_2 = interpolate.interp1d(run_2_temp,run_2_counts, kind='linear', fill_value='extrapolation')
run_2_temp_new = np.linspace(20,50,0.1)
run_2_count_new = inter_run_2(run_2_temp_new)

def f(wave,temp1,temp2):
    signal_1 = inter_run_1(temp1)
    signal_2 = inter_run_2(temp2)
    signal = signal_1 + signal_1
    return signal

popt, pcov = scipy.optimize.curve_fit(f,xdata,ydata,p0=[30,50])
print(popt, pcov)