Esteban Martinez, Andres Heredia

Introduction

The purpose of this lab is to find out the effects of mass on the oscillation of a spring scale. We will put several weights on the scale and record the oscillation time five times, after which we will take the average.

Procedure

$$Oscillation$$

mass (g)	avg. time (s)
50	.29
100	.44
150	.47
200	.53
250	.61
300	.71



In [1]:

    
%matplotlib inline

Data Analysis



In [8]:

    
import matplotlib.pyplot as plt
import numpy as np
from scipy.optimize import curve_fit

mass = [ 50, 100, 150, 200, 250, 300]
time = [ .29, .44, .47, .53, .61, .71]
xx = np.linspace(0,400,10)
def lin_model( x, a, b):
    return a*x + b
a,b = curve_fit(lin_model, mass, time)[0]
print(a,b)
plt.title('Oscillation Time on a Spring Scale')
plt.ylabel ('Time (s)')
plt.xlabel ('Mass (g)')
plt.plot(xx, lin_model(xx, a, b))
plt.plot(mass,time,'ro')









    



0.00152571428745 0.24133333303






    Out[8]:





[<matplotlib.lines.Line2D at 0x7b7c940>]

Conclusion



In [ ]: