Python lab script

#*************************************************************
#
# Experiment: E3, Measuring an electric current, voltage and resistance.
# Names:      Fraser McDonald & Robert Kyle
# Date:       06/11/2018
#
#*************************************************************

import numpy as np
import matplotlib.pyplot as plt
import lab1



#********************************************************
# Load the data from file "E3data.txt"
# Create an numpy array "d" with the voltage in the 1st
# column and the current in the 2nd column.
#
# d = ....
#********************************************************

d = np.loadtxt("E2data.txt")


#********************************************************
# Use d to create variables for the voltage and current
# Use the variable names V and I.
#
# What are the units?
#
# suggestion:  V = ...
#              I = ...
#********************************************************

V=d[:,0]
I=d[:,1]

#********************************************************
# Convert V and I to SI units (Volts and Ampere) if necessary
#
# suggestion:  V = V * ...;   I = I * ...;
#********************************************************





#********************************************************
# Plot your raw data with
#  (a)  V on the x-axis
#  (b)  I on the y-axis.
#  (c)  circle symbol for the data points
#  (e)  labels for x and y axes
#  (f)  a helpful title
#********************************************************
plt.figure(1)
plt.plot(V,I,"ro")
plt.xlabel("Voltage (V)")
plt.ylabel("Current (a)")
plt.title("Relationship between Voltage and Current")


#********************************************************
# Create a linear fit for the data:
#
# Use the command  c,m,dm = lab1.fitLine( V, I)
#
# m  - is the gradient of the fitted line.
# c  - is the offset of the fitted line.
# dm - is the the error of the gradient.
#********************************************************
c,m,dm = lab1.fitLine( V, I)



#********************************************************
# Plot the straight line on top of your linearized data:
# (a) generate an array with x-data points, call it "xr"
#     e.g. from 1 to 1.5 with 0.1 spacint
#                  xr = np.arange(1, 1.5, 0.1)
# (b) calculate the corresponding y-values for a straight
#     line, e.g.
#                  yr = m*xr + c
# (c) plot yr vs. xr with a line style
#********************************************************
xr= np.arange(0.4,1.5,0.005)
yr=(m * xr) + c
plt.plot(xr,yr,"-")


#********************************************************
# Calculate the resitance R
#
# Use the eq. 3 of the script  (I = 1/R*V) to determine R
# from the gradient of you fit (y = m  *x + c).
#
# R = ...
#********************************************************

print("Gradient = ",m)
R = 1/m

#********************************************************
# Calculate the error of R
#
# (Help : The error dR of the derived quatity R = 1/m depends
# on the error dm of parameter m with dR/R = dm/m.)
#
# dR = dm/m * ...
#********************************************************

dR=(dm/m*R)
print("Measurement Results")
print('R',R)
print("dR",dR)

#********************************************************
# do not forget to print this script and the figures
#********************************************************