In [35]:

    

import numpy as np
import numpy.ma as ma
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline


    

In [36]:

    

# ADC Voltage
RAIL_VOLTAGE = 3.3
ADC_QUANTIZATION = 2 ** 12  # for 12 bit ADC


    

In [37]:

    

dat = np.genfromtxt('2016-04-26_calibration_mod.txt')


    

In [76]:

    

currents = np.array([0.1, 1.0, 2.0, 3.0])
values = np.array([])
for i in currents:
    mask=[a != i for a in dat[:,0]]
    mx = ma.masked_array(dat[:,1], mask)
    values = np.append(values, int(mx.mean()) * RAIL_VOLTAGE / ADC_QUANTIZATION)


    

In [40]:

    

slope, intercept, r_value, p_value, std_err = stats.linregress(currents, values)


    

In [41]:

    

line = currents * slope + intercept


    

In [77]:

    

plt.plot(currents, line, 'r-')
plt.errorbar(currents,values,xerr=std_err, fmt='.', color='blue')
plt.xlabel('Calibration current [mA]')
plt.ylabel('Measured current [mA]')
plt.title('Relation between reference and measured currents')
plt.grid(1)


    

In [80]:

    

print('slope: {}'.format(slope))


    

    




slope: 0.9801675611142459

In [81]:

    

print('intercept: {}'.format(intercept))


    

    




intercept: -0.012535071714850021

In [82]:

    

def get_calibrated_value(adc_int_value):
    value = adc_int_value * RAIL_VOLTAGE / ADC_QUANTIZATION
    return (value - intercept) / slope


    

In [83]:

    

get_calibrated_value(3633)


    

    
Out[83]:





2.9989898945806144

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