Carbon Dioxide Difference Analysis

We can infer the infiltration rate from the rate of carbon dioxide decrease.

$$ \frac{\Delta\ concentration}{\Delta t} \cdot \frac{1}{outside\ concentration - inside\ concentration}$$
In [1]:
%matplotlib notebook
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('Indoor_3-3-2016.csv', 
                   skiprows=3,       # ignore the first 3 rows of data
                   sep=';',          # semicolon is used to separate data values
                   index_col=1,      # use column 1 as the dates to index the data
                   parse_dates=True) # convert the date string into a date object
In [2]:
inside_concentration = data['CO2']
concentration_slope = data['CO2'].diff()/data['Timestamp'].diff()
concentration_slope = concentration_slope.where(concentration_slope<0, 0)
infiltration_rate = concentration_slope/(inside_concentration - 400.)*3600

ax = infiltration_rate.plot()
ax.set_ylim((-1,1))
ax.grid()
#data['CO2'].plot()
In [3]:
infiltration_rate.describe()
Out[3]:
count    216.000000
mean      -0.122748
std        0.309181
min       -3.177570
25%       -0.106454
50%       -0.073708
75%       -0.038764
max        0.000000
dtype: float64
In [4]:
infiltration_rate.hist(bins=50)
Out[4]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f060d6e1dd8>
In [5]:
data['CO2'].plot()
Out[5]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f060d6e1dd8>
In [6]:
time2 = '2016-02-23 12:15:00'
time1 = '2016-02-23 12:45:00'
rise = data['CO2'][time2] - data['CO2'][time1]
run = (data['Timestamp'][time2] - data['Timestamp'][time1])


print(data['CO2'][time2])
print(rise)
print('run =',run)
print('slope =', rise/run)
rise / run * 3600 / (data['CO2'][time2]-400)

1802
79
run = -1800
slope = -0.0438888888889
Out[6]:
-0.11269614835948645
In [7]:
time2 = '2016-02-24 05:00:00'
time1 = '2016-02-24 07:00:00'

data[time2:time1]
Out[7]:
Timestamp Temperature Humidity CO2 Noise Pressure
Timezone : America/Los_Angeles
2016-02-24 05:15:00 1456319700 19.4 55 722 36 1015.2
2016-02-24 05:45:00 1456321500 19.3 55 710 37 1015.3
2016-02-24 06:15:00 1456323300 19.3 55 696 37 1015.6
2016-02-24 06:45:00 1456325100 19.2 54 687 37 1015.8
In [8]:
time2 = '2016-02-24 05:45:00'
time1 = '2016-02-24 06:15:00'

rise = data['CO2'][time2] - data['CO2'][time1]
run = (data['Timestamp'][time2] - data['Timestamp'][time1])


print(data['CO2'][time2])
print(rise)
print('run =', run)
print('slope =', rise/run)
rise / run * 3600 / (data['CO2'][time2]-400)

710
14
run = -1800
slope = -0.00777777777778
Out[8]:
-0.090322580645161285
In [9]:
time2 = '2016-02-23 09:15:00'
time1 = '2016-02-23 08:45:00'
rise = data['CO2'][time2] - data['CO2'][time1]
run = (data['Timestamp'][time2] - data['Timestamp'][time1])

rise/run
Out[9]:
-0.0016666666666666668