Carbon Dioxide Difference Analysis¶

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

$$ \frac{\Delta\ concentration}{\Delta t} \cdot \frac{1}{outside\ concentration - inside\ concentration}$$

%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

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()

infiltration_rate.describe()

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

infiltration_rate.hist(bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x7f060d6e1dd8>

data['CO2'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f060d6e1dd8>

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

-0.11269614835948645

time2 = '2016-02-24 05:00:00'
time1 = '2016-02-24 07:00:00'

data[time2:time1]

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

-0.090322580645161285

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

-0.0016666666666666668

	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