2017-05-18-191452.ipynb

Kernel: Python 3 (Anaconda)

The final results

import pandas as pd
import numpy as np

Here I'm reading in the 2016-2017 Combined Netatmo Data

PPM = pd.read_csv('Netatmo2016_2017CO2ppm.csv', parse_dates=True, index_col=1)
#PPM.head()

Graph of CO2 concentrations of the year

%matplotlib inline
PPM['ppm'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f94c35a03c8>

df3 = PPM
#df3.head(5)

Creating CO2 slope of $\Delta$ ppm / minute

delta_CO2 = df3['ppm'].diff(1)
df3['Time'] = df3.index
delta_time = df3['Time'].diff(1) / np.timedelta64(1,'m')

df3['slopes']= delta_CO2 / delta_time

#df3['neg_slopes'] = df3['slopes']<0

#df3.head()

%matplotlib inline
df3['slopes'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f94c2edca58>

using Equation:

$Infiltration = CO2 slopes* ETC Volume / (Inside ppm - Outside ppm)$

df3['ETCVolume'] = 41000
df3['diff_ppm'] = df3['ppm']-400
#del df3['neg_slopes']

df3['infiltration'] = (df3['slopes']*df3['ETCVolume'])/df3['diff_ppm']

df3.head(5)

	Unnamed: 0	ppm	Time	slopes	ETCVolume	diff_ppm	infiltration
Time
2016-02-19 13:26:00	0	NaN	2016-02-19 13:26:00	NaN	41000	NaN	NaN
2016-02-19 13:27:00	1	718.0	2016-02-19 13:27:00	NaN	41000	318.0	NaN
2016-02-19 13:27:00	2	NaN	2016-02-19 13:27:00	NaN	41000	NaN	NaN
2016-02-19 13:31:00	3	337.0	2016-02-19 13:31:00	NaN	41000	-63.0	NaN
2016-02-19 13:36:00	4	332.0	2016-02-19 13:36:00	-1.0	41000	-68.0	602.941176

df3['ACH'] = (df3['infiltration']*60) / 41000

%matplotlib inline
df3['ACH'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f94c2ec7fd0>

%matplotlib inline
df3['infiltration'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f94c352d6d8>

Isolating Negative Slopes

infiltration = df3['infiltration']

neg_infiltration = infiltration[infiltration<0]

%matplotlib inline
neg_infiltration.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f94c39a8630>

neg_infiltration.describe()

count    4.737700e+04
mean             -inf
std               NaN
min              -inf
25%     -1.245570e+03
50%     -4.555556e+02
75%     -1.763441e+02
max     -4.313519e+00
Name: infiltration, dtype: float64