CoCalc -- FinalMethods.ipynb

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GEP475GROUPINEEDANAP

Project: Justin Hoijer - GEP 475 SP2018 Course

Path: InfiltrationHUB / WorkingSets / FinalMethods.ipynb

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Kernel: Python 3 (Ubuntu Linux)

Infiltration Calculations

Things needed to make calculations more accurate:

No inf or zeros present in data
Find true asymtote for CO2_inside (seems to go below 400 "outside ppm")
Get accurate ETC volume. (used 40,000 ft $^3$ here)
Isolate Negative slopes
Determine best days/times to isolate further

VolumetricFlowRate:

CFM_{CO_2} = CFM_{allair} * ( \% CO_2 outside - \% CO_2 inside)

VolumetricFlowRate = CFM_{CO_2} / (\% CO_2 outside - \% CO_2 inside)

$\Delta CO_2$ = $CO_2$ cubic feet per minute (cfm)
Volumetric Flow Rate = Total cubic feet per minute (cfm)
$CO_2$ _outside = proportion (unitless)
$CO_2$ _inside = proportion (unitless)

Infiltration:

Infiltration = \frac{\Delta CO2}{(CO2\_outside - CO2\_inside)} * \frac{1}{Volume} * \frac{60 min}{hour}

Infiltration = "air changes" Per Minute ( $\large{\frac{1}{min}}$ )
$\Delta CO_2$ = $CO_2$ cubic feet per minute (cfm)
$CO_2$ _outside = proportion (unitless)
$CO_2$ _inside = proportion (unitless)
Volume = ETC cubic feet ( $ft^{3}$ )

In [57]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

More Data Manipulation

Starting with just ppm and timestamp

Finshing withing percent CO2 inside and outside, as well as cfm of CO2, and volume of ETC.

In [58]:

df1 = pd.read_csv('NetatmoCleanedForInfiltrationAnalysis.csv', parse_dates=True)
df1.drop(df1.columns[[0]], axis =1 , inplace = True)
df1.head()

	CO2	Time
0	718.0	2/19/16 13:27
1	337.0	2/19/16 13:31
2	332.0	2/19/16 13:36
3	328.0	2/19/16 13:41
4	307.0	2/19/16 13:46

In [59]:

df1['Time'] = pd.to_datetime(df1.Time)
df1.head()

	CO2	Time
0	718.0	2016-02-19 13:27:00
1	337.0	2016-02-19 13:31:00
2	332.0	2016-02-19 13:36:00
3	328.0	2016-02-19 13:41:00
4	307.0	2016-02-19 13:46:00

In [60]:

df1.rename(index=str, columns={"CO2": "ppm_inside"}, inplace = True)
df1.head()

	ppm_inside	Time
0	718.0	2016-02-19 13:27:00
1	337.0	2016-02-19 13:31:00
2	332.0	2016-02-19 13:36:00
3	328.0	2016-02-19 13:41:00
4	307.0	2016-02-19 13:46:00

In [61]:

df1['TimeDelta'] = df1['Time'].diff(1)
df1['TimeDelta'] = df1.TimeDelta /  np.timedelta64(1, 'm')
df1['ppm_outside'] = 400
df1['ETC_Volume'] = 4e4
df1['changeinppminside'] = df1['ppm_inside'].diff(1)
df1.head(4)

	ppm_inside	Time	TimeDelta	ppm_outside	ETC_Volume	changeinppminside
0	718.0	2016-02-19 13:27:00	NaN	400	40000.0	NaN
1	337.0	2016-02-19 13:31:00	4.0	400	40000.0	-381.0
2	332.0	2016-02-19 13:36:00	5.0	400	40000.0	-5.0
3	328.0	2016-02-19 13:41:00	5.0	400	40000.0	-4.0

Cfm = percent change inside * Volume of ETC

In [62]:

df1['percentchangeinside'] = df1['changeinppminside'] / 1e4
df1.head()

	ppm_inside	Time	TimeDelta	ppm_outside	ETC_Volume	changeinppminside	percentchangeinside
0	718.0	2016-02-19 13:27:00	NaN	400	40000.0	NaN	NaN
1	337.0	2016-02-19 13:31:00	4.0	400	40000.0	-381.0	-0.0381
2	332.0	2016-02-19 13:36:00	5.0	400	40000.0	-5.0	-0.0005
3	328.0	2016-02-19 13:41:00	5.0	400	40000.0	-4.0	-0.0004
4	307.0	2016-02-19 13:46:00	5.0	400	40000.0	-21.0	-0.0021

In [63]:

df1['CFM_CO2'] = (df1['percentchangeinside'] / df1['TimeDelta'])* df1['ETC_Volume']
df1.head()

	ppm_inside	Time	TimeDelta	ppm_outside	ETC_Volume	changeinppminside	percentchangeinside	CFM_CO2
0	718.0	2016-02-19 13:27:00	NaN	400	40000.0	NaN	NaN	NaN
1	337.0	2016-02-19 13:31:00	4.0	400	40000.0	-381.0	-0.0381	-381.0
2	332.0	2016-02-19 13:36:00	5.0	400	40000.0	-5.0	-0.0005	-4.0
3	328.0	2016-02-19 13:41:00	5.0	400	40000.0	-4.0	-0.0004	-3.2
4	307.0	2016-02-19 13:46:00	5.0	400	40000.0	-21.0	-0.0021	-16.8

In [64]:

df1['percent_Inside'] = df1['ppm_inside'] / 1e4
df1['percent_Outside'] = df1['ppm_outside'] / 1e4
df1.head()

	ppm_inside	Time	TimeDelta	ppm_outside	ETC_Volume	changeinppminside	percentchangeinside	CFM_CO2	percent_Inside	percent_Outside
0	718.0	2016-02-19 13:27:00	NaN	400	40000.0	NaN	NaN	NaN	0.0718	0.04
1	337.0	2016-02-19 13:31:00	4.0	400	40000.0	-381.0	-0.0381	-381.0	0.0337	0.04
2	332.0	2016-02-19 13:36:00	5.0	400	40000.0	-5.0	-0.0005	-4.0	0.0332	0.04
3	328.0	2016-02-19 13:41:00	5.0	400	40000.0	-4.0	-0.0004	-3.2	0.0328	0.04
4	307.0	2016-02-19 13:46:00	5.0	400	40000.0	-21.0	-0.0021	-16.8	0.0307	0.04

In [65]:

df1.drop(df1.columns[[0, 2, 3, 5, 6,]], axis =1 , inplace = True)
df1.head()

	Time	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside
0	2016-02-19 13:27:00	40000.0	NaN	0.0718	0.04
1	2016-02-19 13:31:00	40000.0	-381.0	0.0337	0.04
2	2016-02-19 13:36:00	40000.0	-4.0	0.0332	0.04
3	2016-02-19 13:41:00	40000.0	-3.2	0.0328	0.04
4	2016-02-19 13:46:00	40000.0	-16.8	0.0307	0.04

In [66]:

df1.percent_Inside.describe()

count    100985.000000
mean          0.054765
std           0.030451
min           0.020100
25%           0.036200
50%           0.043800
75%           0.061500
max           0.277700
Name: percent_Inside, dtype: float64

In [67]:

df1.to_csv('InfiltrationFINALCleanedData.csv')

In [68]:

df2 = pd.read_csv('InfiltrationFINALCleanedData.csv', parse_dates=True, index_col=0)
df2.iloc[32]

Time               2016-02-19 16:07:00
ETC_Volume                       40000
CFM_CO2                           12.8
percent_Inside                  0.0409
percent_Outside                   0.04
Name: 32, dtype: object

In [69]:

df2 = df2[df2['percent_Inside']>(df2['percent_Outside'] + 0.02)]
df2 = df2[df2['CFM_CO2'] < 0]
df2 = df2.replace([np.inf, -np.inf], np.nan)
df2.dropna()
df2.describe()

	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside
count	13010.0	13010.000000	13010.000000	1.301000e+04
mean	40000.0	-10.777121	0.095751	4.000000e-02
std	0.0	18.090360	0.031318	8.195149e-15
min	40000.0	-498.400000	0.060100	4.000000e-02
25%	40000.0	-12.000000	0.069325	4.000000e-02
50%	40000.0	-6.400000	0.089700	4.000000e-02
75%	40000.0	-3.200000	0.111000	4.000000e-02
max	40000.0	-0.408371	0.273300	4.000000e-02

In [70]:

df2.head()

	Time	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside
844	2016-02-22 12:12:00	40000.0	-54.4	0.0816	0.04
846	2016-02-22 12:22:00	40000.0	-4.8	0.0825	0.04
847	2016-02-22 12:27:00	40000.0	-16.8	0.0804	0.04
848	2016-02-22 12:32:00	40000.0	-12.0	0.0789	0.04
849	2016-02-22 12:37:00	40000.0	-22.4	0.0761	0.04

In [71]:

df2['CFM_CO2'].hist(bins = 50, range = (-80, 0))

<matplotlib.axes._subplots.AxesSubplot at 0x7f754f36c0f0>

In [72]:

df2['CFM_ETC'] = df2['CFM_CO2'] / (df2['percent_Inside'] - df2['percent_Outside'])
df2.describe()

	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside	CFM_ETC
count	13010.0	13010.000000	13010.000000	1.301000e+04	13010.000000
mean	40000.0	-10.777121	0.095751	4.000000e-02	-230.943259
std	0.0	18.090360	0.031318	8.195149e-15	394.273245
min	40000.0	-498.400000	0.060100	4.000000e-02	-16950.495050
25%	40000.0	-12.000000	0.069325	4.000000e-02	-261.949742
50%	40000.0	-6.400000	0.089700	4.000000e-02	-136.850291
75%	40000.0	-3.200000	0.111000	4.000000e-02	-65.573770
max	40000.0	-0.408371	0.273300	4.000000e-02	-4.208311

In [73]:

df2['CFM_ETC'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f754e3131d0>

In [101]:

df2['CFM_ETC'].hist(bins = 10, range = (-1500, -1000))

<matplotlib.axes._subplots.AxesSubplot at 0x7f754df58198>

In [75]:

df2['ACH'] = (df2['CFM_ETC'] / df2['ETC_Volume']) * 60
df2.describe()

	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside	CFM_ETC	ACH
count	13010.0	13010.000000	13010.000000	1.301000e+04	13010.000000	13010.000000
mean	40000.0	-10.777121	0.095751	4.000000e-02	-230.943259	-0.346415
std	0.0	18.090360	0.031318	8.195149e-15	394.273245	0.591410
min	40000.0	-498.400000	0.060100	4.000000e-02	-16950.495050	-25.425743
25%	40000.0	-12.000000	0.069325	4.000000e-02	-261.949742	-0.392925
50%	40000.0	-6.400000	0.089700	4.000000e-02	-136.850291	-0.205275
75%	40000.0	-3.200000	0.111000	4.000000e-02	-65.573770	-0.098361
max	40000.0	-0.408371	0.273300	4.000000e-02	-4.208311	-0.006312

In [100]:

#df2['CFM_ETC'].plot.density( range = 200 )

In [77]:

df2['ACH'].hist(bins = 10, range = (-10, 0))

<matplotlib.axes._subplots.AxesSubplot at 0x7f754e50c550>

In [78]:

df2.set_index('Time',inplace=True)
df2.head()

	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside	CFM_ETC	ACH
Time
2016-02-22 12:12:00	40000.0	-54.4	0.0816	0.04	-1307.692308	-1.961538
2016-02-22 12:22:00	40000.0	-4.8	0.0825	0.04	-112.941176	-0.169412
2016-02-22 12:27:00	40000.0	-16.8	0.0804	0.04	-415.841584	-0.623762
2016-02-22 12:32:00	40000.0	-12.0	0.0789	0.04	-308.483290	-0.462725
2016-02-22 12:37:00	40000.0	-22.4	0.0761	0.04	-620.498615	-0.930748

In [79]:

df2['ACH'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f754e8c6c18>

In [80]:

df2.reset_index(inplace = True)
df2.head(1)

	Time	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside	CFM_ETC	ACH
0	2016-02-22 12:12:00	40000.0	-54.4	0.0816	0.04	-1307.692308	-1.961538

In [81]:

df2['Time'] = pd.to_datetime(df2.Time)

In [82]:

df2['Day'] = df2.Time.dt.weekday_name
df2.head()

	Time	ETC_Volume	CFM_CO2	percent_Inside	percent_Outside	CFM_ETC	ACH	Day
0	2016-02-22 12:12:00	40000.0	-54.4	0.0816	0.04	-1307.692308	-1.961538	Monday
1	2016-02-22 12:22:00	40000.0	-4.8	0.0825	0.04	-112.941176	-0.169412	Monday
2	2016-02-22 12:27:00	40000.0	-16.8	0.0804	0.04	-415.841584	-0.623762	Monday
3	2016-02-22 12:32:00	40000.0	-12.0	0.0789	0.04	-308.483290	-0.462725	Monday
4	2016-02-22 12:37:00	40000.0	-22.4	0.0761	0.04	-620.498615	-0.930748	Monday

In [83]:

#df3.Day.value_counts()

In [84]:

InfiltrationData = df2.drop(df2.columns[[1,2,3,4,5]], axis =1 )
InfiltrationData.to_csv('FinalData.csv')

In [85]:

df3 = pd.read_csv('FinalData.csv', index_col = 0,  parse_dates=True)
df3.head()

	Time	ACH	Day
0	2016-02-22 12:12:00	-1.961538	Monday
1	2016-02-22 12:22:00	-0.169412	Monday
2	2016-02-22 12:27:00	-0.623762	Monday
3	2016-02-22 12:32:00	-0.462725	Monday
4	2016-02-22 12:37:00	-0.930748	Monday

In [86]:

df3.dtypes

Time     object
ACH     float64
Day      object
dtype: object

In [87]:

df3['Time'] = pd.to_datetime(df2.Time)
#df3['Day'] = pd.to_datetime(df2.Time)

In [88]:

df3.dtypes

Time    datetime64[ns]
ACH            float64
Day             object
dtype: object

In [89]:

df3 = df3[df3['ACH'] < -0.6]
df3 = df3[df3['ACH'] > -5.0]
df3.describe()

	ACH
count	1645.000000
mean	-1.167449
std	0.755820
min	-4.958678
25%	-1.273469
50%	-0.871972
75%	-0.708861
max	-0.600000

In [90]:

df3.head()

	Time	ACH	Day
0	2016-02-22 12:12:00	-1.961538	Monday
2	2016-02-22 12:27:00	-0.623762	Monday
4	2016-02-22 12:37:00	-0.930748	Monday
8	2016-02-22 13:03:00	-0.766667	Monday
10	2016-02-22 13:13:00	-1.320388	Monday

In [91]:

Fridays = df3[df3['Day'] == 'Friday']
Fridays.set_index('Time',inplace=True)
Fridays.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f754e76a668>

In [92]:

Tuesdays = df3[df3['Day'] == 'Tuesday']
Tuesdays.set_index('Time',inplace=True)
Tuesdays.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f754e767208>

In [93]:

Fridays.hist(bins = 100, range = (-2 , -0.5))

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f754e7f0828>]],
      dtype=object)

In [94]:

Tuesdays.hist(bins = 100, range = (-2 , -0.5))

array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f754e719c18>]],
      dtype=object)

In [95]:

Tuesdays.plot.density()

<matplotlib.axes._subplots.AxesSubplot at 0x7f754e249b00>

In [96]:

Tuesdays.describe()

	ACH
count	376.000000
mean	-1.202934
std	0.763339
min	-4.902857
25%	-1.289797
50%	-0.919636
75%	-0.740748
max	-0.600000

In [0]: