====== Python Code ======

Python was used to parse the data received from the sensors.

The below program was used to get a graph based on the values received from the sensor. The data was also saved in a csv format after. Data was then combined from all the locations and the pandas tool was used to find the mean value and the ratio of the mean vs the concentration of CO<sub>2</sub> in clean air. 

<file   php CO2.php>

<PHP>
# Importing library
import csv
import pandas as pd
import serial
import time
import matplotlib.pyplot as plt

# make sure the 'COM#' is set according the Windows Device Manager
ser = serial.Serial('insert_device_com', 9600, timeout=1)
time.sleep(2)

data = []
for i in range(100):
    line = ser.readline()   # read a byte string
    if line:
        string = line.decode()  # convert the byte string to a unicode string
        num = float(string) # convert the unicode string to an int
        num2 = int(num)
        data.append(num2) # add int to data list
ser.close()

# build the plot
plt.plot(data)
plt.xlabel('Time[s]')
plt.ylabel('CO2 [ppm]')
plt.title('CO2 vs. Time')
plt.show()


CO2 = pd.DataFrame(data) #convert data to a pandas dataframe
  
# opening the csv file in 'w+' mode

file = open('CO2_(insert-initial.csv', 'w+', newline ='')
CO2.to_csv(file,index = False)

#combining data taken from all three locations 

L = pd.read_csv('CO2_L.csv')  #reading the csv files
K = pd.read_csv('CO2_K.csv')
B = pd.read_csv('CO2_B.csv')
df = pd.concat([L,K,B], axis = 1) #combining all the files horizontally

# calculating the mean for each location
mean = df.mean()
ppm = 400 #defining ppm of CO2 in clean air
ratio = mean/ppm #calculating the ratio of ppm of each location with the ppm of clean air

#adding the mean and ratio to the dataframe
df.loc['mean'] = mean 
df.loc['ratio'] = mean/ppm

#converting the dataframe to a csv file
file1 = open('CO2.csv', 'w+', newline ='')
df.to_csv(file1,index = False)
</PHP>

</file>

The below program was used to get a graph based on the sound in decibels vs time. The data was received from the sensor LM393. 
<file   php Sound.php>

<PHP>
# Importing library
import csv
import pandas as pd
import serial
import time
import matplotlib.pyplot as plt

# make sure the 'COM#' is set according the Windows Device Manager
ser = serial.Serial('insert_device_com', 9600, timeout=1)
time.sleep(2)

data = []
for i in range(100):
    line = ser.readline()   # read a byte string
    if line:
        string = line.decode()  # convert the byte string to a unicode string
        num = float(string) # convert the unicode string to an int
        num2 = int(num)
        data.append(num2) # add int to data list
ser.close()

# build the plot
plt.plot(data)
plt.xlabel('Time[ms]')
plt.ylabel('Sound[dB]')
plt.title('Sound vs. Time')
plt.show()

  

</PHP>


</file>
[[amc2021:groupl:report:start|Back to report]]