Completed data communicator.

.gitignore

@@ -1,2 +1,3 @@
 
 .DS_Store
+Data_Analysis_Python/.ipynb_checkpoints/Data Classification-checkpoint.ipynb

Data_Analysis_Python/Data Classification.ipynb

@@ -239,7 +239,7 @@
     "    \n",
     "analysis_condition = ListTable()\n",
     "\n",
-    "analysis_condition.append(['Min', 'Max', 'Range', '75%','25%','Median', 'Mean', 'STD', 'Var','Kurtosis','Skewness'])"
+    "analysis_condition.append(['Min', 'Max', 'Range', '75%', '25%', 'Median', 'Mean', 'STD', 'Var','Kurtosis','Skewness'])"
    ]
   },
   {
@@ -8240,7 +8240,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.7.2"
   }
  },
  "nbformat": 4,

Data_Analysis_Python/data_communicator.py

@@ -1,7 +1,10 @@
 # Importing the required libraries
+import numpy as np
+import scipy.signal
 import serial
 import serial.tools.list_ports
 import sys
+import urllib.request
 
 # Viewing all the available ports
 ports = serial.tools.list_ports.comports()
@@ -11,12 +14,54 @@
 	print("{}: {} [{}]".format(port, desc, hwid))
 
 # Initialising the COM and BAUD rate
-COM = '/dev/cu.usbmodem14201'
+COM = '/dev/cu.usbmodem14101'
 BAUD = 9600
 
-# Declaring a Serial object
-arduino_serial = serial.Serial(COM, BAUD, timeout = .1)
-
 while True:
-	val = str(arduino_serial.readline().decode().strip('\r\n'))
+	input_value = input('\n\nPress return to start recording data: ')
+
+	# Declaring a Serial object
+	arduino_serial = serial.Serial(COM, BAUD, timeout = .1)
+	input_values = []
+
+	# Getting the data from serial port and adding it an array
+	while len(input_values) < 50:
+		value = str(arduino_serial.readline().decode().strip('\r\n'))
+
+		try:
+			if len(value) > 0 and int(value):
+				input_values.append(int(value))
+		except:
+			continue
+
+	print('\nThe recorded values are: ', input_values)
+
+	# Filtering of the input values
+	filtered_values = []
+
+	mean_value = np.mean(input_values)
+	value_25 = np.percentile(input_values, 25)
+	value_75 = np.percentile(input_values, 75)
+
+	for value in input_values:
+		filtered_values.append(value_75 if value > mean_value else value_25)
+	print('\nThe filtered values are: ', filtered_values)
+
+	# Extraction of the signal parameters
+	extracted_parameters = [np.amin(filtered_values), np.amax(filtered_values),
+							np.ptp(filtered_values), np.percentile(filtered_values, 75),
+							np.percentile(filtered_values, 25), np.median(filtered_values),
+							np.mean(filtered_values), round(np.std(filtered_values),2),
+							round(np.var(filtered_values),2), round(scipy.stats.kurtosis(filtered_values),2),
+							round(scipy.stats.skew(filtered_values),2)]
+
+	print('\nThe parameters of this signal are: ', extracted_parameters)
+
+	# Calling the web API
+	web_api_url = "https://pacific-harbor-19774.herokuapp.com/predict?myvar=" + str(extracted_parameters[0]) + "," + str(extracted_parameters[1]) + "," + str(extracted_parameters[2]) + "," + str(extracted_parameters[3]) + "," + str(extracted_parameters[4]) + "," + str(extracted_parameters[5]) + "," + str(extracted_parameters[6]) + "," + str(extracted_parameters[7]) + "," + str(extracted_parameters[8]) + "," + str(extracted_parameters[9]) + "," + str(extracted_parameters[10])
+
+	print('\nThe web API URL is: ', web_api_url)
+	urllib.request.urlopen(web_api_url)
 
+	# Closing the Serial connection
+	arduino_serial.close()