mqtt

Mqtt.py

@@ -0,0 +1,84 @@
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+import matplotlib.pyplot as plt
+from sklearn import linear_model
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+
+df = pd.read_csv(r'C:\Users\ojt98\OneDrive\Desktop\Python\weather_data_24hr.csv')
+#df=pd.get_dummies(df, prefix='weather', columns=['weather'])
+
+X=df[["windspeedKPH", "humidity",  "avgtempC"]]
+y=df[["accidents"]]
+
+lm = linear_model.LinearRegression()
+model = lm.fit(X,y)
+predictions = lm.predict(X)
+#print(predictions[0:5])
+
+print(lm.score(X,y)) ##R^2
+arr=lm.coef_
+print(arr)
+
+for word in arr:
+    one=(word[0])
+
+for word in arr:
+    two=(word[1])
+
+for word in arr:
+    three=(word[2])
+
+
+
+##pull live data into this below...
+
+#accidents=one(windspeedKPH)+  two(humidity)+   three(avgtempC)
+
+
+# python_sub.py
+#mosquitto_pub -h mqtt.dioty.co -p 8883 -u <your user-id> -P <your password> -t <topic> -m <message> -r --cafile ./dioty_ca.crt
+
+import paho.mqtt.client as mqtt
+import json, os
+from datetime import date, datetime
+import time
+
+def on_connect(client, userdata, flags, rc):
+    if rc==0:
+        client.connected_flag = True                        
+        print("connected OK")
+        client.subscribe("iot/twiningo")
+    else:
+        print("Bad connection Returned code=",rc)
+
+def on_message( client, userdata, msg ):
+    print('in on_message')
+    """ callback function for messages received """
+    topic = msg.topic
+    data = msg.payload.decode("utf8")
+    print(topic)
+    print(data)
+
+client = mqtt.Client()
+client.on_connect = on_connect
+client.on_message = on_message
+client.username_pw_set("iot/twiningo", "JLjsTIe2Ws")
+client.tls_set("mqtt.crt")
+client.connect("mqtt.coventry.ac.uk", 8883)
+client.loop_forever()
+
+
+
+
+
+
+
+
+
+
+
+
+

Regression.py

@@ -13,35 +13,14 @@ df = pd.read_csv(r'C:\Users\ojt98\OneDrive\Desktop\Python\weather_data_24hr.csv'
 X=df[["windspeedKPH", "humidity",  "avgtempC"]]
 y=df[["accidents"]]
 
-
-#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)
-
-#regressor = LinearRegression()
-
-#regressor.fit(X_train, y_train)
-
-#y_pred =regressor.predict(X_test)
-
-#X = sm.add_constant(X)
-##Train-test split
-#train_X, test_X, train_y, test_y = train_test_split(X, y, train_size = 0.8, random_state = 42)
-##Linear regression model 
-#model = sm.OLS(train_y, train_X)
-#model = model.fit()
-#print(model.summary2())
-
-
-
-
-
 lm = linear_model.LinearRegression()
 model = lm.fit(X,y)
 predictions = lm.predict(X)
-print(predictions[0:5])
+#print(predictions[0:5])
 
-print(lm.score(X,y))
+print(lm.score(X,y)) ##R^2
 arr=lm.coef_
-
+print(arr)
 
 for word in arr:
     one=(word[0])
@@ -52,11 +31,15 @@ for word in arr:
 for word in arr:
     three=(word[2])
 
+
+
 ##pull live data into this below...
 
 #accidents=one(windspeedKPH)+  two(humidity)+   three(avgtempC)
 
+
 # python_sub.py
+#mosquitto_pub -h mqtt.dioty.co -p 8883 -u <your user-id> -P <your password> -t <topic> -m <message> -r --cafile ./dioty_ca.crt
 
 import paho.mqtt.client as mqtt
 import json, os
@@ -82,7 +65,34 @@ def on_message( client, userdata, msg ):
 client = mqtt.Client()
 client.on_connect = on_connect
 client.on_message = on_message
-client.username_pw_set("iot", "JLjsTIe2Ws")
+client.username_pw_set("iot/twiningo", "JLjsTIe2Ws")
 client.tls_set("mqtt.crt")
 client.connect("mqtt.coventry.ac.uk", 8883)
 client.loop_forever()
+
+
+
+
+#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)
+
+#regressor = LinearRegression()
+
+#regressor.fit(X_train, y_train)
+
+#y_pred =regressor.predict(X_test)
+
+#X = sm.add_constant(X)
+##Train-test split
+#train_X, test_X, train_y, test_y = train_test_split(X, y, train_size = 0.8, random_state = 42)
+##Linear regression model 
+#model = sm.OLS(train_y, train_X)
+#model = model.fit()
+#print(model.summary2())
+
+
+
+
+
+
+
+