tabla

Environmental.py

@@ -30,6 +30,53 @@ def FilteringData(b,e):
 
     return luxesf, tempf, humf, powf
 
+# Pearson and Spearman correlation
+def Correlation(l,t,h,p,c):
+    # Empty 4x4 array
+    arr = np.empty((4,4))
+    # Joints luxesf, tempf, humf, powf in one array
+    l=np.array(l).reshape(-1,1)
+    t=np.array(t).reshape(-1,1)
+    h=np.array(h).reshape(-1,1)
+    p=np.array(p).reshape(-1,1)
+    aa=np.hstack((l,t,h,p))
+    
+    for a in range(0,4):
+        for t in range (0,4):
+            if c=='p':corr, j = pearsonr(aa[:,a],aa[:,t]) # If c iquals s, pearson correlation is calculated
+            elif c=='s': corr, j = spearmanr(aa[:,a],aa[:,t]) # If c iquals s, pearson correlation is calculated
+            else: print("Elija un tipo de correlacion valida: pearson(p) o spearman(s)")
+            arr[a][t]=corr
+    return arr
+# Table of correlation Function
+def Tablecorrelation(data,title):
+    fig, ax = plt.subplots()
+    table = ax.table(cellText=np.around(data, decimals=4),
+                     rowLabels=['Lux','Temp','Hum','Pow'],
+                     colLabels=['Lux','Temp','Hum','Pow'],
+                     loc='center')
+    table.set_fontsize(10)
+    table.scale(1.2,1.2)
+    ax.axis('off')
+    plt.title(title)
+    plt.savefig(f'{title}.png')
+    plt.show()
+#Filtered data from 9Hrs to 16Hrs
+l, t, h, p = FilteringData(9,16)
+#Importing scipy library
+from scipy.stats import pearsonr
+from scipy.stats import spearmanr
+#Correlation calculation
+#SPEARMAN
+Spearmancorrelation = Correlation(l, t, h, p,'s')
+print('Spearman correlation')
+print(Spearmancorrelation)
+Tablecorrelation(Spearmancorrelation,'Spearman correlation')
+#PEARSON
+pearsoncorreltaion = Correlation(l, t, h, p,'p')
+print('Pearson correlation')
+print(pearsoncorreltaion)
+Tablecorrelation(pearsoncorreltaion,'Pearson correltaion')
 ############## PLOTTING DATA FUNCTION ##############
 #Inputs: Luxes, Temperature, Humidity and Power
 #Output: Vs graph 4x4