@@ -0,0 +1,62 @@
+from collections import defaultdict
+def compute_distribution(v):
+    """
+    v: vector de valores enteros
+    devuelve un diccionario con la probabilidad de cada valor
+    computado como la frecuencia de ocurrencia
+    """
+    d= defaultdict(int)
+    for e in v: d[e]+=1
+    s= float(sum(d.values()))
+    return dict((k, v/s) for k, v in d.items())
+def entropy(y):
+    """
+    Computa la entropia de un vector discreto
+    """
+    # P(Y)
+    Py= compute_distribution(y)
+    res=0.0
+    for k, v in Py.items():
+        res+=v*np.log2(v)
+    return -res
+  
+def conditional_entropy(x, y):
+    """
+    x: vector de numeros reales
+    y: vector de numeros enteros
+    devuelve H(Y|X)
+    """
+    # discretizacion de X 
+    #print(int(x.size/10))
+    # https://stats.stackexchange.com/questions/179674/number-of-bins-when-computing-mutual-information#:~:text=3%20Answers&text=There%20is%20no%20best%20number,on%20histograms%20have%20been%20proposed.
+    # Choosing number of bins
+    #hx, bx= histogram(x, bins=int(x.size/10), density=True)
+    hx, bx= np.histogram(x, bins=int(np.sqrt(x.size/5)),density=True)
+
+    Py= compute_distribution(y)
+    Px= compute_distribution(np.digitize(x,bx))
+
+    res= 0
+    for ey in set(y):
+        # P(X | Y)
+        x1= x[y==ey]
+        condPxy= compute_distribution(np.digitize(x1,bx))
+
+        for k, v in condPxy.items():
+            res+= (v*Py[ey]*(np.log2(Px[k]) - np.log2(v*Py[ey])))
+    return res
+            
+def mutual_information(x,y):
+    return entropy(y) - conditional_entropy(x,y)
+    
+def mutual_information_ufunc(x, y, dim='time'):
+    return xr.apply_ufunc(
+        mutual_information,
+        x,
+        y,
+        input_core_dims=[[dim], [dim]],
+        dask="parallelized",
+        output_dtypes=[float],
+    )
+  
+ mi = mutual_information_ufunc(x.chunk({'time': -1}),y.chunk({'time': -1}))