sabiharustam / voltcycle

    return vector1, vector2

def critical_idx(x, y): ## Finds index where data set is no longer linear 

    """

    This function takes x and y values callculate the derrivative of x and y, and calculate moving average of 5 and 15 points.

    Finds intercepts of different moving average curves and return the indexs of the first intercepts.

    """

    k = np.diff(y)/(np.diff(x)) #calculated slops of x and y

    ## Calculate moving average for 5 and 15 points.

    ## This two arbitrary number can be tuned to get better fitting.

    ave5 = []

    ave15 = []

    for i in range(len(k)-10):  # The reason to minus 5 is to prevent j from running out of index.

        a = 0 

        for j in range(0,10):

            a = a + k[i+j]

        ave5.append(round(a/10, 5)) # keeping 9 desimal points for more accuracy

    for i in range(len(k)-15): 

        b = 0 

        for j in range(0,15):

            b = b + k[i+j]

        ave15.append(round(b/15, 5))

    ave5i = np.asarray(ave5)

    #print(ave10i)

    ave15i = np.asarray(ave15)

    #print(ave15i)

    ## Find intercepts of different moving average curves

    idx = np.argwhere(np.diff(np.sign(ave15i - ave5i[:len(ave15i)])!= 0)).reshape(-1)+0 #reshape into one row.

    return idx[5]

# This is based on the method 1 where user can't choose the baseline.

# If wanted to add that, choose method2.

    return vector1, vector2

def critical_idx(x, y): ## Finds index where data set is no longer linear 

    """

    This function takes x and y values callculate the derrivative of x and y, and calculate moving average of 5 and 15 points.

    Finds intercepts of different moving average curves and return the indexs of the first intercepts.

    """

    k = np.diff(y)/(np.diff(x)) #calculated slops of x and y

    ## Calculate moving average for 5 and 15 points.

    ## This two arbitrary number can be tuned to get better fitting.

    ave5 = []

    ave15 = []

    for i in range(len(k)-10):  # The reason to minus 5 is to prevent j from running out of index.

        a = 0 

        for j in range(0,10):

            a = a + k[i+j]

        ave5.append(round(a/10, 5)) # keeping 9 desimal points for more accuracy

    for i in range(len(k)-15): 

        b = 0 

        for j in range(0,15):

            b = b + k[i+j]

        ave15.append(round(b/15, 5))

    ave5i = np.asarray(ave5)

    #print(ave10i)

    ave15i = np.asarray(ave15)

    #print(ave15i)

    ## Find intercepts of different moving average curves

    idx = np.argwhere(np.diff(np.sign(ave15i - ave5i[:len(ave15i)])!= 0)).reshape(-1)+0 #reshape into one row.

    return idx[5]

# This is based on the method 1 where user can't choose the baseline.

# If wanted to add that, choose method2.

    return vector1, vector2

def critical_idx(x, y): ## Finds index where data set is no longer linear 

    """

    This function takes x and y values callculate the derrivative of x and y, and calculate moving average of 5 and 15 points.

    Finds intercepts of different moving average curves and return the indexs of the first intercepts.

    """

    k = np.diff(y)/(np.diff(x)) #calculated slops of x and y

    ## Calculate moving average for 5 and 15 points.

    ## This two arbitrary number can be tuned to get better fitting.

    ave5 = []

    ave15 = []

    for i in range(len(k)-10):  # The reason to minus 5 is to prevent j from running out of index.

        a = 0 

        for j in range(0,10):

            a = a + k[i+j]

        ave5.append(round(a/10, 5)) # keeping 9 desimal points for more accuracy

    for i in range(len(k)-15): 

        b = 0 

        for j in range(0,15):

            b = b + k[i+j]

        ave15.append(round(b/15, 5))

    ave5i = np.asarray(ave5)

    #print(ave10i)

    ave15i = np.asarray(ave15)

    #print(ave15i)

    ## Find intercepts of different moving average curves

    idx = np.argwhere(np.diff(np.sign(ave15i - ave5i[:len(ave15i)])!= 0)).reshape(-1)+0 #reshape into one row.

    return idx[5]

# This is based on the method 1 where user can't choose the baseline.

# If wanted to add that, choose method2.

    return vector1, vector2

def critical_idx(x, y): ## Finds index where data set is no longer linear 

    """

    This function takes x and y values callculate the derrivative of x and y, and calculate moving average of 5 and 15 points.

    Finds intercepts of different moving average curves and return the indexs of the first intercepts.

    """

    k = np.diff(y)/(np.diff(x)) #calculated slops of x and y

    ## Calculate moving average for 5 and 15 points.

    ## This two arbitrary number can be tuned to get better fitting.

    ave5 = []

    ave15 = []

    for i in range(len(k)-10):  # The reason to minus 5 is to prevent j from running out of index.

        a = 0 

        for j in range(0,10):

            a = a + k[i+j]

        ave5.append(round(a/10, 5)) # keeping 9 desimal points for more accuracy

    for i in range(len(k)-15): 

        b = 0 

        for j in range(0,15):

            b = b + k[i+j]

        ave15.append(round(b/15, 5))

    ave5i = np.asarray(ave5)

    #print(ave10i)

    ave15i = np.asarray(ave15)

    #print(ave15i)

    ## Find intercepts of different moving average curves

    idx = np.argwhere(np.diff(np.sign(ave15i - ave5i[:len(ave15i)])!= 0)).reshape(-1)+0 #reshape into one row.

    return idx[5]

# This is based on the method 1 where user can't choose the baseline.

# If wanted to add that, choose method2.

    return vector1, vector2

def critical_idx(x, y): ## Finds index where data set is no longer linear 

    """

    This function takes x and y values callculate the derrivative of x and y, and calculate moving average of 5 and 15 points.

    Finds intercepts of different moving average curves and return the indexs of the first intercepts.

    """

    k = np.diff(y)/(np.diff(x)) #calculated slops of x and y

    ## Calculate moving average for 5 and 15 points.

    ## This two arbitrary number can be tuned to get better fitting.

    ave10 = []

    ave15 = []

    for i in range(len(k)-10):  # The reason to minus 5 is to prevent j from running out of index.

        a = 0 

        for j in range(0,5):

            a = a + k[i+j]

        ave10.append(round(a/5, 5)) # keeping 9 desimal points for more accuracy

    for i in range(len(k)-15): 

        b = 0 

        for j in range(0,10):

            b = b + k[i+j]

        ave15.append(round(b/10, 5))

    ave10i = np.asarray(ave5)

    print(ave10i)

    ave15i = np.asarray(ave15)

    print(ave15i)

    ## Find intercepts of different moving average curves

    idx = np.argwhere(np.diff(np.sign(ave15i - ave10i[:len(ave15i)])!= 0)).reshape(-1)+0 #reshape into one row.

    return idx[1]

# This is based on the method 1 where user can't choose the baseline.

# If wanted to add that, choose method2.