sabiharustam / voltcycle

    return vector1, vector2

def critical_idx(arr_x, arr_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.

    ----------

    Parameters

    ----------

    x : Numpy array.

    y : Numpy array.

    Normally, for the use of this function, it expects numpy array

    that came out from split function. For example, output of

    split.df['potentials'] could be input for this function as x.

    -------

    Returns

    -------

    This function returns 5th index of the intercepts of different moving average curves.

    User can change this function according to baseline

    branch method 2 to get various indexes..

    """

    assert isinstance(arr_x, np.ndarray), "Input should be numpy array"

    assert isinstance(arr_y == np.ndarray), "Input should be numpy array"

    if arr_x.shape[0] != arr_y.shape[0]:

        raise ValueError("x and y must have same first dimension, but "

                         "have shapes {} and {}".format(arr_x.shape, arr_y.shape))

    k_val = np.diff(arr_y)/(np.diff(arr_x)) #calculated slops of x and y

    ## Calculate moving average for 10 and 15 points.

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

    ave10 = []

    ave15 = []

    for i in range(len(k_val)-10):

        # The reason to minus 10 is to prevent j from running out of index.

        a_val = 0

        for j in range(0, 5):

            a_val = a_val + k_val[i+j]

        ave10.append(round(a_val/10, 5))

        # keeping 5 desimal points for more accuracy

        # This numbers affect how sensitive to noise.

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

        b_val = 0

        for j in range(0, 15):

            b_val = b_val + k_val[i+j]

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

    ave10i = np.asarray(ave10)

    ave15i = np.asarray(ave15)

    ## Find intercepts of different moving average curves

    #reshape into one row.

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

    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(arr_x, arr_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.

    ----------

    Parameters

    ----------

    x : Numpy array.

    y : Numpy array.

    Normally, for the use of this function, it expects numpy array

    that came out from split function. For example, output of

    split.df['potentials'] could be input for this function as x.

    -------

    Returns

    -------

    This function returns 5th index of the intercepts of different moving average curves.

    User can change this function according to baseline

    branch method 2 to get various indexes..

    """

    assert isinstance(arr_x, np.ndarray), "Input should be numpy array"

    assert isinstance(arr_y == np.ndarray), "Input should be numpy array"

    if arr_x.shape[0] != arr_y.shape[0]:

        raise ValueError("x and y must have same first dimension, but "

                         "have shapes {} and {}".format(arr_x.shape, arr_y.shape))

    k_val = np.diff(arr_y)/(np.diff(arr_x)) #calculated slops of x and y

    ## Calculate moving average for 10 and 15 points.

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

    ave10 = []

    ave15 = []

    for i in range(len(k_val)-10):

	# The reason to minus 10 is to prevent j from running out of index.

        a_val = 0

        for j in range(0, 5):

            a_val = a_val + k_val[i+j]

        ave10.append(round(a_val/10, 5))

	# keeping 5 desimal points for more accuracy

	# This numbers affect how sensitive to noise.

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

        b_val = 0

        for j in range(0, 15):

            b_val = b_val + k_val[i+j]

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

    ave10i = np.asarray(ave10)

    ave15i = np.asarray(ave15)

    ## Find intercepts of different moving average curves

    #reshape into one row.

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

    return idx[5]

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

# If wanted to add that, choose method2.