sabiharustam / voltcycle

    y_base = y_fitted_line(m, b, x)

    return y_base

def peak_detection_fxn(data_y):

    """The function takes an input of the column containing the y variables in the dataframe,

    associated with the current. The function calls the split function, which splits the

    column into two arrays, one of the positive and one of the negative values.

    This is because cyclic voltammetry delivers negative peaks, but the peakutils function works

    better with positive peaks. The function also runs on the middle 80% of data to eliminate

    unnecessary noise and messy values associated with pseudo-peaks.The vectors are then imported

    into the peakutils.indexes function to determine the significant peak for each array.

    The values are stored in a list, with the first index corresponding to the top peak and the

    second corresponding to the bottom peak.

    Parameters

    ______________

    y column: must be a column from a pandas dataframe

    Returns

    _____________

    A list with the index of the peaks from the top curve and bottom curve.

    """

    # initialize storage list

    index_list = []

    # split data into above and below the baseline

    col_y1, col_y2 = split(data_y) # removed main. head.

    # detemine length of data and what 10% of the data is

    len_y = len(col_y1)

    ten_percent = int(np.around(0.1*len_y))

    # adjust both input columns to be the middle 80% of data

    # (take of the first and last 10% of data)

    # this avoid detecting peaks from electrolysis

    # (from water splitting and not the molecule itself,

    # which can form random "peaks")

    mod_col_y2 = col_y2[ten_percent:len_y-ten_percent]

    mod_col_y1 = col_y1[ten_percent:len_y-ten_percent]

    # run peakutils package to detect the peaks for both top and bottom

    peak_top = peakutils.indexes(mod_col_y2, thres=0.99, min_dist=20)

    peak_bottom = peakutils.indexes(abs(mod_col_y1), thres=0.99, min_dist=20)

    # detemine length of both halves of data

    len_top = len(peak_top)

    len_bot = len(peak_bottom)

    # append the values to the storage list

    # manipulate values by adding the ten_percent value back

    # (as the indecies have moved)

    # to detect the actual peaks and not the modified values

    index_list.append(peak_top[int(len_top/2)]+ten_percent)

    index_list.append(peak_bottom[int(len_bot/2)]+ten_percent)

    # return storage list

    # first value is the top, second value is the bottom

    return index_list

def peak_values(DataFrame_x, DataFrame_y):

import main

def peak_detection_fxn(data_y):

    """The function takes an input of the column containing the y variables in the dataframe,

    associated with the current. The function calls the split function, which splits the

    column into two arrays, one of the positive and one of the negative values.

    This is because cyclic voltammetry delivers negative peaks, but the peakutils function works

    better with positive peaks. The function also runs on the middle 80% of data to eliminate

    unnecessary noise and messy values associated with pseudo-peaks.The vectors are then imported

    into the peakutils.indexes function to determine the significant peak for each array.

    The values are stored in a list, with the first index corresponding to the top peak and the

    second corresponding to the bottom peak.

    Parameters

    ______________

    y column: must be a column from a pandas dataframe

    Returns

    _____________

    A list with the index of the peaks from the top curve and bottom curve.

    """

    # initialize storage list

    index_list = []

    # split data into above and below the baseline

    col_y1, col_y2 = main.split(data_y)

    # detemine length of data and what 10% of the data is

    len_y = len(col_y1)

    ten_percent = int(np.around(0.1*len_y))

    # adjust both input columns to be the middle 80% of data

    # (take of the first and last 10% of data)

    # this avoid detecting peaks from electrolysis

    # (from water splitting and not the molecule itself,

    # which can form random "peaks")

    mod_col_y2 = col_y2[ten_percent:len_y-ten_percent]

    mod_col_y1 = col_y1[ten_percent:len_y-ten_percent]

    # run peakutils package to detect the peaks for both top and bottom

    peak_top = peakutils.indexes(mod_col_y2, thres=0.99, min_dist=20)

    peak_bottom = peakutils.indexes(abs(mod_col_y1), thres=0.99, min_dist=20)

    # detemine length of both halves of data

    len_top = len(peak_top)

    len_bot = len(peak_bottom)

    # append the values to the storage list

    # manipulate values by adding the ten_percent value back

    # (as the indecies have moved)

    # to detect the actual peaks and not the modified values

    index_list.append(peak_top[int(len_top/2)]+ten_percent)

    index_list.append(peak_bottom[int(len_bot/2)]+ten_percent)

    # return storage list

    # first value is the top, second value is the bottom

    return index_list