workingFunctions.py

from __future__ import division, print_function
import _pickle as pickle
import os
import sys
import numpy as np


def loadObj(path):
    with open(path, 'rb') as input:
        obj = pickle.load(input)
    return obj

def dumpObj(obj,path):
    #if not os.path.exists(path):
    try:
        os.makedirs(os.path.dirname(path))
    except OSError:
        pass
    with open(path, 'wb') as output:
        pickle.dump(obj, output, -1)
    print('Dumped: %s' %path.split('/')[-1]  )

# def folderListAll(path, ending = None):
#     data_list =[]
#     for (dirpath, dirnames, filenames) in os.walk(path):
#         for i in filenames:
#             if ending:
#                 if i.endswith(ending):data_list.append(dirpath+i)
#             else:
#                 data_list.append(dirpath+i)
#     data_list.sort()
#     return data_list

def folderListAll(path, ending = None):
    ## Returns all file names in folder
    data_list =[]
    for (dirpath, dirnames, filenames) in os.walk(path):
        for i in filenames:
            if ending:
                if i.endswith(ending):data_list.append(dirpath.replace(path,'')+'/'+i)
            else:
                data_list.append(dirpath.replace(path,'')+'/'+i)
    data_list.sort()
    return data_list

def allFilesInFolder(path, ending = None):
    ##Returns a list With whole path name in file names
    data_list =[]
    for (dirpath, dirnames, filenames) in os.walk(path):
        for i in filenames:
            if ending:
                if i.endswith(ending):data_list.append(path+'/'+i)
            else:
                data_list.append(path+'/'+i)
    data_list.sort()
    return data_list

def fileList(path, ending = None):
    data_list =[]
    for (dirpath, dirnames, filenames) in os.walk(path):
        if ending is not None:
            for i in filenames:
                if i.endswith(ending):
                    data_list.append(i)
        else:
            for i in filenames:
                data_list.append(i)
    data_list.sort()
    return data_list

def print_progress(count, total, status=''):
    sys.stdout.flush()
    bar_len = 15
    filled_len = int(round(bar_len * count / float(total)))

    percents = round(100.0 * count / float(total), 1)
    bar = '=' * filled_len + '-' * (bar_len - filled_len)
    if count == total:
        print('[%s] %s%s ...%s\r' % (bar, percents, '%', status))
    sys.stdout.flush()  # As suggested by Rom Ruben (see: http://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console/27871113#comment50529068_27871113)
    sys.stdout.write('[%s] %s%s ...%s\r' % (bar, percents, '%', status))
    
def smooth(y, window_size, order, deriv=0, rate=1):
    r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
    The Savitzky-Golay filter removes high frequency noise from data.
    It has the advantage of preserving the original shape and
    features of the signal better than other types of filtering
    approaches, such as moving averages techniques.
    Parameters
    ----------
    y : array_like, shape (N,)
        the values of the time history of the signal.
    window_size : int
        the length of the window. Must be an odd integer number.
    order : int
        the order of the polynomial used in the filtering.
        Must be less then `window_size` - 1.
    deriv: int
        the order of the derivative to compute (default = 0 means only smoothing)
    Returns
    -------
    ys : ndarray, shape (N)
        the smoothed signal (or it's n-th derivative).
    Notes
    -----
    The Savitzky-Golay is a type of low-pass filter, particularly
    suited for smoothing noisy data. The main idea behind this
    approach is to make for each point a least-square fit with a
    polynomial of high order over a odd-sized window centered at
    the point.
    Examples
    --------
    t = np.linspace(-4, 4, 500)
    y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
    ysg = savitzky_golay(y, window_size=31, order=4)
    import matplotlib.pyplot as plt
    plt.plot(t, y, label='Noisy signal')
    plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
    plt.plot(t, ysg, 'r', label='Filtered signal')
    plt.legend()
    plt.show()
    References
    ----------
    .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
       Data by Simplified Least Squares Procedures. Analytical
       Chemistry, 1964, 36 (8), pp 1627-1639.
    .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
       W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
       Cambridge University Press ISBN-13: 9780521880688
    """
    from math import factorial
    
    try:
        window_size = np.abs(np.int(window_size))
        order = np.abs(np.int(order))
    except ValueError:
        raise ValueError("window_size and order have to be of type int")
    if window_size % 2 != 1 or window_size < 1:
        raise TypeError("window_size size must be a positive odd number")
    if window_size < order + 2:
        raise TypeError("window_size is too small for the polynomials order")
    order_range = range(order+1)
    half_window = (window_size -1) // 2
    # precompute coefficients
    b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
    m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
    # pad the signal at the extremes with
    # values taken from the signal itself
    firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
    lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
    y = np.concatenate((firstvals, y, lastvals))
    return np.convolve( m[::-1], y, mode='valid')


####### Colors

def color_dict(gradient):
  ''' Takes in a list of RGB sub-lists and returns dictionary of
    colors in RGB and hex form for use in a graphing function
    defined later on '''
  return {"hex":[RGB_to_hex(RGB) for RGB in gradient],
      "r":[RGB[0] for RGB in gradient],
      "g":[RGB[1] for RGB in gradient],
      "b":[RGB[2] for RGB in gradient]}

def hex_to_RGB(hex):
    ''' "#FFFFFF" -> [255,255,255] '''
    # Pass 16 to the integer function for change of base
    return [int(hex[i:i+2], 16) for i in range(1,6,2)]

def RGB_to_hex(RGB):
  ''' [255,255,255] -> "#FFFFFF" '''
  # Components need to be integers for hex to make sense
  RGB = [int(x) for x in RGB]
  return "#"+"".join(["0{0:x}".format(v) if v < 16 else
            "{0:x}".format(v) for v in RGB])


def RGB_tree_colors():
    return [
        RGB_to_hex([165,42,42]), #darkred
        RGB_to_hex([255,0,0]), #red
        RGB_to_hex([218,165,32]), #sand
        RGB_to_hex([255,225,0]), #yellow
        #RGB_to_hex([255,165,0]), #sand
        RGB_to_hex([50,205,50]), #green
        RGB_to_hex([135,206,250]),   #faint blue
        RGB_to_hex([0,0,255]),   #blue
        RGB_to_hex([0,0,100])   #navi
        
    ]


def linear_gradient(start_hex, finish_hex="#FFFFFF", n=10):
  ''' returns a gradient list of (n) colors between
    two hex colors. start_hex and finish_hex
    should be the full six-digit color string,
    inlcuding the number sign ("#FFFFFF") '''
  # Starting and ending colors in RGB form
  s = hex_to_RGB(start_hex)
  f = hex_to_RGB(finish_hex)
  # Initilize a list of the output colors with the starting color
  RGB_list = [s]
  # Calcuate a color at each evenly spaced value of t from 1 to n
  for t in range(1, n):
    # Interpolate RGB vector for color at the current value of t
    curr_vector = [
      int(s[j] + (float(t)/(n-1))*(f[j]-s[j]))
      for j in range(3)
    ]
    # Add it to our list of output colors
    RGB_list.append(curr_vector)

  return color_dict(RGB_list)


def polylinear_gradient(colors, n):
  ''' returns a list of colors forming linear gradients between
      all sequential pairs of colors. "n" specifies the total
      number of desired output colors '''
  # The number of colors per individual linear gradient
  #n_out = int(float(n) / (len(colors) - 1))
  n_out = int(float(n) / (len(colors)-1))
  #print n_out
  # returns dictionary defined by color_dict()
  gradient_dict = linear_gradient(colors[0], colors[1], n_out)

  #if len(colors) > 1:
  for col in range(1, len(colors)-1):
      next = linear_gradient(colors[col], colors[col+1], n_out)
      for k in ("hex", "r", "g", "b"):
        # Exclude first point to avoid duplicates
        gradient_dict[k] += next[k][1:]

  return gradient_dict
#######Colors ENDE


def detect_peaks(x, mph=None, mpd=1, threshold=0, edge='rising',
                 kpsh=False, valley=False, show=False, ax=None):

    """Detect peaks in data based on their amplitude and other features.
    __author__ = "Marcos Duarte, https://github.com/demotu/BMC"
    __version__ = "1.0.4"
    __license__ = "MIT"

    Parameters
    ----------
    x : 1D array_like
        data.
    mph : {None, number}, optional (default = None)
        detect peaks that are greater than minimum peak height.
    mpd : positive integer, optional (default = 1)
        detect peaks that are at least separated by minimum peak distance (in
        number of data).
    threshold : positive number, optional (default = 0)
        detect peaks (valleys) that are greater (smaller) than `threshold`
        in relation to their immediate neighbors.
    edge : {None, 'rising', 'falling', 'both'}, optional (default = 'rising')
        for a flat peak, keep only the rising edge ('rising'), only the
        falling edge ('falling'), both edges ('both'), or don't detect a
        flat peak (None).
    kpsh : bool, optional (default = False)
        keep peaks with same height even if they are closer than `mpd`.
    valley : bool, optional (default = False)
        if True (1), detect valleys (local minima) instead of peaks.
    show : bool, optional (default = False)
        if True (1), plot data in matplotlib figure.
    ax : a matplotlib.axes.Axes instance, optional (default = None).

    Returns
    -------
    ind : 1D array_like
        indeces of the peaks in `x`.

    Notes
    -----
    The detection of valleys instead of peaks is performed internally by simply
    negating the data: `ind_valleys = detect_peaks(-x)`
    
    The function can handle NaN's 

    See this IPython Notebook [1]_.

    References
    ----------
    .. [1] http://nbviewer.ipython.org/github/demotu/BMC/blob/master/notebooks/DetectPeaks.ipynb

    Examples
    --------
    >>> from detect_peaks import detect_peaks
    >>> x = np.random.randn(100)
    >>> x[60:81] = np.nan
    >>> # detect all peaks and plot data
    >>> ind = detect_peaks(x, show=True)
    >>> print(ind)

    >>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
    >>> # set minimum peak height = 0 and minimum peak distance = 20
    >>> detect_peaks(x, mph=0, mpd=20, show=True)

    >>> x = [0, 1, 0, 2, 0, 3, 0, 2, 0, 1, 0]
    >>> # set minimum peak distance = 2
    >>> detect_peaks(x, mpd=2, show=True)

    >>> x = np.sin(2*np.pi*5*np.linspace(0, 1, 200)) + np.random.randn(200)/5
    >>> # detection of valleys instead of peaks
    >>> detect_peaks(x, mph=0, mpd=20, valley=True, show=True)

    >>> x = [0, 1, 1, 0, 1, 1, 0]
    >>> # detect both edges
    >>> detect_peaks(x, edge='both', show=True)

    >>> x = [-2, 1, -2, 2, 1, 1, 3, 0]
    >>> # set threshold = 2
    >>> detect_peaks(x, threshold = 2, show=True)
    """

    x = np.atleast_1d(x).astype('float64')
    if x.size < 3:
        return np.array([], dtype=int)
    if valley:
        x = -x
    # find indices of all peaks
    dx = x[1:] - x[:-1]
    # handle NaN's
    indnan = np.where(np.isnan(x))[0]
    if indnan.size:
        x[indnan] = np.inf
        dx[np.where(np.isnan(dx))[0]] = np.inf
    ine, ire, ife = np.array([[], [], []], dtype=int)
    if not edge:
        ine = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) > 0))[0]
    else:
        if edge.lower() in ['rising', 'both']:
            ire = np.where((np.hstack((dx, 0)) <= 0) & (np.hstack((0, dx)) > 0))[0]
        if edge.lower() in ['falling', 'both']:
            ife = np.where((np.hstack((dx, 0)) < 0) & (np.hstack((0, dx)) >= 0))[0]
    ind = np.unique(np.hstack((ine, ire, ife)))
    # handle NaN's
    if ind.size and indnan.size:
        # NaN's and values close to NaN's cannot be peaks
        ind = ind[np.in1d(ind, np.unique(np.hstack((indnan, indnan-1, indnan+1))), invert=True)]
    # first and last values of x cannot be peaks
    if ind.size and ind[0] == 0:
        ind = ind[1:]
    if ind.size and ind[-1] == x.size-1:
        ind = ind[:-1]
    # remove peaks < minimum peak height
    if ind.size and mph is not None:
        ind = ind[x[ind] >= mph]
    # remove peaks - neighbors < threshold
    if ind.size and threshold > 0:
        dx = np.min(np.vstack([x[ind]-x[ind-1], x[ind]-x[ind+1]]), axis=0)
        ind = np.delete(ind, np.where(dx < threshold)[0])
    # detect small peaks closer than minimum peak distance
    if ind.size and mpd > 1:
        ind = ind[np.argsort(x[ind])][::-1]  # sort ind by peak height
        idel = np.zeros(ind.size, dtype=bool)
        for i in range(ind.size):
            if not idel[i]:
                # keep peaks with the same height if kpsh is True
                idel = idel | (ind >= ind[i] - mpd) & (ind <= ind[i] + mpd) \
                    & (x[ind[i]] > x[ind] if kpsh else True)
                idel[i] = 0  # Keep current peak
        # remove the small peaks and sort back the indices by their occurrence
        ind = np.sort(ind[~idel])

    if show:
        if indnan.size:
            x[indnan] = np.nan
        if valley:
            x = -x
        _plot(x, mph, mpd, threshold, edge, valley, ax, ind)

    return ind


def _plot(x, mph, mpd, threshold, edge, valley, ax, ind):
    """Plot results of the detect_peaks function, see its help."""
    try:
        import matplotlib.pyplot as plt
    except ImportError:
        print('matplotlib is not available.')
    else:
        if ax is None:
            _, ax = plt.subplots(1, 1, figsize=(8, 4))

        ax.plot(x, 'b', lw=1)
        if ind.size:
            label = 'valley' if valley else 'peak'
            label = label + 's' if ind.size > 1 else label
            ax.plot(ind, x[ind], '+', mfc=None, mec='r', mew=2, ms=8,
                    label='%d %s' % (ind.size, label))
            ax.legend(loc='best', framealpha=.5, numpoints=1)
        ax.set_xlim(-.02*x.size, x.size*1.02-1)
        ymin, ymax = x[np.isfinite(x)].min(), x[np.isfinite(x)].max()
        yrange = ymax - ymin if ymax > ymin else 1
        ax.set_ylim(ymin - 0.1*yrange, ymax + 0.1*yrange)
        ax.set_xlabel('Data #', fontsize=14)
        ax.set_ylabel('Amplitude', fontsize=14)
        mode = 'Valley detection' if valley else 'Peak detection'
        ax.set_title("%s (mph=%s, mpd=%d, threshold=%s, edge='%s')"
                     % (mode, str(mph), mpd, str(threshold), edge))
        # plt.grid()
        plt.show()