Python Debug.printWarning方法代码示例

# 需要导入模块: from PythonTools import Debug [as 别名]
# 或者: from PythonTools.Debug import printWarning [as 别名]
def correlation(array, maxtau = 200, step_type = "tau", flag_normalize = True, flag_verbose = False):
    """
    Calculation of the correlation using the method Jan used.
    The method is slow and probably wrong. Use correlation_fft instead.

    For every iteration, the copied array will be 'rolled' or 'rotated' left by 1 for maxtau times. The copied array will be multiplied with the original array, but only the elements with a certain step between them will be used. The larger the step size, the quicker the method but also the more noisy the result will become.

    INPUT:
    array (ndarray): 1-d array with the data
    maxtau (int): the maximum shift, also the maximum to where the correlation is calculated. This directly affects the speed of the calculation. (with N^2?)
    step_type ("1", "tau"): The step size. With "1" the step size is 1, this will result in a longer calculation but less noise. With "tau" the step size is the current "tau". The calculation will be faster but the result will be noisier.
    flag_normalize (BOOL, True): see note below.

    CHANGELOG:
    20120215: Introduced step_type
    20130131/RB: introduced flag_normalize
    20130204/RB: tested if 'array2 = numpy.roll(array2, -1)' is better nested in the itertools call, but it makes no real change on the speed of the function. No changes made.
    """
    DEBUG.verbose("Correlation Jan-style", flag_verbose)
    
    array = array - numpy.mean(array)

    array2 = numpy.copy(array)

    c = numpy.zeros(maxtau)

    for i in range(0, maxtau):

        array2 = numpy.roll(array2, -1)

        if step_type == "tau":
            step = i+1
        elif step_type == "1":
            step = 1
        else:   
            DEBUG.printWarning("step_type is not recognized, will use 'tau'", inspect.stack())
            step = i+1

        a = list(itertools.islice(array * array2, None, len(array)-i-1, step))

        c[i] = numpy.sum(a) / len(a)

    if flag_normalize:
        return c/c[0]
    else:
        return c

# 需要导入模块: from PythonTools import Debug [as 别名]
# 或者: from PythonTools.Debug import printWarning [as 别名]
def find_axes_indices(axis, val_min, val_max, flag_verbose = False):
    """
    Find the indices of values val_min and val_max in axis, such that val_min and val_max are included. 
    Because the intended use is to slice an array data[val_min_i:val_max_i], 1 is added to val_max_i.
    If the val_min or val_max exceed the axis, then the index is 0 or -1, respectively.
    
    BEHAVIOR:
    axis = [3,4,5,6]
    if val_min == 4.5: val_min_i = 1
    if val_min == 5: val_min_i = 1
    if val_min == 1: val_min_i = 0
    
    if val_max = 4.5: val_max_i = 3: axis[0:3] = [3,4,5]
    if val_max = 4: val_max_i = 2: axis[0:2] = [3,4]
    if val_max = 10: val_max_i = -1: axis[0:-1] = [3,4,5]
    
    CHANGELOG:
    201108xx/RB: originated in contourplot
    20130213/RB: moved to separate function. Better handling of edge cases.
    
    """
    if val_min > val_max:
        DEBUG.printWarning("val_min > val_max ({v1} and {v2}), will give strange result.".format(v1 = val_min, v2 = val_max), inspect.stack())
    
    temp = numpy.where(axis < val_min)
    if len(temp[0]) == 0:
        val_min_i = 0
    else:
        val_min_i = temp[0][-1]

    temp = numpy.where(axis > val_max)
    if len(temp[0]) == 0:
        val_max_i = -1
    else:
        val_max_i = temp[0][0] + 1
        if val_max_i == len(axis):
            val_max_i = -1

    return val_min_i, val_max_i

# 需要导入模块: from PythonTools import Debug [as 别名]
# 或者: from PythonTools.Debug import printWarning [as 别名]
def import_data_LV_A(path, base_filename):
    """
    Imports data for 'LV_file_format.1'
    
    """

    try:
        # data
        path_and_filename = path + base_filename + ".csv"
        data = numpy.loadtxt(path_and_filename, dtype = "float", delimiter = ",")
        data = data.T

        # time axis in fringes
        path_and_filename = path + base_filename + "_t1.csv"
        t1_axis = numpy.loadtxt(path_and_filename, dtype = "float", delimiter = ",") 

        # frequency axis
        path_and_filename = path + base_filename + "_w3.csv"
        w3_axis = numpy.loadtxt(path_and_filename, dtype = "float", delimiter = ",") 

        # phase
        path_and_filename = path + base_filename + "_phase.txt"
        f = open(path_and_filename)
        for line in f:
            temp = line
        f.close()
        if temp == "NaN":
            DEBUG.printWarning("Phase is Not-a-Number, will be set to 0 ", inspect.stack())
            phase = 0
        else:
            phase = float(temp)

        # last pump
        path_and_filename = path + base_filename + "_lastpump.txt"
        f = open(path_and_filename)
        for line in f:
            lastpump = line
        f.close()

        # determine number of fringes
        n_fringes = int((len(t1_axis)+1)/2)
        n_pixels = len(w3_axis)

        # convert NaN to zeros
        data = numpy.nan_to_num(data)

        # labview measures 4000-N to 4000+N, we want the data split into 4000-N to 4000 (non-rephasing) and 4000 to 4000+N (rephasing)
        R = data[n_fringes-1:, :]
        NR = numpy.flipud(data[:n_fringes, :])

        # for the FFT, we don't want 4000 to be zero. The axes run from 0 to N
        # also: calculate the axis in fs        
        t1fr_axis = numpy.arange(n_fringes)
        t1fs_axis = numpy.arange(n_fringes) * CONST.hene_fringe_fs

        # return everything
        return R, NR, t1fs_axis, t1fr_axis, w3_axis, phase, lastpump, n_fringes, n_pixels

    except IOError:
        DEBUG.printError("Unable to import LabView data from file " + path + base_filename, inspect.stack())
        # raise
        return False