IR_image_tools.py 82.1 KB
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# -*- coding: utf-8 -*-
"""
Created on Wed May  9 14:56:32 2018
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Version: 3.4.0
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@author: Holger Niemann, Peter Drewelow, Yu Gao
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mainly to clean up the downloadversionIRdata code
Tools for:
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    checking IR images,
    calculate gain and offset again from
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    check backgroundframes
    check coldframes
    ...
"""
import numpy as np
import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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from IR_config_constants import portcamdict, IRCAMBadPixels_path, parameter_file_path
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import os
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import datetime
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#import h5py
#import glob
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def get_OP_by_time(time_ns=None, shot_no=None, program_str=None):
    '''Derives operation phase (OP) of W7-X based on either:
       a nanosacond time stamp, a MDSplus style shot no. or a program ID.
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       INPUT
       -----
          time_ns:      - integer of nanosecond time stamp,
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                         e.g. 1511972727249834301 (OPTIONAL)
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          shot_no      - integer of MDSplus style shot number,
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                         e.g. 171207022 (OPTIONAL)
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          program_str  - string of CoDaQ ArchiveDB style prgram number or date,
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                         e.g. '20171207.022' or '20171207' (OPTIONAL)
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       RESULT
       ------
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          conn         - MDSplus connection object, to be used in e.g. 1511972727249834301
                         read_MDSplus_image_simple(), read_MDSplus_metadata()
   '''
    # derive operation phase (OP) from time as nanosecond time stamp or string
    if time_ns is not None:
        dateOP = datetime.datetime.utcfromtimestamp(time_ns/1e9)
    elif shot_no is not None:
        dateOP = datetime.datetime.strptime(str(shot_no)[:6], '%y%m%d')
    elif program_str is not None:
        dateOP = datetime.datetime.strptime(program_str[:8], '%Y%m%d')
    else:
        raise Exception('get_OP_by_time: ERROR! neither time, shot no. or program ID provided')
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    if dateOP.year == 2017:
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        if dateOP.month > 8 and dateOP.month < 12:
            OP = "OP1.2a"
        elif dateOP.month == 8 and dateOP.day >= 28:
            OP = "OP1.2a"
        elif dateOP.month == 12 and dateOP.day < 8:
            OP = "OP1.2a"
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        else:
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            OP = None
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    elif dateOP.year >= 2018:
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        return "OP1.2b"
    elif dateOP.year <= 2016 and dateOP.year >= 2015:
        if (dateOP.year == 2016 and dateOP.month <= 3) or (dateOP.year == 2015 and dateOP.month == 12):
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            OP = "OP1.1"
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        else:
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            OP = None
    return OP

def bestimmtheitsmass_general(data, fit):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    R = 0
    if len(fit) == len(data):
        mittel = np.sum(data)/len(data)
        qam = quad_abweich_mittel(fit, mittel)
        R = qam/(qam+quad_abweich(data, fit))
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    else:
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        print("bestimmtheitsmass_general: Arrays must have same dimensions")
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    return R
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def bestimmheitsmass_linear(data, fit, debugmode=False):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    R2 = 0
    if len(fit) == len(data):
        mittel_D = np.mean(data)#np.sum(data)/len(data)
        mittel_F = np.mean(fit)
        R2 = quad_abweich_mittel(fit, mittel_D)/quad_abweich_mittel(data, mittel_D)
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        if debugmode:
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            print(mittel_D, mittel_F, quad_abweich_mittel(fit, mittel_D),
                  quad_abweich_mittel(data, mittel_D), R2)
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    else:
        print("bestimmtheitsmass_linear: Arrays must have same dimensions")
    return R2
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def quad_abweich_mittel(data, mittel):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    R = 0
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    for i in data:
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        R = R+(i-mittel)**2
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    return R
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def quad_abweich(data, fit):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    R = 0
    if len(fit) == len(data):
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        for i in range(len(data)):
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            R = R+(data[i]-fit[i])**2
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    else:
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        print("quad_abweich: Arrays must have same dimensions")
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    return R

def find_nearest(array, value):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    #a=array
    a = [x - value for x in array]
    mini = np.min(np.abs(a))
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    try: idx = a.index(mini)
    except: idx = a.index(-mini)
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    return idx#array[idx]
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def check_coldframe(coldframe, references=None, threshold=0.5, plot_it=False):
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    '''
    return true/false and the quality factor
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    shapi = np.shape(coldframe)
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    ##function  (np.arange(0,768)-384)**(2)/900-50
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    datasets = []
    for i in [int(shapi[1]//4), int(shapi[1]//2), int(shapi[1]//4*3)]:
        dataline = coldframe[0:shapi[0], i]
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        datasets.append(dataline-np.mean(dataline))
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    if references == None:
        references = []
        for dat in datasets:
            mini = np.mean(dat[shapi[0]/2-50:shapi[0]/2+50])
            a = (np.mean(dat[0:50])-mini)/(int(shapi[0]/2))**2
            reference = a*(np.arange(0, shapi[0])-int(shapi[0]/2))**(2)+mini
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            references.append(reference)
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    bestimmtheit = []
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    if plot_it:
        plt.figure()
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        plt.imshow(coldframe, vmin=np.mean(coldframe)-500, vmax=np.mean(coldframe)+500)
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        plt.figure()
    for i_dat in range(len(datasets)):
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        dat = datasets[i_dat]
        reference = references[i_dat]
        bestimmtheit.append(bestimmtheitsmass_general(dat, reference))
        if plot_it:
            plt.plot(dat, label='data')
            plt.plot(reference, label='reference')
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#            print(int(shapi[0]/2),1*(np.max(datasets[-1])-mini),mini)
            plt.legend()
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    if np.mean(bestimmtheit) > threshold:
        return True, bestimmtheit
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    else:
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        return False, bestimmtheit
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def check_coldframe_by_refframe(coldframe, reference_frame, threshold=0.8, plot_it=False):
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    '''
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    references = []
    shapi = np.shape(reference_frame)
    for i in [int(shapi[1]//5), int(shapi[1]//2), int(shapi[1]//4*3)]:
        dataline = reference_frame[0:shapi[0], i]
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        references.append(dataline-np.mean(dataline))
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    return check_coldframe(coldframe, references, threshold, plot_it)

def check_backgroundframe(backgroundframe, threshold=50):
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    '''
    return true or false
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    shapi = np.shape(backgroundframe)
    valid = True
    dataset = []
    for i in [int(shapi[1]//4), int(shapi[1]//2), int(shapi[1]//4*3)]:
        referenceline = backgroundframe[0:shapi[0], i]
        meanref = referenceline-np.mean(referenceline)
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        dataset.append(np.max(meanref)-np.min(meanref))
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    if np.mean(dataset) < threshold:
        valid = False
    return valid, np.mean(dataset)

def read_bad_pixels_from_file(port, shot_no=None, program=None, time_ns=None):
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    '''Reads bad pixels stored in *.bpx file on E4 server.
       Requires one of the optional arguments shot_no or program.
        IN
            port            - integer of port no of camera
            shot_no         - integer of MDSplus style shot number, e.g. 171207022 (OPTIONAL)
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            program         - string of CoDaQ ArchiveDB style prgram number or date,
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                              e.g. '20171207.022' or '20171207' (OPTIONAL)
        OUT
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            bad_pixle_list  - list of tuples (row,column) of pixel coordinates
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                              as integer
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        INPUT
        ------
        
        RESULT
        ------
        
        NOTE
        ------
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    '''
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    if shot_no is not None:
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        OP = get_OP_by_time(shot_no=shot_no)
    elif program is not None:
        OP = get_OP_by_time(program_str=program)
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    elif time_ns is not None:
        OP = get_OP_by_time(time_ns=time_ns)
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    else:
        raise Exception('read_bad_pixels_from_file: ERROR! Need either shot no. or program string.')
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    port_name = 'AEF{0}'.format(port)
    bad_pixel_file = 'badpixel_{0}.bpx'.format(portcamdict[OP][port_name][6:])
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    try:
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        data = np.genfromtxt(IRCAMBadPixels_path+bad_pixel_file, dtype=int)
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        bad_pixle_list = list(zip(data[:, 1], data[:, 0]))
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    except:
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        bad_pixle_list = []
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    return bad_pixle_list

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def find_outlier_pixels(frame, tolerance=3, plot_it=False):#worry_about_edges=True,
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    '''
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    This function finds the bad pixels in a 2D dataset.
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    Tolerance is the number of standard deviations used for cutoff.
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    frame = np.array(frame)#, dtype=int)
    from scipy.ndimage import median_filter
    blurred = median_filter(frame, size=9)
    difference = frame - blurred
    threshold = tolerance*np.std(difference)
    mean = np.mean(difference)
    if plot_it:
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        fig = plt.figure()
        fig.suptitle('find_outlier_pixels: histogram')
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        plt.hist(difference.ravel(), 50, log=True, histtype='stepfilled')
        plt.axvline(mean, linewidth=2, color='k', label='mean')
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        x1 = mean - np.std(difference)
        x2 = mean + np.std(difference)
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        plt.axvspan(x1, x2, linewidth=2, facecolor='g', alpha=0.1, label='standard deviation')
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        x1 = mean - tolerance*np.std(difference)
        x2 = mean + tolerance*np.std(difference)
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        plt.axvspan(x1, x2, linewidth=2, facecolor='r', alpha=0.1, label='threshold for bad pixel')
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        plt.legend()
        plt.show()
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    #find the hot pixels
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    bad_pixels = np.transpose(np.nonzero((np.abs(difference) > threshold)))
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    bad_pixels = (bad_pixels).tolist()
    bad_pixels = [tuple(l) for l in bad_pixels]
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    if plot_it:
        plt.figure()
        plt.imshow(frame)
        for i in range(len(bad_pixels)):
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            plt.scatter(bad_pixels[i][1], bad_pixels[i][0], c='None')
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        plt.show()
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    return bad_pixels

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def correct_images(images, badpixels, verbose=0):
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    '''
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    if type(badpixels) != int:
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        if type(images) == list:
            # return corrected images also as list of 2D arrays
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            for i in range(len(images)):
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                images[i] = restore_bad_pixels(images[i], np.invert(badpixels == 1), verbose=verbose-1)
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        else:
            # keep shape
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            images = restore_bad_pixels(images, np.invert(badpixels == 1), verbose=verbose-1)
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#        for i in range(len(images)):
#            images[i]=(restore_pixels(images[i],np.invert(badpixels==1))).astype(np.float32)
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        if verbose > 0:
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            print("correct_images: done")
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    return images

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def restore_bad_pixels(frames, bad_pixel, by_list=True, check_neighbours=True, plot_it=False, verbose=0):
    """Restore bad pixel by interpolation of adjacent pixels. Optionally make
       sure that adjacent pixels are not bad (time consuming). Default is to use
       a list of bad pixels and a for loop. For many bad pixels consider using
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       the optinal alternative using a bad pixel mask.
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       INPUT
       ------
           frames              - either list of frames as 2D numpy array,
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                                  or 3D numpy array (frame number, n_rows, n_cols),
                                  or 2D numpy array (n_rows, n_cols)
            bad_pixel           - either list of tuples of bad pixel coordinates,
                                  or mask of pixel status (good=True, bad=False)
            by_list             - boolean of whether to use a list and a for loop (True),
                                  or to use a mask of bad pixel and array operations (False)
                                  (OPTIONAL: if not provided, True (list) is default)
            check_neighbours    - boolean of whether to check if neighbours of a bad pixel
                                  are not bad either before computing a mean
                                  (works only in list mode!)
                                  (OPTIONAL: if not provided, check is on)
            plot_it             - boolean to decide whether to plot intermediate
                                  results or not
                                  (OPTIONAL: if not provided, switched off)
            verbose             - integer of feedback level (amount of prints)
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                                  (OPTIONAL: if not provided, only ERROR output) 
       RESULT
       ------
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            frames              - 3D numpy array (frame number, n_rows, n_cols) of
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                                  corrected frames
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       NOTE
       ------
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    """

    # make sure frames is correctly shaped
    if type(frames) == list:
        frames = np.array(frames)
        frame_shape = 'list'
    else:
        if len(np.shape(frames)) == 2:
            frames = np.array([frames])
            frame_shape = '2D'
        elif len(np.shape(frames)) == 3:
            frame_shape = '3D'
            pass
        else:
            raise Exception('restore_bad_pixels: ERROR! Unexpected shape of frames.')
    frame_dtype = frames.dtype
#    frames = frames.astype(float)
    n_frames, n_rows, n_cols = np.shape(frames)
    if plot_it:
        start_frame = np.copy(frames[0])
    
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    # make sure bad pixel are provided as mask and list  
    if type(bad_pixel) is list:
        blist = bad_pixel
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        bmask = np.ones([n_rows, n_cols], dtype=bool)
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        for pix in blist:
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            try:
                bmask[pix] = False
            except Exception as E:
                Warning(E)
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        bmask = np.invert(bmask)
    else:
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        if np.shape(bad_pixel)[0] == n_rows and np.shape(bad_pixel)[1] == n_cols:
            bmask = np.invert(bad_pixel)            
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            x, y = np.where(bmask)
            blist = list(zip(x, y))
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        else:
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            raise Exception('restore_bad_pixels: ERROR! bad_pixel in bad shape {0}'.format(np.shape(bad_pixel)))
            
    if verbose > 0:
        print('restore_bad_pixels: {0} bad pixels to be restored: {1} ... '.format(len(blist), blist[:3]))    
    
    # expand frame by rows and columns of zeros to simplify treatment of edges
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    frames = np.dstack([np.zeros([n_frames, n_rows], dtype=frame_dtype), frames, np.zeros([n_frames, n_rows], dtype=frame_dtype)])
    frames = np.hstack([np.zeros([n_frames, 1, n_cols+2], dtype=frame_dtype), frames, np.zeros([n_frames, 1, n_cols+2], dtype=frame_dtype)])
    bmask = np.vstack([np.zeros([1, n_cols], dtype=bool), bmask, np.zeros([1, n_cols], dtype=bool)])
    bmask = np.hstack([np.zeros([n_rows+2, 1], dtype=bool), bmask, np.zeros([n_rows+2, 1], dtype=bool)])
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    # define number of neighbours (up to 4) ina an array of expanded frame size
    n_neighbours = np.ones([n_frames, n_rows+2, n_cols+2])*4
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    n_neighbours[:, 1, :] = 3
    n_neighbours[:, -2, :] = 3
    n_neighbours[:, :, 1] = 3
    n_neighbours[:, :, -2] = 3
    n_neighbours[:, 1, 1] = 2
    n_neighbours[:, 1, -2] = 2
    n_neighbours[:, -2, 1] = 2
    n_neighbours[:, -2, -2] = 2
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    if by_list:
        # ===== correct bad pixels using the list of bad pixels =====
        #
        
        for pos in blist:
            # Note:
            # pos points to real frame coordinates, while bmask, n_neighbours have been expanded!
            
            if check_neighbours:
                # takes only neighbours that are not bad
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                pos_l = np.where(bmask[pos[0]+1,:pos[1]+1] == False)[0]
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                if len(pos_l) != 0:
                    pos_l = pos_l[-1]
                else: 
                    pos_l = pos[1]+1
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                pos_r = np.where(bmask[pos[0]+1,pos[1]+1:] == False)[0]
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                if len(pos_r) != 0:
                    pos_r = pos_r[0] + pos[1]+1
                else: 
                    pos_r = pos[1]+2
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                pos_t = np.where(bmask[:pos[0]+1,pos[1]+1] == False)[0]
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                if len(pos_t) != 0:
                    pos_t = pos_t[-1]
                else: 
                    pos_t = pos[0]+1
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                pos_b = np.where(bmask[pos[0]+1:,pos[1]+1] == False)[0]
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                if len(pos_b) != 0:
                    pos_b = pos_b[0] + pos[0]+1
                else: 
                    pos_b = pos[0]+2
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            else:
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                # insensitive to neighbours being bad as well!
                pos_l = pos[1]
                pos_r = pos[1]+2
                pos_t = pos[0]
                pos_b = pos[0]+2
            average = (frames[:,pos[0]+1,pos_l].astype(float) + 
                       frames[:,pos[0]+1,pos_r].astype(float) + 
                       frames[:,pos_t,pos[1]+1].astype(float) + 
                       frames[:,pos_b,pos[1]+1].astype(float)) / n_neighbours[:,pos[0]+1,pos[1]+1]
            frames[:,pos[0]+1,pos[1]+1] = average.astype(frame_dtype)
        frames = frames[:,1:-1,1:-1]
        
    else:
        # ======= correct bad pixels using the bad pixel mask =======
        #
        # (insensitive to neighbours being bad as well!)
   
        # prepare mask arrays for neighbours by shifting it to left, right, top and bottom
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        bmask_l = np.hstack([bmask[:, 1:], np.zeros([n_rows+2, 1], dtype=bool)])
        bmask_r = np.hstack([np.zeros([n_rows+2, 1], dtype=bool), bmask[:, :-1]])
        bmask_t = np.vstack([bmask[1:, :], np.zeros([1, n_cols+2], dtype=bool)])
        bmask_b = np.vstack([np.zeros([1, n_cols+2], dtype=bool), bmask[:-1, :]])
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        # -----------------------------------
        # restore by mask
        #
        frames[:,bmask] = ( (frames[:,bmask_l].astype(float) + 
                             frames[:,bmask_r].astype(float) + 
                             frames[:,bmask_t].astype(float) + 
                             frames[:,bmask_b].astype(float)) / n_neighbours[:,bmask] ).astype(frame_dtype)
        frames = frames[:,1:-1,1:-1]
    
    # plot comparison
    if plot_it:
        plt.figure()
        plt.title('bad pixel correction of first frame')
        m = np.mean(start_frame)
        s = np.std(start_frame)
        plt.imshow(start_frame, vmin=m-s, vmax=m+s)
        plt.colorbar()
        x,y = zip(*blist)
        plt.scatter(y,x, marker='o', s=5, c='r', linewidths=1)
        plt.tight_layout()
        plt.show()

    if frame_shape == 'list':
        frames = list(frames)
    elif frame_shape == '2D' and len(np.shape(frames))==3:
        frames = frames[0]
        
    return frames

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def generate_new_hot_image(cold,reference_cold,reference_hot):
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    '''
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    if cold is None or reference_cold is None or reference_hot is None:
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        raise Exception("generate_new_hot_image: Cannot Calculate new Hot image, if images are missing!")
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    else:
        return reference_hot+(cold-reference_cold)
    
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def calculate_gain_offset_image_pix(cold_image,hot_image=None,reference_cold=None,reference_hot=None,verbose=0):    
    '''
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    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
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    '''
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    if hot_image is None:
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        hot_image=generate_new_hot_image(cold_image,reference_cold,reference_hot)
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    if verbose>0:
        print("calculate_gain_offset_image_pix: calculate gain and offset")        
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    Sh_ref =  hot_image[ ( np.int( np.shape(hot_image)[0]   /2  )  ) ][np.int( (np.shape(hot_image)[1]   /2  ) ) ]          
    Sc_ref =  cold_image[ ( np.int(  (np.shape(cold_image)[0])  /2 )  ) ][( np.int(  (np.shape(cold_image)[1])  /2 ) ) ]  
    Gain_rel =  ( Sh_ref  - Sc_ref ) / ( hot_image  - cold_image)    
    Off_h_rel = Sh_ref -   hot_image*Gain_rel
    Off_c_rel = Sc_ref -   cold_image*Gain_rel    
    Offset_rel  = ( Off_h_rel + Off_c_rel ) /2
    return Gain_rel,Offset_rel
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def calculate_gain_offset_image(cold_image,hot_image=None,reference_cold=None,reference_hot=None,verbose=0):    
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    if hot_image is None:
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        hot_image=generate_new_hot_image(cold_image,reference_cold,reference_hot)
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    if verbose>0:
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        print("calculate_gain_offset_image: calculate gain and offset")  
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#    Sh_ref =  hot_image[ ( np.int( np.shape(hot_image)[0]   /2  )  ) ][np.int( (np.shape(hot_image)[1]   /2  ) ) ]          
#    Sc_ref =  cold_image[ ( np.int(  (np.shape(cold_image)[0])  /2 )  ) ][( np.int(  (np.shape(cold_image)[1])  /2 ) ) ]  
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#    print(hot_image[( np.int( np.shape(hot_image)[0]/2) )-2: (np.int( np.shape(hot_image)[0]/2))+3,np.int((np.shape(hot_image)[1]/2))-2:np.int((np.shape(hot_image)[1]/2))+3 ])
#    print(cold_image[( np.int( np.shape(hot_image)[0]/2) )-2: (np.int( np.shape(hot_image)[0]/2))+3,np.int((np.shape(hot_image)[1]/2))-2:np.int((np.shape(hot_image)[1]/2))+3 ])
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    Sh_ref = np.mean(hot_image[( np.int(np.shape(hot_image)[0]/2))-2: (np.int(np.shape(hot_image)[0]/2))+3,np.int((np.shape(hot_image)[1]/2))-2:np.int((np.shape(hot_image)[1]/2))+3])    
    Sc_ref = np.mean(cold_image[( np.int(np.shape(cold_image)[0]/2))-2: (np.int(np.shape(cold_image)[0]/2))+3,np.int((np.shape(cold_image)[1]/2))-2:np.int((np.shape(cold_image)[1]/2))+3])    
    difference_image = hot_image  - cold_image
    indexlist = np.where(difference_image==0)
    difference_image[indexlist] = 0.001
    Gain_rel = ( Sh_ref  - Sc_ref ) / ( difference_image)    
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    Gain_rel[indexlist]=0
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    Off_h_rel = Sh_ref - hot_image*Gain_rel
    Off_c_rel = Sc_ref - cold_image*Gain_rel    
    Offset_rel = ( Off_h_rel + Off_c_rel ) /2
    return Gain_rel, Offset_rel    
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#%% functions from Yu Gao
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#""" functions by Yu Gao"""
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#outdated by download_hot_cold_reference in downloadversionIRdata. removed to remove dependency on data on the E4-drive    
#==============================================================================
# def load_ref_images(port, exposuretime, verbose=0):
#     '''
#     load the reference cold and hot frame during calibration from local files.
#     @port: e.g. 'AEF10'
#     @exposuretime: int number.
#     '''
#     cameraname = portcamdict['OP1.2a'][port]
#     foldername = cameraname.split('_')[0] + '_' + cameraname.split('_')[2]
#     scanpath = os.path.join(IRCamRefImagespath, foldername)
#     coldref, hotref = [], []
#     for filename in glob.iglob(scanpath + '\*' + str(int(exposuretime)) + 'us.h5', recursive=True):
#         if 'hot' in filename:
#             if verbose>0:
#                 print('load_ref_images: read from ',filename)
#             with h5py.File(filename, 'r') as h5in:
#                 hotref = h5in[os.path.basename(filename)].value
#         elif 'cold' in filename:
#             if verbose>0:
#                 print('load_ref_images: read from ',filename)
#             with h5py.File(filename, 'r') as h5in:
#                 coldref = h5in[os.path.basename(filename)].value
#     return coldref, hotref
#==============================================================================
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def reconstruct_coldframe (exposuretime, sT, a, bnew, coldref):
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    """
    INPUT
    ------
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        exposuretime: integer
            the exposure time
        sT: 
        a:
        bnew:
        coldref: numpy array
            the reference cold frame as the base for the reconstruction        
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    RESULT
    ------
    
    NOTE
    ------
    """
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    cirebuild = a * sT + bnew * exposuretime + coldref
    return cirebuild
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#%% other functions
def check_dublicates(array):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    a = array
    import collections
    return [item for item, count in collections.Counter(a).items() if count > 1]
    
def check_dublicates_2(array):
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    """
    INPUT
    ------
    
    RESULT
    ------
    
    NOTE
    ------
    """
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    seen = set()
    uniq = []
    for x in array:
        if x not in seen:
            uniq.append(x)
            seen.add(x)
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    return uniq,seen

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def get_work_list(pipepath,typ="q"):
    """
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    INPUT
    ------
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        pipepath: string
            the path to the folder where the files are located
        typ: string
            the typ of data which is requested in the working list\n 
            possiblities: q, Aw, qpeak, width, load\n 
            or anything else for the problematic programs
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    RESULT
    ------
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        cam_programs: list
            a list containing two coloumns, cameras and programs
        reasons: list, optional, only for problematic programs
            a list showing the reasons, why data are not processed
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    NOTE
    ------
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    """
    today=datetime.datetime.now()    
    cam_programs=[]
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    if typ in ('q_old','load_old'):
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        f=open(pipepath+str(today.year)+str(today.month)+"_"+typ+"_requests.txt")
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    elif typ in ('q','load','qpeak','Aw','width'):
        f=open(pipepath+"Auto_"+typ+"_requests.txt")
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    else:
        reasons=[]
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        f = open(pipepath+"problematic_programs.txt")
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    for line in f:
        koline=line.split("\t")
        if len(koline)>1:
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            prog = koline[0]
            if typ in ('q','load'):
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                cam_programs.append((prog,koline[1].split("\n")[0]))    
            else:
                cam_programs.append((prog,koline[1]))                    
                reasons.append(koline[2].split("\n")[0])
    f.close()
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    if typ in ('q','load'):
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        bla=check_dublicates_2(cam_programs)
        cam_programs=bla[0]
        return cam_programs
    else:
        return cam_programs,reasons

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#%% functions regarding wetted area calculation

def read_finger_info(file_name=None, OP='OP1.2b', verbose=0):
    '''Read divertor finger information from file. The referenced fingers are those
       defined in the IR profile mapping, i.e. the target modlues 5 and 6 are
       divided in inner and outer fingers (see 'finger_part' in result dictionary).
    
       INPUT
       -----
           file_name: str, optional
               file name of csv fiel with finger information
               (OPTIONAL: default is None, i.e. decide by OP)
           OP: str, optional
               label of operation phase of interest, e.g. 'OP1.2b'
               (OPTIONAL: default is 'OP1.2b', i.e. load TDU file)
           verbose: integer, optional
               feedback level (details of print messages)
               (OPTIONAL: if not provided, only ERROR output)
       RESULT
       ------
           finger_dic: dictionary
               dictionary with keys 'ID', 'target', 'target_element','n_profiles', 'width', 'finger_part' (see NOTES)
       NOTES
       -----
           contents of result dictionary:
               * 'ID' numpy array of integers of continuous finger number
               * 'target' list of strings of target identifier ('h_l' horizontal low-iota, 'h_m' horizontal middle part, 'h_h' horizontal high-iota, 'v' vertical)
               * 'target_element' numpy array of integers of target module number (1..9 on horizontal target, 1..3 on vertical target)
               * 'n_profiles' numpy array of integers of number of profiles defined on this finger
               * 'width' numpy array of floats of centre width of finger in meters
               * 'finger_part' numpy array of integers indicating with 0 this is a full finger and with 1 this is the second part of the previous finger
    '''
    if file_name is None:
        # assume OP is given
        if OP.startswith('OP1'):
            file_name='finger_info_TDU.csv'
        elif OP.startswith('OP2'):
            file_name='finger_info_HHF.csv'
    full_path = os.path.join(parameter_file_path, file_name)
    print(full_path)
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    if verbose > 0:
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        print('read_finger_info: reading from file {0} in {1}'.format(file_name, parameter_file_path))
    if not os.path.isfile(full_path):
        raise Exception('read_finger_info: ERROR! file not found')
    
    finger_dic = {'ID': [],
                  'target': [],
                  'target_element':[],
                  'n_profiles':[],
                  'width':[],
                  'finger_part':[]}
    
    data = np.genfromtxt(full_path, delimiter=';', dtype=(int, "|S3", int, int, float, int))
    for i in range(len(data)):
        finger_dic['ID'].append( data[i][0] )
        finger_dic['target'].append( data[i][1].decode('UTF-8') )
        finger_dic['target_element'].append( data[i][2] )
        finger_dic['n_profiles'].append( data[i][3] )
        finger_dic['width'].append( data[i][4] )
        finger_dic['finger_part'].append( data[i][5] )
    
    finger_dic['ID'] = np.array(finger_dic['ID'])
    finger_dic['target_element'] = np.array(finger_dic['target_element'])
    finger_dic['n_profiles'] = np.array(finger_dic['n_profiles'])
    finger_dic['width'] = np.array(finger_dic['width'])
    finger_dic['finger_part'] = np.array(finger_dic['finger_part'])
    
    return finger_dic


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def derive_strike_line_width_per_module(heat_flux, mapping,mode='average', q_max=None,
                                  profile_average_range=3, noise_threshold=2E5,
                                  ports_loaded=None, verbose=0):
    ''' Derive strike-line width of heat flux array by integrating the total power load
        and dividing with a peak heat flux value.
        The peak heat flux value is either:
            1) the maximum within each divertor finger (mode='finger')
            2) the maximum of a whole divertor module (mode='module')
            3) the maximum of the mean divertor averaged toroidally (mode='average').
        Mode average is default. In case the input heat flux has only 2 dimensions 
        (from one divertor), mode 'average' and 'module' result in the same.
        Returned are the strike-line width(s) and the corresponding q_max value(s).
    
       INPUT
       -----
           heat_flux: numpy array
               array of heat fluxes from THEODOR on the profiles defined 
               in the IR mapping; can be 2D (one divertor), or 3D (multiple divertor modules)
           mapping: dictionary
               IR profile mapping information as returned by 
               downloadversionIRdara.download_heatflux_mapping_reference();
               minimum necessary keys are 'finger_ID' and 's'
           mode: str, optional
               label to identify the normalization mode, either 
               'module' (normalize by peak heat flux per torus module),
               'average' (normalize by peak heat flux in the toroidally mean heat flux pattern),
               'finger' (normalize by peak heat flux per finger in each torus module)
               (OPTIONAL: default is 'average')
           q_max: float or numpy array, optional
               either single peak heat flux value or a peak heat flux for each 
               divertor module or each finger (depends on mode)
               (OPTIONAL: default is None, i.e. derived based on mode)
           profile_average_range: int, optional
               number of central profiles on each finger to average the 
               integral heat flux on (this avoids hot leading edges and shadowed edges)
               (OPTIONAL: default is 3 profiles)
           noise_threshold: float, optional
               minimum heat flux level to crop heat_flux to, if heat flux has negative values
               (OPTIONAL: default is 200kW/m²)
           ports_loaded: list or str or int, optional if mode not 'average'
               label of divertor modules provided in heat_flux array for plots; 
               int of port number for single divertor data and list of 
               port numbers for heat flux from multiple divertor modules; #
               gets renamed if a mean heat flux pattern is used (mode 'average')
               (OPTIONAL: default is None, i.e. label will be 'w_s')
           verbose: integer, optional
               feedback level (details of print messages)
               (OPTIONAL: if not provided, only ERROR output)
       RESULT
       ------
           total_mean_width: float or numpy array
               mean strike-line width in a shape that depends on the mode (see NOTES)
           q_max: float or numpy array
               peak heat flux used for normalizatin in a shape that depends on the mode (see NOTES)
       NOTES
       -----
           The shape of the results varies depending on the dimension of the input
               * 2D: singel divertor modules heat flux
               * 3D: heat flux from multiple divertor modules
           and the mode to derive q_max ('module', 'average', 'finger'):
               * '2D' + 'module' or 'average' --> one value for total_wetted_area and q_max
               * '3D' + 'average' --> 1D numpy arrays with two values for total_wetted_area and q_max (upper and lower divertors)
               * '3D' + 'module' --> 1D numpy arays with a value for each torus module (first dimension of heat_flux)
               * '2D' + 'finger' --> 1D numpy arays with a value for each divertor finger
               * '3D' + 'finger' --> 2D numpy arays with a value for each torus module and each divertor finger
    '''
    #check input
    heat_flux_dim = len(np.shape(heat_flux))
    if mode == 'average' and ports_loaded == None and heat_flux_dim>2:
        raise Exception("derive_strike_line_width_per_module: ports must be specified in average mode since V3.3.2")
    elif mode == 'average' and heat_flux_dim > 2:
        try:
            llen=len(ports_loaded)
        except:
            raise Exception("derive_strike_line_width_per_module: each divertor need a description to calcualte proper the wetted area!")
        else:
            if llen!=len(heat_flux):
                raise Exception("derive_strike_line_width_per_module: number of given divertors and number of descriptions does not match!")
    # prepare mapping and finger information
    finger_dic = read_finger_info(verbose=verbose-1)
    finger_ID = finger_dic['ID']
    profile_no = mapping['Finger_ID'][0]
    
    # find profile IDs of central profiles on each finger
    central_profiles_on_finger = []
    is_central_profile = []
    for i_finger in range(len(finger_ID)):
        n_profiles = finger_dic['n_profiles'][i_finger]
        i_profile_start = n_profiles//2 - profile_average_range//2 -1
        central_profiles = i_finger*100 + np.arange(i_profile_start, i_profile_start+profile_average_range)
        central_profiles_on_finger.append(central_profiles)
        is_central_profile.append(np.logical_or.reduce([profile_no == centre_profile for centre_profile in central_profiles]))
    central_profiles_on_finger = np.array(central_profiles_on_finger)

    if np.nanmin(heat_flux) < 0:
        heat_flux[heat_flux<noise_threshold] = 0
        if verbose>0:
            print('derive_strike_line_width_per_module: set heat_flux < {0:.1f}kW/m² to 0'.format(noise_threshold/1E3))
                
    # reduce dimension of heat_flux if in 'average' mode
    if heat_flux_dim == 2 and mode == 'average':
        mode = 'module'
    elif heat_flux_dim == 3 and mode == 'average':
#        heat_flux = np.nanmean(heat_flux, axis=0)
        ## sort the divertors
        updiv=[]
        downdiv=[]
        for i in range(len(ports_loaded)):
            # entries in the array/list are either int or str or even float
            try:
                port=int(ports_loaded[i])
            except: #okay it is not an int or an int like string
                ## what can it be? 'AEFXX'? But what about OP2 with the A or K ports? Still be 3 letters
                port=int(ports_loaded[i][3:])
            if port%10==0:
                downdiv.append(heat_flux[i])
            else:
                updiv.append(heat_flux[i])
        heat_flux=np.array([np.nanmean(np.asarray(updiv),axis=0),np.nanmean(np.asarray(downdiv),axis=0)])
        del downdiv,updiv
#        heat_flux_dim = 3
        ports_loaded = [1,0]#'upper divertor','lower divertor']#'mean heat flux'
        mode = 'module'
        if verbose>0:
            print('derive_strike_line_width_per_module: averaged 3D heat flux array over first dimension')
            
    if heat_flux_dim==3:
        # assume dimensions: toroidal index (camera ports), row, column
        n_ports = np.shape(heat_flux)[0]#, n_rows, n_cols 
        if verbose>0:
            print('derive_strike_line_width_per_module: deriving wetted area on {0} divertor modules in {1} mode...'.format(n_ports, mode))
        # derive q_max for normalization of integral
        if q_max is None:
            # derive q_max on every finger
            q_max_on_finger = []
            for i_finger in range(len(finger_ID)):
                q_max_on_finger.append([np.nanmax(h[is_central_profile[i_finger]]) for h in heat_flux])
            q_max_on_finger = np.array(q_max_on_finger)
            q_max_on_finger[q_max_on_finger==0] = 1
            if mode == 'module':
                # one value per torus half module
                q_max = np.nanmax(q_max_on_finger, axis=0)
            elif mode == 'finger':
                # one value per finger in each torus half module
                q_max = q_max_on_finger
            q_max[q_max==0] = 1
        # integrate over profiles
#        goodcounter=np.zeros([n_ports])
        finger_strikeline_width = np.zeros([len(finger_ID), n_ports])
        strikeline_int = np.zeros([len(finger_ID), n_ports])
        for i_finger in range(len(finger_ID)):
            # initialize temporary line integral for each module
            central_line_integral = np.zeros(n_ports)
            # integrate over each central profile and average    
            for i_profile in central_profiles_on_finger[i_finger]:
                ij_profile = np.where(profile_no==i_profile)
                s = mapping['s'][ij_profile]
                h = heat_flux[:,ij_profile[0],ij_profile[1]]
                central_line_integral += np.nan_to_num( np.trapz(h, x=s, axis=1) )            
            # complete averaging process
            central_line_integral = central_line_integral / profile_average_range
            central_line_integral = central_line_integral *((central_line_integral>noise_threshold*max(s))*1)
#            goodcounter+=(central_line_integral>noise_threshold*max(s))*1
            # normalize by q_max and multiply with width of finger
            strikeline_int[i_finger,:] = central_line_integral
            if mode == 'module':
                finger_strikeline_width[i_finger,:] = central_line_integral / q_max #* finger_dic['width'][i_finger]                
            elif mode == 'finger':
                finger_strikeline_width[i_finger,:] = central_line_integral / q_max[i_finger] #* finger_dic['width'][i_finger]                                    
        
    elif heat_flux_dim==2:
        # assume dimensions: row, column
        if verbose>0:
            print('derive_strike_line_width_per_module: deriving wetted area on single divertor module in {0} mode...'.format(mode))
        # derive q_max for normalization of integral
        if q_max is None:
            if mode == 'average' or mode == 'module':
                # one value
                q_max = np.nanmax(heat_flux[np.logical_or.reduce(is_central_profile)])
            elif mode == 'finger':
                # one value per finger
                q_max = []
                for i_finger in range(len(finger_ID)):
                    q_max.append( np.nanmax(heat_flux[is_central_profile[i_finger]]) )
                q_max = np.array(q_max)
                q_max[q_max==0] = 1
        # integrate over profiles
#        goodcounter=0
        finger_strikeline_width = np.zeros([len(finger_ID)])
        strikeline_int = np.zeros([len(finger_ID)])
        for i_finger in range(len(finger_ID)):
            # integrate over each central profile and average
            central_line_integral = 0
            for i_profile in central_profiles_on_finger[i_finger]:
                ij_profile = np.where(profile_no==i_profile)
                s = mapping['s'][ij_profile]            
                h = heat_flux[ij_profile[0],ij_profile[1]]
                central_line_integral += np.nan_to_num( np.trapz(h, x=s, axis=0) )
            central_line_integral = central_line_integral / profile_average_range
            central_line_integral = central_line_integral *((central_line_integral>noise_threshold*max(s))*1)
#            goodcounter+=(central_line_integral>noise_threshold*s)*1
            strikeline_int[i_finger] = central_line_integral
            if mode == 'average' or mode == 'module':
                finger_strikeline_width[i_finger] = central_line_integral / q_max #* finger_dic['width'][i_finger]
            elif mode == 'finger':
                finger_strikeline_width[i_finger] = central_line_integral / q_max[i_finger] #* finger_dic['width'][i_finger]
                
    
    # merge half-fingers of TM5 and TM6
    if np.any(finger_dic['finger_part']):
        if verbose>0:
            print('derive_strike_line_width_per_module: merge wetted area on half fingers of TM05 and TM06')
        new_finger_ID = np.copy(finger_ID)
        # scan backwards over fingers, merge and delete second finger halfs
        for i_finger in finger_ID[:0:-1]:
            if finger_dic['finger_part'][i_finger]:
                new_finger_ID = np.delete(new_finger_ID, i_finger)
                if heat_flux_dim==3 and mode != 'average':
                    finger_strikeline_width[i_finger-1,:] = finger_strikeline_width[i_finger-1,:] + finger_strikeline_width[i_finger,:] 
                    strikeline_int[i_finger-1,:] = strikeline_int[i_finger-1,:] + strikeline_int[i_finger,:] 
                else:
                    finger_strikeline_width[i_finger-1] = finger_strikeline_width[i_finger-1] + finger_strikeline_width[i_finger] 
                    strikeline_int[i_finger-1] = strikeline_int[i_finger-1] + strikeline_int[i_finger] 
                finger_strikeline_width = np.delete(finger_strikeline_width, i_finger, axis=0)
                strikeline_int=np.delete(strikeline_int, i_finger, axis=0)
                if mode == 'finger' and heat_flux_dim==3:
                    q_max[i_finger-1,:] = np.maximum(q_max[i_finger-1,:], q_max[i_finger,:])
                    q_max = np.delete(q_max, i_finger, axis=0)
                elif mode == 'finger' and heat_flux_dim==2:
                    q_max[i_finger-1] = np.maximum(q_max[i_finger-1], q_max[i_finger])
                    q_max = np.delete(q_max, i_finger, axis=0)
    
    # sum up
    # 'average' mode: sum all fingers over all torus modules --> wetted area of all divertors
    #                divide by n_ports --> get average wetted area per divertor
    # 'module' mode:  in each torus module sum over wetted area on all fingers 
    #                --> individual wetted areas per divertor
    # 'finger' mode: do not sum, since each finger was normalized with a differen q_max
    #                --> individual wetted areas per finger
    # if only one divertors heat flux is given, proceed as in local mode
    if mode == 'finger':
        total_mean_width = finger_strikeline_width
    else:
        Weights=np.nan_to_num(strikeline_int/np.sum(strikeline_int,axis=0))
#        print(Weights)
        WS=np.sum(Weights,axis=0)
        bla=np.where(WS==0)[0]
        for b in bla:
            Weights[:,b]=Weights[:,b]+1
        total_mean_width = np.average(finger_strikeline_width,axis=0,weights=Weights)

    return total_mean_width, q_max

def derive_peaking_factor_per_module(heat_flux, mapping,mode='average', q_max=None,
                                  profile_average_range=3, noise_threshold=2E5,
                                  ports_loaded=None, verbose=0):
    ''' Derive peaking of heat flux array by dividing the peak heat flux value 
        with the mean heat flux value the total power load.
        The peak heat flux value is either:
            1) the maximum within each divertor finger (mode='finger')
            2) the maximum of a whole divertor module (mode='module')
            3) the maximum of the mean divertor averaged toroidally (mode='average').
        Mode average is default. In case the input heat flux has only 2 dimensions 
        (from one divertor), mode 'average' and 'module' result in the same.
        Returned are the strike-line width(s) and the corresponding q_max value(s).
    
       INPUT
       -----
           heat_flux: numpy array
               array of heat fluxes from THEODOR on the profiles defined 
               in the IR mapping; can be 2D (one divertor), or 3D (multiple divertor modules)
           mapping: dictionary
               IR profile mapping information as returned by 
               downloadversionIRdara.download_heatflux_mapping_reference();
               minimum necessary keys are 'finger_ID' and 's'
           mode: str, optional
               label to identify the normalization mode, either 
               'module' (normalize by peak heat flux per torus module),
               'average' (normalize by peak heat flux in the toroidally mean heat flux pattern),
               'finger' (normalize by peak heat flux per finger in each torus module)
               (OPTIONAL: default is 'average')
           q_max: float or numpy array, optional
               either single peak heat flux value or a peak heat flux for each 
               divertor module or each finger (depends on mode)
               (OPTIONAL: default is None, i.e. derived based on mode)
           profile_average_range: int, optional
               number of central profiles on each finger to average the 
               integral heat flux on (this avoids hot leading edges and shadowed edges)
               (OPTIONAL: default is 3 profiles)
           noise_threshold: float, optional
               minimum heat flux level to crop heat_flux to, if heat flux has negative values
               (OPTIONAL: default is 200kW/m²)
           ports_loaded: list or str or int, optional if mode not 'average'
               label of divertor modules provided in heat_flux array for plots; 
               int of port number for single divertor data and list of 
               port numbers for heat flux from multiple divertor modules; #
               gets renamed if a mean heat flux pattern is used (mode 'average')
               (OPTIONAL: default is None, i.e. label will be 'w_s')
           verbose: integer, optional
               feedback level (details of print messages)
               (OPTIONAL: if not provided, only ERROR output)
       RESULT
       ------
           mean_peaking_factor: float or numpy array
               mean peaking factor in a shape that depends on the mode (see NOTES)
           q_max: float or numpy array
               peak heat flux used for normalizatin in a shape that depends on the mode (see NOTES)
       NOTES
       -----
           The shape of the results varies depending on the dimension of the input
               * 2D: singel divertor modules heat flux
               * 3D: heat flux from multiple divertor modules
           and the mode to derive q_max ('module', 'average', 'finger'):
               * '2D' + 'module' or 'average' --> one value for total_wetted_area and q_max
               * '3D' + 'average' --> 1D numpy arrays with two values for total_wetted_area and q_max (upper and lower divertors)
               * '3D' + 'module' --> 1D numpy arays with a value for each torus module (first dimension of heat_flux)
               * '2D' + 'finger' --> 1D numpy arays with a value for each divertor finger
               * '3D' + 'finger' --> 2D numpy arays with a value for each torus module and each divertor finger
    '''
    #check input
    heat_flux_dim = len(np.shape(heat_flux))
    if mode == 'average' and ports_loaded == None and heat_flux_dim>2:
        raise Exception("derive_peaking_factor_per_module: ports must be specified in average mode since V3.3.2")
    elif mode == 'average' and heat_flux_dim>2:
        try:
            llen=len(ports_loaded)
        except:
            raise Exception("derive_peaking_factor_per_module: each divertor need a description to calcualte proper the wetted area!")
        else:
            if llen!=len(heat_flux):
                raise Exception("derive_peaking_factor_per_module: number of given divertors and number of descriptions does not match!")
    # prepare mapping and finger information
    finger_dic = read_finger_info(verbose=verbose-1)
    finger_ID = finger_dic['ID']
    profile_no = mapping['Finger_ID'][0]
    
    # find profile IDs of central profiles on each finger
    central_profiles_on_finger = []
    is_central_profile = []
    for i_finger in range(len(finger_ID)):
        n_profiles = finger_dic['n_profiles'][i_finger]
        i_profile_start = n_profiles//2 - profile_average_range//2 -1
        central_profiles = i_finger*100 + np.arange(i_profile_start, i_profile_start+profile_average_range)
        central_profiles_on_finger.append(central_profiles)
        is_central_profile.append( np.logical_or.reduce([profile_no == centre_profile for centre_profile in central_profiles]) )
    central_profiles_on_finger = np.array(central_profiles_on_finger)
    
    if np.nanmin(heat_flux) < 0:
        heat_flux[heat_flux<noise_threshold] = 0
        if verbose>0:
            print('derive_peaking_factor_per_module: set heat_flux < {0:.1f}kW/m² to 0'.format(noise_threshold/1E3))
                
    # reduce dimension of heat_flux if in 'average' mode
    if heat_flux_dim == 2 and mode == 'average':
        mode = 'module'
    elif heat_flux_dim == 3 and mode == 'average':
#        heat_flux = np.nanmean(heat_flux, axis=0)
        ## sort the divertors
        updiv=[]
        downdiv=[]
        for i in range(len(ports_loaded)):
            # entries in the array/list are either int or str or even float
            try:
                port=int(ports_loaded[i])
            except: #okay it is not an int or an int like string
                ## what can it be? 'AEFXX'? But what about OP2 with the A or K ports? Still be 3 letters
                port=int(ports_loaded[i][3:])
            if port%10==0:
                downdiv.append(heat_flux[i])
            else:
                updiv.append(heat_flux[i])
        heat_flux=np.array([np.nanmean(np.asarray(updiv),axis=0),np.nanmean(np.asarray(downdiv),axis=0)])
        del downdiv,updiv
#        heat_flux_dim = 3
        ports_loaded = [1,0]#'upper divertor','lower divertor']#'mean heat flux'
        mode = 'module'
        if verbose>0:
            print('derive_peaking_factor_per_module: averaged 3D heat flux array over first dimension')
            
    if heat_flux_dim==3:
        # assume dimensions: toroidal index (camera ports), row, column
        n_ports = np.shape(heat_flux)[0]#, n_rows, n_cols
        if verbose>0:
            print('derive_peaking_factor_per_module: deriving wetted area on {0} divertor modules in {1} mode...'.format(n_ports, mode))
        # derive q_max for normalization of integral
        if q_max is None:
            # derive q_max on every finger
            q_max_on_finger = []
            for i_finger in range(len(finger_ID)):
                q_max_on_finger.append([np.nanmax(h[is_central_profile[i_finger]]) for h in heat_flux])
            q_max_on_finger = np.array(q_max_on_finger)
            q_max_on_finger[q_max_on_finger==0] = 1
            if mode == 'module':
                # one value per torus half module
                q_max = np.nanmax(q_max_on_finger, axis=0)
            elif mode == 'finger':
                # one value per finger in each torus half module
                q_max = q_max_on_finger
            q_max[q_max==0] = 1
        # integrate over profiles
#        goodcounter=np.zeros([n_ports])
        finger_strikeline_width = np.zeros([len(finger_ID), n_ports])
        strikeline_int = np.zeros([len(finger_ID), n_ports])
        for i_finger in range(len(finger_ID)):
            # initialize temporary line integral for each module
            central_line_integral = np.zeros(n_ports)
            # integrate over each central profile and average    
            for i_profile in central_profiles_on_finger[i_finger]:
                ij_profile = np.where(profile_no==i_profile)
#                s = mapping['s'][ij_profile]
                h = heat_flux[:,ij_profile[0],ij_profile[1]]
                hh=[]
                for ele in h:
                    hh=np.append(hh,np.mean(ele[np.where(ele>0)]))
                central_line_integral += np.nan_to_num(hh)#np.mean(h[:][np.where(h[:]>0)]))#np.trapz(h, x=s, axis=1) )            
            # complete averaging process
            central_line_integral = central_line_integral / profile_average_range
#            central_line_integral = central_line_integral *((central_line_integral>noise_threshold*max(s))*1)
#            goodcounter+=(central_line_integral>noise_threshold*max(s))*1
            # normalize by q_max and multiply with width of finger
            strikeline_int[i_finger,:] = central_line_integral
            if mode == 'module':
                finger_strikeline_width[i_finger,:] = q_max / central_line_integral #/ q_max #* finger_dic['width'][i_finger]                
            elif mode == 'finger':
                if np.min(q_max[i_finger] / central_line_integral) < 1:
                    print("here comes something strange", q_max[i_finger], central_line_integral, np.shape(h))
                finger_strikeline_width[i_finger,:] = q_max[i_finger] / central_line_integral #/ q_max[i_finger] #* finger_dic['width'][i_finger]                                    
        
    elif heat_flux_dim==2:
        # assume dimensions: row, column
        if verbose>0:
            print('derive_peaking_factor_per_module: deriving peaking factor on single divertor module in {0} mode...'.format(mode))
        # derive q_max for normalization of integral
        if q_max is None:
            if mode == 'average' or mode == 'module':
                # one value
                q_max = np.nanmax(heat_flux[np.logical_or.reduce(is_central_profile)])
            elif mode == 'finger':
                # one value per finger
                q_max = []
                for i_finger in range(len(finger_ID)):
                    q_max.append( np.nanmax(heat_flux[is_central_profile[i_finger]]) )
                q_max = np.array(q_max)
                q_max[q_max==0] = 1
        # integrate over profiles
#        goodcounter=0
        finger_strikeline_width = np.zeros([len(finger_ID)])
        strikeline_int = np.zeros([len(finger_ID)])
        for i_finger in range(len(finger_ID)):
            # integrate over each central profile and average
            central_line_integral = 0
            for i_profile in central_profiles_on_finger[i_finger]:
                ij_profile = np.where(profile_no==i_profile)
#                s = mapping['s'][ij_profile]            
                h = heat_flux[ij_profile[0],ij_profile[1]]
                central_line_integral += np.nan_to_num(np.mean(h[np.where(h>0)]))#np.trapz(h, x=s, axis=0) )
            central_line_integral = central_line_integral / profile_average_range
#            central_line_integral = central_line_integral *((central_line_integral>noise_threshold*max(s))*1)
#            goodcounter+=(central_line_integral>noise_threshold*s)*1
            strikeline_int[i_finger] = central_line_integral
            if mode == 'average' or mode == 'module':
                finger_strikeline_width[i_finger] = q_max / central_line_integral   #* finger_dic['width'][i_finger]
            elif mode == 'finger':
                if q_max[i_finger] / central_line_integral < 1:
                    print("here comes something strange", q_max[i_finger], central_line_integral)
                finger_strikeline_width[i_finger] = q_max[i_finger] / central_line_integral   #* finger_dic['width'][i_finger]
                
    
    # merge half-fingers of TM5 and TM6
    if np.any(finger_dic['finger_part']):
        if verbose > 0:
            print('derive_peaking_factor_per_module: merge wetted area on half fingers of TM05 and TM06')
        new_finger_ID = np.copy(finger_ID)
        # scan backwards over fingers, merge and delete second finger halfs
        for i_finger in finger_ID[:0:-1]:
            if finger_dic['finger_part'][i_finger]:
                new_finger_ID = np.delete(new_finger_ID, i_finger)
                if heat_flux_dim==3 and mode != 'average':
                    finger_strikeline_width[i_finger-1,:] = finger_strikeline_width[i_finger-1,:] + finger_strikeline_width[i_finger,:] 
                    strikeline_int[i_finger-1,:] = strikeline_int[i_finger-1,:] + strikeline_int[i_finger,:] 
                else:
                    finger_strikeline_width[i_finger-1] = finger_strikeline_width[i_finger-1] + finger_strikeline_width[i_finger] 
                    strikeline_int[i_finger-1] = strikeline_int[i_finger-1] + strikeline_int[i_finger] 
                finger_strikeline_width = np.delete(finger_strikeline_width, i_finger, axis=0)
                strikeline_int=np.delete(strikeline_int, i_finger, axis=0)
                if mode == 'finger' and heat_flux_dim==3:
                    q_max[i_finger-1,:] = np.maximum(q_max[i_finger-1,:], q_max[i_finger,:])
                    q_max = np.delete(q_max, i_finger, axis=0)
                elif mode == 'finger' and heat_flux_dim==2:
                    q_max[i_finger-1] = np.maximum(q_max[i_finger-1], q_max[i_finger])
                    q_max = np.delete(q_max, i_finger, axis=0)
    
    # sum up
    # 'average' mode: sum all fingers over all torus modules --> wetted area of all divertors
    #                divide by n_ports --> get average wetted area per divertor
    # 'module' mode:  in each torus module sum over wetted area on all fingers 
    #                --> individual wetted areas per divertor
    # 'finger' mode: do not sum, since each finger was normalized with a differen q_max
    #                --> individual wetted areas per finger
    # if only one divertors heat flux is given, proceed as in local mode
    if mode == 'finger':
        peaking_factor = finger_strikeline_width
    else:
        Weights=np.nan_to_num(strikeline_int/np.sum(strikeline_int,axis=0))
        #deal with unloaded parts
        inf_pos=np.where(np.isinf(finger_strikeline_width))
        finger_strikeline_width[inf_pos] = 0
        Weights[inf_pos] = 0
#        print(Weights)
#        print(finger_strikeline_width)
        WS=np.sum(Weights,axis=0)
        bla=np.where(WS==0)[0]
        for b in bla:
            Weights[:,b]=Weights[:,b]+1
        peaking_factor = np.average(finger_strikeline_width,axis=0,weights=Weights)

    return peaking_factor, q_max

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def derive_wetted_area_per_module(heat_flux, mapping, mode='average', q_max=None,
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                                  profile_average_range=3, noise_threshold=2E5,
                                  ports_loaded=None, plot_it=False, verbose=0):
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    ''' Derive wetted area of heat flux array by integrating the total power load
        and dividing with a peak heat flux value.
        The peak heat flux value is either:
            1) the maximum within each divertor finger (mode='finger')
            2) the maximum of a whole divertor module (mode='module')
            3) the maximum of the mean divertor averaged toroidally (mode='average').
        Mode average is default. In case the input heat flux has only 2 dimensions 
        (from one divertor), mode 'average' and 'module' result in the same.
        Returned are the wetted area(s) and the corresponding q_max value(s).
    
       INPUT
       -----
           heat_flux: numpy array
               array of heat fluxes from THEODOR on the profiles defined 
               in the IR mapping; can be 2D (one divertor), or 3D (multiple divertor modules)
           mapping: dictionary
               IR profile mapping information as returned by 
               downloadversionIRdara.download_heatflux_mapping_reference();
               minimum necessary keys are 'finger_ID' and 's'
           mode: str, optional
               label to identify the normalization mode, either 
               'module' (normalize by peak heat flux per torus module),
               'average' (normalize by peak heat flux in the toroidally mean heat flux pattern),
               'finger' (normalize by peak heat flux per finger in each torus module)
               (OPTIONAL: default is 'average')
           q_max: float or numpy array, optional
               either single peak heat flux value or a peak heat flux for each 
               divertor module or each finger (depends on mode)
               (OPTIONAL: default is None, i.e. derived based on mode)
           profile_average_range: int, optional
               number of central profiles on each finger to average the 
               integral heat flux on (this avoids hot leading edges and shadowed edges)
               (OPTIONAL: default is 3 profiles)
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           noise_threshold: float, optional
               minimum heat flux level to crop heat_flux to, if heat flux has negative values
               (OPTIONAL: default is 200kW/m²)
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           ports_loaded: list or str or int, optional if mode not 'average'
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               label of divertor modules provided in heat_flux array for plots; 
               int of port number for single divertor data and list of 
               port numbers for heat flux from multiple divertor modules; #
               gets renamed if a mean heat flux pattern is used (mode 'average')
               (OPTIONAL: default is None, i.e. label will be 'A_w')
           plot_it: bool, optional
               switch of whether to plot intermediate results or not
               (OPTIONAL: deafult is NOT to plot)
           verbose: integer, optional
               feedback level (details of print messages)
               (OPTIONAL: if not provided, only ERROR output)
       RESULT
       ------
           total_wetted_area: float or numpy array
               wetted area in a shape that depends on the mode (see NOTES)
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               in average it is (upper,lower)
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           q_max: float or numpy array
               peak heat flux used for normalizatin in a shape that depends on the mode (see NOTES)
       NOTES
       -----
           The shape of the results varies depending on the dimension of the input
               * 2D: singel divertor modules heat flux
               * 3D: heat flux from multiple divertor modules
           and the mode to derive q_max ('module', 'average', 'finger'):
               * '2D' + 'module' or 'average' --> one value for total_wetted_area and q_max
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               * '3D' + 'average' --> 1D numpy arrays with two values for total_wetted_area and q_max (upper and lower divertors)
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               * '3D' + 'module' --> 1D numpy arays with a value for each torus module (first dimension of heat_flux)
               * '2D' + 'finger' --> 1D numpy arays with a value for each divertor finger
               * '3D' + 'finger' --> 2D numpy arays with a value for each torus module and each divertor finger
    '''
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    #check input
    heat_flux_dim = len(np.shape(heat_flux))
    if mode == 'average' and ports_loaded == None and heat_flux_dim>2:
        raise Exception("derive_wetted_area_per_module: ports must be specified in average mode since V3.3.2")
    elif mode == 'average' and heat_flux_dim>2:
        try:
            llen=len(ports_loaded)
        except:
            raise Exception("derive_wetted_area_per_module: each divertor need a description to calcualte proper the wetted area!")
        else:
            if llen!=len(heat_flux):
                raise Exception("derive_wetted_area_per_module: number of given divertors and number of descriptions does not match!")
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    # prepare mapping and finger information
    finger_dic = read_finger_info(verbose=verbose-1)
    finger_ID = finger_dic['ID']
    profile_no = mapping['Finger_ID'][0]
    
    # find profile IDs of central profiles on each finger
    central_profiles_on_finger = []
    is_central_profile = []
    for i_finger in range(len(finger_ID)):
        n_profiles = finger_dic['n_profiles'][i_finger]
        i_profile_start = n_profiles//2 - profile_average_range//2 -1
        central_profiles = i_finger*100 + np.arange(i_profile_start, i_profile_start+profile_average_range)
        central_profiles_on_finger.append(central_profiles)
        is_central_profile.append( np.logical_or.reduce([profile_no == centre_profile for centre_profile in central_profiles]) )
    central_profiles_on_finger = np.array(central_profiles_on_finger)
    
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    if np.nanmin(heat_flux) < 0:
        heat_flux[heat_flux<noise_threshold] = 0
        if verbose>0:
            print('derive_wetted_area_per_module: set heat_flux < {0:.1f}kW/m² to 0'.format(noise_threshold/1E3))
            
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    # reduce dimension of heat_flux if in 'average' mode
    if heat_flux_dim == 2 and mode == 'average':
        mode = 'module'
    elif heat_flux_dim == 3 and mode == 'average':
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#        heat_flux = np.nanmean(heat_flux, axis=0)
        ## sort the divertors
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        updiv=[]
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        downdiv=[]
        for i in range(len(ports_loaded)):
            # entries in the array/list are either int or str or even float
            try:
                port=int(ports_loaded[i])
            except: #okay it is not an int or an int like string
                ## what can it be? 'AEFXX'? But what about OP2 with the A or K ports? Still be 3 letters
                port=int(ports_loaded[i][3:])
            if port%10==0:
                downdiv.append(heat_flux[i])
            else:
                updiv.append(heat_flux[i])
        heat_flux=np.array([np.nanmean(np.asarray(updiv),axis=0),np.nanmean(np.asarray(downdiv),axis=0)])
        del downdiv,updiv
#        heat_flux_dim = 3
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        ports_loaded = [1,0]#'upper divertor','lower divertor']#'mean heat flux'
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        mode = 'module'
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        if verbose>0:
            print('derive_wetted_area_per_module: averaged 3D heat flux array over first dimension')
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        if plot_it:
            fig,ax=plt.subplots(1,2)
            ax[0].imshow(heat_flux[0]/1e6,vmin=0,vmax=5)
            ax[1].imshow(heat_flux[1]/1e6,vmin=0,vmax=5)
    elif heat_flux_dim == 3 and mode == 'average_central_max':
        ## assuming that the strike lines on each divertor are shifted a little bit radially to each other, shift the profiles so that maximimas agree in position
        # if not p max is lower and the strike-line and wetted area would to too large.        
        ## sort the divertors
        updiv=[]
        downdiv=[]
        for i in range(len(ports_loaded)):
            try:
                port=int(ports_loaded[i])
            except: #okay it is not an int or an int like string
                port=int(ports_loaded[i][3:])
            if port%10==0:
                downdiv.append(heat_flux[i])
            else:
                updiv.append(heat_flux[i])
        dummyup=updiv[0].copy()
        for i in range(1,len(updiv)):            
            for j in range(len(finger_ID)):                
                for k in range(finger_dic['n_profiles'][j]):
                    loc=np.where(mapping['Finger_ID'][0]==j*100+k)
                    line1=updiv[0][loc]
                    line2=updiv[i][loc]
                    if np.argmax(line1)!=0 and np.argmax(line2)!=0:
                        pmax_div=np.argmax(line1)-np.argmax(line2)
                        if pmax_div<0:
                            pstart=0
                            pend=len(loc[0])+pmax_div
                        else:
                            pstart=pmax_div
                            pend=len(loc[0])-pmax_div
                    else:
                        pmax_div=0
                        pstart=0
                        pend=len(loc[0])
#                    print(pend,max(loc[0]),pmax_div,np.shape(dummyup),max(loc[0]),max(loc[1]))
                    for y in range(pstart,pend):
                        dummyup[loc[0][y],loc[1][0]]=dummyup[loc[0][y],loc[1][0]]+updiv[i][loc[0][y]+pmax_div,loc[1][0]]
        dummyup=dummyup/len(updiv)
        dummydwn=downdiv[0].copy()
        for i in range(1,len(downdiv)):            
            for j in range(len(finger_ID)):                
                for k in range(finger_dic['n_profiles'][j]):
                    loc=np.where(mapping['Finger_ID'][0]==j*100+k)
                    line1=downdiv[0][loc]
                    line2=downdiv[i][loc]
                    if np.argmax(line1)!=0 and np.argmax(line2)!=0:
                        pmax_div=np.argmax(line1)-np.argmax(line2)
                        if pmax_div<0:
                            pstart=0
                            pend=len(loc[0])+pmax_div
                        else:
                            pstart=pmax_div
                            pend=len(loc[0])-pmax_div
                    else:
                        pmax_div = 0
                        pstart  =0
                        pend=