# Path: MEHI/utils/tool.py
# def exeTime(func):
#     '''
#     Usage:
#      - just put '@exeTime'(with out quotation) before your function
#      - will show the running time
#     '''
#     import time
#     def wraper(*args, **args2):
#         s = time.time()
#         ret = func(*args, **args2)
#         e = time.time()
#         msg = "%3.3fs taken for {%s}" % (e-s, func.__name__)
#         log('time')(msg)
#         return ret
#     return wraper
# 
# def bar(level):
#     '''
#     Usage:
#      - this will show a progress bar
#      - e.g. bar('info')(msg, i, 100)
#     '''
#     from MEHI.utils.configuration import loglevel
#     if level in loglevel:
#         def wraper(msg, i, end):
#             if i <= end:
#                 sys.stdout.write('[' + bcolors.decode(level) + level + bcolors.decode('endc') + '] ('+ str(i)+'/' + str(end) + ') '+ msg + '\r')
#                 sys.stdout.flush()
#             if i == end:
#                 print
#         return wraper
#     else:
#         def wraper(msg, i, end):
#             pass
#         return wraper
# 
# def log(level):
#     '''
#     Usage:
#      - this will just show info
#      - e.g. log('info')(msg)
#     '''
#     from MEHI.utils.configuration import loglevel
#     if level in loglevel:
#         def wraper(msg):
#             print '[' + bcolors.decode(level) + level + bcolors.decode('endc') + '] '+ msg
#         return wraper
#     else:
#         def wraper(msg):
#             pass
#         return wraper


from MEHI.utils.tool import exeTime, bar, log
    from skimage.filters import threshold_otsu, threshold_adaptive
    from MEHI.udf._phansalkar import phansalkar as _phansalkar
    from skimage.morphology import disk, binary_opening, remove_small_objects
    from skimage.morphology import watershed, remove_small_objects
    from scipy import ndimage
    from skimage.feature import peak_local_max
    from skimage.measure import regionprops
    from skimage.morphology import watershed, remove_small_objects
    from scipy import ndimage
    from skimage.feature import peak_local_max
    from MEHI.udf._moment import moment
    from scipy import ndimage as ndi
import numpy as np
    import scipy.ndimage as ndi
    import scipy.ndimage as ndi
    import pandas as pd
    import scipy.cluster.hierarchy as hier
    import pandas as pd

        except TypeError:
            v = df[col].iloc[0]
        values[col] = v
    return values

@exeTime
def fusion(labeled_stack, image_stack, min_radius, max_radius):  
    prop = properties(labeled_stack, image_stack, min_radius, max_radius)
    prop.to_pickle('prop.pkl')
    prop = clustering(prop, 10)
    log('info')("clustering over")
    cell_table = prop.groupby(level='label').apply(df_average, 'intensitysum')
    cell_table.to_pickle("cell_table.pkl")
    cell_table.to_csv("cell_show.csv")
    #cell_map = labeln(properties, labeled_stack)
    del cell_table['tag']
    return cell_table

def debug(labeled_stack, image_stack, min_radius, max_radiusm, prop):
    prop = clustering(prop,6)
    log('info')("clustering over")
    cell_table = prop.groupby(level='label').apply(df_average, 'intensitysum')
    cell_table.to_pickle("cell_table.pkl")
    cell_table.to_csv("cell_show.csv")
    #cell_map = labeln(properties, labeled_stack)
    del cell_table['tag']
    return cell_table




# Path: MEHI/utils/tool.py
# def exeTime(func):
#     '''
#     Usage:
#      - just put '@exeTime'(with out quotation) before your function
#      - will show the running time
#     '''
#     import time
#     def wraper(*args, **args2):
#         s = time.time()
#         ret = func(*args, **args2)
#         e = time.time()
#         msg = "%3.3fs taken for {%s}" % (e-s, func.__name__)
#         log('time')(msg)
#         return ret
#     return wraper
# 
# def bar(level):
#     '''
#     Usage:
#      - this will show a progress bar
#      - e.g. bar('info')(msg, i, 100)
#     '''
#     from MEHI.utils.configuration import loglevel
#     if level in loglevel:
#         def wraper(msg, i, end):
#             if i <= end:
#                 sys.stdout.write('[' + bcolors.decode(level) + level + bcolors.decode('endc') + '] ('+ str(i)+'/' + str(end) + ') '+ msg + '\r')
#                 sys.stdout.flush()
#             if i == end:
#                 print
#         return wraper
#     else:
#         def wraper(msg, i, end):
#             pass
#         return wraper
# 
# def log(level):
#     '''
#     Usage:
#      - this will just show info
#      - e.g. log('info')(msg)
#     '''
#     from MEHI.utils.configuration import loglevel
#     if level in loglevel:
#         def wraper(msg):
#             print '[' + bcolors.decode(level) + level + bcolors.decode('endc') + '] '+ msg
#         return wraper
#     else:
#         def wraper(msg):
#             pass
#         return wraper


# Path: MEHI/paralleled/segmentation.py
from MEHI.utils.tool import exeTime, bar, log
    from skimage.filters import threshold_otsu, threshold_adaptive
    from MEHI.udf._phansalkar import phansalkar as _phansalkar
    from skimage.morphology import disk, binary_opening, remove_small_objects
    from skimage.morphology import watershed, remove_small_objects
    from scipy import ndimage
    from skimage.feature import peak_local_max
    from skimage.measure import regionprops
    from skimage.morphology import watershed, remove_small_objects
    from scipy import ndimage
    from skimage.feature import peak_local_max
    from MEHI.udf._moment import moment
    from scipy import ndimage as ndi
import numpy as np
    import scipy.ndimage as ndi
    import scipy.ndimage as ndi
    import pandas as pd
    import scipy.cluster.hierarchy as hier
    import pandas as pd

        except TypeError:
            v = df[col].iloc[0]
        values[col] = v
    return values

@exeTime
def fusion(labeled_stack, image_stack, min_radius, max_radius):  
    prop = properties(labeled_stack, image_stack, min_radius, max_radius)
    prop.to_pickle('prop.pkl')
    prop = clustering(prop, 10)
    log('info')("clustering over")
    cell_table = prop.groupby(level='label').apply(df_average, 'intensitysum')
    cell_table.to_pickle("cell_table.pkl")
    cell_table.to_csv("cell_show.csv")
    #cell_map = labeln(properties, labeled_stack)
    del cell_table['tag']
    return cell_table

def debug(labeled_stack, image_stack, min_radius, max_radiusm, prop):
    prop = clustering(prop,6)
    log('info')("clustering over")
    cell_table = prop.groupby(level='label').apply(df_average, 'intensitysum')
    cell_table.to_pickle("cell_table.pkl")
    cell_table.to_csv("cell_show.csv")
    #cell_map = labeln(properties, labeled_stack)
    del cell_table['tag']
    return cell_table




@exeTime
