def split_up_down(data, col=None, folder=None):
"""Splits the DataFile data into several files where the column *col* is either rising or falling
Args:
data (:py:class:`Stoner.Core.DataFile`): object containign the data to be sorted
col (index): is something that :py:meth:`Stoner.Core.DataFile.find_col` can use
folder (:py:class:`Stoner.Folders.DataFolder` or None): if this is an instance of :py:class:`Stoner.Folders.DataFolder` then add
rising and falling files to groups of this DataFolder, otherwise create a new one
Returns:
A :py:class:`Sonter.Folder.DataFolder` object with two groups, rising and falling
"""
a = _SC_.Data(data)
if col is None:
_=a._col_args()
col=_.xcol
width = len(a) / 10
if width % 2 == 0: # Ensure the window for Satvisky Golay filter is odd
width += 1
setas=a.setas.clone
a.setas=""
peaks = list(a.peaks(col, width,xcol=None, peaks=True, troughs=False,full_data=False))
troughs = list(a.peaks(col, width, xcol=None, peaks=False, troughs=True,full_data=False))
a.setas=setas
if len(peaks) > 0 and len(troughs) > 0: #Ok more than up down here
order = peaks[0] < troughs[0]
elif len(peaks) > 0: #Rise then fall
order = True
elif len(troughs) > 0: # Fall then rise
order = False
else: #No peaks or troughs so just return a single rising
ret=_SF_(readlist=False)
ret+=data
return ret
splits = [0, len(a)]
splits.extend(peaks)
splits.extend(troughs)
splits.sort()
splits=[int(s) for s in splits]
if not isinstance(folder, _SF_): # Create a new DataFolder object
output = _SF_(readlist=False)
else:
output = folder
output.add_group("rising")
output.add_group("falling")
if order:
risefall=["rising","falling"]
else:
risefall=["falling","rising"]
for i in range(len(splits)-1):
working=data.clone
working.data = data.data[splits[i]:splits[i+1],:]
output.groups[risefall[i%2]].append(working)
return output
def split(data, col=None, folder=None, spliton=0, rising=True, falling=False, skip=0):
"""Splits the DataFile data into several files where the column *col* is either rising or falling
Args:
data (:py:class:`Stoner.Core.DataFile`): object containign the data to be sorted
col (index): is something that :py:meth:`Stoner.Core.DataFile.find_col` can use
folder (:py:class:`Stoner.Folders.DataFolder` or None): if this is an instance of :py:class:`Stoner.Folders.DataFolder` then add
rising and falling files to groups of this DataFolder, otherwise create a new one
spliton (str or float): Define where to split the data, 'peak' to split on peaks, 'trough' to split
on troughs, 'both' to split on peaks and troughs or number to split at that number
rising (bool): whether to split on threshold crossing when data is rising
falling (bool): whether to split on threshold crossing when data is falling
skip (int): skip this number of splitons each time. eg skip=1 picks out odd crossings
Returns:
A :py:class:`Sonter.Folder.DataFolder` object with two groups, rising and falling
"""
if col is None:
col = data.setas["x"]
d=_SC_.Data(data)
if not isinstance(folder, _SF_): # Create a new DataFolder object
output = _SF_()
else:
output = folder
if isinstance(spliton, int_types+(float,)):
spl=d.threshold(threshold=float(spliton),col=col,rising=rising,falling=falling,all_vals=True)
elif spliton in ['peaks','troughs','both']:
width = len(d) / 10
if width % 2 == 0: # Ensure the window for Satvisky Golay filter is odd
width += 1
if spliton=='peaks':
spl = list(d.peaks(col, width, xcol=False, peaks=True, troughs=False))
elif spliton=='troughs':
spl = list(d.peaks(col, width, xcol=False, peaks=False, troughs=True))
else:
spl = list(d.peaks(col, width, xcol=False, peaks=True, troughs=True))
else:
raise ValueError('Did not recognise spliton')
spl = [spl[i] for i in range(len(spl)) if i%(skip+1)==0]
spl.extend([0,len(d)])
spl.sort()
for i in range(len(spl)-1):
tmp=d.clone
tmp.data=tmp[spl[i]:spl[i+1]]
output.files.append(tmp)
return output