def test_hlog(self):
hlpos = trans.hlog(_xpos)
hlneg = trans.hlog(_xneg)
assert_almost_equal((hlpos[-1] - _ymax) / _ymax, 0, decimal=2)
assert_almost_equal(hlpos, -hlneg[::-1]) # check symmetry
# test that values get larger as b decreases
hlpos10 = trans.hlog(_xpos, b=10)
self.assertTrue(np.all(hlpos10 >= hlpos))
# check that converges to tlog for large values
tlpos = trans.tlog(_xpos)
i = np.where(_xpos > 1e4)[0]
tlpos_large = tlpos[i]
hlpos_large = hlpos10[i]
d = (hlpos_large - tlpos_large) / hlpos_large
assert_almost_equal(d, np.zeros(len(d)), decimal=2)
# test spline option
transformation = Transformation(transform='hlog', direction='forward')
result1 = transformation(_xall, use_spln=True)
result2 = transformation(_xall, use_spln=False)
d = (result1 - result2) / result1
assert_almost_equal(d, np.zeros(len(d)), decimal=2)
def transform(self, transform, direction='forward', share_transform=True,
channels=None, return_all=True, auto_range=True,
use_spln=True, get_transformer=False, ID=None,
apply_now=True,
args=(), **kwargs):
'''
Apply transform to each Measurement in the Collection.
Return a new Collection with transformed data.
{_containers_held_in_memory_warning}
Parameters
----------
{FCMeasurement_transform_pars}
ID : hashable | None
ID for the resulting collection. If None is passed, the original ID is used.
Returns
-------
new : FCCollection
New collection containing the transformed measurements.
transformer : Transformation
The Transformation applied to the measurements.
Only returned if get_transformer=True & share_transform=True.
Examples
--------
{FCMeasurement_transform_examples}
'''
new = self.copy()
if share_transform:
channel_meta = self.values()[0].channels
channel_names = self.values()[0].channel_names
if channels is None:
channels = list(channel_names)
else:
channels = to_list(channels)
## create transformer
if isinstance(transform, Transformation):
transformer = transform
else:
if auto_range: # determine transformation range
if 'd' in kwargs:
warnings.warn('Encountered both auto_range=True and user-specified range '
'value in parameter d.\n '
'Range value specified in parameter d is used.')
else:
# the -1 below because the channel numbers begin from 1 instead of 0 (this is fragile code)
ranges = [float(r['$PnR']) for i, r in channel_meta.iterrows() if
channel_names[i - 1] in channels]
if not np.allclose(ranges, ranges[0]):
raise Exception('Not all specified channels have the same '
'data range, therefore they cannot be '
'transformed together.')
if transform in {'hlog', 'tlog', 'hlog_inv', 'tlog_inv'}:
# Hacky fix to make sure that 'd' is provided only
# for hlog / tlog transformations
kwargs['d'] = np.log10(ranges[0])
transformer = Transformation(transform, direction, args, **kwargs)
if use_spln:
xmax = self.apply(lambda x: x[channels].max().max(), applyto='data').max().max()
xmin = self.apply(lambda x: x[channels].min().min(), applyto='data').min().min()
transformer.set_spline(xmin, xmax)
## transform all measurements
for k, v in new.iteritems():
new[k] = v.transform(transformer, channels=channels, return_all=return_all,
use_spln=use_spln, apply_now=apply_now)
else:
for k, v in new.iteritems():
new[k] = v.transform(transform, direction=direction, channels=channels,
return_all=return_all, auto_range=auto_range,
get_transformer=False,
use_spln=use_spln, apply_now=apply_now, args=args, **kwargs)
if ID is not None:
new.ID = ID
if share_transform and get_transformer:
return new, transformer
else:
return new
def transform(self, transform, direction='forward',
channels=None, return_all=True, auto_range=True,
use_spln=True, get_transformer=False, ID=None,
apply_now=True,
args=(), **kwargs):
"""
Applies a transformation to the specified channels.
The transformation parameters are shared between all transformed channels.
If different parameters need to be applied to different channels,
use several calls to `transform`.
Parameters
----------
{FCMeasurement_transform_pars}
ID : hashable | None
ID for the resulting collection. If None is passed, the original ID is used.
Returns
-------
new : FCMeasurement
New measurement containing the transformed data.
transformer : Transformation
The Transformation applied to the input measurement.
Only returned if get_transformer=True.
Examples
--------
{FCMeasurement_transform_examples}
"""
# Create new measurement
new = self.copy()
data = new.data
channels = to_list(channels)
if channels is None:
channels = data.columns
## create transformer
if isinstance(transform, Transformation):
transformer = transform
else:
if auto_range: # determine transformation range
if 'd' in kwargs:
warnings.warn(
'Encountered both auto_range=True and user-specified range value in '
'parameter d.\n Range value specified in parameter d is used.')
else:
channel_meta = self.channels
# the -1 below because the channel numbers begin from 1 instead of 0
# (this is fragile code)
ranges = [float(r['$PnR']) for i, r in channel_meta.iterrows() if
self.channel_names[i - 1] in channels]
if not np.allclose(ranges, ranges[0]):
raise Exception("""Not all specified channels have the same data range,
therefore they cannot be transformed together.\n
HINT: Try transforming one channel at a time.
You'll need to provide the name of the channel in the transform.""")
if transform in {'hlog', 'tlog', 'hlog_inv', 'tlog_inv'}:
# Hacky fix to make sure that 'd' is provided only
# for hlog / tlog transformations
kwargs['d'] = np.log10(ranges[0])
transformer = Transformation(transform, direction, args, **kwargs)
## create new data
transformed = transformer(data[channels], use_spln)
if return_all:
new_data = data
else:
new_data = data.filter(channels)
new_data[channels] = transformed
## update new Measurement
new.data = new_data
if ID is not None:
new.ID = ID
if get_transformer:
return new, transformer
else:
return new
def transform(self, transform, direction='forward', share_transform=True,
channels=None, return_all=True, auto_range=True,
use_spln=True, get_transformer=False, ID=None,
apply_now=True,
args=(), **kwargs):
'''
Apply transform to each Measurement in the Collection.
Return a new Collection with transformed data.
{_containers_held_in_memory_warning}
Parameters
----------
{FCMeasurement_transform_pars}
ID : hashable | None
ID for the resulting collection. If None is passed, the original ID is used.
Returns
-------
new : FCCollection
New collection containing the transformed measurements.
transformer : Transformation
The Transformation applied to the measurements.
Only returned if get_transformer=True & share_transform=True.
Examples
--------
{FCMeasurement_transform_examples}
'''
new = self.copy()
if share_transform:
channel_meta = self.values()[0].channels
channel_names = self.values()[0].channel_names
if channels is None:
channels = list(channel_names)
else:
channels = to_list(channels)
## create transformer
if isinstance(transform, Transformation):
transformer = transform
else:
if auto_range: # determine transformation range
if 'd' in kwargs:
warnings.warn('Encountered both auto_range=True and user-specified range '
'value in parameter d.\n '
'Range value specified in parameter d is used.')
else:
# the -1 below because the channel numbers begin from 1 instead of 0 (this is fragile code)
ranges = [float(r['$PnR']) for i, r in channel_meta.iterrows() if
channel_names[i - 1] in channels]
if not np.allclose(ranges, ranges[0]):
raise Exception('Not all specified channels have the same '
'data range, therefore they cannot be '
'transformed together.')
kwargs['d'] = np.log10(ranges[0])
transformer = Transformation(transform, direction, args, **kwargs)
if use_spln:
xmax = self.apply(lambda x: x[channels].max().max(), applyto='data').max().max()
xmin = self.apply(lambda x: x[channels].min().min(), applyto='data').min().min()
transformer.set_spline(xmin, xmax)
## transform all measurements
for k, v in new.iteritems():
new[k] = v.transform(transformer, channels=channels, return_all=return_all,
use_spln=use_spln, apply_now=apply_now)
else:
for k, v in new.iteritems():
new[k] = v.transform(transform, direction=direction, channels=channels,
return_all=return_all, auto_range=auto_range,
get_transformer=False,
use_spln=use_spln, apply_now=apply_now, args=args, **kwargs)
if ID is not None:
new.ID = ID
if share_transform and get_transformer:
return new, transformer
else:
return new