Commit 76e8b7b2 authored by Thomas Vincent's avatar Thomas Vincent

code typos

parent c87d1787
......@@ -397,16 +397,16 @@ class QSpaceConverter(object):
def normalizer(self, normalizer):
"""Name of dataset in measurement to use for normalization"""
if normalizer is not None:
normalizer = str(normalizer)
normalizer = str(normalizer)
# Check for valid input in all entries
with XsocsH5.XsocsH5(self.__xsocsH5_f) as xsocsH5:
for entry in xsocsH5.entries():
if xsocsH5.measurement(
entry=entry, measurement=normalizer) is None:
raise ValueError(
'normalizer %s is not available in measurement group of entry %s' %
normalizer, entry)
# Check for valid input in all entries
with XsocsH5.XsocsH5(self.__xsocsH5_f) as xsocsH5:
for entry in xsocsH5.entries():
if xsocsH5.measurement(
entry=entry, measurement=normalizer) is None:
raise ValueError(
'normalizer %s is not available in measurement group of entry %s' %
(normalizer, entry))
self.__params['normalizer'] = normalizer
......@@ -542,7 +542,6 @@ class QSpaceConverter(object):
# y = np.linspace(scan_params['motor_1_start'],
# scan_params['motor_1_end'], steps_1, endpoint=False)
# x_pos = x_pos[]
#
# x_pos.shape = (n_y, n_x)
......@@ -981,7 +980,7 @@ class QSpaceConverter(object):
shared_shifted_shape,))
if disp_times:
class myTimes(object):
class MyTimes(object):
def __init__(self):
self.t_histo = 0.
self.t_sum = 0.
......@@ -1006,7 +1005,7 @@ class QSpaceConverter(object):
self.t_write += t_write_
self.t_w_lock += t_w_lock_
res_times = myTimes()
res_times = MyTimes()
callback = res_times.update
else:
callback = None
......@@ -1100,7 +1099,7 @@ class QSpaceConverter(object):
def is_running(self):
"""Returns True if a conversion is in progress."""
return self.status == QSpaceConverter.RUNNING
#self.__thread and self.__thread.is_alive()
# self.__thread and self.__thread.is_alive()
@output_f.setter
def output_f(self, output_f):
......@@ -1348,7 +1347,7 @@ def _to_qspace(th_idx,
# histo = np.frombuffer(histo_shared, dtype='int32')
# histo.shape = qspace_size
histo = histo_shared
mask = histo > 0
# mask = histo > 0
# h_lut = np.frombuffer(h_lut_shared, dtype=h_lut_dtype)
# h_lut.shape = (n_xy, -1)
......
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