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Commit 9cdb6d2b authored by Damien Naudet's avatar Damien Naudet
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Removed the 'processed' group. 

Renamed two classes.
Refactored two methods.
Changed the get_cumul method.
parent 29a0ed7e
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kmap/io/XsocsH5.py
View file @ 9cdb6d2b
......@@ -48,12 +48,9 @@ class XsocsH5(XsocsH5Base):
TOP_ENTRY = 'global'
positioners_tpl = '/{0}/instrument/positioners'
img_data_tpl = '/{0}/measurement/image/data'
entry_cumul_tpl = '/processed/{0}/cumul'
entry_processed_tpl = '/processed/{0}/'
processed_grp = '/processed'
measurement_tpl = '/{0}/measurement'
measurement_command_tpl = '/processed/{0}/command'
detector_tpl = '/{0}/instrument/detector'
scan_params_tpl = '/{0}/scan'
def __init__(self, h5_f, mode='r'):
super(XsocsH5, self).__init__(h5_f, mode=mode)
......@@ -107,16 +104,6 @@ class XsocsH5(XsocsH5Base):
for param in param_names]
return h5_file.get(path.format(param_names), _np.array(None))[()]
def __command_params(self, entry, param_names):
with self._get_file() as h5_file:
path = self.measurement_command_tpl.format(entry) + '/{0}'
if isinstance(param_names, (list, set, tuple)):
return OrderedDict([(param, h5_file.get(path.format(param),
_np.array(None))[()])
for param in param_names])
return {param_names: h5_file.get(path.format(param_names),
_np.array(None))[()]}
def beam_energy(self, entry):
return self.__detector_params(entry, 'beam_energy')
......@@ -152,16 +139,24 @@ class XsocsH5(XsocsH5Base):
def dset_shape(self, path):
return self._get_array_data(path, shape=True)
def image_cumul(self, entry):
def image_cumul(self, entry, dtype=None):
"""
Returns the summed intensity for each image.
:param dtype: dtype passed to the numpy.sum function.
Default is numpy.double.
:type dtype: numpy.dtype
"""
#if entry == self.TOP_ENTRY:
#path = self.full_cumul_tpl
#else:
path = self.entry_cumul_tpl.format(entry)
cumul = self._get_array_data(path)
return cumul
if dtype is None:
dtype = _np.double
with self.image_dset_ctx(entry) as ctx:
shape = ctx.shape
intensity = _np.ndarray(shape=(shape[0],), dtype=dtype)
img_buffer = _np.array(ctx[0], dtype=dtype)
for idx in range(1, shape[0]):
ctx.read_direct(img_buffer, idx)
intensity[idx] = _np.sum(img_buffer)
return intensity
def scan_positions(self, entry):
path = self.measurement_tpl.format(entry)
......@@ -190,13 +185,20 @@ class XsocsH5(XsocsH5Base):
return result
def scan_params(self, entry):
return self.__command_params(entry,
['motor_0', 'motor_0_start',
'motor_0_end', 'motor_0_steps',
'motor_1', 'motor_1_start',
'motor_1_end', 'motor_1_steps',
'delay'])
param_names = ['motor_0', 'motor_0_start',
'motor_0_end', 'motor_0_steps',
'motor_1', 'motor_1_start',
'motor_1_end', 'motor_1_steps',
'delay']
with self._get_file() as h5_file:
path = self.scan_params_tpl.format(entry) + '/{0}'
if isinstance(param_names, (list, set, tuple)):
return OrderedDict([(param, h5_file.get(path.format(param),
_np.array(None))[
()])
for param in param_names])
return {param_names: h5_file.get(path.format(param_names),
_np.array(None))[()]}
def positioner(self, entry, positioner):
path = self.positioners_tpl.format(entry) + '/' + positioner
......@@ -230,11 +232,11 @@ class XsocsH5(XsocsH5Base):
del image_dset
class XsocsH5_Writer(XsocsH5):
class XsocsH5Writer(XsocsH5):
def __init__(self, h5_f, mode='a', **kwargs):
self.mode = mode
super(XsocsH5_Writer, self).__init__(h5_f, mode=mode, **kwargs)
super(XsocsH5Writer, self).__init__(h5_f, mode=mode, **kwargs)
def __set_detector_params(self, entry, params):
with self._get_file() as h5_file:
......@@ -242,11 +244,11 @@ class XsocsH5_Writer(XsocsH5):
for param_name, param_value in params.items():
self._set_scalar_data(path.format(param_name), param_value)
def __set_measurement_params(self, entry, params):
with self._get_file() as h5_file:
path = self.measurement_command_tpl.format(entry) + '/{0}'
for param_name, param_value in params.items():
self._set_scalar_data(path.format(param_name), param_value)
# def __set_measurement_params(self, entry, params):
# with self._get_file() as h5_file:
# path = self.scan_params_tpl.format(entry) + '/{0}'
# for param_name, param_value in params.items():
# self._set_scalar_data(path.format(param_name), param_value)
def set_beam_energy(self, beam_energy, entry):
return self.__set_detector_params(entry, {'beam_energy': beam_energy})
......@@ -282,16 +284,20 @@ class XsocsH5_Writer(XsocsH5):
motor_1_steps,
delay,
**kwargs):
self.__set_measurement_params(entry,
{'motor_0': _np.string_(motor_0),
'motor_0_start': float(motor_0_start),
'motor_0_end': float(motor_0_end),
'motor_0_steps': int(motor_0_steps),
'motor_1': _np.string_(motor_1),
'motor_1_start': float(motor_1_start),
'motor_1_end': float(motor_1_end),
'motor_1_steps': int(motor_1_steps),
'delay': float(delay)})
params = OrderedDict([('motor_0', _np.string_(motor_0)),
('motor_0_start', float(motor_0_start)),
('motor_0_end', float(motor_0_end)),
('motor_0_steps', int(motor_0_steps)),
('motor_1', _np.string_(motor_1)),
('motor_1_start', float(motor_1_start)),
('motor_1_end', float(motor_1_end)),
('motor_1_steps', int(motor_1_steps)),
('delay', float(delay))])
with self._get_file():
path = self.scan_params_tpl.format(entry) + '/{0}'
for param_name, param_value in params.items():
self._set_scalar_data(path.format(param_name), param_value)
def create_entry(self, entry):
with self._get_file() as h5_file:
......@@ -320,7 +326,7 @@ class XsocsH5_Writer(XsocsH5):
grp = entry_grp.require_group('measurement/image')
grp.attrs['NX_class'] = _np.string_('NXcollection')
## creating some links
# creating some links
grp = entry_grp.require_group('measurement/image')
det_grp = entry_grp.require_group('instrument/detector')
grp['info'] = det_grp
......@@ -328,29 +334,9 @@ class XsocsH5_Writer(XsocsH5):
self._update_entries()
def set_image_cumul(self,
entry,
cumul,
**kwargs):
with self._get_file() as h5_file:
#if entry == self.TOP_ENTRY:
#path = self.full_cumul_tpl
#else:
path = self.entry_cumul_tpl.format(entry)
dset = h5_file.require_dataset(path,
shape=cumul.shape,
dtype=cumul.dtype,
**kwargs)
dset[:] = cumul
del dset
class XsocsH5_Master_Writer(XsocsH5_Writer):
class XsocsH5MasterWriter(XsocsH5Writer):
def add_entry_file(self, entry, entry_file):
with self._get_file() as h5_file:
h5_file[entry] = _h5py.ExternalLink(entry_file, entry)
processed_grp = h5_file.require_group(self.processed_grp)
grp_path = self.entry_processed_tpl.format(entry)
processed_grp[grp_path] = _h5py.ExternalLink(entry_file,
grp_path)
kmap/util/id01_spec.py
View file @ 9cdb6d2b
......@@ -969,7 +969,7 @@ class _MergeThread(Thread):
mode = 'w'
# trying to access the file (erasing it if necessary)
with XsocsH5.XsocsH5_Master_Writer(master_f, mode=mode) as m_h5f:
with XsocsH5.XsocsH5MasterWriter(master_f, mode=mode) as m_h5f:
pass
if self.__n_proc is None:
......@@ -1025,7 +1025,7 @@ class _MergeThread(Thread):
cumul = None
if valid:
with XsocsH5.XsocsH5_Master_Writer(master_f, mode='a') as m_h5f:
with XsocsH5.XsocsH5MasterWriter(master_f, mode='a') as m_h5f:
items = self.__scans.items()
for proc_idx, (scan_id, infos) in enumerate(items):
entry_fn = infos['output']
......@@ -1034,28 +1034,28 @@ class _MergeThread(Thread):
#entry = entry_fn.rpartition('.')[0]
#m_h5f.add_file_link(entry, entry_fn, entry)
entry_fn = os.path.join(self.__output_dir, entry_fn)
# entry_fn = os.path.join(self.__output_dir, entry_fn)
# computing the cumulated sum only if all individual
# datasets have the same size. This should always be the
# case... but who knows, if someone one day modify the code
# to allow data from two different scans to be merged...
with XsocsH5.XsocsH5(entry_fn) as entry_h5:
if proc_idx == 0:
cumul = entry_h5.image_cumul(entry)
elif cumul is None:
pass
else:
cumul_data = entry_h5.image_cumul(entry)
if cumul_data.shape == cumul.shape:
cumul += cumul_data
else:
cumul = None
if cumul is not None:
m_h5f.set_image_cumul(m_h5f.TOP_ENTRY,
cumul,
compression=self.__compression)
# with XsocsH5.XsocsH5(entry_fn) as entry_h5:
# if proc_idx == 0:
# cumul = entry_h5.image_cumul(entry)
# elif cumul is None:
# pass
# else:
# cumul_data = entry_h5.image_cumul(entry)
# if cumul_data.shape == cumul.shape:
# cumul += cumul_data
# else:
# cumul = None
# if cumul is not None:
# m_h5f.set_image_cumul(m_h5f.TOP_ENTRY,
# cumul,
# compression=self.__compression)
if self.__callback:
self.__callback((True, master_f) if valid else (False, None))
......@@ -1148,7 +1148,7 @@ def _add_edf_data(scan_id,
if g_term_evt.is_set(): # noqa
raise Exception('Merge of scan {0} aborted.'.format(scan_id))
with XsocsH5.XsocsH5_Writer(entry_fn, mode='w') as entry_h5f:
with XsocsH5.XsocsH5Writer(entry_fn, mode='w') as entry_h5f:
entry_h5f.create_entry(entry)
progress[proc_idx] = 1
......@@ -1200,7 +1200,7 @@ def _add_edf_data(scan_id,
else:
cumul_dtype = np.float64
cumul_array = np.zeros((n_images,), dtype=cumul_dtype)
# cumul_array = np.zeros((n_images,), dtype=cumul_dtype)
with entry_h5f.image_dset_ctx(entry=entry,
create=True,
......@@ -1216,10 +1216,10 @@ def _add_edf_data(scan_id,
raise Exception('Merge of scan {0} aborted.'
''.format(scan_id))
data = edf_file.GetData(i)
image_dset[i, :, :] = data
cumul_array[i] = data.sum()
entry_h5f.set_image_cumul(entry, cumul_array)
# data = edf_file.GetData(i)
# image_dset[i, :, :] = data
# cumul_array[i] = data.sum()
# entry_h5f.set_image_cumul(entry, cumul_array)
except Exception as ex:
print(ex)
......
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