GitLab will be upgraded on June 23rd evening. During the upgrade the service will be unavailable, sorry for the inconvenience.

Commit 2a181683 authored by Thomas Vincent's avatar Thomas Vincent

Remove unused files

parent fe8b0612
#!/usr/bin/python
# coding: utf8
# /*##########################################################################
#
# Copyright (c) 2015-2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
from __future__ import absolute_import
__authors__ = ["D. Naudet"]
__date__ = "01/01/2017"
__license__ = "MIT"
class Fitter(object):
_AXIS_NAMES = ('qx', 'qy', 'qz') # at some point these should not be hard coded?
qx = property(lambda self: self._qx)
""" qx axis values """
qy = property(lambda self: self._qy)
""" qy axis values """
qz = property(lambda self: self._qz)
""" qz axis values """
shared_results = property(lambda self: self._shared_results)
""" FitSharedResults instance use by this fitter """
def __init__(self, qx, qy, qz,
shared_results):
super(Fitter, self).__init__()
self._shared_results = shared_results
self._qx = qx
self._qy = qy
self._qz = qz
def fit(self, i_fit, i_cube, qx_profile, qy_profile, qz_profile):
"""
Performs the fit and stores the result in this instance's
FitSharedResult object.
:param i_fit: index of the fit result (in the FitH5 file). This
index is to be used when storing the result in the FitSharedResult
instance.
:param i_cube: index of the cube (in the QSpaceH5 file)
:param qx_profile: qx profile
:param qy_profile: qy profile
:param qz_profile: qz profile
:return:
"""
raise NotImplementedError('Not implemented.')
#!/usr/bin/python
# coding: utf8
# /*##########################################################################
#
# Copyright (c) 2015-2016 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
from __future__ import absolute_import
__authors__ = ["D. Naudet"]
__date__ = "01/06/2016"
__license__ = "MIT"
import ctypes
import multiprocessing.sharedctypes as mp_sharedctypes
import numpy as np
class FitTypes(object):
ALLOWED = range(3)
GAUSSIAN, CENTROID, SILX = ALLOWED
class FitSharedResults(object):
_AXIS_NAMES = ('qx', 'qy', 'qz') # at some point these should not be hard coded?
def __init__(self,
n_points=None,
n_params=None,
n_peaks=1,
shared_results=None,
shared_status=None):
super(FitSharedResults, self).__init__()
assert n_points is not None
assert n_params is not None
assert n_peaks >= 1
self._shared_qx_results = None
self._shared_qy_results = None
self._shared_qz_results = None
self._shared_qx_status = None
self._shared_qy_status = None
self._shared_qz_status = None
self._npy_qx_results = None
self._npy_qy_results = None
self._npy_qz_results = None
self._npy_qx_status = None
self._npy_qy_status = None
self._npy_qz_status = None
self._n_points = n_points
self._n_params = n_params
self._n_peaks = n_peaks
self._init_shared_results(shared_results)
self._init_shared_status(shared_status)
def _init_shared_results(self, shared_results=None):
if shared_results is None:
self._shared_qx_results = mp_sharedctypes.RawArray(
ctypes.c_double,
self._n_points * self._n_params * self._n_peaks)
self._shared_qy_results = mp_sharedctypes.RawArray(
ctypes.c_double,
self._n_points * self._n_params * self._n_peaks)
self._shared_qz_results = mp_sharedctypes.RawArray(
ctypes.c_double,
self._n_points * self._n_params * self._n_peaks)
set_value = np.nan
else:
self._shared_qx_results = shared_results[0]
self._shared_qy_results = shared_results[1]
self._shared_qz_results = shared_results[2]
set_value = None
self._init_npy_results(set_value=set_value)
def _init_shared_status(self, shared_status=None):
if shared_status is None:
self._shared_qx_status = mp_sharedctypes.RawArray(
ctypes.c_int8, self._n_points)
self._shared_qy_status = mp_sharedctypes.RawArray(
ctypes.c_int8, self._n_points)
self._shared_qz_status = mp_sharedctypes.RawArray(
ctypes.c_int8, self._n_points)
else:
self._shared_qx_status = shared_status[0]
self._shared_qy_status = shared_status[1]
self._shared_qz_status = shared_status[2]
self._init_npy_status()
def _init_npy_results(self, set_value=None):
self._npy_qx_results = np.frombuffer(self._shared_qx_results)
self._npy_qx_results.shape = (self._n_points,
self._n_peaks * self._n_params)
self._npy_qy_results = np.frombuffer(self._shared_qy_results)
self._npy_qy_results.shape = (self._n_points,
self._n_peaks * self._n_params)
self._npy_qz_results = np.frombuffer(self._shared_qz_results)
self._npy_qz_results.shape = (self._n_points,
self._n_peaks * self._n_params)
if set_value is not None:
self._npy_qx_results[...] = set_value
self._npy_qy_results[...] = set_value
self._npy_qz_results[...] = set_value
def _init_npy_status(self):
self._npy_qx_status = np.frombuffer(self._shared_qx_status,
dtype=np.int8)
self._npy_qy_status = np.frombuffer(self._shared_qy_status,
dtype=np.int8)
self._npy_qz_status = np.frombuffer(self._shared_qz_status,
dtype=np.int8)
def set_results(self, dimension, fit_idx, results, status):
"""
Sets a result value for qx/qy/qz at index fit_idx
:param dimension: which component of q - either qx,qy or qz
:param fit_idx:
:param results:
:param status:
:return:
"""
getattr(self, "_npy_%s_results"%dimension)[fit_idx] = results
getattr(self, "_npy_%s_status"%dimension)[fit_idx] = status
def local_copy(self):
"""
Returns a local (local to the thread this instance in running in) copy
of FitSharedResults.
:return:
"""
shared_results = (self._shared_qx_results,
self._shared_qy_results,
self._shared_qz_results)
shared_status = (self._shared_qx_status,
self._shared_qy_status,
self._shared_qz_status)
return FitSharedResults(n_points=self._n_points,
n_params=self._n_params,
n_peaks=self._n_peaks,
shared_results=shared_results,
shared_status=shared_status)
def fit_results(self, *args, **kwargs):
"""
Returns the fit results.
:param args:
:param kwargs:
:return: FitResult instance
"""
raise NotImplementedError('')
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment