share QSpace projection to axes code

xsocs/gui/process/FitWidget.py

@@ -34,7 +34,7 @@ import logging
 from silx.gui import qt as Qt
 
 from ...io.QSpaceH5 import QSpaceH5
-from ...util import bin_centers_to_range_step
+from ...util import bin_centers_to_range_step, project
 
 from ..widgets.Containers import GroupBox
 from ..widgets.RoiAxisWidget import RoiAxisWidget
@@ -196,13 +196,9 @@ class FitWidget(Qt.QWidget):
 
         :param int index: QSpace index
         """
-        qspace = self.__qspaceH5.qspace_slice(index)
-        dim2_sum = qspace.sum(axis=0).sum(axis=0)
-        cube_sum_dim2 = qspace.sum(axis=2)
-        dim1_sum = cube_sum_dim2.sum(axis=0)
-        dim0_sum = cube_sum_dim2.sum(axis=1)
+        projections = project(self.__qspaceH5.qspace_slice(index))
 
-        for profile, slider in zip((dim0_sum, dim1_sum, dim2_sum),
+        for profile, slider in zip(projections,
                                    self.roiWidget().sliders()):
             slider.setGroovePixmapFromProfile(profile, colormap='jet')
 

xsocs/gui/view/FitView.py

@@ -47,6 +47,7 @@ from .fitview.DropPlotWidget import DropPlotWidget
 
 from .fitview.Plotter import GaussianPlotter, CentroidPlotter
 from ...process.fit.peak_fit import background_estimation, BackgroundTypes
+from ...util import project
 
 
 _logger = logging.getLogger(__name__)
@@ -270,17 +271,11 @@ class FitView(Qt.QMainWindow):
         with self.__qspaceH5:
             cube = self.__qspaceH5.qspace_slice(xIdx)
             histo = self.__qspaceH5.histo
-            mask = np.where(histo > 0)
-            weights = histo[mask]
-            cube[mask] /= weights
             xAcqQX = self.__qspaceH5.qx
             xAcqQY = self.__qspaceH5.qy
             xAcqQZ = self.__qspaceH5.qz
 
-        yAcqQZ = cube.sum(axis=0).sum(axis=0)
-        cube_sum_z = cube.sum(axis=2)
-        yAcqQY = cube_sum_z.sum(axis=0)
-        yAcqQX = cube_sum_z.sum(axis=1)
+        yAcqQX, yAcqQY, yAcqQZ = project(cube, histo)
 
         # Get plotter
         if entry == 'Gaussian':

xsocs/process/fit/peak_fit.py

@@ -36,7 +36,6 @@ import ctypes
 import multiprocessing
 from threading import Thread
 import multiprocessing.sharedctypes as mp_sharedctypes
-import warnings
 
 import numpy as np
 from scipy.optimize import leastsq
@@ -46,7 +45,7 @@ from silx.math.fit import snip1d
 from ... import config
 from ...io import QSpaceH5
 from ...io.FitH5 import BackgroundTypes
-from ...util import gaussian
+from ...util import gaussian, project
 from .fitresults import FitResult
 
 
@@ -317,28 +316,6 @@ def _fit_process(index,
     return result
 
 
-def project(data, hits=None):
-    """Sum data along each axis
-
-    :param numpy.ndarray data: 3D histogram
-    :param Union[numpy.ndarray,None] hits:
-        Number of bin count of the histogram or None to ignore
-    :return: Projections on each axis of the dataset
-    :rtype: List[numpy.ndarray]
-    """
-    if hits is not None:
-        with warnings.catch_warnings():
-            warnings.simplefilter('ignore', RuntimeWarning)
-            data /= hits
-        data[hits <= 0] = 0
-
-    dim2_sum = data.sum(axis=0).sum(axis=0)
-    data_sum_dim2 = data.sum(axis=2)
-    dim1_sum = data_sum_dim2.sum(axis=0)
-    dim0_sum = data_sum_dim2.sum(axis=1)
-    return dim0_sum, dim1_sum, dim2_sum
-
-
 # Center of mass
 
 def centroid(axis, signal):

xsocs/util/__init__.py

@@ -32,6 +32,7 @@ __date__ = "01/03/2016"
 
 
 import sys
+import warnings
 import numpy
 
 
@@ -72,3 +73,25 @@ def gaussian(x, area, center, sigma):
     """
     return (area * (1. / (_SQRT_2_PI * sigma)) *
             numpy.exp(-0.5 * ((x - center) / sigma) ** 2))
+
+
+def project(data, hits=None):
+    """Sum data along each axis
+
+    :param numpy.ndarray data: 3D histogram
+    :param Union[numpy.ndarray,None] hits:
+        Number of bin count of the histogram or None to ignore
+    :return: Projections on each axis of the dataset
+    :rtype: List[numpy.ndarray]
+    """
+    if hits is not None:
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', RuntimeWarning)
+            data /= hits
+        data[hits <= 0] = 0
+
+    dim2_sum = data.sum(axis=0).sum(axis=0)
+    data_sum_dim2 = data.sum(axis=2)
+    dim1_sum = data_sum_dim2.sum(axis=0)
+    dim0_sum = data_sum_dim2.sum(axis=1)
+    return dim0_sum, dim1_sum, dim2_sum