[operation/gradient removal] deduce delta from the unique values

id06workflow/core/operation/mapping.py

@@ -171,9 +171,6 @@ class GradientRemoval(_MappingBase):
         raise ValueError('Not available for Gradient removal yet')
 
     def compute(self):
-        # TODO: why delta = 0.567
-        delta = 0.567
-
         # TODO: for now we ask experiment for the intensity mapping if computed
         mapping = self._experiment.getOperation(IntensityMapping.KEY)
         if mapping is None:
@@ -182,21 +179,22 @@ class GradientRemoval(_MappingBase):
             return
 
         self.__dim = self.apply_gradient_correction(self.data_flatten,
-                                                    delta=delta,
                                                     mapping=mapping)
         self.__intensity_map = mapping.intensity_map
         self.registerOperation()
 
-    @staticmethod
-    def apply_gradient_correction(data, delta, mapping):
+    def apply_gradient_correction(self, data, mapping):
         assert isinstance(mapping, IntensityMapping)
-        # TODO: number of element: for now only square matrices but if evolve ?
+
+        for axis, dim in mapping.dims.items():
+            unique_values = numpy.array(self._experiment.dims.get(axis).unique_values)
+            _delta = unique_values.max() - unique_values.min()
+            _logger.info('delta for axis %s: %s' % (axis, _delta))
             corr2, corr1 = numpy.meshgrid(
-            numpy.linspace(-delta, delta, data.shape[2]),
+                    numpy.linspace(-_delta, _delta, data.shape[2]),
                     numpy.linspace(0, 0, data.shape[1]))
             _gradients = {}
 
-        for axis, dim in mapping.dims.items():
             _mean = dim.mean + corr2
             _variance = dim.variance + corr2
             _skewness = dim.skewness + corr2