Refactor datastore

src/dtscalibration/datastore.py

@@ -213,6 +213,12 @@ def sections(self):
     def sections(self):
         self.attrs["_sections"] = yaml.dump(None)
 
+    @sections.setter
+    def sections(self, value):
+        msg = "Not possible anymore. Instead, pass the sections as an argument to \n" \
+              "ds.dts.calibrate_single_ended() or ds.dts.calibrate_double_ended()."
+        raise NotImplementedError(msg)
+
     def check_reference_section_values(self):
         """
         Checks if the values of the used sections are of the right datatype
@@ -870,9 +876,6 @@ def variance_stokes_constant(self, st_label, sections=None, reshape_residuals=Tr
         dtscalibration/python-dts-calibration/blob/main/examples/notebooks/\
         04Calculate_variance_Stokes.ipynb>`_
         """
-
-        #         var_I, resid = variance_stokes_constant_util(st_label, sections=None, reshape_residuals=True)
-        # def variance_stokes_constant_util(st_label, sections=None, reshape_residuals=True):
         def func_fit(p, xs):
             return p[:xs, None] * p[None, xs:]
 
@@ -1014,7 +1017,12 @@ def variance_stokes_exponential(
 
         Parameters
         ----------
-        reshape_residuals
+        suppress_info : bool, optional
+            Suppress print statements.
+        use_statsmodels : bool, optional
+            Use statsmodels to fit the exponential. If `False`, use scipy.
+        reshape_residuals : bool, optional
+            Reshape the residuals to the shape of the Stokes intensity
         st_label : str
             label of the Stokes, anti-Stokes measurement.
             E.g., st, ast, rst, rast
@@ -1700,6 +1708,9 @@ def calibration_single_ended(
             variance of the estimate of dalpha.
             Covariances between alpha and other parameters are not accounted
             for.
+        fix_alpha : Tuple[array-like, array-like], optional
+            A tuple containing two array-likes. The first array-like is the integrated
+            differential attenuation of length x, and the second item is its variance.
 
         Returns
         -------
@@ -3029,7 +3040,7 @@ def create_da_ta2(no, i_splice, direction="fw", chunks=None):
                 arr = da.concatenate(
                     (
                         da.zeros(
-                            (1, i_splice, 1), chunks=((1, i_splice, 1)), dtype=bool
+                            (1, i_splice, 1), chunks=(1, i_splice, 1), dtype=bool
                         ),
                         da.ones(
                             (1, no - i_splice, 1),
@@ -3045,7 +3056,7 @@ def create_da_ta2(no, i_splice, direction="fw", chunks=None):
                         da.ones((1, i_splice, 1), chunks=(1, i_splice, 1), dtype=bool),
                         da.zeros(
                             (1, no - i_splice, 1),
-                            chunks=((1, no - i_splice, 1)),
+                            chunks=(1, no - i_splice, 1),
                             dtype=bool,
                         ),
                     ),
@@ -3826,7 +3837,7 @@ def create_da_ta2(no, i_splice, direction="fw", chunks=None):
                 arr = da.concatenate(
                     (
                         da.zeros(
-                            (1, i_splice, 1), chunks=((1, i_splice, 1)), dtype=bool
+                            (1, i_splice, 1), chunks=(1, i_splice, 1), dtype=bool
                         ),
                         da.ones(
                             (1, no - i_splice, 1),
@@ -3842,7 +3853,7 @@ def create_da_ta2(no, i_splice, direction="fw", chunks=None):
                         da.ones((1, i_splice, 1), chunks=(1, i_splice, 1), dtype=bool),
                         da.zeros(
                             (1, no - i_splice, 1),
-                            chunks=((1, no - i_splice, 1)),
+                            chunks=(1, no - i_splice, 1),
                             dtype=bool,
                         ),
                     ),