[#159] Restructuring binning

datastock/_class01.py

@@ -14,7 +14,6 @@
 from . import _class01_show as _show
 from . import _class01_compute as _compute
 from . import _class01_domain as _domain
-from . import _class01_binning as _binning
 from . import _class01_interpolate as _interpolate
 from . import _class01_uniformize as _uniformize
 from . import _export_dataframe
@@ -778,99 +777,6 @@ def get_domain_ref(
 
         return _domain.domain_ref(coll=self, domain=domain)
 
-    # ---------------------
-    # Binning
-    # ---------------------
-
-    def binning(
-        self,
-        data=None,
-        data_units=None,
-        axis=None,
-        # binning
-        bins0=None,
-        bins1=None,
-        bin_data0=None,
-        bin_data1=None,
-        bin_units0=None,
-        # kind of binning
-        integrate=None,
-        statistic=None,
-        # options
-        safety_ratio=None,
-        dref_vector=None,
-        verb=None,
-        returnas=None,
-        # storing
-        store=None,
-        store_keys=None,
-    ):
-        """ Return the binned data
-
-        data:  the data on which to apply binning, can be
-            - a list of np.ndarray to be binned
-                (any dimension as long as they all have the same)
-            - a list of keys to ddata items sharing the same refs
-
-        data_units: str only necessary if data is a list of arrays
-
-        axis: int or array of int indices
-            the axis of data along which to bin
-            data will be flattened along all those axis priori to binning
-            If None, assumes bin_data is not variable and uses all its axis
-
-        bins0: the bins (centers), can be
-            - a 1d vector of monotonous bins
-            - a int, used to compute a bins vector from max(data), min(data)
-
-        bin_data0: the data used to compute binning indices, can be:
-            - a str, key to a ddata item
-            - a np.ndarray
-            - a list of any of the above if each data has diff. size along axis
-
-        bin_units: str
-            only used if integrate = True and bin_data is a np.ndarray
-
-        integrate: bool
-            flag indicating whether binning is used for integration
-            Implies that:
-                Only usable for 1d binning (axis has to be a single index)
-                data is multiplied by bin_data0 step prior to binning
-
-        statistic: str
-            the statistic kwd feed to scipy.stats.binned_statistic()
-            automatically set to 'sum' if integrate = True
-
-        store: bool
-            If True, will sotre the result in ddata
-            Only possible if all (data, bin_data and bin) are provided as keys
-
-        """
-
-        return _binning.binning(
-            coll=self,
-            data=data,
-            data_units=data_units,
-            axis=axis,
-            # binning
-            bins0=bins0,
-            bins1=bins1,
-            bin_data0=bin_data0,
-            bin_data1=bin_data1,
-            bin_units0=bin_units0,
-            # kind of binning
-            integrate=integrate,
-            statistic=statistic,
-            # options
-            safety_ratio=safety_ratio,
-            dref_vector=dref_vector,
-            verb=verb,
-            returnas=returnas,
-            # storing
-            store=store,
-            store_keys=store_keys,
-        )
-
     # ---------------------
     # Interpolation
     # ---------------------