From cf1c54f8bef8b2d954ab9a03c301a02e0cb2d22a Mon Sep 17 00:00:00 2001 From: github-actions Date: Mon, 8 Jul 2024 21:56:36 +0000 Subject: [PATCH] Update documentation --- .../user_guide_1_binning_fake_data_13_0.png | Bin 119713 -> 114922 bytes .../user_guide_1_binning_fake_data_8_0.png | Bin 265466 -> 258458 bytes sed/latest/_modules/index.html | 8 +- sed/latest/_modules/sed/binning/binning.html | 91 +++--- .../_modules/sed/binning/numba_bin.html | 40 ++- sed/latest/_modules/sed/binning/utils.html | 42 +-- sed/latest/_modules/sed/calibrator/delay.html | 93 +++--- .../_modules/sed/calibrator/energy.html | 253 ++++++++------- .../_modules/sed/calibrator/momentum.html | 230 +++++++------ sed/latest/_modules/sed/core/config.html | 38 ++- sed/latest/_modules/sed/core/dfops.html | 101 +++--- sed/latest/_modules/sed/core/metadata.html | 59 +++- sed/latest/_modules/sed/core/processor.html | 289 ++++++++--------- sed/latest/_modules/sed/dataset/dataset.html | 8 +- 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zN8W1feWUZd&S-cY_aHwGnfwbkJKYzDh9Gl!oWmj_t~$;c|8}_25pX*M;0xO7|pBqDkplR6Q&_CzKS^vTHcJo@H!uDvbmzTqgf zJ!44LkoRA6R_3uC_G(2JpZQUP{l8+2<=Tl`kqaIwX;J>ig?WqX`470k1iOw?O0TCi zP?Jbw0jfVeZ~9?CA?#13s>}T}*W$|yHz=;zdrpG*^|n7b85kI9$35(OKivK{nZkz} zO8?aJq}cR(yL5SfaeVXb+$^j2y|iZM@^ne_Xj+IyohQl%;LgZaVBaH9b8~;JzX9ODi@kJp2@1 z{{?*M{IMr@P|~pbZ&cG7Y?0I7{~bXUL6&;@AIEK)zYHNJ?T7)x{y$@aT@8^+tN+{c okB;>H|BM6vABPYBkC%NSvu2MzwVGb606;Gp2?g;wQG=lW4~b*UGynhq diff --git a/sed/latest/_modules/index.html b/sed/latest/_modules/index.html index e77ce59..c614414 100644 --- a/sed/latest/_modules/index.html +++ b/sed/latest/_modules/index.html @@ -7,7 +7,7 @@ - Overview: module code — SED 0.1.10a6 documentation + Overview: module code — SED 0.1.10a5 documentation @@ -34,7 +34,7 @@ - + @@ -43,7 +43,7 @@ @@ -121,7 +121,7 @@ -

SED 0.1.10a6 documentation

+

SED 0.1.10a5 documentation

 diff --git a/sed/latest/_modules/sed/binning/binning.html b/sed/latest/_modules/sed/binning/binning.html index 7e2cd42..e7024a0 100644 --- a/sed/latest/_modules/sed/binning/binning.html +++ b/sed/latest/_modules/sed/binning/binning.html @@ -7,7 +7,7 @@ - sed.binning.binning — SED 0.1.10a6 documentation + sed.binning.binning — SED 0.1.10a5 documentation @@ -34,7 +34,7 @@ - + @@ -43,7 +43,7 @@ @@ -121,7 +121,7 @@ -

SED 0.1.10a6 documentation

+

SED 0.1.10a5 documentation

@@ -447,14 +447,13 @@

Source code for sed.binning.binning

 """This module contains the binning functions of the sed.binning module
-
 """
+from __future__ import annotations
+
 import gc
+from collections.abc import Sequence
 from functools import reduce
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
 from typing import Union
 
 import dask.dataframe
@@ -476,33 +475,27 @@ 
Source code for sed.binning.binning
 

 [docs]
 def bin_partition(
-    part: Union[dask.dataframe.DataFrame, pd.DataFrame],
-    bins: Union[
-        int,
-        dict,
-        Sequence[int],
-        Sequence[np.ndarray],
-        Sequence[tuple],
-    ] = 100,
+    part: dask.dataframe.DataFrame | pd.DataFrame,
+    bins: int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple] = 100,
     axes: Sequence[str] = None,
-    ranges: Sequence[Tuple[float, float]] = None,
+    ranges: Sequence[tuple[float, float]] = None,
     hist_mode: str = "numba",
-    jitter: Union[list, dict] = None,
+    jitter: list | dict = None,
     return_edges: bool = False,
     skip_test: bool = False,
-) -> Union[np.ndarray, Tuple[np.ndarray, list]]:
+) -> np.ndarray | tuple[np.ndarray, list]:
     """Compute the n-dimensional histogram of a single dataframe partition.
 
     Args:
-        part (Union[dask.dataframe.DataFrame, pd.DataFrame]): dataframe on which
+        part (dask.dataframe.DataFrame | pd.DataFrame): dataframe on which
             to perform the histogram. Usually a partition of a dask DataFrame.
-        bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple], optional):
+        bins (int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple], optional):
             Definition of the bins. Can  be any of the following cases:
 
                 - an integer describing the number of bins for all dimensions. This
                   requires "ranges" to be defined as well.
                 - A sequence containing one entry of the following types for each
-                  dimension:
+                  dimenstion:
 
                     - an integer describing the number of bins. This requires "ranges"
                       to be defined as well.
@@ -520,7 +513,7 @@ 
Source code for sed.binning.binning
             the order of the dimensions in the resulting array. Only not required if
             bins are provided as dictionary containing the axis names.
             Defaults to None.
-        ranges (Sequence[Tuple[float, float]], optional): Sequence of tuples containing
+        ranges (Sequence[tuple[float, float]], optional): Sequence of tuples containing
             the start and end point of the binning range. Required if bins given as
             int or Sequence[int]. Defaults to None.
         hist_mode (str, optional): Histogram calculation method.
@@ -529,18 +522,18 @@ 
Source code for sed.binning.binning
                 - "numba" use a numba powered similar method.
 
             Defaults to "numba".
-        jitter (Union[list, dict], optional): a list of the axes on which to apply
+        jitter (list | dict, optional): a list of the axes on which to apply
             jittering. To specify the jitter amplitude or method (normal or uniform
             noise) a dictionary can be passed. This should look like
             jitter={'axis':{'amplitude':0.5,'mode':'uniform'}}.
-            This example also shows the default behavior, in case None is
+            This example also shows the default behaviour, in case None is
             passed in the dictionary, or jitter is a list of strings.
             Warning: this is not the most performing approach. Applying jitter
             on the dataframe before calling the binning is much faster.
             Defaults to None.
         return_edges (bool, optional): If True, returns a list of D arrays
             describing the bin edges for each dimension, similar to the
-            behavior of ``np.histogramdd``. Defaults to False.
+            behaviour of ``np.histogramdd``. Defaults to False.
         skip_test (bool, optional): Turns off input check and data transformation.
             Defaults to False as it is intended for internal use only.
             Warning: setting this True might make error tracking difficult.
@@ -552,8 +545,8 @@ 
Source code for sed.binning.binning
             present in the dataframe
 
     Returns:
-        Union[np.ndarray, Tuple[np.ndarray, list]]: 2-element tuple returned only when
-        returnEdges is True. Otherwise only hist is returned.
+        np.ndarray | tuple[np.ndarray: 2-element tuple returned only when
+        return_edges is True. Otherwise only hist is returned.
 
         - **hist**: The result of the n-dimensional binning
         - **edges**: A list of D arrays describing the bin edges for each dimension.
@@ -572,19 +565,19 @@ 
Source code for sed.binning.binning
             raise TypeError(
                 "axes needs to be of type 'List[str]' if tests are skipped!",
             )
-        bins = cast(Union[List[int], List[np.ndarray]], bins)
-        axes = cast(List[str], axes)
-        ranges = cast(List[Tuple[float, float]], ranges)
+        bins = cast(Union[list[int], list[np.ndarray]], bins)
+        axes = cast(list[str], axes)
+        ranges = cast(list[tuple[float, float]], ranges)
 
     # convert bin centers to bin edges:
     if all(isinstance(x, np.ndarray) for x in bins):
-        bins = cast(List[np.ndarray], bins)
+        bins = cast(list[np.ndarray], bins)
         for i, bin_centers in enumerate(bins):
             bins[i] = bin_centers_to_bin_edges(bin_centers)
     else:
-        bins = cast(List[int], bins)
+        bins = cast(list[int], bins)
         # shift ranges by half a bin size to align the bin centers to the given ranges,
-        # as the histogram functions interpret the ranges as limits for the edges.
+        # as the histogram functions interprete the ranges as limits for the edges.
         for i, nbins in enumerate(bins):
             halfbinsize = (ranges[i][1] - ranges[i][0]) / (nbins) / 2
             ranges[i] = (
@@ -656,18 +649,12 @@ 
Source code for sed.binning.binning
 [docs]
 def bin_dataframe(
     df: dask.dataframe.DataFrame,
-    bins: Union[
-        int,
-        dict,
-        Sequence[int],
-        Sequence[np.ndarray],
-        Sequence[tuple],
-    ] = 100,
+    bins: int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple] = 100,
     axes: Sequence[str] = None,
-    ranges: Sequence[Tuple[float, float]] = None,
+    ranges: Sequence[tuple[float, float]] = None,
     hist_mode: str = "numba",
     mode: str = "fast",
-    jitter: Union[list, dict] = None,
+    jitter: list | dict = None,
     pbar: bool = True,
     n_cores: int = N_CPU - 1,
     threads_per_worker: int = 4,
@@ -681,13 +668,13 @@ 
Source code for sed.binning.binning
     Args:
         df (dask.dataframe.DataFrame): a dask.DataFrame on which to perform the
             histogram.
-            bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple], optional):
+        bins (int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple], optional):
             Definition of the bins. Can be any of the following cases:
 
                 - an integer describing the number of bins for all dimensions. This
                   requires "ranges" to be defined as well.
                 - A sequence containing one entry of the following types for each
-                  dimension:
+                  dimenstion:
 
                     - an integer describing the number of bins. This requires "ranges"
                       to be defined as well.
@@ -705,7 +692,7 @@ 
Source code for sed.binning.binning
             the order of the dimensions in the resulting array. Only not required if
             bins are provided as dictionary containing the axis names.
             Defaults to None.
-        ranges (Sequence[Tuple[float, float]], optional): Sequence of tuples containing
+        ranges (Sequence[tuple[float, float]], optional): Sequence of tuples containing
             the start and end point of the binning range. Required if bins given as
             int or Sequence[int]. Defaults to None.
         hist_mode (str, optional): Histogram calculation method.
@@ -722,11 +709,11 @@ 
Source code for sed.binning.binning
                 - 'legacy': Single-core recombination of partition results.
 
             Defaults to "fast".
-        jitter (Union[list, dict], optional): a list of the axes on which to apply
+        jitter (list | dict, optional): a list of the axes on which to apply
             jittering. To specify the jitter amplitude or method (normal or uniform
             noise) a dictionary can be passed. This should look like
             jitter={'axis':{'amplitude':0.5,'mode':'uniform'}}.
-            This example also shows the default behavior, in case None is
+            This example also shows the default behaviour, in case None is
             passed in the dictionary, or jitter is a list of strings.
             Warning: this is not the most performing approach. applying jitter
             on the dataframe before calling the binning is much faster.
@@ -757,14 +744,14 @@ 
Source code for sed.binning.binning
     # create the coordinate axes for the xarray output
     # if provided as array, they are interpreted as bin centers
     if isinstance(bins[0], np.ndarray):
-        bins = cast(List[np.ndarray], bins)
+        bins = cast(list[np.ndarray], bins)
         coords = dict(zip(axes, bins))
     elif ranges is None:
         raise ValueError(
             "bins is not an array and range is none. this shouldn't happen.",
         )
     else:
-        bins = cast(List[int], bins)
+        bins = cast(list[int], bins)
         coords = {
             ax: np.linspace(r[0], r[1], n, endpoint=False) for ax, r, n in zip(axes, ranges, bins)
         }
@@ -933,7 +920,7 @@ 
Source code for sed.binning.binning
     bin_centers: np.ndarray,
     time_unit: float,
 ) -> xr.DataArray:
-    """Get a normalization histogram from a timed dataframe.
+    """Get a normalization histogram from a timed datafram.
 
     Args:
         df (dask.dataframe.DataFrame): a dask.DataFrame on which to perform the
@@ -963,7 +950,7 @@ 
Source code for sed.binning.binning
 
 
 def apply_jitter_on_column(
-    df: Union[dask.dataframe.core.DataFrame, pd.DataFrame],
+    df: dask.dataframe.core.DataFrame | pd.DataFrame,
     amp: float,
     col: str,
     mode: str = "uniform",
diff --git a/sed/latest/_modules/sed/binning/numba_bin.html b/sed/latest/_modules/sed/binning/numba_bin.html
index f78ddd2..9376529 100644
--- a/sed/latest/_modules/sed/binning/numba_bin.html
+++ b/sed/latest/_modules/sed/binning/numba_bin.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.binning.numba_bin — SED 0.1.10a6 documentation
+    sed.binning.numba_bin — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -448,14 +448,12 @@
   
Source code for sed.binning.numba_bin
 """This file contains code for binning using numba precompiled code for the
 sed.binning module
-
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import Any
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import numba
 import numpy as np
@@ -472,7 +470,7 @@ 
Source code for sed.binning.numba_bin
     bit integers.
 
     Args:
-        sample (np.ndarray): The data to be histogram'd with shape N,D.
+        sample (np.ndarray): The data to be histogrammed with shape N,D.
         bins (Sequence[int]): The number of bins for each dimension D.
         ranges (np.ndarray): A sequence of length D, each an optional (lower,
             upper) tuple giving the outer bin edges to be used if the edges are
@@ -497,7 +495,7 @@ 
Source code for sed.binning.numba_bin
 
     for i in range(ndims):
         delta[i] = 1 / ((ranges[i, 1] - ranges[i, 0]) / bins[i])
-        strides[i] = hist.strides[i] // hist.itemsize
+        strides[i] = hist.strides[i] // hist.itemsize  # pylint: disable=E1136
 
     for t in range(sample.shape[0]):
         is_inside = True
@@ -559,7 +557,7 @@ 
Source code for sed.binning.numba_bin
 def _hist_from_bins(
     sample: np.ndarray,
     bins: Sequence[np.ndarray],
-    shape: Tuple,
+    shape: tuple,
 ) -> np.ndarray:
     """Numba powered binning method, similar to np.histogramdd.
 
@@ -569,7 +567,7 @@ 
Source code for sed.binning.numba_bin
         sample (np.ndarray) : the array of shape (N,D) on which to compute the histogram
         bins (Sequence[np.ndarray]): array of shape (N,D) defining the D bins on which
             to compute the histogram, i.e. the desired output axes.
-        shape (Tuple): shape of the resulting array. Workaround for the fact numba
+        shape (tuple): shape of the resulting array. Workaround for the fact numba
             does not allow to create tuples.
     Returns:
         hist: the computed n-dimensional histogram
@@ -607,10 +605,10 @@ 
Source code for sed.binning.numba_bin
 [docs]
 def numba_histogramdd(
     sample: np.ndarray,
-    bins: Union[int, Sequence[int], Sequence[np.ndarray], np.ndarray],
+    bins: int | Sequence[int] | Sequence[np.ndarray] | np.ndarray,
     ranges: Sequence = None,
-) -> Tuple[np.ndarray, List[np.ndarray]]:
-    """Multidimensional histogram function, powered by Numba.
+) -> tuple[np.ndarray, list[np.ndarray]]:
+    """Multidimensional histogramming function, powered by Numba.
 
     Behaves in total much like numpy.histogramdd. Returns uint32 arrays.
     This was chosen because it has a significant performance improvement over
@@ -620,8 +618,8 @@ 
Source code for sed.binning.numba_bin
     sizes.
 
     Args:
-        sample (np.ndarray): The data to be histogram'd with shape N,D
-        bins (Union[int, Sequence[int], Sequence[np.ndarray], np.ndarray]): The number
+        sample (np.ndarray): The data to be histogrammed with shape N,D
+        bins (int | Sequence[int] | Sequence[np.ndarray] | np.ndarray): The number
             of bins for each dimension D, or a sequence of bin edges on which to calculate
             the histogram.
         ranges (Sequence, optional): The range(s) to use for binning when bins is a sequence
@@ -634,7 +632,7 @@ 
Source code for sed.binning.numba_bin
         RuntimeError: Internal shape error after binning
 
     Returns:
-        Tuple[np.ndarray, List[np.ndarray]]: 2-element tuple of The computed histogram
+        tuple[np.ndarray, list[np.ndarray]]: 2-element tuple of The computed histogram
         and s list of D arrays describing the bin edges for each dimension.
 
         - **hist**: The computed histogram
@@ -666,7 +664,7 @@ 
Source code for sed.binning.numba_bin
 
     # method == "array"
     if isinstance(bins[0], np.ndarray):
-        bins = cast(List[np.ndarray], list(bins))
+        bins = cast(list[np.ndarray], list(bins))
         hist = _hist_from_bins(
             sample,
             tuple(bins),
@@ -692,7 +690,7 @@ 
Source code for sed.binning.numba_bin
     bins = tuple(bins)
 
     # Create edge arrays
-    edges: List[Any] = []
+    edges: list[Any] = []
     nbin = np.empty(num_cols, int)
 
     for i in range(num_cols):
diff --git a/sed/latest/_modules/sed/binning/utils.html b/sed/latest/_modules/sed/binning/utils.html
index 222b725..de0841f 100644
--- a/sed/latest/_modules/sed/binning/utils.html
+++ b/sed/latest/_modules/sed/binning/utils.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.binning.utils — SED 0.1.10a6 documentation
+    sed.binning.utils — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -447,13 +447,11 @@
                   
   
Source code for sed.binning.utils
 """This file contains helper functions for the sed.binning module
-
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import numpy as np
 
@@ -466,16 +464,10 @@ 
Source code for sed.binning.utils
 

 [docs]
 def simplify_binning_arguments(
-    bins: Union[
-        int,
-        dict,
-        Sequence[int],
-        Sequence[np.ndarray],
-        Sequence[tuple],
-    ],
+    bins: int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple],
     axes: Sequence[str] = None,
-    ranges: Sequence[Tuple[float, float]] = None,
-) -> Tuple[Union[List[int], List[np.ndarray]], List[str], List[Tuple[float, float]]]:
+    ranges: Sequence[tuple[float, float]] = None,
+) -> tuple[list[int] | list[np.ndarray], list[str], list[tuple[float, float]]]:
     """Convert the flexible input for defining bins into a
     simple "axes" "bins" "ranges" tuple.
 
@@ -483,13 +475,13 @@ 
Source code for sed.binning.utils
     binning functions defined here.
 
     Args:
-        bins (int, dict, Sequence[int], Sequence[np.ndarray], Sequence[tuple]):
+        bins (int | dict | Sequence[int] | Sequence[np.ndarray] | Sequence[tuple]):
             Definition of the bins. Can  be any of the following cases:
 
                 - an integer describing the number of bins for all dimensions. This
                   requires "ranges" to be defined as well.
                 - A sequence containing one entry of the following types for each
-                  dimension:
+                  dimenstion:
 
                     - an integer describing the number of bins. This requires "ranges"
                       to be defined as well.
@@ -506,7 +498,7 @@ 
Source code for sed.binning.utils
             the order of the dimensions in the resulting array. Only not required if
             bins are provided as dictionary containing the axis names.
             Defaults to None.
-        ranges (Sequence[Tuple[float, float]], optional): Sequence of tuples containing
+        ranges (Sequence[tuple[float, float]], optional): Sequence of tuples containing
             the start and end point of the binning range. Required if bins given as
             int or Sequence[int]. Defaults to None.
 
@@ -517,7 +509,7 @@ 
Source code for sed.binning.utils
         AttributeError: Shape mismatch
 
     Returns:
-        Tuple[Union[List[int], List[np.ndarray]], List[Tuple[float, float]]]: Tuple
+        tuple[list[int] | list[np.ndarray], list[str], list[tuple[float, float]]]: Tuple
         containing lists of bin centers, axes, and ranges.
     """
     # if bins is a dictionary: unravel to axes and bins
@@ -563,7 +555,7 @@ 
Source code for sed.binning.utils
 
     # if bins are provided as int, check that ranges are present
     if all(isinstance(x, (int, np.int64)) for x in bins):
-        bins = cast(List[int], list(bins))
+        bins = cast(list[int], list(bins))
         if ranges is None:
             raise AttributeError(
                 "Must provide a range if bins is an integer or list of integers",
@@ -573,9 +565,9 @@ 
Source code for sed.binning.utils
                 f"Ranges must be a sequence, not {type(ranges)}.",
             )
 
-    # otherwise, all bins should be of type np.ndarray here
+    # otherwise, all bins should by np.ndarrays here
     elif all(isinstance(x, np.ndarray) for x in bins):
-        bins = cast(List[np.ndarray], list(bins))
+        bins = cast(list[np.ndarray], list(bins))
     else:
         raise TypeError(f"Could not interpret bins of type {type(bins)}")
 
diff --git a/sed/latest/_modules/sed/calibrator/delay.html b/sed/latest/_modules/sed/calibrator/delay.html
index 781f036..c42dc4d 100644
--- a/sed/latest/_modules/sed/calibrator/delay.html
+++ b/sed/latest/_modules/sed/calibrator/delay.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.calibrator.delay — SED 0.1.10a6 documentation
+    sed.calibrator.delay — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -448,14 +448,12 @@
   
Source code for sed.calibrator.delay
 """sed.calibrator.delay module. Code for delay calibration.
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from copy import deepcopy
 from datetime import datetime
 from typing import Any
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe
 import h5py
@@ -496,32 +494,32 @@ 
Source code for sed.calibrator.delay
             "corrected_delay_column",
             self.delay_column,
         )
-        self.calibration: Dict[str, Any] = self._config["delay"].get("calibration", {})
-        self.offsets: Dict[str, Any] = self._config["delay"].get("offsets", {})
+        self.calibration: dict[str, Any] = self._config["delay"].get("calibration", {})
+        self.offsets: dict[str, Any] = self._config["delay"].get("offsets", {})
 
 

 [docs]
     def append_delay_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         adc_column: str = None,
         delay_column: str = None,
-        calibration: Dict[str, Any] = None,
-        adc_range: Union[Tuple, List, np.ndarray] = None,
-        delay_range: Union[Tuple, List, np.ndarray] = None,
+        calibration: dict[str, Any] = None,
+        adc_range: tuple | list | np.ndarray = None,
+        delay_range: tuple | list | np.ndarray = None,
         time0: float = None,
-        delay_range_mm: Union[Tuple, List, np.ndarray] = None,
+        delay_range_mm: tuple | list | np.ndarray = None,
         datafile: str = None,
         p1_key: str = None,
         p2_key: str = None,
         t0_key: str = None,
         verbose: bool = True,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and append the delay axis to the events dataframe, by converting
         values from an analog-digital-converter (ADC).
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): The dataframe where
+            df (pd.DataFrame | dask.dataframe.DataFrame): The dataframe where
                 to apply the delay calibration to.
             adc_column (str, optional): Source column for delay calibration.
                 Defaults to config["dataframe"]["adc_column"].
@@ -529,14 +527,14 @@ 
Source code for sed.calibrator.delay
                 Defaults to config["dataframe"]["delay_column"].
             calibration (dict, optional): Calibration dictionary with parameters for
                 delay calibration.
-            adc_range (Union[Tuple, List, np.ndarray], optional): The range of used
+            adc_range (tuple | list | np.ndarray, optional): The range of used
                 ADC values. Defaults to config["delay"]["adc_range"].
-            delay_range (Union[Tuple, List, np.ndarray], optional): Range of scanned
+            delay_range (tuple | list | np.ndarray, optional): Range of scanned
                 delay values in ps. If omitted, the range is calculated from the
                 delay_range_mm and t0 values.
             time0 (float, optional): Pump-Probe overlap value of the delay coordinate.
                 If omitted, it is searched for in the data files.
-            delay_range_mm (Union[Tuple, List, np.ndarray], optional): Range of scanned
+            delay_range_mm (tuple | list | np.ndarray, optional): Range of scanned
                 delay stage in mm. If omitted, it is searched for in the data files.
             datafile (str, optional): Datafile in which delay parameters are searched
                 for. Defaults to None.
@@ -554,8 +552,8 @@ 
Source code for sed.calibrator.delay
             NotImplementedError: Raised if no sufficient information passed.
 
         Returns:
-            Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added column
-            and delay calibration metadata dictionary.
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: dataframe with added column
+            and delay calibration metdata dictionary.
         """
         # pylint: disable=duplicate-code
         if calibration is None:
@@ -662,39 +660,40 @@ 
Source code for sed.calibrator.delay
     def add_offsets(
         self,
         df: dask.dataframe.DataFrame,
-        offsets: Dict[str, Any] = None,
+        offsets: dict[str, Any] = None,
         constant: float = None,
         flip_delay_axis: bool = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = 1.0,
-        preserve_mean: Union[bool, Sequence[bool]] = False,
-        reductions: Union[str, Sequence[str]] = None,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = 1.0,
+        preserve_mean: bool | Sequence[bool] = False,
+        reductions: str | Sequence[str] = None,
         delay_column: str = None,
         verbose: bool = True,
-    ) -> Tuple[dask.dataframe.DataFrame, dict]:
+    ) -> tuple[dask.dataframe.DataFrame, dict]:
         """Apply an offset to the delay column based on a constant or other columns.
 
         Args:
             df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
-            offsets (Dict, optional): Dictionary of delay offset parameters.
+            offsets (dict, optional): Dictionary of delay offset parameters.
             constant (float, optional): The constant to shift the delay axis by.
             flip_delay_axis (bool, optional): Whether to flip the time axis. Defaults to False.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[int, Sequence[int]]): weights to apply to the columns.
+            columns (str | Sequence[str]): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float]): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool]): Whether to subtract the mean of the column
+                before applying the shift. Defaults to False.
+            reductions (str | Sequence[str]): The reduction to apply to the column. Should be an
+                available method of dask.dataframe.Series. For example "mean". In this case the
+                function is applied to the column to generate a single value for the whole dataset.
+                If None, the shift is applied per-dataframe-row. Defaults to None. Currently only
+                "mean" is supported.
             delay_column (str, optional): Name of the column containing the delay values.
             verbose (bool, optional): Option to print out diagnostic information.
                 Defaults to True.
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the shifted delay axis.
-            dict: Metadata dictionary.
+            tuple[dask.dataframe.DataFrame, dict]: Dataframe with the shifted delay axis and
+            Metadata dictionary.
         """
         if offsets is None:
             offsets = deepcopy(self.offsets)
@@ -702,7 +701,7 @@ 
Source code for sed.calibrator.delay
         if delay_column is None:
             delay_column = self.delay_column
 
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
         }
 
@@ -838,7 +837,7 @@ 
Source code for sed.calibrator.delay
     p1_key: str,
     p2_key: str,
     t0_key: str,
-) -> Tuple:
+) -> tuple:
     """
     Read delay stage ranges from hdf5 file
 
@@ -866,18 +865,18 @@ 
Source code for sed.calibrator.delay
 

 [docs]
 def mm_to_ps(
-    delay_mm: Union[float, np.ndarray],
+    delay_mm: float | np.ndarray,
     time0_mm: float,
-) -> Union[float, np.ndarray]:
-    """Converts a delay stage position in mm into a relative delay in picoseconds
+) -> float | np.ndarray:
+    """Converts a delaystage position in mm into a relative delay in picoseconds
     (double pass).
 
     Args:
-        delay_mm (Union[float, Sequence[float]]): Delay stage position in mm
+        delay_mm (float | np.ndarray): Delay stage position in mm
         time0_mm (float): Delay stage position of pump-probe overlap in mm
 
     Returns:
-        Union[float, Sequence[float]]: Relative delay in picoseconds
+        float | np.ndarray: Relative delay in picoseconds
     """
     delay_ps = (delay_mm - time0_mm) / 0.15
     return delay_ps

diff --git a/sed/latest/_modules/sed/calibrator/energy.html b/sed/latest/_modules/sed/calibrator/energy.html
index 1ea72b2..0f0e3a9 100644
--- a/sed/latest/_modules/sed/calibrator/energy.html
+++ b/sed/latest/_modules/sed/calibrator/energy.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.calibrator.energy — SED 0.1.10a6 documentation
+    sed.calibrator.energy — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,19 +449,17 @@ 
Source code for sed.calibrator.energy
 """sed.calibrator.energy module. Code for energy calibration and
 correction. Mostly ported from https://github.com/mpes-kit/mpes.
 """
+from __future__ import annotations
+
 import itertools as it
 import warnings as wn
+from collections.abc import Sequence
 from copy import deepcopy
 from datetime import datetime
 from functools import partial
 from typing import Any
 from typing import cast
-from typing import Dict
-from typing import List
 from typing import Literal
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import bokeh.plotting as pbk
 import dask.dataframe
@@ -543,9 +541,9 @@ 
Source code for sed.calibrator.energy
 
         self._config = config
 
-        self.featranges: List[Tuple] = []  # Value ranges for feature detection
+        self.featranges: list[tuple] = []  # Value ranges for feature detection
         self.peaks: np.ndarray = np.asarray([])
-        self.calibration: Dict[str, Any] = self._config["energy"].get("calibration", {})
+        self.calibration: dict[str, Any] = self._config["energy"].get("calibration", {})
 
         self.tof_column = self._config["dataframe"]["tof_column"]
         self.tof_ns_column = self._config["dataframe"].get("tof_ns_column", None)
@@ -564,8 +562,8 @@ 
Source code for sed.calibrator.energy
         self.color_clip = self._config["energy"]["color_clip"]
         self.sector_delays = self._config["dataframe"].get("sector_delays", None)
         self.sector_id_column = self._config["dataframe"].get("sector_id_column", None)
-        self.offsets: Dict[str, Any] = self._config["energy"].get("offsets", {})
-        self.correction: Dict[str, Any] = self._config["energy"].get("correction", {})
+        self.offsets: dict[str, Any] = self._config["energy"].get("offsets", {})
+        self.correction: dict[str, Any] = self._config["energy"].get("correction", {})
 
     @property
     def ntraces(self) -> int:
@@ -632,10 +630,10 @@ 
Source code for sed.calibrator.energy
 [docs]
     def bin_data(
         self,
-        data_files: List[str],
-        axes: List[str] = None,
-        bins: List[int] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        data_files: list[str],
+        axes: list[str] = None,
+        bins: list[int] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         biases: np.ndarray = None,
         bias_key: str = None,
         **kwds,
@@ -643,12 +641,12 @@ 
Source code for sed.calibrator.energy
         """Bin data from single-event files, and load into class.
 
         Args:
-            data_files (List[str]): list of file names to bin
-            axes (List[str], optional): bin axes. Defaults to
+            data_files (list[str]): list of file names to bin
+            axes (list[str], optional): bin axes. Defaults to
                 config["dataframe"]["tof_column"].
-            bins (List[int], optional): number of bins.
+            bins (list[int], optional): number of bins.
                 Defaults to config["energy"]["bins"].
-            ranges (Sequence[Tuple[float, float]], optional): bin ranges.
+            ranges (Sequence[tuple[float, float]], optional): bin ranges.
                 Defaults to config["energy"]["ranges"].
             biases (np.ndarray, optional): Bias voltages used.
                 If not provided, biases are extracted from the file meta data.
@@ -664,7 +662,7 @@ 
Source code for sed.calibrator.energy
             ranges_ = [
                 np.array(self._config["energy"]["ranges"]) / 2 ** (self.binning - 1),
             ]
-            ranges = [cast(Tuple[float, float], tuple(v)) for v in ranges_]
+            ranges = [cast(tuple[float, float], tuple(v)) for v in ranges_]
         # pylint: disable=duplicate-code
         hist_mode = kwds.pop("hist_mode", self._config["binning"]["hist_mode"])
         mode = kwds.pop("mode", self._config["binning"]["mode"])
@@ -750,7 +748,7 @@ 
Source code for sed.calibrator.energy
 [docs]
     def adjust_ranges(
         self,
-        ranges: Tuple,
+        ranges: tuple,
         ref_id: int = 0,
         traces: np.ndarray = None,
         peak_window: int = 7,
@@ -761,7 +759,7 @@ 
Source code for sed.calibrator.energy
         (containing the peaks) among all traces.
 
         Args:
-            ranges (Tuple):
+            ranges (tuple):
                 Collection of feature detection ranges, within which an algorithm
                 (i.e. 1D peak detector) with look for the feature.
             ref_id (int, optional): Index of the reference trace. Defaults to 0.
@@ -842,8 +840,8 @@ 
Source code for sed.calibrator.energy
                 plot_segs[itr].set_ydata(traceseg)
                 plot_segs[itr].set_xdata(tofseg)
 
-                plot_peaks[itr].set_xdata(self.peaks[itr, 0])
-                plot_peaks[itr].set_ydata(self.peaks[itr, 1])
+                plot_peaks[itr].set_xdata([self.peaks[itr, 0]])
+                plot_peaks[itr].set_ydata([self.peaks[itr, 1]])
 
             fig.canvas.draw_idle()
 
@@ -893,7 +891,7 @@ 
Source code for sed.calibrator.energy
 [docs]
     def add_ranges(
         self,
-        ranges: Union[List[Tuple], Tuple],
+        ranges: list[tuple] | tuple,
         ref_id: int = 0,
         traces: np.ndarray = None,
         infer_others: bool = True,
@@ -903,14 +901,14 @@ 
Source code for sed.calibrator.energy
         """Select or extract the equivalent feature ranges (containing the peaks) among all traces.
 
         Args:
-            ranges (Union[List[Tuple], Tuple]):
+            ranges (list[tuple] | tuple):
                 Collection of feature detection ranges, within which an algorithm
                 (i.e. 1D peak detector) with look for the feature.
             ref_id (int, optional): Index of the reference trace. Defaults to 0.
             traces (np.ndarray, optional): Collection of energy dispersion curves.
                 Defaults to self.traces_normed.
             infer_others (bool, optional): Option to infer the feature detection range
-                in other traces from a given one using a time warp algorithm.
+                in other traces from a given one using a time warp algorthm.
                 Defaults to True.
             mode (str, optional): Specification on how to change the feature ranges
                 ('append' or 'replace'). Defaults to "replace".
@@ -923,7 +921,7 @@ 
Source code for sed.calibrator.energy
         # Infer the corresponding feature detection range of other traces by alignment
         if infer_others:
             assert isinstance(ranges, tuple)
-            newranges: List[Tuple] = []
+            newranges: list[tuple] = []
 
             for i in range(self.ntraces):
                 pathcorr = find_correspondence(
@@ -949,14 +947,14 @@ 
Source code for sed.calibrator.energy
 [docs]
     def feature_extract(
         self,
-        ranges: List[Tuple] = None,
+        ranges: list[tuple] = None,
         traces: np.ndarray = None,
         peak_window: int = 7,
     ):
         """Select or extract the equivalent landmarks (e.g. peaks) among all traces.
 
         Args:
-            ranges (List[Tuple], optional):  List of ranges in each trace to look for
+            ranges (list[tuple], optional):  List of ranges in each trace to look for
                 the peak feature, [start, end]. Defaults to self.featranges.
             traces (np.ndarray, optional): Collection of 1D spectra to use for
                 calibration. Defaults to self.traces_normed.
@@ -1082,7 +1080,7 @@ 
Source code for sed.calibrator.energy
     def view(  # pylint: disable=dangerous-default-value
         self,
         traces: np.ndarray,
-        segs: List[Tuple] = None,
+        segs: list[tuple] = None,
         peaks: np.ndarray = None,
         show_legend: bool = True,
         backend: str = "matplotlib",
@@ -1096,7 +1094,7 @@ 
Source code for sed.calibrator.energy
 
         Args:
             traces (np.ndarray): Matrix of traces to visualize.
-            segs (List[Tuple], optional): Segments to be highlighted in the
+            segs (list[tuple], optional): Segments to be highlighted in the
                 visualization. Defaults to None.
             peaks (np.ndarray, optional): Peak positions for labelling the traces.
                 Defaults to None.
@@ -1130,7 +1128,7 @@ 
Source code for sed.calibrator.energy
 
         if backend == "matplotlib":
             figsize = kwds.pop("figsize", (12, 4))
-            fig, ax = plt.subplots(figsize=figsize)
+            fig_plt, ax = plt.subplots(figsize=figsize)
             for itr, trace in enumerate(traces):
                 if align:
                     ax.plot(
@@ -1255,17 +1253,17 @@ 
Source code for sed.calibrator.energy
 [docs]
     def append_energy_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         tof_column: str = None,
         energy_column: str = None,
         calibration: dict = None,
         verbose: bool = True,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and append the energy axis to the events dataframe.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]):
+            df (pd.DataFrame | dask.dataframe.DataFrame):
                 Dataframe to apply the energy axis calibration to.
             tof_column (str, optional): Label of the source column.
                 Defaults to config["dataframe"]["tof_column"].
@@ -1284,7 +1282,7 @@ 
Source code for sed.calibrator.energy
             NotImplementedError: Raised if an invalid calib_type is found.
 
         Returns:
-            Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added column
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: dataframe with added column
             and energy calibration metadata dictionary.
         """
         if tof_column is None:
@@ -1371,15 +1369,15 @@ 
Source code for sed.calibrator.energy
 [docs]
     def append_tof_ns_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         tof_column: str = None,
         tof_ns_column: str = None,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Converts the time-of-flight time from steps to time in ns.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to convert.
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to convert.
             tof_column (str, optional): Name of the column containing the
                 time-of-flight steps. Defaults to config["dataframe"]["tof_column"].
             tof_ns_column (str, optional): Name of the column to store the
@@ -1390,8 +1388,8 @@ 
Source code for sed.calibrator.energy
                 Defaults to config["energy"]["tof_binning"].
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the new columns.
-            dict: Metadata dictionary.
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with the new columns
+            and Metadata dictionary.
         """
         binwidth = kwds.pop("binwidth", self.binwidth)
         binning = kwds.pop("binning", self.binning)
@@ -1409,7 +1407,7 @@ 
Source code for sed.calibrator.energy
             binning,
             df[tof_column].astype("float64"),
         )
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
             "binwidth": binwidth,
             "binning": binning,
@@ -1431,7 +1429,7 @@ 
Source code for sed.calibrator.energy
         """
         if calibration is None:
             calibration = self.calibration
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         metadata["applied"] = True
         metadata["calibration"] = deepcopy(calibration)
         metadata["tof"] = deepcopy(self.tof)
@@ -1449,7 +1447,7 @@ 
Source code for sed.calibrator.energy
         image: xr.DataArray,
         correction_type: str = None,
         amplitude: float = None,
-        center: Tuple[float, float] = None,
+        center: tuple[float, float] = None,
         correction: dict = None,
         apply: bool = False,
         **kwds,
@@ -1469,7 +1467,7 @@ 
Source code for sed.calibrator.energy
                 Defaults to config["energy"]["correction_type"].
             amplitude (float, optional): Amplitude of the time-of-flight correction
                 term. Defaults to config["energy"]["correction"]["correction_type"].
-            center (Tuple[float, float], optional): Center (x/y) coordinates for the
+            center (tuple[float, float], optional): Center (x/y) coordinates for the
                 correction. Defaults to config["energy"]["correction"]["center"].
             correction (dict, optional): Correction dict. Defaults to the config values
                 and is updated from provided and adjusted parameters.
@@ -1620,9 +1618,9 @@ 
Source code for sed.calibrator.energy
             )
 
             trace1.set_ydata(correction_x)
-            line1.set_xdata(x=x_center)
+            line1.set_xdata([x_center])
             trace2.set_ydata(correction_y)
-            line2.set_xdata(x=y_center)
+            line2.set_xdata([y_center])
 
             fig.canvas.draw_idle()
 
@@ -1642,7 +1640,7 @@ 
Source code for sed.calibrator.energy
                 update(correction["amplitude"], x_center, y_center, diameter=correction["diameter"])
             except KeyError as exc:
                 raise ValueError(
-                    "Parameter 'diameter' required for correction type 'spherical', ",
+                    "Parameter 'diameter' required for correction type 'sperical', ",
                     "but not present!",
                 ) from exc
 
@@ -1798,7 +1796,7 @@ 
Source code for sed.calibrator.energy
 [docs]
     def apply_energy_correction(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         tof_column: str = None,
         new_tof_column: str = None,
         correction_type: str = None,
@@ -1806,11 +1804,11 @@ 
Source code for sed.calibrator.energy
         correction: dict = None,
         verbose: bool = True,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Apply correction to the time-of-flight (TOF) axis of single-event data.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): The dataframe where
+            df (pd.DataFrame | dask.dataframe.DataFrame): The dataframe where
                 to apply the energy correction to.
             tof_column (str, optional): Name of the source column to convert.
                 Defaults to config["dataframe"]["tof_column"].
@@ -1827,7 +1825,7 @@ 
Source code for sed.calibrator.energy
                 Defaults to config["energy"]["correction_type"].
             amplitude (float, optional): Amplitude of the time-of-flight correction
                 term. Defaults to config["energy"]["correction"]["correction_type"].
-            correction (dict, optional): Correction dictionary containing parameters
+            correction (dict, optional): Correction dictionary containing paramters
                 for the correction. Defaults to self.correction or
                 config["energy"]["correction"].
             verbose (bool, optional): Option to print out diagnostic information.
@@ -1847,7 +1845,7 @@ 
Source code for sed.calibrator.energy
                   asymmetric 2D Lorentz profile, X-direction.
 
         Returns:
-            Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added column
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: dataframe with added column
             and Energy correction metadata dictionary.
         """
         if correction is None:
@@ -1908,7 +1906,7 @@ 
Source code for sed.calibrator.energy
         """
         if correction is None:
             correction = self.correction
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         metadata["applied"] = True
         metadata["correction"] = deepcopy(correction)
 
@@ -1923,11 +1921,11 @@ 
Source code for sed.calibrator.energy
         tof_column: str = None,
         sector_id_column: str = None,
         sector_delays: np.ndarray = None,
-    ) -> Tuple[dask.dataframe.DataFrame, dict]:
+    ) -> tuple[dask.dataframe.DataFrame, dict]:
         """Aligns the time-of-flight axis of the different sections of a detector.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+            df (dask.dataframe.DataFrame): Dataframe to use.
             tof_column (str, optional): Name of the column containing the time-of-flight values.
                 Defaults to config["dataframe"]["tof_column"].
             sector_id_column (str, optional): Name of the column containing the sector id values.
@@ -1936,8 +1934,8 @@ 
Source code for sed.calibrator.energy
                 config["dataframe"]["sector_delays"].
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the new columns.
-            dict: Metadata dictionary.
+            tuple[dask.dataframe.DataFrame, dict]: Dataframe with the new columns and Metadata
+            dictionary.
         """
         if sector_delays is None:
             sector_delays = self.sector_delays
@@ -1959,7 +1957,7 @@ 
Source code for sed.calibrator.energy
             return val.astype(np.float32)
 
         df[tof_column] = df.map_partitions(align_sector, meta=(tof_column, np.float32))
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
             "sector_delays": sector_delays,
         }
@@ -1970,16 +1968,16 @@ 
Source code for sed.calibrator.energy
 [docs]
     def add_offsets(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame] = None,
-        offsets: Dict[str, Any] = None,
+        df: pd.DataFrame | dask.dataframe.DataFrame = None,
+        offsets: dict[str, Any] = None,
         constant: float = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = None,
-        preserve_mean: Union[bool, Sequence[bool]] = False,
-        reductions: Union[str, Sequence[str]] = None,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = None,
+        preserve_mean: bool | Sequence[bool] = False,
+        reductions: str | Sequence[str] = None,
         energy_column: str = None,
         verbose: bool = True,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Apply an offset to the energy column by the values of the provided columns.
 
         If no parameter is passed to this function, the offset is applied as defined in the
@@ -1987,25 +1985,26 @@ 
Source code for sed.calibrator.energy
         and the offset is applied using the ``dfops.apply_offset_from_columns()`` function.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
             offsets (Dict, optional): Dictionary of energy offset parameters.
             constant (float, optional): The constant to shift the energy axis by.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[float, Sequence[float]]): weights to apply to the columns.
+            columns (str | Sequence[str]): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float]): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool]): Whether to subtract the mean of the column
+                before applying the shift. Defaults to False.
+            reductions (str | Sequence[str]): The reduction to apply to the column. Should be an
+                available method of dask.dataframe.Series. For example "mean". In this case the
+                function is applied to the column to generate a single value for the whole dataset.
+                If None, the shift is applied per-dataframe-row. Defaults to None. Currently only
+                "mean" is supported.
             energy_column (str, optional): Name of the column containing the energy values.
             verbose (bool, optional): Option to print out diagnostic information.
                 Defaults to True.
 
         Returns:
-            dask.dataframe.DataFrame: Dataframe with the new columns.
-            dict: Metadata dictionary.
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with the new columns
+            and Metadata dictionary.
         """
         if offsets is None:
             offsets = deepcopy(self.offsets)
@@ -2013,7 +2012,7 @@ 
Source code for sed.calibrator.energy
         if energy_column is None:
             energy_column = self.energy_column
 
-        metadata: Dict[str, Any] = {
+        metadata: dict[str, Any] = {
             "applied": True,
         }
 
@@ -2145,17 +2144,17 @@ 
Source code for sed.calibrator.energy
 
 

 [docs]
-def extract_bias(files: List[str], bias_key: str) -> np.ndarray:
+def extract_bias(files: list[str], bias_key: str) -> np.ndarray:
     """Read bias values from hdf5 files
 
     Args:
-        files (List[str]): List of filenames
+        files (list[str]): List of filenames
         bias_key (str): hdf5 path to the bias value
 
     Returns:
         np.ndarray: Array of bias values.
     """
-    bias_list: List[float] = []
+    bias_list: list[float] = []
     for file in files:
         with h5py.File(file, "r") as file_handle:
             if bias_key[0] == "@":
@@ -2170,21 +2169,21 @@ 
Source code for sed.calibrator.energy
 

 [docs]
 def correction_function(
-    x: Union[float, np.ndarray],
-    y: Union[float, np.ndarray],
+    x: float | np.ndarray,
+    y: float | np.ndarray,
     correction_type: str,
-    center: Tuple[float, float],
+    center: tuple[float, float],
     amplitude: float,
     **kwds,
-) -> Union[float, np.ndarray]:
+) -> float | np.ndarray:
     """Calculate the TOF correction based on the given X/Y coordinates and a model.
 
     Args:
-        x (float): x coordinate
-        y (float): y coordinate
+        x (float | np.ndarray): x coordinate
+        y (float | np.ndarray): y coordinate
         correction_type (str): type of correction. One of
             "spherical", "Lorentzian", "Gaussian", or "Lorentzian_asymmetric"
-        center (Tuple[int, int]): center position of the distribution (x,y)
+        center (tuple[int, int]): center position of the distribution (x,y)
         amplitude (float): Amplitude of the correction
         **kwds: Keyword arguments:
 
@@ -2199,7 +2198,7 @@ 
Source code for sed.calibrator.energy
               asymmetric 2D Lorentz profile, X-direction.
 
     Returns:
-        float: calculated correction value
+        float | np.ndarray: calculated correction value
     """
     if correction_type == "spherical":
         try:
@@ -2359,21 +2358,21 @@ 
Source code for sed.calibrator.energy
 [docs]
 def range_convert(
     x: np.ndarray,
-    xrng: Tuple,
+    xrng: tuple,
     pathcorr: np.ndarray,
-) -> Tuple:
+) -> tuple:
     """Convert value range using a pairwise path correspondence (e.g. obtained
     from time warping algorithm).
 
     Args:
         x (np.ndarray): Values of the x axis (e.g. time-of-flight values).
-        xrng (Tuple): Boundary value range on the x axis.
+        xrng (tuple): Boundary value range on the x axis.
         pathcorr (np.ndarray): Path correspondence between two 1D arrays in the
             following form,
             [(id_1_trace_1, id_1_trace_2), (id_2_trace_1, id_2_trace_2), ...]
 
     Returns:
-        Tuple: Transformed range according to the path correspondence.
+        tuple: Transformed range according to the path correspondence.
     """
     pathcorr = np.asarray(pathcorr)
     xrange_trans = []
@@ -2409,7 +2408,7 @@ 
Source code for sed.calibrator.energy
 def peaksearch(
     traces: np.ndarray,
     tof: np.ndarray,
-    ranges: List[Tuple] = None,
+    ranges: list[tuple] = None,
     pkwindow: int = 3,
     plot: bool = False,
 ) -> np.ndarray:
@@ -2418,7 +2417,7 @@ 
Source code for sed.calibrator.energy
     Args:
         traces (np.ndarray): Collection of 1D spectra.
         tof (np.ndarray): Time-of-flight values.
-        ranges (List[Tuple], optional): List of ranges for peak detection in the format
+        ranges (list[tuple], optional): List of ranges for peak detection in the format
         [(LowerBound1, UpperBound1), (LowerBound2, UpperBound2), ....].
             Defaults to None.
         pkwindow (int, optional): Window width of a peak (amounts to lookahead in
@@ -2458,8 +2457,8 @@ 
Source code for sed.calibrator.energy
 def _datacheck_peakdetect(
     x_axis: np.ndarray,
     y_axis: np.ndarray,
-) -> Tuple[np.ndarray, np.ndarray]:
-    """Input format checking for 1D peakdetect algorithm
+) -> tuple[np.ndarray, np.ndarray]:
+    """Input format checking for 1D peakdtect algorithm
 
     Args:
         x_axis (np.ndarray): x-axis array
@@ -2469,7 +2468,7 @@ 
Source code for sed.calibrator.energy
         ValueError: Raised if x and y values don't have the same length.
 
     Returns:
-        Tuple[np.ndarray, np.ndarray]: Tuple of checked (x/y) arrays.
+        tuple[np.ndarray, np.ndarray]: Tuple of checked (x/y) arrays.
     """
 
     if x_axis is None:
@@ -2494,7 +2493,7 @@ 
Source code for sed.calibrator.energy
     x_axis: np.ndarray = None,
     lookahead: int = 200,
     delta: int = 0,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Function for detecting local maxima and minima in a signal.
     Discovers peaks by searching for values which are surrounded by lower
     or larger values for maxima and minima respectively
@@ -2521,7 +2520,7 @@ 
Source code for sed.calibrator.energy
         ValueError: Raised if lookahead and delta are out of range.
 
     Returns:
-        Tuple[np.ndarray, np.ndarray]: Tuple of positions of the positive peaks,
+        tuple[np.ndarray, np.ndarray]: Tuple of positions of the positive peaks,
         positions of the negative peaks
     """
     max_peaks = []
@@ -2542,7 +2541,7 @@ 
Source code for sed.calibrator.energy
 
     # maxima and minima candidates are temporarily stored in
     # mx and mn respectively
-    _min, _max = np.Inf, -np.Inf
+    _min, _max = np.inf, -np.inf
 
     # Only detect peak if there is 'lookahead' amount of points after it
     for index, (x, y) in enumerate(
@@ -2557,15 +2556,15 @@ 
Source code for sed.calibrator.energy
             _min_pos = x
 
         # Find local maxima
-        if y < _max - delta and _max != np.Inf:
+        if y < _max - delta and _max != np.inf:
             # Maxima peak candidate found
             # look ahead in signal to ensure that this is a peak and not jitter
             if y_axis[index : index + lookahead].max() < _max:
                 max_peaks.append([_max_pos, _max])
                 dump.append(True)
                 # Set algorithm to only find minima now
-                _max = np.Inf
-                _min = np.Inf
+                _max = np.inf
+                _min = np.inf
 
                 if index + lookahead >= length:
                     # The end is within lookahead no more peaks can be found
@@ -2576,15 +2575,15 @@ 
Source code for sed.calibrator.energy
             #    mxpos = x_axis[np.where(y_axis[index:index+lookahead]==mx)]
 
         # Find local minima
-        if y > _min + delta and _min != -np.Inf:
+        if y > _min + delta and _min != -np.inf:
             # Minima peak candidate found
             # look ahead in signal to ensure that this is a peak and not jitter
             if y_axis[index : index + lookahead].min() > _min:
                 min_peaks.append([_min_pos, _min])
                 dump.append(False)
                 # Set algorithm to only find maxima now
-                _min = -np.Inf
-                _max = -np.Inf
+                _min = -np.inf
+                _max = -np.inf
 
                 if index + lookahead >= length:
                     # The end is within lookahead no more peaks can be found
@@ -2611,13 +2610,13 @@ 
Source code for sed.calibrator.energy
 

 [docs]
 def fit_energy_calibration(
-    pos: Union[List[float], np.ndarray],
-    vals: Union[List[float], np.ndarray],
+    pos: list[float] | np.ndarray,
+    vals: list[float] | np.ndarray,
     binwidth: float,
     binning: int,
     ref_id: int = 0,
     ref_energy: float = None,
-    t: Union[List[float], np.ndarray] = None,
+    t: list[float] | np.ndarray = None,
     energy_scale: str = "kinetic",
     verbose: bool = True,
     **kwds,
@@ -2627,16 +2626,16 @@ 
Source code for sed.calibrator.energy
     function d/(t-t0)**2.
 
     Args:
-        pos (Union[List[float], np.ndarray]): Positions of the spectral landmarks
+        pos (list[float] | np.ndarray): Positions of the spectral landmarks
             (e.g. peaks) in the EDCs.
-        vals (Union[List[float], np.ndarray]): Bias voltage value associated with
+        vals (list[float] | np.ndarray): Bias voltage value associated with
             each EDC.
         binwidth (float): Time width of each original TOF bin in ns.
         binning (int): Binning factor of the TOF values.
         ref_id (int, optional): Reference dataset index. Defaults to 0.
-        ref_energy (float, optional): Energy value of the feature in the reference
+        ref_energy (float, optional): Energy value of the feature in the refence
             trace (eV). required to output the calibration. Defaults to None.
-        t (Union[List[float], np.ndarray], optional): Array of TOF values. Required
+        t (list[float] | np.ndarray, optional): Array of TOF values. Required
             to calculate calibration trace. Defaults to None.
         energy_scale (str, optional): Direction of increasing energy scale.
 
@@ -2656,7 +2655,7 @@ 
Source code for sed.calibrator.energy
     Returns:
         dict: A dictionary of fitting parameters including the following,
 
-        - "coeffs": Fitted function coefficients.
+        - "coeffs": Fitted function coefficents.
         - "axis": Fitted energy axis.
     """
     vals = np.asarray(vals)
@@ -2750,12 +2749,12 @@ 
Source code for sed.calibrator.energy
 

 [docs]
 def poly_energy_calibration(
-    pos: Union[List[float], np.ndarray],
-    vals: Union[List[float], np.ndarray],
+    pos: list[float] | np.ndarray,
+    vals: list[float] | np.ndarray,
     order: int = 3,
     ref_id: int = 0,
     ref_energy: float = None,
-    t: Union[List[float], np.ndarray] = None,
+    t: list[float] | np.ndarray = None,
     aug: int = 1,
     method: str = "lstsq",
     energy_scale: str = "kinetic",
@@ -2770,15 +2769,15 @@ 
Source code for sed.calibrator.energy
 
 
     Args:
-        pos (Union[List[float], np.ndarray]): Positions of the spectral landmarks
+        pos (list[float] | np.ndarray): Positions of the spectral landmarks
             (e.g. peaks) in the EDCs.
-        vals (Union[List[float], np.ndarray]): Bias voltage value associated with
+        vals (list[float] | np.ndarray): Bias voltage value associated with
             each EDC.
         order (int, optional): Polynomial order of the fitting function. Defaults to 3.
         ref_id (int, optional): Reference dataset index. Defaults to 0.
-        ref_energy (float, optional): Energy value of the feature in the reference
+        ref_energy (float, optional): Energy value of the feature in the refence
             trace (eV). required to output the calibration. Defaults to None.
-        t (Union[List[float], np.ndarray], optional): Array of TOF values. Required
+        t (list[float] | np.ndarray, optional): Array of TOF values. Required
             to calculate calibration trace. Defaults to None.
         aug (int, optional): Fitting dimension augmentation
             (1=no change, 2=double, etc). Defaults to 1.
@@ -2907,7 +2906,7 @@ 
Source code for sed.calibrator.energy
 

 [docs]
 def tof2evpoly(
-    poly_a: Union[List[float], np.ndarray],
+    poly_a: list[float] | np.ndarray,
     energy_offset: float,
     t: float,
 ) -> float:
@@ -2915,7 +2914,7 @@ 
Source code for sed.calibrator.energy
     conversion formula.
 
     Args:
-        poly_a (Union[List[float], np.ndarray]): Polynomial coefficients.
+        poly_a (list[float] | np.ndarray): Polynomial coefficients.
         energy_offset (float): Energy offset in eV.
         t (float): TOF value in bin number.
 
diff --git a/sed/latest/_modules/sed/calibrator/momentum.html b/sed/latest/_modules/sed/calibrator/momentum.html
index 4a15598..d6eb762 100644
--- a/sed/latest/_modules/sed/calibrator/momentum.html
+++ b/sed/latest/_modules/sed/calibrator/momentum.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.calibrator.momentum — SED 0.1.10a6 documentation
+    sed.calibrator.momentum — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,14 +449,12 @@ 
Source code for sed.calibrator.momentum
 """sed.calibrator.momentum module. Code for momentum calibration and distortion
 correction. Mostly ported from https://github.com/mpes-kit/mpes.
 """
+from __future__ import annotations
+
 import itertools as it
 from copy import deepcopy
 from datetime import datetime
 from typing import Any
-from typing import Dict
-from typing import List
-from typing import Tuple
-from typing import Union
 
 import bokeh.palettes as bp
 import bokeh.plotting as pbk
@@ -490,9 +488,9 @@ 
Source code for sed.calibrator.momentum
     Momentum distortion correction and momentum calibration workflow functions.
 
     Args:
-        data (Union[xr.DataArray, np.ndarray], optional): Multidimensional hypervolume
+        data (xr.DataArray | np.ndarray, optional): Multidimensional hypervolume
             containing the data. Defaults to None.
-        bin_ranges (List[Tuple], optional): Binning ranges of the data volume, if
+        bin_ranges (list[tuple], optional): Binning ranges of the data volume, if
             provided as np.ndarray. Defaults to None.
         rotsym (int, optional): Rotational symmetry of the data. Defaults to 6.
         config (dict, optional): Config dictionary. Defaults to None.
@@ -500,17 +498,17 @@ 
Source code for sed.calibrator.momentum
 
     def __init__(
         self,
-        data: Union[xr.DataArray, np.ndarray] = None,
-        bin_ranges: List[Tuple] = None,
+        data: xr.DataArray | np.ndarray = None,
+        bin_ranges: list[tuple] = None,
         rotsym: int = 6,
         config: dict = None,
     ):
         """Constructor of the MomentumCorrector class.
 
         Args:
-            data (Union[xr.DataArray, np.ndarray], optional): Multidimensional
+            data (xr.DataArray | np.ndarray, optional): Multidimensional
                 hypervolume containing the data. Defaults to None.
-            bin_ranges (List[Tuple], optional): Binning ranges of the data volume,
+            bin_ranges (list[tuple], optional): Binning ranges of the data volume,
                 if provided as np.ndarray. Defaults to None.
             rotsym (int, optional): Rotational symmetry of the data. Defaults to 6.
             config (dict, optional): Config dictionary. Defaults to None.
@@ -525,7 +523,7 @@ 
Source code for sed.calibrator.momentum
         self.slice: np.ndarray = None
         self.slice_corrected: np.ndarray = None
         self.slice_transformed: np.ndarray = None
-        self.bin_ranges: List[Tuple] = self._config["momentum"].get("bin_ranges", [])
+        self.bin_ranges: list[tuple] = self._config["momentum"].get("bin_ranges", [])
 
         if data is not None:
             self.load_data(data=data, bin_ranges=bin_ranges)
@@ -540,7 +538,7 @@ 
Source code for sed.calibrator.momentum
         self.include_center: bool = False
         self.use_center: bool = False
         self.pouter: np.ndarray = None
-        self.pcent: Tuple[float, ...] = None
+        self.pcent: tuple[float, ...] = None
         self.pouter_ord: np.ndarray = None
         self.prefs: np.ndarray = None
         self.ptargs: np.ndarray = None
@@ -556,10 +554,10 @@ 
Source code for sed.calibrator.momentum
         self.cdeform_field_bkp: np.ndarray = None
         self.inverse_dfield: np.ndarray = None
         self.dfield_updated: bool = False
-        self.transformations: Dict[str, Any] = self._config["momentum"].get("transformations", {})
-        self.correction: Dict[str, Any] = self._config["momentum"].get("correction", {})
-        self.adjust_params: Dict[str, Any] = {}
-        self.calibration: Dict[str, Any] = self._config["momentum"].get("calibration", {})
+        self.transformations: dict[str, Any] = self._config["momentum"].get("transformations", {})
+        self.correction: dict[str, Any] = self._config["momentum"].get("correction", {})
+        self.adjust_params: dict[str, Any] = {}
+        self.calibration: dict[str, Any] = self._config["momentum"].get("calibration", {})
 
         self.x_column = self._config["dataframe"]["x_column"]
         self.y_column = self._config["dataframe"]["y_column"]
@@ -605,15 +603,15 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def load_data(
         self,
-        data: Union[xr.DataArray, np.ndarray],
-        bin_ranges: List[Tuple] = None,
+        data: xr.DataArray | np.ndarray,
+        bin_ranges: list[tuple] = None,
     ):
         """Load binned data into the momentum calibrator class
 
         Args:
-            data (Union[xr.DataArray, np.ndarray]):
+            data (xr.DataArray | np.ndarray):
                 2D or 3D data array, either as np.ndarray or xr.DataArray.
-            bin_ranges (List[Tuple], optional):
+            bin_ranges (list[tuple], optional):
                 Binning ranges. Needs to be provided in case the data are given
                 as np.ndarray. Otherwise, they are determined from the coords of
                 the xr.DataArray. Defaults to None.
@@ -722,9 +720,7 @@ 
Source code for sed.calibrator.momentum
             axmax = np.max(self.slice, axis=(0, 1))
             if axmin < axmax:
                 img.set_clim(axmin, axmax)
-            ax.set_title(
-                f"Plane[{start}:{stop}]",
-            )
+            ax.set_title(f"Plane[{start}:{stop}]")
             fig.canvas.draw_idle()
 
             plane_slider.close()
@@ -745,13 +741,13 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def select_slice(
         self,
-        selector: Union[slice, List[int], int],
+        selector: slice | list[int] | int,
         axis: int = 2,
     ):
         """Select (hyper)slice from a (hyper)volume.
 
         Args:
-            selector (Union[slice, List[int], int]):
+            selector (slice | list[int] | int):
                 Selector along the specified axis to extract the slice (image). Use
                 the construct slice(start, stop, step) to select a range of images
                 and sum them. Use an integer to specify only a particular slice.
@@ -798,7 +794,7 @@ 
Source code for sed.calibrator.momentum
                 Option to calculate symmetry scores. Defaults to False.
             **kwds: Keyword arguments.
 
-                - **symtype** (str): Type of symmetry scores to calculate
+                - **symtype** (str): Type of symmetry scores to calculte
                   if symscores is True. Defaults to "rotation".
 
         Raises:
@@ -1070,7 +1066,7 @@ 
Source code for sed.calibrator.momentum
         use_center: bool = None,
         fixed_center: bool = True,
         interp_order: int = 1,
-        ascale: Union[float, list, tuple, np.ndarray] = None,
+        ascale: float | list | tuple | np.ndarray = None,
         verbose: bool = True,
         **kwds,
     ) -> np.ndarray:
@@ -1088,13 +1084,13 @@ 
Source code for sed.calibrator.momentum
             interp_order (int, optional):
                 Order of interpolation (see ``scipy.ndimage.map_coordinates()``).
                 Defaults to 1.
-            ascale: (Union[float, np.ndarray], optional): Scale parameter determining a relative
-                scale for each symmetry feature. If provided as single float, rotsym has to be 4.
-                This parameter describes the relative scaling between the two orthogonal symmetry
-                directions (for an orthorhombic system). This requires the correction points to be
-                located along the principal axes (X/Y points of the Brillouin zone). Otherwise, an
-                array with ``rotsym`` elements is expected, containing relative scales for each
-                feature. Defaults to an array of equal scales.
+            ascale: (float | list | tuple | np.ndarray, optional): Scale parameter determining a
+                relative scale for each symmetry feature. If provided as single float, rotsym has
+                to be 4. This parameter describes the relative scaling between the two orthogonal
+                symmetry directions (for an orthorhombic system). This requires the correction
+                points to be located along the principal axes (X/Y points of the Brillouin zone).
+                Otherwise, an array with ``rotsym`` elements is expected, containing relative
+                scales for each feature. Defaults to an array of equal scales.
             verbose (bool, optional): Option to report the used landmarks for correction.
                 Defaults to True.
             **kwds: keyword arguments:
@@ -1261,7 +1257,7 @@ 
Source code for sed.calibrator.momentum
             self.slice_corrected = corrected_image
 
         if verbose:
-            print("Calculated thin spline correction based on the following landmarks:")
+            print("Calulated thin spline correction based on the following landmarks:")
             print(f"pouter: {self.pouter}")
             if use_center:
                 print(f"pcent: {self.pcent}")
@@ -1375,7 +1371,7 @@ 
Source code for sed.calibrator.momentum
                 - rotation_auto.
                 - scaling.
                 - scaling_auto.
-                - homography.
+                - homomorphy.
 
             keep (bool, optional): Option to keep the specified coordinate transform in
                 the class. Defaults to False.
@@ -1495,7 +1491,7 @@ 
Source code for sed.calibrator.momentum
             )
             self.slice_transformed = slice_transformed
         else:
-            # if external image is provided, apply only the new additional transformation
+            # if external image is provided, apply only the new addional tranformation
             slice_transformed = ndi.map_coordinates(
                 image,
                 [rdeform, cdeform],
@@ -1519,7 +1515,7 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def pose_adjustment(
         self,
-        transformations: Dict[str, Any] = None,
+        transformations: dict[str, Any] = None,
         apply: bool = False,
         reset: bool = True,
         verbose: bool = True,
@@ -1531,7 +1527,7 @@ 
Source code for sed.calibrator.momentum
 
         Args:
             transformations (dict, optional): Dictionary with transformations.
-                Defaults to self.transformations or config["momentum"]["transformations"].
+                Defaults to self.transformations or config["momentum"]["transformtions"].
             apply (bool, optional):
                 Option to directly apply the provided transformations.
                 Defaults to False.
@@ -1759,7 +1755,7 @@ 
Source code for sed.calibrator.momentum
         image: np.ndarray = None,
         origin: str = "lower",
         cmap: str = "terrain_r",
-        figsize: Tuple[int, int] = (4, 4),
+        figsize: tuple[int, int] = (4, 4),
         points: dict = None,
         annotated: bool = False,
         backend: str = "matplotlib",
@@ -1767,7 +1763,7 @@ 
Source code for sed.calibrator.momentum
         scatterkwds: dict = {},
         cross: bool = False,
         crosshair: bool = False,
-        crosshair_radii: List[int] = [50, 100, 150],
+        crosshair_radii: list[int] = [50, 100, 150],
         crosshair_thickness: int = 1,
         **kwds,
     ):
@@ -1778,7 +1774,7 @@ 
Source code for sed.calibrator.momentum
             origin (str, optional): Figure origin specification ('lower' or 'upper').
                 Defaults to "lower".
             cmap (str, optional):  Colormap specification. Defaults to "terrain_r".
-            figsize (Tuple[int, int], optional): Figure size. Defaults to (4, 4).
+            figsize (tuple[int, int], optional): Figure size. Defaults to (4, 4).
             points (dict, optional): Points for annotation. Defaults to None.
             annotated (bool, optional): Option to add annotation. Defaults to False.
             backend (str, optional): Visualization backend specification. Defaults to
@@ -1795,7 +1791,7 @@ 
Source code for sed.calibrator.momentum
                 self.pcent. Defaults to False.
             crosshair (bool, optional): Display option to plot circles around center
                 self.pcent. Works only in bokeh backend. Defaults to False.
-            crosshair_radii (List[int], optional): Pixel radii of circles to plot when
+            crosshair_radii (list[int], optional): Pixel radii of circles to plot when
                 crosshair option is activated. Defaults to [50, 100, 150].
             crosshair_thickness (int, optional): Thickness of crosshair circles.
                 Defaults to 1.
@@ -1814,7 +1810,7 @@ 
Source code for sed.calibrator.momentum
             txtsize = kwds.pop("textsize", 12)
 
         if backend == "matplotlib":
-            fig, ax = plt.subplots(figsize=figsize)
+            fig_plt, ax = plt.subplots(figsize=figsize)
             ax.imshow(image.T, origin=origin, cmap=cmap, **imkwds)
 
             if cross:
@@ -1909,30 +1905,31 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def select_k_range(
         self,
-        point_a: Union[np.ndarray, List[int]] = None,
-        point_b: Union[np.ndarray, List[int]] = None,
+        point_a: np.ndarray | list[int] = None,
+        point_b: np.ndarray | list[int] = None,
         k_distance: float = None,
-        k_coord_a: Union[np.ndarray, List[float]] = None,
-        k_coord_b: Union[np.ndarray, List[float]] = np.array([0.0, 0.0]),
+        k_coord_a: np.ndarray | list[float] = None,
+        k_coord_b: np.ndarray | list[float] = np.array([0.0, 0.0]),
         equiscale: bool = True,
         apply: bool = False,
     ):
-        """Interactive selection function for features for the Momentum axes calibration. It allows
-        the user to select the pixel positions of two symmetry points (a and b) and the k-space
-        distance of the two. Alternatively, the coordinates of both points can be provided. See the
-        equiscale option for details on the specifications of point coordinates.
+        """Interactive selection function for features for the Momentum axes calibra-
+        tion. It allows the user to select the pixel positions of two symmetry points
+        (a and b) and the k-space distance of the two. Alternatively, the corrdinates
+        of both points can be provided. See the equiscale option for details on the
+        specifications of point coordinates.
 
         Args:
-            point_a (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_a (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point a.
-            point_b (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_b (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point b. Defaults to the center pixel of the image, defined by
                 config["momentum"]["center_pixel"].
             k_distance (float, optional): The known momentum space distance between the
                 two symmetry points.
-            k_coord_a (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_a (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points a. Only valid if equiscale=False.
-            k_coord_b (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_b (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points b. Only valid if equiscale=False. Defaults to
                 the k-space center np.array([0.0, 0.0]).
             equiscale (bool, optional): Option to adopt equal scale along both the x
@@ -2062,11 +2059,11 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def calibrate(
         self,
-        point_a: Union[np.ndarray, List[int]],
-        point_b: Union[np.ndarray, List[int]],
+        point_a: np.ndarray | list[int],
+        point_b: np.ndarray | list[int],
         k_distance: float = None,
-        k_coord_a: Union[np.ndarray, List[float]] = None,
-        k_coord_b: Union[np.ndarray, List[float]] = np.array([0.0, 0.0]),
+        k_coord_a: np.ndarray | list[float] = None,
+        k_coord_b: np.ndarray | list[float] = np.array([0.0, 0.0]),
         equiscale: bool = True,
         image: np.ndarray = None,
     ) -> dict:
@@ -2077,16 +2074,16 @@ 
Source code for sed.calibrator.momentum
         of point coordinates.
 
         Args:
-            point_a (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_a (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point a.
-            point_b (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_b (np.ndarray | list[int], optional): Pixel coordinates of the
                 symmetry point b. Defaults to the center pixel of the image, defined by
                 config["momentum"]["center_pixel"].
             k_distance (float, optional): The known momentum space distance between the
                 two symmetry points.
-            k_coord_a (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_a (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points a. Only valid if equiscale=False.
-            k_coord_b (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_b (np.ndarray | list[float], optional): Momentum coordinate
                 of the symmetry points b. Only valid if equiscale=False. Defaults to
                 the k-space center np.array([0.0, 0.0]).
             equiscale (bool, optional): Option to adopt equal scale along both the x
@@ -2182,20 +2179,20 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def apply_corrections(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         x_column: str = None,
         y_column: str = None,
         new_x_column: str = None,
         new_y_column: str = None,
         verbose: bool = True,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and replace the X and Y values with their distortion-corrected
         version.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to apply
-                the distortion correction to.
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to apply
+                the distotion correction to.
             x_column (str, optional): Label of the 'X' column before momentum
                 distortion correction. Defaults to config["momentum"]["x_column"].
             y_column (str, optional): Label of the 'Y' column before momentum
@@ -2217,7 +2214,7 @@ 
Source code for sed.calibrator.momentum
                 Additional keyword arguments are passed to ``apply_dfield``.
 
         Returns:
-            Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]: Dataframe with
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with
             added columns and momentum correction metadata dictionary.
         """
         if x_column is None:
@@ -2274,7 +2271,7 @@ 
Source code for sed.calibrator.momentum
         Returns:
             dict: generated correction metadata dictionary.
         """
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         if len(self.correction) > 0:
             metadata["correction"] = self.correction
             metadata["correction"]["applied"] = True
@@ -2289,11 +2286,11 @@ 
Source code for sed.calibrator.momentum
             metadata["registration"]["creation_date"] = datetime.now().timestamp()
             metadata["registration"]["applied"] = True
             metadata["registration"]["depends_on"] = (
-                "/entry/process/registration/transformations/rot_z"
+                "/entry/process/registration/tranformations/rot_z"
                 if "angle" in metadata["registration"] and metadata["registration"]["angle"]
-                else "/entry/process/registration/transformations/trans_y"
+                else "/entry/process/registration/tranformations/trans_y"
                 if "xtrans" in metadata["registration"] and metadata["registration"]["xtrans"]
-                else "/entry/process/registration/transformations/trans_x"
+                else "/entry/process/registration/tranformations/trans_x"
                 if "ytrans" in metadata["registration"] and metadata["registration"]["ytrans"]
                 else "."
             )
@@ -2317,7 +2314,7 @@ 
Source code for sed.calibrator.momentum
                     [0.0, 1.0, 0.0],
                 )
                 metadata["registration"]["trans_y"]["depends_on"] = (
-                    "/entry/process/registration/transformations/trans_x"
+                    "/entry/process/registration/tranformations/trans_x"
                     if "ytrans" in metadata["registration"] and metadata["registration"]["ytrans"]
                     else "."
                 )
@@ -2333,9 +2330,9 @@ 
Source code for sed.calibrator.momentum
                     (metadata["registration"]["center"], [0.0]),
                 )
                 metadata["registration"]["rot_z"]["depends_on"] = (
-                    "/entry/process/registration/transformations/trans_y"
+                    "/entry/process/registration/tranformations/trans_y"
                     if "xtrans" in metadata["registration"] and metadata["registration"]["xtrans"]
-                    else "/entry/process/registration/transformations/trans_x"
+                    else "/entry/process/registration/tranformations/trans_x"
                     if "ytrans" in metadata["registration"] and metadata["registration"]["ytrans"]
                     else "."
                 )
@@ -2347,19 +2344,19 @@ 
Source code for sed.calibrator.momentum
 [docs]
     def append_k_axis(
         self,
-        df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+        df: pd.DataFrame | dask.dataframe.DataFrame,
         x_column: str = None,
         y_column: str = None,
         new_x_column: str = None,
         new_y_column: str = None,
         calibration: dict = None,
         **kwds,
-    ) -> Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]:
+    ) -> tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]:
         """Calculate and append the k axis coordinates (kx, ky) to the events dataframe.
 
         Args:
-            df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to apply the
-                distortion correction to.
+            df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to apply the
+                distotion correction to.
             x_column (str, optional): Label of the source 'X' column.
                 Defaults to config["momentum"]["corrected_x_column"] or
                 config["momentum"]["x_column"] (whichever is present).
@@ -2376,7 +2373,7 @@ 
Source code for sed.calibrator.momentum
                 to the calibration dictionary.
 
         Returns:
-            Tuple[Union[pd.DataFrame, dask.dataframe.DataFrame], dict]: Dataframe with
+            tuple[pd.DataFrame | dask.dataframe.DataFrame, dict]: Dataframe with
             added columns and momentum calibration metadata dictionary.
         """
         if x_column is None:
@@ -2406,7 +2403,7 @@ 
Source code for sed.calibrator.momentum
             calibration["creation_date"] = datetime.now().timestamp()
 
         try:
-            (df[new_x_column], df[new_y_column]) = detector_coordinates_2_k_coordinates(
+            (df[new_x_column], df[new_y_column]) = detector_coordiantes_2_k_koordinates(
                 r_det=df[x_column],
                 c_det=df[y_column],
                 r_start=calibration["rstart"],
@@ -2442,7 +2439,7 @@ 
Source code for sed.calibrator.momentum
         """
         if calibration is None:
             calibration = self.calibration
-        metadata: Dict[Any, Any] = {}
+        metadata: dict[Any, Any] = {}
         try:
             metadata["creation_date"] = calibration["creation_date"]
         except KeyError:
@@ -2469,7 +2466,7 @@ 
Source code for sed.calibrator.momentum
         cmap_name (str): Name of the colormap/palette.
 
     Returns:
-        list: List of colors in hex representation (a bokeh palette).
+        list: List of colors in hex representation (a bokoeh palette).
     """
     if cmap_name in bp.all_palettes.keys():
         palette_func = getattr(bp, cmap_name)
@@ -2488,38 +2485,33 @@ 
Source code for sed.calibrator.momentum
 [docs]
 def dictmerge(
     main_dict: dict,
-    other_entries: Union[List[dict], Tuple[dict], dict],
+    other_entries: list[dict] | tuple[dict] | dict,
 ) -> dict:
     """Merge a dictionary with other dictionaries.
 
     Args:
         main_dict (dict): Main dictionary.
-        other_entries (Union[List[dict], Tuple[dict], dict]):
+        other_entries (list[dict] | tuple[dict] | dict):
             Other dictionary or composite dictionarized elements.
 
     Returns:
         dict: Merged dictionary.
     """
-    if isinstance(
-        other_entries,
-        (
-            list,
-            tuple,
-        ),
-    ):  # Merge main_dict with a list or tuple of dictionaries
+    # Merge main_dict with a list or tuple of dictionaries
+    if isinstance(other_entries, (list, tuple)):
         for oth in other_entries:
             main_dict = {**main_dict, **oth}
-
-    elif isinstance(other_entries, dict):  # Merge D with a single dictionary
+    # Merge D with a single dictionary
+    elif isinstance(other_entries, dict):
         main_dict = {**main_dict, **other_entries}
 
     return main_dict

 
 
 
-

-[docs]
-def detector_coordinates_2_k_coordinates(
+

+[docs]
+def detector_coordiantes_2_k_koordinates(
     r_det: float,
     c_det: float,
     r_start: float,
@@ -2530,8 +2522,8 @@ 
Source code for sed.calibrator.momentum
     c_conversion: float,
     r_step: float,
     c_step: float,
-) -> Tuple[float, float]:
-    """Conversion from detector coordinates (r_det, c_det) to momentum coordinates
+) -> tuple[float, float]:
+    """Conversion from detector coordinates (rdet, cdet) to momentum coordinates
     (kr, kc).
 
     Args:
@@ -2547,7 +2539,7 @@ 
Source code for sed.calibrator.momentum
         c_step (float): Column stepping factor.
 
     Returns:
-        Tuple[float, float]: Converted momentum space row/column coordinates.
+        tuple[float, float]: Converted momentum space row/column coordinates.
     """
     r_det0 = r_start + r_step * r_center
     c_det0 = c_start + c_step * c_center
@@ -2561,30 +2553,30 @@ 
Source code for sed.calibrator.momentum
 

 [docs]
 def apply_dfield(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     dfield: np.ndarray,
     x_column: str,
     y_column: str,
     new_x_column: str,
     new_y_column: str,
-    detector_ranges: List[Tuple],
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+    detector_ranges: list[tuple],
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Application of the inverse displacement-field to the dataframe coordinates.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to apply the
-            distortion correction to.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to apply the
+            distotion correction to.
         dfield (np.ndarray): The distortion correction field. 3D matrix,
             with column and row distortion fields stacked along the first dimension.
         x_column (str): Label of the 'X' source column.
         y_column (str): Label of the 'Y' source column.
         new_x_column (str): Label of the 'X' destination column.
         new_y_column (str): Label of the 'Y' destination column.
-        detector_ranges (List[Tuple]): tuple of pixel ranges of the detector x/y
+        detector_ranges (list[tuple]): tuple of pixel ranges of the detector x/y
             coordinates
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added columns
+        pd.DataFrame | dask.dataframe.DataFrame: dataframe with added columns
     """
     x = df[x_column]
     y = df[y_column]
@@ -2605,18 +2597,18 @@ 
Source code for sed.calibrator.momentum
 def generate_inverse_dfield(
     rdeform_field: np.ndarray,
     cdeform_field: np.ndarray,
-    bin_ranges: List[Tuple],
-    detector_ranges: List[Tuple],
+    bin_ranges: list[tuple],
+    detector_ranges: list[tuple],
 ) -> np.ndarray:
-    """Generate inverse deformation field using interpolation with griddata.
+    """Generate inverse deformation field using inperpolation with griddata.
     Assuming the binning range of the input ``rdeform_field`` and ``cdeform_field``
     covers the whole detector.
 
     Args:
         rdeform_field (np.ndarray): Row-wise deformation field.
         cdeform_field (np.ndarray): Column-wise deformation field.
-        bin_ranges (List[Tuple]): Detector ranges of the binned coordinates.
-        detector_ranges (List[Tuple]): Ranges of detector coordinates to interpolate to.
+        bin_ranges (list[tuple]): Detector ranges of the binned coordinates.
+        detector_ranges (list[tuple]): Ranges of detector coordinates to interpolate to.
 
     Returns:
         np.ndarray: The calculated inverse deformation field (row/column)
@@ -2680,14 +2672,14 @@ 
Source code for sed.calibrator.momentum
 
 

 [docs]
-def load_dfield(file: str) -> Tuple[np.ndarray, np.ndarray]:
+def load_dfield(file: str) -> tuple[np.ndarray, np.ndarray]:
     """Load inverse dfield from file
 
     Args:
         file (str): Path to file containing the inverse dfield
 
     Returns:
-        np.ndarray: the loaded inverse deformation field
+        tuple[np.ndarray, np.ndarray]: the loaded inverse row and column deformation fields
     """
     rdeform_field: np.ndarray = None
     cdeform_field: np.ndarray = None
diff --git a/sed/latest/_modules/sed/core/config.html b/sed/latest/_modules/sed/core/config.html
index 26a585d..90151e0 100644
--- a/sed/latest/_modules/sed/core/config.html
+++ b/sed/latest/_modules/sed/core/config.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.core.config — SED 0.1.10a6 documentation
+    sed.core.config — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -448,13 +448,14 @@
   
Source code for sed.core.config
 """This module contains a config library for loading yaml/json files into dicts
 """
+from __future__ import annotations
+
 import copy
 import json
 import os
 import platform
 from importlib.util import find_spec
 from pathlib import Path
-from typing import Union
 
 import yaml
 from platformdirs import user_config_path
@@ -467,14 +468,11 @@ 
Source code for sed.core.config
 

 [docs]
 def parse_config(
-    config: Union[dict, str] = None,
-    folder_config: Union[dict, str] = None,
-    user_config: Union[dict, str] = None,
-    system_config: Union[dict, str] = None,
-    default_config: Union[
-        dict,
-        str,
-    ] = f"{package_dir}/config/default.yaml",
+    config: dict | str = None,
+    folder_config: dict | str = None,
+    user_config: dict | str = None,
+    system_config: dict | str = None,
+    default_config: (dict | str) = f"{package_dir}/config/default.yaml",
     verbose: bool = True,
 ) -> dict:
     """Load the config dictionary from a file, or pass the provided config dictionary.
@@ -484,21 +482,21 @@ 
Source code for sed.core.config
     can be also passed as optional arguments (file path strings or dictionaries).
 
     Args:
-        config (Union[dict, str], optional): config dictionary or file path.
+        config (dict | str, optional): config dictionary or file path.
                 Files can be *json* or *yaml*. Defaults to None.
-        folder_config (Union[ dict, str, ], optional): working-folder-based config dictionary
+        folder_config (dict | str, optional): working-folder-based config dictionary
             or file path. The loaded dictionary is completed with the folder-based values,
             taking preference over user, system and default values. Defaults to the file
             "sed_config.yaml" in the current working directory.
-        user_config (Union[ dict, str, ], optional): user-based config dictionary
+        user_config (dict | str, optional): user-based config dictionary
             or file path. The loaded dictionary is completed with the user-based values,
             taking preference over system and default values.
             Defaults to the file ".sed/config.yaml" in the current user's home directory.
-        system_config (Union[ dict, str, ], optional): system-wide config dictionary
+        system_config (dict | str, optional): system-wide config dictionary
             or file path. The loaded dictionary is completed with the system-wide values,
             taking preference over default values. Defaults to the file "/etc/sed/config.yaml"
             on linux, and "%ALLUSERSPROFILE%/sed/config.yaml" on windows.
-        default_config (Union[ dict, str, ], optional): default config dictionary
+        default_config (dict | str, optional): default config dictionary
             or file path. The loaded dictionary is completed with the default values.
             Defaults to *package_dir*/config/default.yaml".
         verbose (bool, optional): Option to report loaded config files. Defaults to True.
@@ -631,10 +629,10 @@ 
Source code for sed.core.config
 def save_config(config_dict: dict, config_path: str, overwrite: bool = False):
     """Function to save a given config dictionary to a json or yaml file. Normally, it loads any
     existing file of the given name, and keeps any existing dictionary keys not present in the
-    provided dictionary. The overwrite option creates a fully empty dictionary first.
+    provided dictionary. The overwrite option creates a fully empty dictionry first.
 
     Args:
-        config_dict (dict): The dictionary to save.
+        config_dict (dict): The dictionry to save.
         config_path (str): A string containing the path to the file where to save the dictionary
             to.
         overwrite (bool, optional): Option to overwrite an existing file with the given dictionary.
diff --git a/sed/latest/_modules/sed/core/dfops.html b/sed/latest/_modules/sed/core/dfops.html
index 6637f74..c75fb61 100644
--- a/sed/latest/_modules/sed/core/dfops.html
+++ b/sed/latest/_modules/sed/core/dfops.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.core.dfops — SED 0.1.10a6 documentation
+    sed.core.dfops — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -451,9 +451,10 @@ 
Source code for sed.core.dfops
 """
 # Note: some of the functions presented here were
 # inspired by https://github.com/mpes-kit/mpes
+from __future__ import annotations
+
+from collections.abc import Sequence
 from typing import Callable
-from typing import Sequence
-from typing import Union
 
 import dask.dataframe
 import numpy as np
@@ -464,30 +465,30 @@ 
Source code for sed.core.dfops
 

 [docs]
 def apply_jitter(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
-    cols: Union[str, Sequence[str]],
-    cols_jittered: Union[str, Sequence[str]] = None,
-    amps: Union[float, Sequence[float]] = 0.5,
+    df: pd.DataFrame | dask.dataframe.DataFrame,
+    cols: str | Sequence[str],
+    cols_jittered: str | Sequence[str] = None,
+    amps: float | Sequence[float] = 0.5,
     jitter_type: str = "uniform",
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Add jittering to one or more dataframe columns.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to add
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to add
             noise/jittering to.
-        cols (Union[str, Sequence[str]]): Names of the columns to add jittering to.
-        cols_jittered (Union[str, Sequence[str]], optional): Names of the columns
+        cols (str | Sequence[str]): Names of the columns to add jittering to.
+        cols_jittered (str | Sequence[str], optional): Names of the columns
             with added jitter. Defaults to None.
-        amps (Union[float, Sequence[float]], optional): Amplitude scalings for the
+        amps (float | Sequence[float], optional): Amplitude scalings for the
             jittering noise. If one number is given, the same is used for all axes.
-            For normal noise, the added noise will have stdev [-amp, +amp], for
+            For normal noise, the added noise will have sdev [-amp, +amp], for
             uniform noise it will cover the interval [-amp, +amp].
             Defaults to 0.5.
         jitter_type (str, optional): the type of jitter to add. 'uniform' or 'normal'
             distributed noise. Defaults to "uniform".
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: dataframe with added columns.
+        pd.DataFrame | dask.dataframe.DataFrame: dataframe with added columns.
     """
     assert cols is not None, "cols needs to be provided!"
     assert jitter_type in (
@@ -524,17 +525,17 @@ 
Source code for sed.core.dfops
 

 [docs]
 def drop_column(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
-    column_name: Union[str, Sequence[str]],
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+    df: pd.DataFrame | dask.dataframe.DataFrame,
+    column_name: str | Sequence[str],
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Delete columns.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
-        column_name (Union[str, Sequence[str]])): List of column names to be dropped.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
+        column_name (str | Sequence[str]): List of column names to be dropped.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with dropped columns.
+        pd.DataFrame | dask.dataframe.DataFrame: Dataframe with dropped columns.
     """
     out_df = df.drop(column_name, axis=1)
 
@@ -545,15 +546,15 @@ 
Source code for sed.core.dfops
 

 [docs]
 def apply_filter(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     col: str,
     lower_bound: float = -np.inf,
     upper_bound: float = np.inf,
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Application of bound filters to a specified column (can be used consecutively).
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
         col (str): Name of the column to filter. Passing "index" for col will
             filter on the index in each dataframe partition.
         lower_bound (float, optional): The lower bound used in the filtering.
@@ -562,7 +563,7 @@ 
Source code for sed.core.dfops
             Defaults to np.inf.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: The filtered dataframe.
+        pd.DataFrame | dask.dataframe.DataFrame: The filtered dataframe.
     """
     df = df.copy()
     if col == "index":
@@ -591,14 +592,14 @@ 
Source code for sed.core.dfops
     timestamps in the dataframe.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (dask.dataframe.DataFrame): Dataframe to use.
         time_stamps (np.ndarray): Time stamps of the values to add
         data (np.ndarray): Values corresponding at the time stamps in time_stamps
         dest_column (str): destination column name
         time_stamp_column (str): Time stamp column name
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with added column
+        dask.dataframe.DataFrame: Dataframe with added column
     """
     if time_stamp_column not in df.columns:
         raise ValueError(f"{time_stamp_column} not found in dataframe!")
@@ -625,23 +626,23 @@ 
Source code for sed.core.dfops
 

 [docs]
 def map_columns_2d(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     map_2d: Callable,
     x_column: str,
     y_column: str,
     **kwds,
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Apply a 2-dimensional mapping simultaneously to two dimensions.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
         map_2d (Callable): 2D mapping function.
         x_column (str): The X column of the dataframe to apply mapping to.
         y_column (str): The Y column of the dataframe to apply mapping to.
         **kwds: Additional arguments for the 2D mapping function.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with mapped columns.
+        pd.DataFrame | dask.dataframe.DataFrame: Dataframe with mapped columns.
     """
     new_x_column = kwds.pop("new_x_column", x_column)
     new_y_column = kwds.pop("new_y_column", y_column)
@@ -661,7 +662,7 @@ 
Source code for sed.core.dfops
 def forward_fill_lazy(
     df: dask.dataframe.DataFrame,
     columns: Sequence[str] = None,
-    before: Union[str, int] = "max",
+    before: str | int = "max",
     compute_lengths: bool = False,
     iterations: int = 2,
 ) -> dask.dataframe.DataFrame:
@@ -669,14 +670,14 @@ 
Source code for sed.core.dfops
 
     Allows forward filling between partitions. This is useful for dataframes
     that have sparse data, such as those with many NaNs.
-    Running the forward filling multiple times can fix the issue of having
+    Runnin the forward filling multiple times can fix the issue of having
     entire partitions consisting of NaNs. By default we run this twice, which
     is enough to fix the issue for dataframes with no consecutive partitions of NaNs.
 
     Args:
         df (dask.dataframe.DataFrame): The dataframe to forward fill.
-        columns (list): The columns to forward fill. If None, fills all columns
-        before (int, str, optional): The number of rows to include before the current partition.
+        columns (list, optional): The columns to forward fill. If None, fills all columns
+        before (str | int, optional): The number of rows to include before the current partition.
             if 'max' it takes as much as possible from the previous partition, which is
             the size of the smallest partition in the dataframe. Defaults to 'max'.
         compute_lengths (bool, optional): Whether to compute the length of each partition
@@ -726,7 +727,7 @@ 
Source code for sed.core.dfops
 def backward_fill_lazy(
     df: dask.dataframe.DataFrame,
     columns: Sequence[str] = None,
-    after: Union[str, int] = "max",
+    after: str | int = "max",
     compute_lengths: bool = False,
     iterations: int = 1,
 ) -> dask.dataframe.DataFrame:
@@ -738,8 +739,8 @@ 
Source code for sed.core.dfops
 
     Args:
         df (dask.dataframe.DataFrame): The dataframe to forward fill.
-        columns (list): The columns to forward fill. If None, fills all columns
-        after (int, str, optional): The number of rows to include after the current partition.
+        columns (list, optional): The columns to forward fill. If None, fills all columns
+        after (str | int, optional): The number of rows to include after the current partition.
             if 'max' it takes as much as possible from the previous partition, which is
             the size of the smallest partition in the dataframe. Defaults to 'max'.
         compute_lengths (bool, optional): Whether to compute the length of each partition
@@ -789,10 +790,10 @@ 
Source code for sed.core.dfops
 def offset_by_other_columns(
     df: dask.dataframe.DataFrame,
     target_column: str,
-    offset_columns: Union[str, Sequence[str]],
-    weights: Union[float, Sequence[float]],
-    reductions: Union[str, Sequence[str]] = None,
-    preserve_mean: Union[bool, Sequence[bool]] = False,
+    offset_columns: str | Sequence[str],
+    weights: float | Sequence[float],
+    reductions: str | Sequence[str] = None,
+    preserve_mean: bool | Sequence[bool] = False,
     inplace: bool = True,
     rename: str = None,
 ) -> dask.dataframe.DataFrame:
@@ -801,13 +802,13 @@ 
Source code for sed.core.dfops
     Args:
         df (dask.dataframe.DataFrame): Dataframe to use. Currently supports only dask dataframes.
         target_column (str): Name of the column to apply the offset to.
-        offset_columns (str): Name of the column(s) to use for the offset.
-        weights (Union[float, Sequence[float]]): weights to apply on each column before adding.
-            Used also for changing sign.
-        reductions (Union[str, Sequence[str]], optional): Reduction function to use for the offset.
+        offset_columns (str | Sequence[str]): Name of the column(s) to use for the offset.
+        weights (float | Sequence[float]): weights to apply on each column before adding. Used also
+            for changing sign.
+        reductions (str | Sequence[str], optional): Reduction function to use for the offset.
             Defaults to "mean". Currently, only mean is supported.
-        preserve_mean (Union[bool, Sequence[bool]], optional): Whether to subtract the mean of the
-            offset column. Defaults to False. If a list is given, it must have the same length as
+        preserve_mean (bool | Sequence[bool], optional): Whether to subtract the mean of the offset
+            column. Defaults to False. If a list is given, it must have the same length as
             offset_columns. Otherwise the value passed is used for all columns.
         inplace (bool, optional): Whether to apply the offset inplace.
             If false, the new column will have the name provided by rename, or has the same name as
diff --git a/sed/latest/_modules/sed/core/metadata.html b/sed/latest/_modules/sed/core/metadata.html
index b852175..605e57c 100644
--- a/sed/latest/_modules/sed/core/metadata.html
+++ b/sed/latest/_modules/sed/core/metadata.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.core.metadata — SED 0.1.10a6 documentation
+    sed.core.metadata — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -448,10 +448,11 @@
   
Source code for sed.core.metadata
 """This is a metadata handler class from the sed package
 """
+from __future__ import annotations
+
 import json
 from copy import deepcopy
 from typing import Any
-from typing import Dict
 
 from sed.core.config import complete_dictionary
 
@@ -460,18 +461,49 @@ 
Source code for sed.core.metadata
 [docs]
 class MetaHandler:
     """This class provides methods to manipulate metadata dictionaries,
-    and give a nice representation of them."""
+    and give a nice representation of them.
+
+    Args:
+        meta (dict, optional): Pre-existing metadata dict. Defaults to None.
+    """
 
-    def __init__(self, meta: Dict = None) -> None:
+    def __init__(self, meta: dict = None) -> None:
+        """Constructor.
+
+        Args:
+            meta (dict, optional): Pre-existing metadata dict. Defaults to None.
+        """
         self._m = deepcopy(meta) if meta is not None else {}
 
-    def __getitem__(self, val: Any) -> None:
+    def __getitem__(self, val: Any) -> Any:
+        """Function for getting a value
+
+        Args:
+            val (Any): Metadata category key
+
+        Returns:
+            Any: The metadata category entry.
+        """
         return self._m[val]
 
     def __repr__(self) -> str:
+        """String representation function as json
+
+        Returns:
+            str: Summary string.
+        """
         return json.dumps(self._m, default=str, indent=4)
 
-    def _format_attributes(self, attributes, indent=0):
+    def _format_attributes(self, attributes: dict, indent: int = 0) -> str:
+        """Function to summarize a dictionary as html
+
+        Args:
+            attributes (dict): dictionary to summarize
+            indent (int, optional): Indentation value. Defaults to 0.
+
+        Returns:
+            str: Generated html summary.
+        """
         INDENT_FACTOR = 20
         html = ""
         for key, value in attributes.items():
@@ -492,11 +524,16 @@ 
Source code for sed.core.metadata
         return html
 
     def _repr_html_(self) -> str:
+        """Summary function as html
+
+        Returns:
+            str: Generated html summary
+        """
         html = self._format_attributes(self._m)
         return html
 
     @property
-    def metadata(self) -> Dict:
+    def metadata(self) -> dict:
         """Property returning the metadata dict.
         Returns:
             dict: Dictionary of metadata.
@@ -516,7 +553,7 @@ 
Source code for sed.core.metadata
         Args:
             entry: dictionary containing the metadata to add.
             name: name of the dictionary key under which to add entry.
-            duplicate_policy: Control behavior in case the 'name' key
+            duplicate_policy: Control behaviour in case the 'name' key
                 is already present in the metadata dictionary. Can be any of:
 
                     - "raise": raises a DuplicateEntryError.
diff --git a/sed/latest/_modules/sed/core/processor.html b/sed/latest/_modules/sed/core/processor.html
index d1eadb6..51f9d29 100644
--- a/sed/latest/_modules/sed/core/processor.html
+++ b/sed/latest/_modules/sed/core/processor.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.core.processor — SED 0.1.10a6 documentation
+    sed.core.processor — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,15 +449,13 @@ 
Source code for sed.core.processor
 """This module contains the core class for the sed package
 
 """
+from __future__ import annotations
+
 import pathlib
+from collections.abc import Sequence
 from datetime import datetime
 from typing import Any
 from typing import cast
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as ddf
 import matplotlib.pyplot as plt
@@ -498,11 +496,11 @@ 
Source code for sed.core.processor
 
     Args:
         metadata (dict, optional): Dict of external Metadata. Defaults to None.
-        config (Union[dict, str], optional): Config dictionary or config file name.
+        config (dict | str, optional): Config dictionary or config file name.
             Defaults to None.
-        dataframe (Union[pd.DataFrame, ddf.DataFrame], optional): dataframe to load
+        dataframe (pd.DataFrame | ddf.DataFrame, optional): dataframe to load
             into the class. Defaults to None.
-        files (List[str], optional): List of files to pass to the loader defined in
+        files (list[str], optional): List of files to pass to the loader defined in
             the config. Defaults to None.
         folder (str, optional): Folder containing files to pass to the loader
             defined in the config. Defaults to None.
@@ -518,9 +516,9 @@ 
Source code for sed.core.processor
     def __init__(
         self,
         metadata: dict = None,
-        config: Union[dict, str] = None,
-        dataframe: Union[pd.DataFrame, ddf.DataFrame] = None,
-        files: List[str] = None,
+        config: dict | str = None,
+        dataframe: pd.DataFrame | ddf.DataFrame = None,
+        files: list[str] = None,
         folder: str = None,
         runs: Sequence[str] = None,
         collect_metadata: bool = False,
@@ -532,11 +530,11 @@ 
Source code for sed.core.processor
 
         Args:
             metadata (dict, optional): Dict of external Metadata. Defaults to None.
-            config (Union[dict, str], optional): Config dictionary or config file name.
+            config (dict | str, optional): Config dictionary or config file name.
                 Defaults to None.
-            dataframe (Union[pd.DataFrame, ddf.DataFrame], optional): dataframe to load
+            dataframe (pd.DataFrame | ddf.DataFrame, optional): dataframe to load
                 into the class. Defaults to None.
-            files (List[str], optional): List of files to pass to the loader defined in
+            files (list[str], optional): List of files to pass to the loader defined in
                 the config. Defaults to None.
             folder (str, optional): Folder containing files to pass to the loader
                 defined in the config. Defaults to None.
@@ -564,9 +562,9 @@ 
Source code for sed.core.processor
         else:
             self.verbose = verbose
 
-        self._dataframe: Union[pd.DataFrame, ddf.DataFrame] = None
-        self._timed_dataframe: Union[pd.DataFrame, ddf.DataFrame] = None
-        self._files: List[str] = []
+        self._dataframe: pd.DataFrame | ddf.DataFrame = None
+        self._timed_dataframe: pd.DataFrame | ddf.DataFrame = None
+        self._files: list[str] = []
 
         self._binned: xr.DataArray = None
         self._pre_binned: xr.DataArray = None
@@ -658,20 +656,20 @@ 
Source code for sed.core.processor
     #     self.view_event_histogram(dfpid=2, backend="matplotlib")
 
     @property
-    def dataframe(self) -> Union[pd.DataFrame, ddf.DataFrame]:
+    def dataframe(self) -> pd.DataFrame | ddf.DataFrame:
         """Accessor to the underlying dataframe.
 
         Returns:
-            Union[pd.DataFrame, ddf.DataFrame]: Dataframe object.
+            pd.DataFrame | ddf.DataFrame: Dataframe object.
         """
         return self._dataframe
 
     @dataframe.setter
-    def dataframe(self, dataframe: Union[pd.DataFrame, ddf.DataFrame]):
+    def dataframe(self, dataframe: pd.DataFrame | ddf.DataFrame):
         """Setter for the underlying dataframe.
 
         Args:
-            dataframe (Union[pd.DataFrame, ddf.DataFrame]): The dataframe object to set.
+            dataframe (pd.DataFrame | ddf.DataFrame): The dataframe object to set.
         """
         if not isinstance(dataframe, (pd.DataFrame, ddf.DataFrame)) or not isinstance(
             dataframe,
@@ -685,20 +683,20 @@ 
Source code for sed.core.processor
         self._dataframe = dataframe
 
     @property
-    def timed_dataframe(self) -> Union[pd.DataFrame, ddf.DataFrame]:
+    def timed_dataframe(self) -> pd.DataFrame | ddf.DataFrame:
         """Accessor to the underlying timed_dataframe.
 
         Returns:
-            Union[pd.DataFrame, ddf.DataFrame]: Timed Dataframe object.
+            pd.DataFrame | ddf.DataFrame: Timed Dataframe object.
         """
         return self._timed_dataframe
 
     @timed_dataframe.setter
-    def timed_dataframe(self, timed_dataframe: Union[pd.DataFrame, ddf.DataFrame]):
+    def timed_dataframe(self, timed_dataframe: pd.DataFrame | ddf.DataFrame):
         """Setter for the underlying timed dataframe.
 
         Args:
-            timed_dataframe (Union[pd.DataFrame, ddf.DataFrame]): The timed dataframe object to set
+            timed_dataframe (pd.DataFrame | ddf.DataFrame): The timed dataframe object to set
         """
         if not isinstance(timed_dataframe, (pd.DataFrame, ddf.DataFrame)) or not isinstance(
             timed_dataframe,
@@ -738,20 +736,20 @@ 
Source code for sed.core.processor
 
 
     @property
-    def config(self) -> Dict[Any, Any]:
+    def config(self) -> dict[Any, Any]:
         """Getter attribute for the config dictionary
 
         Returns:
-            Dict: The config dictionary.
+            dict: The config dictionary.
         """
         return self._config
 
     @property
-    def files(self) -> List[str]:
+    def files(self) -> list[str]:
         """Getter attribute for the list of files
 
         Returns:
-            List[str]: The list of loaded files
+            list[str]: The list of loaded files
         """
         return self._files
 
@@ -784,7 +782,7 @@ 
Source code for sed.core.processor
         """Getter attribute for the normalization histogram
 
         Returns:
-            xr.DataArray: The normalization histogram
+            xr.DataArray: The normalizazion histogram
         """
         if self._normalization_histogram is None:
             raise ValueError("No normalization histogram available, generate histogram first!")
@@ -792,17 +790,17 @@ 
Source code for sed.core.processor
 
 

 [docs]
-    def cpy(self, path: Union[str, List[str]]) -> Union[str, List[str]]:
+    def cpy(self, path: str | list[str]) -> str | list[str]:
         """Function to mirror a list of files or a folder from a network drive to a
         local storage. Returns either the original or the copied path to the given
         path. The option to use this functionality is set by
         config["core"]["use_copy_tool"].
 
         Args:
-            path (Union[str, List[str]]): Source path or path list.
+            path (str | list[str]): Source path or path list.
 
         Returns:
-            Union[str, List[str]]: Source or destination path or path list.
+            str | list[str]: Source or destination path or path list.
         """
         if self.use_copy_tool:
             if isinstance(path, list):
@@ -823,9 +821,9 @@ 
Source code for sed.core.processor
 [docs]
     def load(
         self,
-        dataframe: Union[pd.DataFrame, ddf.DataFrame] = None,
+        dataframe: pd.DataFrame | ddf.DataFrame = None,
         metadata: dict = None,
-        files: List[str] = None,
+        files: list[str] = None,
         folder: str = None,
         runs: Sequence[str] = None,
         collect_metadata: bool = False,
@@ -834,11 +832,11 @@ 
Source code for sed.core.processor
         """Load tabular data of single events into the dataframe object in the class.
 
         Args:
-            dataframe (Union[pd.DataFrame, ddf.DataFrame], optional): data in tabular
+            dataframe (pd.DataFrame | ddf.DataFrame, optional): data in tabular
                 format. Accepts anything which can be interpreted by pd.DataFrame as
                 an input. Defaults to None.
             metadata (dict, optional): Dict of external Metadata. Defaults to None.
-            files (List[str], optional): List of file paths to pass to the loader.
+            files (list[str], optional): List of file paths to pass to the loader.
                 Defaults to None.
             runs (Sequence[str], optional): List of run identifiers to pass to the
                 loader. Defaults to None.
@@ -883,7 +881,7 @@ 
Source code for sed.core.processor
             )
         elif files is not None:
             dataframe, timed_dataframe, metadata = self.loader.read_dataframe(
-                files=cast(List[str], self.cpy(files)),
+                files=cast(list[str], self.cpy(files)),
                 metadata=metadata,
                 collect_metadata=collect_metadata,
                 **kwds,
@@ -954,10 +952,10 @@ 
Source code for sed.core.processor
 [docs]
     def bin_and_load_momentum_calibration(
         self,
-        df_partitions: Union[int, Sequence[int]] = 100,
-        axes: List[str] = None,
-        bins: List[int] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        df_partitions: int | Sequence[int] = 100,
+        axes: list[str] = None,
+        bins: list[int] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         plane: int = 0,
         width: int = 5,
         apply: bool = False,
@@ -968,13 +966,13 @@ 
Source code for sed.core.processor
         interactive view, and load it into the momentum corrector class.
 
         Args:
-            df_partitions (Union[int, Sequence[int]], optional): Number of dataframe partitions
+            df_partitions (int | Sequence[int], optional): Number of dataframe partitions
                 to use for the initial binning. Defaults to 100.
-            axes (List[str], optional): Axes to bin.
+            axes (list[str], optional): Axes to bin.
                 Defaults to config["momentum"]["axes"].
-            bins (List[int], optional): Bin numbers to use for binning.
+            bins (list[int], optional): Bin numbers to use for binning.
                 Defaults to config["momentum"]["bins"].
-            ranges (List[Tuple], optional): Ranges to use for binning.
+            ranges (Sequence[tuple[float, float]], optional): Ranges to use for binning.
                 Defaults to config["momentum"]["ranges"].
             plane (int, optional): Initial value for the plane slider. Defaults to 0.
             width (int, optional): Initial value for the width slider. Defaults to 5.
@@ -1009,7 +1007,7 @@ 
Source code for sed.core.processor
     ):
         """2. Step of the distortion correction workflow: Define feature points in
         momentum space. They can be either manually selected using a GUI tool, be
-        provided as list of feature points, or auto-generated using a
+        ptovided as list of feature points, or auto-generated using a
         feature-detection algorithm.
 
         Args:
@@ -1061,7 +1059,7 @@ 
Source code for sed.core.processor
         **kwds,
     ):
         """3. Step of the distortion correction workflow: Generate the correction
-        function restoring the symmetry in the image using a splinewarp algorithm.
+        function restoring the symmetry in the image using a splinewarp algortihm.
 
         Args:
             use_center (bool, optional): Option to use the position of the
@@ -1150,7 +1148,7 @@ 
Source code for sed.core.processor
 [docs]
     def pose_adjustment(
         self,
-        transformations: Dict[str, Any] = None,
+        transformations: dict[str, Any] = None,
         apply: bool = False,
         use_correction: bool = True,
         reset: bool = True,
@@ -1163,8 +1161,8 @@ 
Source code for sed.core.processor
         the image.
 
         Args:
-            transformations (dict, optional): Dictionary with transformations.
-                Defaults to self.transformations or config["momentum"]["transformations"].
+            transformations (dict[str, Any], optional): Dictionary with transformations.
+                Defaults to self.transformations or config["momentum"]["transformtions"].
             apply (bool, optional): Option to directly apply the provided
                 transformations. Defaults to False.
             use_correction (bool, option): Whether to use the spline warp correction
@@ -1183,7 +1181,7 @@ 
Source code for sed.core.processor
         if verbose is None:
             verbose = self.verbose
 
-        # Generate homography as default if no distortion correction has been applied
+        # Generate homomorphy as default if no distortion correction has been applied
         if self.mc.slice_corrected is None:
             if self.mc.slice is None:
                 self.mc.slice = np.zeros(self._config["momentum"]["bins"][0:2])
@@ -1318,11 +1316,11 @@ 
Source code for sed.core.processor
 [docs]
     def calibrate_momentum_axes(
         self,
-        point_a: Union[np.ndarray, List[int]] = None,
-        point_b: Union[np.ndarray, List[int]] = None,
+        point_a: np.ndarray | list[int] = None,
+        point_b: np.ndarray | list[int] = None,
         k_distance: float = None,
-        k_coord_a: Union[np.ndarray, List[float]] = None,
-        k_coord_b: Union[np.ndarray, List[float]] = np.array([0.0, 0.0]),
+        k_coord_a: np.ndarray | list[float] = None,
+        k_coord_b: np.ndarray | list[float] = np.array([0.0, 0.0]),
         equiscale: bool = True,
         apply=False,
     ):
@@ -1333,18 +1331,18 @@ 
Source code for sed.core.processor
         the points.
 
         Args:
-            point_a (Union[np.ndarray, List[int]]): Pixel coordinates of the first
+            point_a (np.ndarray | list[int], optional): Pixel coordinates of the first
                 point used for momentum calibration.
-            point_b (Union[np.ndarray, List[int]], optional): Pixel coordinates of the
+            point_b (np.ndarray | list[int], optional): Pixel coordinates of the
                 second point used for momentum calibration.
                 Defaults to config["momentum"]["center_pixel"].
             k_distance (float, optional): Momentum distance between point a and b.
-                Needs to be provided if no specific k-coordinates for the two points
+                Needs to be provided if no specific k-koordinates for the two points
                 are given. Defaults to None.
-            k_coord_a (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_a (np.ndarray | list[float], optional): Momentum coordinate
                 of the first point used for calibration. Used if equiscale is False.
                 Defaults to None.
-            k_coord_b (Union[np.ndarray, List[float]], optional): Momentum coordinate
+            k_coord_b (np.ndarray | list[float], optional): Momentum coordinate
                 of the second point used for calibration. Defaults to [0.0, 0.0].
             equiscale (bool, optional): Option to apply different scales to kx and ky.
                 If True, the distance between points a and b, and the absolute
@@ -1481,11 +1479,11 @@ 
Source code for sed.core.processor
         self,
         correction_type: str = None,
         amplitude: float = None,
-        center: Tuple[float, float] = None,
+        center: tuple[float, float] = None,
         apply=False,
         **kwds,
     ):
-        """1. step of the energy correction workflow: Opens an interactive plot to
+        """1. step of the energy crrection workflow: Opens an interactive plot to
         adjust the parameters for the TOF/energy correction. Also pre-bins the data if
         they are not present yet.
 
@@ -1501,7 +1499,7 @@ 
Source code for sed.core.processor
                 Defaults to config["energy"]["correction_type"].
             amplitude (float, optional): Amplitude of the correction.
                 Defaults to config["energy"]["correction"]["amplitude"].
-            center (Tuple[float, float], optional): Center X/Y coordinates for the
+            center (tuple[float, float], optional): Center X/Y coordinates for the
                 correction. Defaults to config["energy"]["correction"]["center"].
             apply (bool, optional): Option to directly apply the provided or default
                 correction parameters. Defaults to False.
@@ -1570,7 +1568,7 @@ 
Source code for sed.core.processor
         verbose: bool = None,
         **kwds,
     ):
-        """2. step of the energy correction workflow: Apply the energy correction
+        """2. step of the energy correction workflow: Apply the enery correction
         parameters stored in the class to the dataframe.
 
         Args:
@@ -1628,11 +1626,11 @@ 
Source code for sed.core.processor
 [docs]
     def load_bias_series(
         self,
-        binned_data: Union[xr.DataArray, Tuple[np.ndarray, np.ndarray, np.ndarray]] = None,
-        data_files: List[str] = None,
-        axes: List[str] = None,
-        bins: List = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        binned_data: xr.DataArray | tuple[np.ndarray, np.ndarray, np.ndarray] = None,
+        data_files: list[str] = None,
+        axes: list[str] = None,
+        bins: list = None,
+        ranges: Sequence[tuple[float, float]] = None,
         biases: np.ndarray = None,
         bias_key: str = None,
         normalize: bool = None,
@@ -1643,16 +1641,16 @@ 
Source code for sed.core.processor
         single-event files, or load binned bias/TOF traces.
 
         Args:
-            binned_data (Union[xr.DataArray, Tuple[np.ndarray, np.ndarray, np.ndarray]], optional):
+            binned_data (xr.DataArray | tuple[np.ndarray, np.ndarray, np.ndarray], optional):
                 Binned data If provided as DataArray, Needs to contain dimensions
                 config["dataframe"]["tof_column"] and config["dataframe"]["bias_column"]. If
                 provided as tuple, needs to contain elements tof, biases, traces.
-            data_files (List[str], optional): list of file paths to bin
-            axes (List[str], optional): bin axes.
+            data_files (list[str], optional): list of file paths to bin
+            axes (list[str], optional): bin axes.
                 Defaults to config["dataframe"]["tof_column"].
-            bins (List, optional): number of bins.
+            bins (list, optional): number of bins.
                 Defaults to config["energy"]["bins"].
-            ranges (Sequence[Tuple[float, float]], optional): bin ranges.
+            ranges (Sequence[tuple[float, float]], optional): bin ranges.
                 Defaults to config["energy"]["ranges"].
             biases (np.ndarray, optional): Bias voltages used. If missing, bias
                 voltages are extracted from the data files.
@@ -1693,7 +1691,7 @@ 
Source code for sed.core.processor
 
         elif data_files is not None:
             self.ec.bin_data(
-                data_files=cast(List[str], self.cpy(data_files)),
+                data_files=cast(list[str], self.cpy(data_files)),
                 axes=axes,
                 bins=bins,
                 ranges=ranges,
@@ -1724,7 +1722,7 @@ 
Source code for sed.core.processor
 [docs]
     def find_bias_peaks(
         self,
-        ranges: Union[List[Tuple], Tuple],
+        ranges: list[tuple] | tuple,
         ref_id: int = 0,
         infer_others: bool = True,
         mode: str = "replace",
@@ -1740,7 +1738,7 @@ 
Source code for sed.core.processor
         Alternatively, a list of ranges for all traces can be provided.
 
         Args:
-            ranges (Union[List[Tuple], Tuple]): Tuple of TOF values indicating a range.
+            ranges (list[tuple] | tuple): Tuple of TOF values indicating a range.
                 Alternatively, a list of ranges for all traces can be given.
             ref_id (int, optional): The id of the trace the range refers to.
                 Defaults to 0.
@@ -1751,7 +1749,7 @@ 
Source code for sed.core.processor
             radius (int, optional): Radius parameter for fast_dtw.
                 Defaults to config["energy"]["fastdtw_radius"].
             peak_window (int, optional): Peak_window parameter for the peak detection
-                algorithm. amount of points that have to have to behave monotonously
+                algorthm. amount of points that have to have to behave monotoneously
                 around a peak. Defaults to config["energy"]["peak_window"].
             apply (bool, optional): Option to directly apply the provided parameters.
                 Defaults to False.
@@ -2000,10 +1998,10 @@ 
Source code for sed.core.processor
     def add_energy_offset(
         self,
         constant: float = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = None,
-        reductions: Union[str, Sequence[str]] = None,
-        preserve_mean: Union[bool, Sequence[bool]] = None,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = None,
+        reductions: str | Sequence[str] = None,
+        preserve_mean: bool | Sequence[bool] = None,
         preview: bool = False,
         verbose: bool = None,
     ) -> None:
@@ -2011,15 +2009,16 @@ 
Source code for sed.core.processor
 
         Args:
             constant (float, optional): The constant to shift the energy axis by.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[float, Sequence[float]]): weights to apply to the columns.
+            columns (str | Sequence[str], optional): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float], optional): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            reductions (str | Sequence[str], optional): The reduction to apply to the column.
+                Should be an available method of dask.dataframe.Series. For example "mean". In this
+                case the function is applied to the column to generate a single value for the whole
+                dataset. If None, the shift is applied per-dataframe-row. Defaults to None.
+                Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool], optional): Whether to subtract the mean of the
+                column before applying the shift. Defaults to False.
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
             verbose (bool, optional): Option to print out diagnostic information.
@@ -2229,7 +2228,7 @@ 
Source code for sed.core.processor
 [docs]
     def calibrate_delay_axis(
         self,
-        delay_range: Tuple[float, float] = None,
+        delay_range: tuple[float, float] = None,
         datafile: str = None,
         preview: bool = False,
         verbose: bool = None,
@@ -2239,7 +2238,7 @@ 
Source code for sed.core.processor
         them from a file.
 
         Args:
-            delay_range (Tuple[float, float], optional): The scanned delay range in
+            delay_range (tuple[float, float], optional): The scanned delay range in
                 picoseconds. Defaults to None.
             datafile (str, optional): The file from which to read the delay ranges.
                 Defaults to None.
@@ -2351,10 +2350,10 @@ 
Source code for sed.core.processor
         self,
         constant: float = None,
         flip_delay_axis: bool = None,
-        columns: Union[str, Sequence[str]] = None,
-        weights: Union[float, Sequence[float]] = 1.0,
-        reductions: Union[str, Sequence[str]] = None,
-        preserve_mean: Union[bool, Sequence[bool]] = False,
+        columns: str | Sequence[str] = None,
+        weights: float | Sequence[float] = 1.0,
+        reductions: str | Sequence[str] = None,
+        preserve_mean: bool | Sequence[bool] = False,
         preview: bool = False,
         verbose: bool = None,
     ) -> None:
@@ -2363,15 +2362,16 @@ 
Source code for sed.core.processor
         Args:
             constant (float, optional): The constant to shift the delay axis by.
             flip_delay_axis (bool, optional): Option to reverse the direction of the delay axis.
-            columns (Union[str, Sequence[str]]): Name of the column(s) to apply the shift from.
-            weights (Union[float, Sequence[float]]): weights to apply to the columns.
+            columns (str | Sequence[str], optional): Name of the column(s) to apply the shift from.
+            weights (float | Sequence[float], optional): weights to apply to the columns.
                 Can also be used to flip the sign (e.g. -1). Defaults to 1.
-            preserve_mean (bool): Whether to subtract the mean of the column before applying the
-                shift. Defaults to False.
-            reductions (str): The reduction to apply to the column. Should be an available method
-                of dask.dataframe.Series. For example "mean". In this case the function is applied
-                to the column to generate a single value for the whole dataset. If None, the shift
-                is applied per-dataframe-row. Defaults to None. Currently only "mean" is supported.
+            reductions (str | Sequence[str], optional): The reduction to apply to the column.
+                Should be an available method of dask.dataframe.Series. For example "mean". In this
+                case the function is applied to the column to generate a single value for the whole
+                dataset. If None, the shift is applied per-dataframe-row. Defaults to None.
+                Currently only "mean" is supported.
+            preserve_mean (bool | Sequence[bool], optional): Whether to subtract the mean of the
+                column before applying the shift. Defaults to False.
             preview (bool, optional): Option to preview the first elements of the data frame.
                 Defaults to False.
             verbose (bool, optional): Option to print out diagnostic information.
@@ -2498,16 +2498,16 @@ 
Source code for sed.core.processor
 [docs]
     def add_jitter(
         self,
-        cols: List[str] = None,
-        amps: Union[float, Sequence[float]] = None,
+        cols: list[str] = None,
+        amps: float | Sequence[float] = None,
         **kwds,
     ):
         """Add jitter to the selected dataframe columns.
 
         Args:
-            cols (List[str], optional): The columns onto which to apply jitter.
+            cols (list[str], optional): The colums onto which to apply jitter.
                 Defaults to config["dataframe"]["jitter_cols"].
-            amps (Union[float, Sequence[float]], optional): Amplitude scalings for the
+            amps (float | Sequence[float], optional): Amplitude scalings for the
                 jittering noise. If one number is given, the same is used for all axes.
                 For uniform noise (default) it will cover the interval [-amp, +amp].
                 Defaults to config["dataframe"]["jitter_amps"].
@@ -2613,7 +2613,7 @@ 
Source code for sed.core.processor
                     time_stamp_column=time_stamp_column,
                     **kwds,
                 )
-        metadata: List[Any] = []
+        metadata: list[Any] = []
         metadata.append(dest_column)
         metadata.append(time_stamps)
         metadata.append(data)
@@ -2624,22 +2624,22 @@ 
Source code for sed.core.processor
 [docs]
     def pre_binning(
         self,
-        df_partitions: Union[int, Sequence[int]] = 100,
-        axes: List[str] = None,
-        bins: List[int] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        df_partitions: int | Sequence[int] = 100,
+        axes: list[str] = None,
+        bins: list[int] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         **kwds,
     ) -> xr.DataArray:
         """Function to do an initial binning of the dataframe loaded to the class.
 
         Args:
-            df_partitions (Union[int, Sequence[int]], optional): Number of dataframe partitions to
+            df_partitions (int | Sequence[int], optional): Number of dataframe partitions to
                 use for the initial binning. Defaults to 100.
-            axes (List[str], optional): Axes to bin.
+            axes (list[str], optional): Axes to bin.
                 Defaults to config["momentum"]["axes"].
-            bins (List[int], optional): Bin numbers to use for binning.
+            bins (list[int], optional): Bin numbers to use for binning.
                 Defaults to config["momentum"]["bins"].
-            ranges (List[Tuple], optional): Ranges to use for binning.
+            ranges (Sequence[tuple[float, float]], optional): Ranges to use for binning.
                 Defaults to config["momentum"]["ranges"].
             **kwds: Keyword argument passed to ``compute``.
 
@@ -2659,7 +2659,7 @@ 
Source code for sed.core.processor
             ranges_[2] = np.asarray(ranges_[2]) / 2 ** (
                 self._config["dataframe"]["tof_binning"] - 1
             )
-            ranges = [cast(Tuple[float, float], tuple(v)) for v in ranges_]
+            ranges = [cast(tuple[float, float], tuple(v)) for v in ranges_]
 
         assert self._dataframe is not None, "dataframe needs to be loaded first!"
 
@@ -2676,23 +2676,16 @@ 
Source code for sed.core.processor
 [docs]
     def compute(
         self,
-        bins: Union[
-            int,
-            dict,
-            tuple,
-            List[int],
-            List[np.ndarray],
-            List[tuple],
-        ] = 100,
-        axes: Union[str, Sequence[str]] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
-        normalize_to_acquisition_time: Union[bool, str] = False,
+        bins: int | dict | tuple | list[int] | list[np.ndarray] | list[tuple] = 100,
+        axes: str | Sequence[str] = None,
+        ranges: Sequence[tuple[float, float]] = None,
+        normalize_to_acquisition_time: bool | str = False,
         **kwds,
     ) -> xr.DataArray:
         """Compute the histogram along the given dimensions.
 
         Args:
-            bins (int, dict, tuple, List[int], List[np.ndarray], List[tuple], optional):
+            bins (int | dict | tuple | list[int] | list[np.ndarray] | list[tuple], optional):
                 Definition of the bins. Can be any of the following cases:
 
                 - an integer describing the number of bins in on all dimensions
@@ -2703,13 +2696,13 @@ 
Source code for sed.core.processor
                 - a dictionary made of the axes as keys and any of the above as values.
 
                 This takes priority over the axes and range arguments. Defaults to 100.
-            axes (Union[str, Sequence[str]], optional): The names of the axes (columns)
+            axes (str | Sequence[str], optional): The names of the axes (columns)
                 on which to calculate the histogram. The order will be the order of the
                 dimensions in the resulting array. Defaults to None.
-            ranges (Sequence[Tuple[float, float]], optional): list of tuples containing
+            ranges (Sequence[tuple[float, float]], optional): list of tuples containing
                 the start and end point of the binning range. Defaults to None.
-            normalize_to_acquisition_time (Union[bool, str]): Option to normalize the
-                result to the acquisition time. If a "slow" axis was scanned, providing
+            normalize_to_acquisition_time (bool | str): Option to normalize the
+                result to the acquistion time. If a "slow" axis was scanned, providing
                 the name of the scanned axis will compute and apply the corresponding
                 normalization histogram. Defaults to False.
             **kwds: Keyword arguments:
@@ -2759,7 +2752,7 @@ 
Source code for sed.core.processor
             "threadpool_API",
             self._config["binning"]["threadpool_API"],
         )
-        df_partitions: Union[int, Sequence[int]] = kwds.pop("df_partitions", None)
+        df_partitions: int | Sequence[int] = kwds.pop("df_partitions", None)
         if isinstance(df_partitions, int):
             df_partitions = list(range(0, min(df_partitions, self._dataframe.npartitions)))
         if df_partitions is not None:
@@ -2884,7 +2877,7 @@ 
Source code for sed.core.processor
         if axis not in self._binned.coords:
             raise ValueError(f"Axis '{axis}' not found in binned data!")
 
-        df_partitions: Union[int, Sequence[int]] = kwds.pop("df_partitions", None)
+        df_partitions: int | Sequence[int] = kwds.pop("df_partitions", None)
         if isinstance(df_partitions, int):
             df_partitions = list(range(0, min(df_partitions, self._dataframe.npartitions)))
         if use_time_stamps or self._timed_dataframe is None:
@@ -2929,7 +2922,7 @@ 
Source code for sed.core.processor
         ncol: int = 2,
         bins: Sequence[int] = None,
         axes: Sequence[str] = None,
-        ranges: Sequence[Tuple[float, float]] = None,
+        ranges: Sequence[tuple[float, float]] = None,
         backend: str = "bokeh",
         legend: bool = True,
         histkwds: dict = None,
@@ -2942,12 +2935,12 @@ 
Source code for sed.core.processor
         Args:
             dfpid (int): Number of the data frame partition to look at.
             ncol (int, optional): Number of columns in the plot grid. Defaults to 2.
-            bins (Sequence[int], optional): Number of bins to use for the specified
+            bins (Sequence[int], optional): Number of bins to use for the speicified
                 axes. Defaults to config["histogram"]["bins"].
             axes (Sequence[str], optional): Names of the axes to display.
                 Defaults to config["histogram"]["axes"].
-            ranges (Sequence[Tuple[float, float]], optional): Value ranges of all
-                specified axes. Defaults to config["histogram"]["ranges"].
+            ranges (Sequence[tuple[float, float]], optional): Value ranges of all
+                specified axes. Defaults toconfig["histogram"]["ranges"].
             backend (str, optional): Backend of the plotting library
                 ('matplotlib' or 'bokeh'). Defaults to "bokeh".
             legend (bool, optional): Option to include a legend in the histogram plots.
@@ -3032,7 +3025,7 @@ 
Source code for sed.core.processor
                 - "*.h5", "*.hdf5": Saves an HDF5 file.
                 - "*.nxs", "*.nexus": Saves a NeXus file.
 
-            **kwds: Keyword arguments, which are passed to the writer functions:
+            **kwds: Keyword argumens, which are passed to the writer functions:
                 For TIFF writing:
 
                 - **alias_dict**: Dictionary of dimension aliases to use.
@@ -3043,9 +3036,9 @@ 
Source code for sed.core.processor
 
                 For NeXus:
 
-                - **reader**: Name of the pynxtools reader to use.
+                - **reader**: Name of the nexustools reader to use.
                   Defaults to config["nexus"]["reader"]
-                - **definition**: NeXus application definition to use for saving.
+                - **definiton**: NeXus application definition to use for saving.
                   Must be supported by the used ``reader``. Defaults to
                   config["nexus"]["definition"]
                 - **input_files**: A list of input files to pass to the reader.
diff --git a/sed/latest/_modules/sed/dataset/dataset.html b/sed/latest/_modules/sed/dataset/dataset.html
index 5f4d38d..b3c4d33 100644
--- a/sed/latest/_modules/sed/dataset/dataset.html
+++ b/sed/latest/_modules/sed/dataset/dataset.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.dataset.dataset — SED 0.1.10a6 documentation
+    sed.dataset.dataset — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
diff --git a/sed/latest/_modules/sed/diagnostics.html b/sed/latest/_modules/sed/diagnostics.html
index f9dbbaa..5f7dfa5 100644
--- a/sed/latest/_modules/sed/diagnostics.html
+++ b/sed/latest/_modules/sed/diagnostics.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.diagnostics — SED 0.1.10a6 documentation
+    sed.diagnostics — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,8 +449,9 @@ 
Source code for sed.diagnostics
 """This module contains diagnostic output functions for the sed module
 
 """
-from typing import Sequence
-from typing import Tuple
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 import bokeh.plotting as pbk
 import matplotlib.pyplot as plt
@@ -507,7 +508,7 @@ 
Source code for sed.diagnostics
     ncol: int,
     rvs: Sequence,
     rvbins: Sequence,
-    rvranges: Sequence[Tuple[float, float]],
+    rvranges: Sequence[tuple[float, float]],
     backend: str = "bokeh",
     legend: bool = True,
     histkwds: dict = None,
@@ -521,7 +522,7 @@ 
Source code for sed.diagnostics
         ncol (int): Number of columns in the plot grid.
         rvs (Sequence): List of names for the random variables (rvs).
         rvbins (Sequence): Bin values for all random variables.
-        rvranges (Sequence[Tuple[float, float]]): Value ranges of all random variables.
+        rvranges (Sequence[tuple[float, float]]): Value ranges of all random variables.
         backend (str, optional): Backend for making the plot ('matplotlib' or 'bokeh').
             Defaults to "bokeh".
         legend (bool, optional): Option to include a legend in each histogram plot.
diff --git a/sed/latest/_modules/sed/io/hdf5.html b/sed/latest/_modules/sed/io/hdf5.html
index 2ea1d2a..29c0429 100644
--- a/sed/latest/_modules/sed/io/hdf5.html
+++ b/sed/latest/_modules/sed/io/hdf5.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.io.hdf5 — SED 0.1.10a6 documentation
+    sed.io.hdf5 — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,7 +449,7 @@ 
Source code for sed.io.hdf5
 """This module contains hdf5 file input/output functions for the sed.io module
 
 """
-from typing import Union
+from __future__ import annotations
 
 import h5py
 import numpy as np
@@ -496,17 +496,17 @@ 
Source code for sed.io.hdf5
                 print(f"Saved {key} as string.")
             except BaseException as exc:
                 raise ValueError(
-                    f"Unknown error occurred, cannot save {item} of type {type(item)}.",
+                    f"Unknown error occured, cannot save {item} of type {type(item)}.",
                 ) from exc
 
 
 def recursive_parse_metadata(
-    node: Union[h5py.Group, h5py.Dataset],
+    node: h5py.Group | h5py.Dataset,
 ) -> dict:
     """Recurses through an hdf5 file, and parse it into a dictionary.
 
     Args:
-        node (Union[h5py.Group, h5py.Dataset]): hdf5 group or dataset to parse into
+        node (h5py.Group | h5py.Dataset): hdf5 group or dataset to parse into
             dictionary.
 
     Returns:
@@ -594,7 +594,7 @@ 
Source code for sed.io.hdf5
         ValueError: Raised if data or axes are not found in the file.
 
     Returns:
-        xr.DataArray: output xarray data
+        xr.DataArray: output xarra data
     """
     with h5py.File(faddr, mode) as h5_file:
         # Reading data array
diff --git a/sed/latest/_modules/sed/io/nexus.html b/sed/latest/_modules/sed/io/nexus.html
index 04c486f..748cd67 100644
--- a/sed/latest/_modules/sed/io/nexus.html
+++ b/sed/latest/_modules/sed/io/nexus.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.io.nexus — SED 0.1.10a6 documentation
+    sed.io.nexus — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -447,12 +447,13 @@
                   
   
Source code for sed.io.nexus
 """This module contains NuXus file input/output functions for the sed.io module.
-The conversion is based on the pynxtools from the FAIRmat NFDI consortium.
+The conversion is based on the nexusutils from the FAIRmat NFDI consortium.
 For details, see https://github.com/nomad-coe/nomad-parser-nexus
 
 """
-from typing import Sequence
-from typing import Union
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 import xarray as xr
 from pynxtools.dataconverter.convert import convert
@@ -465,7 +466,7 @@ 
Source code for sed.io.nexus
     faddr: str,
     reader: str,
     definition: str,
-    input_files: Union[str, Sequence[str]],
+    input_files: str | Sequence[str],
     **kwds,
 ):
     """Saves the x-array provided to a NeXus file at faddr, using the provided reader,
@@ -476,10 +477,9 @@ 
Source code for sed.io.nexus
             data._attrs["metadata"].
         faddr (str): The file path to save to.
         reader (str): The name of the NeXus reader to use.
-        definition (str): The NeXus definition to use.
-        input_files (Union[str, Sequence[str]]): The file path or paths to the additional files to
-            use.
-        **kwds: Keyword arguments for ``pynxtools.dataconverter.convert.convert()``.
+        definition (str): The NeXus definiton to use.
+        input_files (str | Sequence[str]): The file path or paths to the additional files to use.
+        **kwds: Keyword arguments for ``nexusutils.dataconverter.convert``.
     """
 
     if isinstance(input_files, str):
diff --git a/sed/latest/_modules/sed/io/tiff.html b/sed/latest/_modules/sed/io/tiff.html
index cdd8d44..b0f7d32 100644
--- a/sed/latest/_modules/sed/io/tiff.html
+++ b/sed/latest/_modules/sed/io/tiff.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.io.tiff — SED 0.1.10a6 documentation
+    sed.io.tiff — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,9 +449,10 @@ 
Source code for sed.io.tiff
 """This module contains tiff file input/output functions for the sed.io module
 
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from pathlib import Path
-from typing import Sequence
-from typing import Union
 
 import numpy as np
 import tifffile
@@ -487,20 +488,20 @@ 
Source code for sed.io.tiff
 

 [docs]
 def to_tiff(
-    data: Union[xr.DataArray, np.ndarray],
-    faddr: Union[Path, str],
+    data: xr.DataArray | np.ndarray,
+    faddr: Path | str,
     alias_dict: dict = None,
 ):
     """Save an array as a .tiff stack compatible with ImageJ
 
     Args:
-        data (Union[xr.DataArray, np.ndarray]): data to be saved. If a np.ndarray,
+        data (xr.DataArray | np.ndarray): data to be saved. If a np.ndarray,
             the order is retained. If it is an xarray.DataArray, the order is inferred
             from axis_dict instead. ImageJ likes tiff files with axis order as
             TZCYXS. Therefore, best axis order in input should be: Time, Energy,
             posY, posX. The channels 'C' and 'S' are automatically added and can
             be ignored.
-        faddr (Union[Path, str]): full path and name of file to save.
+        faddr Path | str): full path and name of file to save.
         alias_dict (dict, optional): name pairs for correct axis ordering. Keys should
             be any of T,Z,C,Y,X,S. The Corresponding value should be a dimension of the
             xarray or the dimension number if a numpy array. This is used to sort the
@@ -513,7 +514,7 @@ 
Source code for sed.io.tiff
         NotImplementedError: if data is not 2,3 or 4 dimensional
         TypeError: if data is not a np.ndarray or an xarray.DataArray
     """
-    out: Union[np.ndarray, xr.DataArray] = None
+    out: np.ndarray | xr.DataArray = None
     if isinstance(data, np.ndarray):
         # TODO: add sorting by dictionary keys
         dim_expansions = {2: [0, 1, 2, 5], 3: [0, 2, 5], 4: [2, 5]}
@@ -625,7 +626,7 @@ 
Source code for sed.io.tiff
 

 [docs]
 def load_tiff(
-    faddr: Union[str, Path],
+    faddr: str | Path,
     coords: dict = None,
     dims: Sequence = None,
     attrs: dict = None,
@@ -637,7 +638,7 @@ 
Source code for sed.io.tiff
     only as np.ndarray.
 
     Args:
-        faddr (Union[str, Path]): Path to file to load.
+        faddr (str | Path): Path to file to load.
         coords (dict, optional): The axes describing the data, following the tiff
             stack order. Defaults to None.
         dims (Sequence, optional): the order of the coordinates provided, considering
diff --git a/sed/latest/_modules/sed/loader/base/loader.html b/sed/latest/_modules/sed/loader/base/loader.html
index 0ad4d30..06e2d9c 100644
--- a/sed/latest/_modules/sed/loader/base/loader.html
+++ b/sed/latest/_modules/sed/loader/base/loader.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.base.loader — SED 0.1.10a6 documentation
+    sed.loader.base.loader — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -446,17 +446,16 @@
                 

                   
   
Source code for sed.loader.base.loader
-"""The abstract class off of which to implement loaders."""
+"""The abstract class off of which to implement loaders.
+"""
+from __future__ import annotations
+
 import os
 from abc import ABC
 from abc import abstractmethod
+from collections.abc import Sequence
 from copy import deepcopy
 from typing import Any
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as ddf
 import numpy as np
@@ -482,7 +481,7 @@ 
Source code for sed.loader.base.loader
 
     __name__ = "BaseLoader"
 
-    supported_file_types: List[str] = []
+    supported_file_types: list[str] = []
 
     def __init__(
         self,
@@ -490,35 +489,35 @@ 
Source code for sed.loader.base.loader
     ):
         self._config = config if config is not None else {}
 
-        self.files: List[str] = []
-        self.runs: List[str] = []
-        self.metadata: Dict[Any, Any] = {}
+        self.files: list[str] = []
+        self.runs: list[str] = []
+        self.metadata: dict[Any, Any] = {}
 
 

 [docs]
     @abstractmethod
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = None,
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[ddf.DataFrame, ddf.DataFrame, dict]:
+    ) -> tuple[ddf.DataFrame, ddf.DataFrame, dict]:
         """Reads data from given files, folder, or runs and returns a dask dataframe
         and corresponding metadata.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (str | Sequence[str], optional): File path(s) to process.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
+            folders (str | Sequence[str], optional): Path to folder(s) where files
                 are stored. Path has priority such that if it's specified, the specified
                 files will be ignored. Defaults to None.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding
                 files will be located in the location provided by ``folders``. Takes
-                precedence over ``files`` and ``folders``. Defaults to None.
+                precendence over ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): File type to read ('parquet', 'json', 'csv', etc).
                 If a folder path is given, all files with the specified extension are
                 read into the dataframe in the reading order. Defaults to None.
@@ -529,7 +528,7 @@ 
Source code for sed.loader.base.loader
             **kwds: keyword arguments. See description in respective loader.
 
         Returns:
-            Tuple[ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
+            tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
             read from specified files.
         """
 
@@ -584,21 +583,21 @@ 
Source code for sed.loader.base.loader
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = None,
         **kwds,
-    ) -> List[str]:
+    ) -> list[str]:
         """Locate the files for a given run identifier.
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to None.
             extension (str, optional): The file extension. Defaults to None.
             kwds: Keyword arguments
 
         Return:
-            List[str]: List of files for the given run.
+            list[str]: List of files for the given run.
         """
         raise NotImplementedError

 
@@ -610,7 +609,7 @@ 
Source code for sed.loader.base.loader
         self,
         fids: Sequence[int] = None,
         **kwds,
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Create count rate data for the files specified in ``fids``.
 
         Args:
@@ -619,7 +618,7 @@ 
Source code for sed.loader.base.loader
             kwds: Keyword arguments
 
         Return:
-            Tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
+            tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
             into the scan.
         """
         return None, None

diff --git a/sed/latest/_modules/sed/loader/flash/loader.html b/sed/latest/_modules/sed/loader/flash/loader.html
index e3c9b21..191fd5d 100644
--- a/sed/latest/_modules/sed/loader/flash/loader.html
+++ b/sed/latest/_modules/sed/loader/flash/loader.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.flash.loader — SED 0.1.10a6 documentation
+    sed.loader.flash.loader — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -453,15 +453,14 @@ 
Source code for sed.loader.flash.loader
 The dataframe is a amalgamation of all h5 files for a combination of runs, where the NaNs are
 automatically forward filled across different files.
 This can then be saved as a parquet for out-of-sed processing and reread back to access other
-sed functionality.
+sed funtionality.
 """
+from __future__ import annotations
+
 import time
+from collections.abc import Sequence
 from functools import reduce
 from pathlib import Path
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as dd
 import h5py
@@ -499,16 +498,16 @@ 
Source code for sed.loader.flash.loader
         self.multi_index = ["trainId", "pulseId", "electronId"]
         self.index_per_electron: MultiIndex = None
         self.index_per_pulse: MultiIndex = None
-        self.failed_files_error: List[str] = []
+        self.failed_files_error: list[str] = []
 
 

 [docs]
-    def initialize_paths(self) -> Tuple[List[Path], Path]:
+    def initialize_paths(self) -> tuple[list[Path], Path]:
         """
         Initializes the paths based on the configuration.
 
         Returns:
-            Tuple[List[Path], Path]: A tuple containing a list of raw data directories
+            tuple[list[Path], Path]: A tuple containing a list of raw data directories
             paths and the parquet data directory path.
 
         Raises:
@@ -569,23 +568,23 @@ 
Source code for sed.loader.flash.loader
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = "h5",
         **kwds,
-    ) -> List[str]:
+    ) -> list[str]:
         """Returns a list of filenames for a given run located in the specified directory
         for the specified data acquisition (daq).
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to config["core"]["base_folder"].
             extension (str, optional): The file extension. Defaults to "h5".
             kwds: Keyword arguments:
                 - daq (str): The data acquisition identifier.
 
         Returns:
-            List[str]: A list of path strings representing the collected file names.
+            list[str]: A list of path strings representing the collected file names.
 
         Raises:
             FileNotFoundError: If no files are found for the given run in the directory.
@@ -604,7 +603,7 @@ 
Source code for sed.loader.flash.loader
         # Generate the file patterns to search for in the directory
         file_pattern = f"{stream_name_prefixes[daq]}_run{run_id}_*." + extension
 
-        files: List[Path] = []
+        files: list[Path] = []
         # Use pathlib to search for matching files in each directory
         for folder in folders:
             files.extend(
@@ -625,7 +624,7 @@ 
Source code for sed.loader.flash.loader
 
 
     @property
-    def available_channels(self) -> List:
+    def available_channels(self) -> list:
         """Returns the channel names that are available for use,
         excluding pulseId, defined by the json file"""
         available_channels = list(self._config["dataframe"]["channels"].keys())
@@ -634,17 +633,17 @@ 
Source code for sed.loader.flash.loader
 
 

 [docs]
-    def get_channels(self, formats: Union[str, List[str]] = "", index: bool = False) -> List[str]:
+    def get_channels(self, formats: str | list[str] = "", index: bool = False) -> list[str]:
         """
         Returns a list of channels associated with the specified format(s).
 
         Args:
-            formats (Union[str, List[str]]): The desired format(s)
-                                ('per_pulse', 'per_electron', 'per_train', 'all').
+            formats (str | list[str]): The desired format(s)
+                ('per_pulse', 'per_electron', 'per_train', 'all').
             index (bool): If True, includes channels from the multi_index.
 
         Returns:
-            List[str]: A list of channels with the specified format(s).
+            list[str]: A list of channels with the specified format(s).
         """
         # If 'formats' is a single string, convert it to a list for uniform processing.
         if isinstance(formats, str):
@@ -781,7 +780,7 @@ 
Source code for sed.loader.flash.loader
         self,
         h5_file: h5py.File,
         channel: str,
-    ) -> Tuple[Series, np.ndarray]:
+    ) -> tuple[Series, np.ndarray]:
         """
         Returns a numpy array for a given channel name for a given file.
 
@@ -790,7 +789,7 @@ 
Source code for sed.loader.flash.loader
             channel (str): The name of the channel.
 
         Returns:
-            Tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
+            tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
             for the channel's data.
 
         """
@@ -878,15 +877,15 @@ 
Source code for sed.loader.flash.loader
             DataFrame: The pandas DataFrame for the channel's data.
 
         Notes:
-            - For auxiliary channels, the macrobunch resolved data is repeated 499 times to be
-              compared to electron resolved data for each auxiliary channel. The data is then
+            - For auxillary channels, the macrobunch resolved data is repeated 499 times to be
+              compared to electron resolved data for each auxillary channel. The data is then
               converted to a multicolumn DataFrame.
             - For all other pulse resolved channels, the macrobunch resolved data is exploded
               to a DataFrame and the MultiIndex is set.
 
         """
 
-        # Special case for auxiliary channels
+        # Special case for auxillary channels
         if channel == "dldAux":
             # Checks the channel dictionary for correct slices and creates a multicolumn DataFrame
             data_frames = (
@@ -949,7 +948,7 @@ 
Source code for sed.loader.flash.loader
         self,
         h5_file: h5py.File,
         channel: str,
-    ) -> Union[Series, DataFrame]:
+    ) -> Series | DataFrame:
         """
         Returns a pandas DataFrame for a given channel name from a given file.
 
@@ -962,7 +961,7 @@ 
Source code for sed.loader.flash.loader
             channel (str): The name of the channel.
 
         Returns:
-            Union[Series, DataFrame]: A pandas Series or DataFrame representing the channel's data.
+            Series | DataFrame: A pandas Series or DataFrame representing the channel's data.
 
         Raises:
             ValueError: If the channel has an undefined format.
@@ -1105,7 +1104,7 @@ 
Source code for sed.loader.flash.loader
 
 

 [docs]
-    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> Union[bool, Exception]:
+    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> bool | Exception:
         """
         Converts an HDF5 file to Parquet format to create a buffer file.
 
@@ -1116,6 +1115,9 @@ 
Source code for sed.loader.flash.loader
             h5_path (Path): Path to the input HDF5 file.
             parquet_path (Path): Path to the output Parquet file.
 
+        Returns:
+            bool | Exception: Collected exceptions, if any.
+
         Raises:
             ValueError: If an error occurs during the conversion process.
 
@@ -1139,7 +1141,7 @@ 
Source code for sed.loader.flash.loader
         data_parquet_dir: Path,
         detector: str,
         force_recreate: bool,
-    ) -> Tuple[List[Path], List, List]:
+    ) -> tuple[list[Path], list, list]:
         """
         Handles the conversion of buffer files (h5 to parquet) and returns the filenames.
 
@@ -1149,7 +1151,7 @@ 
Source code for sed.loader.flash.loader
             force_recreate (bool): Forces recreation of buffer files
 
         Returns:
-            Tuple[List[Path], List, List]: Three lists, one for
+            tuple[list[Path], list, list]: Three lists, one for
             parquet file paths, one for metadata and one for schema.
 
         Raises:
@@ -1247,7 +1249,7 @@ 
Source code for sed.loader.flash.loader
         load_parquet: bool = False,
         save_parquet: bool = False,
         force_recreate: bool = False,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame]:
+    ) -> tuple[dd.DataFrame, dd.DataFrame]:
         """
         Handles loading and saving of parquet files based on the provided parameters.
 
@@ -1262,7 +1264,7 @@ 
Source code for sed.loader.flash.loader
             save_parquet (bool, optional): Saves the entire dataframe into a parquet.
             force_recreate (bool, optional): Forces recreation of buffer file.
         Returns:
-            tuple: A tuple containing two dataframes:
+            tuple[dd.DataFrame, dd.DataFrame]: A tuple containing two dataframes:
             - dataframe_electron: Dataframe containing the loaded/augmented electron data.
             - dataframe_pulse: Dataframe containing the loaded/augmented timed data.
 
@@ -1304,7 +1306,7 @@ 
Source code for sed.loader.flash.loader
             dataframe = dd.read_parquet(filenames, calculate_divisions=True)
 
             # Channels to fill NaN values
-            channels: List[str] = self.get_channels(["per_pulse", "per_train"])
+            channels: list[str] = self.get_channels(["per_pulse", "per_train"])
 
             overlap = min(file.num_rows for file in metadata)
 
@@ -1373,31 +1375,32 @@ 
Source code for sed.loader.flash.loader
 [docs]
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "h5",
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame, dict]:
+    ) -> tuple[dd.DataFrame, dd.DataFrame, dict]:
         """
         Read express data from the DAQ, generating a parquet in between.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process. Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files are stored
+            files (str | Sequence[str], optional): File path(s) to process. Defaults to None.
+            folders (str | Sequence[str], optional): Path to folder(s) where files are stored
                 Path has priority such that if it's specified, the specified files will be ignored.
                 Defaults to None.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding files will
-                be located in the location provided by ``folders``. Takes precedence over
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding files will
+                be located in the location provided by ``folders``. Takes precendence over
                 ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): The file extension type. Defaults to "h5".
             metadata (dict, optional): Additional metadata. Defaults to None.
             collect_metadata (bool, optional): Whether to collect metadata. Defaults to False.
 
         Returns:
-            Tuple[dd.DataFrame, dict]: A tuple containing the concatenated DataFrame and metadata.
+            tuple[dd.DataFrame, dd.DataFrame, dict]: A tuple containing the concatenated DataFrame
+            and metadata.
 
         Raises:
             ValueError: If neither 'runs' nor 'files'/'data_raw_dir' is provided.
diff --git a/sed/latest/_modules/sed/loader/flash/metadata.html b/sed/latest/_modules/sed/loader/flash/metadata.html
index 90b2854..1de53e3 100644
--- a/sed/latest/_modules/sed/loader/flash/metadata.html
+++ b/sed/latest/_modules/sed/loader/flash/metadata.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.flash.metadata — SED 0.1.10a6 documentation
+    sed.loader.flash.metadata — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -450,10 +450,9 @@ 
Source code for sed.loader.flash.metadata
 The module provides a MetadataRetriever class for retrieving metadata
 from a Scicat Instance based on beamtime and run IDs.
 """
+from __future__ import annotations
 
 import warnings
-from typing import Dict
-from typing import Optional
 
 import requests
 
@@ -466,7 +465,7 @@ 
Source code for sed.loader.flash.metadata
     on beamtime and run IDs.
     """
 
-    def __init__(self, metadata_config: Dict, scicat_token: str = None) -> None:
+    def __init__(self, metadata_config: dict, scicat_token: str = None) -> None:
         """
         Initializes the MetadataRetriever class.
 
@@ -495,15 +494,15 @@ 
Source code for sed.loader.flash.metadata
         self,
         beamtime_id: str,
         runs: list,
-        metadata: Optional[Dict] = None,
-    ) -> Dict:
+        metadata: dict = None,
+    ) -> dict:
         """
         Retrieves metadata for a given beamtime ID and list of runs.
 
         Args:
             beamtime_id (str): The ID of the beamtime.
             runs (list): A list of run IDs.
-            metadata (Dict, optional): The existing metadata dictionary.
+            metadata (dict, optional): The existing metadata dictionary.
             Defaults to None.
 
         Returns:
@@ -528,7 +527,7 @@ 
Source code for sed.loader.flash.metadata
         return metadata

 
 
-    def _get_metadata_per_run(self, pid: str) -> Dict:
+    def _get_metadata_per_run(self, pid: str) -> dict:
         """
         Retrieves metadata for a specific run based on the PID.
 
@@ -536,13 +535,13 @@ 
Source code for sed.loader.flash.metadata
             pid (str): The PID of the run.
 
         Returns:
-            Dict: The retrieved metadata.
+            dict: The retrieved metadata.
 
         Raises:
             Exception: If the request to retrieve metadata fails.
         """
         headers2 = dict(self.headers)
-        headers2["Authorization"] = "Bearer {}".format(self.token)
+        headers2["Authorization"] = f"Bearer {self.token}"
 
         try:
             dataset_response = requests.get(
@@ -554,7 +553,9 @@ 
Source code for sed.loader.flash.metadata
             # Check if response is an empty object because wrong url for older implementation
             if not dataset_response.content:
                 dataset_response = requests.get(
-                    self._create_old_dataset_url(pid), headers=headers2, timeout=10
+                    self._create_old_dataset_url(pid),
+                    headers=headers2,
+                    timeout=10,
                 )
             # If the dataset request is successful, return the retrieved metadata
             # as a JSON object
@@ -566,12 +567,16 @@ 
Source code for sed.loader.flash.metadata
 
     def _create_old_dataset_url(self, pid: str) -> str:
         return "{burl}/{url}/%2F{npid}".format(
-            burl=self.url, url="Datasets", npid=self._reformat_pid(pid)
+            burl=self.url,
+            url="Datasets",
+            npid=self._reformat_pid(pid),
         )
 
     def _create_new_dataset_url(self, pid: str) -> str:
         return "{burl}/{url}/{npid}".format(
-            burl=self.url, url="Datasets", npid=self._reformat_pid(pid)
+            burl=self.url,
+            url="Datasets",
+            npid=self._reformat_pid(pid),
         )
 
     def _reformat_pid(self, pid: str) -> str:
diff --git a/sed/latest/_modules/sed/loader/generic/loader.html b/sed/latest/_modules/sed/loader/generic/loader.html
index 3e65594..e3bf9f1 100644
--- a/sed/latest/_modules/sed/loader/generic/loader.html
+++ b/sed/latest/_modules/sed/loader/generic/loader.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.generic.loader — SED 0.1.10a6 documentation
+    sed.loader.generic.loader — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -451,10 +451,9 @@ 
Source code for sed.loader.generic.loader
 Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
+from __future__ import annotations
+
+from collections.abc import Sequence
 
 import dask.dataframe as ddf
 import numpy as np
@@ -481,25 +480,25 @@ 
Source code for sed.loader.generic.loader
 [docs]
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "parquet",
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[ddf.DataFrame, ddf.DataFrame, dict]:
+    ) -> tuple[ddf.DataFrame, ddf.DataFrame, dict]:
         """Read stored files from a folder into a dataframe.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (str | Sequence[str], optional): File path(s) to process.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
+            folders (str | Sequence[str], optional): Path to folder(s) where files
                 are stored. Path has priority such that if it's specified, the specified
                 files will be ignored. Defaults to None.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding
                 files will be located in the location provided by ``folders``. Takes
-                precedence over ``files`` and ``folders``. Defaults to None.
+                precendence over ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): File type to read ('parquet', 'json', 'csv', etc).
                 If a folder path is given, all files with the specified extension are
                 read into the dataframe in the reading order. Defaults to "parquet".
@@ -512,11 +511,11 @@ 
Source code for sed.loader.generic.loader
 
         Raises:
             ValueError: Raised if neither files nor folder provided.
-            FileNotFoundError: Raised if the files or folder cannot be found.
+            FileNotFoundError: Raised if the fileds or folder cannot be found.
             ValueError: Raised if the file type is not supported.
 
         Returns:
-            Tuple[ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
+            tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed dataframe and metadata
             read from specified files.
         """
         # pylint: disable=duplicate-code
@@ -557,21 +556,21 @@ 
Source code for sed.loader.generic.loader
     def get_files_from_run_id(
         self,
         run_id: str,  # noqa: ARG002
-        folders: Union[str, Sequence[str]] = None,  # noqa: ARG002
+        folders: str | Sequence[str] = None,  # noqa: ARG002
         extension: str = None,  # noqa: ARG002
         **kwds,  # noqa: ARG002
-    ) -> List[str]:
+    ) -> list[str]:
         """Locate the files for a given run identifier.
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to None.
             extension (str, optional): The file extension. Defaults to "h5".
             kwds: Keyword arguments
 
         Return:
-            str: Path to the location of run data.
+            list[str]: Path to the location of run data.
         """
         raise NotImplementedError

 
@@ -582,7 +581,7 @@ 
Source code for sed.loader.generic.loader
         self,
         fids: Sequence[int] = None,  # noqa: ARG002
         **kwds,  # noqa: ARG002
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Create count rate data for the files specified in ``fids``.
 
         Args:
@@ -591,7 +590,7 @@ 
Source code for sed.loader.generic.loader
             kwds: Keyword arguments
 
         Return:
-            Tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
+            tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
             into the scan.
         """
         # TODO
diff --git a/sed/latest/_modules/sed/loader/loader_interface.html b/sed/latest/_modules/sed/loader/loader_interface.html
index d768938..da3d4b4 100644
--- a/sed/latest/_modules/sed/loader/loader_interface.html
+++ b/sed/latest/_modules/sed/loader/loader_interface.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.loader_interface — SED 0.1.10a6 documentation
+    sed.loader.loader_interface — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -448,10 +448,11 @@
   
Source code for sed.loader.loader_interface
 """Interface to select a specified loader
 """
+from __future__ import annotations
+
 import glob
 import importlib.util
 import os
-from typing import List
 
 from sed.loader.base.loader import BaseLoader
 
@@ -496,11 +497,11 @@ 
Source code for sed.loader.loader_interface
 
 

 [docs]
-def get_names_of_all_loaders() -> List[str]:
+def get_names_of_all_loaders() -> list[str]:
     """Helper function to populate a list of all available loaders.
 
     Returns:
-        List[str]: List of all detected loader names.
+        list[str]: List of all detected loader names.
     """
     path_prefix = f"{os.path.dirname(__file__)}{os.sep}" if os.path.dirname(__file__) else ""
     files = glob.glob(os.path.join(path_prefix, "*", "loader.py"))
diff --git a/sed/latest/_modules/sed/loader/mirrorutil.html b/sed/latest/_modules/sed/loader/mirrorutil.html
index f6f5d50..026edf1 100644
--- a/sed/latest/_modules/sed/loader/mirrorutil.html
+++ b/sed/latest/_modules/sed/loader/mirrorutil.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.mirrorutil — SED 0.1.10a6 documentation
+    sed.loader.mirrorutil — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -449,15 +449,16 @@ 
Source code for sed.loader.mirrorutil
 """
 module sed.loader.mirrorutil, code for transparently mirroring file system trees to a
 second (local) location. This is speeds up binning of data stored on network drives
-tremendously.
+tremendiously.
 Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
+from __future__ import annotations
+
 import errno
 import os
 import shutil
 from datetime import datetime
-from typing import List
 
 import dask as d
 from dask.diagnostics import ProgressBar
@@ -469,7 +470,7 @@ 
Source code for sed.loader.mirrorutil
     """File collecting and sorting class.
 
     Args:
-        source (str): Source path for the copy tool.
+        source (str): Dource path for the copy tool.
         dest (str): Destination path for the copy tool.
     """
 
@@ -720,7 +721,7 @@ 
Source code for sed.loader.mirrorutil
             proceed = input()
         if proceed == "y":
             shutil.rmtree(oldest_scan)
-            print("Removed successfully!")
+            print("Removed sucessfully!")
         else:
             print("Aborted.")

 

@@ -753,7 +754,7 @@ 
Source code for sed.loader.mirrorutil
         ValueError: Raised if sdir not inside of source
 
     Returns:
-        str: The mapped target directory inside dest
+        str: The mapped targed directory inside dest
     """
 
     if not os.path.isdir(sdir):
@@ -782,7 +783,7 @@ 
Source code for sed.loader.mirrorutil
 # replacement for os.makedirs, which is independent of umask
 

 [docs]
-def mymakedirs(path: str, mode: int, gid: int) -> List[str]:
+def mymakedirs(path: str, mode: int, gid: int) -> list[str]:
     """Creates a directory path iteratively from its root
 
     Args:
@@ -791,7 +792,7 @@ 
Source code for sed.loader.mirrorutil
         gid (int): Group id of created directories
 
     Returns:
-        str: Path of created directories
+        list[str]: Path of created directories
     """
 
     if not path or os.path.exists(path):
diff --git a/sed/latest/_modules/sed/loader/mpes/loader.html b/sed/latest/_modules/sed/loader/mpes/loader.html
index 2a13436..9e8f846 100644
--- a/sed/latest/_modules/sed/loader/mpes/loader.html
+++ b/sed/latest/_modules/sed/loader/mpes/loader.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.mpes.loader — SED 0.1.10a6 documentation
+    sed.loader.mpes.loader — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -451,15 +451,13 @@ 
Source code for sed.loader.mpes.loader
 Mostly ported from https://github.com/mpes-kit/mpes.
 @author: L. Rettig
 """
+from __future__ import annotations
+
 import datetime
 import glob
 import json
 import os
-from typing import Dict
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
+from collections.abc import Sequence
 from urllib.error import HTTPError
 from urllib.error import URLError
 from urllib.request import urlopen
@@ -480,7 +478,7 @@ 
Source code for sed.loader.mpes.loader
 def hdf5_to_dataframe(
     files: Sequence[str],
     group_names: Sequence[str] = None,
-    alias_dict: Dict[str, str] = None,
+    alias_dict: dict[str, str] = None,
     time_stamps: bool = False,
     time_stamp_alias: str = "timeStamps",
     ms_markers_group: str = "msMarkers",
@@ -494,7 +492,7 @@ 
Source code for sed.loader.mpes.loader
         files (List[str]): A list of the file paths to load.
         group_names (List[str], optional): hdf5 group names to load. Defaults to load
             all groups containing "Stream"
-        alias_dict (Dict[str, str], optional): Dictionary of aliases for the dataframe
+        alias_dict (dict[str, str], optional): Dictionary of aliases for the dataframe
             columns. Keys are the hdf5 groupnames, and values the aliases. If an alias
             is not found, its group name is used. Defaults to read the attribute
             "Name" from each group.
@@ -521,7 +519,7 @@ 
Source code for sed.loader.mpes.loader
     if group_names == []:
         group_names, alias_dict = get_groups_and_aliases(
             h5file=test_proc,
-            search_pattern="Stream",
+            seach_pattern="Stream",
         )
 
     column_names = [alias_dict.get(group, group) for group in group_names]
@@ -563,7 +561,7 @@ 
Source code for sed.loader.mpes.loader
 def hdf5_to_timed_dataframe(
     files: Sequence[str],
     group_names: Sequence[str] = None,
-    alias_dict: Dict[str, str] = None,
+    alias_dict: dict[str, str] = None,
     time_stamps: bool = False,
     time_stamp_alias: str = "timeStamps",
     ms_markers_group: str = "msMarkers",
@@ -578,7 +576,7 @@ 
Source code for sed.loader.mpes.loader
         files (List[str]): A list of the file paths to load.
         group_names (List[str], optional): hdf5 group names to load. Defaults to load
             all groups containing "Stream"
-        alias_dict (Dict[str, str], optional): Dictionary of aliases for the dataframe
+        alias_dict (dict[str, str], optional): Dictionary of aliases for the dataframe
             columns. Keys are the hdf5 groupnames, and values the aliases. If an alias
             is not found, its group name is used. Defaults to read the attribute
             "Name" from each group.
@@ -605,7 +603,7 @@ 
Source code for sed.loader.mpes.loader
     if group_names == []:
         group_names, alias_dict = get_groups_and_aliases(
             h5file=test_proc,
-            search_pattern="Stream",
+            seach_pattern="Stream",
         )
 
     column_names = [alias_dict.get(group, group) for group in group_names]
@@ -646,31 +644,31 @@ 
Source code for sed.loader.mpes.loader
 [docs]
 def get_groups_and_aliases(
     h5file: h5py.File,
-    search_pattern: str = None,
+    seach_pattern: str = None,
     alias_key: str = "Name",
-) -> Tuple[List[str], Dict[str, str]]:
+) -> tuple[list[str], dict[str, str]]:
     """Read groups and aliases from a provided hdf5 file handle
 
     Args:
         h5file (h5py.File):
             The hdf5 file handle
-        search_pattern (str, optional):
+        seach_pattern (str, optional):
             Search pattern to select groups. Defaults to include all groups.
         alias_key (str, optional):
             Attribute key where aliases are stored. Defaults to "Name".
 
     Returns:
-        Tuple[List[str], Dict[str, str]]:
+        tuple[list[str], dict[str, str]]:
             The list of groupnames and the alias dictionary parsed from the file
     """
     # get group names:
     group_names = list(h5file)
 
     # Filter the group names
-    if search_pattern is None:
+    if seach_pattern is None:
         filtered_group_names = group_names
     else:
-        filtered_group_names = [name for name in group_names if search_pattern in name]
+        filtered_group_names = [name for name in group_names if seach_pattern in name]
 
     alias_dict = {}
     for name in filtered_group_names:
@@ -794,7 +792,7 @@ 
Source code for sed.loader.mpes.loader
             timestamp of a file. Defaults to "FirstEventTimeStamp".
 
     Returns:
-        np.ndarray: the array of the values at evenly spaced timing obtained from
+        np.ndarray: the array of the values at evently spaced timing obtained from
         the ms_markers.
     """
 
@@ -842,7 +840,7 @@ 
Source code for sed.loader.mpes.loader
 

 [docs]
 def get_attribute(h5group: h5py.Group, attribute: str) -> str:
-    """Reads, decodes and returns an attribute from an hdf5 group
+    """Reads, decodes and returns an attrubute from an hdf5 group
 
     Args:
         h5group (h5py.Group):
@@ -869,7 +867,7 @@ 
Source code for sed.loader.mpes.loader
 def get_count_rate(
     h5file: h5py.File,
     ms_markers_group: str = "msMarkers",
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Create count rate in the file from the msMarker column.
 
     Args:
@@ -878,7 +876,7 @@ 
Source code for sed.loader.mpes.loader
             are stored. Defaults to "msMarkers".
 
     Returns:
-        Tuple[np.ndarray, np.ndarray]: The count rate in Hz and the seconds into the
+        tuple[np.ndarray, np.ndarray]: The count rate in Hz and the seconds into the
         scan.
     """
     ms_markers = np.asarray(h5file[ms_markers_group])
@@ -905,7 +903,7 @@ 
Source code for sed.loader.mpes.loader
             are stored. Defaults to "msMarkers".
 
     Return:
-        float: The acquisition time of the file in seconds.
+        float: The acquision time of the file in seconds.
     """
     secs = h5file[ms_markers_group].len() / 1000
 
@@ -920,7 +918,7 @@ 
Source code for sed.loader.mpes.loader
     archiver_channel: str,
     ts_from: float,
     ts_to: float,
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Extract time stamps and corresponding data from and EPICS archiver instance
 
     Args:
@@ -930,7 +928,7 @@ 
Source code for sed.loader.mpes.loader
         ts_to (float): ending time stamp of the range of interest
 
     Returns:
-        Tuple[List, List]: The extracted time stamps and corresponding data
+        tuple[np.ndarray, np.ndarray]: The extracted time stamps and corresponding data
     """
     iso_from = datetime.datetime.utcfromtimestamp(ts_from).isoformat()
     iso_to = datetime.datetime.utcfromtimestamp(ts_to).isoformat()
@@ -974,27 +972,27 @@ 
Source code for sed.loader.mpes.loader
 [docs]
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "h5",
         metadata: dict = None,
         collect_metadata: bool = False,
         time_stamps: bool = False,
         **kwds,
-    ) -> Tuple[ddf.DataFrame, ddf.DataFrame, dict]:
+    ) -> tuple[ddf.DataFrame, ddf.DataFrame, dict]:
         """Read stored hdf5 files from a list or from folder and returns a dask
         dataframe and corresponding metadata.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process.
+            files (str | Sequence[str], optional): File path(s) to process.
                 Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files
+            folders (str | Sequence[str], optional): Path to folder(s) where files
                 are stored. Path has priority such that if it's specified, the specified
                 files will be ignored. Defaults to None.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding
                 files will be located in the location provided by ``folders``. Takes
-                precedence over ``files`` and ``folders``. Defaults to None.
+                precendence over ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): File extension to use. If a folder path is given,
                 all files with the specified extension are read into the dataframe
                 in the reading order. Defaults to "h5".
@@ -1020,7 +1018,7 @@ 
Source code for sed.loader.mpes.loader
             FileNotFoundError: Raised if a file or folder is not found.
 
         Returns:
-            Tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed Dask
+            tuple[ddf.DataFrame, ddf.DataFrame, dict]: Dask dataframe, timed Dask
             dataframe and metadata read from specified files.
         """
         # if runs is provided, try to locate the respective files relative to the provided folder.
@@ -1114,21 +1112,21 @@ 
Source code for sed.loader.mpes.loader
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = "h5",
         **kwds,  # noqa: ARG002
-    ) -> List[str]:
+    ) -> list[str]:
         """Locate the files for a given run identifier.
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to config["core"]["base_folder"]
             extension (str, optional): The file extension. Defaults to "h5".
             kwds: Keyword arguments
 
         Return:
-            List[str]: List of file path strings to the location of run data.
+            list[str]: List of file path strings to the location of run data.
         """
         if folders is None:
             folders = self._config["core"]["paths"]["data_raw_dir"]
@@ -1136,7 +1134,7 @@ 
Source code for sed.loader.mpes.loader
         if isinstance(folders, str):
             folders = [folders]
 
-        files: List[str] = []
+        files: list[str] = []
         for folder in folders:
             run_files = natsorted(
                 glob.glob(
@@ -1158,11 +1156,11 @@ 
Source code for sed.loader.mpes.loader
 
 

 [docs]
-    def get_start_and_end_time(self) -> Tuple[float, float]:
+    def get_start_and_end_time(self) -> tuple[float, float]:
         """Extract the start and end time stamps from the loaded files
 
         Returns:
-            Tuple[float, float]: A tuple containing the start and end time stamps
+            tuple[float, float]: A tuple containing the start and end time stamps
         """
         h5file = h5py.File(self.files[0])
         timestamps = hdf5_to_array(
@@ -1322,13 +1320,13 @@ 
Source code for sed.loader.mpes.loader
                 print("Contrast aperture size not found.")
 
         # Storing the lens modes corresponding to lens voltages.
-        # Use lens voltages present in first lens_mode entry.
+        # Use lens volages present in first lens_mode entry.
         lens_list = self._config["metadata"]["lens_mode_config"][
             next(iter(self._config["metadata"]["lens_mode_config"]))
         ].keys()
 
         lens_volts = np.array(
-            [metadata["file"].get(f"KTOF:Lens:{lens}:V", np.NaN) for lens in lens_list],
+            [metadata["file"].get(f"KTOF:Lens:{lens}:V", np.nan) for lens in lens_list],
         )
         for mode, value in self._config["metadata"]["lens_mode_config"].items():
             lens_volts_config = np.array([value[k] for k in lens_list])
@@ -1375,7 +1373,7 @@ 
Source code for sed.loader.mpes.loader
         self,
         fids: Sequence[int] = None,
         **kwds,
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Create count rate from the msMarker column for the files specified in
         ``fids``.
 
@@ -1387,7 +1385,7 @@ 
Source code for sed.loader.mpes.loader
                 - **ms_markers_group**: Name of the hdf5 group containing the ms-markers
 
         Returns:
-            Tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
+            tuple[np.ndarray, np.ndarray]: Arrays containing countrate and seconds
             into the scan.
         """
         if fids is None:
diff --git a/sed/latest/_modules/sed/loader/sxp/loader.html b/sed/latest/_modules/sed/loader/sxp/loader.html
index f6be18c..b83e7ba 100644
--- a/sed/latest/_modules/sed/loader/sxp/loader.html
+++ b/sed/latest/_modules/sed/loader/sxp/loader.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.sxp.loader — SED 0.1.10a6 documentation
+    sed.loader.sxp.loader — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -454,16 +454,15 @@ 
Source code for sed.loader.sxp.loader
 The dataframe is a amalgamation of all h5 files for a combination of runs, where the NaNs are
 automatically forward filled across different files.
 This can then be saved as a parquet for out-of-sed processing and reread back to access other
-sed functionality.
+sed funtionality.
 Most of the structure is identical to the FLASH loader.
 """
+from __future__ import annotations
+
 import time
+from collections.abc import Sequence
 from functools import reduce
 from pathlib import Path
-from typing import List
-from typing import Sequence
-from typing import Tuple
-from typing import Union
 
 import dask.dataframe as dd
 import h5py
@@ -500,17 +499,17 @@ 
Source code for sed.loader.sxp.loader
         self.multi_index = ["trainId", "pulseId", "electronId"]
         self.index_per_electron: MultiIndex = None
         self.index_per_pulse: MultiIndex = None
-        self.failed_files_error: List[str] = []
-        self.array_indices: List[List[slice]] = None
+        self.failed_files_error: list[str] = []
+        self.array_indices: list[list[slice]] = None
 
 

 [docs]
-    def initialize_paths(self) -> Tuple[List[Path], Path]:
+    def initialize_paths(self) -> tuple[list[Path], Path]:
         """
         Initializes the paths based on the configuration.
 
         Returns:
-            Tuple[List[Path], Path]: A tuple containing a list of raw data directories
+            tuple[List[Path], Path]: A tuple containing a list of raw data directories
             paths and the parquet data directory path.
 
         Raises:
@@ -562,23 +561,23 @@ 
Source code for sed.loader.sxp.loader
     def get_files_from_run_id(
         self,
         run_id: str,
-        folders: Union[str, Sequence[str]] = None,
+        folders: str | Sequence[str] = None,
         extension: str = "h5",
         **kwds,
-    ) -> List[str]:
+    ) -> list[str]:
         """Returns a list of filenames for a given run located in the specified directory
         for the specified data acquisition (daq).
 
         Args:
             run_id (str): The run identifier to locate.
-            folders (Union[str, Sequence[str]], optional): The directory(ies) where the raw
+            folders (str | Sequence[str], optional): The directory(ies) where the raw
                 data is located. Defaults to config["core"]["base_folder"].
             extension (str, optional): The file extension. Defaults to "h5".
             kwds: Keyword arguments:
                 - daq (str): The data acquisition identifier.
 
         Returns:
-            List[str]: A list of path strings representing the collected file names.
+            list[str]: A list of path strings representing the collected file names.
 
         Raises:
             FileNotFoundError: If no files are found for the given run in the directory.
@@ -602,7 +601,7 @@ 
Source code for sed.loader.sxp.loader
         # Generate the file patterns to search for in the directory
         file_pattern = f"**/{stream_name_prefixes[daq]}{run_id}{stream_name_postfix}*." + extension
 
-        files: List[Path] = []
+        files: list[Path] = []
         # Use pathlib to search for matching files in each directory
         for folder in folders:
             files.extend(
@@ -623,7 +622,7 @@ 
Source code for sed.loader.sxp.loader
 
 
     @property
-    def available_channels(self) -> List:
+    def available_channels(self) -> list:
         """Returns the channel names that are available for use,
         excluding pulseId, defined by the json file"""
         available_channels = list(self._config["dataframe"]["channels"].keys())
@@ -633,13 +632,13 @@ 
Source code for sed.loader.sxp.loader
 
 

 [docs]
-    def get_channels(self, formats: Union[str, List[str]] = "", index: bool = False) -> List[str]:
+    def get_channels(self, formats: str | list[str] = "", index: bool = False) -> list[str]:
         """
         Returns a list of channels associated with the specified format(s).
 
         Args:
-            formats (Union[str, List[str]]): The desired format(s)
-                                ('per_pulse', 'per_electron', 'per_train', 'all').
+            formats (str | list[str]): The desired format(s)
+                ('per_pulse', 'per_electron', 'per_train', 'all').
             index (bool): If True, includes channels from the multi_index.
 
         Returns:
@@ -812,7 +811,7 @@ 
Source code for sed.loader.sxp.loader
         self,
         h5_file: h5py.File,
         channel: str,
-    ) -> Tuple[Series, np.ndarray]:
+    ) -> tuple[Series, np.ndarray]:
         """
         Returns a numpy array for a given channel name for a given file.
 
@@ -821,7 +820,7 @@ 
Source code for sed.loader.sxp.loader
             channel (str): The name of the channel.
 
         Returns:
-            Tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
+            tuple[Series, np.ndarray]: A tuple containing the train ID Series and the numpy array
             for the channel's data.
 
         """
@@ -880,7 +879,7 @@ 
Source code for sed.loader.sxp.loader
         """
         if self.array_indices is None or len(self.array_indices) != np_array.shape[0]:
             raise RuntimeError(
-                "macrobunch_indices not set correctly, internal inconsistency detected.",
+                "macrobunch_indices not set correctly, internal inconstency detected.",
             )
         train_data = []
         for i, _ in enumerate(self.array_indices):
@@ -922,15 +921,15 @@ 
Source code for sed.loader.sxp.loader
             DataFrame: The pandas DataFrame for the channel's data.
 
         Notes:
-            - For auxiliary channels, the macrobunch resolved data is repeated 499 times to be
-              compared to electron resolved data for each auxiliary channel. The data is then
+            - For auxillary channels, the macrobunch resolved data is repeated 499 times to be
+              compared to electron resolved data for each auxillary channel. The data is then
               converted to a multicolumn DataFrame.
             - For all other pulse resolved channels, the macrobunch resolved data is exploded
               to a DataFrame and the MultiIndex is set.
 
         """
 
-        # Special case for auxiliary channels
+        # Special case for auxillary channels
         if channel == "dldAux":
             # Checks the channel dictionary for correct slices and creates a multicolumn DataFrame
             data_frames = (
@@ -993,7 +992,7 @@ 
Source code for sed.loader.sxp.loader
         self,
         h5_file: h5py.File,
         channel: str,
-    ) -> Union[Series, DataFrame]:
+    ) -> Series | DataFrame:
         """
         Returns a pandas DataFrame for a given channel name from a given file.
 
@@ -1006,7 +1005,7 @@ 
Source code for sed.loader.sxp.loader
             channel (str): The name of the channel.
 
         Returns:
-            Union[Series, DataFrame]: A pandas Series or DataFrame representing the channel's data.
+            Series | DataFrame: A pandas Series or DataFrame representing the channel's data.
 
         Raises:
             ValueError: If the channel has an undefined format.
@@ -1149,7 +1148,7 @@ 
Source code for sed.loader.sxp.loader
 
 

 [docs]
-    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> Union[bool, Exception]:
+    def create_buffer_file(self, h5_path: Path, parquet_path: Path) -> bool | Exception:
         """
         Converts an HDF5 file to Parquet format to create a buffer file.
 
@@ -1160,6 +1159,9 @@ 
Source code for sed.loader.sxp.loader
             h5_path (Path): Path to the input HDF5 file.
             parquet_path (Path): Path to the output Parquet file.
 
+        Returns:
+            bool | Exception: Collected exceptions if any.
+
         Raises:
             ValueError: If an error occurs during the conversion process.
 
@@ -1183,7 +1185,7 @@ 
Source code for sed.loader.sxp.loader
         data_parquet_dir: Path,
         detector: str,
         force_recreate: bool,
-    ) -> Tuple[List[Path], List, List]:
+    ) -> tuple[list[Path], list, list]:
         """
         Handles the conversion of buffer files (h5 to parquet) and returns the filenames.
 
@@ -1193,7 +1195,7 @@ 
Source code for sed.loader.sxp.loader
             force_recreate (bool): Forces recreation of buffer files
 
         Returns:
-            Tuple[List[Path], List, List]: Three lists, one for
+            tuple[list[Path], list, list]: Three lists, one for
             parquet file paths, one for metadata and one for schema.
 
         Raises:
@@ -1293,7 +1295,7 @@ 
Source code for sed.loader.sxp.loader
         load_parquet: bool = False,
         save_parquet: bool = False,
         force_recreate: bool = False,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame]:
+    ) -> tuple[dd.DataFrame, dd.DataFrame]:
         """
         Handles loading and saving of parquet files based on the provided parameters.
 
@@ -1308,7 +1310,7 @@ 
Source code for sed.loader.sxp.loader
             save_parquet (bool, optional): Saves the entire dataframe into a parquet.
             force_recreate (bool, optional): Forces recreation of buffer file.
         Returns:
-            tuple: A tuple containing two dataframes:
+            tuple[dd.DataFrame, dd.DataFrame]: A tuple containing two dataframes:
             - dataframe_electron: Dataframe containing the loaded/augmented electron data.
             - dataframe_pulse: Dataframe containing the loaded/augmented timed data.
 
@@ -1349,7 +1351,7 @@ 
Source code for sed.loader.sxp.loader
             dataframe = dd.read_parquet(filenames, calculate_divisions=True)
 
             # Channels to fill NaN values
-            channels: List[str] = self.get_channels(["per_pulse", "per_train"])
+            channels: list[str] = self.get_channels(["per_pulse", "per_train"])
 
             overlap = min(file.num_rows for file in metadata)
 
@@ -1417,31 +1419,32 @@ 
Source code for sed.loader.sxp.loader
 [docs]
     def read_dataframe(
         self,
-        files: Union[str, Sequence[str]] = None,
-        folders: Union[str, Sequence[str]] = None,
-        runs: Union[str, Sequence[str]] = None,
+        files: str | Sequence[str] = None,
+        folders: str | Sequence[str] = None,
+        runs: str | Sequence[str] = None,
         ftype: str = "h5",
         metadata: dict = None,
         collect_metadata: bool = False,
         **kwds,
-    ) -> Tuple[dd.DataFrame, dd.DataFrame, dict]:
+    ) -> tuple[dd.DataFrame, dd.DataFrame, dict]:
         """
         Read express data from the DAQ, generating a parquet in between.
 
         Args:
-            files (Union[str, Sequence[str]], optional): File path(s) to process. Defaults to None.
-            folders (Union[str, Sequence[str]], optional): Path to folder(s) where files are stored
+            files (str | Sequence[str], optional): File path(s) to process. Defaults to None.
+            folders (str | Sequence[str], optional): Path to folder(s) where files are stored
                 Path has priority such that if it's specified, the specified files will be ignored.
                 Defaults to None.
-            runs (Union[str, Sequence[str]], optional): Run identifier(s). Corresponding files will
-                be located in the location provided by ``folders``. Takes precedence over
+            runs (str | Sequence[str], optional): Run identifier(s). Corresponding files will
+                be located in the location provided by ``folders``. Takes precendence over
                 ``files`` and ``folders``. Defaults to None.
             ftype (str, optional): The file extension type. Defaults to "h5".
             metadata (dict, optional): Additional metadata. Defaults to None.
             collect_metadata (bool, optional): Whether to collect metadata. Defaults to False.
 
         Returns:
-            Tuple[dd.DataFrame, dict]: A tuple containing the concatenated DataFrame and metadata.
+            tuple[dd.DataFrame, dd.DataFrame, dict]: A tuple containing the concatenated DataFrame,
+            timed DataFrame, and metadata.
 
         Raises:
             ValueError: If neither 'runs' nor 'files'/'data_raw_dir' is provided.
diff --git a/sed/latest/_modules/sed/loader/utils.html b/sed/latest/_modules/sed/loader/utils.html
index 785f8bf..768cb0a 100644
--- a/sed/latest/_modules/sed/loader/utils.html
+++ b/sed/latest/_modules/sed/loader/utils.html
@@ -7,7 +7,7 @@
   
     
     
-    sed.loader.utils — SED 0.1.10a6 documentation
+    sed.loader.utils — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -448,11 +448,11 @@
   
Source code for sed.loader.utils
 """Utilities for loaders
 """
+from __future__ import annotations
+
+from collections.abc import Sequence
 from glob import glob
 from typing import cast
-from typing import List
-from typing import Sequence
-from typing import Union
 
 import dask.dataframe
 import numpy as np
@@ -471,7 +471,7 @@ 
Source code for sed.loader.utils
     f_end: int = None,
     f_step: int = 1,
     file_sorting: bool = True,
-) -> List[str]:
+) -> list[str]:
     """Collects and sorts files with specified extension from a given folder.
 
     Args:
@@ -487,13 +487,13 @@ 
Source code for sed.loader.utils
             Defaults to True.
 
     Returns:
-        List[str]: List of collected file names.
+        list[str]: List of collected file names.
     """
     try:
         files = glob(folder + "/*." + extension)
 
         if file_sorting:
-            files = cast(List[str], natsorted(files))
+            files = cast(list[str], natsorted(files))
 
         if f_start is not None and f_end is not None:
             files = files[slice(f_start, f_end, f_step)]
@@ -508,7 +508,7 @@ 
Source code for sed.loader.utils
 
 

 [docs]
-def parse_h5_keys(h5_file: File, prefix: str = "") -> List[str]:
+def parse_h5_keys(h5_file: File, prefix: str = "") -> list[str]:
     """Helper method which parses the channels present in the h5 file
     Args:
         h5_file (h5py.File): The H5 file object.
@@ -516,7 +516,7 @@ 
Source code for sed.loader.utils
         Defaults to an empty string.
 
     Returns:
-        List[str]: A list of channel names in the H5 file.
+        list[str]: A list of channel names in the H5 file.
 
     Raises:
         Exception: If an error occurs while parsing the keys.
@@ -603,19 +603,19 @@ 
Source code for sed.loader.utils
 

 [docs]
 def split_dld_time_from_sector_id(
-    df: Union[pd.DataFrame, dask.dataframe.DataFrame],
+    df: pd.DataFrame | dask.dataframe.DataFrame,
     tof_column: str = None,
     sector_id_column: str = None,
     sector_id_reserved_bits: int = None,
     config: dict = None,
-) -> Union[pd.DataFrame, dask.dataframe.DataFrame]:
+) -> pd.DataFrame | dask.dataframe.DataFrame:
     """Converts the 8s time in steps to time in steps and sectorID.
 
     The 8s detector encodes the dldSectorID in the 3 least significant bits of the
     dldTimeSteps channel.
 
     Args:
-        df (Union[pd.DataFrame, dask.dataframe.DataFrame]): Dataframe to use.
+        df (pd.DataFrame | dask.dataframe.DataFrame): Dataframe to use.
         tof_column (str, optional): Name of the column containing the
             time-of-flight steps. Defaults to config["dataframe"]["tof_column"].
         sector_id_column (str, optional): Name of the column containing the
@@ -624,7 +624,7 @@ 
Source code for sed.loader.utils
         config (dict, optional): Configuration dictionary. Defaults to None.
 
     Returns:
-        Union[pd.DataFrame, dask.dataframe.DataFrame]: Dataframe with the new columns.
+        pd.DataFrame | dask.dataframe.DataFrame: Dataframe with the new columns.
     """
     if tof_column is None:
         if config is None:
diff --git a/sed/latest/_static/documentation_options.js b/sed/latest/_static/documentation_options.js
index df85b7e..073578e 100644
--- a/sed/latest/_static/documentation_options.js
+++ b/sed/latest/_static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.1.10a6',
+    VERSION: '0.1.10a5',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/sed/latest/genindex.html b/sed/latest/genindex.html
index 0189b47..2d5ea03 100644
--- a/sed/latest/genindex.html
+++ b/sed/latest/genindex.html
@@ -7,7 +7,7 @@
   
     
     
-    Index — SED 0.1.10a6 documentation
+    Index — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -121,7 +121,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -697,7 +697,7 @@ 
D

   

     
diff --git a/sed/latest/misc/contributing.html b/sed/latest/misc/contributing.html
index 8fe7d67..6c62640 100644
--- a/sed/latest/misc/contributing.html
+++ b/sed/latest/misc/contributing.html
@@ -8,7 +8,7 @@
     
     
 
-    Contributing to sed — SED 0.1.10a6 documentation
+    Contributing to sed — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
diff --git a/sed/latest/misc/contribution.html b/sed/latest/misc/contribution.html
index 9fa6c6c..d35970d 100644
--- a/sed/latest/misc/contribution.html
+++ b/sed/latest/misc/contribution.html
@@ -8,7 +8,7 @@
     
     
 
-    Development — SED 0.1.10a6 documentation
+    Development — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
diff --git a/sed/latest/misc/maintain.html b/sed/latest/misc/maintain.html
index 07b4630..2374b9b 100644
--- a/sed/latest/misc/maintain.html
+++ b/sed/latest/misc/maintain.html
@@ -8,7 +8,7 @@
     
     
 
-    How to Maintain — SED 0.1.10a6 documentation
+    How to Maintain — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
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diff --git a/sed/latest/py-modindex.html b/sed/latest/py-modindex.html
index c02bad5..08ebd84 100644
--- a/sed/latest/py-modindex.html
+++ b/sed/latest/py-modindex.html
@@ -7,7 +7,7 @@
   
     
     
-    Python Module Index — SED 0.1.10a6 documentation
+    Python Module Index — SED 0.1.10a5 documentation
   
   
   
@@ -34,7 +34,7 @@
 
   
 
-    
+    
     
     
     
@@ -43,7 +43,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
diff --git a/sed/latest/search.html b/sed/latest/search.html
index 2d82818..1a5158c 100644
--- a/sed/latest/search.html
+++ b/sed/latest/search.html
@@ -6,7 +6,7 @@
 
   
     
-    Search - SED 0.1.10a6 documentation
+    Search - SED 0.1.10a5 documentation
   
   
   
@@ -33,7 +33,7 @@
 
   
 
-    
+    
     
     
     
@@ -42,7 +42,7 @@
     
   
@@ -123,7 +123,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
diff --git a/sed/latest/searchindex.js b/sed/latest/searchindex.js
index fdb470b..2f3f6a7 100644
--- a/sed/latest/searchindex.js
+++ b/sed/latest/searchindex.js
@@ -1 +1 @@
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"sed_kernel": 18, "sedprocessor": 8, "see": [1, 3, 6, 8, 13, 16, 18], "seg": 6, "segment": 6, "select": [1, 3, 6, 8, 13, 16], "select_k_rang": 6, "select_slic": 6, "selector": [6, 13], "self": [6, 8], "separ": [6, 13], "sequenc": [5, 6, 8, 10, 11, 12, 13], "seri": [6, 8, 13], "set": [1, 3, 5, 6, 7, 8, 13, 17], "setup": 16, "sever": 0, "shall": 16, "shape": [5, 6, 8], "shell": 3, "shift": [6, 8], "ship": 16, "should": [1, 5, 6, 8, 9, 12, 16], "show": [5, 6, 8], "show_legend": 6, "showcas": 15, "sig_mov": 6, "sig_stil": 6, "sigma": [6, 16], "sigma_radiu": 16, "sign": [6, 8, 10, 16], "signal": [6, 8], "signific": [5, 13, 15], "significantli": 3, "similar": [5, 10, 13], "simpl": [5, 15], "simpli": 15, "simplify_binning_argu": 5, "simul": 15, "simultan": 10, "sinc": 16, "singl": [0, 5, 6, 8, 11, 12, 13, 15, 16], "single_event_data": 9, "sis8300": 16, "situat": 6, "size": [5, 6, 10, 13, 16], "skip": [3, 18], "skip_test": 5, "slice": [6, 8, 16], "slice_correct": 6, "slider": [6, 8], "slow": [3, 8], "small": [1, 15], "smallest": 10, "smooth": [6, 8, 16], "so": [3, 12, 16], "solv": 6, "some": 13, "soon": 16, "sort": [12, 13], "sourc": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18], "space": [3, 6, 8, 13], "span": [6, 8], "spars": 10, "spawn": [5, 8], "spec": [6, 13], "special": 16, "specif": [3, 6, 8, 13], "specifi": [5, 6, 8, 9, 10, 13], "spectra": 6, "spectral": 6, "spectroscopi": [0, 1], "speed": 13, "speicifi": 8, "spheric": [6, 8], "sphinx": 3, "spline": [6, 8, 16], "spline_warp_estim": [6, 8], "splinewarp": 8, "split": 13, "split_channel_bitwis": 13, "split_dld_time_from_sector_id": 13, "split_sector_id_from_dld_tim": 16, "squar": 6, "stack": [6, 8, 12], "stage": [6, 16], "stamp": [8, 10, 13, 16], "standard": [6, 12], "start": [2, 5, 6, 8, 13, 16], "static": [6, 9, 16], "step": [3, 5, 6, 8, 13, 16], "still": 16, "stop": 6, "storag": [8, 16], "store": [5, 6, 8, 9, 13, 16], "str": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14], "stream": [13, 16], "stream_0": 16, "stream_1": 16, "stream_2": 16, "stream_4": 16, "stream_name_prefix": 16, "string": [5, 7, 12, 13], "structur": [13, 15, 16], "subclass": [1, 6], "subdir": 9, "subfunct": 12, "submit": 18, "subplot": 15, "subsequ": 16, "substitu": 8, "subtract": [6, 8, 10], "succes": 16, "success": [1, 3], "successfulli": 9, "suffici": 6, "suffix": 10, "sum": [6, 15], "sum_n": 6, "support": [6, 7, 8, 10, 13], "supported_file_typ": 13, "sure": [3, 16], "surround": 6, "sxp": 13, "sy": 15, "sym": 6, "symmetr": 6, "symmetri": [6, 8, 16], "symscor": 6, "symtyp": 6, "sync": 16, "system": [6, 7, 13, 16], "system_config": [7, 16], "t": [3, 6, 8, 12, 16], "t0": [6, 8, 16], "t0_kei": [6, 16], "t0_valu": 6, "t_n": 16, "ta": 6, "tab": 3, "tabl": 15, "tabular": 8, "tag": 3, "take": [5, 7, 8, 10, 13], "taken": 6, "targ": 13, "targcent": 6, "target": [6, 13], "target_column": 10, "tas2": 9, "td": 16, "temp_rbv": 16, "term": 6, "terrain_r": 6, "test": [1, 9, 16], "text": 11, "than": [8, 12], "thei": [6, 7, 8, 16], "them": [6, 8, 14, 16], "theme": 0, "therefor": 12, "thi": [0, 1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18], "thick": 6, "thin": 6, "those": [10, 18], "thread": [5, 8, 16], "threadpool_api": [5, 8, 16], "threadpool_limit": 8, "threads_per_work": [5, 8, 16], "three": 13, "through": [0, 1], "tht": 16, "tif": 8, "tiff": [8, 12], "time": [0, 6, 8, 10, 12, 13, 15, 16], "time0": [6, 16], "time0_mm": 6, "time1": 16, "time_offset": 6, "time_stamp": [8, 10, 13], "time_stamp_alia": [8, 13, 16], "time_stamp_column": 10, "timed_datafram": 8, "timed_dataframe_unit_tim": 16, "timestamp": [8, 10, 13, 16], "timinginfo": 16, "tion": 6, "titl": 6, "tm": 16, "tmat": 6, "to_h5": 12, "to_nexu": 12, "to_tiff": 12, "toconfig": 8, "tof": [6, 8, 16], "tof2ev": 6, "tof2evpoli": 6, "tof2n": 6, "tof_bin": [6, 16], "tof_binwidth": [6, 16], "tof_column": [6, 8, 13, 16], "tof_dist": 6, "tof_fermi": [6, 16], "tof_ns_column": [6, 8, 16], "tof_step_to_n": 8, "tof_voltag": 16, "tof_width": [6, 16], "tofvoltag": 16, "tog": 6, "token": [3, 13, 16], "toml": 3, "too": 8, "took": 16, "tool": [6, 8, 13, 16], "tooltip": 11, "top": 6, "total": [5, 15], "toward": 6, "tqdm": [5, 8], "trace": [6, 8, 16], "traces_norm": 6, "track": [0, 5], "trail": 14, "train": 13, "train_id": 13, "trainid": 13, "transform": [5, 6, 8, 17], "transform_typ": 6, "transformt": [6, 8], "translat": [6, 8], "transpar": 13, "trarp": [0, 16], "tree": 13, "tremendi": 13, "tri": [8, 12], "trigger": 3, "true": [5, 6, 7, 8, 9, 10, 11, 13, 15, 16], "trx": 16, "try": [5, 15, 16], "trz": 16, "ts_from": 13, "ts_to": 13, "tupl": [5, 6, 8, 11, 13], "turn": 5, "tutori": [3, 9], "twice": 10, "two": [3, 6, 8, 10, 13], "type": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 18], "typeerror": [5, 7, 8, 12], "typic": 16, "tzcyx": 12, "ubid_offset": 16, "uca": 16, "udld": 16, "ufa": 16, "uint32": 5, "uint64": 5, "unbin": 16, "uncategoris": 16, "undefin": [6, 13], "under": [8, 14], "underli": 8, "understand": 3, "uniform": [5, 6, 8, 10], "unimpl": 5, "union": 6, "unit": [8, 16], "unix": 13, "unmodifi": 8, "unnecessari": 3, "up": [1, 3, 6, 13], "updat": [3, 6, 13], "update_deform": 6, "upload": 3, "upper": 6, "upper_bound": [8, 10], "upperbound1": 6, "upperbound2": 6, "url": [9, 13, 16], "us": [0, 1, 3, 6, 8, 10, 12, 13, 15, 16, 18], "usag": [0, 13], "use_cent": [6, 8, 16], "use_copy_tool": [8, 16], "use_correct": 8, "use_time_stamp": 8, "user": [0, 1, 3, 5, 6, 7, 13, 15, 16, 18], "user_config": [7, 16], "user_path": 9, "usual": 5, "util": [0, 5], "v": [3, 16], "v0": 3, "val": [5, 6], "valid": [6, 8, 13], "valu": [5, 6, 7, 8, 10, 11, 12, 13, 16], "valueerror": [5, 6, 8, 9, 12, 13], "variabl": [1, 11], "variat": 16, "variou": [13, 16], "vector": 6, "venv": 18, "verbos": [6, 7, 8], "veri": 5, "version": [0, 1, 3, 6, 13, 17], "vert": 6, "vertex": 6, "vertic": [6, 11], "view": [3, 6, 8], "view_event_histogram": 8, "virtual": [3, 18], "visit": 3, "visual": [6, 16], "visula": 16, "vital": 16, "volt": 6, "voltag": [6, 8, 16], "volum": [5, 6], "w": [8, 12], "wa": [5, 8], "walk": 1, "wall": 15, "warn": [5, 12], "warp": [6, 8], "wave": 13, "we": [1, 10, 15, 16], "web": 9, "weight": [6, 8, 10], "welcom": 1, "well": [5, 6], "wesp": 13, "wether": 13, "when": [5, 6, 8, 9, 14], "where": [5, 6, 7, 8, 13, 16], "whether": [1, 6, 8, 9, 10, 13, 16], "which": [1, 5, 6, 8, 10, 13, 14, 16], "whichev": 6, "while": 13, "whole": [6, 8], "whose": 6, "wide": 7, "width": [6, 8, 16], "window": [6, 7, 16, 18], "wise": 6, "within": [6, 8, 13], "work": [3, 6, 7, 8, 9, 16, 18], "worker": 16, "workflow": [0, 2, 3, 6, 8, 16], "workflow_dispatch": 3, "would": [1, 3, 15], "wrapper": 8, "write": [1, 3, 8, 12], "writer": 8, "written": 6, "wrong": 5, "wse2": 9, "x": [6, 8, 10, 12, 16], "x_axi": 6, "x_center": [6, 16], "x_column": [6, 10, 16], "x_width": [6, 16], "xarra": 12, "xarrai": [5, 8, 12], "xaxi": 6, "xgs600": 16, "xm": 16, "xr": [5, 6, 8, 12], "xrng": 6, "xtran": [6, 8], "y": [6, 8, 10, 12, 16], "y_axi": 6, "y_center": [6, 16], "y_column": [6, 10, 16], "y_width": [6, 16], "yaml": [7, 8, 16], "year": 16, "yet": [8, 16], "ym": 16, "yml": 3, "you": [0, 1, 3, 15, 18], "your": [1, 18], "yournameload": 1, "yourusernam": 1, "ytran": [6, 8], "z": 12, "z1": 16, "z2": 16, "zain": 9, "zenodo": 9, "zero": 6, "zip": 15, "zone": 6, "zraw": 16}, "titles": ["SED documentation", "Contributing to sed", "Development", "How to Maintain", "API", "Binning", "Calibrator", "Config", "Core", "Dataset", "Dataframe Operations", "Diagnostics", "IO", "Data loader", "Metadata", "Binning demonstration on locally generated fake data", "Configuration", "User Guide", "Installation", "Workflows"], "titleterms": {"Not": 9, "Or": 9, "The": 9, "abstract": 13, "ad": 9, "advanc": 17, "after": 9, "all": 9, "allow": 9, "along": 15, "ani": 9, "anoth": 9, "api": [0, 4, 9], "attribut": 9, "baseload": 13, "basic": 17, "behavior": 9, "berlin": 16, "bin": [5, 15], "calibr": 6, "can": 9, "commun": 0, "comput": 15, "concept": 17, "config": 7, "configur": 16, "continu": 9, "contribut": [0, 1], "core": 8, "correct": 6, "custom": 9, "dask": 15, "data": [9, 13, 15], "datafram": [10, 15], "dataset": 9, "default": [9, 16], "defin": 15, "delai": 6, "delet": 9, "demonstr": 15, "desi": 16, "develop": [1, 2, 18], "diagnost": 11, "distribut": 15, "document": [0, 3, 9], "download": 9, "energi": 6, "exampl": [0, 9, 16], "exist": 9, "extract": 9, "fake": 15, "fals": 9, "fhi": 16, "file": [9, 16], "flash": 16, "flashload": 13, "from": 9, "function": 5, "gener": 15, "genericload": 13, "get": [1, 9], "guid": [0, 17], "guidelin": 1, "ha": 9, "helper": 5, "hextof": 16, "how": 3, "i": 9, "instal": [17, 18], "instanc": 9, "interfac": 13, "interrupt": 9, "io": 12, "json": 9, "just": 9, "keyword": 9, "loader": [1, 13], "local": 15, "locat": 9, "main": 5, "maintain": 3, "metadata": 14, "meti": 16, "microscop": 16, "momentum": [6, 16], "mpe": 16, "mpesload": 13, "name": 9, "need": 9, "one": 9, "onli": 9, "oper": 10, "panda": 15, "partit": 15, "path": 9, "present": 9, "process": 9, "provid": 9, "pull": 1, "rang": 15, "reextract": 9, "releas": 3, "remov": 9, "remove_zip": 9, "request": 1, "restart": 9, "root_dir": 9, "sed": [0, 1, 17], "see": 9, "set": [9, 16], "similar": 9, "some": 9, "start": 1, "stop": 9, "sxploader": 13, "thi": 9, "transform": 15, "try": 9, "two": 9, "us": [5, 9], "use_exist": 9, "user": [9, 17], "util": 13, "version": 18, "where": 9, "workflow": [1, 19], "would": 9, "zip": 9}})
\ No newline at end of file
diff --git a/sed/latest/sed/api.html b/sed/latest/sed/api.html
index fadc638..31ffcd1 100644
--- a/sed/latest/sed/api.html
+++ b/sed/latest/sed/api.html
@@ -8,7 +8,7 @@
     
     
 
-    API — SED 0.1.10a6 documentation
+    API — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
diff --git a/sed/latest/sed/binning.html b/sed/latest/sed/binning.html
index 38938f7..ac0c85c 100644
--- a/sed/latest/sed/binning.html
+++ b/sed/latest/sed/binning.html
@@ -8,7 +8,7 @@
     
     
 
-    Binning — SED 0.1.10a6 documentation
+    Binning — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -478,22 +478,21 @@ 
Binning#<
 
sed.binning module easy access APIs

 

 

-sed.binning.bin_dataframe(df, bins=100, axes=None, ranges=None, hist_mode='numba', mode='fast', jitter=None, pbar=True, n_cores=9, threads_per_worker=4, threadpool_api='blas', return_partitions=False, **kwds)[source]#

+sed.binning.bin_dataframe(df, bins=100, axes=None, ranges=None, hist_mode='numba', mode='fast', jitter=None, pbar=True, n_cores=3, threads_per_worker=4, threadpool_api='blas', return_partitions=False, **kwds)[source]#
 
Computes the n-dimensional histogram on columns of a dataframe,
 parallelized.

 

 
Parameters:

 

 
Raises:

@@ -586,15 +584,15 @@ 
Binning#<
 

 
Parameters:

 

 
Returns:

 
2-element tuple returned only when
-returnEdges is True. Otherwise only hist is returned.

+return_edges is True. Otherwise only hist is returned.

 

 
Return type:

-
Union[np.ndarray, Tuple[np.ndarray, list]]

+
np.ndarray | tuple[np.ndarray

 

 

 

@@ -699,7 +697,7 @@ 
Binning#<
 

 

 sed.binning.numba_bin.numba_histogramdd(sample, bins, ranges=None)[source]#

-
Multidimensional histogram function, powered by Numba.

+
Multidimensional histogramming function, powered by Numba.

 
Behaves in total much like numpy.histogramdd. Returns uint32 arrays.
 This was chosen because it has a significant performance improvement over
 uint64 for large binning volumes. Be aware that this can cause overflows
@@ -709,8 +707,8 @@ 
Binning#<
 

 
Parameters:

 

 
Return type:

-
Tuple[np.ndarray, List[np.ndarray]]

+
tuple[np.ndarray, list[np.ndarray]]

 

 

 

@@ -752,13 +750,13 @@ 
Binning#<
 

 
Parameters:

 

 
Return type:

-
Tuple[Union[List[int], List[np.ndarray]], List[Tuple[float, float]]]

+
tuple[list[int] | list[np.ndarray], list[str], list[tuple[float, float]]]

 

 

 
diff --git a/sed/latest/sed/calibrator.html b/sed/latest/sed/calibrator.html
index f0c336b..b057051 100644
--- a/sed/latest/sed/calibrator.html
+++ b/sed/latest/sed/calibrator.html
@@ -8,7 +8,7 @@
     
     
 
-    Calibrator — SED 0.1.10a6 documentation
+    Calibrator — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -485,9 +485,9 @@ 
Calibrator
 
Parameters:

 

 
Returns:

-
the loaded inverse deformation field

+
the loaded inverse row and column deformation fields

 

 
Return type:

-
np.ndarray

+
tuple[np.ndarray, np.ndarray]

 

 
 
@@ -1357,12 +1358,12 @@ 
Calibrator
 
Parameters:

 

     
@@ -475,7 +475,7 @@
 
This module contains a config library for loading yaml/json files into dicts

 

 

-sed.core.config.parse_config(config=None, folder_config=None, user_config=None, system_config=None, default_config='/Users/zain/Documents/Work/sed_fresh_copy/sed/sed/config/default.yaml', verbose=True)[source]#

+sed.core.config.parse_config(config=None, folder_config=None, user_config=None, system_config=None, default_config='/home/runner/work/sed/sed/sed/config/default.yaml', verbose=True)[source]#
 
Load the config dictionary from a file, or pass the provided config dictionary.
 The content of the loaded config dictionary is then completed from a set of pre-configured
 config files in hierarchical order, by adding missing items. These additional config files
@@ -484,21 +484,21 @@
 

 
Parameters:

 
    
-
  • config (Union[dict, str], optional) – config dictionary or file path.
+
  • config (dict | str, optional) – config dictionary or file path.
 Files can be json or yaml. Defaults to None.
    • 
-
  • folder_config (Union[ dict, str, ], optional) – working-folder-based config dictionary
+
  • folder_config (dict | str, optional) – working-folder-based config dictionary
 or file path. The loaded dictionary is completed with the folder-based values,
 taking preference over user, system and default values. Defaults to the file
 “sed_config.yaml” in the current working directory.
    • 
-
  • user_config (Union[ dict, str, ], optional) – user-based config dictionary
+
  • user_config (dict | str, optional) – user-based config dictionary
 or file path. The loaded dictionary is completed with the user-based values,
 taking preference over system and default values.
 Defaults to the file “.sed/config.yaml” in the current user’s home directory.
    • 
-
  • system_config (Union[ dict, str, ], optional) – system-wide config dictionary
+
  • system_config (dict | str, optional) – system-wide config dictionary
 or file path. The loaded dictionary is completed with the system-wide values,
 taking preference over default values. Defaults to the file “/etc/sed/config.yaml”
 on linux, and “%ALLUSERSPROFILE%/sed/config.yaml” on windows.
    • 
-
  • default_config (Union[ dict, str, ], optional) – default config dictionary
+
  • default_config (dict | str, optional) – default config dictionary
 or file path. The loaded dictionary is completed with the default values.
 Defaults to package_dir/config/default.yaml”.
    • 
 
  • verbose (bool, optional) – Option to report loaded config files. Defaults to True.
    • 
@@ -547,11 +547,11 @@
 sed.core.config.save_config(config_dict, config_path, overwrite=False)[source]#
 
    Function to save a given config dictionary to a json or yaml file. Normally, it loads any
 existing file of the given name, and keeps any existing dictionary keys not present in the
-provided dictionary. The overwrite option creates a fully empty dictionary first.
    
+provided dictionary. The overwrite option creates a fully empty dictionry first.
    
 
    
 
    Parameters:
    
 
      
-
    • config_dict (dict) – The dictionary to save.
      • 
+
    • config_dict (dict) – The dictionry to save.
      • 
 
    • config_path (str) – A string containing the path to the file where to save the dictionary
 to.
      • 
 
    • overwrite (bool, optional) – Option to overwrite an existing file with the given dictionary.
diff --git a/sed/latest/sed/core.html b/sed/latest/sed/core.html
index 28bb012..e955f18 100644
--- a/sed/latest/sed/core.html
+++ b/sed/latest/sed/core.html
@@ -8,7 +8,7 @@
     
     
 
-    Core — SED 0.1.10a6 documentation
+    Core — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
      SED 0.1.10a6 documentation
      
+    
      SED 0.1.10a5 documentation
      
   
 

     
@@ -484,11 +484,11 @@
 
Parameters:

 
    
 
  • metadata (dict, optional) – Dict of external Metadata. Defaults to None.
    • 
-
  • config (Union[dict, str], optional) – Config dictionary or config file name.
+
  • config (dict | str, optional) – Config dictionary or config file name.
 Defaults to None.
    • 
-
  • dataframe (Union[pd.DataFrame, ddf.DataFrame], optional) – dataframe to load
+
  • dataframe (pd.DataFrame | ddf.DataFrame, optional) – dataframe to load
 into the class. Defaults to None.
    • 
-
  • files (List[str], optional) – List of files to pass to the loader defined in
+
  • files (list[str], optional) – List of files to pass to the loader defined in
 the config. Defaults to None.
    • 
 
  • folder (str, optional) – Folder containing files to pass to the loader
 defined in the config. Defaults to None.
    • 
@@ -504,28 +504,28 @@
 
 
    
 
    
-property dataframe: DataFrame | DataFrame#
    
+property dataframe: pd.DataFrame | ddf.DataFrame#
 
    Accessor to the underlying dataframe.
    
 
    
 
    Returns:
    
 
    Dataframe object.
    
 
    
 
    Return type:
    
-
    Union[pd.DataFrame, ddf.DataFrame]
    
+
    pd.DataFrame | ddf.DataFrame
    
 
    
 
    
 
    
 
 
    
 
    
-property timed_dataframe: DataFrame | DataFrame#
    
+property timed_dataframe: pd.DataFrame | ddf.DataFrame#
 
    Accessor to the underlying timed_dataframe.
    
 
    
 
    Returns:
    
 
    Timed Dataframe object.
    
 
    
 
    Return type:
    
-
    Union[pd.DataFrame, ddf.DataFrame]
    
+
    pd.DataFrame | ddf.DataFrame
    
 
    
 
    
 
    
@@ -560,28 +560,28 @@
 
 
    
 
    
-property config: Dict[Any, Any]#
    
+property config: dict[Any, Any]#
 
    Getter attribute for the config dictionary
    
 
    
 
    Returns:
    
 
    The config dictionary.
    
 
    
 
    Return type:
    
-
    Dict
    
+
    dict
    
 
    
 
    
 
    
 
 
    
 
    
-property files: List[str]#
    
+property files: list[str]#
 
    Getter attribute for the list of files
    
 
    
 
    Returns:
    
 
    The list of loaded files
    
 
    
 
    Return type:
    
-
    List[str]
    
+
    list[str]
    
 
    
 
    
 
    
@@ -620,7 +620,7 @@
 
    Getter attribute for the normalization histogram
    
 
    
 
    Returns:
    
-
    The normalization histogram
    
+
    The normalizazion histogram
    
 
    
 
    Return type:
    
 
    xr.DataArray
    
@@ -637,13 +637,13 @@
 config[“core”][“use_copy_tool”].
    
 
    
 
    Parameters:
    
-
    path (Union[str, List[str]]) – Source path or path list.
    
+
    path (str | list[str]) – Source path or path list.
    
 
    
 
    Returns:
    
 
    Source or destination path or path list.
    
 
    
 
    Return type:
    
-
    Union[str, List[str]]
    
+
    str | list[str]
    
 
    
 
    
 
    
@@ -655,11 +655,11 @@
 
    
 
    Parameters:
    
 
      
-
    • dataframe (Union[pd.DataFrame, ddf.DataFrame], optional) – data in tabular
+
    • dataframe (pd.DataFrame | ddf.DataFrame, optional) – data in tabular
 format. Accepts anything which can be interpreted by pd.DataFrame as
 an input. Defaults to None.
      • 
 
    • metadata (dict, optional) – Dict of external Metadata. Defaults to None.
      • 
-
    • files (List[str], optional) – List of file paths to pass to the loader.
+
    • files (list[str], optional) – List of file paths to pass to the loader.
 Defaults to None.
      • 
 
    • runs (Sequence[str], optional) – List of run identifiers to pass to the
 loader. Defaults to None.
      • 
@@ -702,13 +702,13 @@
 
      
 
      Parameters:
      
 
        
-
      • df_partitions (Union[int, Sequence[int]], optional) – Number of dataframe partitions
+
      • df_partitions (int | Sequence[int], optional) – Number of dataframe partitions
 to use for the initial binning. Defaults to 100.
        • 
-
      • axes (List[str], optional) – Axes to bin.
+
      • axes (list[str], optional) – Axes to bin.
 Defaults to config[“momentum”][“axes”].
        • 
-
      • bins (List[int], optional) – Bin numbers to use for binning.
+
      • bins (list[int], optional) – Bin numbers to use for binning.
 Defaults to config[“momentum”][“bins”].
        • 
-
      • ranges (List[Tuple], optional) – Ranges to use for binning.
+
      • ranges (Sequence[tuple[float, float]], optional) – Ranges to use for binning.
 Defaults to config[“momentum”][“ranges”].
        • 
 
      • plane (int, optional) – Initial value for the plane slider. Defaults to 0.
        • 
 
      • width (int, optional) – Initial value for the width slider. Defaults to 5.
        • 
@@ -725,7 +725,7 @@
 define_features(features=None, rotation_symmetry=6, auto_detect=False, include_center=True, apply=False, **kwds)[source]#
 
        2. Step of the distortion correction workflow: Define feature points in
 momentum space. They can be either manually selected using a GUI tool, be
-provided as list of feature points, or auto-generated using a
+ptovided as list of feature points, or auto-generated using a
 feature-detection algorithm.
        
 
        
 
        Parameters:
        
@@ -750,7 +750,7 @@
 
        
 generate_splinewarp(use_center=None, verbose=None, **kwds)[source]#
        
 
        3. Step of the distortion correction workflow: Generate the correction
-function restoring the symmetry in the image using a splinewarp algorithm.
        
+function restoring the symmetry in the image using a splinewarp algortihm.
        
 
        
 
        Parameters:
        
 
          
@@ -790,8 +790,8 @@
 
          
 
          Parameters:
          
 
            
-
          • transformations (dict, optional) – Dictionary with transformations.
-Defaults to self.transformations or config[“momentum”][“transformations”].
            • 
+
          • transformations (dict[str, Any], optional) – Dictionary with transformations.
+Defaults to self.transformations or config[“momentum”][“transformtions”].
            • 
 
          • apply (bool, optional) – Option to directly apply the provided
 transformations. Defaults to False.
            • 
 
          • use_correction (bool, option) – Whether to use the spline warp correction
@@ -864,18 +864,18 @@
 
            
 
            Parameters:
            
 
              
-
            • point_a (Union[np.ndarray, List[int]]) – Pixel coordinates of the first
+
            • point_a (np.ndarray | list[int], optional) – Pixel coordinates of the first
 point used for momentum calibration.
              • 
-
            • point_b (Union[np.ndarray, List[int]], optional) – Pixel coordinates of the
+
            • point_b (np.ndarray | list[int], optional) – Pixel coordinates of the
 second point used for momentum calibration.
 Defaults to config[“momentum”][“center_pixel”].
              • 
 
            • k_distance (float, optional) – Momentum distance between point a and b.
-Needs to be provided if no specific k-coordinates for the two points
+Needs to be provided if no specific k-koordinates for the two points
 are given. Defaults to None.
              • 
-
            • k_coord_a (Union[np.ndarray, List[float]], optional) – Momentum coordinate
+
            • k_coord_a (np.ndarray | list[float], optional) – Momentum coordinate
 of the first point used for calibration. Used if equiscale is False.
 Defaults to None.
              • 
-
            • k_coord_b (Union[np.ndarray, List[float]], optional) – Momentum coordinate
+
            • k_coord_b (np.ndarray | list[float], optional) – Momentum coordinate
 of the second point used for calibration. Defaults to [0.0, 0.0].
              • 
 
            • equiscale (bool, optional) – Option to apply different scales to kx and ky.
 If True, the distance between points a and b, and the absolute
@@ -929,7 +929,7 @@
 
              
 
              
 adjust_energy_correction(correction_type=None, amplitude=None, center=None, apply=False, **kwds)[source]#
              
-
              1. step of the energy correction workflow: Opens an interactive plot to
+
              1. step of the energy crrection workflow: Opens an interactive plot to
 adjust the parameters for the TOF/energy correction. Also pre-bins the data if
 they are not present yet.
              
 
              
@@ -947,7 +947,7 @@
 
              • 
 
            • amplitude (float, optional) – Amplitude of the correction.
 Defaults to config[“energy”][“correction”][“amplitude”].
              • 
-
            • center (Tuple[float, float], optional) – Center X/Y coordinates for the
+
            • center (tuple[float, float], optional) – Center X/Y coordinates for the
 correction. Defaults to config[“energy”][“correction”][“center”].
              • 
 
            • apply (bool, optional) – Option to directly apply the provided or default
 correction parameters. Defaults to False.
              • 
@@ -976,7 +976,7 @@
 
              
 
              
 apply_energy_correction(correction=None, preview=False, verbose=None, **kwds)[source]#
              
-
              2. step of the energy correction workflow: Apply the energy correction
+
              2. step of the energy correction workflow: Apply the enery correction
 parameters stored in the class to the dataframe.
              
 
              
 
              Parameters:
              
@@ -1001,15 +1001,15 @@
 
              
 
              Parameters:
              
 
                
-
              • binned_data (Union[xr.DataArray, Tuple[np.ndarray, np.ndarray, np.ndarray]], optional) – Binned data If provided as DataArray, Needs to contain dimensions
+
              • binned_data (xr.DataArray | tuple[np.ndarray, np.ndarray, np.ndarray], optional) – Binned data If provided as DataArray, Needs to contain dimensions
 config[“dataframe”][“tof_column”] and config[“dataframe”][“bias_column”]. If
 provided as tuple, needs to contain elements tof, biases, traces.
                • 
-
              • data_files (List[str], optional) – list of file paths to bin
                • 
-
              • axes (List[str], optional) – bin axes.
+
              • data_files (list[str], optional) – list of file paths to bin
                • 
+
              • axes (list[str], optional) – bin axes.
 Defaults to config[“dataframe”][“tof_column”].
                • 
-
              • bins (List, optional) – number of bins.
+
              • bins (list, optional) – number of bins.
 Defaults to config[“energy”][“bins”].
                • 
-
              • ranges (Sequence[Tuple[float, float]], optional) – bin ranges.
+
              • ranges (Sequence[tuple[float, float]], optional) – bin ranges.
 Defaults to config[“energy”][“ranges”].
                • 
 
              • biases (np.ndarray, optional) – Bias voltages used. If missing, bias
 voltages are extracted from the data files.
                • 
@@ -1040,7 +1040,7 @@
 
                
 
                Parameters:
                
 
                  
-
                • ranges (Union[List[Tuple], Tuple]) – Tuple of TOF values indicating a range.
+
                • ranges (list[tuple] | tuple) – Tuple of TOF values indicating a range.
 Alternatively, a list of ranges for all traces can be given.
                  • 
 
                • ref_id (int, optional) – The id of the trace the range refers to.
 Defaults to 0.
                  • 
@@ -1051,7 +1051,7 @@
 
                • radius (int, optional) – Radius parameter for fast_dtw.
 Defaults to config[“energy”][“fastdtw_radius”].
                  • 
 
                • peak_window (int, optional) – Peak_window parameter for the peak detection
-algorithm. amount of points that have to have to behave monotonously
+algorthm. amount of points that have to have to behave monotoneously
 around a peak. Defaults to config[“energy”][“peak_window”].
                  • 
 
                • apply (bool, optional) – Option to directly apply the provided parameters.
 Defaults to False.
                  • 
@@ -1142,15 +1142,16 @@
 
                  Parameters:
                  
 
                    
 
                  • constant (float, optional) – The constant to shift the energy axis by.
                    • 
-
                  • columns (Union[str, Sequence[str]]) – Name of the column(s) to apply the shift from.
                    • 
-
                  • weights (Union[float, Sequence[float]]) – weights to apply to the columns.
+
                  • columns (str | Sequence[str], optional) – Name of the column(s) to apply the shift from.
                    • 
+
                  • weights (float | Sequence[float], optional) – weights to apply to the columns.
 Can also be used to flip the sign (e.g. -1). Defaults to 1.
                    • 
-
                  • preserve_mean (bool) – Whether to subtract the mean of the column before applying the
-shift. Defaults to False.
                    • 
-
                  • reductions (str) – The reduction to apply to the column. Should be an available method
-of dask.dataframe.Series. For example “mean”. In this case the function is applied
-to the column to generate a single value for the whole dataset. If None, the shift
-is applied per-dataframe-row. Defaults to None. Currently only “mean” is supported.
                    • 
+
                  • reductions (str | Sequence[str], optional) – The reduction to apply to the column.
+Should be an available method of dask.dataframe.Series. For example “mean”. In this
+case the function is applied to the column to generate a single value for the whole
+dataset. If None, the shift is applied per-dataframe-row. Defaults to None.
+Currently only “mean” is supported.
                    • 
+
                  • preserve_mean (bool | Sequence[bool], optional) – Whether to subtract the mean of the
+column before applying the shift. Defaults to False.
                    • 
 
                  • preview (bool, optional) – Option to preview the first elements of the data frame.
 Defaults to False.
                    • 
 
                  • verbose (bool, optional) – Option to print out diagnostic information.
@@ -1161,7 +1162,7 @@
 
                    ValueError – If the energy column is not in the dataframe.
                    
 
                    
 
                    Return type:
                    
-
                    None
                    
+
                    None
                    
 
                    
 
                
 
              
@@ -1229,7 +1230,7 @@
 
              
 
              Parameters:
              
 
                
-
              • delay_range (Tuple[float, float], optional) – The scanned delay range in
+
              • delay_range (tuple[float, float], optional) – The scanned delay range in
 picoseconds. Defaults to None.
                • 
 
              • datafile (str, optional) – The file from which to read the delay ranges.
 Defaults to None.
                • 
@@ -1271,15 +1272,16 @@
 
                  
 
                • constant (float, optional) – The constant to shift the delay axis by.
                  • 
 
                • flip_delay_axis (bool, optional) – Option to reverse the direction of the delay axis.
                  • 
-
                • columns (Union[str, Sequence[str]]) – Name of the column(s) to apply the shift from.
                  • 
-
                • weights (Union[float, Sequence[float]]) – weights to apply to the columns.
+
                • columns (str | Sequence[str], optional) – Name of the column(s) to apply the shift from.
                  • 
+
                • weights (float | Sequence[float], optional) – weights to apply to the columns.
 Can also be used to flip the sign (e.g. -1). Defaults to 1.
                  • 
-
                • preserve_mean (bool) – Whether to subtract the mean of the column before applying the
-shift. Defaults to False.
                  • 
-
                • reductions (str) – The reduction to apply to the column. Should be an available method
-of dask.dataframe.Series. For example “mean”. In this case the function is applied
-to the column to generate a single value for the whole dataset. If None, the shift
-is applied per-dataframe-row. Defaults to None. Currently only “mean” is supported.
                  • 
+
                • reductions (str | Sequence[str], optional) – The reduction to apply to the column.
+Should be an available method of dask.dataframe.Series. For example “mean”. In this
+case the function is applied to the column to generate a single value for the whole
+dataset. If None, the shift is applied per-dataframe-row. Defaults to None.
+Currently only “mean” is supported.
                  • 
+
                • preserve_mean (bool | Sequence[bool], optional) – Whether to subtract the mean of the
+column before applying the shift. Defaults to False.
                  • 
 
                • preview (bool, optional) – Option to preview the first elements of the data frame.
 Defaults to False.
                  • 
 
                • verbose (bool, optional) – Option to print out diagnostic information.
@@ -1290,7 +1292,7 @@
 
                  ValueError – If the delay column is not in the dataframe.
                  
 
                  
 
                  Return type:
                  
-
                  None
                  
+
                  None
                  
 
                  
 
              
 
              
@@ -1340,9 +1342,9 @@
 
              
 
              Parameters:
              
 
                
-
              • cols (List[str], optional) – The columns onto which to apply jitter.
+
              • cols (list[str], optional) – The colums onto which to apply jitter.
 Defaults to config[“dataframe”][“jitter_cols”].
                • 
-
              • amps (Union[float, Sequence[float]], optional) – Amplitude scalings for the
+
              • amps (float | Sequence[float], optional) – Amplitude scalings for the
 jittering noise. If one number is given, the same is used for all axes.
 For uniform noise (default) it will cover the interval [-amp, +amp].
 Defaults to config[“dataframe”][“jitter_amps”].
                • 
@@ -1381,13 +1383,13 @@
 
                
 
                Parameters:
                
 
                  
-
                • df_partitions (Union[int, Sequence[int]], optional) – Number of dataframe partitions to
+
                • df_partitions (int | Sequence[int], optional) – Number of dataframe partitions to
 use for the initial binning. Defaults to 100.
                  • 
-
                • axes (List[str], optional) – Axes to bin.
+
                • axes (list[str], optional) – Axes to bin.
 Defaults to config[“momentum”][“axes”].
                  • 
-
                • bins (List[int], optional) – Bin numbers to use for binning.
+
                • bins (list[int], optional) – Bin numbers to use for binning.
 Defaults to config[“momentum”][“bins”].
                  • 
-
                • ranges (List[Tuple], optional) – Ranges to use for binning.
+
                • ranges (Sequence[tuple[float, float]], optional) – Ranges to use for binning.
 Defaults to config[“momentum”][“ranges”].
                  • 
 
                • **kwds – Keyword argument passed to compute.
                  • 
 
                
@@ -1408,7 +1410,7 @@
 
                
 
                Parameters:
                
 
                  
-
                • bins (int, dict, tuple, List[int], List[np.ndarray], List[tuple], optional) – 
                  Definition of the bins. Can be any of the following cases:
                  
+
                • bins (int | dict | tuple | list[int] | list[np.ndarray] | list[tuple], optional) – 
                  Definition of the bins. Can be any of the following cases:
                  
 
                    
 
                  • an integer describing the number of bins in on all dimensions
                    • 
 
                  • a tuple of 3 numbers describing start, end and step of the binning
@@ -1419,13 +1421,13 @@
 
                  
 
                  This takes priority over the axes and range arguments. Defaults to 100.
                  
 
                  • 
-
                • axes (Union[str, Sequence[str]], optional) – The names of the axes (columns)
+
                • axes (str | Sequence[str], optional) – The names of the axes (columns)
 on which to calculate the histogram. The order will be the order of the
 dimensions in the resulting array. Defaults to None.
                  • 
-
                • ranges (Sequence[Tuple[float, float]], optional) – list of tuples containing
+
                • ranges (Sequence[tuple[float, float]], optional) – list of tuples containing
 the start and end point of the binning range. Defaults to None.
                  • 
-
                • normalize_to_acquisition_time (Union[bool, str]) – Option to normalize the
-result to the acquisition time. If a “slow” axis was scanned, providing
+
                • normalize_to_acquisition_time (bool | str) – Option to normalize the
+result to the acquistion time. If a “slow” axis was scanned, providing
 the name of the scanned axis will compute and apply the corresponding
 normalization histogram. Defaults to False.
                  • 
 
                • **kwds – 
                  Keyword arguments:
                  
@@ -1517,12 +1519,12 @@
 
                    
 
                  • dfpid (int) – Number of the data frame partition to look at.
                    • 
 
                  • ncol (int, optional) – Number of columns in the plot grid. Defaults to 2.
                    • 
-
                  • bins (Sequence[int], optional) – Number of bins to use for the specified
+
                  • bins (Sequence[int], optional) – Number of bins to use for the speicified
 axes. Defaults to config[“histogram”][“bins”].
                    • 
 
                  • axes (Sequence[str], optional) – Names of the axes to display.
 Defaults to config[“histogram”][“axes”].
                    • 
-
                  • ranges (Sequence[Tuple[float, float]], optional) – Value ranges of all
-specified axes. Defaults to config[“histogram”][“ranges”].
                    • 
+
                  • ranges (Sequence[tuple[float, float]], optional) – Value ranges of all
+specified axes. Defaults toconfig[“histogram”][“ranges”].
                    • 
 
                  • backend (str, optional) – Backend of the plotting library
 (‘matplotlib’ or ‘bokeh’). Defaults to “bokeh”.
                    • 
 
                  • legend (bool, optional) – Option to include a legend in the histogram plots.
@@ -1556,7 +1558,7 @@
 
                  • ”.nxs”, “.nexus”: Saves a NeXus file.
                    • 
 
                  
 
                  • 
-
                • **kwds – 
                  Keyword arguments, which are passed to the writer functions:
+
                • **kwds – 
                  Keyword argumens, which are passed to the writer functions:
 For TIFF writing:
                  
 
                    
 
                  • alias_dict: Dictionary of dimension aliases to use.
                    • 
@@ -1567,9 +1569,9 @@
 
                  
 
                  For NeXus:
                  
 
                    
-
                  • reader: Name of the pynxtools reader to use.
+
                  • reader: Name of the nexustools reader to use.
 Defaults to config[“nexus”][“reader”]
                    • 
-
                  • definition: NeXus application definition to use for saving.
+
                  • definiton: NeXus application definition to use for saving.
 Must be supported by the used reader. Defaults to
 config[“nexus”][“definition”]
                    • 
 
                  • input_files: A list of input files to pass to the reader.
diff --git a/sed/latest/sed/dataset.html b/sed/latest/sed/dataset.html
index 1841f09..f9689e2 100644
--- a/sed/latest/sed/dataset.html
+++ b/sed/latest/sed/dataset.html
@@ -8,7 +8,7 @@
     
     
 
-    Dataset — SED 0.1.10a6 documentation
+    Dataset — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
                    SED 0.1.10a6 documentation
                    
+    
                    SED 0.1.10a5 documentation
                    
   
 

     
@@ -707,7 +707,8 @@ 
Default datasets.json        "subdirs": [
             "Scan049_1",
             "energycal_2019_01_08"
-        ]
+        ],
+        "data_path": "/Users/zain/Documents/Work/sed_fresh_copy/sed/tutorial/datasets/WSe2"
     },
     "Gd_W110": {
         "url": "https://zenodo.org/records/10658470/files/single_event_data.zip",
@@ -759,7 +760,7 @@ 
Default datasets.json
 

-json_path = {'folder': './datasets.json', 'module': '/Users/zain/Documents/Work/sed_fresh_copy/sed/sed/dataset/datasets.json', 'user': '/Users/zain/Library/Application Support/sed/datasets.json'}#

+json_path = {'folder': './datasets.json', 'module': '/home/runner/work/sed/sed/sed/dataset/datasets.json', 'user': '/home/runner/.config/sed/datasets.json'}#
 

 
 

diff --git a/sed/latest/sed/dfops.html b/sed/latest/sed/dfops.html
index 7022a6e..b755749 100644
--- a/sed/latest/sed/dfops.html
+++ b/sed/latest/sed/dfops.html
@@ -8,7 +8,7 @@
     
     
 
-    Dataframe Operations — SED 0.1.10a6 documentation
+    Dataframe Operations — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
SED 0.1.10a6 documentation

+    
SED 0.1.10a5 documentation

   
 

     
@@ -481,14 +481,14 @@
 

 
Parameters:

 
    
-
  • df (Union[pd.DataFrame, dask.dataframe.DataFrame]) – Dataframe to add
+
  • df (pd.DataFrame | dask.dataframe.DataFrame) – Dataframe to add
 noise/jittering to.
    • 
-
  • cols (Union[str, Sequence[str]]) – Names of the columns to add jittering to.
    • 
-
  • cols_jittered (Union[str, Sequence[str]], optional) – Names of the columns
+
  • cols (str | Sequence[str]) – Names of the columns to add jittering to.
    • 
+
  • cols_jittered (str | Sequence[str], optional) – Names of the columns
 with added jitter. Defaults to None.
    • 
-
  • amps (Union[float, Sequence[float]], optional) – Amplitude scalings for the
+
  • amps (float | Sequence[float], optional) – Amplitude scalings for the
 jittering noise. If one number is given, the same is used for all axes.
-For normal noise, the added noise will have stdev [-amp, +amp], for
+For normal noise, the added noise will have sdev [-amp, +amp], for
 uniform noise it will cover the interval [-amp, +amp].
 Defaults to 0.5.
    • 
 
  • jitter_type (str, optional) – the type of jitter to add. ‘uniform’ or ‘normal’
@@ -499,7 +499,7 @@
 
    dataframe with added columns.
    
 
    
 
    Return type:
    
-
    Union[pd.DataFrame, dask.dataframe.DataFrame]
    
+
    pd.DataFrame | dask.dataframe.DataFrame
    
 
    
 

 
@@ -511,15 +511,15 @@
 

 
Parameters:

 
    
-
  • df (Union[pd.DataFrame, dask.dataframe.DataFrame]) – Dataframe to use.
    • 
-
  • column_name (Union[str, Sequence[str]])) – List of column names to be dropped.
    • 
+
  • df (pd.DataFrame | dask.dataframe.DataFrame) – Dataframe to use.
    • 
+
  • column_name (str | Sequence[str]) – List of column names to be dropped.
    • 
 

 

 
Returns:

 
Dataframe with dropped columns.

 

 
Return type:

-
Union[pd.DataFrame, dask.dataframe.DataFrame]

+
pd.DataFrame | dask.dataframe.DataFrame

 

 

 
@@ -531,7 +531,7 @@
 

 
Parameters:

 
    
-
  • df (Union[pd.DataFrame, dask.dataframe.DataFrame]) – Dataframe to use.
    • 
+
  • df (pd.DataFrame | dask.dataframe.DataFrame) – Dataframe to use.
    • 
 
  • col (str) – Name of the column to filter. Passing “index” for col will
 filter on the index in each dataframe partition.
    • 
 
  • lower_bound (float, optional) – The lower bound used in the filtering.
@@ -544,7 +544,7 @@
 
    The filtered dataframe.
    
 
    
 
    Return type:
    
-
    Union[pd.DataFrame, dask.dataframe.DataFrame]
    
+
    pd.DataFrame | dask.dataframe.DataFrame
    
 
    
 

 
@@ -557,7 +557,7 @@
 

 
Parameters:

 
    
-
  • df (Union[pd.DataFrame, dask.dataframe.DataFrame]) – Dataframe to use.
    • 
+
  • df (dask.dataframe.DataFrame) – Dataframe to use.
    • 
 
  • time_stamps (np.ndarray) – Time stamps of the values to add
    • 
 
  • data (np.ndarray) – Values corresponding at the time stamps in time_stamps
    • 
 
  • dest_column (str) – destination column name
    • 
@@ -568,7 +568,7 @@
 
    Dataframe with added column
    
 
    
 
    Return type:
    
-
    Union[pd.DataFrame, dask.dataframe.DataFrame]
    
+
    dask.dataframe.DataFrame
    
 
    
 

 
@@ -580,7 +580,7 @@
 

 
Parameters:

 
    
-
  • df (Union[pd.DataFrame, dask.dataframe.DataFrame]) – Dataframe to use.
    • 
+
  • df (pd.DataFrame | dask.dataframe.DataFrame) – Dataframe to use.
    • 
 
  • map_2d (Callable) – 2D mapping function.
    • 
 
  • x_column (str) – The X column of the dataframe to apply mapping to.
    • 
 
  • y_column (str) – The Y column of the dataframe to apply mapping to.
    • 
@@ -591,7 +591,7 @@
 
    Dataframe with mapped columns.
    
 
    
 
    Return type:
    
-
    Union[pd.DataFrame, dask.dataframe.DataFrame]
    
+
    pd.DataFrame | dask.dataframe.DataFrame
    
 
    
 

 
@@ -602,15 +602,15 @@
 
Forward fill the specified columns multiple times in a dask dataframe.

 
Allows forward filling between partitions. This is useful for dataframes
 that have sparse data, such as those with many NaNs.
-Running the forward filling multiple times can fix the issue of having
+Runnin the forward filling multiple times can fix the issue of having
 entire partitions consisting of NaNs. By default we run this twice, which
 is enough to fix the issue for dataframes with no consecutive partitions of NaNs.

 

 
Parameters:

 
    
 
  • df (dask.dataframe.DataFrame) – The dataframe to forward fill.
    • 
-
  • columns (list) – The columns to forward fill. If None, fills all columns
    • 
-
  • before (int, str, optional) – The number of rows to include before the current partition.
+
  • columns (list, optional) – The columns to forward fill. If None, fills all columns
    • 
+
  • before (str | int, optional) – The number of rows to include before the current partition.
 if ‘max’ it takes as much as possible from the previous partition, which is
 the size of the smallest partition in the dataframe. Defaults to ‘max’.
    • 
 
  • compute_lengths (bool, optional) – Whether to compute the length of each partition
    • 
@@ -637,8 +637,8 @@
 
    Parameters:
    
 
      
 
    • df (dask.dataframe.DataFrame) – The dataframe to forward fill.
      • 
-
    • columns (list) – The columns to forward fill. If None, fills all columns
      • 
-
    • after (int, str, optional) – The number of rows to include after the current partition.
+
    • columns (list, optional) – The columns to forward fill. If None, fills all columns
      • 
+
    • after (str | int, optional) – The number of rows to include after the current partition.
 if ‘max’ it takes as much as possible from the previous partition, which is
 the size of the smallest partition in the dataframe. Defaults to ‘max’.
      • 
 
    • compute_lengths (bool, optional) – Whether to compute the length of each partition
      • 
@@ -663,13 +663,13 @@
 
        
 
      • df (dask.dataframe.DataFrame) – Dataframe to use. Currently supports only dask dataframes.
        • 
 
      • target_column (str) – Name of the column to apply the offset to.
        • 
-
      • offset_columns (str) – Name of the column(s) to use for the offset.
        • 
-
      • weights (Union[float, Sequence[float]]) – weights to apply on each column before adding.
-Used also for changing sign.
        • 
-
      • reductions (Union[str, Sequence[str]], optional) – Reduction function to use for the offset.
+
      • offset_columns (str | Sequence[str]) – Name of the column(s) to use for the offset.
        • 
+
      • weights (float | Sequence[float]) – weights to apply on each column before adding. Used also
+for changing sign.
        • 
+
      • reductions (str | Sequence[str], optional) – Reduction function to use for the offset.
 Defaults to “mean”. Currently, only mean is supported.
        • 
-
      • preserve_mean (Union[bool, Sequence[bool]], optional) – Whether to subtract the mean of the
-offset column. Defaults to False. If a list is given, it must have the same length as
+
      • preserve_mean (bool | Sequence[bool], optional) – Whether to subtract the mean of the offset
+column. Defaults to False. If a list is given, it must have the same length as
 offset_columns. Otherwise the value passed is used for all columns.
        • 
 
      • inplace (bool, optional) – Whether to apply the offset inplace.
 If false, the new column will have the name provided by rename, or has the same name as
diff --git a/sed/latest/sed/diagnostic.html b/sed/latest/sed/diagnostic.html
index 5bd8050..93a455d 100644
--- a/sed/latest/sed/diagnostic.html
+++ b/sed/latest/sed/diagnostic.html
@@ -8,7 +8,7 @@
     
     
 
-    Diagnostics — SED 0.1.10a6 documentation
+    Diagnostics — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
        SED 0.1.10a6 documentation
        
+    
        SED 0.1.10a5 documentation
        
   
 

     
@@ -507,7 +507,7 @@
 
  • ncol (int) – Number of columns in the plot grid.
    • 
 
  • rvs (Sequence) – List of names for the random variables (rvs).
    • 
 
  • rvbins (Sequence) – Bin values for all random variables.
    • 
-
  • rvranges (Sequence[Tuple[float, float]]) – Value ranges of all random variables.
    • 
+
  • rvranges (Sequence[tuple[float, float]]) – Value ranges of all random variables.
    • 
 
  • backend (str, optional) – Backend for making the plot (‘matplotlib’ or ‘bokeh’).
 Defaults to “bokeh”.
    • 
 
  • legend (bool, optional) – Option to include a legend in each histogram plot.
diff --git a/sed/latest/sed/io.html b/sed/latest/sed/io.html
index 820de4c..5c5bdfb 100644
--- a/sed/latest/sed/io.html
+++ b/sed/latest/sed/io.html
@@ -8,7 +8,7 @@
     
     
 
-    IO — SED 0.1.10a6 documentation
+    IO — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
    SED 0.1.10a6 documentation
    
+    
    SED 0.1.10a5 documentation
    
   
 
    • 
     
@@ -489,7 +489,7 @@
 
    ValueError – Raised if data or axes are not found in the file.
    
 
    
 
    Returns:
    
-
    output xarray data
    
+
    output xarra data
    
 
    
 
    Return type:
    
 
    xr.DataArray
    
@@ -526,7 +526,7 @@
 
    
 
    Parameters:
    
 
      
-
    • faddr (Union[str, Path]) – Path to file to load.
      • 
+
    • faddr (str | Path) – Path to file to load.
      • 
 
    • coords (dict, optional) – The axes describing the data, following the tiff
 stack order. Defaults to None.
      • 
 
    • dims (Sequence, optional) – the order of the coordinates provided, considering
@@ -552,19 +552,20 @@
 
      
 
      Parameters:
      
 
        
-
      • data (Union[xr.DataArray, np.ndarray]) – data to be saved. If a np.ndarray,
+
      • data (xr.DataArray | np.ndarray) – data to be saved. If a np.ndarray,
 the order is retained. If it is an xarray.DataArray, the order is inferred
 from axis_dict instead. ImageJ likes tiff files with axis order as
 TZCYXS. Therefore, best axis order in input should be: Time, Energy,
 posY, posX. The channels ‘C’ and ‘S’ are automatically added and can
 be ignored.
        • 
-
      • faddr (Union[Path, str]) – full path and name of file to save.
        • 
+
      • str) (faddr Path |) – full path and name of file to save.
        • 
 
      • alias_dict (dict, optional) – name pairs for correct axis ordering. Keys should
 be any of T,Z,C,Y,X,S. The Corresponding value should be a dimension of the
 xarray or the dimension number if a numpy array. This is used to sort the
 data in the correct order for imagej standards. If None it tries to guess
 the order from the name of the axes or assumes T,Z,C,Y,X,S order for numpy
 arrays. Defaults to None.
        • 
+
      • faddr (Path | str)
        • 
 
      
 
      
 
      Raises:
      
@@ -589,10 +590,9 @@
 data._attrs[“metadata”].
      • 
 
    • faddr (str) – The file path to save to.
      • 
 
    • reader (str) – The name of the NeXus reader to use.
      • 
-
    • definition (str) – The NeXus definition to use.
      • 
-
    • input_files (Union[str, Sequence[str]]) – The file path or paths to the additional files to
-use.
      • 
-
    • **kwds – Keyword arguments for pynxtools.dataconverter.convert.convert().
      • 
+
    • definition (str) – The NeXus definiton to use.
      • 
+
    • input_files (str | Sequence[str]) – The file path or paths to the additional files to use.
      • 
+
    • **kwds – Keyword arguments for nexusutils.dataconverter.convert.
      • 
 
    
 
    
 
    
diff --git a/sed/latest/sed/loader.html b/sed/latest/sed/loader.html
index ae901c1..3c6b465 100644
--- a/sed/latest/sed/loader.html
+++ b/sed/latest/sed/loader.html
@@ -8,7 +8,7 @@
     
     
 
-    Data loader — SED 0.1.10a6 documentation
+    Data loader — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
    SED 0.1.10a6 documentation
    
+    
    SED 0.1.10a5 documentation
    
   
 
    
     
@@ -508,7 +508,7 @@ 
    Data loader
    List of all detected loader names.
    
 
 
    Return type:
    
-
    List[str]
    
+
    list[str]
    
 
    
 
 
@@ -534,7 +534,7 @@ 
    Data loader
 
    
-supported_file_types: typing.List[str] = []#
    
+supported_file_types: list[str] = []#
 
    
 
 
    
@@ -545,14 +545,14 @@ 
    Data loader
 
    Parameters:
    
 
    
 
@@ -581,7 +581,7 @@ 
    Data loaderParameters:
 
    
 
    Return type:
    
-
    List[str]
    
+
    list[str]
    
 
    
 
 
@@ -613,7 +613,7 @@ 
    Data loaderReturn type:
-
    Tuple[np.ndarray, np.ndarray]
    
+
    tuple[np.ndarray, np.ndarray]
    
 
    
 
 
@@ -669,7 +669,7 @@ 
    Data loader
 
    
-supported_file_types: List[str] = ['parquet', 'csv', 'json']#
    
+supported_file_types: list[str] = ['parquet', 'csv', 'json']#
 
    
 
 
    
@@ -679,14 +679,14 @@ 
    Data loader
 
    Parameters:
    
 
    
 
@@ -723,7 +723,7 @@ 
    Data loaderParameters:
 
    
 
    Return type:
    
-
    str
    
+
    list[str]
    
 
    
 
 
@@ -755,7 +755,7 @@ 
    Data loaderReturn type:
-
    Tuple[np.ndarray, np.ndarray]
    
+
    tuple[np.ndarray, np.ndarray]
    
 
    
 
 
@@ -806,7 +806,7 @@ 
    Data loader
 
    
-sed.loader.mpes.loader.get_groups_and_aliases(h5file, search_pattern=None, alias_key='Name')[source]#
    
+sed.loader.mpes.loader.get_groups_and_aliases(h5file, seach_pattern=None, alias_key='Name')[source]#
 
    Read groups and aliases from a provided hdf5 file handle
    
 
    
 
    Parameters:
    
 
      
 
    • h5file (h5py.File) – The hdf5 file handle
      • 
-
    • search_pattern (str, optional) – Search pattern to select groups. Defaults to include all groups.
      • 
+
    • seach_pattern (str, optional) – Search pattern to select groups. Defaults to include all groups.
      • 
 
    • alias_key (str, optional) – Attribute key where aliases are stored. Defaults to “Name”.
      • 
 
    
 
    
@@ -880,7 +880,7 @@ 
    Data loader
    The list of groupnames and the alias dictionary parsed from the file
    
 
    
 
    Return type:
    
-
    Tuple[List[str], Dict[str, str]]
    
+
    tuple[list[str], dict[str, str]]
    
 
    
 
 
@@ -931,7 +931,7 @@ 
    Data loaderReturns:
-
    the array of the values at evenly spaced timing obtained from
+
    the array of the values at evently spaced timing obtained from
 the ms_markers.
    
 
    
 
    Return type:
    
@@ -943,7 +943,7 @@ 
    Data loader
 
    
 sed.loader.mpes.loader.get_attribute(h5group, attribute)[source]#
    
-
    Reads, decodes and returns an attribute from an hdf5 group
    
+
    Reads, decodes and returns an attrubute from an hdf5 group
    
 
    
 
    Parameters:
    
 
    
 
    
@@ -995,7 +995,7 @@ 
    Data loaderReturns:
-
    The acquisition time of the file in seconds.
    
+
    The acquision time of the file in seconds.
    
 
    
 
    Return type:
    
 
    float
    
@@ -1020,7 +1020,7 @@ 
    Data loader
    The extracted time stamps and corresponding data
    
 
    
 
    Return type:
    
-
    Tuple[List, List]
    
+
    tuple[np.ndarray, np.ndarray]
    
 
    
 
 
@@ -1041,7 +1041,7 @@ 
    Data loader
 
    
-supported_file_types: List[str] = ['h5']#
    
+supported_file_types: list[str] = ['h5']#
 
    
 
 
    
@@ -1052,14 +1052,14 @@ 
    Data loader
 
    Parameters:
    
 
    
 
@@ -1106,7 +1106,7 @@ 
    Data loaderParameters:
 
    
 
    Return type:
    
-
    List[str]
    
+
    list[str]
    
 
    
 
 
@@ -1130,7 +1130,7 @@ 
    Data loader
    A tuple containing the start and end time stamps
    
 
 
    Return type:
    
-
    Tuple[float, float]
    
+
    tuple[float, float]
    
 
    
 
 
@@ -1178,7 +1178,7 @@ 
    Data loaderReturn type:
-
    Tuple[np.ndarray, np.ndarray]
    
+
    tuple[np.ndarray, np.ndarray]
    
 
    
 
 
@@ -1226,7 +1226,7 @@ 
    Data loader
 
    
 class sed.loader.flash.loader.FlashLoader(config)[source]#
    
@@ -1241,7 +1241,7 @@ 
    Data loader
 
    
-supported_file_types: List[str] = ['h5']#
    
+supported_file_types: list[str] = ['h5']#
 
    
 
 
    
@@ -1254,7 +1254,7 @@ 
    Data loaderReturn type:
-
    Tuple[List[Path], Path]
    
+
    tuple[list[Path], Path]
    
 
    
 
    Raises:
    
 
    
@@ -1307,7 +1307,7 @@ 
    Data loader
 
    Parameters:
    
 
      
-
    • formats (Union[str, List[str]]) – The desired format(s)
+
    • formats (str | list[str]) – The desired format(s)
 (‘per_pulse’, ‘per_electron’, ‘per_train’, ‘all’).
      • 
 
    • index (bool) – If True, includes channels from the multi_index.
      • 
 
    
@@ -1316,7 +1316,7 @@ 
    Data loader
    A list of channels with the specified format(s).
    
 
    
 
    Return type:
    
-
    List[str]
    
+
    list[str]
    
 
    
 
 
@@ -1400,7 +1400,7 @@ 
    Data loaderReturn type:
-
    Tuple[Series, np.ndarray]
    
+
    tuple[Series, np.ndarray]
    
 
    
 
 
@@ -1451,8 +1451,8 @@ 
    Data loaderNotes
    
 
    
     
@@ -482,12 +482,12 @@
 and give a nice representation of them.
    
 
    
 
    Parameters:
    
-
    meta (typing.Optional[typing.Dict], default: None)
    
+
    meta (dict, optional) – Pre-existing metadata dict. Defaults to None.
    
 
    
 
    
 
    
 
    
-property metadata: Dict#
    
+property metadata: dict#
 
    Property returning the metadata dict.
 :returns: Dictionary of metadata.
 :rtype: dict
    
@@ -502,7 +502,7 @@
 
      
 
    • entry (typing.Any) – dictionary containing the metadata to add.
      • 
 
    • name (str) – name of the dictionary key under which to add entry.
      • 
-
    • duplicate_policy (str, default: 'raise') – 
      Control behavior in case the ‘name’ key
+
    • duplicate_policy (str, default: 'raise') – 
      Control behaviour in case the ‘name’ key
 is already present in the metadata dictionary. Can be any of:
      
 
      
 
        
diff --git a/sed/latest/user_guide/1_binning_fake_data.html b/sed/latest/user_guide/1_binning_fake_data.html
index 9047964..677b4e7 100644
--- a/sed/latest/user_guide/1_binning_fake_data.html
+++ b/sed/latest/user_guide/1_binning_fake_data.html
@@ -8,7 +8,7 @@
     
     
 
-    Binning demonstration on locally generated fake data — SED 0.1.10a6 documentation
+    Binning demonstration on locally generated fake data — SED 0.1.10a5 documentation
   
   
   
@@ -36,7 +36,7 @@
 
   
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
     
@@ -127,7 +127,7 @@
   
   
   
-    
        SED 0.1.10a6 documentation
        
+    
        SED 0.1.10a5 documentation
        
   
 
      
     
@@ -540,33 +540,33 @@ 
      Generate Fake Data
 
      -CPU times: user 1 s, sys: 49.9 ms, total: 1.05 s
-Wall time: 1.18 s
+CPU times: user 1.33 s, sys: 51.7 ms, total: 1.38 s
+Wall time: 1.38 s
 
    
 
    
 
    
@@ -773,15 +773,15 @@ 
    Compute distributed binning on the partitioned dask dataframe
 
    
 
    
-
    
+
    
 
    
 
    
 
    
 
    
 
    
 
    -CPU times: user 1.78 s, sys: 1.28 s, total: 3.06 s
-Wall time: 681 ms
+CPU times: user 887 ms, sys: 353 ms, total: 1.24 s
+Wall time: 789 ms
 
    
 
    
 
    
@@ -810,7 +810,7 @@ 
    Compute distributed binning on the partitioned dask dataframe
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diff --git a/sed/latest/user_guide/1_binning_fake_data.ipynb b/sed/latest/user_guide/1_binning_fake_data.ipynb
index 1fc0aff..2d4ad17 100644
--- a/sed/latest/user_guide/1_binning_fake_data.ipynb
+++ b/sed/latest/user_guide/1_binning_fake_data.ipynb
@@ -19,10 +19,10 @@
    "id": "fb045e17-fa89-4c11-9d51-7f06e80d96d5",
    "metadata": {
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-     "iopub.execute_input": "2024-07-08T17:09:19.569493Z",
-     "iopub.status.busy": "2024-07-08T17:09:19.568984Z",
-     "iopub.status.idle": "2024-07-08T17:09:22.439931Z",
-     "shell.execute_reply": "2024-07-08T17:09:22.439464Z"
+     "iopub.execute_input": "2024-07-08T21:56:09.683840Z",
+     "iopub.status.busy": "2024-07-08T21:56:09.683631Z",
+     "iopub.status.idle": "2024-07-08T21:56:12.294341Z",
+     "shell.execute_reply": "2024-07-08T21:56:12.293658Z"
     }
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@@ -54,10 +54,10 @@
    "id": "2aa8df59-224a-46a2-bb77-0277ff504996",
    "metadata": {
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-     "iopub.execute_input": "2024-07-08T17:09:22.443081Z",
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-     "iopub.status.idle": "2024-07-08T17:09:22.460938Z",
-     "shell.execute_reply": "2024-07-08T17:09:22.460585Z"
+     "iopub.execute_input": "2024-07-08T21:56:12.297467Z",
+     "iopub.status.busy": "2024-07-08T21:56:12.296944Z",
+     "iopub.status.idle": "2024-07-08T21:56:12.323119Z",
+     "shell.execute_reply": "2024-07-08T21:56:12.322522Z"
     }
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    "outputs": [
@@ -90,33 +90,33 @@
        "  \n",
        "    \n",
        "      0\n",
-       "      1.099223\n",
-       "      2.358426\n",
-       "      1.273596\n",
+       "      -2.352188\n",
+       "      0.018544\n",
+       "      0.087294\n",
        "    \n",
        "    \n",
        "      1\n",
-       "      -1.225348\n",
-       "      -1.008053\n",
-       "      -0.421557\n",
+       "      0.661999\n",
+       "      -2.894941\n",
+       "      0.220710\n",
        "    \n",
        "    \n",
        "      2\n",
-       "      0.269306\n",
-       "      -0.709568\n",
-       "      1.330315\n",
+       "      -0.924308\n",
+       "      -1.126615\n",
+       "      -0.637220\n",
        "    \n",
        "    \n",
        "      3\n",
-       "      -2.039608\n",
-       "      -0.134934\n",
-       "      2.528361\n",
+       "      -0.797949\n",
+       "      0.051009\n",
+       "      -1.290132\n",
        "    \n",
        "    \n",
        "      4\n",
-       "      -0.289571\n",
-       "      2.037927\n",
-       "      0.832904\n",
+       "      -0.019662\n",
+       "      0.205863\n",
+       "      1.269046\n",
        "    \n",
        "    \n",
        "      ...\n",
@@ -126,33 +126,33 @@
        "    \n",
        "    \n",
        "      99995\n",
-       "      0.243452\n",
-       "      -1.305578\n",
-       "      1.034903\n",
+       "      1.733657\n",
+       "      -0.240760\n",
+       "      -0.523856\n",
        "    \n",
        "    \n",
        "      99996\n",
-       "      1.188316\n",
-       "      0.425674\n",
-       "      -1.380265\n",
+       "      0.933419\n",
+       "      -1.249297\n",
+       "      -0.200826\n",
        "    \n",
        "    \n",
        "      99997\n",
-       "      -1.674443\n",
-       "      1.715874\n",
-       "      0.104226\n",
+       "      -0.211517\n",
+       "      0.836925\n",
+       "      -0.111336\n",
        "    \n",
        "    \n",
        "      99998\n",
-       "      -0.466612\n",
-       "      -1.209274\n",
-       "      -0.092987\n",
+       "      1.461525\n",
+       "      0.306676\n",
+       "      -0.803900\n",
        "    \n",
        "    \n",
        "      99999\n",
-       "      0.294573\n",
-       "      -1.874502\n",
-       "      -1.150481\n",
+       "      0.532887\n",
+       "      -1.725969\n",
+       "      2.022515\n",
        "    \n",
        "  \n",
        "\n",
@@ -161,17 +161,17 @@
       ],
       "text/plain": [
        "           posx      posy    energy\n",
-       "0      1.099223  2.358426  1.273596\n",
-       "1     -1.225348 -1.008053 -0.421557\n",
-       "2      0.269306 -0.709568  1.330315\n",
-       "3     -2.039608 -0.134934  2.528361\n",
-       "4     -0.289571  2.037927  0.832904\n",
+       "0     -2.352188  0.018544  0.087294\n",
+       "1      0.661999 -2.894941  0.220710\n",
+       "2     -0.924308 -1.126615 -0.637220\n",
+       "3     -0.797949  0.051009 -1.290132\n",
+       "4     -0.019662  0.205863  1.269046\n",
        "...         ...       ...       ...\n",
-       "99995  0.243452 -1.305578  1.034903\n",
-       "99996  1.188316  0.425674 -1.380265\n",
-       "99997 -1.674443  1.715874  0.104226\n",
-       "99998 -0.466612 -1.209274 -0.092987\n",
-       "99999  0.294573 -1.874502 -1.150481\n",
+       "99995  1.733657 -0.240760 -0.523856\n",
+       "99996  0.933419 -1.249297 -0.200826\n",
+       "99997 -0.211517  0.836925 -0.111336\n",
+       "99998  1.461525  0.306676 -0.803900\n",
+       "99999  0.532887 -1.725969  2.022515\n",
        "\n",
        "[100000 rows x 3 columns]"
       ]
@@ -202,10 +202,10 @@
    "id": "a7601cd7-cd51-40a9-8fc7-8b7d32ff15d0",
    "metadata": {
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-     "iopub.status.busy": "2024-07-08T17:09:22.463363Z",
-     "iopub.status.idle": "2024-07-08T17:09:22.466269Z",
-     "shell.execute_reply": "2024-07-08T17:09:22.465965Z"
+     "iopub.execute_input": "2024-07-08T21:56:12.325619Z",
+     "iopub.status.busy": "2024-07-08T21:56:12.325238Z",
+     "iopub.status.idle": "2024-07-08T21:56:12.329235Z",
+     "shell.execute_reply": "2024-07-08T21:56:12.328683Z"
     }
    },
    "outputs": [],
@@ -230,10 +230,10 @@
    "id": "758a0e95-7a03-4d44-9dae-e6bd2334554c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-08T17:09:22.468375Z",
-     "iopub.status.busy": "2024-07-08T17:09:22.468183Z",
-     "iopub.status.idle": "2024-07-08T17:09:23.647705Z",
-     "shell.execute_reply": "2024-07-08T17:09:23.647247Z"
+     "iopub.execute_input": "2024-07-08T21:56:12.331599Z",
+     "iopub.status.busy": "2024-07-08T21:56:12.331394Z",
+     "iopub.status.idle": "2024-07-08T21:56:13.717885Z",
+     "shell.execute_reply": "2024-07-08T21:56:13.717243Z"
     }
    },
    "outputs": [
@@ -241,8 +241,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 1 s, sys: 49.9 ms, total: 1.05 s\n",
-      "Wall time: 1.18 s\n"
+      "CPU times: user 1.33 s, sys: 51.7 ms, total: 1.38 s\n",
+      "Wall time: 1.38 s\n"
      ]
     }
    ],
@@ -263,16 +263,16 @@
    "id": "c4f2b55f-11b3-4456-abd6-b0865749df96",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-08T17:09:23.650321Z",
-     "iopub.status.busy": "2024-07-08T17:09:23.650113Z",
-     "iopub.status.idle": "2024-07-08T17:09:24.086051Z",
-     "shell.execute_reply": "2024-07-08T17:09:24.085576Z"
+     "iopub.execute_input": "2024-07-08T21:56:13.720290Z",
+     "iopub.status.busy": "2024-07-08T21:56:13.720066Z",
+     "iopub.status.idle": "2024-07-08T21:56:14.354099Z",
+     "shell.execute_reply": "2024-07-08T21:56:14.353377Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "
    "
       ]
@@ -301,10 +301,10 @@
    "id": "ba0416b3-b4b6-4b18-8ed3-a76ab4889892",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-08T17:09:24.089428Z",
-     "iopub.status.busy": "2024-07-08T17:09:24.089248Z",
-     "iopub.status.idle": "2024-07-08T17:09:24.106079Z",
-     "shell.execute_reply": "2024-07-08T17:09:24.105691Z"
+     "iopub.execute_input": "2024-07-08T21:56:14.357790Z",
+     "iopub.status.busy": "2024-07-08T21:56:14.357537Z",
+     "iopub.status.idle": "2024-07-08T21:56:14.371964Z",
+     "shell.execute_reply": "2024-07-08T21:56:14.371329Z"
     }
    },
    "outputs": [
@@ -415,22 +415,22 @@
    "id": "cbed3261-187c-498d-8ee0-0c3a3c8a8c1e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-07-08T17:09:24.107975Z",
-     "iopub.status.busy": "2024-07-08T17:09:24.107833Z",
-     "iopub.status.idle": "2024-07-08T17:09:24.792595Z",
-     "shell.execute_reply": "2024-07-08T17:09:24.791945Z"
+     "iopub.execute_input": "2024-07-08T21:56:14.374578Z",
+     "iopub.status.busy": "2024-07-08T21:56:14.374213Z",
+     "iopub.status.idle": "2024-07-08T21:56:15.166965Z",
+     "shell.execute_reply": "2024-07-08T21:56:15.166330Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2ba03d9838e04889b706931effa08ed0",
+       "model_id": "63d07608761045ecb8879835cda9c412",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "  0%|          | 0/6 [00:00"
       ]
@@ -514,12 +514,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.9.19"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "1ae28a6b4ba449a19926fcc54b986682": {
+     "29853c78541442e0b54ffee6104555c5": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -571,7 +571,7 @@
        "width": null
       }
      },
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@@ -586,14 +586,14 @@
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-        "IPY_MODEL_8d2679906f464c4d8a62ec21995e6985"
+        "IPY_MODEL_b2e336f2a6494cb58cd09ee38e347246",
+        "IPY_MODEL_701249fcbac14731adee2c8baec82b02",
+        "IPY_MODEL_ecc1589858e04e6283e979b0867ffb6e"
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-       "layout": "IPY_MODEL_355b6d5be12d480d832e865768793745"
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@@ -609,15 +609,31 @@
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        "description_tooltip": null,
-       "layout": "IPY_MODEL_a67ebd2127104917aed511743330c89d",
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+       "max": 17.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_6aeb07ce82f845808734a570d23327f7",
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-     "355b6d5be12d480d832e865768793745": {
+     "72c5bb347cff4e409da48ad68b2db1cd": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
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+     "8b8fb3e8b4ce47cda6a4a465f2a2dacc": {
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@@ -669,23 +685,7 @@
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-     "6aeb07ce82f845808734a570d23327f7": {
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-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
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-     "6f2cac48f9a640e5ad9b4c137c7f75be": {
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@@ -700,22 +700,7 @@
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-     "7d147107f1d64db093dbe611337e5bfc": {
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@@ -730,34 +715,13 @@
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-       "layout": "IPY_MODEL_1ae28a6b4ba449a19926fcc54b986682",
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-     "a67ebd2127104917aed511743330c89d": {
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@@ -809,7 +773,22 @@
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-     "f39b23e20aa84238928e932d37e1864e": {
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+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
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+       "_model_module_version": "1.5.0",
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@@ -860,6 +839,27 @@
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+       "placeholder": "​",
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+       "value": " 17/17 [00:00<00:00, 25.17it/s]"
+      }
      }
     },
     "version_major": 2,
diff --git a/sed/latest/user_guide/config.html b/sed/latest/user_guide/config.html
index ba9979b..d95dfde 100644
--- a/sed/latest/user_guide/config.html
+++ b/sed/latest/user_guide/config.html
@@ -8,7 +8,7 @@
     
     
 
-    Configuration — SED 0.1.10a6 documentation
+    Configuration — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
-    
    SED 0.1.10a6 documentation
    
+    
    SED 0.1.10a5 documentation
    
   
 
    
     
@@ -524,9 +524,9 @@ 
    Default configuration settings  adc_binning: 1
   # list of columns to apply jitter to.
   jitter_cols: ["@x_column", "@y_column", "@tof_column"]
-  # Jitter amplitude or list of jitter amplitudes. Should equal half the digital step size of each jitter_column
+  # Jitter amplitude or list of jitter amplitudes. Should equal half the digitial step size of each jitter_column
   jitter_amps: 0.5
-  # Time stepping in seconds of the successive events in the timed dataframe
+  # Time stepping in seconds of the succesive events in the timed dataframe
   timed_dataframe_unit_time: 0.001
 
 energy:
@@ -544,7 +544,7 @@ 
    Default configuration settings  fastdtw_radius: 2
   # Window around a peak to make sure that no other peaks are present
   peak_window: 7
-  # Method to use for energy calibration
+  # Mehtod to use for energy calibration
   calibration_method: "lmfit"
   # Energy scale to use for energy calibration
   energy_scale: "kinetic"
@@ -553,11 +553,11 @@ 
    Default configuration settings  tof_fermi: 132250
   # TOF range to visualize for the correction tool around tof_fermi
   tof_width: [-600, 1000]
-  # x-integration range for the correction tool around the center pixel
+  # x-intergration range for the correction tool around the center pixel
   x_width: [-20, 20]
-  # y-integration range for the correction tool around the center pixel
+  # y-intergration range for the correction tool around the center pixel
   y_width: [-20, 20]
-  # High intensity cutoff for the visualization tool
+  # High intensity cutoff for the visulaization tool
   color_clip: 300
 
 
@@ -592,7 +592,7 @@ 
    Default configuration settingsbinning:
   # Histogram computation mode to use.
   hist_mode: "numba"
-  # Mode for histogram recombination to use
+  # Mode for hostogram recombination to use
   mode: fast
   # Whether to display a progress bar
   pbar: True
@@ -604,7 +604,7 @@ 
    Default configuration settingshistogram:
   # number of bins used for histogram visualization
   bins: [80, 80, 80]
-  # default axes to use for histogram visualization.
+  # default axes to use for histgram visualization.
   # Axes names starting with "@" refer to keys in the "dataframe" section
   axes: ["@x_column", "@y_column", "@tof_column"]
   # default ranges to use for histogram visualization (in unbinned detector coordinates)
@@ -623,7 +623,7 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
   copy_tool_source: "/path/to/data/"
   # path to the root or the local data storage
   copy_tool_dest: "/path/to/localDataStore/"
-  # optional keywords for the copy tool:
+  # optional keyworkds for the copy tool:
   copy_tool_kwds:
     # number of parallel copy jobs
     ntasks: 20
@@ -641,11 +641,11 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
     Stream_4: "ADC"
   # dataframe column name for the time stamp column
   time_stamp_alias: "timeStamps"
-  # hdf5 group name containing eventIDs occurring at every millisecond (used to calculate timestamps)
+  # hdf5 group name containing eventIDs occuring at every millisecond (used to calculate timestamps)
   ms_markers_group: "msMarkers"
   # hdf5 attribute containing the timestamp of the first event in a file
   first_event_time_stamp_key: "FirstEventTimeStamp"
-  # Time stepping in seconds of the successive events in the timed dataframe
+  # Time stepping in seconds of the succesive events in the timed dataframe
   timed_dataframe_unit_time: 0.001
   # list of columns to apply jitter to
   jitter_cols: ["X", "Y", "t", "ADC"]
@@ -712,7 +712,7 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
   fastdtw_radius: 2
   # Window around a peak to make sure that no other peaks are present
   peak_window: 7
-  # Method to use for energy calibration
+  # Mehtod to use for energy calibration
   calibration_method: "lmfit"
   # Energy scale to use for energy calibration
   energy_scale: "kinetic"
@@ -721,11 +721,11 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
   tof_fermi: 132250
   # TOF range to visualize for the correction tool around tof_fermi
   tof_width: [-600, 1000]
-  # x-integration range for the correction tool around the center pixel
+  # x-intergration range for the correction tool around the center pixel
   x_width: [-20, 20]
-  # y-integration range for the correction tool around the center pixel
+  # y-intergration range for the correction tool around the center pixel
   y_width: [-20, 20]
-  # High intensity cutoff for the visualization tool
+  # High intensity cutoff for the visulaization tool
   color_clip: 300
   correction:
     # Correction type
@@ -771,9 +771,9 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
   sigma_radius: 1
   # default momentum calibration
   calibration:
-    # x momentum scaling factor
+    # x momentum scaleing factor
     kx_scale: 0.010729535670610963
-    # y momentum scaling factor
+    # y momentum scaleing factor
     ky_scale: 0.010729535670610963
     # x BZ center pixel
     x_center: 256.0
@@ -811,7 +811,7 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
 binning:
   # Histogram computation mode to use.
   hist_mode: "numba"
-  # Mode for histogram recombination to use
+  # Mode for hostogram recombination to use
   mode: "fast"
   # Whether to display a progress bar
   pbar: True
@@ -825,7 +825,7 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
 histogram:
   # number of bins used for histogram visualization
   bins: [80, 80, 80, 80]
-  # default axes to use for histogram visualization.
+  # default axes to use for histgram visualization.
   # Axes names starting with "@" refer to keys in the "dataframe" section
   axes: ["@x_column", "@y_column", "@tof_column", "@adc_column"]
   # default ranges to use for histogram visualization (in unbinned detector coordinates)
@@ -923,7 +923,7 @@ 
    Example configuration file for mpes (METIS momentum microscope at FHI-Berlin
   reader: "mpes"
   # NeXus application definition to use for saving
   definition: "NXmpes"
-  # List containing additional input files to be handed to the pynxtools converter tool,
+  # List conatining additional input files to be handed to the pynxtools converter tool,
   # e.g. containing a configuration file, and additional metadata.
   input_files: ["../sed/config/NXmpes_config.json"]
 
    
@@ -938,7 +938,7 @@ 
    Example configuration file for flash (HEXTOF momentum microscope at FLASH, D
   loader: flash
   # the beamline where experiment took place
   beamline: pg2
-  # the ID number of the beamtime
+  # the ID number of the beamtimme
   beamtime_id: 11013410
   # the year of the beamtime
   year: 2023
@@ -1022,7 +1022,7 @@ 
    Example configuration file for flash (HEXTOF momentum microscope at FLASH, D
   # channelAlias:
   #   format: per_pulse/per_electron/per_train
   #   group_name: the hdf5 group path
-  #   slice: if the group contains multidimensional data, where to slice
+  #   slice: if the group contains multidim data, where to slice
 
   channels:
     # The timestamp
@@ -1056,8 +1056,8 @@ 
    Example configuration file for flash (HEXTOF momentum microscope at FLASH, D
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
       slice: 3
 
-    # The auxiliary channel has a special structure where the group further contains
-    # a multidimensional structure so further aliases are defined below
+    # The auxillary channel has a special structure where the group further contains
+    # a multidim structure so further aliases are defined below
     dldAux:
       format: per_pulse
       group_name: "/uncategorised/FLASH.EXP/HEXTOF.DAQ/DLD1/"
diff --git a/sed/latest/user_guide/index.html b/sed/latest/user_guide/index.html
index a9f2e36..f129665 100644
--- a/sed/latest/user_guide/index.html
+++ b/sed/latest/user_guide/index.html
@@ -9,7 +9,7 @@
     
 
 
-    User Guide — SED 0.1.10a6 documentation
+    User Guide — SED 0.1.10a5 documentation
   
   
   
@@ -36,7 +36,7 @@
 
   
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
     
@@ -127,7 +127,7 @@
   
   
   
-    
    SED 0.1.10a6 documentation
    
+    
    SED 0.1.10a5 documentation
    
   
 
    
     
diff --git a/sed/latest/user_guide/installation.html b/sed/latest/user_guide/installation.html
index 4fbfa8f..78a6d64 100644
--- a/sed/latest/user_guide/installation.html
+++ b/sed/latest/user_guide/installation.html
@@ -8,7 +8,7 @@
     
     
 
-    Installation — SED 0.1.10a6 documentation
+    Installation — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
@@ -44,7 +44,7 @@
     
     
@@ -124,7 +124,7 @@
   
   
   
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    SED 0.1.10a6 documentation
    
+    
    SED 0.1.10a5 documentation
    
   
 
    
     
diff --git a/sed/latest/workflows/index.html b/sed/latest/workflows/index.html
index c84b89d..39914d0 100644
--- a/sed/latest/workflows/index.html
+++ b/sed/latest/workflows/index.html
@@ -8,7 +8,7 @@
     
     
 
-    Workflows — SED 0.1.10a6 documentation
+    Workflows — SED 0.1.10a5 documentation
   
   
   
@@ -35,7 +35,7 @@
 
   
 
-    
+    
     
     
     
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    SED 0.1.10a6 documentation
    
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