diff --git a/specsanalyzer/latest/_modules/index.html b/specsanalyzer/latest/_modules/index.html index ef73a53..1a4734d 100644 --- a/specsanalyzer/latest/_modules/index.html +++ b/specsanalyzer/latest/_modules/index.html @@ -7,7 +7,7 @@ - Overview: module code — specsanalyzer 0.5.1.dev2+g5d28e05 documentation + Overview: module code — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation @@ -29,7 +29,7 @@ - + @@ -37,7 +37,7 @@ - + @@ -46,7 +46,7 @@ @@ -54,7 +54,7 @@ - + @@ -116,7 +116,7 @@ -

specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+

specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

diff --git a/specsanalyzer/latest/_modules/specsanalyzer/config.html b/specsanalyzer/latest/_modules/specsanalyzer/config.html index 46ffdac..354b898 100644 --- a/specsanalyzer/latest/_modules/specsanalyzer/config.html +++ b/specsanalyzer/latest/_modules/specsanalyzer/config.html @@ -7,7 +7,7 @@ - specsanalyzer.config — specsanalyzer 0.5.1.dev2+g5d28e05 documentation + specsanalyzer.config — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation @@ -29,7 +29,7 @@ - + @@ -37,7 +37,7 @@ - + @@ -46,7 +46,7 @@ @@ -54,7 +54,7 @@ - + @@ -116,7 +116,7 @@ -

specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+

specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

@@ -551,22 +551,22 @@

Source code for specsanalyzer.config

 """This module contains a config library for loading yaml/json files into dicts"""
-from __future__ import annotations
+from __future__ import annotations
 
-import json
-import os
-import platform
-from importlib.util import find_spec
-from pathlib import Path
+import json
+import os
+import platform
+from importlib.util import find_spec
+from pathlib import Path
 
-import yaml
+import yaml
 
 package_dir = os.path.dirname(find_spec("specsanalyzer").origin)
 
 
 

 [docs]
-def parse_config(
+def parse_config(
     config: dict | str = None,
     folder_config: dict | str = None,
     user_config: dict | str = None,
@@ -694,7 +694,7 @@ 
Source code for specsanalyzer.config
 
 

 [docs]
-def load_config(config_path: str) -> dict:
+def load_config(config_path: str) -> dict:
     """Loads config parameter files.
 
     Args:
@@ -728,7 +728,7 @@ 
Source code for specsanalyzer.config
 
 

 [docs]
-def save_config(config_dict: dict, config_path: str, overwrite: bool = False):
+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.
@@ -761,7 +761,7 @@ 
Source code for specsanalyzer.config
 
 

 [docs]
-def complete_dictionary(dictionary: dict, base_dictionary: dict) -> dict:
+def complete_dictionary(dictionary: dict, base_dictionary: dict) -> dict:
     """Iteratively completes a dictionary from a base dictionary, by adding keys that are missing
     in the dictionary, and are present in the base dictionary.
 
diff --git a/specsanalyzer/latest/_modules/specsanalyzer/convert.html b/specsanalyzer/latest/_modules/specsanalyzer/convert.html
index 3df1dbd..819e7c5 100644
--- a/specsanalyzer/latest/_modules/specsanalyzer/convert.html
+++ b/specsanalyzer/latest/_modules/specsanalyzer/convert.html
@@ -7,7 +7,7 @@
   
     
     
-    specsanalyzer.convert — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsanalyzer.convert — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -551,15 +551,15 @@
                   
   
Source code for specsanalyzer.convert
 """Specsanalyzer image conversion module"""
-from __future__ import annotations
+from __future__ import annotations
 
-import numpy as np
-from scipy.ndimage import map_coordinates
+import numpy as np
+from scipy.ndimage import map_coordinates
 
 
 

 [docs]
-def get_damatrix_from_calib2d(
+def get_damatrix_from_calib2d(
     lens_mode: str,
     kinetic_energy: float,
     pass_energy: float,
@@ -594,7 +594,7 @@ 
Source code for specsanalyzer.convert
     except KeyError as exc:
         raise KeyError(
             "The supported modes were not found in the calib2d dictionary",
-        ) from exc
+        ) from exc
 
     if lens_mode in supported_angle_modes:
         # given the lens mode get all the retardation ratios available
@@ -653,7 +653,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def bisection(array: np.ndarray, value: float) -> int:
+def bisection(array: np.ndarray, value: float) -> int:
     """
     Auxiliary function to find the closest rr index from https://stackoverflow.com/questions/2566412/
     find-nearest-value-in-numpy-array
@@ -697,7 +697,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def second_closest_rr(rrvec: np.ndarray, closest_rr_index: int) -> int:
+def second_closest_rr(rrvec: np.ndarray, closest_rr_index: int) -> int:
     """Return closest_rr_index+1 unless you are at the edge of the rrvec.
 
     Args:
@@ -719,7 +719,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def get_rr_da(
+def get_rr_da(
     lens_mode: str,
     calib2d_dict: dict,
 ) -> tuple[np.ndarray, np.ndarray]:
@@ -748,7 +748,7 @@ 
Source code for specsanalyzer.convert
     except KeyError as exc:
         raise KeyError(
             "The supported modes were not found in the calib2d dictionary",
-        ) from exc
+        ) from exc
 
     if lens_mode in supported_angle_modes:
         rr_array = np.array(list(calib2d_dict[lens_mode]["rr"]))
@@ -762,7 +762,7 @@ 
Source code for specsanalyzer.convert
         except KeyError as exc:
             raise ValueError(
                 "Da values do not exist for the given mode.",
-            ) from exc
+            ) from exc
 
         da_matrix = np.zeros([dim1, dim2, dim3])
         for count, item in enumerate(rr_array):
@@ -796,7 +796,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def calculate_polynomial_coef_da(
+def calculate_polynomial_coef_da(
     da_matrix: np.ndarray,
     kinetic_energy: float,
     pass_energy: float,
@@ -838,7 +838,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def zinner(
+def zinner(
     kinetic_energy: np.ndarray,
     angle: np.ndarray,
     da_poly_matrix: np.ndarray,
@@ -869,7 +869,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def zinner_diff(
+def zinner_diff(
     kinetic_energy: np.ndarray,
     angle: np.ndarray,
     da_poly_matrix: np.ndarray,
@@ -904,7 +904,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def mcp_position_mm(
+def mcp_position_mm(
     kinetic_energy: np.ndarray,
     angle: np.ndarray,
     a_inner: float,
@@ -945,7 +945,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def calculate_matrix_correction(
+def calculate_matrix_correction(
     kinetic_energy: float,
     pass_energy: float,
     nx_pixels: int,
@@ -1039,7 +1039,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def calculate_jacobian(
+def calculate_jacobian(
     angular_correction_matrix: np.ndarray,
     e_correction: np.ndarray,
     ek_axis: np.ndarray,
@@ -1067,7 +1067,7 @@ 
Source code for specsanalyzer.convert
 
 

 [docs]
-def physical_unit_data(
+def physical_unit_data(
     image: np.ndarray,
     angular_correction_matrix: np.ndarray,
     e_correction: float,
diff --git a/specsanalyzer/latest/_modules/specsanalyzer/core.html b/specsanalyzer/latest/_modules/specsanalyzer/core.html
index bc04951..2992396 100644
--- a/specsanalyzer/latest/_modules/specsanalyzer/core.html
+++ b/specsanalyzer/latest/_modules/specsanalyzer/core.html
@@ -7,7 +7,7 @@
   
     
     
-    specsanalyzer.core — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsanalyzer.core — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -551,35 +551,35 @@
                   
   
Source code for specsanalyzer.core
 """This is the specsanalyzer core class"""
-from __future__ import annotations
-
-import os
-from typing import Any
-from typing import Generator
-
-import imutils
-import ipywidgets as ipw
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import xarray as xr
-from IPython.display import display
-
-from specsanalyzer import io
-from specsanalyzer.config import complete_dictionary
-from specsanalyzer.config import parse_config
-from specsanalyzer.convert import calculate_matrix_correction
-from specsanalyzer.convert import get_damatrix_from_calib2d
-from specsanalyzer.convert import physical_unit_data
-from specsanalyzer.img_tools import crop_xarray
-from specsanalyzer.img_tools import fourier_filter_2d
+from __future__ import annotations
+
+import os
+from typing import Any
+from typing import Generator
+
+import imutils
+import ipywidgets as ipw
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import xarray as xr
+from IPython.display import display
+
+from specsanalyzer import io
+from specsanalyzer.config import complete_dictionary
+from specsanalyzer.config import parse_config
+from specsanalyzer.convert import calculate_matrix_correction
+from specsanalyzer.convert import get_damatrix_from_calib2d
+from specsanalyzer.convert import physical_unit_data
+from specsanalyzer.img_tools import crop_xarray
+from specsanalyzer.img_tools import fourier_filter_2d
 
 package_dir = os.path.dirname(__file__)
 
 
 

 [docs]
-class SpecsAnalyzer:
+class SpecsAnalyzer:
     """SpecsAnalyzer: A class to convert photoemission data from a SPECS Phoibos analyzer from
     camera image coordinates into physical units (energy, angle, position).
 
@@ -589,7 +589,7 @@ 
Source code for specsanalyzer.core
         **kwds: Keyword arguments passed to ``parse_config``.
     """
 
-    def __init__(
+    def __init__(
         self,
         metadata: dict[Any, Any] = {},
         config: dict[Any, Any] | str = {},
@@ -619,7 +619,7 @@ 
Source code for specsanalyzer.core
 
         self._correction_matrix_dict: dict[Any, Any] = {}
 
-    def __repr__(self):
+    def __repr__(self):
         if self._config is None:
             pretty_str = "No configuration available"
         else:
@@ -630,23 +630,23 @@ 
Source code for specsanalyzer.core
         return pretty_str if pretty_str is not None else ""
 
     @property
-    def config(self) -> dict:
+    def config(self) -> dict:
         """Get config"""
         return self._config
 
     @property
-    def calib2d(self) -> dict:
+    def calib2d(self) -> dict:
         """Get calib2d dict"""
         return self._calib2d
 
     @property
-    def correction_matrix_dict(self) -> dict:
+    def correction_matrix_dict(self) -> dict:
         """Get correction_matrix_dict"""
         return self._correction_matrix_dict
 
 

 [docs]
-    def convert_image(
+    def convert_image(
         self,
         raw_img: np.ndarray,
         lens_mode: str,
@@ -914,7 +914,7 @@ 
Source code for specsanalyzer.core
 
 

 [docs]
-    def crop_tool(
+    def crop_tool(
         self,
         raw_img: np.ndarray,
         lens_mode: str,
@@ -1053,7 +1053,7 @@ 
Source code for specsanalyzer.core
             max=data_array.data.max(),
         )
 
-        def update(hline, vline, v_vals):
+        def update(hline, vline, v_vals):
             lineh1.set_ydata([hline[0]])
             lineh2.set_ydata([hline[1]])
             linev1.set_xdata([vline[0]])
@@ -1068,7 +1068,7 @@ 
Source code for specsanalyzer.core
             v_vals=clim_slider,
         )
 
-        def cropit(val):  # pylint: disable=unused-argument
+        def cropit(val):  # pylint: disable=unused-argument
             ang_min = min(hline_slider.value)
             ang_max = max(hline_slider.value)
             ek_min = min(vline_slider.value)
@@ -1131,7 +1131,7 @@ 
Source code for specsanalyzer.core
 
 

 [docs]
-    def fft_tool(
+    def fft_tool(
         self,
         raw_image: np.ndarray,
         apply: bool = False,
@@ -1252,7 +1252,7 @@ 
Source code for specsanalyzer.core
             max=int(np.log10(np.abs(img).max())) + 1,
         )
 
-        def update(v_vals, pos_x, pos_y, sigma_x, sigma_y, amplitude):
+        def update(v_vals, pos_x, pos_y, sigma_x, sigma_y, amplitude):
             fft_filter_peaks = create_fft_params(amplitude, pos_x, pos_y, sigma_x, sigma_y)
             msk = fourier_filter_2d(raw_image, peaks=fft_filter_peaks, ret="mask")
             filtered_new = fourier_filter_2d(raw_image, peaks=fft_filter_peaks, ret="filtered")
@@ -1289,7 +1289,7 @@ 
Source code for specsanalyzer.core
             v_vals=clim_slider,
         )
 
-        def apply_fft(apply: bool):  # pylint: disable=unused-argument
+        def apply_fft(apply: bool):  # pylint: disable=unused-argument
             amplitude = amplitude_slider.value
             pos_x = pos_x_slider.value
             pos_y = pos_y_slider.value
@@ -1329,7 +1329,7 @@ 
Source code for specsanalyzer.core
 
 

 [docs]
-def create_fft_params(
+def create_fft_params(
     amplitude: float,
     pos_x: float,
     pos_y: float,
diff --git a/specsanalyzer/latest/_modules/specsanalyzer/img_tools.html b/specsanalyzer/latest/_modules/specsanalyzer/img_tools.html
index e1f4b2d..e07eef1 100644
--- a/specsanalyzer/latest/_modules/specsanalyzer/img_tools.html
+++ b/specsanalyzer/latest/_modules/specsanalyzer/img_tools.html
@@ -7,7 +7,7 @@
   
     
     
-    specsanalyzer.img_tools — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsanalyzer.img_tools — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -551,17 +551,17 @@
                   
   
Source code for specsanalyzer.img_tools
 """This module contains image manipulation tools for the specsanalyzer package"""
-from __future__ import annotations
+from __future__ import annotations
 
-from typing import Sequence
+from typing import Sequence
 
-import numpy as np
-import xarray as xr
+import numpy as np
+import xarray as xr
 
 
 

 [docs]
-def gauss2d(
+def gauss2d(
     x: float | np.ndarray,
     y: float | np.ndarray,
     mx: float,
@@ -592,7 +592,7 @@ 
Source code for specsanalyzer.img_tools
 
 

 [docs]
-def fourier_filter_2d(
+def fourier_filter_2d(
     image: np.ndarray,
     peaks: Sequence[dict],
     ret: str = "filtered",
@@ -641,7 +641,7 @@ 
Source code for specsanalyzer.img_tools
             raise KeyError(
                 f"The peaks input is supposed to be a list of dicts with the "
                 "following structure: pos_x, pos_y, sigma_x, sigma_y, amplitude.",
-            ) from exc
+            ) from exc
 
     # apply mask to the FFT, and transform back
     filtered = np.fft.irfft2(np.fft.ifftshift(image_fft * mask, axes=0))
@@ -661,7 +661,7 @@ 
Source code for specsanalyzer.img_tools
 
 

 [docs]
-def crop_xarray(
+def crop_xarray(
     data_array: xr.DataArray,
     x_min: float,
     x_max: float,
diff --git a/specsanalyzer/latest/_modules/specsanalyzer/io.html b/specsanalyzer/latest/_modules/specsanalyzer/io.html
index d219e61..2dd2be3 100644
--- a/specsanalyzer/latest/_modules/specsanalyzer/io.html
+++ b/specsanalyzer/latest/_modules/specsanalyzer/io.html
@@ -7,7 +7,7 @@
   
     
     
-    specsanalyzer.io — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsanalyzer.io — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -551,17 +551,17 @@
                   
   
Source code for specsanalyzer.io
 """This module contains file input/output functions for the specsanalyzer module"""
-from __future__ import annotations
+from __future__ import annotations
 
-from pathlib import Path
-from typing import Any
-from typing import Sequence
+from pathlib import Path
+from typing import Any
+from typing import Sequence
 
-import h5py
-import numpy as np
-import tifffile
-import xarray as xr
-from pynxtools.dataconverter.convert import convert
+import h5py
+import numpy as np
+import tifffile
+import xarray as xr
+from pynxtools.dataconverter.convert import convert
 
 _IMAGEJ_DIMS_ORDER = "TZCYXS"
 _IMAGEJ_DIMS_ALIAS = {
@@ -591,7 +591,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def recursive_write_metadata(h5group: h5py.Group, node: dict):
+def recursive_write_metadata(h5group: h5py.Group, node: dict):
     """Recurses through a python dictionary and writes it into an hdf5 file.
 
     Args:
@@ -631,13 +631,13 @@ 
Source code for specsanalyzer.io
             except BaseException as exc:
                 raise ValueError(
                     f"Unknown error occurred, cannot save {item} of type {type(item)}.",
-                ) from exc

+                ) from exc

 
 
 
 

 [docs]
-def recursive_parse_metadata(
+def recursive_parse_metadata(
     node: h5py.Group | h5py.Dataset,
 ) -> dict:
     """Recurses through an hdf5 file, and parse it into a dictionary.
@@ -668,7 +668,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def to_h5(data: xr.DataArray, faddr: str, mode: str = "w"):
+def to_h5(data: xr.DataArray, faddr: str, mode: str = "w"):
     """Save xarray formatted data to hdf5
 
     Args:
@@ -720,7 +720,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def load_h5(faddr: str, mode: str = "r") -> xr.DataArray:
+def load_h5(faddr: str, mode: str = "r") -> xr.DataArray:
     """Read xarray data from formatted hdf5 file
 
     Args:
@@ -737,7 +737,7 @@ 
Source code for specsanalyzer.io
         except KeyError as exc:
             raise ValueError(
                 f"Wrong Data Format, the BinnedData were not found. The error was{exc}.",
-            ) from exc
+            ) from exc
 
         # Reading the axes
         bin_axes = []
@@ -750,7 +750,7 @@ 
Source code for specsanalyzer.io
         except KeyError as exc:
             raise ValueError(
                 f"Wrong Data Format, the axes were not found. The error was {exc}",
-            ) from exc
+            ) from exc
 
         # load metadata
         metadata = None
@@ -780,7 +780,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def to_tiff(
+def to_tiff(
     data: xr.DataArray | np.ndarray,
     faddr: Path | str,
     alias_dict: dict = None,
@@ -845,7 +845,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def _sort_dims_for_imagej(dims: list, alias_dict: dict = None) -> list:
+def _sort_dims_for_imagej(dims: list, alias_dict: dict = None) -> list:
     """Guess the order of the dimensions from the alias dictionary
 
     Args:
@@ -871,7 +871,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def _fill_missing_dims(dims: list, alias_dict: dict = None) -> list:
+def _fill_missing_dims(dims: list, alias_dict: dict = None) -> list:
     """Guess the order of the dimensions from the alias dictionary
 
     Args:
@@ -925,7 +925,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def load_tiff(
+def load_tiff(
     faddr: str | Path,
     coords: dict = None,
     dims: Sequence = None,
@@ -975,7 +975,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def to_nexus(
+def to_nexus(
     data: xr.DataArray,
     faddr: str,
     reader: str,
@@ -1014,7 +1014,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def get_pair_from_list(list_line: list) -> list:
+def get_pair_from_list(list_line: list) -> list:
     """Returns key value pair for the read function
     where a line in the file contains '=' character.
 
@@ -1047,7 +1047,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def read_calib2d(filepath: str) -> list:
+def read_calib2d(filepath: str) -> list:
     """Reads the calib2d file into a convenient list for the parser
     function containing useful and cleaned data.
 
@@ -1090,7 +1090,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def parse_calib2d_to_dict(filepath: str) -> dict:
+def parse_calib2d_to_dict(filepath: str) -> dict:
     """Parses the given calib2d file into a nested dictionary structure
     to provide parameters for image conversion.
 
@@ -1133,7 +1133,7 @@ 
Source code for specsanalyzer.io
 
 

 [docs]
-def get_modes_from_calib_dict(calib_dict: dict) -> tuple[list, list]:
+def get_modes_from_calib_dict(calib_dict: dict) -> tuple[list, list]:
     """create a list of supported modes, divided in spatial and angular modes
 
     Args:
diff --git a/specsanalyzer/latest/_modules/specsscan/core.html b/specsanalyzer/latest/_modules/specsscan/core.html
index 28631c1..3f76eab 100644
--- a/specsanalyzer/latest/_modules/specsscan/core.html
+++ b/specsanalyzer/latest/_modules/specsscan/core.html
@@ -7,7 +7,7 @@
   
     
     
-    specsscan.core — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsscan.core — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -551,33 +551,33 @@
                   
   
Source code for specsscan.core
 """This is the SpecsScan core class"""
-from __future__ import annotations
-
-import copy
-import os
-import pathlib
-from importlib.util import find_spec
-from logging import warn
-from pathlib import Path
-from typing import Any
-from typing import Sequence
-
-import matplotlib
-import numpy as np
-import xarray as xr
-from tqdm.auto import tqdm
-
-from specsanalyzer import SpecsAnalyzer
-from specsanalyzer.config import parse_config
-from specsanalyzer.io import to_h5
-from specsanalyzer.io import to_nexus
-from specsanalyzer.io import to_tiff
-from specsscan.helpers import get_coords
-from specsscan.helpers import get_scan_path
-from specsscan.helpers import handle_meta
-from specsscan.helpers import load_images
-from specsscan.helpers import parse_info_to_dict
-from specsscan.helpers import parse_lut_to_df
+from __future__ import annotations
+
+import copy
+import os
+import pathlib
+from importlib.util import find_spec
+from logging import warn
+from pathlib import Path
+from typing import Any
+from typing import Sequence
+
+import matplotlib
+import numpy as np
+import xarray as xr
+from tqdm.auto import tqdm
+
+from specsanalyzer import SpecsAnalyzer
+from specsanalyzer.config import parse_config
+from specsanalyzer.io import to_h5
+from specsanalyzer.io import to_nexus
+from specsanalyzer.io import to_tiff
+from specsscan.helpers import get_coords
+from specsscan.helpers import get_scan_path
+from specsscan.helpers import handle_meta
+from specsscan.helpers import load_images
+from specsscan.helpers import parse_info_to_dict
+from specsscan.helpers import parse_lut_to_df
 
 
 package_dir = os.path.dirname(find_spec("specsscan").origin)
@@ -585,7 +585,7 @@ 
Source code for specsscan.core
 
 

 [docs]
-class SpecsScan:
+class SpecsScan:
     """SpecsAnalyzer class for loading scans and data from SPECS Phoibos electron analyzers,
     generated with the ARPESControl software at Fritz Haber Institute, Berlin, and EPFL, Lausanne.
 
@@ -595,7 +595,7 @@ 
Source code for specsscan.core
         **kwds: Keyword arguments passed to ``parse_config``.
     """
 
-    def __init__(
+    def __init__(
         self,
         metadata: dict = {},
         config: dict | str = {},
@@ -635,7 +635,7 @@ 
Source code for specsscan.core
         self._result: xr.DataArray = None
 
     # pylint: disable=duplicate-code
-    def __repr__(self):
+    def __repr__(self):
         if self._config is None:
             pretty_str = "No configuration available"
         else:
@@ -646,12 +646,12 @@ 
Source code for specsscan.core
         return pretty_str if pretty_str is not None else ""
 
     @property
-    def config(self):
+    def config(self):
         """Get config"""
         return self._config
 
     @config.setter
-    def config(self, config: dict | str):
+    def config(self, config: dict | str):
         """Set config"""
         self._config = parse_config(
             config,
@@ -663,13 +663,13 @@ 
Source code for specsscan.core
             self.spa = SpecsAnalyzer()
 
     @property
-    def result(self):
+    def result(self):
         """Get result xarray"""
         return self._result
 
 

 [docs]
-    def load_scan(
+    def load_scan(
         self,
         scan: int,
         path: str | Path = "",
@@ -812,10 +812,10 @@ 
Source code for specsscan.core
             "/entry/sample/transformations/sample_azimuth": "Azimuth",
         }
 
-        # store link information for resolved axis coordinates
+        # store data for resolved axis coordinates
         for k, v in depends_dict.items():
             if v in axis_dict:
-                self._scan_info[axis_dict[v]] = "@link:/entry/data/" + k
+                self._scan_info[axis_dict[v]] = res_xarray.coords[k].data
 
         for name in res_xarray.dims:
             try:
@@ -846,7 +846,7 @@ 
Source code for specsscan.core
 
 

 [docs]
-    def crop_tool(self, scan: int = None, path: Path | str = "", **kwds):
+    def crop_tool(self, scan: int = None, path: Path | str = "", **kwds):
         """Cropping tool interface to crop_tool method of the SpecsAnalyzer class.
 
         Args:
@@ -871,7 +871,7 @@ 
Source code for specsscan.core
             try:
                 image = self.metadata["loader"]["raw_data"][0]
             except KeyError as exc:
-                raise ValueError("No image loaded, load image first!") from exc
+                raise ValueError("No image loaded, load image first!") from exc
 
         self.spa.crop_tool(
             image,
@@ -885,7 +885,7 @@ 
Source code for specsscan.core
 
 

 [docs]
-    def fft_tool(self, scan: int = None, path: Path | str = "", **kwds):
+    def fft_tool(self, scan: int = None, path: Path | str = "", **kwds):
         """FFT tool to play around with the peak parameters in the Fourier plane. Built to filter
         out the meshgrid appearing in the raw data images. The optimized parameters are stored in
         the class config dict under fft_filter_peaks.
@@ -915,7 +915,7 @@ 
Source code for specsscan.core
             try:
                 image = self.metadata["loader"]["raw_data"][0]
             except KeyError as exc:
-                raise ValueError("No image loaded, load image first!") from exc
+                raise ValueError("No image loaded, load image first!") from exc
 
         self.spa.fft_tool(
             image,
@@ -925,7 +925,7 @@ 
Source code for specsscan.core
 
 

 [docs]
-    def check_scan(
+    def check_scan(
         self,
         scan: int,
         delays: Sequence[int] | int,
@@ -1054,7 +1054,7 @@ 
Source code for specsscan.core
 
 

 [docs]
-    def save(
+    def save(
         self,
         faddr: str,
         **kwds,
@@ -1140,7 +1140,7 @@ 
Source code for specsscan.core
 
 

 [docs]
-    def process_sweep_scan(
+    def process_sweep_scan(
         self,
         raw_data: list[np.ndarray],
         kinetic_energy: np.ndarray,
diff --git a/specsanalyzer/latest/_modules/specsscan/helpers.html b/specsanalyzer/latest/_modules/specsscan/helpers.html
index c57255f..e239268 100644
--- a/specsanalyzer/latest/_modules/specsscan/helpers.html
+++ b/specsanalyzer/latest/_modules/specsscan/helpers.html
@@ -7,7 +7,7 @@
   
     
     
-    specsscan.helpers — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsscan.helpers — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -551,27 +551,27 @@
                   
   
Source code for specsscan.helpers
 """This script contains helper functions used by the specsscan class"""
-from __future__ import annotations
+from __future__ import annotations
 
-import datetime as dt
-import json
-from pathlib import Path
-from typing import Any
-from typing import Sequence
-from urllib.error import HTTPError
-from urllib.error import URLError
-from urllib.request import urlopen
+import datetime as dt
+import json
+from pathlib import Path
+from typing import Any
+from typing import Sequence
+from urllib.error import HTTPError
+from urllib.error import URLError
+from urllib.request import urlopen
 
-import numpy as np
-import pandas as pd
-from tqdm.auto import tqdm
+import numpy as np
+import pandas as pd
+from tqdm.auto import tqdm
 
-from specsanalyzer.config import complete_dictionary
+from specsanalyzer.config import complete_dictionary
 
 
 

 [docs]
-def get_scan_path(path: Path | str, scan: int, basepath: Path | str) -> Path:
+def get_scan_path(path: Path | str, scan: int, basepath: Path | str) -> Path:
     """Returns the path to the given scan.
 
     Args:
@@ -607,7 +607,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def load_images(
+def load_images(
     scan_path: Path,
     df_lut: pd.DataFrame = None,
     iterations: np.ndarray | slice | Sequence[int] | Sequence[slice] = None,
@@ -681,7 +681,7 @@ 
Source code for specsscan.helpers
                 "the chosen data. In case of a single scan, "
                 f"try without passing iterations inside the "
                 "load_scan method.",
-            ) from exc
+            ) from exc
 
         print(f"Averaging over {avg_dim}...")
         for dim in tqdm(raw_2d_sliced):
@@ -718,7 +718,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def get_raw2d(scan_list: list[str], raw_array: np.ndarray) -> np.ndarray:
+def get_raw2d(scan_list: list[str], raw_array: np.ndarray) -> np.ndarray:
     """Converts a 1-D array of raw scan names into 2-D based on the number of iterations
 
     Args:
@@ -762,7 +762,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def parse_lut_to_df(scan_path: Path) -> pd.DataFrame:
+def parse_lut_to_df(scan_path: Path) -> pd.DataFrame:
     """Loads the contents of LUT.txt file into a pandas data frame to be used as metadata.
 
     Args:
@@ -794,7 +794,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def get_coords(
+def get_coords(
     scan_path: Path,
     scan_type: str,
     scan_info: dict[Any, Any],
@@ -842,7 +842,7 @@ 
Source code for specsscan.helpers
             dim = df_new.columns[index]
 
         else:
-            raise FileNotFoundError("scanvector.txt file not found!") from exc
+            raise FileNotFoundError("scanvector.txt file not found!") from exc
 
     if scan_type == "delay":
         t_0 = scan_info["TimeZero"]
@@ -855,7 +855,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def compare_coords(axis_data: np.ndarray) -> tuple[np.ndarray, int]:
+def compare_coords(axis_data: np.ndarray) -> tuple[np.ndarray, int]:
     """Identifies the most changing column in a given 2-D numpy array.
 
     Args:
@@ -881,7 +881,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def parse_info_to_dict(path: Path) -> dict:
+def parse_info_to_dict(path: Path) -> dict:
     """Parses the contents of info.txt file into a dictionary
 
     Args:
@@ -911,7 +911,7 @@ 
Source code for specsscan.helpers
                     info_dict[key] = value
 
     except FileNotFoundError as exc:
-        raise FileNotFoundError("info.txt file not found.") from exc
+        raise FileNotFoundError("info.txt file not found.") from exc
 
     return info_dict

 
@@ -919,7 +919,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def handle_meta(
+def handle_meta(
     df_lut: pd.DataFrame,
     scan_info: dict,
     config: dict,
@@ -1062,7 +1062,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def get_archiver_data(
+def get_archiver_data(
     archiver_url: str,
     archiver_channel: str,
     ts_from: float,
@@ -1093,7 +1093,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def find_scan(path: Path, scan: int) -> list[Path]:
+def find_scan(path: Path, scan: int) -> list[Path]:
     """Search function to locate the scan folder
 
     Args:
@@ -1127,7 +1127,7 @@ 
Source code for specsscan.helpers
 
 

 [docs]
-def find_scan_type(
+def find_scan_type(
     path: Path,
     scan_type: str,
 ):
diff --git a/specsanalyzer/latest/_static/documentation_options.js b/specsanalyzer/latest/_static/documentation_options.js
index 5543657..8493108 100644
--- a/specsanalyzer/latest/_static/documentation_options.js
+++ b/specsanalyzer/latest/_static/documentation_options.js
@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '0.5.1.dev2+g5d28e05',
+    VERSION: '0.5.1.dev10+g0c6ed22',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/specsanalyzer/latest/_static/pygments.css b/specsanalyzer/latest/_static/pygments.css
index 012e6a0..d7dd577 100644
--- a/specsanalyzer/latest/_static/pygments.css
+++ b/specsanalyzer/latest/_static/pygments.css
@@ -6,11 +6,11 @@ html[data-theme="light"] .highlight span.linenos.special { color: #000000; backg
 html[data-theme="light"] .highlight .hll { background-color: #fae4c2 }
 html[data-theme="light"] .highlight { background: #fefefe; color: #080808 }
 html[data-theme="light"] .highlight .c { color: #515151 } /* Comment */
-html[data-theme="light"] .highlight .err { color: #a12236 } /* Error */
-html[data-theme="light"] .highlight .k { color: #6730c5 } /* Keyword */
-html[data-theme="light"] .highlight .l { color: #7f4707 } /* Literal */
+html[data-theme="light"] .highlight .err { color: #A12236 } /* Error */
+html[data-theme="light"] .highlight .k { color: #6730C5 } /* Keyword */
+html[data-theme="light"] .highlight .l { color: #7F4707 } /* Literal */
 html[data-theme="light"] .highlight .n { color: #080808 } /* Name */
-html[data-theme="light"] .highlight .o { color: #00622f } /* Operator */
+html[data-theme="light"] .highlight .o { color: #00622F } /* Operator */
 html[data-theme="light"] .highlight .p { color: #080808 } /* Punctuation */
 html[data-theme="light"] .highlight .ch { color: #515151 } /* Comment.Hashbang */
 html[data-theme="light"] .highlight .cm { color: #515151 } /* Comment.Multiline */
@@ -18,135 +18,135 @@ html[data-theme="light"] .highlight .cp { color: #515151 } /* Comment.Preproc */
 html[data-theme="light"] .highlight .cpf { color: #515151 } /* Comment.PreprocFile */
 html[data-theme="light"] .highlight .c1 { color: #515151 } /* Comment.Single */
 html[data-theme="light"] .highlight .cs { color: #515151 } /* Comment.Special */
-html[data-theme="light"] .highlight .gd { color: #005b82 } /* Generic.Deleted */
+html[data-theme="light"] .highlight .gd { color: #005B82 } /* Generic.Deleted */
 html[data-theme="light"] .highlight .ge { font-style: italic } /* Generic.Emph */
-html[data-theme="light"] .highlight .gh { color: #005b82 } /* Generic.Heading */
+html[data-theme="light"] .highlight .gh { color: #005B82 } /* Generic.Heading */
 html[data-theme="light"] .highlight .gs { font-weight: bold } /* Generic.Strong */
-html[data-theme="light"] .highlight .gu { color: #005b82 } /* Generic.Subheading */
-html[data-theme="light"] .highlight .kc { color: #6730c5 } /* Keyword.Constant */
-html[data-theme="light"] .highlight .kd { color: #6730c5 } /* Keyword.Declaration */
-html[data-theme="light"] .highlight .kn { color: #6730c5 } /* Keyword.Namespace */
-html[data-theme="light"] .highlight .kp { color: #6730c5 } /* Keyword.Pseudo */
-html[data-theme="light"] .highlight .kr { color: #6730c5 } /* Keyword.Reserved */
-html[data-theme="light"] .highlight .kt { color: #7f4707 } /* Keyword.Type */
-html[data-theme="light"] .highlight .ld { color: #7f4707 } /* Literal.Date */
-html[data-theme="light"] .highlight .m { color: #7f4707 } /* Literal.Number */
-html[data-theme="light"] .highlight .s { color: #00622f } /* Literal.String */
+html[data-theme="light"] .highlight .gu { color: #005B82 } /* Generic.Subheading */
+html[data-theme="light"] .highlight .kc { color: #6730C5 } /* Keyword.Constant */
+html[data-theme="light"] .highlight .kd { color: #6730C5 } /* Keyword.Declaration */
+html[data-theme="light"] .highlight .kn { color: #6730C5 } /* Keyword.Namespace */
+html[data-theme="light"] .highlight .kp { color: #6730C5 } /* Keyword.Pseudo */
+html[data-theme="light"] .highlight .kr { color: #6730C5 } /* Keyword.Reserved */
+html[data-theme="light"] .highlight .kt { color: #7F4707 } /* Keyword.Type */
+html[data-theme="light"] .highlight .ld { color: #7F4707 } /* Literal.Date */
+html[data-theme="light"] .highlight .m { color: #7F4707 } /* Literal.Number */
+html[data-theme="light"] .highlight .s { color: #00622F } /* Literal.String */
 html[data-theme="light"] .highlight .na { color: #912583 } /* Name.Attribute */
-html[data-theme="light"] .highlight .nb { color: #7f4707 } /* Name.Builtin */
-html[data-theme="light"] .highlight .nc { color: #005b82 } /* Name.Class */
-html[data-theme="light"] .highlight .no { color: #005b82 } /* Name.Constant */
-html[data-theme="light"] .highlight .nd { color: #7f4707 } /* Name.Decorator */
-html[data-theme="light"] .highlight .ni { color: #00622f } /* Name.Entity */
-html[data-theme="light"] .highlight .ne { color: #6730c5 } /* Name.Exception */
-html[data-theme="light"] .highlight .nf { color: #005b82 } /* Name.Function */
-html[data-theme="light"] .highlight .nl { color: #7f4707 } /* Name.Label */
+html[data-theme="light"] .highlight .nb { color: #7F4707 } /* Name.Builtin */
+html[data-theme="light"] .highlight .nc { color: #005B82 } /* Name.Class */
+html[data-theme="light"] .highlight .no { color: #005B82 } /* Name.Constant */
+html[data-theme="light"] .highlight .nd { color: #7F4707 } /* Name.Decorator */
+html[data-theme="light"] .highlight .ni { color: #00622F } /* Name.Entity */
+html[data-theme="light"] .highlight .ne { color: #6730C5 } /* Name.Exception */
+html[data-theme="light"] .highlight .nf { color: #005B82 } /* Name.Function */
+html[data-theme="light"] .highlight .nl { color: #7F4707 } /* Name.Label */
 html[data-theme="light"] .highlight .nn { color: #080808 } /* Name.Namespace */
 html[data-theme="light"] .highlight .nx { color: #080808 } /* Name.Other */
-html[data-theme="light"] .highlight .py { color: #005b82 } /* Name.Property */
-html[data-theme="light"] .highlight .nt { color: #005b82 } /* Name.Tag */
-html[data-theme="light"] .highlight .nv { color: #a12236 } /* Name.Variable */
-html[data-theme="light"] .highlight .ow { color: #6730c5 } /* Operator.Word */
+html[data-theme="light"] .highlight .py { color: #005B82 } /* Name.Property */
+html[data-theme="light"] .highlight .nt { color: #005B82 } /* Name.Tag */
+html[data-theme="light"] .highlight .nv { color: #A12236 } /* Name.Variable */
+html[data-theme="light"] .highlight .ow { color: #6730C5 } /* Operator.Word */
 html[data-theme="light"] .highlight .pm { color: #080808 } /* Punctuation.Marker */
 html[data-theme="light"] .highlight .w { color: #080808 } /* Text.Whitespace */
-html[data-theme="light"] .highlight .mb { color: #7f4707 } /* Literal.Number.Bin */
-html[data-theme="light"] .highlight .mf { color: #7f4707 } /* Literal.Number.Float */
-html[data-theme="light"] .highlight .mh { color: #7f4707 } /* Literal.Number.Hex */
-html[data-theme="light"] .highlight .mi { color: #7f4707 } /* Literal.Number.Integer */
-html[data-theme="light"] .highlight .mo { color: #7f4707 } /* Literal.Number.Oct */
-html[data-theme="light"] .highlight .sa { color: #00622f } /* Literal.String.Affix */
-html[data-theme="light"] .highlight .sb { color: #00622f } /* Literal.String.Backtick */
-html[data-theme="light"] .highlight .sc { color: #00622f } /* Literal.String.Char */
-html[data-theme="light"] .highlight .dl { color: #00622f } /* Literal.String.Delimiter */
-html[data-theme="light"] .highlight .sd { color: #00622f } /* Literal.String.Doc */
-html[data-theme="light"] .highlight .s2 { color: #00622f } /* Literal.String.Double */
-html[data-theme="light"] .highlight .se { color: #00622f } /* Literal.String.Escape */
-html[data-theme="light"] .highlight .sh { color: #00622f } /* Literal.String.Heredoc */
-html[data-theme="light"] .highlight .si { color: #00622f } /* Literal.String.Interpol */
-html[data-theme="light"] .highlight .sx { color: #00622f } /* Literal.String.Other */
-html[data-theme="light"] .highlight .sr { color: #a12236 } /* Literal.String.Regex */
-html[data-theme="light"] .highlight .s1 { color: #00622f } /* Literal.String.Single */
-html[data-theme="light"] .highlight .ss { color: #005b82 } /* Literal.String.Symbol */
-html[data-theme="light"] .highlight .bp { color: #7f4707 } /* Name.Builtin.Pseudo */
-html[data-theme="light"] .highlight .fm { color: #005b82 } /* Name.Function.Magic */
-html[data-theme="light"] .highlight .vc { color: #a12236 } /* Name.Variable.Class */
-html[data-theme="light"] .highlight .vg { color: #a12236 } /* Name.Variable.Global */
-html[data-theme="light"] .highlight .vi { color: #a12236 } /* Name.Variable.Instance */
-html[data-theme="light"] .highlight .vm { color: #7f4707 } /* Name.Variable.Magic */
-html[data-theme="light"] .highlight .il { color: #7f4707 } /* Literal.Number.Integer.Long */
+html[data-theme="light"] .highlight .mb { color: #7F4707 } /* Literal.Number.Bin */
+html[data-theme="light"] .highlight .mf { color: #7F4707 } /* Literal.Number.Float */
+html[data-theme="light"] .highlight .mh { color: #7F4707 } /* Literal.Number.Hex */
+html[data-theme="light"] .highlight .mi { color: #7F4707 } /* Literal.Number.Integer */
+html[data-theme="light"] .highlight .mo { color: #7F4707 } /* Literal.Number.Oct */
+html[data-theme="light"] .highlight .sa { color: #00622F } /* Literal.String.Affix */
+html[data-theme="light"] .highlight .sb { color: #00622F } /* Literal.String.Backtick */
+html[data-theme="light"] .highlight .sc { color: #00622F } /* Literal.String.Char */
+html[data-theme="light"] .highlight .dl { color: #00622F } /* Literal.String.Delimiter */
+html[data-theme="light"] .highlight .sd { color: #00622F } /* Literal.String.Doc */
+html[data-theme="light"] .highlight .s2 { color: #00622F } /* Literal.String.Double */
+html[data-theme="light"] .highlight .se { color: #00622F } /* Literal.String.Escape */
+html[data-theme="light"] .highlight .sh { color: #00622F } /* Literal.String.Heredoc */
+html[data-theme="light"] .highlight .si { color: #00622F } /* Literal.String.Interpol */
+html[data-theme="light"] .highlight .sx { color: #00622F } /* Literal.String.Other */
+html[data-theme="light"] .highlight .sr { color: #A12236 } /* Literal.String.Regex */
+html[data-theme="light"] .highlight .s1 { color: #00622F } /* Literal.String.Single */
+html[data-theme="light"] .highlight .ss { color: #005B82 } /* Literal.String.Symbol */
+html[data-theme="light"] .highlight .bp { color: #7F4707 } /* Name.Builtin.Pseudo */
+html[data-theme="light"] .highlight .fm { color: #005B82 } /* Name.Function.Magic */
+html[data-theme="light"] .highlight .vc { color: #A12236 } /* Name.Variable.Class */
+html[data-theme="light"] .highlight .vg { color: #A12236 } /* Name.Variable.Global */
+html[data-theme="light"] .highlight .vi { color: #A12236 } /* Name.Variable.Instance */
+html[data-theme="light"] .highlight .vm { color: #7F4707 } /* Name.Variable.Magic */
+html[data-theme="light"] .highlight .il { color: #7F4707 } /* Literal.Number.Integer.Long */
 html[data-theme="dark"] .highlight pre { line-height: 125%; }
 html[data-theme="dark"] .highlight td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
 html[data-theme="dark"] .highlight span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
 html[data-theme="dark"] .highlight td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 html[data-theme="dark"] .highlight span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
 html[data-theme="dark"] .highlight .hll { background-color: #ffd9002e }
-html[data-theme="dark"] .highlight { background: #2b2b2b; color: #f8f8f2 }
-html[data-theme="dark"] .highlight .c { color: #ffd900 } /* Comment */
-html[data-theme="dark"] .highlight .err { color: #ffa07a } /* Error */
-html[data-theme="dark"] .highlight .k { color: #dcc6e0 } /* Keyword */
-html[data-theme="dark"] .highlight .l { color: #ffd900 } /* Literal */
-html[data-theme="dark"] .highlight .n { color: #f8f8f2 } /* Name */
-html[data-theme="dark"] .highlight .o { color: #abe338 } /* Operator */
-html[data-theme="dark"] .highlight .p { color: #f8f8f2 } /* Punctuation */
-html[data-theme="dark"] .highlight .ch { color: #ffd900 } /* Comment.Hashbang */
-html[data-theme="dark"] .highlight .cm { color: #ffd900 } /* Comment.Multiline */
-html[data-theme="dark"] .highlight .cp { color: #ffd900 } /* Comment.Preproc */
-html[data-theme="dark"] .highlight .cpf { color: #ffd900 } /* Comment.PreprocFile */
-html[data-theme="dark"] .highlight .c1 { color: #ffd900 } /* Comment.Single */
-html[data-theme="dark"] .highlight .cs { color: #ffd900 } /* Comment.Special */
-html[data-theme="dark"] .highlight .gd { color: #00e0e0 } /* Generic.Deleted */
+html[data-theme="dark"] .highlight { background: #2b2b2b; color: #F8F8F2 }
+html[data-theme="dark"] .highlight .c { color: #FFD900 } /* Comment */
+html[data-theme="dark"] .highlight .err { color: #FFA07A } /* Error */
+html[data-theme="dark"] .highlight .k { color: #DCC6E0 } /* Keyword */
+html[data-theme="dark"] .highlight .l { color: #FFD900 } /* Literal */
+html[data-theme="dark"] .highlight .n { color: #F8F8F2 } /* Name */
+html[data-theme="dark"] .highlight .o { color: #ABE338 } /* Operator */
+html[data-theme="dark"] .highlight .p { color: #F8F8F2 } /* Punctuation */
+html[data-theme="dark"] .highlight .ch { color: #FFD900 } /* Comment.Hashbang */
+html[data-theme="dark"] .highlight .cm { color: #FFD900 } /* Comment.Multiline */
+html[data-theme="dark"] .highlight .cp { color: #FFD900 } /* Comment.Preproc */
+html[data-theme="dark"] .highlight .cpf { color: #FFD900 } /* Comment.PreprocFile */
+html[data-theme="dark"] .highlight .c1 { color: #FFD900 } /* Comment.Single */
+html[data-theme="dark"] .highlight .cs { color: #FFD900 } /* Comment.Special */
+html[data-theme="dark"] .highlight .gd { color: #00E0E0 } /* Generic.Deleted */
 html[data-theme="dark"] .highlight .ge { font-style: italic } /* Generic.Emph */
-html[data-theme="dark"] .highlight .gh { color: #00e0e0 } /* Generic.Heading */
+html[data-theme="dark"] .highlight .gh { color: #00E0E0 } /* Generic.Heading */
 html[data-theme="dark"] .highlight .gs { font-weight: bold } /* Generic.Strong */
-html[data-theme="dark"] .highlight .gu { color: #00e0e0 } /* Generic.Subheading */
-html[data-theme="dark"] .highlight .kc { color: #dcc6e0 } /* Keyword.Constant */
-html[data-theme="dark"] .highlight .kd { color: #dcc6e0 } /* Keyword.Declaration */
-html[data-theme="dark"] .highlight .kn { color: #dcc6e0 } /* Keyword.Namespace */
-html[data-theme="dark"] .highlight .kp { color: #dcc6e0 } /* Keyword.Pseudo */
-html[data-theme="dark"] .highlight .kr { color: #dcc6e0 } /* Keyword.Reserved */
-html[data-theme="dark"] .highlight .kt { color: #ffd900 } /* Keyword.Type */
-html[data-theme="dark"] .highlight .ld { color: #ffd900 } /* Literal.Date */
-html[data-theme="dark"] .highlight .m { color: #ffd900 } /* Literal.Number */
-html[data-theme="dark"] .highlight .s { color: #abe338 } /* Literal.String */
-html[data-theme="dark"] .highlight .na { color: #ffd900 } /* Name.Attribute */
-html[data-theme="dark"] .highlight .nb { color: #ffd900 } /* Name.Builtin */
-html[data-theme="dark"] .highlight .nc { color: #00e0e0 } /* Name.Class */
-html[data-theme="dark"] .highlight .no { color: #00e0e0 } /* Name.Constant */
-html[data-theme="dark"] .highlight .nd { color: #ffd900 } /* Name.Decorator */
-html[data-theme="dark"] .highlight .ni { color: #abe338 } /* Name.Entity */
-html[data-theme="dark"] .highlight .ne { color: #dcc6e0 } /* Name.Exception */
-html[data-theme="dark"] .highlight .nf { color: #00e0e0 } /* Name.Function */
-html[data-theme="dark"] .highlight .nl { color: #ffd900 } /* Name.Label */
-html[data-theme="dark"] .highlight .nn { color: #f8f8f2 } /* Name.Namespace */
-html[data-theme="dark"] .highlight .nx { color: #f8f8f2 } /* Name.Other */
-html[data-theme="dark"] .highlight .py { color: #00e0e0 } /* Name.Property */
-html[data-theme="dark"] .highlight .nt { color: #00e0e0 } /* Name.Tag */
-html[data-theme="dark"] .highlight .nv { color: #ffa07a } /* Name.Variable */
-html[data-theme="dark"] .highlight .ow { color: #dcc6e0 } /* Operator.Word */
-html[data-theme="dark"] .highlight .pm { color: #f8f8f2 } /* Punctuation.Marker */
-html[data-theme="dark"] .highlight .w { color: #f8f8f2 } /* Text.Whitespace */
-html[data-theme="dark"] .highlight .mb { color: #ffd900 } /* Literal.Number.Bin */
-html[data-theme="dark"] .highlight .mf { color: #ffd900 } /* Literal.Number.Float */
-html[data-theme="dark"] .highlight .mh { color: #ffd900 } /* Literal.Number.Hex */
-html[data-theme="dark"] .highlight .mi { color: #ffd900 } /* Literal.Number.Integer */
-html[data-theme="dark"] .highlight .mo { color: #ffd900 } /* Literal.Number.Oct */
-html[data-theme="dark"] .highlight .sa { color: #abe338 } /* Literal.String.Affix */
-html[data-theme="dark"] .highlight .sb { color: #abe338 } /* Literal.String.Backtick */
-html[data-theme="dark"] .highlight .sc { color: #abe338 } /* Literal.String.Char */
-html[data-theme="dark"] .highlight .dl { color: #abe338 } /* Literal.String.Delimiter */
-html[data-theme="dark"] .highlight .sd { color: #abe338 } /* Literal.String.Doc */
-html[data-theme="dark"] .highlight .s2 { color: #abe338 } /* Literal.String.Double */
-html[data-theme="dark"] .highlight .se { color: #abe338 } /* Literal.String.Escape */
-html[data-theme="dark"] .highlight .sh { color: #abe338 } /* Literal.String.Heredoc */
-html[data-theme="dark"] .highlight .si { color: #abe338 } /* Literal.String.Interpol */
-html[data-theme="dark"] .highlight .sx { color: #abe338 } /* Literal.String.Other */
-html[data-theme="dark"] .highlight .sr { color: #ffa07a } /* Literal.String.Regex */
-html[data-theme="dark"] .highlight .s1 { color: #abe338 } /* Literal.String.Single */
-html[data-theme="dark"] .highlight .ss { color: #00e0e0 } /* Literal.String.Symbol */
-html[data-theme="dark"] .highlight .bp { color: #ffd900 } /* Name.Builtin.Pseudo */
-html[data-theme="dark"] .highlight .fm { color: #00e0e0 } /* Name.Function.Magic */
-html[data-theme="dark"] .highlight .vc { color: #ffa07a } /* Name.Variable.Class */
-html[data-theme="dark"] .highlight .vg { color: #ffa07a } /* Name.Variable.Global */
-html[data-theme="dark"] .highlight .vi { color: #ffa07a } /* Name.Variable.Instance */
-html[data-theme="dark"] .highlight .vm { color: #ffd900 } /* Name.Variable.Magic */
-html[data-theme="dark"] .highlight .il { color: #ffd900 } /* Literal.Number.Integer.Long */
\ No newline at end of file
+html[data-theme="dark"] .highlight .gu { color: #00E0E0 } /* Generic.Subheading */
+html[data-theme="dark"] .highlight .kc { color: #DCC6E0 } /* Keyword.Constant */
+html[data-theme="dark"] .highlight .kd { color: #DCC6E0 } /* Keyword.Declaration */
+html[data-theme="dark"] .highlight .kn { color: #DCC6E0 } /* Keyword.Namespace */
+html[data-theme="dark"] .highlight .kp { color: #DCC6E0 } /* Keyword.Pseudo */
+html[data-theme="dark"] .highlight .kr { color: #DCC6E0 } /* Keyword.Reserved */
+html[data-theme="dark"] .highlight .kt { color: #FFD900 } /* Keyword.Type */
+html[data-theme="dark"] .highlight .ld { color: #FFD900 } /* Literal.Date */
+html[data-theme="dark"] .highlight .m { color: #FFD900 } /* Literal.Number */
+html[data-theme="dark"] .highlight .s { color: #ABE338 } /* Literal.String */
+html[data-theme="dark"] .highlight .na { color: #FFD900 } /* Name.Attribute */
+html[data-theme="dark"] .highlight .nb { color: #FFD900 } /* Name.Builtin */
+html[data-theme="dark"] .highlight .nc { color: #00E0E0 } /* Name.Class */
+html[data-theme="dark"] .highlight .no { color: #00E0E0 } /* Name.Constant */
+html[data-theme="dark"] .highlight .nd { color: #FFD900 } /* Name.Decorator */
+html[data-theme="dark"] .highlight .ni { color: #ABE338 } /* Name.Entity */
+html[data-theme="dark"] .highlight .ne { color: #DCC6E0 } /* Name.Exception */
+html[data-theme="dark"] .highlight .nf { color: #00E0E0 } /* Name.Function */
+html[data-theme="dark"] .highlight .nl { color: #FFD900 } /* Name.Label */
+html[data-theme="dark"] .highlight .nn { color: #F8F8F2 } /* Name.Namespace */
+html[data-theme="dark"] .highlight .nx { color: #F8F8F2 } /* Name.Other */
+html[data-theme="dark"] .highlight .py { color: #00E0E0 } /* Name.Property */
+html[data-theme="dark"] .highlight .nt { color: #00E0E0 } /* Name.Tag */
+html[data-theme="dark"] .highlight .nv { color: #FFA07A } /* Name.Variable */
+html[data-theme="dark"] .highlight .ow { color: #DCC6E0 } /* Operator.Word */
+html[data-theme="dark"] .highlight .pm { color: #F8F8F2 } /* Punctuation.Marker */
+html[data-theme="dark"] .highlight .w { color: #F8F8F2 } /* Text.Whitespace */
+html[data-theme="dark"] .highlight .mb { color: #FFD900 } /* Literal.Number.Bin */
+html[data-theme="dark"] .highlight .mf { color: #FFD900 } /* Literal.Number.Float */
+html[data-theme="dark"] .highlight .mh { color: #FFD900 } /* Literal.Number.Hex */
+html[data-theme="dark"] .highlight .mi { color: #FFD900 } /* Literal.Number.Integer */
+html[data-theme="dark"] .highlight .mo { color: #FFD900 } /* Literal.Number.Oct */
+html[data-theme="dark"] .highlight .sa { color: #ABE338 } /* Literal.String.Affix */
+html[data-theme="dark"] .highlight .sb { color: #ABE338 } /* Literal.String.Backtick */
+html[data-theme="dark"] .highlight .sc { color: #ABE338 } /* Literal.String.Char */
+html[data-theme="dark"] .highlight .dl { color: #ABE338 } /* Literal.String.Delimiter */
+html[data-theme="dark"] .highlight .sd { color: #ABE338 } /* Literal.String.Doc */
+html[data-theme="dark"] .highlight .s2 { color: #ABE338 } /* Literal.String.Double */
+html[data-theme="dark"] .highlight .se { color: #ABE338 } /* Literal.String.Escape */
+html[data-theme="dark"] .highlight .sh { color: #ABE338 } /* Literal.String.Heredoc */
+html[data-theme="dark"] .highlight .si { color: #ABE338 } /* Literal.String.Interpol */
+html[data-theme="dark"] .highlight .sx { color: #ABE338 } /* Literal.String.Other */
+html[data-theme="dark"] .highlight .sr { color: #FFA07A } /* Literal.String.Regex */
+html[data-theme="dark"] .highlight .s1 { color: #ABE338 } /* Literal.String.Single */
+html[data-theme="dark"] .highlight .ss { color: #00E0E0 } /* Literal.String.Symbol */
+html[data-theme="dark"] .highlight .bp { color: #FFD900 } /* Name.Builtin.Pseudo */
+html[data-theme="dark"] .highlight .fm { color: #00E0E0 } /* Name.Function.Magic */
+html[data-theme="dark"] .highlight .vc { color: #FFA07A } /* Name.Variable.Class */
+html[data-theme="dark"] .highlight .vg { color: #FFA07A } /* Name.Variable.Global */
+html[data-theme="dark"] .highlight .vi { color: #FFA07A } /* Name.Variable.Instance */
+html[data-theme="dark"] .highlight .vm { color: #FFD900 } /* Name.Variable.Magic */
+html[data-theme="dark"] .highlight .il { color: #FFD900 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/specsanalyzer/latest/genindex.html b/specsanalyzer/latest/genindex.html
index 4db373f..de981a6 100644
--- a/specsanalyzer/latest/genindex.html
+++ b/specsanalyzer/latest/genindex.html
@@ -7,7 +7,7 @@
   
     
     
-    Index — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Index — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -54,7 +54,7 @@
     
   
   
-  
+  
   
   
   
@@ -116,7 +116,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/getting_started.html b/specsanalyzer/latest/getting_started.html
index b269505..3385c12 100644
--- a/specsanalyzer/latest/getting_started.html
+++ b/specsanalyzer/latest/getting_started.html
@@ -8,7 +8,7 @@
     
     
 
-    specsanalyzer — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    specsanalyzer — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/index.html b/specsanalyzer/latest/index.html
index f5682b1..7feb9aa 100644
--- a/specsanalyzer/latest/index.html
+++ b/specsanalyzer/latest/index.html
@@ -8,7 +8,7 @@
     
     
 
-    Welcome to specsanalyzer’s documentation! — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Welcome to specsanalyzer’s documentation! — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -49,7 +49,7 @@
     
@@ -58,7 +58,7 @@
     
   
   
-  
+  
   
   
   
@@ -120,7 +120,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/misc/maintain.html b/specsanalyzer/latest/misc/maintain.html
index c73d495..eb0f29e 100644
--- a/specsanalyzer/latest/misc/maintain.html
+++ b/specsanalyzer/latest/misc/maintain.html
@@ -8,7 +8,7 @@
     
     
 
-    1. How to maintain — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    1. How to maintain — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -56,7 +56,7 @@
     
   
   
-  
+  
   
   
   
@@ -118,7 +118,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/objects.inv b/specsanalyzer/latest/objects.inv
index 84d8a7b..8014bdf 100644
Binary files a/specsanalyzer/latest/objects.inv and b/specsanalyzer/latest/objects.inv differ
diff --git a/specsanalyzer/latest/py-modindex.html b/specsanalyzer/latest/py-modindex.html
index cd9a73d..c37e02b 100644
--- a/specsanalyzer/latest/py-modindex.html
+++ b/specsanalyzer/latest/py-modindex.html
@@ -7,7 +7,7 @@
   
     
     
-    Python Module Index — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Python Module Index — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -29,7 +29,7 @@
   
 
 
-    
+    
   
   
   
@@ -37,7 +37,7 @@
   
 
 
-    
+    
     
     
     
@@ -46,7 +46,7 @@
     
@@ -55,7 +55,7 @@
 
   
   
-  
+  
 
 
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/search.html b/specsanalyzer/latest/search.html
index 93d4ad5..d968e1b 100644
--- a/specsanalyzer/latest/search.html
+++ b/specsanalyzer/latest/search.html
@@ -6,7 +6,7 @@
 
   
     
-    Search - specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Search - specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -28,7 +28,7 @@
   
 
 
-    
+    
   
   
   
@@ -36,7 +36,7 @@
   
 
 
-    
+    
     
     
     
@@ -45,7 +45,7 @@
     
@@ -56,7 +56,7 @@
     
   
   
-  
+  
   
   
   
@@ -118,7 +118,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/searchindex.js b/specsanalyzer/latest/searchindex.js
index 7d0e089..7d9ed09 100644
--- a/specsanalyzer/latest/searchindex.js
+++ b/specsanalyzer/latest/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"API": [[3, "module-specsanalyzer.config"]], "Adjusting offsets and angle": [[10, "Adjusting-offsets-and-angle"]], "Config": [[3, null]], "Configuration and calib2d file": [[0, "configuration-and-calib2d-file"]], "Contributing": [[1, null]], "Conversion into spatially resolved modes": [[10, "Conversion-into-spatially-resolved-modes"]], "Conversion using conversion_parameters dict": [[10, "Conversion-using-conversion_parameters-dict"]], "Core functions  (specsanalyzer.core)": [[5, null]], "Core functions  (specsscan.core)": [[8, null]], "Cropping data": [[11, "Cropping-data"]], "Default specsanalyzer configuration settings": [[3, "default-specsanalyzer-configuration-settings"]], "Default specsscan configuration settings": [[3, "default-specsscan-configuration-settings"]], "Example 1: SpecsAnalyzer conversion": [[10, null]], "Example 2: SpecsScan loading": [[11, null]], "Example 3: Export to NeXus": [[12, null]], "Example 4: Sweep Scan loading": [[13, null]], "Example configuration file for the trARPES setup at FHI-Berlin": [[3, "example-configuration-file-for-the-trarpes-setup-at-fhi-berlin"]], "For Contributors": [[0, "for-contributors"]], "Getting Started": [[1, null]], "Helpers": [[9, null]], "How to maintain": [[2, null]], "Image conversion": [[10, "Image-conversion"]], "Indices and tables": [[1, "indices-and-tables"]], "Installation": [[0, "installation"]], "Loading data": [[11, "Loading-data"]], "Loading with selected iterations": [[11, "Loading-with-selected-iterations"]], "Pip (for users)": [[0, "pip-for-users"]], "Poetry (for maintainers)": [[0, "poetry-for-maintainers"]], "Removal of Mesh Artifact": [[11, "Removal-of-Mesh-Artifact"]], "Removal of mesh artefact": [[10, "Removal-of-mesh-artefact"]], "Saving": [[11, "Saving"]], "SpecsAnalyzer Core Modules": [[1, null]], "SpecsScan Core Modules": [[1, null]], "Welcome to specsanalyzer\u2019s documentation!": [[1, null]], "convert functions (specsanalyzer.convert)": [[4, null]], "image tool functions (specsanalyzer.img_tools)": [[6, null]], "io functions  (specsanalyzer.io)": [[7, null]], "specsanalyzer": [[0, null]]}, "docnames": ["getting_started", "index", "misc/maintain", "specsanalyzer/config", "specsanalyzer/convert", "specsanalyzer/core", "specsanalyzer/img_tools", "specsanalyzer/io", "specsscan/core", "specsscan/helpers", "tutorial/1_specsanalyzer_conversion_examples", "tutorial/2_specsscan_example", "tutorial/3_specsscan_conversion_to_NeXus", "tutorial/4_specsscan_load_sweep_scan"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1}, "filenames": ["getting_started.rst", "index.rst", "misc/maintain.rst", "specsanalyzer/config.rst", "specsanalyzer/convert.rst", "specsanalyzer/core.rst", "specsanalyzer/img_tools.rst", "specsanalyzer/io.rst", "specsscan/core.rst", "specsscan/helpers.rst", "tutorial/1_specsanalyzer_conversion_examples.ipynb", "tutorial/2_specsscan_example.ipynb", "tutorial/3_specsscan_conversion_to_NeXus.ipynb", "tutorial/4_specsscan_load_sweep_scan.ipynb"], "indexentries": {"_fill_missing_dims() (in module specsanalyzer.io)": [[7, "specsanalyzer.io._fill_missing_dims", false]], "_sort_dims_for_imagej() (in module specsanalyzer.io)": [[7, "specsanalyzer.io._sort_dims_for_imagej", false]], "bisection() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.bisection", false]], "calculate_jacobian() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.calculate_jacobian", false]], "calculate_matrix_correction() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.calculate_matrix_correction", false]], "calculate_polynomial_coef_da() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.calculate_polynomial_coef_da", false]], "calib2d (specsanalyzer.core.specsanalyzer property)": [[5, "specsanalyzer.core.SpecsAnalyzer.calib2d", false]], "check_scan() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.check_scan", false]], "compare_coords() (in module specsscan.helpers)": [[9, "specsscan.helpers.compare_coords", false]], "complete_dictionary() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.complete_dictionary", false]], "config (specsanalyzer.core.specsanalyzer property)": [[5, "specsanalyzer.core.SpecsAnalyzer.config", false]], "config (specsscan.core.specsscan property)": [[8, "specsscan.core.SpecsScan.config", false]], "convert_image() (specsanalyzer.core.specsanalyzer method)": [[5, "specsanalyzer.core.SpecsAnalyzer.convert_image", false]], "correction_matrix_dict (specsanalyzer.core.specsanalyzer property)": [[5, "specsanalyzer.core.SpecsAnalyzer.correction_matrix_dict", false]], "create_fft_params() (in module specsanalyzer.core)": [[5, "specsanalyzer.core.create_fft_params", false]], "crop_tool() (specsanalyzer.core.specsanalyzer method)": [[5, "specsanalyzer.core.SpecsAnalyzer.crop_tool", false]], "crop_tool() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.crop_tool", false]], "crop_xarray() (in module specsanalyzer.img_tools)": [[6, "specsanalyzer.img_tools.crop_xarray", false]], "fft_tool() (specsanalyzer.core.specsanalyzer method)": [[5, "specsanalyzer.core.SpecsAnalyzer.fft_tool", false]], "fft_tool() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.fft_tool", false]], "find_scan() (in module specsscan.helpers)": [[9, "specsscan.helpers.find_scan", false]], "find_scan_type() (in module specsscan.helpers)": [[9, "specsscan.helpers.find_scan_type", false]], "fourier_filter_2d() (in module specsanalyzer.img_tools)": [[6, "specsanalyzer.img_tools.fourier_filter_2d", false]], "gauss2d() (in module specsanalyzer.img_tools)": [[6, "specsanalyzer.img_tools.gauss2d", false]], "get_archiver_data() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_archiver_data", false]], "get_coords() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_coords", false]], "get_damatrix_from_calib2d() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.get_damatrix_from_calib2d", false]], "get_modes_from_calib_dict() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.get_modes_from_calib_dict", false]], "get_pair_from_list() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.get_pair_from_list", false]], "get_raw2d() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_raw2d", false]], "get_rr_da() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.get_rr_da", false]], "get_scan_path() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_scan_path", false]], "handle_meta() (in module specsscan.helpers)": [[9, "specsscan.helpers.handle_meta", false]], "load_config() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.load_config", false]], "load_h5() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.load_h5", false]], "load_images() (in module specsscan.helpers)": [[9, "specsscan.helpers.load_images", false]], "load_scan() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.load_scan", false]], "load_tiff() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.load_tiff", false]], "mcp_position_mm() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.mcp_position_mm", false]], "module": [[3, "module-specsanalyzer.config", false], [4, "module-specsanalyzer.convert", false], [5, "module-specsanalyzer.core", false], [6, "module-specsanalyzer.img_tools", false], [7, "module-specsanalyzer.io", false], [8, "module-specsscan.core", false], [9, "module-specsscan.helpers", false]], "parse_calib2d_to_dict() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.parse_calib2d_to_dict", false]], "parse_config() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.parse_config", false]], "parse_info_to_dict() (in module specsscan.helpers)": [[9, "specsscan.helpers.parse_info_to_dict", false]], "parse_lut_to_df() (in module specsscan.helpers)": [[9, "specsscan.helpers.parse_lut_to_df", false]], "physical_unit_data() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.physical_unit_data", false]], "process_sweep_scan() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.process_sweep_scan", false]], "read_calib2d() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.read_calib2d", false]], "recursive_parse_metadata() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.recursive_parse_metadata", false]], "recursive_write_metadata() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.recursive_write_metadata", false]], "result (specsscan.core.specsscan property)": [[8, "specsscan.core.SpecsScan.result", false]], "save() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.save", false]], "save_config() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.save_config", false]], "second_closest_rr() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.second_closest_rr", false]], "specsanalyzer (class in specsanalyzer.core)": [[5, "specsanalyzer.core.SpecsAnalyzer", false]], "specsanalyzer.config": [[3, "module-specsanalyzer.config", false]], "specsanalyzer.convert": [[4, "module-specsanalyzer.convert", false]], "specsanalyzer.core": [[5, "module-specsanalyzer.core", false]], "specsanalyzer.img_tools": [[6, "module-specsanalyzer.img_tools", false]], "specsanalyzer.io": [[7, "module-specsanalyzer.io", false]], "specsscan (class in specsscan.core)": [[8, "specsscan.core.SpecsScan", false]], "specsscan.core": [[8, "module-specsscan.core", false]], "specsscan.helpers": [[9, "module-specsscan.helpers", false]], "to_h5() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.to_h5", false]], "to_nexus() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.to_nexus", false]], "to_tiff() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.to_tiff", false]], "zinner() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.zinner", false]], "zinner_diff() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.zinner_diff", false]]}, "objects": {"specsanalyzer": [[3, 0, 0, "-", "config"], [4, 0, 0, "-", "convert"], [5, 0, 0, "-", "core"], [6, 0, 0, "-", "img_tools"], [7, 0, 0, "-", "io"]], "specsanalyzer.config": [[3, 1, 1, "", "complete_dictionary"], [3, 1, 1, "", "load_config"], [3, 1, 1, "", "parse_config"], [3, 1, 1, "", "save_config"]], "specsanalyzer.convert": [[4, 1, 1, "", "bisection"], [4, 1, 1, "", "calculate_jacobian"], [4, 1, 1, "", "calculate_matrix_correction"], [4, 1, 1, "", "calculate_polynomial_coef_da"], [4, 1, 1, "", "get_damatrix_from_calib2d"], [4, 1, 1, "", "get_rr_da"], [4, 1, 1, "", "mcp_position_mm"], [4, 1, 1, "", "physical_unit_data"], [4, 1, 1, "", "second_closest_rr"], [4, 1, 1, "", "zinner"], [4, 1, 1, "", "zinner_diff"]], "specsanalyzer.core": [[5, 2, 1, "", "SpecsAnalyzer"], [5, 1, 1, "", "create_fft_params"]], "specsanalyzer.core.SpecsAnalyzer": [[5, 3, 1, "", "calib2d"], [5, 3, 1, "", "config"], [5, 4, 1, "", "convert_image"], [5, 3, 1, "", "correction_matrix_dict"], [5, 4, 1, "", "crop_tool"], [5, 4, 1, "", "fft_tool"]], "specsanalyzer.img_tools": [[6, 1, 1, "", "crop_xarray"], [6, 1, 1, "", "fourier_filter_2d"], [6, 1, 1, "", "gauss2d"]], "specsanalyzer.io": [[7, 1, 1, "", "_fill_missing_dims"], [7, 1, 1, "", "_sort_dims_for_imagej"], [7, 1, 1, "", "get_modes_from_calib_dict"], [7, 1, 1, "", "get_pair_from_list"], [7, 1, 1, "", "load_h5"], [7, 1, 1, "", "load_tiff"], [7, 1, 1, "", "parse_calib2d_to_dict"], [7, 1, 1, "", "read_calib2d"], [7, 1, 1, "", "recursive_parse_metadata"], [7, 1, 1, "", "recursive_write_metadata"], [7, 1, 1, "", "to_h5"], [7, 1, 1, "", "to_nexus"], [7, 1, 1, "", "to_tiff"]], "specsscan": [[8, 0, 0, "-", "core"], [9, 0, 0, "-", "helpers"]], "specsscan.core": [[8, 2, 1, "", "SpecsScan"]], "specsscan.core.SpecsScan": [[8, 4, 1, "", "check_scan"], [8, 3, 1, "", "config"], [8, 4, 1, "", "crop_tool"], [8, 4, 1, "", "fft_tool"], [8, 4, 1, "", "load_scan"], [8, 4, 1, "", "process_sweep_scan"], [8, 3, 1, "", "result"], [8, 4, 1, "", "save"]], "specsscan.helpers": [[9, 1, 1, "", "compare_coords"], [9, 1, 1, "", "find_scan"], [9, 1, 1, "", "find_scan_type"], [9, 1, 1, "", "get_archiver_data"], [9, 1, 1, "", "get_coords"], [9, 1, 1, "", "get_raw2d"], [9, 1, 1, "", "get_scan_path"], [9, 1, 1, "", "handle_meta"], [9, 1, 1, "", "load_images"], [9, 1, 1, "", "parse_info_to_dict"], [9, 1, 1, "", "parse_lut_to_df"]]}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "property", "Python property"], "4": ["py", "method", "Python method"]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:property", "4": "py:method"}, "terms": {"": [3, 7, 12], "0": [3, 5, 10, 11, 12, 13], "00": 12, "000000": 10, "0033": 10, "00645": 3, "01": 10, "02": [10, 12], "0258": 10, "03": [10, 12], "04": 10, "04428571e": 10, "05": 10, "066": 10, "07597844332538181": 12, "07597844332538357": 13, "08": [11, 12], "0x7f5c65f7a650": 12, "0x7f5c6eb1e1a0": 12, "0x7f883933cfa0": 11, "0x7f8839437250": 11, "0x7f883975eaa0": 11, "0x7f88399c7790": 11, "0x7f883acec0a0": 11, "0x7f93adb4a8f0": 10, "0x7f93adbce3b0": 10, "0x7f93adc7e080": 10, "0x7f93b5991c60": 10, "0x7f93b5db6d40": 10, "0x7f93b8547010": 10, "0x7fc9a00eb760": 13, "1": [1, 3, 4, 6, 8, 9, 11, 12, 13], "10": [3, 8, 9, 10, 11, 12, 13], "1024": 3, "108": 3, "109": 3, "11": [10, 11, 12], "116": 11, "12": [10, 11], "120": 12, "13": [10, 11, 13], "1376": 3, "14": [10, 11], "14195": 12, "1496": 12, "15": [3, 8, 9, 10, 11, 12, 13], "150": 12, "16453159041394336": 12, "16571429e": 10, "16732026143790849": 13, "17668": 3, "19828571e": 10, "19t10": 12, "2": [1, 6, 7, 9, 10, 12, 13], "20": 12, "2017": 12, "2020": 9, "20521429e": 10, "20f": 12, "21": 12, "224": 12, "23": 11, "2566412": 4, "270": 12, "2857142857142865": 10, "2d": [5, 6, 9], "3": [1, 3, 7, 8, 10, 11, 13], "30": 12, "300": 12, "34": 12, "34014286e": 10, "35": [10, 12], "36678571e": 10, "37997143e": 10, "39": [10, 11, 12], "3d": [9, 11], "4": [1, 3, 7, 10, 11, 12], "4450": 11, "4sigma": 12, "5": [3, 10, 11, 12], "500": 12, "53542857e": 10, "54": [3, 10], "55": 12, "59685714e": 10, "5e": 12, "6": [4, 10, 11, 12], "6455": 13, "7": [10, 11, 12], "70": 12, "7142857142857135": 10, "74571429e": 10, "78": 3, "79": 3, "8": [3, 10, 11, 12], "80": [3, 12], "800": 12, "800nm": 12, "81": 3, "82": [10, 11], "8449673202614381": 13, "85": 11, "85771429e": 10, "8771428571428571": 10, "88": 11, "8840087145969499": 12, "8965456312395133": 13, "9": [10, 11], "9117413199045858": 12, "95942857e": 10, "A": [0, 3, 4, 5, 8, 9, 12], "For": [3, 8, 9, 11], "If": [0, 7, 9, 10], "In": [3, 11], "It": 3, "Its": 8, "No": 10, "One": 11, "The": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "These": [3, 10, 12], "To": 0, "Will": 3, "__epicsarchiver_host__": 3, "_attr": 7, "_fill_missing_dim": 7, "_sort_dims_for_imagej": 7, "a_inn": [4, 10], "a_rang": [4, 10], "about": 6, "abov": 4, "access": [0, 11], "accessor": 11, "accord": 6, "acquir": 0, "activ": 0, "ad": [3, 7, 11], "add": 7, "addit": [3, 8, 9, 11, 13], "address": 12, "administr": 3, "affili": 12, "after": [4, 11, 12], "again": 11, "alia": 7, "alias": 8, "alias_dict": [7, 8], "all": [0, 3, 8, 9, 11, 13], "allusersprofil": 3, "along": [3, 4, 6, 11, 12], "alreadi": 11, "also": [3, 10, 11], "altern": [3, 10, 11], "amplitud": [3, 5, 6, 8, 11], "an": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "analyz": [0, 3, 4, 5, 8, 10], "analyzer_dispers": 3, "ang_range_max": [5, 11, 12, 13], "ang_range_min": [5, 11, 12, 13], "angl": [0, 3, 4, 5, 8, 11], "angle_axi": 4, "angle_offset_px": [3, 4, 10, 13], "angular": [0, 3, 4, 7, 11], "angular0": [3, 12], "angular1": [3, 12], "angular_correction_matrix": 4, "angular_resolut": 12, "ani": [3, 7, 9], "anoth": 11, "api": 0, "appear": [5, 8], "append": 8, "appli": [3, 5, 8, 10, 11], "applic": [3, 8], "apply_fft_filt": [3, 10, 11], "approach": 10, "ar": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13], "archiv": [3, 8, 9], "archiver_channel": 9, "archiver_url": [3, 9], "area": [4, 11], "argument": [3, 5, 7, 8, 10], "around": [4, 5, 8, 12], "arpescontrol": 8, "arrai": [4, 6, 7, 8, 9, 10], "arriv": 4, "artifact": [3, 6, 10], "associ": [4, 11], "assum": 7, "attr": [7, 10, 11], "attribut": [7, 10, 11], "attributeerror": 7, "automat": 7, "autoreload": [10, 11, 12, 13], "auxiliari": 4, "avail": [0, 10], "averag": [8, 9, 11], "average_pow": 12, "avg": 9, "ax": [3, 7, 12], "axi": [3, 4, 5, 7, 8, 9, 11, 12], "axis_data": 9, "axis_dict": 7, "azimuth": 3, "band": [11, 12], "bar": [3, 9], "base": [3, 4, 5, 8, 9], "base_dictionari": 3, "basepath": 9, "beam": 12, "been": 7, "befor": 11, "below": [0, 3, 4], "berlin": [8, 11, 12], "best": [4, 7], "between": [3, 4], "beyond": 11, "bin": [0, 3, 4, 10], "binarysearch": 4, "bisect": 4, "bool": [3, 5, 8, 9], "both": 9, "boundari": [4, 6, 11], "built": [5, 8], "c": 7, "calcul": [4, 6], "calculate_jacobian": 4, "calculate_matrix_correct": 4, "calculate_polynomial_coef_da": 4, "calib2": 10, "calib2d": [3, 4, 5, 7, 8, 10], "calib2d_dict": 4, "calib2d_fil": 3, "calib_dict": 7, "calibr": [0, 3, 7, 10], "camelcas": [5, 8], "camera": 5, "can": [0, 3, 7, 8, 9, 10, 11], "cannot": [3, 7, 9], "carv": [3, 12], "case": 11, "cd": 0, "center": [4, 6, 10], "certain": 4, "chang": [0, 3, 9, 11, 12], "channel": [3, 7, 9], "charact": 7, "check": [0, 5, 8], "check_scan": [8, 9, 11], "chemical_formula": 12, "chosen": 6, "class": [0, 5, 8, 9, 11, 12, 13], "clean": 7, "cleav": 12, "clone": 0, "closest": 4, "closest_rr_index": 4, "coeffici": 4, "collect": [3, 8, 9, 10, 11, 12, 13], "collect_metadata": [8, 9, 12], "column": 9, "com": [0, 4], "combin": 3, "come": 12, "command": 0, "common": 8, "compare_coord": 9, "comparison": 4, "compat": 7, "complet": [3, 7, 11], "complete_dictionari": 3, "concept": 1, "conda": 0, "config": [0, 1, 5, 8, 9, 10, 11, 12, 13], "config_dict": 3, "config_filteron": 10, "config_path": 3, "configur": [4, 7, 10, 11, 13], "consid": 7, "consist": [3, 8, 9], "constructor": 0, "contain": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12], "content": [3, 9], "contribut": 0, "control": 3, "conveni": [7, 11], "convent": [5, 8], "convers": [0, 1, 3, 4, 5, 7, 8, 9, 11, 12, 13], "conversion_paramet": [5, 11], "convert": [0, 1, 5, 7, 8, 9, 10, 11, 12], "convert_imag": [5, 10], "coord": [7, 9], "coordin": [0, 4, 5, 6, 7, 8, 9, 11], "coordinate_depend": 3, "coordinate_map": 3, "copi": 10, "correct": [4, 5, 7], "correction_matrix_dict": 5, "correl": 6, "correspond": [0, 3, 4, 5, 6, 7, 9, 10, 11], "creat": [0, 3, 5, 7, 10, 11, 12, 13], "create_fft_param": 5, "crop": [3, 5, 6, 8, 12, 13], "crop_tool": [5, 8, 11], "crop_xarrai": 6, "crystal": 12, "cube": 12, "current": [3, 4, 7], "cut": 12, "d": [8, 9], "da": 4, "da1": 4, "da3": 4, "da7": 4, "da_matrix": [4, 10], "da_poly_matrix": 4, "dapolymatrix": 4, "data": [0, 3, 5, 6, 7, 8, 9, 10, 12, 13], "data9132_rawdata": 10, "data_arrai": 6, "data_path": [3, 8], "dataarrai": [5, 6, 7, 8], "databas": 4, "dataconvert": 7, "dataepfl": 10, "datafram": [9, 11, 12], "dataset": 7, "de": 12, "de1": [4, 10], "default": [5, 6, 7, 8, 9, 10, 11, 12, 13], "default_config": 3, "defin": [0, 3, 4, 5, 6, 10], "definit": [3, 7, 8, 12], "deg": 4, "degre": 3, "delai": [3, 8, 9, 11, 12], "delimit": 10, "demonstr": [10, 11], "depend": 0, "describ": 7, "descript": [1, 4, 12], "detail": 3, "detector": [0, 3, 4, 10, 11], "determin": [4, 5, 8], "dev": 0, "develop": 0, "deviat": 3, "df_lut": 9, "dict": [3, 4, 5, 6, 7, 8, 9], "dict_kei": 11, "dictionari": [3, 4, 5, 7, 8, 9, 13], "differ": [3, 9, 10, 11, 12, 13], "dim": [4, 6, 7, 9, 10, 11, 12], "dimens": [7, 8, 9, 10, 11], "dimension": [6, 7, 12], "direct": [3, 4, 6], "directli": [3, 5, 8, 11], "directori": [3, 9], "disabl": 3, "dispers": [3, 4, 11, 12], "distanc": 4, "divid": 7, "do": [4, 11], "doc": [0, 10, 11, 12, 13], "document": [0, 11], "doe": 7, "done": [0, 11, 12, 13], "drain_curr": 3, "drive": 11, "duplic": 7, "e": [0, 3, 4, 5, 6, 8, 9, 10, 11], "e_correct": 4, "e_rang": [4, 10], "e_shift": [4, 10], "each": [3, 4, 6, 8], "edg": 4, "edit": 0, "ef": 12, "either": [0, 8, 11], "ek_axi": 4, "ek_range_max": [5, 11, 12, 13], "ek_range_min": [5, 11, 12, 13], "ekin": [3, 8, 10, 11], "electron": [4, 8, 12], "electronanalys": [3, 12], "element": [6, 7], "eln": 3, "eln_data": 8, "email": 12, "emiss": [0, 4], "empti": [3, 8, 9], "enabl": [3, 9], "enable_nested_progress_bar": 3, "end": 9, "energi": [0, 3, 4, 5, 7, 8, 11, 12, 13], "energy_offset_px": [3, 4, 10], "energy_resolut": 12, "energydispers": 3, "engin": 3, "ensur": 0, "entri": [3, 6, 7, 11, 12], "entry_titl": 12, "environ": 0, "epfl": [0, 8], "epic": [3, 8, 9], "epics_channel": 3, "equidist": 13, "eshift": 4, "estim": 4, "etc": [0, 3, 9], "ev": 3, "even": 7, "ex": [8, 9], "exampl": [0, 1], "example_config_fhi": [12, 13], "example_data": 11, "exist": [3, 11], "experiment_summari": 12, "experiment_titl": 12, "explicitli": 10, "explor": 8, "export": 1, "express": 4, "extend": 7, "extens": 8, "extent": 12, "extract": 9, "f": [3, 12], "factor": [3, 4], "faddr": [7, 8], "fairmat": 12, "fals": [3, 5, 8, 9, 10, 11], "faradayweg": 12, "fast": 9, "fast_ax": 9, "featur": 6, "feel": 0, "fermi": 12, "fft": [5, 6, 8, 10, 11], "fft_filter_peak": [3, 5, 8, 10, 11], "fft_tool": [5, 8, 10, 11], "fhi": [0, 11, 12], "figur": [10, 11, 12, 13], "file": [5, 7, 8, 9, 10, 11, 12], "filenam": 8, "filenotfounderror": [3, 9], "filepath": 7, "fill": 12, "filter": [3, 5, 6, 8, 10, 11], "filtered_fft": 6, "find": 4, "find_scan": 9, "find_scan_typ": 9, "first": [0, 3, 6, 11, 12], "fit": 4, "fix": 11, "flag": [3, 6], "float": [4, 5, 6, 7, 8, 9], "fluenc": 12, "folder": [3, 8, 9, 10, 11], "folder_config": 3, "follow": [0, 3, 6, 7, 12], "format": [3, 7, 12], "found": [0, 3, 4, 7, 9, 10, 11, 12, 13], "fourier": [3, 5, 6, 8, 10], "fourier_filter_2d": 6, "fraction": 4, "frame": [8, 9], "free": 0, "frequenc": [5, 8, 12], "fritz": [3, 8, 12], "from": [0, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13], "front": 10, "fsmap": 12, "full": [6, 7], "fulli": 3, "function": [1, 3, 9, 10, 11], "further": [8, 9], "fwhm": 12, "g": [0, 3, 5, 6, 8, 9, 10, 11], "gamma": 12, "gather": [11, 12, 13], "gauss2d": 6, "gaussian": [3, 5, 6, 12], "gener": [8, 11, 12], "get": [4, 5, 8, 11], "get_archiver_data": 9, "get_coord": 9, "get_damatrix_from_calib2d": 4, "get_modes_from_calib_dict": 7, "get_pair_from_list": 7, "get_raw2d": 9, "get_rr_da": 4, "get_scan_path": 9, "getdata": 3, "git": 0, "github": [0, 2], "give": 3, "given": [3, 4, 6, 7, 8, 9, 11, 12], "grid": [3, 6, 8, 10, 11, 13], "group": 7, "gt": [10, 11, 12, 13], "guess": 7, "h5": [8, 11], "h5group": 7, "h5py": 7, "ha": [11, 12], "haber": [3, 8, 12], "handl": [0, 9], "handle_meta": 9, "have": [0, 7, 10], "hdf5": [7, 8, 11], "help": 0, "helper": 1, "here": [3, 11, 13], "hierarch": 3, "high": 4, "highmagnification2": 10, "home": [3, 10, 11, 12, 13], "horizont": [5, 8], "hostedtoolcach": [3, 10, 11, 12, 13], "how": 1, "http": [0, 3, 4], "i": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "ideal": 12, "identifi": 9, "ignor": 7, "igor": 4, "illumin": 11, "imag": [0, 1, 3, 4, 5, 7, 8, 9, 11, 13], "imagej": 7, "img_tool": 1, "import": [0, 10, 11, 12, 13], "importantli": 0, "incident_energi": 12, "incident_energy_spread": 12, "incident_polar": 12, "incident_wavelength": 12, "includ": [7, 11], "increas": 4, "independ": 6, "index": [1, 4, 8, 9, 11], "indexerror": 9, "indic": [4, 6, 8, 9], "individu": [3, 13], "infer": 7, "info": 9, "info_dict": 9, "inform": [6, 7, 11], "init": 0, "initi": 3, "inner": 4, "input": [3, 6, 7, 8, 9], "input_fil": [3, 7, 8], "instanc": [3, 9, 10, 11, 12, 13], "instead": 7, "institut": [3, 8, 12], "instrument": [3, 12], "int": [4, 8, 9], "integ": [4, 8, 9], "integr": 11, "intend": [0, 3], "intens": 6, "interact": [10, 11], "interest": [8, 9, 11], "interfac": [8, 9], "interpol": [4, 8, 10], "introduc": 10, "investig": 12, "io": 1, "ipykernel": 0, "ipython": 0, "item": 3, "iter": [3, 8, 9], "its": [11, 12], "j": 4, "jacobian": 4, "jacobian_determin": 4, "json": [3, 8, 12], "jupyt": 0, "k": 3, "keep": 3, "kei": [3, 7, 11], "kernel": 0, "keyerror": 4, "keyword": [5, 7, 8, 10, 11], "kinet": [4, 5, 8, 13], "kinetic_energi": [3, 4, 5, 8, 10], "kwd": [5, 7, 8, 10], "lab": [8, 12], "labview": 0, "later": [4, 11], "laurenz": 12, "lausann": 8, "len": [3, 4, 5, 7, 8], "lens_mod": [4, 5, 8, 10], "lib": [3, 10, 11, 12, 13], "librari": 3, "like": 7, "line": [4, 6, 7], "linear": 4, "linux": 3, "list": [0, 4, 5, 6, 7, 8, 9, 11], "list_lin": 7, "load": [0, 1, 3, 7, 8, 9, 10, 12], "load_config": 3, "load_ext": [10, 11, 12, 13], "load_h5": 7, "load_imag": 9, "load_scan": [8, 9, 11, 12, 13], "load_tiff": 7, "loader": [11, 12], "loadtxt": 10, "loc": [11, 12], "local": 0, "locat": [0, 9, 11, 12, 13], "look": 3, "low": 4, "lt": [10, 11, 12, 13], "lut": 9, "m": 0, "magnif": [3, 4, 10], "mai": 11, "maintain": 1, "make": [0, 11], "manipul": [6, 9], "manner": 3, "manual": [7, 12], "manufactur": 0, "map": 12, "map_coordin": 4, "mask": 6, "matplotlib": [10, 11, 12, 13], "matric": 4, "matrix": [4, 5], "max": 12, "maxim": 4, "maximum": [6, 9], "mcp": [0, 4, 10, 11], "mcp_position_mm": 4, "measur": [3, 12], "mechan": 3, "merg": [3, 13], "mesh": [5, 8], "meshgrid": [5, 8, 11], "metadata": [5, 7, 8, 9, 11, 12, 13], "method": [8, 9, 11, 12, 13], "might": 13, "millimet": [3, 4], "mimic": 4, "minim": 4, "minimum": 6, "mirror": [9, 11], "mirrori": 3, "mirrorx": [3, 11], "miss": 3, "mitig": 10, "mm": [3, 4], "mm_z": 4, "mode": [0, 4, 5, 7, 8, 11], "modif": 3, "modul": [0, 3, 4, 6, 7], "monoton": 4, "more": 7, "most": [0, 9], "mostli": 3, "mpe": [3, 12], "mpg": 12, "must": [4, 8], "mx": 6, "my": 6, "name": [0, 3, 5, 7, 8, 9, 12], "nameerror": 7, "nap32": 9, "ndarrai": [4, 5, 6, 7, 8, 9], "ndimag": 4, "nearest": 4, "necessari": 3, "need": [0, 4, 7, 8, 9, 12], "neither": [3, 9], "nest": [3, 7, 9], "new": [0, 11], "nexu": [1, 3, 7, 8, 9], "node": 7, "non": [4, 11, 13], "none": [3, 5, 7, 8, 9], "nor": [3, 9], "normal": [3, 4, 5, 8], "notebook": [0, 3, 8, 11, 12], "notimplementederror": 7, "now": 0, "np": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "number": [3, 4, 7, 8, 9, 11, 13], "numpi": [4, 5, 7, 8, 9, 10, 11, 12, 13], "nx": [8, 12], "nx_pixel": [3, 4], "nxmpes_arp": [3, 12], "nxmpes_arpes_config": [3, 12], "nxuser": 12, "ny_pixel": [3, 4], "o": 10, "object": [5, 7, 8, 9, 11], "obtain": 9, "offset": [3, 4], "omg": 3, "onc": 0, "one": [6, 7, 10, 11], "onli": [4, 7, 11], "onto": [8, 13], "open": 10, "opencomp": 0, "oper": [10, 11], "opt": [3, 10, 11, 12, 13], "optim": [5, 8, 10, 11], "option": [0, 3, 5, 6, 7, 8, 9, 11], "order": [3, 4, 7, 11], "org": 0, "other": [3, 5, 11], "otherwis": 7, "out": [0, 4, 5, 8], "output": [7, 12], "outsid": 4, "over": [3, 8, 9, 11], "overlap": [12, 13], "overwrit": [3, 5], "overwritten": 3, "p": 12, "p_rd": [3, 12], "pack": 4, "packag": [0, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13], "package_dir": 3, "page": 1, "pair": 7, "panda": 9, "paramet": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "pars": [7, 9], "parse_calib2d_to_dict": 7, "parse_config": [3, 5, 8], "parse_info_to_dict": 9, "parse_lut_to_df": 9, "parser": 7, "part": 0, "pass": [0, 3, 4, 5, 8, 10, 11], "pass_energi": [4, 5, 8, 10], "path": [3, 5, 7, 8, 9, 10, 11, 12, 13], "pattern": 6, "pd": 9, "peak": [3, 5, 6, 8, 10, 11], "per": [4, 11, 13], "perform": [4, 8, 9, 10, 13], "pesdata": 9, "phi": 3, "phoibo": [0, 5, 8, 10, 11], "phoibos150": [0, 3], "photoelectron": 4, "photoemiss": [4, 5], "physic": [0, 4, 5], "physical_unit_data": 4, "pixel": [3, 4], "pixel_s": [3, 4, 10], "place": [3, 5], "plai": [5, 8], "planck": 12, "plane": [3, 5, 8, 10], "pleas": 10, "plot": [10, 11, 12, 13], "plt": [10, 11, 12, 13], "point": [9, 11, 12], "pol": 12, "polar": 3, "polynomi": 4, "popul": 7, "pos_i": [3, 5, 6, 8, 11], "pos_x": [3, 5, 6, 8, 11], "posi": 7, "posit": [3, 4, 5, 6, 8, 10, 11], "poss": 4, "possibl": 6, "possibli": 3, "posx": 7, "pre": [3, 5], "prefer": 3, "preparation_d": 12, "preparation_descript": 12, "prerequisit": 0, "present": [3, 11], "preserv": 4, "press": 11, "pressur": 3, "pressureac": [3, 12], "previous": [8, 11], "princip": 12, "print": 9, "pro": 4, "probe": 12, "procedur": 0, "process": [8, 11], "process_sweep_scan": 8, "processor": 3, "produc": 13, "program": 4, "progress": [3, 9], "project": 9, "properti": [5, 8], "provid": [0, 3, 5, 6, 7, 8, 9, 10, 11, 13], "pull": 0, "pulse_dur": 12, "pulse_energi": 12, "pump": 12, "pv": 3, "pyenv": 0, "pynxtool": [7, 8, 12], "pypi": 0, "pyplot": [10, 11, 12, 13], "pyproject": 0, "python": [0, 3, 7, 10, 11, 12, 13], "python3": [3, 10, 11, 12, 13], "quadmesh": [10, 11, 12, 13], "queri": 9, "question": 4, "r": 7, "r9132": 10, "rais": [3, 4, 7, 8, 9], "rang": [4, 8, 9, 11, 12, 13], "ratio": 4, "raw": [4, 5, 8, 9], "raw_arrai": 9, "raw_data": 8, "raw_imag": 5, "raw_image_nam": 10, "raw_img": 5, "raw_list": 9, "rbv": 3, "read": [0, 7, 8, 9], "read_calib2d": 7, "reader": [3, 7, 8, 12], "real": 6, "record": 11, "recurs": [0, 7], "recursive_parse_metadata": 7, "recursive_write_metadata": 7, "refer": 2, "regular": 6, "rel": 11, "remov": [3, 6], "renam": 3, "report": 3, "repositori": 0, "repres": 7, "request": [0, 3, 4], "requir": [0, 11], "res_xarrai": [10, 11, 12, 13], "res_xarray_check": 11, "resolut": 12, "resolv": 4, "respect": [3, 4], "result": [3, 4, 8, 11, 12, 13], "ret": 6, "retain": 7, "retard": 4, "retardation_ratio": [4, 10], "retriev": 3, "rettig": 12, "return": [3, 4, 5, 6, 7, 8, 9, 13], "rewrit": 12, "right": 0, "rise": 7, "role": 12, "root": 13, "rotat": 10, "rotation_angl": [10, 13], "row": 4, "row0": 4, "row1": 4, "rr": 4, "rrvec": 4, "rudimentari": 9, "rule": 3, "run": 0, "runner": [10, 11, 12, 13], "s_": [8, 9, 11], "sai": 11, "sampl": [3, 12], "sample_azimuth": 3, "sample_histori": 12, "sample_polar": 3, "sample_tilt": 3, "save": [3, 7, 8, 12], "save_config": 3, "scale": 4, "scan": [0, 1, 4, 8, 9, 11, 12], "scan_info": [9, 11, 12], "scan_list": 9, "scan_path": 9, "scan_typ": 9, "scanvector": 9, "scipi": 4, "script": 9, "search": [1, 3, 8, 9], "second": [4, 6], "second_closest_rr": 4, "see": [0, 3, 11], "select": [5, 8], "sequenc": [6, 7, 8, 9], "server": 11, "set": [0, 4, 5, 8, 11, 12, 13], "sever": 0, "shell": 0, "shift": 4, "ship": 3, "should": [0, 3, 4, 7, 11, 12], "show": 11, "showcas": [10, 11, 12, 13], "sigma": [5, 6], "sigma_i": [3, 5, 6, 8, 11], "sigma_x": [3, 5, 6, 8, 11], "singl": [3, 7, 8, 9, 10, 12], "site": [3, 10, 11, 12, 13], "size": [3, 4, 9], "slice": [8, 9, 11, 12], "slow": 9, "slow_ax": 9, "so": 7, "societi": 12, "softwar": [0, 8, 11], "some": 10, "sort": 7, "sourc": [0, 3, 4, 5, 6, 7, 8, 9, 10], "sp": [11, 12, 13], "sp2": 10, "spa": 10, "spa_param": [3, 12, 13], "spatial": [3, 4, 5, 7, 8], "spatial0": 3, "spatial_resolut": 12, "spawn": 0, "spec": [0, 3, 4, 5, 8, 10], "specs_config": 3, "specs_kernel": 0, "specs_poetri": 0, "specsanalyz": [8, 11, 12, 13], "specsscan": [0, 9, 12, 13], "spectromet": 0, "split": 7, "src": [3, 12, 13], "stack": [7, 8], "stackoverflow": 4, "stamp": [9, 11, 12, 13], "standard": [3, 7], "start": [4, 9], "step": [0, 3, 8, 11, 13], "stoke": 12, "store": [3, 5, 7, 8, 10, 11], "str": [3, 4, 5, 6, 7, 8, 9], "string": [3, 4, 7, 8, 9, 11], "structur": 7, "subfunct": 7, "submit": 0, "submodul": 0, "subsequ": 3, "subtract": [3, 5, 8], "sum": [8, 11, 12, 13], "support": [3, 7, 8], "suppress": 6, "sure": 0, "surfac": 12, "sweep": [1, 8], "sx": 6, "sy": 6, "sync": 0, "system": [0, 3, 4], "system_config": [3, 11], "t": [7, 10, 12], "tabul": 4, "take": 3, "tbte3": 12, "temp_rbv": [3, 12], "tempa": 3, "temperatur": [3, 8, 9], "test": [0, 3, 10, 11, 12, 13], "text": 10, "th": [5, 8], "than": 7, "thei": 3, "them": 3, "therefor": [7, 11], "thi": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "third": [8, 11], "through": 7, "tht": 3, "tif": 8, "tiff": [7, 8], "tilt": [3, 12], "time": [7, 9, 11, 12, 13], "to_h5": 7, "to_nexu": 7, "to_tiff": 7, "todo": 1, "togeth": [0, 3], "toml": 0, "tool": [1, 5, 8, 10, 11], "topfloor": 9, "total_iter": 9, "tqdm_enable_nest": 9, "trans_i": 3, "trans_x": 3, "trans_z": 3, "transform": [3, 4], "trarp": [9, 11, 12], "tri": 7, "true": [3, 10, 11, 12, 13], "trx": 3, "try": 3, "trz": 3, "ts_from": 9, "ts_to": 9, "tsv": 10, "tsv_data": 10, "tupl": [4, 7, 8, 9], "tutori": 0, "two": 0, "txt": 9, "type": [0, 3, 4, 5, 6, 7, 8, 9], "typeerror": [3, 7], "typic": 3, "tzcyx": 7, "uhv": 12, "under": [5, 8, 9], "union": 8, "unit": [3, 4, 5], "unless": 4, "up": [0, 4], "updat": 0, "url": [3, 9], "us": [0, 3, 4, 5, 7, 8, 9, 11, 12, 13], "usag": 0, "user": [3, 11, 12, 13], "user0": 12, "user_config": [3, 11, 12, 13], "v": [3, 5], "valid": [4, 8, 9, 10], "valu": [3, 4, 5, 6, 7, 8], "valueerror": [4, 7, 8, 9], "variabl": 6, "variou": 3, "vector": [4, 12], "venv": 0, "verbos": 3, "version": 0, "vertic": [5, 8], "virtual": 0, "visual": 10, "voltag": [3, 8], "w": [7, 8, 10], "wa": 8, "want": 6, "warn": [7, 11, 13], "we": 11, "well": [2, 11], "when": [7, 8, 9], "where": [3, 6, 7, 8, 11], "which": [6, 8, 9, 10, 11, 12], "while": 11, "wide": [0, 3], "wideanglemod": [5, 8, 10], "widget": [10, 11, 12, 13], "width": [3, 5, 8], "wiki": 2, "window": 3, "wish": 0, "within": 0, "withing": 4, "without": [6, 10], "work": [0, 3, 4, 5, 8, 10, 11, 12, 13], "work_funct": [4, 5, 8, 10], "workflow": 3, "would": [8, 9], "write": [7, 8], "writer": 8, "wrong": 13, "x": [3, 5, 6, 7], "x64": [3, 10, 11, 12, 13], "x_max": 6, "x_min": 6, "xarrai": [3, 5, 6, 7, 8, 10, 11, 12, 13], "xgs600": [3, 12], "xr": [5, 6, 7, 8], "xuv": 12, "y": [3, 5, 6, 7], "y_max": 6, "y_min": 6, "yaml": [3, 9, 10, 11, 12, 13], "year": 9, "yet": 11, "you": [0, 4, 6], "your": [0, 3, 10], "z": [3, 7], "zinner": 4, "zinner_diff": 4, "\u00b5j": 12}, "titles": ["specsanalyzer", "Welcome to specsanalyzer\u2019s documentation!", "1. How to maintain", "Config", "2. convert functions (specsanalyzer.convert)", "1. Core functions  (specsanalyzer.core)", "3. image tool functions (specsanalyzer.img_tools)", "4. io functions  (specsanalyzer.io)", "1. Core functions  (specsscan.core)", "2. Helpers", "Example 1: SpecsAnalyzer conversion", "Example 2: SpecsScan loading", "Example 3: Export to NeXus", "Example 4: Sweep Scan loading"], "titleterms": {"": 1, "1": 10, "2": 11, "3": 12, "4": 13, "For": 0, "adjust": 10, "angl": 10, "api": 3, "artefact": 10, "artifact": 11, "berlin": 3, "calib2d": 0, "config": 3, "configur": [0, 3], "contribut": 1, "contributor": 0, "convers": 10, "conversion_paramet": 10, "convert": 4, "core": [1, 5, 8], "crop": 11, "data": 11, "default": 3, "dict": 10, "document": 1, "exampl": [3, 10, 11, 12, 13], "export": 12, "fhi": 3, "file": [0, 3], "function": [4, 5, 6, 7, 8], "get": 1, "helper": 9, "how": 2, "imag": [6, 10], "img_tool": 6, "indic": 1, "instal": 0, "io": 7, "iter": 11, "load": [11, 13], "maintain": [0, 2], "mesh": [10, 11], "mode": 10, "modul": 1, "nexu": 12, "offset": 10, "pip": 0, "poetri": 0, "remov": [10, 11], "resolv": 10, "save": 11, "scan": 13, "select": 11, "set": 3, "setup": 3, "spatial": 10, "specsanalyz": [0, 1, 3, 4, 5, 6, 7, 10], "specsscan": [1, 3, 8, 11], "start": 1, "sweep": 13, "tabl": 1, "tool": 6, "trarp": 3, "us": 10, "user": 0, "welcom": 1}})
\ No newline at end of file
+Search.setIndex({"alltitles": {"API": [[3, "module-specsanalyzer.config"]], "Adjusting offsets and angle": [[10, "Adjusting-offsets-and-angle"]], "Config": [[3, null]], "Configuration and calib2d file": [[0, "configuration-and-calib2d-file"]], "Contributing": [[1, null]], "Conversion into spatially resolved modes": [[10, "Conversion-into-spatially-resolved-modes"]], "Conversion using conversion_parameters dict": [[10, "Conversion-using-conversion_parameters-dict"]], "Core functions  (specsanalyzer.core)": [[5, null]], "Core functions  (specsscan.core)": [[8, null]], "Cropping data": [[11, "Cropping-data"]], "Default specsanalyzer configuration settings": [[3, "default-specsanalyzer-configuration-settings"]], "Default specsscan configuration settings": [[3, "default-specsscan-configuration-settings"]], "Example 1: SpecsAnalyzer conversion": [[10, null]], "Example 2: SpecsScan loading": [[11, null]], "Example 3: Export to NeXus": [[12, null]], "Example 4: Sweep Scan loading": [[13, null]], "Example configuration file for the trARPES setup at FHI-Berlin": [[3, "example-configuration-file-for-the-trarpes-setup-at-fhi-berlin"]], "For Contributors": [[0, "for-contributors"]], "Getting Started": [[1, null]], "Helpers": [[9, null]], "How to maintain": [[2, null]], "Image conversion": [[10, "Image-conversion"]], "Indices and tables": [[1, "indices-and-tables"]], "Installation": [[0, "installation"]], "Loading data": [[11, "Loading-data"]], "Loading with selected iterations": [[11, "Loading-with-selected-iterations"]], "Pip (for users)": [[0, "pip-for-users"]], "Poetry (for maintainers)": [[0, "poetry-for-maintainers"]], "Removal of Mesh Artifact": [[11, "Removal-of-Mesh-Artifact"]], "Removal of mesh artefact": [[10, "Removal-of-mesh-artefact"]], "Saving": [[11, "Saving"]], "SpecsAnalyzer Core Modules": [[1, null]], "SpecsScan Core Modules": [[1, null]], "Welcome to specsanalyzer\u2019s documentation!": [[1, null]], "convert functions (specsanalyzer.convert)": [[4, null]], "image tool functions (specsanalyzer.img_tools)": [[6, null]], "io functions  (specsanalyzer.io)": [[7, null]], "specsanalyzer": [[0, null]]}, "docnames": ["getting_started", "index", "misc/maintain", "specsanalyzer/config", "specsanalyzer/convert", "specsanalyzer/core", "specsanalyzer/img_tools", "specsanalyzer/io", "specsscan/core", "specsscan/helpers", "tutorial/1_specsanalyzer_conversion_examples", "tutorial/2_specsscan_example", "tutorial/3_specsscan_conversion_to_NeXus", "tutorial/4_specsscan_load_sweep_scan"], "envversion": {"nbsphinx": 4, "sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1}, "filenames": ["getting_started.rst", "index.rst", "misc/maintain.rst", "specsanalyzer/config.rst", "specsanalyzer/convert.rst", "specsanalyzer/core.rst", "specsanalyzer/img_tools.rst", "specsanalyzer/io.rst", "specsscan/core.rst", "specsscan/helpers.rst", "tutorial/1_specsanalyzer_conversion_examples.ipynb", "tutorial/2_specsscan_example.ipynb", "tutorial/3_specsscan_conversion_to_NeXus.ipynb", "tutorial/4_specsscan_load_sweep_scan.ipynb"], "indexentries": {"_fill_missing_dims() (in module specsanalyzer.io)": [[7, "specsanalyzer.io._fill_missing_dims", false]], "_sort_dims_for_imagej() (in module specsanalyzer.io)": [[7, "specsanalyzer.io._sort_dims_for_imagej", false]], "bisection() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.bisection", false]], "calculate_jacobian() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.calculate_jacobian", false]], "calculate_matrix_correction() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.calculate_matrix_correction", false]], "calculate_polynomial_coef_da() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.calculate_polynomial_coef_da", false]], "calib2d (specsanalyzer.core.specsanalyzer property)": [[5, "specsanalyzer.core.SpecsAnalyzer.calib2d", false]], "check_scan() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.check_scan", false]], "compare_coords() (in module specsscan.helpers)": [[9, "specsscan.helpers.compare_coords", false]], "complete_dictionary() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.complete_dictionary", false]], "config (specsanalyzer.core.specsanalyzer property)": [[5, "specsanalyzer.core.SpecsAnalyzer.config", false]], "config (specsscan.core.specsscan property)": [[8, "specsscan.core.SpecsScan.config", false]], "convert_image() (specsanalyzer.core.specsanalyzer method)": [[5, "specsanalyzer.core.SpecsAnalyzer.convert_image", false]], "correction_matrix_dict (specsanalyzer.core.specsanalyzer property)": [[5, "specsanalyzer.core.SpecsAnalyzer.correction_matrix_dict", false]], "create_fft_params() (in module specsanalyzer.core)": [[5, "specsanalyzer.core.create_fft_params", false]], "crop_tool() (specsanalyzer.core.specsanalyzer method)": [[5, "specsanalyzer.core.SpecsAnalyzer.crop_tool", false]], "crop_tool() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.crop_tool", false]], "crop_xarray() (in module specsanalyzer.img_tools)": [[6, "specsanalyzer.img_tools.crop_xarray", false]], "fft_tool() (specsanalyzer.core.specsanalyzer method)": [[5, "specsanalyzer.core.SpecsAnalyzer.fft_tool", false]], "fft_tool() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.fft_tool", false]], "find_scan() (in module specsscan.helpers)": [[9, "specsscan.helpers.find_scan", false]], "find_scan_type() (in module specsscan.helpers)": [[9, "specsscan.helpers.find_scan_type", false]], "fourier_filter_2d() (in module specsanalyzer.img_tools)": [[6, "specsanalyzer.img_tools.fourier_filter_2d", false]], "gauss2d() (in module specsanalyzer.img_tools)": [[6, "specsanalyzer.img_tools.gauss2d", false]], "get_archiver_data() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_archiver_data", false]], "get_coords() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_coords", false]], "get_damatrix_from_calib2d() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.get_damatrix_from_calib2d", false]], "get_modes_from_calib_dict() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.get_modes_from_calib_dict", false]], "get_pair_from_list() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.get_pair_from_list", false]], "get_raw2d() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_raw2d", false]], "get_rr_da() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.get_rr_da", false]], "get_scan_path() (in module specsscan.helpers)": [[9, "specsscan.helpers.get_scan_path", false]], "handle_meta() (in module specsscan.helpers)": [[9, "specsscan.helpers.handle_meta", false]], "load_config() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.load_config", false]], "load_h5() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.load_h5", false]], "load_images() (in module specsscan.helpers)": [[9, "specsscan.helpers.load_images", false]], "load_scan() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.load_scan", false]], "load_tiff() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.load_tiff", false]], "mcp_position_mm() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.mcp_position_mm", false]], "module": [[3, "module-specsanalyzer.config", false], [4, "module-specsanalyzer.convert", false], [5, "module-specsanalyzer.core", false], [6, "module-specsanalyzer.img_tools", false], [7, "module-specsanalyzer.io", false], [8, "module-specsscan.core", false], [9, "module-specsscan.helpers", false]], "parse_calib2d_to_dict() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.parse_calib2d_to_dict", false]], "parse_config() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.parse_config", false]], "parse_info_to_dict() (in module specsscan.helpers)": [[9, "specsscan.helpers.parse_info_to_dict", false]], "parse_lut_to_df() (in module specsscan.helpers)": [[9, "specsscan.helpers.parse_lut_to_df", false]], "physical_unit_data() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.physical_unit_data", false]], "process_sweep_scan() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.process_sweep_scan", false]], "read_calib2d() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.read_calib2d", false]], "recursive_parse_metadata() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.recursive_parse_metadata", false]], "recursive_write_metadata() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.recursive_write_metadata", false]], "result (specsscan.core.specsscan property)": [[8, "specsscan.core.SpecsScan.result", false]], "save() (specsscan.core.specsscan method)": [[8, "specsscan.core.SpecsScan.save", false]], "save_config() (in module specsanalyzer.config)": [[3, "specsanalyzer.config.save_config", false]], "second_closest_rr() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.second_closest_rr", false]], "specsanalyzer (class in specsanalyzer.core)": [[5, "specsanalyzer.core.SpecsAnalyzer", false]], "specsanalyzer.config": [[3, "module-specsanalyzer.config", false]], "specsanalyzer.convert": [[4, "module-specsanalyzer.convert", false]], "specsanalyzer.core": [[5, "module-specsanalyzer.core", false]], "specsanalyzer.img_tools": [[6, "module-specsanalyzer.img_tools", false]], "specsanalyzer.io": [[7, "module-specsanalyzer.io", false]], "specsscan (class in specsscan.core)": [[8, "specsscan.core.SpecsScan", false]], "specsscan.core": [[8, "module-specsscan.core", false]], "specsscan.helpers": [[9, "module-specsscan.helpers", false]], "to_h5() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.to_h5", false]], "to_nexus() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.to_nexus", false]], "to_tiff() (in module specsanalyzer.io)": [[7, "specsanalyzer.io.to_tiff", false]], "zinner() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.zinner", false]], "zinner_diff() (in module specsanalyzer.convert)": [[4, "specsanalyzer.convert.zinner_diff", false]]}, "objects": {"specsanalyzer": [[3, 0, 0, "-", "config"], [4, 0, 0, "-", "convert"], [5, 0, 0, "-", "core"], [6, 0, 0, "-", "img_tools"], [7, 0, 0, "-", "io"]], "specsanalyzer.config": [[3, 1, 1, "", "complete_dictionary"], [3, 1, 1, "", "load_config"], [3, 1, 1, "", "parse_config"], [3, 1, 1, "", "save_config"]], "specsanalyzer.convert": [[4, 1, 1, "", "bisection"], [4, 1, 1, "", "calculate_jacobian"], [4, 1, 1, "", "calculate_matrix_correction"], [4, 1, 1, "", "calculate_polynomial_coef_da"], [4, 1, 1, "", "get_damatrix_from_calib2d"], [4, 1, 1, "", "get_rr_da"], [4, 1, 1, "", "mcp_position_mm"], [4, 1, 1, "", "physical_unit_data"], [4, 1, 1, "", "second_closest_rr"], [4, 1, 1, "", "zinner"], [4, 1, 1, "", "zinner_diff"]], "specsanalyzer.core": [[5, 2, 1, "", "SpecsAnalyzer"], [5, 1, 1, "", "create_fft_params"]], "specsanalyzer.core.SpecsAnalyzer": [[5, 3, 1, "", "calib2d"], [5, 3, 1, "", "config"], [5, 4, 1, "", "convert_image"], [5, 3, 1, "", "correction_matrix_dict"], [5, 4, 1, "", "crop_tool"], [5, 4, 1, "", "fft_tool"]], "specsanalyzer.img_tools": [[6, 1, 1, "", "crop_xarray"], [6, 1, 1, "", "fourier_filter_2d"], [6, 1, 1, "", "gauss2d"]], "specsanalyzer.io": [[7, 1, 1, "", "_fill_missing_dims"], [7, 1, 1, "", "_sort_dims_for_imagej"], [7, 1, 1, "", "get_modes_from_calib_dict"], [7, 1, 1, "", "get_pair_from_list"], [7, 1, 1, "", "load_h5"], [7, 1, 1, "", "load_tiff"], [7, 1, 1, "", "parse_calib2d_to_dict"], [7, 1, 1, "", "read_calib2d"], [7, 1, 1, "", "recursive_parse_metadata"], [7, 1, 1, "", "recursive_write_metadata"], [7, 1, 1, "", "to_h5"], [7, 1, 1, "", "to_nexus"], [7, 1, 1, "", "to_tiff"]], "specsscan": [[8, 0, 0, "-", "core"], [9, 0, 0, "-", "helpers"]], "specsscan.core": [[8, 2, 1, "", "SpecsScan"]], "specsscan.core.SpecsScan": [[8, 4, 1, "", "check_scan"], [8, 3, 1, "", "config"], [8, 4, 1, "", "crop_tool"], [8, 4, 1, "", "fft_tool"], [8, 4, 1, "", "load_scan"], [8, 4, 1, "", "process_sweep_scan"], [8, 3, 1, "", "result"], [8, 4, 1, "", "save"]], "specsscan.helpers": [[9, 1, 1, "", "compare_coords"], [9, 1, 1, "", "find_scan"], [9, 1, 1, "", "find_scan_type"], [9, 1, 1, "", "get_archiver_data"], [9, 1, 1, "", "get_coords"], [9, 1, 1, "", "get_raw2d"], [9, 1, 1, "", "get_scan_path"], [9, 1, 1, "", "handle_meta"], [9, 1, 1, "", "load_images"], [9, 1, 1, "", "parse_info_to_dict"], [9, 1, 1, "", "parse_lut_to_df"]]}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "property", "Python property"], "4": ["py", "method", "Python method"]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:property", "4": "py:method"}, "terms": {"": [3, 7, 12], "0": [3, 5, 10, 11, 12, 13], "00": 12, "000000": 10, "0033": 10, "00645": 3, "01": 10, "02": [10, 12], "0258": 10, "03": [10, 12], "04": 10, "04428571e": 10, "05": 10, "066": 10, "07597844332538181": 12, "07597844332538357": 13, "08": [11, 12], "0x7f9a5d6a0280": 11, "0x7f9a5d6f7c70": 11, "0x7f9a5d782aa0": 11, "0x7f9a5d8b3c40": 11, "0x7f9ab13cb1f0": 11, "0x7fdfdbf3f430": 10, "0x7fdfdbf6e4d0": 10, "0x7fdfe00b9810": 10, "0x7fdfe3e52b00": 10, "0x7fdfe3e99660": 10, "0x7fdfe4554dc0": 10, "0x7fe1327c60e0": 12, "0x7fe13375ed40": 12, "0x7ff22834a3b0": 13, "1": [1, 3, 4, 6, 8, 9, 11, 12, 13], "10": [3, 8, 9, 10, 11, 12, 13], "1024": 3, "108": 3, "109": 3, "11": [10, 11, 12], "116": 11, "12": [10, 11], "120": 12, "13": [10, 11, 13], "1376": 3, "14": [10, 11], "14195": 12, "1496": 12, "15": [8, 9, 10, 11], "150": 12, "16": [3, 10, 11, 12, 13], "16453159041394336": 12, "16571429e": 10, "16732026143790849": 13, "17668": 3, "19828571e": 10, "19t10": 12, "2": [1, 6, 7, 9, 10, 12, 13], "20": 12, "2017": 12, "2020": 9, "20521429e": 10, "20f": 12, "21": 12, "224": 12, "23": 11, "2566412": 4, "270": 12, "2857142857142865": 10, "2d": [5, 6, 9], "3": [1, 3, 7, 8, 10, 11, 13], "30": 12, "300": 12, "34": 12, "34014286e": 10, "35": [10, 12], "36678571e": 10, "37997143e": 10, "39": [10, 11, 12], "3d": [9, 11], "4": [1, 3, 7, 10, 11, 12], "4450": 11, "4sigma": 12, "5": [3, 10, 11, 12], "500": 12, "53542857e": 10, "54": [3, 10], "55": 12, "59685714e": 10, "5e": 12, "6": [4, 10, 11, 12], "6455": 13, "7": [10, 11, 12], "70": 12, "7142857142857135": 10, "74571429e": 10, "78": 3, "79": 3, "8": [3, 10, 11, 12], "80": [3, 12], "800": 12, "800nm": 12, "81": 3, "82": [10, 11], "8449673202614381": 13, "85": 11, "85771429e": 10, "8771428571428571": 10, "88": 11, "8840087145969499": 12, "8965456312395133": 13, "9": [10, 11], "9117413199045858": 12, "95942857e": 10, "A": [0, 3, 4, 5, 8, 9, 12], "For": [3, 8, 9, 11], "If": [0, 7, 9, 10], "In": [3, 11], "It": 3, "Its": 8, "No": 10, "One": 11, "The": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "These": [3, 10, 12], "To": 0, "Will": 3, "__epicsarchiver_host__": 3, "_attr": 7, "_fill_missing_dim": 7, "_sort_dims_for_imagej": 7, "a_inn": [4, 10], "a_rang": [4, 10], "about": 6, "abov": 4, "access": [0, 11], "accessor": 11, "accord": 6, "acquir": 0, "activ": 0, "ad": [3, 7, 11], "add": 7, "addit": [3, 8, 9, 11, 13], "address": 12, "administr": 3, "affili": 12, "after": [4, 11, 12], "again": 11, "alia": 7, "alias": 8, "alias_dict": [7, 8], "all": [0, 3, 8, 9, 11, 13], "allusersprofil": 3, "along": [3, 4, 6, 11, 12], "alreadi": 11, "also": [3, 10, 11], "altern": [3, 10, 11], "amplitud": [3, 5, 6, 8, 11], "an": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "analyz": [0, 3, 4, 5, 8, 10], "analyzer_dispers": 3, "ang_range_max": [5, 11, 12, 13], "ang_range_min": [5, 11, 12, 13], "angl": [0, 3, 4, 5, 8, 11], "angle_axi": 4, "angle_offset_px": [3, 4, 10, 13], "angular": [0, 3, 4, 7, 11], "angular0": [3, 12], "angular1": [3, 12], "angular_correction_matrix": 4, "angular_resolut": 12, "ani": [3, 7, 9], "anoth": 11, "api": 0, "appear": [5, 8], "append": 8, "appli": [3, 5, 8, 10, 11], "applic": [3, 8], "apply_fft_filt": [3, 10, 11], "approach": 10, "ar": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13], "archiv": [3, 8, 9], "archiver_channel": 9, "archiver_url": [3, 9], "area": [4, 11], "argument": [3, 5, 7, 8, 10], "around": [4, 5, 8, 12], "arpescontrol": 8, "arrai": [4, 6, 7, 8, 9, 10], "arriv": 4, "artifact": [3, 6, 10], "associ": [4, 11], "assum": 7, "attr": [7, 10, 11], "attribut": [7, 10, 11], "attributeerror": 7, "automat": 7, "autoreload": [10, 11, 12, 13], "auxiliari": 4, "avail": [0, 10], "averag": [8, 9, 11], "average_pow": 12, "avg": 9, "ax": [3, 7, 12], "axi": [3, 4, 5, 7, 8, 9, 11, 12], "axis_data": 9, "axis_dict": 7, "azimuth": 3, "band": [11, 12], "bar": [3, 9], "base": [3, 4, 5, 8, 9], "base_dictionari": 3, "basepath": 9, "beam": 12, "been": 7, "befor": 11, "below": [0, 3, 4], "berlin": [8, 11, 12], "best": [4, 7], "between": [3, 4], "beyond": 11, "bin": [0, 3, 4, 10], "binarysearch": 4, "bisect": 4, "bool": [3, 5, 8, 9], "both": 9, "boundari": [4, 6, 11], "built": [5, 8], "c": 7, "calcul": [4, 6], "calculate_jacobian": 4, "calculate_matrix_correct": 4, "calculate_polynomial_coef_da": 4, "calib2": 10, "calib2d": [3, 4, 5, 7, 8, 10], "calib2d_dict": 4, "calib2d_fil": 3, "calib_dict": 7, "calibr": [0, 3, 7, 10], "camelcas": [5, 8], "camera": 5, "can": [0, 3, 7, 8, 9, 10, 11], "cannot": [3, 7, 9], "carv": [3, 12], "case": 11, "cd": 0, "center": [4, 6, 10], "certain": 4, "chang": [0, 3, 9, 11, 12], "channel": [3, 7, 9], "charact": 7, "check": [0, 5, 8], "check_scan": [8, 9, 11], "chemical_formula": 12, "chosen": 6, "class": [0, 5, 8, 9, 11, 12, 13], "clean": 7, "cleav": 12, "clone": 0, "closest": 4, "closest_rr_index": 4, "coeffici": 4, "collect": [3, 8, 9, 10, 11, 12, 13], "collect_metadata": [8, 9, 12], "column": 9, "com": [0, 4], "combin": 3, "come": 12, "command": 0, "common": 8, "compare_coord": 9, "comparison": 4, "compat": 7, "complet": [3, 7, 11], "complete_dictionari": 3, "concept": 1, "conda": 0, "config": [0, 1, 5, 8, 9, 10, 11, 12, 13], "config_dict": 3, "config_filteron": 10, "config_path": 3, "configur": [4, 7, 10, 11, 13], "consid": 7, "consist": [3, 8, 9], "constructor": 0, "contain": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12], "content": [3, 9], "contribut": 0, "control": 3, "conveni": [7, 11], "convent": [5, 8], "convers": [0, 1, 3, 4, 5, 7, 8, 9, 11, 12, 13], "conversion_paramet": [5, 11], "convert": [0, 1, 5, 7, 8, 9, 10, 11, 12], "convert_imag": [5, 10], "coord": [7, 9], "coordin": [0, 4, 5, 6, 7, 8, 9, 11], "coordinate_depend": 3, "coordinate_map": 3, "copi": 10, "correct": [4, 5, 7], "correction_matrix_dict": 5, "correl": 6, "correspond": [0, 3, 4, 5, 6, 7, 9, 10, 11], "creat": [0, 3, 5, 7, 10, 11, 12, 13], "create_fft_param": 5, "crop": [3, 5, 6, 8, 12, 13], "crop_tool": [5, 8, 11], "crop_xarrai": 6, "crystal": 12, "cube": 12, "current": [3, 4, 7], "cut": 12, "d": [8, 9], "da": 4, "da1": 4, "da3": 4, "da7": 4, "da_matrix": [4, 10], "da_poly_matrix": 4, "dapolymatrix": 4, "data": [0, 3, 5, 6, 7, 8, 9, 10, 12, 13], "data9132_rawdata": 10, "data_arrai": 6, "data_path": [3, 8], "dataarrai": [5, 6, 7, 8], "databas": 4, "dataconvert": 7, "dataepfl": 10, "datafram": [9, 11, 12], "dataset": 7, "de": 12, "de1": [4, 10], "default": [5, 6, 7, 8, 9, 10, 11, 12, 13], "default_config": 3, "defin": [0, 3, 4, 5, 6, 10], "definit": [3, 7, 8, 12], "deg": 4, "degre": 3, "delai": [3, 8, 9, 11, 12], "delimit": 10, "demonstr": [10, 11], "depend": 0, "describ": 7, "descript": [1, 4, 12], "detail": 3, "detector": [0, 3, 4, 10, 11], "determin": [4, 5, 8], "dev": 0, "develop": 0, "deviat": 3, "df_lut": 9, "dict": [3, 4, 5, 6, 7, 8, 9], "dict_kei": 11, "dictionari": [3, 4, 5, 7, 8, 9, 13], "differ": [3, 9, 10, 11, 12, 13], "dim": [4, 6, 7, 9, 10, 11, 12], "dimens": [7, 8, 9, 10, 11], "dimension": [6, 7, 12], "direct": [3, 4, 6], "directli": [3, 5, 8, 11], "directori": [3, 9], "disabl": 3, "dispers": [3, 4, 11, 12], "distanc": 4, "divid": 7, "do": [4, 11], "doc": [0, 10, 11, 12, 13], "document": [0, 11], "doe": 7, "done": [0, 11, 12, 13], "drain_curr": 3, "drive": 11, "duplic": 7, "e": [0, 3, 4, 5, 6, 8, 9, 10, 11], "e_correct": 4, "e_rang": [4, 10], "e_shift": [4, 10], "each": [3, 4, 6, 8], "edg": 4, "edit": 0, "ef": 12, "either": [0, 8, 11], "ek_axi": 4, "ek_range_max": [5, 11, 12, 13], "ek_range_min": [5, 11, 12, 13], "ekin": [3, 8, 10, 11], "electron": [4, 8, 12], "electronanalys": [3, 12], "element": [6, 7], "eln": 3, "eln_data": 8, "email": 12, "emiss": [0, 4], "empti": [3, 8, 9], "enabl": [3, 9], "enable_nested_progress_bar": 3, "end": 9, "energi": [0, 3, 4, 5, 7, 8, 11, 12, 13], "energy_offset_px": [3, 4, 10], "energy_resolut": 12, "energydispers": 3, "engin": 3, "ensur": 0, "entri": [3, 6, 7, 11, 12], "entry_titl": 12, "environ": 0, "epfl": [0, 8], "epic": [3, 8, 9], "epics_channel": 3, "equidist": 13, "eshift": 4, "estim": 4, "etc": [0, 3, 9], "ev": 3, "even": 7, "ex": [8, 9], "exampl": [0, 1], "example_config_fhi": [12, 13], "example_data": 11, "exist": [3, 11], "experiment_summari": 12, "experiment_titl": 12, "explicitli": 10, "explor": 8, "export": 1, "express": 4, "extend": 7, "extens": 8, "extent": 12, "extract": 9, "f": [3, 12], "factor": [3, 4], "faddr": [7, 8], "fairmat": 12, "fals": [3, 5, 8, 9, 10, 11], "faradayweg": 12, "fast": 9, "fast_ax": 9, "featur": 6, "feel": 0, "fermi": 12, "fft": [5, 6, 8, 10, 11], "fft_filter_peak": [3, 5, 8, 10, 11], "fft_tool": [5, 8, 10, 11], "fhi": [0, 11, 12], "figur": [10, 11, 12, 13], "file": [5, 7, 8, 9, 10, 11, 12], "filenam": 8, "filenotfounderror": [3, 9], "filepath": 7, "fill": 12, "filter": [3, 5, 6, 8, 10, 11], "filtered_fft": 6, "find": 4, "find_scan": 9, "find_scan_typ": 9, "first": [0, 3, 6, 11, 12], "fit": 4, "fix": 11, "flag": [3, 6], "float": [4, 5, 6, 7, 8, 9], "fluenc": 12, "folder": [3, 8, 9, 10, 11], "folder_config": 3, "follow": [0, 3, 6, 7, 12], "format": [3, 7, 12], "found": [0, 3, 4, 7, 9, 10, 11, 12, 13], "fourier": [3, 5, 6, 8, 10], "fourier_filter_2d": 6, "fraction": 4, "frame": [8, 9], "free": 0, "frequenc": [5, 8, 12], "fritz": [3, 8, 12], "from": [0, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13], "front": 10, "fsmap": 12, "full": [6, 7], "fulli": 3, "function": [1, 3, 9, 10, 11], "further": [8, 9], "fwhm": 12, "g": [0, 3, 5, 6, 8, 9, 10, 11], "gamma": 12, "gather": [11, 12, 13], "gauss2d": 6, "gaussian": [3, 5, 6, 12], "gener": [8, 11, 12], "get": [4, 5, 8, 11], "get_archiver_data": 9, "get_coord": 9, "get_damatrix_from_calib2d": 4, "get_modes_from_calib_dict": 7, "get_pair_from_list": 7, "get_raw2d": 9, "get_rr_da": 4, "get_scan_path": 9, "getdata": 3, "git": 0, "github": [0, 2], "give": 3, "given": [3, 4, 6, 7, 8, 9, 11, 12], "grid": [3, 6, 8, 10, 11, 13], "group": 7, "gt": [10, 11, 12, 13], "guess": 7, "h5": [8, 11], "h5group": 7, "h5py": 7, "ha": [11, 12], "haber": [3, 8, 12], "handl": [0, 9], "handle_meta": 9, "have": [0, 7, 10], "hdf5": [7, 8, 11], "help": 0, "helper": 1, "here": [3, 11, 13], "hierarch": 3, "high": 4, "highmagnification2": 10, "home": [3, 10, 11, 12, 13], "horizont": [5, 8], "hostedtoolcach": [3, 10, 11, 12, 13], "how": 1, "http": [0, 3, 4], "i": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "ideal": 12, "identifi": 9, "ignor": 7, "igor": 4, "illumin": 11, "imag": [0, 1, 3, 4, 5, 7, 8, 9, 11, 13], "imagej": 7, "img_tool": 1, "import": [0, 10, 11, 12, 13], "importantli": 0, "incident_energi": 12, "incident_energy_spread": 12, "incident_polar": 12, "incident_wavelength": 12, "includ": [7, 11], "increas": 4, "independ": 6, "index": [1, 4, 8, 9, 11], "indexerror": 9, "indic": [4, 6, 8, 9], "individu": [3, 13], "infer": 7, "info": 9, "info_dict": 9, "inform": [6, 7, 11], "init": 0, "initi": 3, "inner": 4, "input": [3, 6, 7, 8, 9], "input_fil": [3, 7, 8], "instanc": [3, 9, 10, 11, 12, 13], "instead": 7, "institut": [3, 8, 12], "instrument": [3, 12], "int": [4, 8, 9], "integ": [4, 8, 9], "integr": 11, "intend": [0, 3], "intens": 6, "interact": [10, 11], "interest": [8, 9, 11], "interfac": [8, 9], "interpol": [4, 8, 10], "introduc": 10, "investig": 12, "io": 1, "ipykernel": 0, "ipython": 0, "item": 3, "iter": [3, 8, 9], "its": [11, 12], "j": 4, "jacobian": 4, "jacobian_determin": 4, "json": [3, 8, 12], "jupyt": 0, "k": 3, "keep": 3, "kei": [3, 7, 11], "kernel": 0, "keyerror": 4, "keyword": [5, 7, 8, 10, 11], "kinet": [4, 5, 8, 13], "kinetic_energi": [3, 4, 5, 8, 10], "kwd": [5, 7, 8, 10], "lab": [8, 12], "labview": 0, "later": [4, 11], "laurenz": 12, "lausann": 8, "len": [3, 4, 5, 7, 8], "lens_mod": [4, 5, 8, 10], "lib": [3, 10, 11, 12, 13], "librari": 3, "like": 7, "line": [4, 6, 7], "linear": 4, "linux": 3, "list": [0, 4, 5, 6, 7, 8, 9, 11], "list_lin": 7, "load": [0, 1, 3, 7, 8, 9, 10, 12], "load_config": 3, "load_ext": [10, 11, 12, 13], "load_h5": 7, "load_imag": 9, "load_scan": [8, 9, 11, 12, 13], "load_tiff": 7, "loader": [11, 12], "loadtxt": 10, "loc": [11, 12], "local": 0, "locat": [0, 9, 11, 12, 13], "look": 3, "low": 4, "lt": [10, 11, 12, 13], "lut": 9, "m": 0, "magnif": [3, 4, 10], "mai": 11, "maintain": 1, "make": [0, 11], "manipul": [6, 9], "manner": 3, "manual": [7, 12], "manufactur": 0, "map": 12, "map_coordin": 4, "mask": 6, "matplotlib": [10, 11, 12, 13], "matric": 4, "matrix": [4, 5], "max": 12, "maxim": 4, "maximum": [6, 9], "mcp": [0, 4, 10, 11], "mcp_position_mm": 4, "measur": [3, 12], "mechan": 3, "merg": [3, 13], "mesh": [5, 8], "meshgrid": [5, 8, 11], "metadata": [5, 7, 8, 9, 11, 12, 13], "method": [8, 9, 11, 12, 13], "might": 13, "millimet": [3, 4], "mimic": 4, "minim": 4, "minimum": 6, "mirror": [9, 11], "mirrori": 3, "mirrorx": [3, 11], "miss": 3, "mitig": 10, "mm": [3, 4], "mm_z": 4, "mode": [0, 4, 5, 7, 8, 11], "modif": 3, "modul": [0, 3, 4, 6, 7], "monoton": 4, "more": 7, "most": [0, 9], "mostli": 3, "mpe": [3, 12], "mpg": 12, "must": [4, 8], "mx": 6, "my": 6, "name": [0, 3, 5, 7, 8, 9, 12], "nameerror": 7, "nap32": 9, "ndarrai": [4, 5, 6, 7, 8, 9], "ndimag": 4, "nearest": 4, "necessari": 3, "need": [0, 4, 7, 8, 9, 12], "neither": [3, 9], "nest": [3, 7, 9], "new": [0, 11], "nexu": [1, 3, 7, 8, 9], "node": 7, "non": [4, 11, 13], "none": [3, 5, 7, 8, 9], "nor": [3, 9], "normal": [3, 4, 5, 8], "notebook": [0, 3, 8, 11, 12], "notimplementederror": 7, "now": 0, "np": [4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "number": [3, 4, 7, 8, 9, 11, 13], "numpi": [4, 5, 7, 8, 9, 10, 11, 12, 13], "nx": [8, 12], "nx_pixel": [3, 4], "nxmpes_arp": [3, 12], "nxmpes_arpes_config": [3, 12], "nxuser": 12, "ny_pixel": [3, 4], "o": 10, "object": [5, 7, 8, 9, 11], "obtain": 9, "offset": [3, 4], "omg": 3, "onc": 0, "one": [6, 7, 10, 11], "onli": [4, 7, 11], "onto": [8, 13], "open": 10, "opencomp": 0, "oper": [10, 11], "opt": [3, 10, 11, 12, 13], "optim": [5, 8, 10, 11], "option": [0, 3, 5, 6, 7, 8, 9, 11], "order": [3, 4, 7, 11], "org": 0, "other": [3, 5, 11], "otherwis": 7, "out": [0, 4, 5, 8], "output": [7, 12], "outsid": 4, "over": [3, 8, 9, 11], "overlap": [12, 13], "overwrit": [3, 5], "overwritten": 3, "p": 12, "p_rd": [3, 12], "pack": 4, "packag": [0, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13], "package_dir": 3, "page": 1, "pair": 7, "panda": 9, "paramet": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "pars": [7, 9], "parse_calib2d_to_dict": 7, "parse_config": [3, 5, 8], "parse_info_to_dict": 9, "parse_lut_to_df": 9, "parser": 7, "part": 0, "pass": [0, 3, 4, 5, 8, 10, 11], "pass_energi": [4, 5, 8, 10], "path": [3, 5, 7, 8, 9, 10, 11, 12, 13], "pattern": 6, "pd": 9, "peak": [3, 5, 6, 8, 10, 11], "per": [4, 11, 13], "perform": [4, 8, 9, 10, 13], "pesdata": 9, "phi": 3, "phoibo": [0, 5, 8, 10, 11], "phoibos150": [0, 3], "photoelectron": 4, "photoemiss": [4, 5], "physic": [0, 4, 5], "physical_unit_data": 4, "pixel": [3, 4], "pixel_s": [3, 4, 10], "place": [3, 5], "plai": [5, 8], "planck": 12, "plane": [3, 5, 8, 10], "pleas": 10, "plot": [10, 11, 12, 13], "plt": [10, 11, 12, 13], "point": [9, 11, 12], "pol": 12, "polar": 3, "polynomi": 4, "popul": 7, "pos_i": [3, 5, 6, 8, 11], "pos_x": [3, 5, 6, 8, 11], "posi": 7, "posit": [3, 4, 5, 6, 8, 10, 11], "poss": 4, "possibl": 6, "possibli": 3, "posx": 7, "pre": [3, 5], "prefer": 3, "preparation_d": 12, "preparation_descript": 12, "prerequisit": 0, "present": [3, 11], "preserv": 4, "press": 11, "pressur": 3, "pressureac": [3, 12], "previous": [8, 11], "princip": 12, "print": 9, "pro": 4, "probe": 12, "procedur": 0, "process": [8, 11], "process_sweep_scan": 8, "processor": 3, "produc": 13, "program": 4, "progress": [3, 9], "project": 9, "properti": [5, 8], "provid": [0, 3, 5, 6, 7, 8, 9, 10, 11, 13], "pull": 0, "pulse_dur": 12, "pulse_energi": 12, "pump": 12, "pv": 3, "pyenv": 0, "pynxtool": [7, 8, 12], "pypi": 0, "pyplot": [10, 11, 12, 13], "pyproject": 0, "python": [0, 3, 7, 10, 11, 12, 13], "python3": [3, 10, 11, 12, 13], "quadmesh": [10, 11, 12, 13], "queri": 9, "question": 4, "r": 7, "r9132": 10, "rais": [3, 4, 7, 8, 9], "rang": [4, 8, 9, 11, 12, 13], "ratio": 4, "raw": [4, 5, 8, 9], "raw_arrai": 9, "raw_data": 8, "raw_imag": 5, "raw_image_nam": 10, "raw_img": 5, "raw_list": 9, "rbv": 3, "read": [0, 7, 8, 9], "read_calib2d": 7, "reader": [3, 7, 8, 12], "real": 6, "record": 11, "recurs": [0, 7], "recursive_parse_metadata": 7, "recursive_write_metadata": 7, "refer": 2, "regular": 6, "rel": 11, "remov": [3, 6], "renam": 3, "report": 3, "repositori": 0, "repres": 7, "request": [0, 3, 4], "requir": [0, 11], "res_xarrai": [10, 11, 12, 13], "res_xarray_check": 11, "resolut": 12, "resolv": 4, "respect": [3, 4], "result": [3, 4, 8, 11, 12, 13], "ret": 6, "retain": 7, "retard": 4, "retardation_ratio": [4, 10], "retriev": 3, "rettig": 12, "return": [3, 4, 5, 6, 7, 8, 9, 13], "rewrit": 12, "right": 0, "rise": 7, "role": 12, "root": 13, "rotat": 10, "rotation_angl": [10, 13], "row": 4, "row0": 4, "row1": 4, "rr": 4, "rrvec": 4, "rudimentari": 9, "rule": 3, "run": 0, "runner": [10, 11, 12, 13], "s_": [8, 9, 11], "sai": 11, "sampl": [3, 12], "sample_azimuth": 3, "sample_histori": 12, "sample_polar": 3, "sample_tilt": 3, "save": [3, 7, 8, 12], "save_config": 3, "scale": 4, "scan": [0, 1, 4, 8, 9, 11, 12], "scan_info": [9, 11, 12], "scan_list": 9, "scan_path": 9, "scan_typ": 9, "scanvector": 9, "scipi": 4, "script": 9, "search": [1, 3, 8, 9], "second": [4, 6], "second_closest_rr": 4, "see": [0, 3, 11], "select": [5, 8], "sequenc": [6, 7, 8, 9], "server": 11, "set": [0, 4, 5, 8, 11, 12, 13], "sever": 0, "shell": 0, "shift": 4, "ship": 3, "should": [0, 3, 4, 7, 11, 12], "show": 11, "showcas": [10, 11, 12, 13], "sigma": [5, 6], "sigma_i": [3, 5, 6, 8, 11], "sigma_x": [3, 5, 6, 8, 11], "singl": [3, 7, 8, 9, 10, 12], "site": [3, 10, 11, 12, 13], "size": [3, 4, 9], "slice": [8, 9, 11, 12], "slow": 9, "slow_ax": 9, "so": 7, "societi": 12, "softwar": [0, 8, 11], "some": 10, "sort": 7, "sourc": [0, 3, 4, 5, 6, 7, 8, 9, 10], "sp": [11, 12, 13], "sp2": 10, "spa": 10, "spa_param": [3, 12, 13], "spatial": [3, 4, 5, 7, 8], "spatial0": 3, "spatial_resolut": 12, "spawn": 0, "spec": [0, 3, 4, 5, 8, 10], "specs_config": 3, "specs_kernel": 0, "specs_poetri": 0, "specsanalyz": [8, 11, 12, 13], "specsscan": [0, 9, 12, 13], "spectromet": 0, "split": 7, "src": [3, 12, 13], "stack": [7, 8], "stackoverflow": 4, "stamp": [9, 11, 12, 13], "standard": [3, 7], "start": [4, 9], "step": [0, 3, 8, 11, 13], "stoke": 12, "store": [3, 5, 7, 8, 10, 11], "str": [3, 4, 5, 6, 7, 8, 9], "string": [3, 4, 7, 8, 9, 11], "structur": 7, "subfunct": 7, "submit": 0, "submodul": 0, "subsequ": 3, "subtract": [3, 5, 8], "sum": [8, 11, 12, 13], "support": [3, 7, 8], "suppress": 6, "sure": 0, "surfac": 12, "sweep": [1, 8], "sx": 6, "sy": 6, "sync": 0, "system": [0, 3, 4], "system_config": [3, 11], "t": [7, 10, 12], "tabul": 4, "take": 3, "tbte3": 12, "temp_rbv": [3, 12], "tempa": 3, "temperatur": [3, 8, 9], "test": [0, 3, 10, 11, 12, 13], "text": 10, "th": [5, 8], "than": 7, "thei": 3, "them": 3, "therefor": [7, 11], "thi": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], "third": [8, 11], "through": 7, "tht": 3, "tif": 8, "tiff": [7, 8], "tilt": [3, 12], "time": [7, 9, 11, 12, 13], "to_h5": 7, "to_nexu": 7, "to_tiff": 7, "todo": 1, "togeth": [0, 3], "toml": 0, "tool": [1, 5, 8, 10, 11], "topfloor": 9, "total_iter": 9, "tqdm_enable_nest": 9, "trans_i": 3, "trans_x": 3, "trans_z": 3, "transform": [3, 4], "trarp": [9, 11, 12], "tri": 7, "true": [3, 10, 11, 12, 13], "trx": 3, "try": 3, "trz": 3, "ts_from": 9, "ts_to": 9, "tsv": 10, "tsv_data": 10, "tupl": [4, 7, 8, 9], "tutori": 0, "two": 0, "txt": 9, "type": [0, 3, 4, 5, 6, 7, 8, 9], "typeerror": [3, 7], "typic": 3, "tzcyx": 7, "uhv": 12, "under": [5, 8, 9], "union": 8, "unit": [3, 4, 5], "unless": 4, "up": [0, 4], "updat": 0, "url": [3, 9], "us": [0, 3, 4, 5, 7, 8, 9, 11, 12, 13], "usag": 0, "user": [3, 11, 12, 13], "user0": 12, "user_config": [3, 11, 12, 13], "v": [3, 5], "valid": [4, 8, 9, 10], "valu": [3, 4, 5, 6, 7, 8], "valueerror": [4, 7, 8, 9], "variabl": 6, "variou": 3, "vector": [4, 12], "venv": 0, "verbos": 3, "version": 0, "vertic": [5, 8], "virtual": 0, "visual": 10, "voltag": [3, 8], "w": [7, 8, 10], "wa": 8, "want": 6, "warn": [7, 11, 13], "we": 11, "well": [2, 11], "when": [7, 8, 9], "where": [3, 6, 7, 8, 11], "which": [6, 8, 9, 10, 11, 12], "while": 11, "wide": [0, 3], "wideanglemod": [5, 8, 10], "widget": [10, 11, 12, 13], "width": [3, 5, 8], "wiki": 2, "window": 3, "wish": 0, "within": 0, "withing": 4, "without": [6, 10], "work": [0, 3, 4, 5, 8, 10, 11, 12, 13], "work_funct": [4, 5, 8, 10], "workflow": 3, "would": [8, 9], "write": [7, 8], "writer": 8, "wrong": 13, "x": [3, 5, 6, 7], "x64": [3, 10, 11, 12, 13], "x_max": 6, "x_min": 6, "xarrai": [3, 5, 6, 7, 8, 10, 11, 12, 13], "xgs600": [3, 12], "xr": [5, 6, 7, 8], "xuv": 12, "y": [3, 5, 6, 7], "y_max": 6, "y_min": 6, "yaml": [3, 9, 10, 11, 12, 13], "year": 9, "yet": 11, "you": [0, 4, 6], "your": [0, 3, 10], "z": [3, 7], "zinner": 4, "zinner_diff": 4, "\u00b5j": 12}, "titles": ["specsanalyzer", "Welcome to specsanalyzer\u2019s documentation!", "1. How to maintain", "Config", "2. convert functions (specsanalyzer.convert)", "1. Core functions  (specsanalyzer.core)", "3. image tool functions (specsanalyzer.img_tools)", "4. io functions  (specsanalyzer.io)", "1. Core functions  (specsscan.core)", "2. Helpers", "Example 1: SpecsAnalyzer conversion", "Example 2: SpecsScan loading", "Example 3: Export to NeXus", "Example 4: Sweep Scan loading"], "titleterms": {"": 1, "1": 10, "2": 11, "3": 12, "4": 13, "For": 0, "adjust": 10, "angl": 10, "api": 3, "artefact": 10, "artifact": 11, "berlin": 3, "calib2d": 0, "config": 3, "configur": [0, 3], "contribut": 1, "contributor": 0, "convers": 10, "conversion_paramet": 10, "convert": 4, "core": [1, 5, 8], "crop": 11, "data": 11, "default": 3, "dict": 10, "document": 1, "exampl": [3, 10, 11, 12, 13], "export": 12, "fhi": 3, "file": [0, 3], "function": [4, 5, 6, 7, 8], "get": 1, "helper": 9, "how": 2, "imag": [6, 10], "img_tool": 6, "indic": 1, "instal": 0, "io": 7, "iter": 11, "load": [11, 13], "maintain": [0, 2], "mesh": [10, 11], "mode": 10, "modul": 1, "nexu": 12, "offset": 10, "pip": 0, "poetri": 0, "remov": [10, 11], "resolv": 10, "save": 11, "scan": 13, "select": 11, "set": 3, "setup": 3, "spatial": 10, "specsanalyz": [0, 1, 3, 4, 5, 6, 7, 10], "specsscan": [1, 3, 8, 11], "start": 1, "sweep": 13, "tabl": 1, "tool": 6, "trarp": 3, "us": 10, "user": 0, "welcom": 1}})
\ No newline at end of file
diff --git a/specsanalyzer/latest/specsanalyzer/config.html b/specsanalyzer/latest/specsanalyzer/config.html
index dc5ce12..04c37df 100644
--- a/specsanalyzer/latest/specsanalyzer/config.html
+++ b/specsanalyzer/latest/specsanalyzer/config.html
@@ -8,7 +8,7 @@
     
     
 
-    Config — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Config — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -576,7 +576,7 @@ 
Config#This module contains a config library for loading yaml/json files into dicts

 

 

-specsanalyzer.config.parse_config(config=None, folder_config=None, user_config=None, system_config=None, default_config='/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml', verbose=True)[source]#

+specsanalyzer.config.parse_config(config=None, folder_config=None, user_config=None, system_config=None, default_config='/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/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
diff --git a/specsanalyzer/latest/specsanalyzer/convert.html b/specsanalyzer/latest/specsanalyzer/convert.html
index 5b734e6..502aa53 100644
--- a/specsanalyzer/latest/specsanalyzer/convert.html
+++ b/specsanalyzer/latest/specsanalyzer/convert.html
@@ -8,7 +8,7 @@
     
     
 
-    2. convert functions (specsanalyzer.convert) — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    2. convert functions (specsanalyzer.convert) — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/specsanalyzer/core.html b/specsanalyzer/latest/specsanalyzer/core.html
index 6d1244c..92e0f7c 100644
--- a/specsanalyzer/latest/specsanalyzer/core.html
+++ b/specsanalyzer/latest/specsanalyzer/core.html
@@ -8,7 +8,7 @@
     
     
 
-    1. Core functions (specsanalyzer.core) — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    1. Core functions (specsanalyzer.core) — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/specsanalyzer/img_tools.html b/specsanalyzer/latest/specsanalyzer/img_tools.html
index d0d9533..cbdfb18 100644
--- a/specsanalyzer/latest/specsanalyzer/img_tools.html
+++ b/specsanalyzer/latest/specsanalyzer/img_tools.html
@@ -8,7 +8,7 @@
     
     
 
-    3. image tool functions (specsanalyzer.img_tools) — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    3. image tool functions (specsanalyzer.img_tools) — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/specsanalyzer/io.html b/specsanalyzer/latest/specsanalyzer/io.html
index 3f6f595..ade9197 100644
--- a/specsanalyzer/latest/specsanalyzer/io.html
+++ b/specsanalyzer/latest/specsanalyzer/io.html
@@ -8,7 +8,7 @@
     
     
 
-    4. io functions (specsanalyzer.io) — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    4. io functions (specsanalyzer.io) — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/specsscan/core.html b/specsanalyzer/latest/specsscan/core.html
index 23c8efd..52910e0 100644
--- a/specsanalyzer/latest/specsscan/core.html
+++ b/specsanalyzer/latest/specsscan/core.html
@@ -8,7 +8,7 @@
     
     
 
-    1. Core functions (specsscan.core) — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    1. Core functions (specsscan.core) — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/specsscan/helpers.html b/specsanalyzer/latest/specsscan/helpers.html
index f4a22cd..a408a57 100644
--- a/specsanalyzer/latest/specsscan/helpers.html
+++ b/specsanalyzer/latest/specsscan/helpers.html
@@ -8,7 +8,7 @@
     
     
 
-    2. Helpers — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    2. Helpers — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
   
   
   
@@ -38,7 +38,7 @@
   
 
 
-    
+    
     
     
     
@@ -47,7 +47,7 @@
     
@@ -57,7 +57,7 @@
     
   
   
-  
+  
   
   
   
@@ -119,7 +119,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
diff --git a/specsanalyzer/latest/tutorial/1_specsanalyzer_conversion_examples.html b/specsanalyzer/latest/tutorial/1_specsanalyzer_conversion_examples.html
index 33293da..6b2a1f4 100644
--- a/specsanalyzer/latest/tutorial/1_specsanalyzer_conversion_examples.html
+++ b/specsanalyzer/latest/tutorial/1_specsanalyzer_conversion_examples.html
@@ -8,7 +8,7 @@
     
     
 
-    Example 1: SpecsAnalyzer conversion — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Example 1: SpecsAnalyzer conversion — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
     
   
   
@@ -39,7 +39,7 @@
   
 
 
-    
+    
     
     
     
@@ -50,7 +50,7 @@
     
@@ -60,7 +60,7 @@
     
   
   
-  
+  
   
   
   
@@ -122,7 +122,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -573,9 +573,9 @@ 
Example 1: SpecsAnalyzer conversion
%load_ext autoreload
 %autoreload 2
-from specsanalyzer import SpecsAnalyzer
-import matplotlib.pyplot as plt
-import numpy as np
+from specsanalyzer import SpecsAnalyzer
+import matplotlib.pyplot as plt
+import numpy as np
 %matplotlib widget
 

 

@@ -597,7 +597,7 @@ 
Image conversion
 
 Configuration loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/tests/data/dataEPFL/config/config.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
 

 

 
convert single image

@@ -657,14 +657,14 @@ 
Image conversion
 
-<matplotlib.collections.QuadMesh at 0x7f93b8547010>
+<matplotlib.collections.QuadMesh at 0x7fdfe4554dc0>
 

 

 

 

 

 

-

+

 

 
Conversion parameters are stored in the attributes

 

@@ -734,14 +734,14 @@ 
Adjusting offsets and angle
 
-<matplotlib.collections.QuadMesh at 0x7f93b5991c60>
+<matplotlib.collections.QuadMesh at 0x7fdfe3e52b00>
 

 

 

 

 

 

-

+

 

 
 

@@ -761,7 +761,7 @@ 
Removal of mesh artefact
 
 Configuration loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/tests/data/dataEPFL/config/config_filterON.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
 

 

 

@@ -786,14 +786,14 @@ 
Removal of mesh artefact
 
-<matplotlib.collections.QuadMesh at 0x7f93b5db6d40>
+<matplotlib.collections.QuadMesh at 0x7fdfe3e99660>
 

 

 

 

 

 

-

+

 

 
Alternatively, one can use the interactive fft tool to optimize the fft peak positions of the grid.

 

@@ -808,19 +808,19 @@ 
Removal of mesh artefact
 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 
The peak parameters are stored in the config dict which can be passed as kwds to the convert_image function

 

@@ -855,14 +855,14 @@ 
Removal of mesh artefact
 
-<matplotlib.collections.QuadMesh at 0x7f93adc7e080>
+<matplotlib.collections.QuadMesh at 0x7fdfe00b9810>
 

 

 

 

 

 

-

+

 

 
 

@@ -898,14 +898,14 @@ 
Conversion into spatially resolved modes
 
-<matplotlib.collections.QuadMesh at 0x7f93adb4a8f0>
+<matplotlib.collections.QuadMesh at 0x7fdfdbf6e4d0>
 

 

 

 

 

 

-

+

 

 
 

@@ -962,7 +962,7 @@ 
Conversion using conversion_parameters dict
 
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
 No valid calib2 file found. Please provide the path to the calib2d file
 corresponding to your detector in the config, or copy into the config
 folder of the package! Without valid calib2d file, calibration parameters
@@ -975,17 +975,17 @@ 
Conversion using conversion_parameters dict
 
-<matplotlib.collections.QuadMesh at 0x7f93adbce3b0>
+<matplotlib.collections.QuadMesh at 0x7fdfdbf3f430>
 

 

 

 

 

 

-

+

 

 
 
 
diff --git a/specsanalyzer/latest/tutorial/2_specsscan_example.html b/specsanalyzer/latest/tutorial/2_specsscan_example.html
index 4c63946..cd63964 100644
--- a/specsanalyzer/latest/tutorial/2_specsscan_example.html
+++ b/specsanalyzer/latest/tutorial/2_specsscan_example.html
@@ -8,7 +8,7 @@
     
     
 
-    Example 2: SpecsScan loading — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Example 2: SpecsScan loading — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
     
   
   
@@ -39,7 +39,7 @@
   
 
 
-    
+    
     
     
     
@@ -50,7 +50,7 @@
     
@@ -60,7 +60,7 @@
     
   
   
-  
+  
   
   
   
@@ -122,7 +122,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -574,9 +574,9 @@ 
Example 2: SpecsScan loading
%load_ext autoreload
 %autoreload 2
-from specsscan import SpecsScan
-import matplotlib.pyplot as plt
-import numpy as np
+from specsscan import SpecsScan
+import matplotlib.pyplot as plt
+import numpy as np
 %matplotlib widget
 

 

@@ -597,8 +597,8 @@ 
Example 2: SpecsScan loading
 
 Configuration loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/tests/data/config.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsscan/config/default.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsscan/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
 

 

 

@@ -621,7 +621,7 @@ 
Loading data
 

 

-

+

 

 

 

@@ -667,14 +667,14 @@ 
Loading data
 
-<matplotlib.collections.QuadMesh at 0x7f883acec0a0>
+<matplotlib.collections.QuadMesh at 0x7f9ab13cb1f0>
 

 

 

 

 

 

-

+

 

 
 

@@ -696,7 +696,7 @@ 
Cropping data
 

 

-

+

 

 

 

@@ -729,19 +729,19 @@ 
Cropping data
 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 
Load the scan again to apply it to all images:

 

@@ -759,7 +759,7 @@ 
Cropping data
 

 

-

+

 

 

 

@@ -795,19 +795,19 @@ 
Removal of Mesh Artifact
 

 

-

+

 

 

 

 

 

-

+

 

 

 

 

 

-

+

 

 
Load the scan again for the new changes to apply to all the images

 

@@ -829,7 +829,7 @@ 
Removal of Mesh Artifact
 

 

-

+

 

 

 

@@ -848,14 +848,14 @@ 
Removal of Mesh Artifact
 
-<matplotlib.collections.QuadMesh at 0x7f88399c7790>
+<matplotlib.collections.QuadMesh at 0x7f9a5d8b3c40>
 

 

 

 

 

 

-

+

 

 
We can e.g. also get a plot along the third dimension, by integrating along the first.

 
One can also access the conversion result from a class accessor:

@@ -874,14 +874,14 @@ 
Removal of Mesh Artifact
 
-<matplotlib.collections.QuadMesh at 0x7f8839437250>
+<matplotlib.collections.QuadMesh at 0x7f9a5d782aa0>
 

 

 

 

 

 

-

+

 

 
The metadata associated with the scan is added as an attribute to the xarray

 

@@ -930,7 +930,7 @@ 
Loading with selected iterations
 

 

-

+

 

 

 

@@ -949,14 +949,14 @@ 
Loading with selected iterations
 
-<matplotlib.collections.QuadMesh at 0x7f883933cfa0>
+<matplotlib.collections.QuadMesh at 0x7f9a5d6a0280>
 

 

 

 

 

 

-

+

 

 
Another useful functionality is to load a 3D scan as a function of iterations averaged over the scan parameter (in this case, mirrorX). This is done using the check_scan method

 

@@ -985,7 +985,7 @@ 
Loading with selected iterations
 

 

-

+

 

 

 

@@ -1004,14 +1004,14 @@ 
Loading with selected iterations
 
-<matplotlib.collections.QuadMesh at 0x7f883975eaa0>
+<matplotlib.collections.QuadMesh at 0x7f9a5d6f7c70>
 

 

 

 

 

 

-

+

 

 
 

@@ -1033,7 +1033,7 @@ 
Saving#
 

 

-

+

 

 

 

@@ -1059,7 +1059,7 @@ 
Saving#
 

 
 
 
diff --git a/specsanalyzer/latest/tutorial/3_specsscan_conversion_to_NeXus.html b/specsanalyzer/latest/tutorial/3_specsscan_conversion_to_NeXus.html
index ca3ea75..b4cdd6f 100644
--- a/specsanalyzer/latest/tutorial/3_specsscan_conversion_to_NeXus.html
+++ b/specsanalyzer/latest/tutorial/3_specsscan_conversion_to_NeXus.html
@@ -8,7 +8,7 @@
     
     
 
-    Example 3: Export to NeXus — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Example 3: Export to NeXus — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
     
   
   
@@ -39,7 +39,7 @@
   
 
 
-    
+    
     
     
     
@@ -50,7 +50,7 @@
     
@@ -60,7 +60,7 @@
     
   
   
-  
+  
   
   
   
@@ -122,7 +122,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -574,9 +574,9 @@ 
Example 3: Export to NeXus
%load_ext autoreload
 %autoreload 2
-from specsscan import SpecsScan
-import matplotlib.pyplot as plt
-import numpy as np
+from specsscan import SpecsScan
+import matplotlib.pyplot as plt
+import numpy as np
 %matplotlib widget
 

 

@@ -665,8 +665,8 @@ 
Example 3: Export to NeXus
 
 User config loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/src/specsscan/config/example_config_FHI.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsscan/config/default.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsscan/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
 

 

 
The load_scan method loads the scan as an xarray along with the metadata needed for nexus conversion.

@@ -687,7 +687,7 @@ 
Example 3: Export to NeXus
 

 

-

+

 

 

 

@@ -734,14 +734,14 @@ 
Example 3: Export to NeXus
 
-<matplotlib.collections.QuadMesh at 0x7f5c6eb1e1a0>
+<matplotlib.collections.QuadMesh at 0x7fe13375ed40>
 

 

 

 

 

 

-

+

 

 
The Fermi surface

 

@@ -759,14 +759,14 @@ 
Example 3: Export to NeXus
 
-<matplotlib.collections.QuadMesh at 0x7f5c65f7a650>
+<matplotlib.collections.QuadMesh at 0x7fe1327c60e0>
 

 

 

 

 

 

-

+

 

 
Save as nexus file

 

@@ -796,7 +796,7 @@ 
Example 3: Export to NeXus
-{"state": {"928e431680194431a69a139f2a1dbc31": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "965bf801c71e4814b035c04c9893840c": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7335fe09b76c43cbbf66d80d2abba6b5": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_928e431680194431a69a139f2a1dbc31", "max": 61.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_965bf801c71e4814b035c04c9893840c", "tabbable": null, "tooltip": null, "value": 61.0}}, "54b1e92117d24d9bbb1bb09c4d402ee4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "fc30fe6b320d4e4eaf2a2640bb098176": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1bd90e5e309448829f8a6788829366e1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_54b1e92117d24d9bbb1bb09c4d402ee4", "placeholder": "\u200b", "style": "IPY_MODEL_fc30fe6b320d4e4eaf2a2640bb098176", "tabbable": null, "tooltip": null, "value": "100%"}}, "fb8dcecb157c4fb8b2a2e04b770059a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c73333283d554831b2201faf2abeb2dc": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "c6ceeff2c5cd46d49f1f155ed31fc191": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fb8dcecb157c4fb8b2a2e04b770059a0", "placeholder": "\u200b", "style": "IPY_MODEL_c73333283d554831b2201faf2abeb2dc", "tabbable": null, "tooltip": null, "value": "\u200761/61\u2007[00:00<00:00,\u2007155.82it/s]"}}, "a8ebf43cba7a486e960b52a1da3208f7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5595902af7764729944db61bc02a6d56": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_1bd90e5e309448829f8a6788829366e1", "IPY_MODEL_7335fe09b76c43cbbf66d80d2abba6b5", "IPY_MODEL_c6ceeff2c5cd46d49f1f155ed31fc191"], "layout": "IPY_MODEL_a8ebf43cba7a486e960b52a1da3208f7", "tabbable": null, "tooltip": null}}, "25bf1247431f469496460ed5794d13b0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ed74cf7ed648485f987d3f005abc7f7a": {"model_name": "MPLCanvasModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_cursor": "pointer", "_data_url": "data:image/png;base64,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", "_dom_classes": [], "_figure_label": "Figure", "_image_mode": "full", "_message": "", "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "MPLCanvasModel", "_rubberband_height": 0, "_rubberband_width": 0, "_rubberband_x": 0, "_rubberband_y": 0, "_size": [640.0, 480.0], "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "MPLCanvasView", "capture_scroll": false, "footer_visible": true, "header_visible": true, "layout": "IPY_MODEL_25bf1247431f469496460ed5794d13b0", "pan_zoom_throttle": 33.0, "resizable": true, "tabbable": null, "toolbar": "IPY_MODEL_4de993da459a4416ac857cadd4f88e8c", "toolbar_position": "left", "toolbar_visible": "fade-in-fade-out", "tooltip": null}}, "ed4d5c1b54824c678091f4ff98200280": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4de993da459a4416ac857cadd4f88e8c": {"model_name": "ToolbarModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_current_action": "", "_dom_classes": [], "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "ToolbarModel", "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "ToolbarView", "button_style": "", "collapsed": true, "layout": "IPY_MODEL_ed4d5c1b54824c678091f4ff98200280", "orientation": "vertical", "tabbable": null, "toolitems": [["Home", "Reset original view", "home", "home"], ["Back", "Back to previous view", "arrow-left", "back"], ["Forward", "Forward to next view", "arrow-right", "forward"], ["Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan"], ["Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom"], ["Download", "Download plot", "floppy-o", "save_figure"]], "tooltip": null}}, "73a033b4ba3b4d9d8eb619bff7eb3ab0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "938cc410b41d4d838d124681e91e328c": {"model_name": "MPLCanvasModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_cursor": "pointer", "_data_url": "data:image/png;base64,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", "_dom_classes": [], "_figure_label": "Figure", "_image_mode": "full", "_message": "", "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "MPLCanvasModel", "_rubberband_height": 0, "_rubberband_width": 0, "_rubberband_x": 0, "_rubberband_y": 0, "_size": [640.0, 480.0], "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "MPLCanvasView", "capture_scroll": false, "footer_visible": true, "header_visible": true, "layout": "IPY_MODEL_73a033b4ba3b4d9d8eb619bff7eb3ab0", "pan_zoom_throttle": 33.0, "resizable": true, "tabbable": null, "toolbar": "IPY_MODEL_1fbf7623d97c4854a2789adfa3a969bd", "toolbar_position": "left", "toolbar_visible": "fade-in-fade-out", "tooltip": null}}, "4ce7afdeaf4d4da4adaceeab39b6d67b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1fbf7623d97c4854a2789adfa3a969bd": {"model_name": "ToolbarModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_current_action": "", "_dom_classes": [], "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "ToolbarModel", "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "ToolbarView", "button_style": "", "collapsed": true, "layout": "IPY_MODEL_4ce7afdeaf4d4da4adaceeab39b6d67b", "orientation": "vertical", "tabbable": null, "toolitems": [["Home", "Reset original view", "home", "home"], ["Back", "Back to previous view", "arrow-left", "back"], ["Forward", "Forward to next view", "arrow-right", "forward"], ["Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan"], ["Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom"], ["Download", "Download plot", "floppy-o", "save_figure"]], "tooltip": null}}}, "version_major": 2, "version_minor": 0}
+{"state": {"13e70ca380af4644986015a69a85724d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1cdef103244742a9aee1e02ca94f3b56": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "d1402350d56c4802a97ea7a342dce657": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_13e70ca380af4644986015a69a85724d", "max": 61.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_1cdef103244742a9aee1e02ca94f3b56", "tabbable": null, "tooltip": null, "value": 61.0}}, "b6dc1d58101e4537bda72b0aeb702ebc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "63dbe054683549d88d6b2b96a0893398": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "9630c133ccb549dc8aa19754b14c76b8": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b6dc1d58101e4537bda72b0aeb702ebc", "placeholder": "\u200b", "style": "IPY_MODEL_63dbe054683549d88d6b2b96a0893398", "tabbable": null, "tooltip": null, "value": "100%"}}, "fdfc12ecf4284c10bda19f9093658e6f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "963f3e2d4f2744519b8ecd552a63a2f2": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "526e16aceecd489d8db211d39a9a8074": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_fdfc12ecf4284c10bda19f9093658e6f", "placeholder": "\u200b", "style": "IPY_MODEL_963f3e2d4f2744519b8ecd552a63a2f2", "tabbable": null, "tooltip": null, "value": "\u200761/61\u2007[00:00<00:00,\u2007153.79it/s]"}}, "6242f45425424547ac11d98420bfdb52": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "144bd6792f884691b627630a0e637344": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_9630c133ccb549dc8aa19754b14c76b8", "IPY_MODEL_d1402350d56c4802a97ea7a342dce657", "IPY_MODEL_526e16aceecd489d8db211d39a9a8074"], "layout": "IPY_MODEL_6242f45425424547ac11d98420bfdb52", "tabbable": null, "tooltip": null}}, "b3837367e79b458999dbd3ad921c5546": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "53abeac89a104fce87891991d4a206f3": {"model_name": "MPLCanvasModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_cursor": "pointer", "_data_url": "data:image/png;base64,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", "_dom_classes": [], "_figure_label": "Figure", "_image_mode": "full", "_message": "", "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "MPLCanvasModel", "_rubberband_height": 0, "_rubberband_width": 0, "_rubberband_x": 0, "_rubberband_y": 0, "_size": [640.0, 480.0], "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "MPLCanvasView", "capture_scroll": false, "footer_visible": true, "header_visible": true, "layout": "IPY_MODEL_b3837367e79b458999dbd3ad921c5546", "pan_zoom_throttle": 33.0, "resizable": true, "tabbable": null, "toolbar": "IPY_MODEL_6797026f8ced44449ad24ea45d18bc48", "toolbar_position": "left", "toolbar_visible": "fade-in-fade-out", "tooltip": null}}, "7018a88c0d3646b39052bf3cf451db50": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6797026f8ced44449ad24ea45d18bc48": {"model_name": "ToolbarModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_current_action": "", "_dom_classes": [], "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "ToolbarModel", "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "ToolbarView", "button_style": "", "collapsed": true, "layout": "IPY_MODEL_7018a88c0d3646b39052bf3cf451db50", "orientation": "vertical", "tabbable": null, "toolitems": [["Home", "Reset original view", "home", "home"], ["Back", "Back to previous view", "arrow-left", "back"], ["Forward", "Forward to next view", "arrow-right", "forward"], ["Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan"], ["Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom"], ["Download", "Download plot", "floppy-o", "save_figure"]], "tooltip": null}}, "910b79621b7d4107b3f1a0ea1774f6cc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c49f379624fd4c2f836606d0c93b8a84": {"model_name": "MPLCanvasModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_cursor": "pointer", "_data_url": "data:image/png;base64,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", "_dom_classes": [], "_figure_label": "Figure", "_image_mode": "full", "_message": "", "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "MPLCanvasModel", "_rubberband_height": 0, "_rubberband_width": 0, "_rubberband_x": 0, "_rubberband_y": 0, "_size": [640.0, 480.0], "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "MPLCanvasView", "capture_scroll": false, "footer_visible": true, "header_visible": true, "layout": "IPY_MODEL_910b79621b7d4107b3f1a0ea1774f6cc", "pan_zoom_throttle": 33.0, "resizable": true, "tabbable": null, "toolbar": "IPY_MODEL_c4a545f3056f4f80a209190a92f4654e", "toolbar_position": "left", "toolbar_visible": "fade-in-fade-out", "tooltip": null}}, "e0d33ad7e70342a6919556c2d81518e4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c4a545f3056f4f80a209190a92f4654e": {"model_name": "ToolbarModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_current_action": "", "_dom_classes": [], "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "ToolbarModel", "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "ToolbarView", "button_style": "", "collapsed": true, "layout": "IPY_MODEL_e0d33ad7e70342a6919556c2d81518e4", "orientation": "vertical", "tabbable": null, "toolitems": [["Home", "Reset original view", "home", "home"], ["Back", "Back to previous view", "arrow-left", "back"], ["Forward", "Forward to next view", "arrow-right", "forward"], ["Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan"], ["Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom"], ["Download", "Download plot", "floppy-o", "save_figure"]], "tooltip": null}}}, "version_major": 2, "version_minor": 0}
 
 
 
diff --git a/specsanalyzer/latest/tutorial/4_specsscan_load_sweep_scan.html b/specsanalyzer/latest/tutorial/4_specsscan_load_sweep_scan.html
index 3b330d7..9c8e68b 100644
--- a/specsanalyzer/latest/tutorial/4_specsscan_load_sweep_scan.html
+++ b/specsanalyzer/latest/tutorial/4_specsscan_load_sweep_scan.html
@@ -8,7 +8,7 @@
     
     
 
-    Example 4: Sweep Scan loading — specsanalyzer 0.5.1.dev2+g5d28e05 documentation
+    Example 4: Sweep Scan loading — specsanalyzer 0.5.1.dev10+g0c6ed22 documentation
   
   
   
@@ -30,7 +30,7 @@
   
 
 
-    
+    
     
   
   
@@ -39,7 +39,7 @@
   
 
 
-    
+    
     
     
     
@@ -50,7 +50,7 @@
     
@@ -60,7 +60,7 @@
     
   
   
-  
+  
   
   
   
@@ -122,7 +122,7 @@
   
   
   
-    
specsanalyzer 0.5.1.dev2+g5d28e05 documentation

+    
specsanalyzer 0.5.1.dev10+g0c6ed22 documentation

   
 

     
@@ -573,9 +573,9 @@ 
Example 4: Sweep Scan loading
%load_ext autoreload
 %autoreload 2
-from specsscan import SpecsScan
-import matplotlib.pyplot as plt
-import numpy as np
+from specsscan import SpecsScan
+import matplotlib.pyplot as plt
+import numpy as np
 %matplotlib widget
 

 

@@ -605,8 +605,8 @@ 
Example 4: Sweep Scan loading
 
 User config loaded from: [/home/runner/work/specsanalyzer/specsanalyzer/docs/src/specsscan/config/example_config_FHI.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsscan/config/default.yaml]
-Default config loaded from: [/opt/hostedtoolcache/Python/3.10.15/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsscan/config/default.yaml]
+Default config loaded from: [/opt/hostedtoolcache/Python/3.10.16/x64/lib/python3.10/site-packages/specsanalyzer/config/default.yaml]
 

 

 
The load_scan method performs the merging of the images and returns the scan as an xarray

@@ -625,7 +625,7 @@ 
Example 4: Sweep Scan loading
 

 

-

+

 

 

 

@@ -647,7 +647,7 @@ 
Example 4: Sweep Scan loading
 

 

-

+

 

 

 

@@ -674,14 +674,14 @@ 
Example 4: Sweep Scan loading
 
-<matplotlib.collections.QuadMesh at 0x7fc9a00eb760>
+<matplotlib.collections.QuadMesh at 0x7ff22834a3b0>
 

 

 

 

 

 

-

+

 

 

 
[ ]:
@@ -692,7 +692,7 @@ 
Example 4: Sweep Scan loading
-{"state": {"9ff2c1d21c274449b0cef24e4ee247dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5a8e09076e1f43e1a9c970beda5f00fb": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "b2a4f58516ae4e15bb069e7f62362c97": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_9ff2c1d21c274449b0cef24e4ee247dc", "max": 80.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_5a8e09076e1f43e1a9c970beda5f00fb", "tabbable": null, "tooltip": null, "value": 80.0}}, "ed2cef29345549aba718ea67770b3e89": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "cfaf77d6e35645c29c6524c9643048b5": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "616fedd503434b43a1c3e92754625ac3": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ed2cef29345549aba718ea67770b3e89", "placeholder": "\u200b", "style": "IPY_MODEL_cfaf77d6e35645c29c6524c9643048b5", "tabbable": null, "tooltip": null, "value": "100%"}}, "7906e63b4c6a4dfe9a4e2c248914e8e7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e3ed776a59f14b39aa9366b5e1355cdf": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "1b3f390e55f74c83a0953cd3635e0785": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7906e63b4c6a4dfe9a4e2c248914e8e7", "placeholder": "\u200b", "style": "IPY_MODEL_e3ed776a59f14b39aa9366b5e1355cdf", "tabbable": null, "tooltip": null, "value": "\u200780/80\u2007[00:00<00:00,\u2007190.19it/s]"}}, "aa63f74e202f409abf04f0f5e0e7758b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c97739724bf346b78ad46589682a43bb": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_616fedd503434b43a1c3e92754625ac3", "IPY_MODEL_b2a4f58516ae4e15bb069e7f62362c97", "IPY_MODEL_1b3f390e55f74c83a0953cd3635e0785"], "layout": "IPY_MODEL_aa63f74e202f409abf04f0f5e0e7758b", "tabbable": null, "tooltip": null}}, "78348c7693a14974be0342d0451cd3c8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "35284d5081004f55ba22199a0e3d8649": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "68e961ae10aa47f2b36ef21e62d11054": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_78348c7693a14974be0342d0451cd3c8", "max": 80.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_35284d5081004f55ba22199a0e3d8649", "tabbable": null, "tooltip": null, "value": 80.0}}, "e9fe20b4c5fa4fd4b17ec7b685cc1135": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d6181e9cb18249e38b385d271f2db1eb": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "8dc6131df1fd46a6abf377fd5883cccd": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e9fe20b4c5fa4fd4b17ec7b685cc1135", "placeholder": "\u200b", "style": "IPY_MODEL_d6181e9cb18249e38b385d271f2db1eb", "tabbable": null, "tooltip": null, "value": "100%"}}, "2de59107bd3647ba878748c5d64170f8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3b0c187b7346411e988fae18a0027736": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "3eae864fc7494d61a0dc5e4da7d3feac": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_2de59107bd3647ba878748c5d64170f8", "placeholder": "\u200b", "style": "IPY_MODEL_3b0c187b7346411e988fae18a0027736", "tabbable": null, "tooltip": null, "value": "\u200780/80\u2007[00:03<00:00,\u200726.50it/s]"}}, "52f8899cbc214614aa5042221f26d82d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f701c3c02ccb401c878e7dd4e2eb2e96": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_8dc6131df1fd46a6abf377fd5883cccd", "IPY_MODEL_68e961ae10aa47f2b36ef21e62d11054", "IPY_MODEL_3eae864fc7494d61a0dc5e4da7d3feac"], "layout": "IPY_MODEL_52f8899cbc214614aa5042221f26d82d", "tabbable": null, "tooltip": null}}, "b25d9e7f30a54c84929da6c50503077d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "458895ff97d84f6fb70f38f0ea49aa0e": {"model_name": "MPLCanvasModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_cursor": "pointer", "_data_url": "data:image/png;base64,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", "_dom_classes": [], "_figure_label": "Figure", "_image_mode": "full", "_message": "", "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "MPLCanvasModel", "_rubberband_height": 0, "_rubberband_width": 0, "_rubberband_x": 0, "_rubberband_y": 0, "_size": [640.0, 480.0], "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "MPLCanvasView", "capture_scroll": false, "footer_visible": true, "header_visible": true, "layout": "IPY_MODEL_b25d9e7f30a54c84929da6c50503077d", "pan_zoom_throttle": 33.0, "resizable": true, "tabbable": null, "toolbar": "IPY_MODEL_097d25c38993413ab236a619a996c140", "toolbar_position": "left", "toolbar_visible": "fade-in-fade-out", "tooltip": null}}, "e1e68a84f0d14e09869d85498efefca3": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "097d25c38993413ab236a619a996c140": {"model_name": "ToolbarModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_current_action": "", "_dom_classes": [], "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "ToolbarModel", "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "ToolbarView", "button_style": "", "collapsed": true, "layout": "IPY_MODEL_e1e68a84f0d14e09869d85498efefca3", "orientation": "vertical", "tabbable": null, "toolitems": [["Home", "Reset original view", "home", "home"], ["Back", "Back to previous view", "arrow-left", "back"], ["Forward", "Forward to next view", "arrow-right", "forward"], ["Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan"], ["Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom"], ["Download", "Download plot", "floppy-o", "save_figure"]], "tooltip": null}}}, "version_major": 2, "version_minor": 0}
+{"state": {"d4aef54665744bb9b4f0349d26b370d5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "e5d8dccc8f4f496c86d8d221ef1a1b83": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "afb116e72293415ca914930e57bb978a": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_d4aef54665744bb9b4f0349d26b370d5", "max": 80.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_e5d8dccc8f4f496c86d8d221ef1a1b83", "tabbable": null, "tooltip": null, "value": 80.0}}, "80903c5c909245a18c94ff02a8db634c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6b5eb0dc467f4a3ebbf7cb9d6727fb13": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "2e71476a498b482182540ac6f43bfcef": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_80903c5c909245a18c94ff02a8db634c", "placeholder": "\u200b", "style": "IPY_MODEL_6b5eb0dc467f4a3ebbf7cb9d6727fb13", "tabbable": null, "tooltip": null, "value": "100%"}}, "b761d705d6f54ca5b1f12c240b6d62df": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "38e5f11cacfa4690b822df559057e524": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "388587a4a28d49e4a7979f352f3073c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b761d705d6f54ca5b1f12c240b6d62df", "placeholder": "\u200b", "style": "IPY_MODEL_38e5f11cacfa4690b822df559057e524", "tabbable": null, "tooltip": null, "value": "\u200780/80\u2007[00:00<00:00,\u2007182.02it/s]"}}, "0868ab19b34d4b0f87229500985602f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3cba6ce8f570427883360fa34591f031": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_2e71476a498b482182540ac6f43bfcef", "IPY_MODEL_afb116e72293415ca914930e57bb978a", "IPY_MODEL_388587a4a28d49e4a7979f352f3073c1"], "layout": "IPY_MODEL_0868ab19b34d4b0f87229500985602f4", "tabbable": null, "tooltip": null}}, "e56ad676414c4edc96a4b9b740e35586": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "62ca9f4de7194f338930ad58700ce175": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7e6509e9ce69445b8fa4cfa33d86c1b8": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_e56ad676414c4edc96a4b9b740e35586", "max": 80.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_62ca9f4de7194f338930ad58700ce175", "tabbable": null, "tooltip": null, "value": 80.0}}, "242faccc8a8f4e6cb63138e2a624cde6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b7e774082c544c60af2f86c9f50b96f9": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "611d87806f0c4b23bf482aa898e741c4": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_242faccc8a8f4e6cb63138e2a624cde6", "placeholder": "\u200b", "style": "IPY_MODEL_b7e774082c544c60af2f86c9f50b96f9", "tabbable": null, "tooltip": null, "value": "100%"}}, "b3bf3cc54e7e4205a261618be5226865": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7ecb7575288445ff9d2ae52df5b6983f": {"model_name": "HTMLStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null}}, "b0c548bb98f04498814529c4a6b7fdeb": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_b3bf3cc54e7e4205a261618be5226865", "placeholder": "\u200b", "style": "IPY_MODEL_7ecb7575288445ff9d2ae52df5b6983f", "tabbable": null, "tooltip": null, "value": "\u200780/80\u2007[00:03<00:00,\u200725.99it/s]"}}, "401f8f6ece19491f8fc1569c12d33e97": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "db19cbbc5e4e4a42ac098aaf91ab01e0": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_611d87806f0c4b23bf482aa898e741c4", "IPY_MODEL_7e6509e9ce69445b8fa4cfa33d86c1b8", "IPY_MODEL_b0c548bb98f04498814529c4a6b7fdeb"], "layout": "IPY_MODEL_401f8f6ece19491f8fc1569c12d33e97", "tabbable": null, "tooltip": null}}, "06846cc88a204857afacbe501506852e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f8c1981ec6e24fbf8fcab242fbd3bcf1": {"model_name": "MPLCanvasModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_cursor": "pointer", "_data_url": "data:image/png;base64,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", "_dom_classes": [], "_figure_label": "Figure", "_image_mode": "full", "_message": "", "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "MPLCanvasModel", "_rubberband_height": 0, "_rubberband_width": 0, "_rubberband_x": 0, "_rubberband_y": 0, "_size": [640.0, 480.0], "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "MPLCanvasView", "capture_scroll": false, "footer_visible": true, "header_visible": true, "layout": "IPY_MODEL_06846cc88a204857afacbe501506852e", "pan_zoom_throttle": 33.0, "resizable": true, "tabbable": null, "toolbar": "IPY_MODEL_2d857532ee7243c49ce94c6bed42ef6c", "toolbar_position": "left", "toolbar_visible": "fade-in-fade-out", "tooltip": null}}, "19c53a93f9ba4e01ae35950039a5845e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2d857532ee7243c49ce94c6bed42ef6c": {"model_name": "ToolbarModel", "model_module": "jupyter-matplotlib", "model_module_version": "^0.11", "state": {"_current_action": "", "_dom_classes": [], "_model_module": "jupyter-matplotlib", "_model_module_version": "^0.11", "_model_name": "ToolbarModel", "_view_count": null, "_view_module": "jupyter-matplotlib", "_view_module_version": "^0.11", "_view_name": "ToolbarView", "button_style": "", "collapsed": true, "layout": "IPY_MODEL_19c53a93f9ba4e01ae35950039a5845e", "orientation": "vertical", "tabbable": null, "toolitems": [["Home", "Reset original view", "home", "home"], ["Back", "Back to previous view", "arrow-left", "back"], ["Forward", "Forward to next view", "arrow-right", "forward"], ["Pan", "Left button pans, Right button zooms\nx/y fixes axis, CTRL fixes aspect", "arrows", "pan"], ["Zoom", "Zoom to rectangle\nx/y fixes axis", "square-o", "zoom"], ["Download", "Download plot", "floppy-o", "save_figure"]], "tooltip": null}}}, "version_major": 2, "version_minor": 0}
 
 
 
diff --git a/specsanalyzer/switcher.json b/specsanalyzer/switcher.json
index 8b3827e..79b6d9d 100644
--- a/specsanalyzer/switcher.json
+++ b/specsanalyzer/switcher.json
@@ -1,7 +1,7 @@
 [
   {
     "name": "latest",
-    "version": "0.5.1.dev2+g5d28e05",
+    "version": "0.5.1.dev10+g0c6ed22",
     "url": "https://opencompes.github.io/docs/specsanalyzer/latest"
   },
   {