diff --git a/docs/API_reference/overview.md b/docs/API_reference/overview.md
index 8751921..3da9a76 100644
--- a/docs/API_reference/overview.md
+++ b/docs/API_reference/overview.md
@@ -19,7 +19,7 @@ This section provides a detailed reference for all modules, classes, and functio
 ## Core functions
 [download](download.md)
 
-[extract_variables_from_files](extract_variables_from_files.md)
+[extraction](processing/extraction.md)
 
 [save](save.md)
 
@@ -57,7 +57,7 @@ This section provides a detailed reference for all modules, classes, and functio
 
 [fold_pitch_angles_and_flux](processing/fold_pitch_angles_and_flux.md)
 
-[convert_string_to_datetime](processing/convert_string_to_datetime.md)
+[convert_string_to_datetime](utilities/convert_string_to_datetime.md)
 
 
 ## Saving standards
diff --git a/docs/API_reference/extract_variables_from_files.md b/docs/API_reference/processing/extraction.md
similarity index 54%
rename from docs/API_reference/extract_variables_from_files.md
rename to docs/API_reference/processing/extraction.md
index 8352630..05a0d4a 100644
--- a/docs/API_reference/extract_variables_from_files.md
+++ b/docs/API_reference/processing/extraction.md
@@ -5,6 +5,6 @@ SPDX-FileContributor: Bernhard Haas
 SPDX-License-Identifier: Apache-2.0
 -->
 
-::: el_paso.extract_variables_from_files.extract_variables_from_files
+::: el_paso.processing.extraction.extract_variables_from_files
 
-::: el_paso.extract_variables_from_files.ExtractionInfo
+::: el_paso.processing.extraction.ExtractionInfo
diff --git a/docs/API_reference/processing/convert_string_to_datetime.md b/docs/API_reference/utilities/convert_string_to_datetime.md
similarity index 65%
rename from docs/API_reference/processing/convert_string_to_datetime.md
rename to docs/API_reference/utilities/convert_string_to_datetime.md
index c143713..4bd26b8 100644
--- a/docs/API_reference/processing/convert_string_to_datetime.md
+++ b/docs/API_reference/utilities/convert_string_to_datetime.md
@@ -5,4 +5,4 @@ SPDX-FileContributor: Bernhard Haas
 SPDX-License-Identifier: Apache-2.0
 -->
 
-::: el_paso.processing.convert_string_to_datetime.convert_string_to_datetime
+::: el_paso.utils.convert_string_to_datetime
diff --git a/docs/getting_started/basic_workflow.md b/docs/getting_started/basic_workflow.md
index 815540b..fd39ba6 100644
--- a/docs/getting_started/basic_workflow.md
+++ b/docs/getting_started/basic_workflow.md
@@ -31,7 +31,7 @@ You can learn more about the EL-PASO download routine in tutorial #1 located in
 
 A [Variable](../API_reference/variable.md) in EL-PASO is a custom class which holds a numpy-array as data and metadata (unit, processing notes, etc).
 
-We can turn the downloaded files into variables by calling the EL-PASO [extract_variables_from_files](../API_reference/extract_variables_from_files.md) routine.
+We can turn the downloaded files into variables by calling the EL-PASO [extract_variables_from_files](../API_reference/processing/extraction.md) routine.
 
 The user has to provide information about under which name or column the variable is storred and its unit:
 
diff --git a/el_paso/__init__.py b/el_paso/__init__.py
index cc37eeb..e915703 100644
--- a/el_paso/__init__.py
+++ b/el_paso/__init__.py
@@ -25,8 +25,10 @@
 from el_paso.save import save
 from el_paso.processing import TimeBinMethod
 from el_paso.download import download
-from el_paso.extract_variables_from_files import extract_variables_from_files, ExtractionInfo
 from el_paso.load_indices_solar_wind_parameters import load_indices_solar_wind_parameters
+from el_paso import utils
+from el_paso.processing import ExtractionInfo
+from el_paso.processing import extract_variables_from_files
 
 # expose RBMDataSet related classes and functions
 from swvo.io.RBMDataSet.custom_enums import (
@@ -73,6 +75,7 @@
     "save",
     "saving_strategies",
     "units",
+    "utils",
 ]
 
 __version__ = "1.0.3rc0"
diff --git a/el_paso/processing/__init__.py b/el_paso/processing/__init__.py
index 66c6d78..84c1012 100644
--- a/el_paso/processing/__init__.py
+++ b/el_paso/processing/__init__.py
@@ -1,5 +1,6 @@
 # SPDX-FileCopyrightText: 2025 GFZ Helmholtz Centre for Geosciences
 # SPDX-FileContributor: Bernhard Haas
+# SPDX-FileContributor: Sahil Jhawar
 #
 # SPDX-License-Identifier: Apache-2.0
 
@@ -13,12 +14,13 @@
 from el_paso.processing.compute_phase_space_density import compute_phase_space_density
 from el_paso.processing.compute_pitch_angles_for_telescopes import compute_pitch_angles_for_telescopes
 from el_paso.processing.construct_pitch_angle_distribution import construct_pitch_angle_distribution
-from el_paso.processing.convert_string_to_datetime import convert_string_to_datetime
+from el_paso.processing.extraction import ExtractionInfo, extract_variables_from_files
 from el_paso.processing.fold_pitch_angles_and_flux import fold_pitch_angles_and_flux
 from el_paso.processing.get_real_time_tipsod import get_real_time_tipsod
 from el_paso.processing.magnetic_field_utils import MagFieldVarTypes
 
 __all__ = [
+    "ExtractionInfo",
     "MagFieldVarTypes",
     "TimeBinMethod",
     "VariableRequest",
@@ -31,7 +33,7 @@
     "compute_phase_space_density",
     "compute_pitch_angles_for_telescopes",
     "construct_pitch_angle_distribution",
-    "convert_string_to_datetime",
+    "extract_variables_from_files",
     "fold_pitch_angles_and_flux",
     "get_real_time_tipsod",
     "magnetic_field_utils",
diff --git a/el_paso/processing/convert_string_to_datetime.py b/el_paso/processing/convert_string_to_datetime.py
deleted file mode 100644
index e19d5a6..0000000
--- a/el_paso/processing/convert_string_to_datetime.py
+++ /dev/null
@@ -1,39 +0,0 @@
-# SPDX-FileCopyrightText: 2025 GFZ Helmholtz Centre for Geosciences
-# SPDX-FileContributor: Bernhard Haas
-#
-# SPDX-License-Identifier: Apache-2.0
-
-from datetime import datetime, timezone
-
-import numpy as np
-from dateutil import parser
-from numpy.typing import NDArray
-
-from el_paso import Variable
-
-
-def convert_string_to_datetime(time_var: Variable, time_format: str | None = None) -> NDArray[np.generic]:
-    """Converts a Variable's string-based time data to UTC datetime objects.
-
-    This function transforms an array of time strings into Python datetime objects,
-    automatically setting the timezone to UTC. If time_format is provided, it uses
-    datetime.strptime for explicit parsing; otherwise, it uses a flexible parser
-    (like dateutil.parser.parse) to infer the format.
-
-    Args:
-        time_var (Variable): The variable containing string-based time data to be
-            converted. Its data is accessed via time_var.get_data().
-        time_format (str | None): The explicit format string (e.g., "%Y-%m-%d %H:%M:%S")
-            used to parse the time data. If None (default), the function uses a
-            flexible parser to infer the correct format.
-
-    Returns:
-        NDArray[np.generic]: A NumPy array of Python datetime objects that are all
-            localized to the UTC timezone.
-    """
-    time_var.metadata.add_processing_note("Converting string-time to datetime")
-
-    if time_format is None:
-        return np.asarray([parser.parse(t).replace(tzinfo=timezone.utc) for t in time_var.get_data()])
-
-    return np.asarray([datetime.strptime(t, time_format).replace(tzinfo=timezone.utc) for t in time_var.get_data()])
diff --git a/el_paso/extract_variables_from_files.py b/el_paso/processing/extraction.py
similarity index 99%
rename from el_paso/extract_variables_from_files.py
rename to el_paso/processing/extraction.py
index d3fe552..937af4a 100644
--- a/el_paso/extract_variables_from_files.py
+++ b/el_paso/processing/extraction.py
@@ -1,5 +1,6 @@
 # SPDX-FileCopyrightText: 2025 GFZ Helmholtz Centre for Geosciences
 # SPDX-FileContributor: Bernhard Haas
+# SPDX-FileContributor: Sahil Jhawar
 #
 # SPDX-License-Identifier: Apache-2.0
 
diff --git a/el_paso/utils.py b/el_paso/utils.py
index d144296..a6680cd 100644
--- a/el_paso/utils.py
+++ b/el_paso/utils.py
@@ -1,5 +1,6 @@
 # SPDX-FileCopyrightText: 2025 GFZ Helmholtz Centre for Geosciences
 # SPDX-FileContributor: Bernhard Haas
+# SPDX-FileContributor: Sahil Jhawar
 #
 # SPDX-License-Identifier: Apache-2.0
 
@@ -15,15 +16,20 @@
 from pathlib import Path
 from typing import Any, ParamSpec, TypeVar
 
+import numpy as np
 import pandas as pd
 import tqdm
+from dateutil import parser
 from packaging import version as version_pkg
 
 if typing.TYPE_CHECKING:
     from collections.abc import Callable, Iterable
     from multiprocessing.pool import MapResult
 
+    from numpy.typing import NDArray
+
     import el_paso as ep
+    from el_paso import Variable
 
 logger = logging.getLogger(__name__)
 
@@ -299,3 +305,30 @@ def make_dict_hashable(dict_input: dict[Any, Any] | None) -> Hashabledict | None
         return dict_input
 
     return Hashabledict(dict_input)
+
+
+def convert_string_to_datetime(time_var: Variable, time_format: str | None = None) -> NDArray[np.generic]:
+    """Converts a Variable's string-based time data to UTC datetime objects.
+
+    This function transforms an array of time strings into Python datetime objects,
+    automatically setting the timezone to UTC. If time_format is provided, it uses
+    datetime.strptime for explicit parsing; otherwise, it uses a flexible parser
+    (like dateutil.parser.parse) to infer the format.
+
+    Args:
+        time_var (Variable): The variable containing string-based time data to be
+            converted. Its data is accessed via time_var.get_data().
+        time_format (str | None): The explicit format string (e.g., "%Y-%m-%d %H:%M:%S")
+            used to parse the time data. If None (default), the function uses a
+            flexible parser to infer the correct format.
+
+    Returns:
+        NDArray[np.generic]: A NumPy array of Python datetime objects that are all
+            localized to the UTC timezone.
+    """
+    time_var.metadata.add_processing_note("Converting string-time to datetime")
+
+    if time_format is None:
+        return np.asarray([parser.parse(t).replace(tzinfo=timezone.utc) for t in time_var.get_data()])
+
+    return np.asarray([datetime.strptime(t, time_format).replace(tzinfo=timezone.utc) for t in time_var.get_data()])
diff --git a/examples/Arase/arase_mepe.py b/examples/Arase/arase_mepe.py
index b07c1ac..6d3ac8f 100644
--- a/examples/Arase/arase_mepe.py
+++ b/examples/Arase/arase_mepe.py
@@ -89,7 +89,7 @@ def process_mepe_level_3(  # noqa: PLR0915
         ),
     ]
 
-    mepe_variables = ep.extract_variables_from_files(
+    mepe_variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
diff --git a/examples/Arase/get_arase_orbit_variables.py b/examples/Arase/get_arase_orbit_variables.py
index d6c8fba..7c6b80a 100644
--- a/examples/Arase/get_arase_orbit_variables.py
+++ b/examples/Arase/get_arase_orbit_variables.py
@@ -49,7 +49,7 @@ def get_arase_orbit_level_2_variables(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
@@ -140,7 +140,7 @@ def get_arase_orbit_level_3_variables(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
diff --git a/examples/Arase/process_arase_xep_realtime.py b/examples/Arase/process_arase_xep_realtime.py
index 7d98fc4..b7d0322 100644
--- a/examples/Arase/process_arase_xep_realtime.py
+++ b/examples/Arase/process_arase_xep_realtime.py
@@ -251,7 +251,7 @@ def _get_xep_variables(
 
     # Bernhard: the header is also in the file, but there is a comment after it, so it cannot be read by pd.read_csv
     xep_header = ("time", "ch1", "ch2", "ch3", "ch4", "ch5", "ch6", "ch7", "ch8")
-    xep_variables = ep.extract_variables_from_files(
+    xep_variables = ep.processing.extract_variables_from_files(
         extraction_infos=extraction_infos,
         data_path=data_path_stem,
         file_name_stem=file_name_stem,
@@ -263,7 +263,7 @@ def _get_xep_variables(
 
     # convert time variable
     # parse time strings
-    datetimes = ep.processing.convert_string_to_datetime(xep_variables["Epoch"])
+    datetimes = ep.utils.convert_string_to_datetime(xep_variables["Epoch"])
     xep_variables["Epoch"].set_data(np.asarray([t.timestamp() for t in datetimes]), unit=ep.units.posixtime)
 
     # add energy variable
@@ -342,7 +342,7 @@ def _get_orb_variables(
         ep.ExtractionInfo(name_or_column="sm_z", unit=ep.units.RE, result_key="sm_z"),
     ]
 
-    orb_variables = ep.extract_variables_from_files(
+    orb_variables = ep.processing.extract_variables_from_files(
         extraction_infos=extraction_infos,
         data_path=data_path_stem,
         file_name_stem=file_name_stem,
@@ -351,7 +351,7 @@ def _get_orb_variables(
         file_cadence="daily",
     )
 
-    datetimes = ep.processing.convert_string_to_datetime(orb_variables["Epoch"])
+    datetimes = ep.utils.convert_string_to_datetime(orb_variables["Epoch"])
     orb_variables["Epoch"].set_data(np.asarray([t.timestamp() for t in datetimes]), unit=ep.units.posixtime)
 
     # convert SM to GEO
diff --git a/examples/Arase/process_pwe_densities.py b/examples/Arase/process_pwe_densities.py
index 63bffbd..3b5d50e 100644
--- a/examples/Arase/process_pwe_densities.py
+++ b/examples/Arase/process_pwe_densities.py
@@ -68,7 +68,7 @@ def process_pwe_density(
         ),
     ]
 
-    pwe_variables = ep.extract_variables_from_files(
+    pwe_variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
diff --git a/examples/ESA/process_ngrm_satellite.py b/examples/ESA/process_ngrm_satellite.py
index 9cf54aa..9b454c1 100644
--- a/examples/ESA/process_ngrm_satellite.py
+++ b/examples/ESA/process_ngrm_satellite.py
@@ -114,7 +114,7 @@ def process_ngrm_electron_fluxes(
         ep.ExtractionInfo(result_key="L", name_or_column="L", unit=ep.units.RE),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
@@ -127,7 +127,7 @@ def process_ngrm_electron_fluxes(
     time_format = "%Y-%m-%dT%H:%M:%S.%fZ" if satellite in ["MTG-I1", "MTG-S1"] else "%Y-%m-%dT%H:%M:%SZ"
 
     # convert iso strings to posixtime
-    datetimes = ep.processing.convert_string_to_datetime(variables["Epoch_iso"], time_format=time_format)
+    datetimes = ep.utils.convert_string_to_datetime(variables["Epoch_iso"], time_format=time_format)
     variables["Epoch"] = ep.Variable(
         data=np.asarray([t.timestamp() for t in datetimes]), original_unit=ep.units.posixtime
     )
diff --git a/examples/GOES/process_goes_r_mps_high.py b/examples/GOES/process_goes_r_mps_high.py
index c628eac..3e7beb1 100644
--- a/examples/GOES/process_goes_r_mps_high.py
+++ b/examples/GOES/process_goes_r_mps_high.py
@@ -107,7 +107,7 @@ def process_goes_r_mps_high(
 
     # fold pitch angles around 90 degree
     local_pa = local_pa_var.get_data(u.degree)
-    local_pa_folded = np.where(local_pa > 90, local_pa - 90, local_pa)  # noqa: PLR2004
+    local_pa_folded = np.where(local_pa > 90, local_pa - 90, local_pa)
     local_pa_var.set_data(local_pa_folded, unit=u.degree)
 
     # sort pitch angles in ascending order and apply to fluxes
@@ -238,7 +238,7 @@ def _get_magn_variables(
         ep.ExtractionInfo(name_or_column="b_brf", unit=u.nT, result_key="b_brf"),
     ]
 
-    return ep.extract_variables_from_files(
+    return ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
@@ -273,7 +273,7 @@ def _get_ephe_variables(
         ep.ExtractionInfo(name_or_column="gse_xyz", unit=u.km, result_key="xgse"),
     ]
 
-    return ep.extract_variables_from_files(
+    return ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
@@ -323,7 +323,7 @@ def _get_mps_high_variables(
         ep.ExtractionInfo(name_or_column="IntElectronEffectiveEnergy", unit=u.keV, result_key="int_energy"),
     ]
 
-    return ep.extract_variables_from_files(
+    return ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
diff --git a/examples/GOES/process_goes_realtime.py b/examples/GOES/process_goes_realtime.py
index 54b46a3..3ba0626 100644
--- a/examples/GOES/process_goes_realtime.py
+++ b/examples/GOES/process_goes_realtime.py
@@ -93,7 +93,7 @@ def process_goes_real_time(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
@@ -106,7 +106,7 @@ def process_goes_real_time(
     logger.info(f"Processing satellite: {sat_name}")
 
     # parse time strings
-    datetimes = ep.processing.convert_string_to_datetime(variables["Epoch"], time_format="%Y-%m-%dT%H:%M:%SZ")
+    datetimes = ep.utils.convert_string_to_datetime(variables["Epoch"], time_format="%Y-%m-%dT%H:%M:%SZ")
     variables["Epoch"].set_data(np.asarray([t.timestamp() for t in datetimes]), ep.units.posixtime)
 
     # generated weighted energy channels
diff --git a/examples/POES/process_poes_meped.py b/examples/POES/process_poes_meped.py
index 157513e..ed6bd37 100644
--- a/examples/POES/process_poes_meped.py
+++ b/examples/POES/process_poes_meped.py
@@ -45,7 +45,6 @@ def process_poes_meped_electron(
     num_cores: int = 32,
     bin_cadence: timedelta = timedelta(minutes=5),
 ) -> None:
-
     data_path_stem = f"{raw_data_path}/YYYY/MM/{satellite_str}/"
     url = f"https://spdf.gsfc.nasa.gov/pub/data/noaa/{satellite_str}/sem2_fluxes-2sec/YYYY/"
     file_name_stem = satellite_str + "_poes-sem2_fluxes-2sec_YYYYMMDD_.{3}.cdf"
@@ -103,7 +102,7 @@ def process_poes_meped_electron(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
@@ -130,7 +129,9 @@ def process_poes_meped_electron(
     variables["FEIU"].transpose_data((0, 2, 1))
 
     # stack pitch angles
-    pa_arr = np.stack((variables["PA_local_t0"].get_data(u.deg), variables["PA_local_t90"].get_data(u.deg))).T.astype(np.float64)
+    pa_arr = np.stack((variables["PA_local_t0"].get_data(u.deg), variables["PA_local_t90"].get_data(u.deg))).T.astype(
+        np.float64
+    )
     variables["PA_local"] = ep.Variable(data=pa_arr, original_unit=u.deg)
 
     del variables["PA_local_t0"]
@@ -207,9 +208,7 @@ def process_poes_meped_electron(
     logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
     logging.getLogger().setLevel(logging.INFO)
 
-    parser = argparse.ArgumentParser(
-        description="Process MEPED data from POES satellites."
-    )
+    parser = argparse.ArgumentParser(description="Process MEPED data from POES satellites.")
     parser.add_argument(
         "--start_time",
         type=str,
@@ -239,17 +238,16 @@ def process_poes_meped_electron(
 
     #    with tempfile.TemporaryDirectory() as tmpdir:
     for sat_str in get_args(poes_satellite_literal):
-        print(f"Processing {sat_str}!")
-        # try:
-        process_poes_meped_electron(
-            start_time=dt_start,
-            end_time=dt_end,
-            satellite_str=sat_str,
-            irbem_lib_path=args.irbem_lib_path,
-            raw_data_path=".",
-            processed_data_path=".",
-            num_cores=64,
-            bin_cadence=timedelta(seconds=10),
-        )
-        # except:
-        #     continue
\ No newline at end of file
+        try:
+            process_poes_meped_electron(
+                start_time=dt_start,
+                end_time=dt_end,
+                satellite_str=sat_str,
+                irbem_lib_path=args.irbem_lib_path,
+                raw_data_path=".",
+                processed_data_path=".",
+                num_cores=64,
+                bin_cadence=timedelta(seconds=10),
+            )
+        except:  # noqa: E722, S112
+            continue
diff --git a/examples/POES/process_poes_ted.py b/examples/POES/process_poes_ted.py
index ee3da2c..51931fb 100644
--- a/examples/POES/process_poes_ted.py
+++ b/examples/POES/process_poes_ted.py
@@ -47,7 +47,6 @@ def process_poes_meped_electron(
     *,
     calculate_Lm_Lstar: bool = False,  # noqa: N803
 ) -> None:
-
     data_path_stem = f"{raw_data_path}/YYYY/MM/{satellite_str}/"
     url = f"https://spdf.gsfc.nasa.gov/pub/data/noaa/{satellite_str}/sem2_fluxes-2sec/YYYY/"
     file_name_stem = satellite_str + "_poes-sem2_fluxes-2sec_YYYYMMDD_.{3}.cdf"
@@ -105,7 +104,7 @@ def process_poes_meped_electron(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
@@ -132,7 +131,9 @@ def process_poes_meped_electron(
 
     variables["FEDU"].transpose_data((0, 2, 1))
     # stack pitch angles
-    pa_arr = np.stack((variables["PA_local_t0"].get_data(u.deg), variables["PA_local_t30"].get_data(u.deg))).T.astype(np.float64)
+    pa_arr = np.stack((variables["PA_local_t0"].get_data(u.deg), variables["PA_local_t30"].get_data(u.deg))).T.astype(
+        np.float64
+    )
     pa_arr = np.where(pa_arr > 90, 180 - pa_arr, pa_arr)
 
     variables["PA_local"] = ep.Variable(data=pa_arr, original_unit=u.deg)
@@ -196,8 +197,10 @@ def process_poes_meped_electron(
     }
 
     if calculate_Lm_Lstar:
-        variables_to_save |= {"position/T89/Lm": magnetic_field_variables["Lm_T89"],
-                              "position/T89/Lstar": magnetic_field_variables["Lstar_T89"]}
+        variables_to_save |= {
+            "position/T89/Lm": magnetic_field_variables["Lm_T89"],
+            "position/T89/Lstar": magnetic_field_variables["Lstar_T89"],
+        }
 
     saving_strategy = ep.saving_strategies.MonthlyNetCDFStrategy(
         base_data_path=Path(processed_data_path) / "POES" / sat_str,
@@ -214,9 +217,7 @@ def process_poes_meped_electron(
     logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))
     logging.getLogger().setLevel(logging.INFO)
 
-    parser = argparse.ArgumentParser(
-        description="Process TED data from POES satellites."
-    )
+    parser = argparse.ArgumentParser(description="Process TED data from POES satellites.")
     parser.add_argument(
         "--start_time",
         type=str,
@@ -246,7 +247,6 @@ def process_poes_meped_electron(
 
     #    with tempfile.TemporaryDirectory() as tmpdir:
     for sat_str in get_args(poes_satellite_literal):
-        print(f"Processing {sat_str}!")
         try:
             process_poes_meped_electron(
                 start_time=dt_start,
@@ -258,5 +258,5 @@ def process_poes_meped_electron(
                 num_cores=64,
                 bin_cadence=timedelta(seconds=2),
             )
-        except:
-            continue
\ No newline at end of file
+        except:  # noqa: E722, S112
+            continue
diff --git a/examples/PROBAV/process_ept_electron_fluxes.py b/examples/PROBAV/process_ept_electron_fluxes.py
index 825ff16..f075bab 100644
--- a/examples/PROBAV/process_ept_electron_fluxes.py
+++ b/examples/PROBAV/process_ept_electron_fluxes.py
@@ -91,7 +91,7 @@ def process_ept_electron_fluxes(
         ep.ExtractionInfo(result_key="lat", name_or_column="Lat", unit=u.deg),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         file_cadence="daily",
diff --git a/examples/VanAllenProbes/process_ect_combined.py b/examples/VanAllenProbes/process_ect_combined.py
index 692f2f7..603a876 100644
--- a/examples/VanAllenProbes/process_ect_combined.py
+++ b/examples/VanAllenProbes/process_ect_combined.py
@@ -86,7 +86,7 @@ def process_ect_combined(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
diff --git a/examples/VanAllenProbes/process_efw_emfisis_density_combined.py b/examples/VanAllenProbes/process_efw_emfisis_density_combined.py
index 0ad2ccd..71e6ed7 100644
--- a/examples/VanAllenProbes/process_efw_emfisis_density_combined.py
+++ b/examples/VanAllenProbes/process_efw_emfisis_density_combined.py
@@ -194,7 +194,7 @@ def _get_efw_variables(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
@@ -244,7 +244,7 @@ def _get_emfisis_variables(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
@@ -294,7 +294,7 @@ def _get_and_time_bin_hiss_derived_densities(
         ),
     ]
 
-    hiss_derived_vars = ep.extract_variables_from_files(
+    hiss_derived_vars = ep.processing.extract_variables_from_files(
         start_time=start_time,
         end_time=end_time,
         file_cadence="single_file",
@@ -304,7 +304,7 @@ def _get_and_time_bin_hiss_derived_densities(
         pd_read_csv_kwargs={"skiprows": 4, "sep": "\t", "dtype": {0: str, 1: np.float64}},
     )
 
-    datetimes = ep.processing.convert_string_to_datetime(hiss_derived_vars["Epoch"], time_format="%Y-%m-%dT%H:%M:%S.%f")
+    datetimes = ep.utils.convert_string_to_datetime(hiss_derived_vars["Epoch"], time_format="%Y-%m-%dT%H:%M:%S.%f")
     timestamps = np.asarray([dt.timestamp() for dt in datetimes])
     hiss_derived_vars["Epoch"].set_data(timestamps, unit=ep.units.posixtime)
 
diff --git a/examples/VanAllenProbes/process_hope_electrons.py b/examples/VanAllenProbes/process_hope_electrons.py
index 516a8c3..eb12781 100644
--- a/examples/VanAllenProbes/process_hope_electrons.py
+++ b/examples/VanAllenProbes/process_hope_electrons.py
@@ -78,7 +78,7 @@ def process_hope_electrons(
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time,
         end_time,
         "daily",
diff --git a/examples/minimal_example.py b/examples/minimal_example.py
index 80356f9..3adcf33 100644
--- a/examples/minimal_example.py
+++ b/examples/minimal_example.py
@@ -66,7 +66,7 @@
     ),
 ]
 
-variables = ep.extract_variables_from_files(
+variables = ep.processing.extract_variables_from_files(
     start_time=start_time,
     end_time=end_time,
     file_cadence="daily",
diff --git a/mkdocs.yml b/mkdocs.yml
index 638f35c..608bb75 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -47,7 +47,7 @@ nav:
           - API_reference/variable.md
       - Core Functions:
           - API_reference/download.md
-          - API_reference/extract_variables_from_files.md
+          - API_reference/processing/extraction.md
           - API_reference/save.md
       - Processing Functions:
           - API_reference/processing/bin_by_time.md
@@ -57,7 +57,6 @@ nav:
           - API_reference/processing/compute_phase_space_density.md
           - API_reference/processing/compute_pitch_angles_for_telescopes.md
           - API_reference/processing/construct_pitch_angle_distribution.md
-          - API_reference/processing/convert_string_to_datetime.md
           - API_reference/processing/fold_pitch_angles_and_flux.md
           - API_reference/processing/calculate_geo_coords_from_tle.md
       - Utilities:
@@ -67,6 +66,7 @@ nav:
           - Release mode: API_reference/utilities/release_mode.md
           - Scripts: API_reference/utilities/scripts.md
           - Units: API_reference/utilities/units.md
+          - API_reference/utilities/convert_string_to_datetime.md
       - Saving Strategies:
           - DataOrg Strategy: API_reference/saving_strategies/data_org.md
           - Monthly H5 Strategy: API_reference/saving_strategies/monthly_h5.md
diff --git a/ruff.toml b/ruff.toml
index 1fd8f5c..44008d1 100644
--- a/ruff.toml
+++ b/ruff.toml
@@ -16,8 +16,9 @@ ignore = [
     "COM812",  # disable trailing whitespace because we use formatter
     "S101",    # allow assert statements
     "RET504",  # allow assignment before return
+    "PLR2004", # allow constants inline
 ]
-exclude = ["*.ipynb", "IRBEM/*", "setup.py"]
+exclude = ["*.ipynb", "setup.py"]
 
 [lint.pydocstyle]
 convention = "google"
diff --git a/tests/comparisons/test_mageph_rbsp.py b/tests/comparisons/test_mageph_rbsp.py
index 891de76..3d6d511 100644
--- a/tests/comparisons/test_mageph_rbsp.py
+++ b/tests/comparisons/test_mageph_rbsp.py
@@ -20,8 +20,6 @@
 from el_paso import InstrumentEnum, MfmEnum, RBMDataSet
 from examples.VanAllenProbes.process_hope_electrons import process_hope_electrons
 
-# ruff: noqa: PLR2004
-
 sat_str_list = ["a", "b"]
 mag_field_list = ["TS04", "T89"]
 
@@ -106,7 +104,7 @@ def test_mageph_rbsp(sat_str: Literal["a", "b"], mag_field: Literal["T89", "TS04
         ),
     ]
 
-    variables = ep.extract_variables_from_files(
+    variables = ep.processing.extract_variables_from_files(
         start_time, end_time, "daily", "tests/comparisons/raw_data", file_name_stem, extraction_infos
     )
 
diff --git a/tutorials/1_download_data_and_extracting_variables.ipynb b/tutorials/1_download_data_and_extracting_variables.ipynb
index de6f57d..781eaa7 100644
--- a/tutorials/1_download_data_and_extracting_variables.ipynb
+++ b/tutorials/1_download_data_and_extracting_variables.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Tutorial: Loading data\n",
     "\n",
-    "This notebook will teach you how to load data in the *EL-PASO* framework. You will load data from a csv file on disk, as well as download a cdf file from an online repository and load data from it. \n",
+    "This notebook will teach you how to load data in the _EL-PASO_ framework. You will load data from a csv file on disk, as well as download a cdf file from an online repository and load data from it.\n",
     "\n",
     "## Loading from a csv file\n",
     "\n",
-    "As a first step, we will load data from example_orbit.csv, which looks like this:"
+    "As a first step, we will load data from example_orbit.csv, which looks like this:\n"
    ]
   },
   {
@@ -22,77 +22,6 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "DATETIME",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "alt(km)",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "lat(deg)",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "lon(deg)",
-         "rawType": "float64",
-         "type": "float"
-        }
-       ],
-       "ref": "7a65f54c-cea7-49f9-9d6f-cd168fb586ec",
-       "rows": [
-        [
-         "0",
-         "2019-07-30 17:04:00",
-         "11362.75841",
-         "11.391923",
-         "154.268925"
-        ],
-        [
-         "1",
-         "2019-07-30 17:09:00",
-         "11516.45781",
-         "8.645016",
-         "156.193306"
-        ],
-        [
-         "2",
-         "2019-07-30 17:14:00",
-         "11650.92313",
-         "5.916772",
-         "158.021787"
-        ],
-        [
-         "3",
-         "2019-07-30 17:19:00",
-         "11765.75939",
-         "3.20951",
-         "159.778169"
-        ],
-        [
-         "4",
-         "2019-07-30 17:24:00",
-         "11860.64721",
-         "0.524741",
-         "161.484253"
-        ]
-       ],
-       "shape": {
-        "columns": 4,
-        "rows": 5
-       }
-      },
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -183,7 +112,7 @@
    "id": "7e86fd9d",
    "metadata": {},
    "source": [
-    "The data holds four columns describing the orbit of a satellite through time, altitude, latitude, and logitude. While we can certainly read in the data using pandas as above, we want to use the EL-PASO SourceFile class, which extracts the necessary data and puts it into variables. For this, we need to tell EL-PASO how to read the file by creating a list of ExtractionInfos. Each ExtractionInfo is used to create one variable based on data from the file and the given unit. The result_key parameter is used later on to identify the variable after loading."
+    "The data holds four columns describing the orbit of a satellite through time, altitude, latitude, and logitude. While we can certainly read in the data using pandas as above, we want to use the EL-PASO SourceFile class, which extracts the necessary data and puts it into variables. For this, we need to tell EL-PASO how to read the file by creating a list of ExtractionInfos. Each ExtractionInfo is used to create one variable based on data from the file and the given unit. The result_key parameter is used later on to identify the variable after loading.\n"
    ]
   },
   {
@@ -191,16 +120,7 @@
    "execution_count": 2,
    "id": "45d8ede0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[91mLogger not instantiated.\u001b[0m\n",
-      "\u001b[91mBasic logger can be instantiated using `logging.basicConfig(level=logging.INFO)`\u001b[0m\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from datetime import datetime, timezone\n",
     "\n",
@@ -240,12 +160,12 @@
    "id": "771a3ead",
    "metadata": {},
    "source": [
-    "Now we are ready to extract the data and put it into the variables. The extract_variables function return a dictionary holding Variables based on the extraction_infos."
+    "Now we are ready to extract the data and put it into the variables. The extract_variables function return a dictionary holding Variables based on the extraction_infos.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "id": "daadc3cd",
    "metadata": {},
    "outputs": [
@@ -263,7 +183,7 @@
     }
    ],
    "source": [
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time,\n",
     "    end_time,\n",
     "    \"single_file\",\n",
@@ -274,7 +194,7 @@
     "\n",
     "print(variables.keys())\n",
     "print(variables[\"Epoch\"].metadata)\n",
-    "#print a slice of data\n",
+    "# print a slice of data\n",
     "print(variables[\"Epoch\"].get_data()[:10])"
    ]
   },
@@ -285,9 +205,9 @@
    "source": [
     "## Download and load a cdf file\n",
     "\n",
-    "This example will show you how to inspect a CDF file and how to extract variables from it using a *SourceFile*.\n",
+    "This example will show you how to inspect a CDF file and how to extract variables from it using a _SourceFile_.\n",
     "\n",
-    "First thing you want to do is knowing what are the contents of the CDF file. For this, a script is provided by EL-PASO, which prints a table with all relevant information:"
+    "First thing you want to do is knowing what are the contents of the CDF file. For this, a script is provided by EL-PASO, which prints a table with all relevant information:\n"
    ]
   },
   {
@@ -371,12 +291,12 @@
    "id": "03466457",
    "metadata": {},
    "source": [
-    "The next step is to think which variables you want to use for the processing and translating them to EL-PASO ExtractionInfos."
+    "The next step is to think which variables you want to use for the processing and translating them to EL-PASO ExtractionInfos.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "bcc0b57c",
    "metadata": {},
    "outputs": [],
@@ -409,12 +329,12 @@
    "id": "d1f5e4ce",
    "metadata": {},
    "source": [
-    "In this example, we want to download the data from the server as this is how it is done for most data sets. The file_name_stem contains a pattern (YYYYMMDD) to describe the date of the file. While loading the data, this pattern will be replaced by the correct date. A similar pattern is used for the download url. The file_name_stem also contains the regex expression '.{6}', which is used to find files with different versions. The most up-to-date version will always be downloaded. "
+    "In this example, we want to download the data from the server as this is how it is done for most data sets. The file_name_stem contains a pattern (YYYYMMDD) to describe the date of the file. While loading the data, this pattern will be replaced by the correct date. A similar pattern is used for the download url. The file_name_stem also contains the regex expression '.{6}', which is used to find files with different versions. The most up-to-date version will always be downloaded.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "5c99faa0",
    "metadata": {},
    "outputs": [
@@ -426,7 +346,7 @@
        " 'FEDU': Variable holding (8346, 11, 72) data points with metadata: VariableMetadata(unit=Unit(\"1 / (keV s sr cm2)\"), original_cadence_seconds=0, source_files=['rbspa_rel04_ect-hope-pa-l3_20170708_v7.4.0.cdf', 'rbspa_rel04_ect-hope-pa-l3_20170709_v7.4.0.cdf'], description='', processing_notes='', standard_name='')}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -450,7 +370,7 @@
     "    skip_existing=True,\n",
     ")\n",
     "\n",
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time, end_time, \"daily\", data_path=\".\", file_name_stem=file_name_stem, extraction_infos=extraction_infos\n",
     ")\n",
     "variables"
@@ -459,7 +379,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "verb4d",
+   "display_name": "EL_PASO_GH (3.13.7)",
    "language": "python",
    "name": "python3"
   },
@@ -473,7 +393,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/2_variable_class.ipynb b/tutorials/2_variable_class.ipynb
index eb31b85..d8b700c 100644
--- a/tutorials/2_variable_class.ipynb
+++ b/tutorials/2_variable_class.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Tutorial: Variable class\n",
     "\n",
-    "This tutorial will teach you how to use the *Variable* class and what it can be used for.\n",
+    "This tutorial will teach you how to use the _Variable_ class and what it can be used for.\n",
     "\n",
     "## Variables from the csv file\n",
     "\n",
-    "We will use the loaded variables from the example_orbit.csv file to showcase how to deal with time and space variables."
+    "We will use the loaded variables from the example_orbit.csv file to showcase how to deal with time and space variables.\n"
    ]
   },
   {
@@ -20,14 +20,6 @@
    "id": "941c7d1f",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[91mLogger not instantiated.\u001b[0m\n",
-      "\u001b[91mBasic logger can be instantiated using `logging.basicConfig(level=logging.INFO)`\u001b[0m\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
@@ -75,7 +67,7 @@
     "    ),\n",
     "]\n",
     "\n",
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time,\n",
     "    end_time,\n",
     "    \"single_file\",\n",
@@ -92,7 +84,7 @@
    "metadata": {},
    "source": [
     "Let's look at the Epoch-variable first. At the end, we want to have the time variable with the unit of posixtime (also often called timestamp, basically seconds since Epoch).\n",
-    "In this case, the Epoch-variable holds strings, so the first step is to convert them into datetimes. There is a designated function for this available: "
+    "In this case, the Epoch-variable holds strings, so the first step is to convert them into datetimes. There is a designated function for this available:\n"
    ]
   },
   {
@@ -102,7 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "datetimes = ep.processing.convert_string_to_datetime(variables[\"Epoch\"])"
+    "datetimes = ep.utils.convert_string_to_datetime(variables[\"Epoch\"])"
    ]
   },
   {
@@ -110,7 +102,7 @@
    "id": "33b6a53e",
    "metadata": {},
    "source": [
-    "Next, we can convert the datetimes into timestamps and store the data into the Epoch-variable. The data of a variable should always be changed by using the set_data function. This also allows you to specify a new unit (ep.units.posixtime in this case). If the unit should not change, you can set the second parameter to \"same\". "
+    "Next, we can convert the datetimes into timestamps and store the data into the Epoch-variable. The data of a variable should always be changed by using the set_data function. This also allows you to specify a new unit (ep.units.posixtime in this case). If the unit should not change, you can set the second parameter to \"same\".\n"
    ]
   },
   {
@@ -131,15 +123,30 @@
    "id": "ccc2265f",
    "metadata": {},
    "source": [
-    "Next, we transform the GDZ coordinates to GEO, which is the standard coordinate system used in *EL-PASO*. For this, we create a new variable. Here we use the function get_data(), which should be used to retrieve the data from a variable.  "
+    "Next, we transform the GDZ coordinates to GEO, which is the standard coordinate system used in _EL-PASO_. For this, we create a new variable. Here we use the function get_data(), which should be used to retrieve the data from a variable.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "a762b8f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "OSError",
+     "evalue": "../IRBEM/libirbem.so: cannot open shared object file: No such file or directory",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mOSError\u001b[39m                                   Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      3\u001b[39m irbem_lib_path = \u001b[33m\"../IRBEM/libirbem.so\"\u001b[39m\n\u001b[32m      4\u001b[39m \n\u001b[32m      5\u001b[39m xGDZ_arr = np.stack((variables[\u001b[33m\"alt\"\u001b[39m].get_data(), variables[\u001b[33m\"lat\"\u001b[39m].get_data(), variables[\u001b[33m\"lon\"\u001b[39m].get_data())).T\n\u001b[32m      6\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m model_coord = Coords(lib_path=irbem_lib_path)\n\u001b[32m      8\u001b[39m \n\u001b[32m      9\u001b[39m \u001b[38;5;66;03m# convert time_array to datenums for transform function\u001b[39;00m\n\u001b[32m     10\u001b[39m xGEO_var = ep.Variable(\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/EL_PASO_GH/el_paso/processing/magnetic_field_utils/irbem.py:953\u001b[39m, in \u001b[36mCoords.__init__\u001b[39m\u001b[34m(self, lib_path)\u001b[39m\n\u001b[32m    949\u001b[39m     lib_path = Path(lib_path)\n\u001b[32m    951\u001b[39m \u001b[38;5;28mself\u001b[39m.irbem_obj_path = lib_path\n\u001b[32m--> \u001b[39m\u001b[32m953\u001b[39m \u001b[38;5;28mself\u001b[39m.irbem_obj_path, \u001b[38;5;28mself\u001b[39m._irbem_obj = \u001b[30;43m_load_shared_object\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mirbem_obj_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/EL_PASO_GH/el_paso/processing/magnetic_field_utils/irbem.py:1075\u001b[39m, in \u001b[36m_load_shared_object\u001b[39m\u001b[34m(path)\u001b[39m\n\u001b[32m   1073\u001b[39m         irbem_obj = ctypes.WinDLL(\u001b[38;5;28mstr\u001b[39m(path))\n\u001b[32m   1074\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1075\u001b[39m         irbem_obj = \u001b[30;43mctypes\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mCDLL\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstr\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1076\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[32m   1077\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mTry using the full path with constructor syntax.\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(err):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.local/share/uv/python/cpython-3.13-linux-x86_64-gnu/lib/python3.13/ctypes/__init__.py:390\u001b[39m, in \u001b[36mCDLL.__init__\u001b[39m\u001b[34m(self, name, mode, handle, use_errno, use_last_error, winmode)\u001b[39m\n\u001b[32m    387\u001b[39m \u001b[38;5;28mself\u001b[39m._FuncPtr = _FuncPtr\n\u001b[32m    389\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m handle \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m390\u001b[39m     \u001b[38;5;28mself\u001b[39m._handle = \u001b[30;43m_dlopen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_name\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mmode\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    391\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    392\u001b[39m     \u001b[38;5;28mself\u001b[39m._handle = handle\n",
+      "\u001b[31mOSError\u001b[39m: ../IRBEM/libirbem.so: cannot open shared object file: No such file or directory"
+     ]
+    }
+   ],
    "source": [
     "from el_paso.processing.magnetic_field_utils.irbem import Coords\n",
     "\n",
@@ -161,12 +168,12 @@
    "id": "0ca82274",
    "metadata": {},
    "source": [
-    "## Variables from the cdf file"
+    "## Variables from the cdf file\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "929bab50",
    "metadata": {},
    "outputs": [],
@@ -205,7 +212,7 @@
     "    skip_existing=True,\n",
     ")\n",
     "\n",
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time, end_time, \"daily\", data_path=\".\", file_name_stem=file_name_stem, extraction_infos=extraction_infos\n",
     ")"
    ]
@@ -215,14 +222,14 @@
    "id": "a7dc76eb",
    "metadata": {},
    "source": [
-    "Handling the epoch variable is sometimes troublesome, as different formats are used. Usually, in cdf files, epoch is stored in the format *tt2000* or *cdf_epoch*, which counts the milliseconds since epoch. To make the conversion between tt2000 and posixtime easier, tt2000 is also defined as a custom el_paso unit. The conversion from one astropy unit to another on is handled by the *convert_to_unit* method.\n",
+    "Handling the epoch variable is sometimes troublesome, as different formats are used. Usually, in cdf files, epoch is stored in the format _tt2000_ or _cdf_epoch_, which counts the milliseconds since epoch. To make the conversion between tt2000 and posixtime easier, tt2000 is also defined as a custom el*paso unit. The conversion from one astropy unit to another on is handled by the \\_convert_to_unit* method.\n",
     "\n",
-    "If needed, you can get datetimes from the posixtimes afterwards."
+    "If needed, you can get datetimes from the posixtimes afterwards.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "fb2b1708",
    "metadata": {},
    "outputs": [
@@ -245,12 +252,12 @@
    "id": "82ed3132",
    "metadata": {},
    "source": [
-    "Retrieving data of Variables is realized through the *get_data()* method. It is optional to specify the unit, which should be used for the returned data. "
+    "Retrieving data of Variables is realized through the _get_data()_ method. It is optional to specify the unit, which should be used for the returned data.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "25916994",
    "metadata": {},
    "outputs": [
@@ -280,12 +287,12 @@
    "id": "6ef58f07",
    "metadata": {},
    "source": [
-    "To change the data of a variable, the *set_data()* method should be used. To keep the units in mind, you always have to specify the new units of the variable. If you are sure, that the units did not change, you can set the new unit to \"same\". "
+    "To change the data of a variable, the _set_data()_ method should be used. To keep the units in mind, you always have to specify the new units of the variable. If you are sure, that the units did not change, you can set the new unit to \"same\".\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "1ec55e23",
    "metadata": {},
    "outputs": [
@@ -316,7 +323,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "verb4d",
+   "display_name": "EL_PASO_GH (3.13.7)",
    "language": "python",
    "name": "python3"
   },
@@ -330,7 +337,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/3_saving_strategies.ipynb b/tutorials/3_saving_strategies.ipynb
index ce4c0f9..33af435 100644
--- a/tutorials/3_saving_strategies.ipynb
+++ b/tutorials/3_saving_strategies.ipynb
@@ -7,9 +7,9 @@
    "source": [
     "# Tutorial: Saving strategies\n",
     "\n",
-    "This tutorial will teach you how to save your processed variables using different *SavingStrategies*.\n",
+    "This tutorial will teach you how to save your processed variables using different _SavingStrategies_.\n",
     "\n",
-    "We will use the loaded variables from the cdf file to showcase how to save them with different formats. We will add additional variables so that we have more to save."
+    "We will use the loaded variables from the cdf file to showcase how to save them with different formats. We will add additional variables so that we have more to save.\n"
    ]
   },
   {
@@ -22,13 +22,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[91mLogger not instantiated.\u001b[0m\n",
-      "\u001b[91mBasic logger can be instantiated using `logging.basicConfig(level=logging.INFO)`\u001b[0m\n",
       "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170730_v7.3.0.cdf\n",
       "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170731_v7.4.0.cdf\n",
       "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170801_v7.3.0.cdf\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.352 seconds\n",
-      "INFO:el_paso.extract_variables_from_files:Extracting variables ...\n"
+      "INFO:el_paso.download:download finished in 0.368 seconds\n",
+      "INFO:el_paso.processing.extraction:Extracting variables ...\n"
      ]
     },
     {
@@ -89,7 +87,7 @@
     "    skip_existing=True,\n",
     ")\n",
     "\n",
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time, end_time, \"daily\", data_path=\".\", file_name_stem=file_name_stem, extraction_infos=extraction_infos\n",
     ")\n",
     "variables"
@@ -102,9 +100,9 @@
    "source": [
     "## Single file strategy\n",
     "\n",
-    "First, we want to save the variables using the *SingleFileStrategy*. This is the simplest way to save variables, as everything is simple put into one file. Dependent on the file ending, different file formats will be saved. Possible formats are \".mat\", \".pickle\", and \".h5\".\n",
+    "First, we want to save the variables using the _SingleFileStrategy_. This is the simplest way to save variables, as everything is simple put into one file. Dependent on the file ending, different file formats will be saved. Possible formats are \".mat\", \".pickle\", and \".h5\".\n",
     "\n",
-    "The units of the variables are not changed when using the SingleFileStrategy. "
+    "The units of the variables are not changed when using the SingleFileStrategy.\n"
    ]
   },
   {
@@ -118,7 +116,7 @@
      "output_type": "stream",
      "text": [
       "INFO:el_paso.saving_strategy:Saving file rbsp_hope_example.pickle...\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.521 seconds\n"
+      "INFO:el_paso.save:save finished in 0.621 seconds\n"
      ]
     }
    ],
@@ -134,7 +132,7 @@
    "id": "0b9e196b",
    "metadata": {},
    "source": [
-    "Let's inspect what got saved. The variables are turned into simple numpy arrays before saving. You can see that also a *metadata* variable has been saved. We will look closer at metadata in a different tutorial."
+    "Let's inspect what got saved. The variables are turned into simple numpy arrays before saving. You can see that also a _metadata_ variable has been saved. We will look closer at metadata in a different tutorial.\n"
    ]
   },
   {
@@ -170,9 +168,9 @@
    "source": [
     "## Data-org strategy\n",
     "\n",
-    "All data at GFZ is stored under a standard, which we call DataOrg (Data Organization). The standard inlcudes monthly files, where variables are split into separate files to reduce loading times. All files are saved as .mat files. By using the *DataOrgStrategy*, the variables are automatically sorted into the corresponding files, based on the key of the variable_dict. Additionally, the variables are converted into the units as described in the standard.\n",
+    "All data at GFZ is stored under a standard, which we call DataOrg (Data Organization). The standard inlcudes monthly files, where variables are split into separate files to reduce loading times. All files are saved as .mat files. By using the _DataOrgStrategy_, the variables are automatically sorted into the corresponding files, based on the key of the variable_dict. Additionally, the variables are converted into the units as described in the standard.\n",
     "\n",
-    "For using it, we have to store the variables in a dictionary with certain keys, as specified by the standard. "
+    "For using it, we have to store the variables in a dictionary with certain keys, as specified by the standard.\n"
    ]
   },
   {
@@ -186,60 +184,46 @@
      "output_type": "stream",
      "text": [
       "INFO:el_paso.saving_strategy:Saving file rbspa_hope_20170701to20170731_flux_ver4.mat...\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable alpha_local!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output alpha_and_energy!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable MLT!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output mlt!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable Lstar!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output lstar!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable Lm!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output lm!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable PSD!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output psd!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable alpha_local!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output alpha_and_energy!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable MLT!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output mlt!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable Lstar!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output lstar!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable Lm!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output lm!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable PSD!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output psd!\n",
       "INFO:el_paso.saving_strategy:Saving file rbspa_hope_20170701to20170731_xGEO_ver4.mat...\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable InvMu!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output invmu_and_invk!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable B_eq!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output bfield!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable R0!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output R0!\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69: UserWarning: Could not find target variable density!\n",
-      "  target_variables = saving_strategy.get_target_variables(\n",
-      "\n",
-      "WARNING:el_paso.save:Saving attempted, but product is missing some required variables for output density!\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "('Time length mismatch! Time length of variable Flux: 8292', 'and of variable Flux: 4182')",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      1\u001b[39m variables_to_save = {\n\u001b[32m      2\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mtime\u001b[39m\u001b[33m\"\u001b[39m: variables[\u001b[33m\"\u001b[39m\u001b[33mEpoch\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m      3\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mFlux\u001b[39m\u001b[33m\"\u001b[39m: variables[\u001b[33m\"\u001b[39m\u001b[33mFEDU\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m      4\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mxGEO\u001b[39m\u001b[33m\"\u001b[39m: variables[\u001b[33m\"\u001b[39m\u001b[33mxGEO\u001b[39m\u001b[33m\"\u001b[39m],\n\u001b[32m      5\u001b[39m }\n\u001b[32m      7\u001b[39m saving_strategy = ep.saving_strategies.DataOrgStrategy(\n\u001b[32m      8\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m, mission=\u001b[33m\"\u001b[39m\u001b[33mRBSP\u001b[39m\u001b[33m\"\u001b[39m, satellite=\u001b[33m\"\u001b[39m\u001b[33mrbspa\u001b[39m\u001b[33m\"\u001b[39m, instrument=\u001b[33m\"\u001b[39m\u001b[33mhope\u001b[39m\u001b[33m\"\u001b[39m, kext=\u001b[33m\"\u001b[39m\u001b[33mT89\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m      9\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[43mep\u001b[49m\u001b[43m.\u001b[49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables_to_save\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaving_strategy\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mend_time\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_var\u001b[49m\u001b[43m=\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mEpoch\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/utils.py:155\u001b[39m, in \u001b[36mtimed_function.<locals>.timed_function_.<locals>.wrap\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m    152\u001b[39m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[32m    153\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mwrap\u001b[39m(*args: P.args, **kwargs: P.kwargs) -> R:\n\u001b[32m    154\u001b[39m     tic = timeit.default_timer()\n\u001b[32m--> \u001b[39m\u001b[32m155\u001b[39m     result = \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    156\u001b[39m     toc = timeit.default_timer()\n\u001b[32m    157\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m func_name:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/save.py:69\u001b[39m, in \u001b[36msave\u001b[39m\u001b[34m(variables_dict, saving_strategy, start_time, end_time, time_var, append)\u001b[39m\n\u001b[32m     66\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m output_file \u001b[38;5;129;01min\u001b[39;00m saving_strategy.output_files:\n\u001b[32m     67\u001b[39m     file_path = saving_strategy.get_file_path(interval_start, interval_end, output_file)\n\u001b[32m---> \u001b[39m\u001b[32m69\u001b[39m     target_variables = \u001b[43msaving_strategy\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget_target_variables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m     70\u001b[39m \u001b[43m        \u001b[49m\u001b[43moutput_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariables_dict\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtime_var\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval_start\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval_end\u001b[49m\n\u001b[32m     71\u001b[39m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     73\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m target_variables \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m     74\u001b[39m         logger.warning(\n\u001b[32m     75\u001b[39m             \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mSaving attempted, but product is missing some required variables for output \u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutput_file.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m!\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     76\u001b[39m             stacklevel=\u001b[32m2\u001b[39m,\n\u001b[32m     77\u001b[39m         )\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/saving_strategy.py:164\u001b[39m, in \u001b[36mSavingStrategy.get_target_variables\u001b[39m\u001b[34m(self, output_file, variables_dict, time_var, start_time, end_time)\u001b[39m\n\u001b[32m    162\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m start_time \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m end_time \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m time_var \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    163\u001b[39m         var_to_save.truncate(time_var, start_time.timestamp(), end_time.timestamp())\n\u001b[32m--> \u001b[39m\u001b[32m164\u001b[39m     var_to_save = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstandardize_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvar_to_save\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname_to_save\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    166\u001b[39m     target_variables[name_to_save] = var_to_save\n\u001b[32m    167\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/saving_strategies/data_org_strategy.py:123\u001b[39m, in \u001b[36mDataOrgStrategy.standardize_variable\u001b[39m\u001b[34m(self, variable, name_in_file)\u001b[39m\n\u001b[32m    105\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mstandardize_variable\u001b[39m(\u001b[38;5;28mself\u001b[39m, variable: Variable, name_in_file: \u001b[38;5;28mstr\u001b[39m) -> Variable:\n\u001b[32m    106\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"Standardizes a variable's units and dimensions based on its predefined name.\u001b[39;00m\n\u001b[32m    107\u001b[39m \n\u001b[32m    108\u001b[39m \u001b[33;03m    This method acts as a proxy, delegating the actual standardization logic\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    121\u001b[39m \u001b[33;03m        ValueError: If an unknown `name_in_file` is encountered..\u001b[39;00m\n\u001b[32m    122\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m123\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mdata_standard\u001b[49m\u001b[43m.\u001b[49m\u001b[43mstandardize_variable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_in_file\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvariable\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/data_standards/data_org_standard.py:51\u001b[39m, in \u001b[36mDataOrgStandard.standardize_variable\u001b[39m\u001b[34m(self, standard_name, variable)\u001b[39m\n\u001b[32m     49\u001b[39m assert_n_dim(variable, \u001b[32m3\u001b[39m, standard_name)\n\u001b[32m     50\u001b[39m shape = variable.get_data().shape\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mconsistency_check\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcheck_time_size\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstandard_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     52\u001b[39m \u001b[38;5;28mself\u001b[39m.consistency_check.check_energy_size(shape[\u001b[32m1\u001b[39m], standard_name)\n\u001b[32m     53\u001b[39m \u001b[38;5;28mself\u001b[39m.consistency_check.check_pitch_angle_size(shape[\u001b[32m2\u001b[39m], standard_name)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/data_standard.py:80\u001b[39m, in \u001b[36mConsistencyCheck.check_time_size\u001b[39m\u001b[34m(self, provided_len_time, name_in_file)\u001b[39m\n\u001b[32m     75\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.len_time.size != provided_len_time:\n\u001b[32m     76\u001b[39m     msg = (\n\u001b[32m     77\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTime length mismatch! Time length of variable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.len_time.name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.len_time.size\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m     78\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mand of variable \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname_in_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mprovided_len_time\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m,\n\u001b[32m     79\u001b[39m     )\n\u001b[32m---> \u001b[39m\u001b[32m80\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n",
-      "\u001b[31mValueError\u001b[39m: ('Time length mismatch! Time length of variable Flux: 8292', 'and of variable Flux: 4182')"
+      "INFO:el_paso.saving_strategy:Could not find target variable InvMu!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output invmu_and_invk!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable B_eq!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output bfield!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable R0!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output R0!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable density!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output density!\n",
+      "INFO:el_paso.saving_strategy:Saving file rbspa_hope_20170801to20170831_flux_ver4.mat...\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable alpha_local!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output alpha_and_energy!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable MLT!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output mlt!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable Lstar!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output lstar!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable Lm!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output lm!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable PSD!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output psd!\n",
+      "INFO:el_paso.saving_strategy:Saving file rbspa_hope_20170801to20170831_xGEO_ver4.mat...\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable InvMu!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output invmu_and_invk!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable B_eq!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output bfield!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable R0!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output R0!\n",
+      "INFO:el_paso.saving_strategy:Could not find target variable density!\n",
+      "INFO:el_paso.save:Saving attempted, but product is missing some required variables for output density!\n",
+      "INFO:el_paso.save:save finished in 6.040 seconds\n"
      ]
     }
    ],
@@ -261,7 +245,7 @@
    "id": "da869bc2",
    "metadata": {},
    "source": [
-    "Let's inspect again, what has been saved. Flux-files and xGEO-files got saved. Note how the metadata struct is again saved and that the units of xGEO got automatically transformed from km to R_E. "
+    "Let's inspect again, what has been saved. Flux-files and xGEO-files got saved. Note how the metadata struct is again saved and that the units of xGEO got automatically transformed from km to R_E.\n"
    ]
   },
   {
@@ -274,8 +258,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of saved files: 2\n",
-      "Saved files: [PosixPath('RBSP/rbspa/Processed_Mat_Files/rbspa_hope_20170701to20170731_flux_ver4.mat'), PosixPath('RBSP/rbspa/Processed_Mat_Files/rbspa_hope_20170701to20170731_xGEO_ver4.mat')]\n",
+      "Number of saved files: 4\n",
+      "Saved files: [PosixPath('RBSP/rbspa/Processed_Mat_Files/rbspa_hope_20170701to20170731_flux_ver4.mat'), PosixPath('RBSP/rbspa/Processed_Mat_Files/rbspa_hope_20170701to20170731_xGEO_ver4.mat'), PosixPath('RBSP/rbspa/Processed_Mat_Files/rbspa_hope_20170801to20170831_flux_ver4.mat'), PosixPath('RBSP/rbspa/Processed_Mat_Files/rbspa_hope_20170801to20170831_xGEO_ver4.mat')]\n",
       "xGEO data keys: dict_keys(['__header__', '__version__', '__globals__', 'time', 'xGEO', 'metadata'])\n",
       "xGEO data metadata: dict_keys(['unit', 'original_cadence_seconds', 'source_files', 'description', 'processing_notes'])\n",
       "xGEO[0,:]: [-3.1846778  -4.3196673   0.15771057] RE\n"
@@ -308,7 +292,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "verb4d",
+   "display_name": "EL_PASO_GH (3.13.7)",
    "language": "python",
    "name": "python3"
   },
@@ -322,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/4_time_binning.ipynb b/tutorials/4_time_binning.ipynb
index 225ba04..bf9dffd 100644
--- a/tutorials/4_time_binning.ipynb
+++ b/tutorials/4_time_binning.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "This tutorial will teach you how to time bin variables as an essential processing step.\n",
     "\n",
-    "We will use the loaded variables from the cdf file for the tutorial."
+    "We will use the loaded variables from the cdf file for the tutorial.\n"
    ]
   },
   {
@@ -22,13 +22,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\u001b[91mLogger not instantiated.\u001b[0m\n",
-      "\u001b[91mBasic logger can be instantiated using `logging.basicConfig(level=logging.INFO)`\u001b[0m\n",
       "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170730_v7.3.0.cdf\n",
       "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170731_v7.4.0.cdf\n",
       "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170801_v7.3.0.cdf\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.348 seconds\n",
-      "INFO:el_paso.extract_variables_from_files:Extracting variables ...\n"
+      "INFO:el_paso.download:download finished in 0.374 seconds\n",
+      "INFO:el_paso.processing.extraction:Extracting variables ...\n"
      ]
     },
     {
@@ -89,7 +87,7 @@
     "    skip_existing=True,\n",
     ")\n",
     "\n",
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time, end_time, \"daily\", data_path=\".\", file_name_stem=file_name_stem, extraction_infos=extraction_infos\n",
     ")\n",
     "variables"
@@ -100,10 +98,10 @@
    "id": "6253847c",
    "metadata": {},
    "source": [
-    "Time binning is one essential processing step, you probably want to use for most satellite missions. The function is part of the *processing* module, which holds functions you can use for processing your variables. The function *bin_by_time* changes the variables in place to reduce memory, because before time binning, data sizes can be quite big. A new time variable is created with the same cadence as provided as an input to the function.\n",
+    "Time binning is one essential processing step, you probably want to use for most satellite missions. The function is part of the _processing_ module, which holds functions you can use for processing your variables. The function _bin_by_time_ changes the variables in place to reduce memory, because before time binning, data sizes can be quite big. A new time variable is created with the same cadence as provided as an input to the function.\n",
     "\n",
     "For every variable, which should be time-binned, you have to provide a corresponding time-bin-method. This dictionary is used as input in the time binning function.\n",
-    "If you want to change the start- and end-time of the binning, you should truncate the variables first. This can be done by the *truncate()* method of variable class."
+    "If you want to change the start- and end-time of the binning, you should truncate the variables first. This can be done by the _truncate()_ method of variable class.\n"
    ]
   },
   {
@@ -116,14 +114,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:py.warnings:/tmp/ipykernel_148707/387006790.py:12: RuntimeWarning: divide by zero encountered in log10\n",
+      "WARNING:py.warnings:/tmp/ipykernel_416965/387006790.py:12: RuntimeWarning: divide by zero encountered in log10\n",
       "  np.log10(variables[\"FEDU\"].get_data()[:, 5, 10]),\n",
       "\n",
       "INFO:el_paso.processing.bin_by_time:Binning by time...\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/utils.py:200: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
+      "WARNING:py.warnings:/home/jhawar/EL_PASO_GH/el_paso/utils.py:205: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
       "  .to_pydatetime()\n",
       "\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.039 seconds\n"
+      "WARNING:py.warnings:/home/jhawar/EL_PASO_GH/el_paso/processing/bin_by_time.py:88: RuntimeWarning: All-NaN slice encountered\n",
+      "  binned_array = np.nanmedian(data, axis=0)\n",
+      "\n",
+      "INFO:el_paso.processing.bin_by_time:bin_by_time finished in 0.065 seconds\n"
      ]
     },
     {
@@ -138,7 +139,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -187,7 +188,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "verb4d",
+   "display_name": "EL_PASO_GH (3.13.7)",
    "language": "python",
    "name": "python3"
   },
@@ -201,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/5_magnetic_field_processing.ipynb b/tutorials/5_magnetic_field_processing.ipynb
index f205e4d..bbc0a84 100644
--- a/tutorials/5_magnetic_field_processing.ipynb
+++ b/tutorials/5_magnetic_field_processing.ipynb
@@ -9,36 +9,15 @@
     "\n",
     "This tutorial will teach you how to perform the processing steps which require magnetic field calculations like mapping along the field line and calculations of adiabatic invariants.\n",
     "\n",
-    "We will use the time-binned variables from the cdf file for the tutorial. We also use energies and pitch angles for this tutorial and fold the pitch angles around 90 degrees assuming a symmetric pitch-angle distribution."
+    "We will use the time-binned variables from the cdf file for the tutorial. We also use energies and pitch angles for this tutorial and fold the pitch angles around 90 degrees assuming a symmetric pitch-angle distribution.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "e36616b5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[91mLogger not instantiated.\u001b[0m\n",
-      "\u001b[91mBasic logger can be instantiated using `logging.basicConfig(level=logging.INFO)`\u001b[0m\n",
-      "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170730_v7.3.0.cdf\n",
-      "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170731_v7.4.0.cdf\n",
-      "INFO:el_paso.download:File already exists, skipping download: rbspa_rel04_ect-hope-pa-l3_20170801_v7.3.0.cdf\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.369 seconds\n",
-      "INFO:el_paso.extract_variables_from_files:Extracting variables ...\n",
-      "INFO:el_paso.processing.bin_by_time:Binning by time...\n",
-      "WARNING:py.warnings:/home/jhawar/anaconda3/envs/verb4d/lib/python3.11/site-packages/el_paso/utils.py:200: UserWarning: Discarding nonzero nanoseconds in conversion.\n",
-      "  .to_pydatetime()\n",
-      "\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.946 seconds\n",
-      "INFO:el_paso.processing.fold_pitch_angles_and_flux:Folding pitch angles and flux ...\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.033 seconds\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import logging\n",
     "import sys\n",
@@ -95,7 +74,7 @@
     "    skip_existing=True,\n",
     ")\n",
     "\n",
-    "variables = ep.extract_variables_from_files(\n",
+    "variables = ep.processing.extract_variables_from_files(\n",
     "    start_time, end_time, \"daily\", data_path=\".\", file_name_stem=file_name_stem, extraction_infos=extraction_infos\n",
     ")\n",
     "\n",
@@ -128,80 +107,23 @@
     "Now we are ready to perform the magnetic field calculations. To make this most efficient, there exists a single function, which calculates all derived variables at once.\n",
     "The use specifies which variables should be calculated, which magnetic field model should be used, and which options for IRBEM are chosen (IRBEM is the underlying library performing the magnetic field calculations).\n",
     "\n",
-    "We will start by calculating just the variables which dependent on the satellite's position but not on the particles. The full list of available variable names can be found HERE. "
+    "We will start by calculating just the variables which dependent on the satellite's position but not on the particles. The full list of available variable names can be found HERE.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "d01555ab",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:OMNI Low Res  data directory: /home/jhawar/.elpaso/OMNI_low_res\n",
-      "INFO:root:Kp Niemegk  data directory: /home/jhawar/.elpaso/KpNiemegk\n",
-      "INFO:root:Reading omni from 2017-07-30 00:00:11+00:00 to 2017-08-01 23:55:11+00:00\n",
-      "INFO:root:Setting NaNs in omni from 2017-08-02 02:55:11+00:00 to 2017-08-01 23:55:11+00:00\n",
-      "INFO:root:Percentage of NaNs in data frame: 0.00%\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating local magnetic field values ...\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.007 seconds\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating magnetic local time ...\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.013 seconds\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating magnetic field and radial distance at the equator ...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/864 [00:01<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:el_paso.utils:\t\tFinished in 1.130 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0, 0.5, 'R_eq (RE)')"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate magnetic field variables\n",
     "from matplotlib import pyplot as plt\n",
+    "from pathlib import Path\n",
     "\n",
     "num_cores = 4\n",
     "irbem_options = [1, 1, 4, 4, 0]\n",
-    "irbem_lib_path = \"../IRBEM/libirbem.so\"\n",
+    "irbem_lib_path = Path(ep.__file__).parent / \"..\" / \"libirbem.so\"\n",
     "\n",
     "variables_to_compute: ep.processing.VariableRequest = [\n",
     "    (\"B_local\", \"T89\"),\n",
@@ -235,111 +157,15 @@
    "source": [
     "Next, we want to calculate equatorial pitch angles and adiabatic invariants. In this case, we have to provide the pitch-angles and energy variables as well.\n",
     "\n",
-    "The calculation of Lstar is quite heavy, so we will only calculate a small time frame for this tutorial."
+    "The calculation of Lstar is quite heavy, so we will only calculate a small time frame for this tutorial.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "7fe26054",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:OMNI Low Res  data directory: /home/jhawar/.elpaso/OMNI_low_res\n",
-      "INFO:root:Kp Niemegk  data directory: /home/jhawar/.elpaso/KpNiemegk\n",
-      "INFO:root:Reading omni from 2017-07-30 00:00:11+00:00 to 2017-07-30 00:10:11+00:00\n",
-      "INFO:root:Setting NaNs in omni from 2017-07-30 03:10:11+00:00 to 2017-07-30 00:10:11+00:00\n",
-      "INFO:root:Percentage of NaNs in data frame: 0.00%\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating local magnetic field values ...\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.001 seconds\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating magnetic local time ...\n",
-      "INFO:el_paso.utils:\t\tFinished in 0.001 seconds\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating magnetic field and radial distance at the equator ...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/3 [00:01<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:el_paso.utils:\t\tFinished in 1.021 seconds\n",
-      "INFO:el_paso.processing.compute_magnetic_field_variables:\tCalculating equatorial pitch angle ...\n",
-      "INFO:el_paso.utils:\t\tEquatorial pitch angle calculation finished in 0.001 seconds\n",
-      "INFO:el_paso.processing.compute_magnetic_field_variables:\tCalculating invariant mu ...\n",
-      "INFO:el_paso.utils:\t\tInvariant mu calculation finished in 0.000 seconds\n",
-      "INFO:el_paso.processing.compute_magnetic_field_variables:\tCalculating invariant K ...\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating Lstar and J ...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      " 67%|██████▋   | 2/3 [00:07<00:03,  3.50s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:el_paso.utils:\t\tFinished in 7.029 seconds\n",
-      "INFO:el_paso.processing.magnetic_field_utils.magnetic_field_functions:\tCalculating mirror points ...\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "  0%|          | 0/3 [00:01<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:el_paso.utils:\t\tFinished in 1.028 seconds\n",
-      "INFO:el_paso.utils:\t\tInvariant K calculation finished in 8.059 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(2.0, 6.0)"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "for var in variables.values():\n",
     "    var.truncate(binned_time_variable, start_time=start_time, end_time=start_time + timedelta(hours=0.2))\n",
@@ -379,7 +205,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "verb4d",
+   "display_name": "EL_PASO_GH (3.13.7)",
    "language": "python",
    "name": "python3"
   },
@@ -393,7 +219,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.13"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/rbsp_hope_example.pickle b/tutorials/rbsp_hope_example.pickle
index 767e18a..5b741fb 100644
Binary files a/tutorials/rbsp_hope_example.pickle and b/tutorials/rbsp_hope_example.pickle differ