diff --git a/.github/workflows/build_and_publish.yaml b/.github/workflows/build_and_publish.yaml
index 57f3386c35..10dea44a46 100644
--- a/.github/workflows/build_and_publish.yaml
+++ b/.github/workflows/build_and_publish.yaml
@@ -19,10 +19,10 @@ jobs:
         ref: 'refs/heads/master'
     - uses: actions/checkout@v4
 
-    - name: Set up Python 3.7
+    - name: Set up Python 3.10
       uses: actions/setup-python@v5
       with:
-        python-version: 3.7
+        python-version: "3.10"
     - name: Cache pip
       uses: actions/cache@v4
       with:
diff --git a/.github/workflows/deploy_documentation.yaml b/.github/workflows/deploy_documentation.yaml
index 332f74ac9a..832635076c 100644
--- a/.github/workflows/deploy_documentation.yaml
+++ b/.github/workflows/deploy_documentation.yaml
@@ -13,10 +13,10 @@ jobs:
         with:
           ref: 'refs/heads/develop'
       - uses: actions/checkout@v4
-      - name: Set up Python 3.7
+      - name: Set up Python 3.10
         uses: actions/setup-python@v5
         with:
-          python-version: 3.7
+          python-version: "3.10"
 
       - name: Cache pip
         uses: actions/cache@v4
diff --git a/.github/workflows/python_version_compatibility.yaml b/.github/workflows/python_version_compatibility.yaml
index 8f19771313..b883f3a549 100644
--- a/.github/workflows/python_version_compatibility.yaml
+++ b/.github/workflows/python_version_compatibility.yaml
@@ -12,11 +12,15 @@ jobs:
     strategy:
       fail-fast: false # continue even if one of the versions fails
       matrix:
-        python_version: ["3.8", "3.9", "3.10", "3.11", "3.x"] # 3.x = latest available
+        python_version: ["3.9", "3.11", "3.x"] # 3.x = latest available
         os: [ubuntu-latest]
         include:
           - python_version: "3.6"
             os: ubuntu-20.04 # python 3.6 is not available with 22.04 on github actions
+          - python_version: "3.7"
+            os: ubuntu-22.04
+          - python_version: "3.8" # technically available on 24.04 but not pre-installed
+            os: ubuntu-22.04
     uses: ./.github/workflows/run_tests.yaml
     with:
       python_version: ${{ matrix.python_version }}
diff --git a/.github/workflows/run_tests.yaml b/.github/workflows/run_tests.yaml
index 9d092e45c8..03fb6394da 100644
--- a/.github/workflows/run_tests.yaml
+++ b/.github/workflows/run_tests.yaml
@@ -9,7 +9,7 @@ on:
         description: Specify Python version to run tests on
         type: string
         required: true
-        default: '3.7'
+        default: '3.10'
       os:
         description: OS to run tests on
         type: string
@@ -20,14 +20,14 @@ on:
         description: Specify Python version to run tests on
         type: string
         required: true
-        default: '3.7'
+        default: '3.10'
       os:
         description: OS to run tests on
         type: string
         default: ubuntu-latest
 
-env: # set the python_version to 3.7 if the workflow is not triggered by workflow_call or workflow_dispatch
-  PYTHON_VERSION: ${{ inputs.python_version || '3.7' }}
+env: # set the python_version to 3.10 if the workflow is not triggered by workflow_call or workflow_dispatch
+  PYTHON_VERSION: ${{ inputs.python_version || '3.10' }}
 
 jobs:
   build:
@@ -190,6 +190,12 @@ jobs:
         export GSLDIR=$(gsl-config --prefix)
         export PYTHONPATH=$(pwd):$PYTHONPATH
         NuRadioReco/test/trigger_tests/run_trigger_test.sh
+    - name: "Test channelGalacticNoiseAdder"
+      if: always()
+      run: |
+        export PYTHONPATH=$(pwd):$PYTHONPATH
+        python install_dev.py --dev galacticnoise --no-interactive
+        python NuRadioReco/examples/StandAloneScripts/B01galacticnoiseadder.py
     - name: "Test all examples"
       if: always()
       run: |
diff --git a/NuRadioMC/SignalProp/analyticraytracing.py b/NuRadioMC/SignalProp/analyticraytracing.py
index ecff29a522..3c67cfffe7 100644
--- a/NuRadioMC/SignalProp/analyticraytracing.py
+++ b/NuRadioMC/SignalProp/analyticraytracing.py
@@ -2681,6 +2681,23 @@ def get_output_parameters(self):
         ]
 
     def get_raytracing_output(self, i_solution):
+        """
+        Get the output of the ray tracing for a specific solution
+
+        Parameters
+        ----------
+        i_solution: int
+            Index of the raytracing solution
+
+        Returns
+        -------
+        output_dict: dict
+            Dictionary containing the output of the ray tracing.
+            The C_0 and C_1 parameters are the parameters of the analytic function that describes the ray path.
+            The solution type is the type of the solution (1: direct, 2:refracted, 3: reflected off the surface).
+            The reflection parameter specifies the number of bottom reflections (in case of reflective layers in the ice).
+            The reflection case specifies if the ray starts upward or downward (1: upward, 2: downward) (only relevant if bottom reflection > 0).
+        """
         if self._config['propagation']['focusing']:
             focusing = self.get_focusing(i_solution, limit=float(self._config['propagation']['focusing_limit']))
         else:
diff --git a/NuRadioMC/SignalProp/examples/birefringence_examples/06_Veff_comparison/06_B_Veff_comparison.py b/NuRadioMC/SignalProp/examples/birefringence_examples/06_Veff_comparison/06_B_Veff_comparison.py
index bacfa46940..ba5cbb8c4f 100644
--- a/NuRadioMC/SignalProp/examples/birefringence_examples/06_Veff_comparison/06_B_Veff_comparison.py
+++ b/NuRadioMC/SignalProp/examples/birefringence_examples/06_Veff_comparison/06_B_Veff_comparison.py
@@ -5,6 +5,7 @@
 # import detector simulation modules
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 from NuRadioMC.simulation import simulation
 
@@ -14,7 +15,7 @@
 logger = logging.getLogger("NuRadioMC.SignalProp.runstrawman")
 
 ###-----------------------------------------
-#   EXAMPLE: Script to calculate the effects of birefringence on the effective volume. 
+#   EXAMPLE: Script to calculate the effects of birefringence on the effective volume.
 #            A full study of this calculation was published here: DOI: https://doi.org/10.22323/1.444.1101
 ###-----------------------------------------
 
@@ -38,7 +39,7 @@ def _detector_simulation_trigger(self, evt, station, det):
                              triggered_channels=None,  # run trigger on all channels
                              number_concidences=1,
                              trigger_name='simple_threshold1')  # the name of the trigger
-        
+
 energies = np.array([19])
 
 Veff_birefringence = []
@@ -81,4 +82,3 @@ def _detector_simulation_trigger(self, evt, station, det):
 plt.xlabel(r'$\log (E)$')
 plt.tight_layout()
 plt.savefig('06_Veff_comp_plot.png', dpi=400)
-
diff --git a/NuRadioMC/SignalProp/propagation_base_class.py b/NuRadioMC/SignalProp/propagation_base_class.py
index 2ec0de7e4d..2a6e6785e9 100644
--- a/NuRadioMC/SignalProp/propagation_base_class.py
+++ b/NuRadioMC/SignalProp/propagation_base_class.py
@@ -25,7 +25,10 @@ class initilization
         medium: medium class
             class describing the index-of-refraction profile
         attenuation_model: string
-            signal attenuation model (so far only "SP1" is implemented)
+            if this parameter is also defined in the config file, the value from the config file
+            will be used. If not, the value from this parameter will be used.
+
+            signal attenuation model
         log_level: logging object
             specify the log level of the ray tracing class
 
@@ -36,10 +39,16 @@ class describing the index-of-refraction profile
 
             default is NOTSET (ie global control)
         n_frequencies_integration: int
-            the number of frequencies for which the frequency dependent attenuation
+            if this parameter is also defined in the config file, the value from the config file
+            will be used. If not, the value from this parameter will be used.
+
+            This parameter specifies the number of frequencies for which the frequency dependent attenuation
             length is being calculated. The attenuation length for all other frequencies
             is obtained via linear interpolation.
         n_reflections: int (default 0)
+            if this parameter is also defined in the config file, the value from the config file
+            will be used. If not, the value from this parameter will be used.
+
             in case of a medium with a reflective layer at the bottom, how many reflections should be considered
         config: nested dictionary
             loaded yaml config file
@@ -60,6 +69,19 @@ class describing the index-of-refraction profile
         self._n_reflections = n_reflections
 
         self._config = config
+        if self._config is not None:
+            if 'n_freq' in self._config['propagation']:
+                if n_frequencies_integration is not None:
+                    self.__logger.warning(f"overriding n_frequencies_integration from config file from {n_frequencies_integration} to {self._config['propagation']['n_freq']}")
+                self._n_frequencies_integration = self._config['propagation']['n_freq']
+            if 'n_reflections' in self._config['propagation']:
+                if n_reflections is not None:
+                    self.__logger.warning(f"overriding n_reflections from config file from {n_reflections} to {self._config['propagation']['n_reflections']}")
+                self._n_reflections = self._config['propagation']['n_reflections']
+            if 'attenuation_model' in self._config['propagation']:
+                if attenuation_model is not None:
+                    self.__logger.warning(f"overriding attenuation_model from config file from {attenuation_model} to {self._config['propagation']['attenuation_model']}")
+                self._attenuation_model = self._config['propagation']['attenuation_model']
         self._detector = detector
         self._max_detector_frequency = None
         if self._detector is not None:
diff --git a/NuRadioMC/examples/01_Veff_simulation/T02RunSimulation.py b/NuRadioMC/examples/01_Veff_simulation/T02RunSimulation.py
index d5278a9281..d572e0daff 100644
--- a/NuRadioMC/examples/01_Veff_simulation/T02RunSimulation.py
+++ b/NuRadioMC/examples/01_Veff_simulation/T02RunSimulation.py
@@ -13,7 +13,6 @@
 highLowThreshold = NuRadioReco.modules.trigger.highLowThreshold.triggerSimulator()
 channelBandPassFilter = NuRadioReco.modules.channelBandPassFilter.channelBandPassFilter()
 
-
 class mySimulation(simulation.simulation):
 
     def _detector_simulation_filter_amp(self, evt, station, det):
@@ -70,4 +69,3 @@ def _detector_simulation_trigger(self, evt, station, det):
                                 config_file=args.config,
                                 file_overwrite=True)
     sim.run()
-
diff --git a/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation.py b/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation.py
index 1e7da6d1fa..7a27375b28 100644
--- a/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation.py
+++ b/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation.py
@@ -4,6 +4,7 @@
 import NuRadioReco.modules.trigger.highLowThreshold
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 from NuRadioMC.simulation import simulation
 
@@ -68,4 +69,3 @@ def _detector_simulation_trigger(self, evt, station, det):
                                 config_file=args.config,
                                 file_overwrite=True)
     sim.run()
-
diff --git a/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation_trigger_channels.py b/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation_trigger_channels.py
index ca8a4c69fe..0fd84963cf 100644
--- a/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation_trigger_channels.py
+++ b/NuRadioMC/examples/01_Veff_simulation_B/T02RunSimulation_trigger_channels.py
@@ -3,6 +3,7 @@
 import NuRadioReco.modules.trigger.highLowThreshold
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 from NuRadioMC.simulation import simulation
 import logging
@@ -69,4 +70,3 @@ def _detector_simulation_trigger(self, evt, station, det):
                                 file_overwrite=True,
                                 trigger_channels=[8,9,2,3])
     sim.run()
-
diff --git a/NuRadioMC/examples/06_webinar/W02RunSimulation.py b/NuRadioMC/examples/06_webinar/W02RunSimulation.py
index 906ca1b9b6..3459951498 100644
--- a/NuRadioMC/examples/06_webinar/W02RunSimulation.py
+++ b/NuRadioMC/examples/06_webinar/W02RunSimulation.py
@@ -5,6 +5,7 @@
 import NuRadioReco.modules.channelResampler
 import NuRadioReco.modules.channelBandPassFilter
 import NuRadioReco.modules.channelGenericNoiseAdder
+
 from NuRadioReco.utilities import units
 import numpy as np
 from NuRadioMC.simulation import simulation
@@ -68,14 +69,14 @@
     functions that controls what the detector does after the electric field arrives
     at the antenna. That allows us to create our own class that inherits from
     the simulation class that we will call mySimulation, and define in it a
-    _detector_simulation_filter_amp and _detector_simulation_trigger 
+    _detector_simulation_filter_amp and _detector_simulation_trigger
     function with all the characteristics of our detector setup.
     """
 
 
     class mySimulation(simulation.simulation):
         """
-        
+
         """
 
         def _detector_simulation_filter_amp(self, evt, station, det):
diff --git a/NuRadioMC/examples/08_RNO_G_trigger_simulation/simulate.py b/NuRadioMC/examples/08_RNO_G_trigger_simulation/simulate.py
new file mode 100644
index 0000000000..6ceab13dcd
--- /dev/null
+++ b/NuRadioMC/examples/08_RNO_G_trigger_simulation/simulate.py
@@ -0,0 +1,273 @@
+#!/bin/env python3
+
+import argparse
+import copy
+import logging
+import numpy as np
+import os
+import secrets
+import datetime as dt
+from scipy import constants
+
+
+from NuRadioMC.EvtGen import generator
+from NuRadioMC.simulation import simulation
+from NuRadioReco.utilities import units
+
+from NuRadioReco.detector.RNO_G import rnog_detector
+
+from NuRadioReco.modules.RNO_G import hardwareResponseIncorporator, triggerBoardResponse
+from NuRadioReco.modules.trigger import highLowThreshold
+
+
+root_seed = secrets.randbits(128)
+deep_trigger_channels = np.array([0, 1, 2, 3])
+
+
+def get_vrms_from_temperature_for_trigger_channels(det, station_id, trigger_channels, temperature):
+
+    vrms_per_channel = []
+    for channel_id in trigger_channels:
+        resp = det.get_signal_chain_response(station_id, channel_id, trigger=True)
+
+        freqs = np.linspace(10, 1200, 1000) * units.MHz
+        filt = resp(freqs)
+
+        # Calculation of Vrms. For details see from elog:1566 and https://en.wikipedia.org/wiki/Johnson%E2%80%93Nyquist_noise
+        # (last two Eqs. in "noise voltage and power" section) or our wiki https://nu-radio.github.io/NuRadioMC/NuRadioMC/pages/HDF5_structure.html
+
+        # Bandwidth, i.e., \Delta f in equation
+        integrated_channel_response = np.trapz(np.abs(filt) ** 2, freqs)
+
+        vrms_per_channel.append(
+            (temperature * 50 * constants.k * integrated_channel_response / units.Hz) ** 0.5
+        )
+
+    return vrms_per_channel
+
+
+def get_fiducial_volume(energy):
+    # Fiducial volume for a Greenland station. From Martin: https://radio.uchicago.edu/wiki/images/2/26/TriggerSimulation_May2023.pdf
+
+    # key: log10(E), value: radius in km
+    max_radius_shallow = {
+        16.25: 1.5, 16.5: 2.1, 16.75: 2.7, 17.0: 3.1, 17.25: 3.7, 17.5: 3.9, 17.75: 4.4,
+        18.00: 4.8, 18.25: 5.1, 18.50: 5.25, 18.75: 5.3, 19.0: 5.6, 100: 6.1,
+    }
+
+    # key: log10(E), value: depth in km
+    min_z_shallow = {
+        16.25: -0.65, 16.50: -0.8, 16.75: -1.2, 17.00: -1.5, 17.25: -1.7, 17.50: -2.0,
+        17.75: -2.1, 18.00: -2.3, 18.25: -2.4, 18.50: -2.55, 100: -2.7,
+    }
+
+    def get_limits(dic, E):
+        # find all energy bins which are higher than E
+        idx = np.arange(len(dic))[E - 10 ** np.array(list(dic.keys())) * units.eV <= 0]
+        assert len(idx), f"Energy {E} is too high. Max energy is {10 ** np.amax(dic.keys()):.1e}."
+
+        # take the lowest energy bin which is higher than E
+        return np.array(list(dic.values()))[np.amin(idx)] * units.km
+
+    r_max = get_limits(max_radius_shallow, energy)
+    print(f"Maximum radius {r_max}")
+    z_min = get_limits(min_z_shallow, energy)
+    print(f"Depth {z_min}")
+
+    volume = {
+        "fiducial_rmax": r_max,
+        "fiducial_rmin": 0 * units.km,
+        "fiducial_zmin": z_min,
+        "fiducial_zmax": 0
+    }
+
+    return volume
+
+
+def RNO_G_HighLow_Thresh(lgRate_per_hz):
+    # Thresholds calculated using the RNO-G hardware (iglu + flower_lp)
+    # This applies for the VPol antennas
+    # parameterization comes from Alan: https://radio.uchicago.edu/wiki/images/e/e6/2023.10.11_Simulating_RNO-G_Trigger.pdf
+    return (-859 + np.sqrt(39392706 - 3602500 * lgRate_per_hz)) / 1441.0
+
+
+class mySimulation(simulation.simulation):
+
+    def __init__(self, *args, **kwargs):
+        # this module is needed in super().__init__ to calculate the vrms
+        self.rnogHarwareResponse = hardwareResponseIncorporator.hardwareResponseIncorporator()
+        self.rnogHarwareResponse.begin(trigger_channels=deep_trigger_channels)
+
+        super().__init__(*args, **kwargs)
+        self.logger = logging.getLogger("NuRadioMC.RNOG_trigger_simulation")
+        self.deep_trigger_channels = deep_trigger_channels
+
+
+        self.highLowThreshold = highLowThreshold.triggerSimulator()
+        self.rnogADCResponse = triggerBoardResponse.triggerBoardResponse()
+        self.rnogADCResponse.begin(adc_input_range=2 * units.volt, clock_offset=0.0, adc_output="voltage")
+
+        # future TODO: Add noise
+        # self.channel_generic_noise_adder = channelGenericNoiseAdder.channelGenericNoiseAdder()
+        # self.channel_generic_noise_adder.begin(seed=self._cfg['seed'])
+
+        self.output_mode = {'Channels': self._config['output']['channel_traces'],
+                            'ElectricFields': self._config['output']['electric_field_traces'],
+                            'SimChannels': self._config['output']['sim_channel_traces'],
+                            'SimElectricFields': self._config['output']['sim_electric_field_traces']}
+
+        self.high_low_trigger_thresholds = {
+            "10mHz": RNO_G_HighLow_Thresh(-2),
+            "100mHz": RNO_G_HighLow_Thresh(-1),
+            "1Hz": RNO_G_HighLow_Thresh(0),
+            "3Hz": RNO_G_HighLow_Thresh(np.log10(3)),
+        }
+
+
+    def _detector_simulation_filter_amp(self, evt, station, det):
+        # apply the amplifiers and filters to get to RADIANT-level
+        self.rnogHarwareResponse.run(evt, station, det, sim_to_data=True)
+
+    def _detector_simulation_trigger(self, evt, station, det):
+
+        vrms_input_to_adc = get_vrms_from_temperature_for_trigger_channels(
+            det, station.get_id(), self.deep_trigger_channels, 300)
+
+        sampling_rate = det.get_sampling_frequency(station.get_id())
+        self.logger.info(f'Radiant sampling rate is {sampling_rate / units.MHz:.1f} MHz')
+
+        # Runs the FLOWER board response
+        vrms_after_gain = self.rnogADCResponse.run(
+            evt, station, det, trigger_channels=self.deep_trigger_channels,
+            vrms=copy.copy(vrms_input_to_adc), digitize_trace=True,
+        )
+
+        for idx, trigger_channel in enumerate(self.deep_trigger_channels):
+            self.logger.info(
+                f'Vrms = {vrms_input_to_adc[idx] / units.mV:.2f} mV / {vrms_after_gain[idx] / units.mV:.2f} mV (after gain).')
+            self._Vrms_per_trigger_channel[station.get_id()][trigger_channel] = vrms_after_gain[idx]
+
+
+        # this is only returning the correct value if digitize_trace=True for self.rnogADCResponse.run(..)
+        flower_sampling_rate = station.get_trigger_channel(self.deep_trigger_channels[0]).get_sampling_rate()
+        self.logger.info(f'Flower sampling rate is {flower_sampling_rate / units.MHz:.1f} MHz')
+
+        for thresh_key, threshold in self.high_low_trigger_thresholds.items():
+
+            threshold_high = {channel_id: threshold * vrms for channel_id, vrms in zip(self.deep_trigger_channels, vrms_after_gain)}
+            threshold_low = {channel_id: -1 * threshold * vrms for channel_id, vrms in zip(self.deep_trigger_channels, vrms_after_gain)}
+
+            self.highLowThreshold.run(
+                evt,
+                station,
+                det,
+                threshold_high=threshold_high,
+                threshold_low=threshold_low,
+                use_digitization=False, #the trace has already been digitized with the rnogADCResponse
+                high_low_window=6 / flower_sampling_rate,
+                coinc_window=20 / flower_sampling_rate,
+                number_concidences=2,
+                triggered_channels=self.deep_trigger_channels,
+                trigger_name=f"deep_high_low_{thresh_key}",
+            )
+
+
+if __name__ == "__main__":
+
+    ABS_PATH_HERE = str(os.path.dirname(os.path.realpath(__file__)))
+    def_data_dir = os.path.join(ABS_PATH_HERE, "data")
+    default_config_path = os.path.join(ABS_PATH_HERE, "../07_RNO_G_simulation/RNO_config.yaml")
+
+    parser = argparse.ArgumentParser(description="Run NuRadioMC simulation")
+    # Sim steering arguments
+    parser.add_argument("--config", type=str, default=default_config_path, help="NuRadioMC yaml config file")
+    parser.add_argument("--detectordescription", '--det', type=str, default=None, help="Path to RNO-G detector description file. If None, query from DB")
+    parser.add_argument("--station_id", type=int, default=None, help="Set station to be used for simulation", required=True)
+
+    # Neutrino arguments
+    parser.add_argument("--energy", '-e', default=1e18, type=float, help="Neutrino energy [eV]")
+    parser.add_argument("--flavor", '-f', default="all", type=str, help="the flavor")
+    parser.add_argument("--interaction_type", '-it', default="ccnc", type=str, help="interaction type cc, nc or ccnc")
+
+    # File meta-variables
+    parser.add_argument("--index", '-i', default=0, type=int, help="counter to create a unique data-set identifier")
+    parser.add_argument("--n_events_per_file", '-n', type=int, default=1e3, help="Number of nu-interactions per file")
+    parser.add_argument("--data_dir", type=str, default=def_data_dir, help="directory name where the library will be created")
+    parser.add_argument("--proposal", action="store_true", help="Use PROPOSAL for simulation")
+    parser.add_argument("--nur_output", action="store_true", help="Write nur files.")
+
+    args = parser.parse_args()
+    kwargs = args.__dict__
+    assert args.station_id is not None, "Please specify a station id with `--station_id`"
+
+    # Defaults for the trigger simulation which are not yet in the hardware DB
+    defaults = {
+        "trigger_adc_sampling_frequency": 0.472,
+        "trigger_adc_nbits": 8,
+        "trigger_adc_noise_nbits": 3.321,
+    }
+
+    det = rnog_detector.Detector(
+        detector_file=args.detectordescription, log_level=logging.INFO,
+        always_query_entire_description=False, select_stations=args.station_id,
+        over_write_handset_values=defaults)
+
+    det.update(dt.datetime(2023, 8, 3))
+
+    volume = get_fiducial_volume(args.energy)
+
+    # Simulate fiducial volume around station
+    pos = det.get_absolute_position(args.station_id)
+    print(f"Simulating around center x0={pos[0]:.2f}m, y0={pos[1]:.2f}m")
+    volume.update({"x0": pos[0], "y0": pos[1]})
+
+    output_path = f"{args.data_dir}/station_{args.station_id}/nu_{args.flavor}_{args.interaction_type}"
+
+    if not os.path.exists(output_path):
+        print("Making dirs", output_path)
+        os.makedirs(output_path, exist_ok=True)
+
+    output_filename = f"{output_path}/{args.flavor}_{args.interaction_type}_1e{np.log10(args.energy):.2f}eV_{args.index:08d}.hdf5"
+
+    flavor_ids = {"e": [12, -12], "mu": [14, -14], "tau": [16, -16], "all": [12, 14, 16, -12, -14, -16]}
+    run_proposal = args.proposal and ("cc" in args.interaction_type) and (args.flavor in ["mu", "tau", "all"])
+    if run_proposal:
+        print(f"Using PROPOSAL for simulation of {args.flavor} {args.interaction_type}")
+
+    input_data = generator.generate_eventlist_cylinder(
+        "on-the-fly",
+        kwargs["n_events_per_file"],
+        args.energy, args.energy,
+        volume,
+        start_event_id=args.index * args.n_events_per_file + 1,
+        flavor=flavor_ids[args.flavor],
+        n_events_per_file=None,
+        deposited=False,
+        proposal=run_proposal,
+        proposal_config="Greenland",
+        start_file_id=0,
+        log_level=None,
+        proposal_kwargs={},
+        max_n_events_batch=args.n_events_per_file,
+        write_events=False,
+        seed=root_seed + args.index,
+        interaction_type=args.interaction_type,
+    )
+
+    if args.nur_output:
+        nur_output_filename = output_filename.replace(".hdf5", ".nur")
+    else:
+        nur_output_filename = None
+
+    sim = mySimulation(
+        inputfilename=input_data,
+        outputfilename=output_filename,
+        det=det,
+        evt_time=dt.datetime(2023, 8, 3),
+        outputfilenameNuRadioReco=nur_output_filename,
+        config_file=args.config,
+        trigger_channels=deep_trigger_channels,
+        # log_level=logging.INFO,
+    )
+
+    sim.run()
\ No newline at end of file
diff --git a/NuRadioMC/examples/Sensitivities/E2_fluxes3.py b/NuRadioMC/examples/Sensitivities/E2_fluxes3.py
index f7d9fa0853..1f9e3ed886 100644
--- a/NuRadioMC/examples/Sensitivities/E2_fluxes3.py
+++ b/NuRadioMC/examples/Sensitivities/E2_fluxes3.py
@@ -528,40 +528,11 @@ def get_ice_cube_hese_range():
 ara_4year_limit *= units.eV * units.cm ** -2 * units.second ** -1 * units.sr ** -1
 ara_4year_limit *= energyBinsPerDecade
 
-# ARA 2023 projection
-'''
-This estimate is built by using the actual recorded livetime for the ARA stations 
-through June 2021. Specifically:
-1747 days of A1
-5627 days of A2 + A3 + A4
-826 days of A5
-
-And then adding projected livetime
-A1: 7/12 of a year for 2021, then 1 year of 2022, and 1 year of 2023
-A2: no more data for 2021, no data for 2022, 1 year of 2023
-A3: 7/12 of a year for 2021, then 1 year of 2022, and 1 year of 2023
-A4: no more data for 2021, no data for 2022, 1 year of 2023
-A5: 7/12 of a year for 2021, then 1 year of 2022, and 1 year of 2023
-
-We do include different effective areas for A1, A2/3/4, and A5,
-since A1 is smaller (only being at 100m), while A5 is larger (having the phased array).
-
-We also included the trigger level and analysis level estimate.
-The analysis level option assumes the A2 analysis efficiency for stations A1-4,
-and a (preliminary) analysis efficiency estimate from the phased-array analysis.
-
-'''
-ara_2023_E_TL, ara_2023_limit_TL, t1, t2 = np.loadtxt(os.path.join(os.path.dirname(__file__), 'data', "limit_ara_2023_projected_trigger.txt"), unpack=True)
-ara_2023_E_TL *= units.GeV
-ara_2023_limit_TL *= units.GeV * units.cm ** -2 * units.second ** -1 * units.sr ** -1
-ara_2023_limit_TL *= energyBinsPerDecade
-ara_2023_limit_TL *= 2.44  # convert to 90%CL limit to be comparable with information from other experiments
-
-ara_2023_E, ara_2023_limit, t1, t2 = np.loadtxt(os.path.join(os.path.dirname(__file__), 'data', "limit_ara_2023_projected_analysis.txt"), unpack=True)
-ara_2023_E *= units.GeV
-ara_2023_limit *= units.GeV * units.cm ** -2 * units.second ** -1 * units.sr ** -1
-ara_2023_limit *= energyBinsPerDecade
-ara_2023_limit *= 2.44  # convert to 90%CL limit to be comparable with information from other experiments
+# ARA phased array 6-month limit
+ara_PA_6month_E, ara_PA_6month_limit, t1, t2 = np.loadtxt(os.path.join(os.path.dirname(__file__), 'data', "limit_ARA_PA_6months.txt"), unpack=True)
+ara_PA_6month_E *= units.eV
+ara_PA_6month_limit *= units.eV * units.cm ** -2 * units.second ** -1 * units.sr ** -1
+ara_PA_6month_limit *= energyBinsPerDecade
 
 ARIANNA_HRA = np.array([[1.00000003e+07, 3.16228005e+07, 9.99999984e+07, 3.16227997e+08,
                          9.99999984e+08, 3.16228010e+09, 9.99999998e+09, 3.16228010e+10,
@@ -631,8 +602,7 @@ def get_E2_limit_figure(diffuse=True,
                         show_anita_I_IV_limit=True,
                         show_auger_limit=True,
                         show_ara=True,
-                        show_ara_2023=False,
-                        show_ara_2023_TL=False,
+                        show_ara_PA=False,
                         show_arianna=True,
                         show_neutrino_best_fit=True,
                         show_neutrino_best_case=True,
@@ -773,8 +743,8 @@ def get_E2_limit_figure(diffuse=True,
                             horizontalalignment='left', color='saddlebrown', rotation=50, fontsize=legendfontsize)
         else:
             ax.annotate('GRAND 10k',
-                xy=(1.3e19 * units.eV / plotUnitsEnergy, 2.6e-8), xycoords='data',
-                horizontalalignment='left', va="bottom", color='saddlebrown', rotation=38, fontsize=legendfontsize)
+                xy=(1.5e19 * units.eV / plotUnitsEnergy, 3.4e-8), xycoords='data',
+                horizontalalignment='left', va="bottom", color='saddlebrown', rotation=40, fontsize=legendfontsize)
 
     if show_grand_200k:
         ax.plot(GRAND_energy / plotUnitsEnergy, GRAND_200k / plotUnitsFlux, linestyle=":", color='saddlebrown',
@@ -830,10 +800,10 @@ def get_E2_limit_figure(diffuse=True,
                     horizontalalignment='center', color='dodgerblue', rotation=0, fontsize=legendfontsize)
 
         # Extrapolation
-        energy_placeholder = np.array(([1e14, 1e19])) * units.eV
-        plt.plot(energy_placeholder / plotUnitsEnergy,
-                 ice_cube_nu_fit(energy_placeholder) * energy_placeholder ** 2 / plotUnitsFlux,
-                 color='dodgerblue', linestyle=':')
+        # energy_placeholder = np.array(([1e14, 1e19])) * units.eV
+        # plt.plot(energy_placeholder / plotUnitsEnergy,
+        #          ice_cube_nu_fit(energy_placeholder) * energy_placeholder ** 2 / plotUnitsFlux,
+        #          color='dodgerblue', linestyle=':')
 
         uplimit = np.copy(nu_mu_data[:, 3])
         uplimit[np.where(nu_mu_data[:, 3] == 0)] = 1
@@ -913,27 +883,16 @@ def get_E2_limit_figure(diffuse=True,
             ax.annotate('ARA',
                     xy=(1e18* units.eV / plotUnitsEnergy, 0.7e-6), xycoords='data',
                     horizontalalignment='left', color='indigo', rotation=0, fontsize=legendfontsize)
-    if show_ara_2023:
-        ax.plot(ara_2023_E / plotUnitsEnergy, ara_2023_limit / plotUnitsFlux, color='grey', linestyle='--')
-        if energyBinsPerDecade == 2:
-            ax.annotate('ARA 2023',
-                        xy=(2E16 * units.eV / plotUnitsEnergy, 6e-7), xycoords='data',
-                        horizontalalignment='left', color='grey', rotation=0, fontsize=legendfontsize)
-        else:
-            ax.annotate('ARA 2023',
-                    xy=(4E17 * units.eV / plotUnitsEnergy, 6e-8), xycoords='data',
-                    horizontalalignment='left', color='grey', rotation=0, fontsize=legendfontsize)
-
-    if show_ara_2023_TL:
-        ax.plot(ara_2023_E_TL / plotUnitsEnergy, ara_2023_limit_TL / plotUnitsFlux, color='grey', linestyle='--')
+    if show_ara_PA:
+        ax.plot(ara_PA_6month_E / plotUnitsEnergy, ara_PA_6month_limit / plotUnitsFlux, color='blue', linestyle='-')
         if energyBinsPerDecade == 2:
-            ax.annotate('ARA 2023 \n(TL)',
-                        xy=(1E16 * units.eV / plotUnitsEnergy, 6e-7), xycoords='data',
+            ax.annotate('ARA PA',
+                        xy=(2.95E1 * units.eV / plotUnitsEnergy, 6e-7), xycoords='data',
                         horizontalalignment='left', color='grey', rotation=0, fontsize=legendfontsize)
         else:
-            ax.annotate('ARA 2023 \n(TL)',
-                    xy=(1E16 * units.eV / plotUnitsEnergy, 6e-8), xycoords='data',
-                    horizontalalignment='left', color='grey', rotation=0, fontsize=legendfontsize)
+            ax.annotate('ARA PA',
+                    xy=(2.9E17 * units.eV / plotUnitsEnergy, 5.2e-6), xycoords='data',
+                    horizontalalignment='left', color='blue', rotation=0, fontsize=legendfontsize)
 
     if show_arianna:
         ax.plot(ARIANNA_HRA[:, 0] / plotUnitsEnergy, ARIANNA_HRA[:, 1] / plotUnitsFlux, color='red')
diff --git a/NuRadioMC/examples/Sensitivities/data/limit_ARA_PA_6months.txt b/NuRadioMC/examples/Sensitivities/data/limit_ARA_PA_6months.txt
new file mode 100644
index 0000000000..4ef5a6b5f5
--- /dev/null
+++ b/NuRadioMC/examples/Sensitivities/data/limit_ARA_PA_6months.txt
@@ -0,0 +1,20 @@
+# Source: Phased Array Six-Month Real Limit
+# Data type: Upper limit
+# Energy bins per decade: 1
+# Column values:
+# Energy [eV]
+# Flux [eV cm^-2 s^-1 sr^-1]
+# Flux band minimum
+# Flux band maximum
+
+1.00E+16  89771.61706761  0 0
+3.16E+16  27085.56086071  0 0
+1.00E+17  8786.44941194   0 0
+3.16E+17  4822.58991603   0 0
+1.00E+18  3210.43840531   0 0
+3.16E+18  3120.34963572     0 0
+1.00E+19  3255.37433081 0 0
+3.16E+19  4246.17439525 0 0
+1.00E+20  6213.49250206 0 0
+3.16E+20  8835.75610193 0 0
+1.00E+21  17992.49247862  0 0
\ No newline at end of file
diff --git a/NuRadioMC/examples/Sensitivities/data/limit_ara_2023_projected_analysis.txt b/NuRadioMC/examples/Sensitivities/data/limit_ara_2023_projected_analysis.txt
deleted file mode 100644
index dd6c2fce26..0000000000
--- a/NuRadioMC/examples/Sensitivities/data/limit_ara_2023_projected_analysis.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-# Source: Projected ARA Limit by 2023 (Trigger Level  SES)
-# Data type: Single Event Sensitivity
-# Energy bins per decade: 1
-# Column values:
-# Energy [GeV]
-# Flux [GeV cm^-2 s^-1 sr^-1]
-# Flux band minimum
-# Flux band maximum
-
-3.162278e+07 1.242111e-07 0 0
-1.000000e+08 4.620724e-08 0 0
-3.162278e+08 2.420521e-08 0 0
-1.000000e+09 1.695089e-08 0 0
-3.162278e+09 1.541511e-08 0 0
-1.000000e+10 1.639581e-08 0 0
-3.162278e+10 1.839849e-08 0 0
-1.000000e+11 2.427857e-08 0 0
diff --git a/NuRadioMC/examples/Sensitivities/data/limit_ara_2023_projected_trigger.txt b/NuRadioMC/examples/Sensitivities/data/limit_ara_2023_projected_trigger.txt
deleted file mode 100644
index 6b77fb35c4..0000000000
--- a/NuRadioMC/examples/Sensitivities/data/limit_ara_2023_projected_trigger.txt
+++ /dev/null
@@ -1,17 +0,0 @@
-# Source: Projected ARA Limit by 2023 (Trigger Level  SES)
-# Data type: Single Event Sensitivity
-# Energy bins per decade: 1
-# Column values:
-# Energy [GeV]
-# Flux [GeV cm^-2 s^-1 sr^-1]
-# Flux band minimum
-# Flux band maximum
-
-3.162278e+07 3.570426e-08 0 0
-1.000000e+08 1.508577e-08 0 0
-3.162278e+08 9.464551e-09 0 0
-1.000000e+09 7.722422e-09 0 0
-3.162278e+09 8.032262e-09 0 0
-1.000000e+10 9.685805e-09 0 0
-3.162278e+10 1.122384e-08 0 0
-1.000000e+11 1.517349e-08 0 0
diff --git a/NuRadioMC/simulation/output_writer_hdf5.py b/NuRadioMC/simulation/output_writer_hdf5.py
index 6ecb7159b0..d7aab8491b 100644
--- a/NuRadioMC/simulation/output_writer_hdf5.py
+++ b/NuRadioMC/simulation/output_writer_hdf5.py
@@ -108,17 +108,20 @@ def add_event_group(self, event_buffer):
 
                 # save event attributes
                 for enum_entry in genattrs:
-                    if evt.has_generator_info(enum_entry):
+                    if evt.has_parameter(enum_entry):
                         if enum_entry.name not in self._mout_attributes:
-                            self._mout_attributes[enum_entry.name] = evt.get_generator_info(enum_entry)
+                            self._mout_attributes[enum_entry.name] = evt.get_parameter(enum_entry)
                         else:  # if the attribute is already present, we need to check if it is the same for all events
-                            assert all(np.atleast_1d(self._mout_attributes[enum_entry.name] == evt.get_generator_info(enum_entry)))
+                            assert all(np.atleast_1d(self._mout_attributes[enum_entry.name] == evt.get_parameter(enum_entry)))
 
                 # save station attributes
                 for stn in evt.get_stations():
-                    station_key_pairs = [[chp.Vrms_NuRadioMC_simulation, "Vrms"], [chp.bandwidth_NuRadioMC_simulation, "bandwidth"]]
+                    station_key_pairs = [[chp.Vrms_NuRadioMC_simulation, "Vrms"], [chp.bandwidth_NuRadioMC_simulation, "bandwidth"],
+                        [chp.Vrms_trigger_NuRadioMC_simulation, "Vrms_trigger"]]
+
+
                     for (key_cp, key_hdf5) in station_key_pairs:
-                        channel_values = [channel[key_cp] for channel in stn.iter_channels(sorted=True)]
+                        channel_values = [channel[key_cp] for channel in stn.iter_channels(sorted=True) if channel.has_parameter(key_cp)]
 
                         if key_hdf5 not in self._mout_groups_attributes[sid]:
                             self._mout_groups_attributes[sid][key_hdf5] = np.array(channel_values)
diff --git a/NuRadioMC/simulation/simulation.py b/NuRadioMC/simulation/simulation.py
index 216b4de969..a69bd4ccfe 100644
--- a/NuRadioMC/simulation/simulation.py
+++ b/NuRadioMC/simulation/simulation.py
@@ -6,50 +6,51 @@
 import copy
 import yaml
 import numpy as np
-from radiotools import helper as hp
-from radiotools import coordinatesystems as cstrans
-from numpy.random import Generator, Philox
 import h5py
 from scipy import constants
-# import detector simulation modules
-from NuRadioMC.SignalGen import askaryan
-from NuRadioMC.SignalGen import emitter as emitter_signalgen
+from numpy.random import Generator, Philox
+
+from radiotools import helper as hp
+from radiotools import coordinatesystems as cstrans
+
 import NuRadioMC.utilities.medium
+from NuRadioMC.SignalGen import askaryan, emitter as emitter_signalgen
 from NuRadioMC.utilities.earth_attenuation import get_weight
 from NuRadioMC.SignalProp import propagation
 from NuRadioMC.simulation.output_writer_hdf5 import outputWriterHDF5
 from NuRadioReco.utilities import units
-import NuRadioReco.modules.io.eventWriter
 from NuRadioReco.utilities.logging import LOGGING_STATUS
-import NuRadioReco.modules.channelSignalReconstructor
+
+import NuRadioReco.modules.io.eventWriter
+import NuRadioReco.modules.channelAddCableDelay
 import NuRadioReco.modules.electricFieldResampler
-import NuRadioReco.modules.channelGenericNoiseAdder
 import NuRadioReco.modules.efieldToVoltageConverterPerEfield
 import NuRadioReco.modules.efieldToVoltageConverter
-import NuRadioReco.modules.channelAddCableDelay
+import NuRadioReco.modules.channelSignalReconstructor
 import NuRadioReco.modules.channelResampler
+import NuRadioReco.modules.channelGenericNoiseAdder
 import NuRadioReco.modules.triggerTimeAdjuster
-from NuRadioReco.detector import detector
+
+from NuRadioReco.detector import detector, antennapattern
 import NuRadioReco.framework.sim_station
 import NuRadioReco.framework.electric_field
 import NuRadioReco.framework.particle
 import NuRadioReco.framework.event
 import NuRadioReco.framework.sim_emitter
-from NuRadioReco.detector import antennapattern
-from NuRadioReco.framework.parameters import electricFieldParameters as efp
-from NuRadioReco.framework.parameters import showerParameters as shp
-from NuRadioReco.framework.parameters import channelParameters as chp
-# parameters describing simulated Monte Carlo particles
-from NuRadioReco.framework.parameters import particleParameters as simp
-from NuRadioReco.framework.parameters import emitterParameters as ep
-# parameters set in the event generator
-from NuRadioReco.framework.parameters import generatorAttributes as genattrs
+
+from NuRadioReco.framework.parameters import (
+    electricFieldParameters as efp, showerParameters as shp,
+    channelParameters as chp, particleParameters as simp, emitterParameters as ep,
+    generatorAttributes as genattrs)
+
 import NuRadioMC.simulation.time_logger
 
 logger = logging.getLogger("NuRadioMC.simulation")
 
+
 # initialize a few NuRadioReco modules
 # TODO: Is this the best way to do it? Better to initialize them on demand
+
 channelAddCableDelay = NuRadioReco.modules.channelAddCableDelay.channelAddCableDelay()
 electricFieldResampler = NuRadioReco.modules.electricFieldResampler.electricFieldResampler()
 efieldToVoltageConverterPerEfield = NuRadioReco.modules.efieldToVoltageConverterPerEfield.efieldToVoltageConverterPerEfield()
@@ -57,10 +58,10 @@
 channelSignalReconstructor = NuRadioReco.modules.channelSignalReconstructor.channelSignalReconstructor()
 channelResampler = NuRadioReco.modules.channelResampler.channelResampler()
 channelGenericNoiseAdder = NuRadioReco.modules.channelGenericNoiseAdder.channelGenericNoiseAdder()
-channelResampler = NuRadioReco.modules.channelResampler.channelResampler()
 eventWriter = NuRadioReco.modules.io.eventWriter.eventWriter()
 triggerTimeAdjuster = NuRadioReco.modules.triggerTimeAdjuster.triggerTimeAdjuster()
 
+
 def merge_config(user, default):
     """
     Merge the user configuration dictionary with the default configuration dictionary recursively.
@@ -87,11 +88,12 @@ def merge_config(user, default):
     return user
 
 
-def calculate_sim_efield(showers, station_id, channel_id,
-                         det, propagator, medium, config,
-                         time_logger=None,
-                         min_efield_amplitude=None,
-                         distance_cut=None):
+def calculate_sim_efield(
+        showers, station_id, channel_id,
+        det, propagator, medium, config,
+        time_logger=None,
+        min_efield_amplitude=None,
+        distance_cut=None):
     """
     Calculate the simulated electric field for a given shower and channel.
 
@@ -155,11 +157,11 @@ def calculate_sim_efield(showers, station_id, channel_id,
             mask_shower_sum = np.abs(vertex_distances - vertex_distances[iSh]) < config['speedup']['distance_cut_sum_length']
             shower_energy_sum = np.sum(shower_energies[mask_shower_sum])
             if np.linalg.norm(x1 - x2) > distance_cut(shower_energy_sum):
-                # logger.warning(f"shower {shower.get_id()} is too far away ({np.linalg.norm(x1 - x2)/units.km:.2f} > {distance_cut(shower_energy_sum)/units.km:.2f}) from station {station_id} channel {channel_id}, skipping shower")
                 time_logger.stop_time('distance cut')
                 continue
-            # logger.warning(f"shower {shower.get_id()} is close enough ({np.linalg.norm(x1 - x2)/units.km:.2f} < {distance_cut(shower_energy_sum)/units.km:.2f}) to station {station_id} channel {channel_id}, continuing with shower")
+
             time_logger.stop_time('distance cut')
+
         time_logger.start_time('ray tracing')
         logger.debug(f"Calculating electric field for shower {shower.get_id()} and station {station_id}, channel {channel_id}")
         shower_direction = -1 * shower.get_axis() # We need the propagation direction here, so we multiply the shower axis with '-1'
@@ -189,6 +191,7 @@ def calculate_sim_efield(showers, station_id, channel_id,
             logger.debug(f'delta_C too large, event unlikely to be observed, (min(Delta_C) = {min(np.abs(delta_Cs))/units.deg:.1f}deg), skipping event')
             continue
         time_logger.stop_time('ray tracing')
+
         for iS in range(n): # loop through all ray tracing solution
             time_logger.start_time('ray tracing (time)')
             # skip individual channels where the viewing angle difference is too large
@@ -201,7 +204,8 @@ def calculate_sim_efield(showers, station_id, channel_id,
             wave_propagation_time = propagator.get_travel_time(iS)  # calculate travel time
             time_logger.start_time('ray tracing (time)')
             if wave_propagation_distance is None or wave_propagation_time is None:
-                logger.warning(f'travel distance or travel time could not be calculated, skipping ray tracing solution. Shower ID: {shower.get_id()} Station ID: {station_id} Channel ID: {channel_id}')
+                logger.warning('travel distance or travel time could not be calculated, skipping ray tracing solution. '
+                               f'Shower ID: {shower.get_id()} Station ID: {station_id} Channel ID: {channel_id}')
                 continue
             kwargs = {}
             # if the input file specifies a specific shower realization, or
@@ -261,25 +265,29 @@ def calculate_sim_efield(showers, station_id, channel_id,
             electric_field[efp.nu_vertex_distance] = wave_propagation_distance
             electric_field[efp.nu_vertex_propagation_time] = wave_propagation_time
             electric_field[efp.nu_viewing_angle] = viewing_angles[iS]
-            electric_field[efp.polarization_angle] = np.arctan2(*polarization_direction_onsky[1:][::-1]) #: electric field polarization in onsky-coordinates. 0 corresponds to polarization in e_theta, 90deg is polarization in e_phi
+            #: electric field polarization in onsky-coordinates. 0 corresponds to polarization in e_theta, 90deg is polarization in e_phi
+            electric_field[efp.polarization_angle] = np.arctan2(*polarization_direction_onsky[1:][::-1])
             electric_field[efp.raytracing_solution] = propagator.get_raytracing_output(iS)
             electric_field[efp.launch_vector] = propagator.get_launch_vector(iS)
 
             if min_efield_amplitude is not None:
                 if np.max(np.abs(electric_field.get_trace())) > min_efield_amplitude:
                     sim_station.set_candidate(True)
-                # if np.max(np.abs(electric_field.get_trace())) < min_efield_amplitude:
-                #     logger.debug(f"Amplitude to low: electric field NOT added to SimStation for shower {shower.get_id()} and station {station_id}, channel {channel_id} with ray tracing solution {iS} and viewing angle {viewing_angles[iS]/units.deg:.1f}deg")
-                #     continue
+
             sim_station.add_electric_field(electric_field)
-            logger.debug(f"Added electric field to SimStation for shower {shower.get_id()} and station {station_id}, channel {channel_id} with ray tracing solution {iS} and viewing angle {viewing_angles[iS]/units.deg:.1f}deg")
+            logger.debug(
+                f"Added electric field to SimStation for shower {shower.get_id()} and station {station_id}, "
+                f"channel {channel_id} with ray tracing solution {iS} and viewing angle {viewing_angles[iS] / units.deg:.1f}deg")
+
     return sim_station
 
-def calculate_sim_efield_for_emitter(emitters, station_id, channel_id,
-                         det, propagator, medium, config,
-                         rnd, antenna_pattern_provider,
-                         time_logger=None,
-                         min_efield_amplitude=None):
+
+def calculate_sim_efield_for_emitter(
+        emitters, station_id, channel_id,
+        det, propagator, medium, config,
+        rnd, antenna_pattern_provider,
+        time_logger=None,
+        min_efield_amplitude=None):
     """
     Calculate the simulated electric field for a given shower and channel.
 
@@ -341,7 +349,8 @@ def calculate_sim_efield_for_emitter(emitters, station_id, channel_id,
         propagator.find_solutions()
         time_logger.stop_time('ray tracing')
         if not propagator.has_solution():
-            logger.debug(f"emitter {emitter.get_id()} and station {station_id}, channel {channel_id} from {x1} to {x2} does not have any ray tracing solution")
+            logger.debug(f"emitter {emitter.get_id()} and station {station_id}, "
+                        f"channel {channel_id} from {x1} to {x2} does not have any ray tracing solution")
             continue
 
         n = propagator.get_number_of_solutions()
@@ -352,8 +361,10 @@ def calculate_sim_efield_for_emitter(emitters, station_id, channel_id,
             wave_propagation_time = propagator.get_travel_time(iS)  # calculate travel time
             time_logger.stop_time('ray tracing (time)')
             if wave_propagation_distance is None or wave_propagation_time is None:
-                logger.warning(f'travel distance or travel time could not be calculated, skipping ray tracing solution. Emitter ID: {emitter.get_id()} Station ID: {station_id} Channel ID: {channel_id}')
+                logger.warning('travel distance or travel time could not be calculated, skipping ray tracing solution. '
+                            f'Emitter ID: {emitter.get_id()} Station ID: {station_id} Channel ID: {channel_id}')
                 continue
+
             # if the input file specifies a specific shower realization, or
             # if the shower was already simulated (e.g. for a different channel or ray tracing solution)
             # use that realization
@@ -398,6 +409,7 @@ def calculate_sim_efield_for_emitter(emitters, station_id, channel_id,
                 eTheta = VEL['theta'] * (-1j) * voltage_spectrum_emitter * frequencies * n_index / c
                 ePhi = VEL['phi'] * (-1j) * voltage_spectrum_emitter * frequencies * n_index / c
                 eR = np.zeros_like(eTheta)
+
             # rescale amplitudes by 1/R, for emitters this is not part of the "SignalGen" class
             eTheta *= 1 / wave_propagation_distance
             ePhi *= 1 / wave_propagation_distance
@@ -439,14 +451,17 @@ def calculate_sim_efield_for_emitter(emitters, station_id, channel_id,
                     sim_station.set_candidate(True)
 
             sim_station.add_electric_field(electric_field)
+
     return sim_station
 
-def apply_det_response_sim(sim_station, det, config,
-                        detector_simulation_filter_amp=None,
-                        evt=None,
-                        event_time=None,
-                        detector_simulation_part1=None,
-                        time_logger=None):
+
+def apply_det_response_sim(
+        sim_station, det, config,
+        detector_simulation_filter_amp=None,
+        evt=None,
+        event_time=None,
+        detector_simulation_part1=None,
+        time_logger=None):
     """
     Apply the detector response to the simulated electric field, i.e., calculate the voltage traces as
     seen by the readout system, per shower, raytracing solution and channel.
@@ -454,7 +469,6 @@ def apply_det_response_sim(sim_station, det, config,
     analog signal chain. The result is a list of SimChannel objects which are added to the
     SimStation object.
 
-
     Parameters
     ----------
     sim_station : sim_station object that contains the electric fields at the observer positions
@@ -479,9 +493,12 @@ def apply_det_response_sim(sim_station, det, config,
 
     Returns nothing. The SimChannels are added to the SimStation object.
     """
-    if time_logger is not None: time_logger.start_time('detector response (sim)')
+    if time_logger is not None:
+        time_logger.start_time('detector response (sim)')
+
     if evt is None:
         evt = NuRadioReco.framework.event.Event(0, 0)
+
     if event_time is not None:
         sim_station.set_station_time(event_time)
 
@@ -493,23 +510,29 @@ def apply_det_response_sim(sim_station, det, config,
     if detector_simulation_part1 is not None:
         detector_simulation_part1(sim_station, det)
     else:
-        efieldToVoltageConverterPerEfield.run(evt, sim_station, det)  # convolve efield with antenna pattern
-        detector_simulation_filter_amp(evt, sim_station, det)
+        # convolve efield with antenna pattern and add cable delay (is not added with efieldToVoltageConverterPEREFIELD)
+        efieldToVoltageConverterPerEfield.run(evt, sim_station, det)
         channelAddCableDelay.run(evt, sim_station, det)
 
+        detector_simulation_filter_amp(evt, sim_station, det)
+
     if config['speedup']['amp_per_ray_solution']:
         channelSignalReconstructor.run(evt, sim_station, det)
-    if time_logger is not None: time_logger.stop_time('detector response (sim)')
-
-def apply_det_response(evt, det, config,
-                        detector_simulation_filter_amp=None,
-                        add_noise=None,
-                        Vrms_per_channel=None,
-                        integrated_channel_response=None,
-                        noiseless_channels=None,
-                        detector_simulation_part2=None,
-                        time_logger=None,
-                        channel_ids=None):
+
+    if time_logger is not None:
+        time_logger.stop_time('detector response (sim)')
+
+
+def apply_det_response(
+        evt, det, config,
+        detector_simulation_filter_amp=None,
+        add_noise=None,
+        Vrms_per_channel=None,
+        integrated_channel_response=None,
+        noiseless_channels=None,
+        detector_simulation_part2=None,
+        time_logger=None,
+        channel_ids=None):
     """
     Apply the detector response to the simulated electric field, i.e., the voltage traces
     seen by the readout system. This function combines all electric fields (from different showers and
@@ -517,7 +540,6 @@ def apply_det_response(evt, det, config,
     analog signal chain. The result is a list of Channel objects which are added to the
     Station object.
 
-
     Parameters
     ----------
     evt : NuRadioReco.framework.event.Event
@@ -550,7 +572,9 @@ def apply_det_response(evt, det, config,
 
     Returns nothing. The Channels are added to the Station object.
     """
-    if time_logger is not None: time_logger.start_time('detector response')
+    if time_logger is not None:
+        time_logger.start_time('detector response')
+
     if detector_simulation_filter_amp is None and detector_simulation_part2 is None:
         logger.error("No detector response function provided. Please provide either detector_simulation_filter_amp or detector_simulation_part2")
         raise ValueError("No detector response function provided. Please provide either detector_simulation_filter_amp or detector_simulation_part2")
@@ -562,7 +586,11 @@ def apply_det_response(evt, det, config,
     else:
         dt = 1. / (config['sampling_rate'])
         # start detector simulation
-        efieldToVoltageConverter.run(evt, station, det, channel_ids=channel_ids)  # convolve efield with antenna pattern
+
+        # convolve efield with antenna pattern and add cable delay (this is also done in the efieldToVoltageConverter
+        # (unlike the efieldToVoltageConverterPEREFIELD))
+        efieldToVoltageConverter.run(evt, station, det, channel_ids=channel_ids)
+
         # downsample trace to internal simulation sampling rate (the efieldToVoltageConverter upsamples the trace to
         # 20 GHz by default to achive a good time resolution when the two signals from the two signal paths are added)
         channelResampler.run(evt, station, det, sampling_rate=1. / dt)
@@ -572,16 +600,20 @@ def apply_det_response(evt, det, config,
             Vrms = {}
             for channel_id in det.get_channel_ids(station.get_id()):
                 norm = integrated_channel_response[station.get_id()][channel_id]
-                Vrms[channel_id] = Vrms_per_channel[station.get_id()][channel_id] / (norm / max_freq) ** 0.5  # normalize noise level to the bandwidth its generated for
-            channelGenericNoiseAdder.run(evt, station, det, amplitude=Vrms, min_freq=0 * units.MHz,
-                                            max_freq=max_freq, type='rayleigh',
-                                            excluded_channels=noiseless_channels[station.get_id()])
+                # normalize noise level to the bandwidth its generated for
+                Vrms[channel_id] = Vrms_per_channel[station.get_id()][channel_id] / (norm / max_freq) ** 0.5
+
+            channelGenericNoiseAdder.run(
+                evt, station, det, amplitude=Vrms, min_freq=0 * units.MHz,
+                max_freq=max_freq, type='rayleigh',
+                excluded_channels=noiseless_channels[station.get_id()])
 
         detector_simulation_filter_amp(evt, station, det)
 
     if time_logger is not None:
         time_logger.stop_time('detector response')
 
+
 def build_dummy_event(station_id, det, config):
     """
     Builds a dummy event for simulation.
@@ -595,8 +627,9 @@ def build_dummy_event(station_id, det, config):
     config : dict
         The NuRadioMC configuration dictionary (from the yaml file)
 
-    Returns:
-        object: The built event object.
+    Returns
+    -------
+    object: The built event object.
     """
 
     evt = NuRadioReco.framework.event.Event(0, 0)
@@ -651,14 +684,16 @@ def build_NuRadioEvents_from_hdf5(fin, fin_attrs, idxs, time_logger=None):
         the output should contain all relevant information from the hdf5 file (except the attributes)
         to perform a NuRadioMC simulation
     """
-    if time_logger is not None: time_logger.start_time('event builder (hdf5 -> nur)')
+    if time_logger is not None:
+        time_logger.start_time('event builder (hdf5 -> nur)')
+
     parent_id = idxs[0]
     event_group_id = fin['event_group_ids'][parent_id]
     event_group = NuRadioReco.framework.event.Event(event_group_id, parent_id)
     # add event generator info event
     for enum_entry in genattrs:
         if enum_entry.name in fin_attrs:
-            event_group.set_generator_info(enum_entry, fin_attrs[enum_entry.name])
+            event_group.set_parameter(enum_entry, fin_attrs[enum_entry.name])
 
     particle_mode = "simulation_mode" not in fin_attrs or fin_attrs['simulation_mode'] != "emitter"
     if particle_mode:  # first case: simulation of a particle interaction which produces showers
@@ -705,10 +740,11 @@ def build_NuRadioEvents_from_hdf5(fin, fin_attrs, idxs, time_logger=None):
             sim_shower[shp.vertex] = np.array([fin['xx'][idx], fin['yy'][idx], fin['zz'][idx]])
             sim_shower[shp.vertex_time] = vertex_time
             sim_shower[shp.type] = fin['shower_type'][idx]
-            if('shower_realization_ARZ' in fin):
+            if 'shower_realization_ARZ' in fin:
                 sim_shower[shp.charge_excess_profile_id] = fin['shower_realization_ARZ'][idx]
-            if('shower_realization_Alvarez2009' in fin):
+            if 'shower_realization_Alvarez2009' in fin:
                 sim_shower[shp.k_L] = fin['shower_realization_Alvarez2009'][idx]
+
             # TODO direct parent does not necessarily need to be the primary in general, but full
             # interaction chain is currently not populated in the input files.
             sim_shower[shp.parent_id] = event_group_id
@@ -726,9 +762,13 @@ def build_NuRadioEvents_from_hdf5(fin, fin_attrs, idxs, time_logger=None):
                     if 'emitter_' + key.name in fin:
                         emitter_obj[key] = fin['emitter_' + key.name][idx]
             event_group.add_sim_emitter(emitter_obj)
-    if time_logger is not None: time_logger.stop_time('event builder (hdf5 -> nur)')
+
+    if time_logger is not None:
+        time_logger.stop_time('event builder (hdf5 -> nur)')
+
     return event_group
 
+
 def get_config(config_file):
     """
     Read the configuration file and return the configuration dictionary.
@@ -751,14 +791,17 @@ def get_config(config_file):
     logger.status('reading default config from %s', config_file_default)
     with open(config_file_default, 'r', encoding="utf-8") as ymlfile:
         cfg = yaml.load(ymlfile, Loader=yaml.FullLoader)
+
     if config_file is not None:
         logger.status('reading local config overrides from %s', config_file)
         with open(config_file, 'r', encoding="utf-8") as ymlfile:
             local_config = yaml.load(ymlfile, Loader=yaml.FullLoader)
             new_cfg = merge_config(local_config, cfg)
             cfg = new_cfg
+
     return cfg
 
+
 def calculate_polarization_vector(shower_axis, launch_vector, config):
     """ calculates the polarization vector in spherical coordinates (eR, eTheta, ePhi)
 
@@ -792,6 +835,7 @@ def calculate_polarization_vector(shower_axis, launch_vector, config):
     logger.error(msg)
     raise ValueError(msg)
 
+
 def increase_signal(station, channel_id, factor):
     """
     increase the signal of a simulated station by a factor of x
@@ -811,6 +855,7 @@ def increase_signal(station, channel_id, factor):
         for sim_channel in sim_channels:
             sim_channel.set_trace(sim_channel.get_trace() * factor, sampling_rate=sim_channel.get_sampling_rate())
 
+
 def calculate_particle_weight(event_group, idx, cfg, fin=None):
     """
     Calculate the (survival) propability of a neutrino reaching the simulation volume.
@@ -853,13 +898,14 @@ def calculate_particle_weight(event_group, idx, cfg, fin=None):
     elif cfg['weights']['weight_mode'] is None:
         primary[simp.weight][simp.weight] = 1.
     else:
-        primary[simp.weight] = get_weight(primary[simp.zenith],
-                                          primary[simp.energy],
-                                          primary[simp.flavor],
-                                          mode=cfg['weights']['weight_mode'],
-                                          cross_section_type=cfg['weights']['cross_section_type'],
-                                          vertex_position=primary[simp.vertex],
-                                          phi_nu=primary[simp.azimuth])
+        primary[simp.weight] = get_weight(
+            primary[simp.zenith],
+            primary[simp.energy],
+            primary[simp.flavor],
+            mode=cfg['weights']['weight_mode'],
+            cross_section_type=cfg['weights']['cross_section_type'],
+            vertex_position=primary[simp.vertex],
+            phi_nu=primary[simp.azimuth])
     # all entries for the event for this primary get the calculated primary's weight
     return primary[simp.weight]
 
@@ -869,8 +915,8 @@ def group_into_events(station, event_group, particle_mode, split_event_time_diff
     """
     Group the signals from a station into multiple events based on signal arrival times.
 
-    Parameters:
-    -----------
+    Parameters
+    ----------
     station : NuRadioReco.framework.station.Station
         The station object containing the signals.
     event_group : NuRadioMC.framework.event.Event
@@ -882,8 +928,8 @@ def group_into_events(station, event_group, particle_mode, split_event_time_diff
     zerosignal : bool, optional
         Flag indicating whether to zero out the signals. Default is False.
 
-    Returns:
-    --------
+    Returns
+    -------
     events : list of NuRadioReco.framework.event.Event
         The list of events created from the grouped signals.
     """
@@ -896,14 +942,17 @@ def group_into_events(station, event_group, particle_mode, split_event_time_diff
     for channel in station.get_sim_station().iter_channels():
         channel_identifiers.append(channel.get_unique_identifier())
         start_times.append(channel.get_trace_start_time())
+
     start_times = np.array(start_times)
     start_times_sort = np.argsort(start_times)
-    delta_start_times = start_times[start_times_sort][1:] - start_times[start_times_sort][:-1]  # this array is sorted in time
+    delta_start_times = np.diff(start_times[start_times_sort])  # this array is sorted in time
     split_event_time_diff = float(split_event_time_diff)
+
     iSplit = np.atleast_1d(np.squeeze(np.argwhere(delta_start_times > split_event_time_diff)))
     n_sub_events = len(iSplit) + 1
     if n_sub_events > 1:
-        logger.info(f"splitting event group id {event_group_id} into {n_sub_events} sub events because time separation larger than {split_event_time_diff/units.ns}ns")
+        logger.info(f"splitting event group id {event_group_id} into {n_sub_events} "
+                    f"sub events because time separation larger than {split_event_time_diff/units.ns}ns")
 
     tmp_station = copy.deepcopy(station)
     events = []
@@ -921,14 +970,19 @@ def group_into_events(station, event_group, particle_mode, split_event_time_diff
             for start_time in start_times[indices]:
                 tmp += f"{start_time/units.ns:.0f}, "
             tmp = tmp[:-2] + " ns"
-            logger.info(f"creating event {iEvent} of event group {event_group_id} ranging rom {iStart} to {iStop} with indices {indices} corresponding to signal times of {tmp}")
+            logger.info(f"creating event {iEvent} of event group {event_group_id} ranging "
+                        f"from {iStart} to {iStop} with indices {indices} corresponding to signal times of {tmp}")
+
         evt = NuRadioReco.framework.event.Event(event_group_id, iEvent)  # create new event
         if particle_mode:
             # add MC particles that belong to this (sub) event to event structure
             # add only primary for now, since full interaction chain is not typically in the input hdf5s
             evt.add_particle(event_group.get_primary())  # add primary particle to event
+
         # copy over generator information from temporary event to event
-        evt._generator_info = event_group._generator_info
+        for enum_entry in genattrs:
+            if event_group.has_parameter(enum_entry):
+                evt.set_parameter(enum_entry, event_group.get_parameter(enum_entry))
 
         station = NuRadioReco.framework.station.Station(tmp_station.get_id())
         sim_station = NuRadioReco.framework.sim_station.SimStation(tmp_station.get_id())
@@ -954,18 +1008,23 @@ def group_into_events(station, event_group, particle_mode, split_event_time_diff
         else:
             for shower_id in shower_ids_of_sub_event:
                 evt.add_sim_emitter(event_group.get_sim_emitter(shower_id))
+
         station.set_sim_station(sim_station)
         station.set_station_time(event_group.get_event_time())
         evt.set_station(station)
         if bool(zerosignal):
             increase_signal(station, None, 0)
+
         events.append(evt)
+
     logger.info(f"created {len(events)} events from event group {event_group_id}")
+
     if time_logger is not None:
         time_logger.stop_time('group into events')
 
     return events
 
+
 def read_input_hdf5(filename):
     """
     Reads input file into memory.
@@ -1006,6 +1065,7 @@ def read_input_hdf5(filename):
     fin_hdf5.close()
     return fin, fin_stations, fin_attrs
 
+
 def remove_all_traces(evt):
     """
     Remove all traces from the event.
@@ -1032,25 +1092,26 @@ def remove_all_traces(evt):
 
 class simulation:
 
-    def __init__(self, inputfilename,
-                 outputfilename,
-                 detectorfile=None,
-                 det=None,
-                 det_kwargs={},
-                 outputfilenameNuRadioReco=None,
-                 debug=False,
-                 evt_time=datetime.datetime(2018, 1, 1),
-                 config_file=None,
-                 log_level=LOGGING_STATUS,
-                 default_detector_station=None,
-                 default_detector_channel=None,
-                 file_overwrite=False,
-                 write_detector=True,
-                 event_list=None,
-                 log_level_propagation=logging.WARNING,
-                 ice_model=None,
-                 trigger_channels = None,
-                 **kwargs):
+    def __init__(
+            self, inputfilename,
+            outputfilename,
+            detectorfile=None,
+            det=None,
+            det_kwargs={},
+            outputfilenameNuRadioReco=None,
+            debug=False,
+            evt_time=datetime.datetime(2018, 1, 1),
+            config_file=None,
+            log_level=LOGGING_STATUS,
+            default_detector_station=None,
+            default_detector_channel=None,
+            file_overwrite=False,
+            write_detector=True,
+            event_list=None,
+            log_level_propagation=logging.WARNING,
+            ice_model=None,
+            trigger_channels = None,
+            **kwargs):
         """
         initialize the NuRadioMC end-to-end simulation
 
@@ -1106,9 +1167,9 @@ def __init__(self, inputfilename,
             logger.warning('Parameter write_mode is deprecated. Define the output format in the config file instead.')
 
         self.__trigger_channel_ids = trigger_channels
-        if(self.__trigger_channel_ids is None):
-            logger.warning("No trigger channels specified. All channels will be simulated even if they don't contribute to any trigger. This can be inefficient. \
-                           Processing time can be saved by specifying the trigger channels.")
+        if self.__trigger_channel_ids is None:
+            logger.warning("No trigger channels specified. All channels will be simulated even if they don't contribute to any trigger. "
+                        "This can be inefficient. Processing time can be saved by specifying the trigger channels.")
         self._log_level = log_level
         self._log_level_ray_propagation = log_level_propagation
         self.__time_logger = NuRadioMC.simulation.time_logger.timeLogger(logger)
@@ -1152,15 +1213,13 @@ def __init__(self, inputfilename,
         self.detector_simulation_part2 = getattr(self, "_detector_simulation_part2", None)
         self.detector_simulation_filter_amp = getattr(self, "_detector_simulation_filter_amp", None)
         self.detector_simulation_trigger = getattr(self, "_detector_simulation_trigger", None)
-        if((self.detector_simulation_filter_amp is None) ^ (self.detector_simulation_trigger is None)):
-            logger.error("Please provide both detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2")
-            raise ValueError("Please provide both detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2")
-        if((self.detector_simulation_part1 is None) ^ (self.detector_simulation_part2 is None)):
-            logger.error("Please provide both detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2")
-            raise ValueError("Please provide both detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2")
-        if((self.detector_simulation_part1 is not None) and (self.detector_simulation_filter_amp is not None)):
-            logger.error("Please provide either detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2")
-            raise ValueError("Please provide either detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2")
+
+        err_msg = "Please provide both detector_simulation_filter_amp and detector_simulation_trigger or detector_simulation_part1 and detector_simulation_part2"
+        if (((self.detector_simulation_filter_amp is None) ^ (self.detector_simulation_trigger is None)) and
+            ((self.detector_simulation_part1 is None) ^ (self.detector_simulation_part2 is None)) and
+            ((self.detector_simulation_part1 is not None) and (self.detector_simulation_filter_amp is not None))):
+            logger.error(err_msg)
+            raise ValueError(err_msg)
 
         # Initialize detector
         self._antenna_pattern_provider = antennapattern.AntennaPatternProvider()
@@ -1186,10 +1245,8 @@ def __init__(self, inputfilename,
 
         prop = propagation.get_propagation_module(self._config['propagation']['module'])
         self._propagator = prop(
-            self._ice, self._config['propagation']['attenuation_model'],
+            self._ice,
             log_level=self._log_level_ray_propagation,
-            n_frequencies_integration=int(self._config['propagation']['n_freq']),
-            n_reflections=int(self._config['propagation']['n_reflections']),
             config=self._config,
             detector=self._det
         )
@@ -1273,6 +1330,8 @@ def __init__(self, inputfilename,
 
         self._Vrms_per_channel = collections.defaultdict(dict)
         self._Vrms_efield_per_channel = collections.defaultdict(dict)
+        self._Vrms_per_trigger_channel = collections.defaultdict(dict)  # Only used for trigger channels in a custom trigger simulation (optional)
+
 
         if noise_temp is not None:
             if noise_temp == "detector":
@@ -1532,24 +1591,28 @@ def run(self):
                 # we loop through all non-trigger channels and simulate the electric fields for all showers.
                 # then we apply the detector response to the electric fields and find the event in which they will be visible in the readout window
                 non_trigger_channels = list(set(self._det.get_channel_ids(station_id)) - set(channel_ids))
-                if (len(non_trigger_channels) > 0):
+                if len(non_trigger_channels):
                     logger.status(f"Simulating non-trigger channels for station {station_id}: {non_trigger_channels}")
                     for iCh, channel_id in enumerate(non_trigger_channels):
                         if particle_mode:
-                            sim_station = calculate_sim_efield(showers=event_group.get_sim_showers(),
-                                                            station_id=station_id, channel_id=channel_id,
-                                                            det=self._det, propagator=self._propagator, medium=self._ice,
-                                                            config=self._config,
-                                                            time_logger=self.__time_logger,
-                                                            min_efield_amplitude=float(self._config['speedup']['min_efield_amplitude']) * self._Vrms_efield_per_channel[station_id][channel_id],
-                                                            distance_cut=self._get_distance_cut)
+                            sim_station = calculate_sim_efield(
+                                showers=event_group.get_sim_showers(),
+                                station_id=station_id, channel_id=channel_id,
+                                det=self._det, propagator=self._propagator, medium=self._ice,
+                                config=self._config,
+                                time_logger=self.__time_logger,
+                                min_efield_amplitude=float(self._config['speedup']['min_efield_amplitude'])
+                                    * self._Vrms_efield_per_channel[station_id][channel_id],
+                                distance_cut=self._get_distance_cut)
                         else:
-                            sim_station = calculate_sim_efield_for_emitter(emitters=event_group.get_sim_emitters(),
-                                                station_id=station_id, channel_id=channel_id,
-                                                det=self._det, propagator=self._propagator, medium=self._ice, config=self._config,
-                                                rnd=self._rnd, antenna_pattern_provider=self._antenna_pattern_provider,
-                                                min_efield_amplitude=float(self._config['speedup']['min_efield_amplitude']) * self._Vrms_efield_per_channel[station_id][channel_id],
-                                                time_logger=self.__time_logger)
+                            sim_station = calculate_sim_efield_for_emitter(
+                                emitters=event_group.get_sim_emitters(),
+                                station_id=station_id, channel_id=channel_id,
+                                det=self._det, propagator=self._propagator, medium=self._ice, config=self._config,
+                                rnd=self._rnd, antenna_pattern_provider=self._antenna_pattern_provider,
+                                min_efield_amplitude=float(self._config['speedup']['min_efield_amplitude'])
+                                    * self._Vrms_efield_per_channel[station_id][channel_id],
+                                time_logger=self.__time_logger)
 
                         # skip to next channel if the efield is below the speed cut
                         if not sim_station.get_electric_fields():
@@ -1562,7 +1625,8 @@ def run(self):
                         apply_det_response_sim(sim_station, self._det, self._config, self.detector_simulation_filter_amp,
                                             event_time=self._evt_time, time_logger=self.__time_logger,
                                             detector_simulation_part1=self.detector_simulation_part1)
-                        logger.debug(f"adding sim_station to station {station_id} for event group {event_group.get_run_number()}, channel {channel_id}")
+
+                        logger.debug(f"Adding sim_station to station {station_id} for event group {event_group.get_run_number()}, channel {channel_id}")
                         station.add_sim_station(sim_station)  # this will add the channels and efields to the existing sim_station object
                         for evt in output_buffer[station_id].values():
                             # determine the trigger that was used to determine the readout window
@@ -1605,23 +1669,29 @@ def run(self):
                             for i, (name, instance, kwargs) in enumerate(evt.iter_modules(station_id)):
                                 if hasattr(instance, "get_filter"):
                                     filt *= instance.get_filter(ff, station_id, channel_id, self._det, **kwargs)
-                            noise = channelGenericNoiseAdder.bandlimited_noise_from_spectrum(len(channel.get_trace()), channel.get_sampling_rate(),
-                                                                                            spectrum=filt, amplitude=self._Vrms_per_channel[station.get_id()][channel_id],
-                                                                                            type='rayleigh', time_domain=False)
+
+                            noise = channelGenericNoiseAdder.bandlimited_noise_from_spectrum(
+                                len(channel.get_trace()), channel.get_sampling_rate(),
+                                spectrum=filt, amplitude=self._Vrms_per_channel[station.get_id()][channel_id],
+                                type='rayleigh', time_domain=False)
+
                             # from NuRadioReco.utilities import fft
                             # logger.warning(f"adding noise to channel {channel.get_id()} with Vrms = {Vrms[channel_id]/units.mV:.4f}mV, realized noise Vrms = {np.std(fft.freq2time(noise, 1/dt))/units.mV:.4f}mV")
                             channel.set_frequency_spectrum(channel.get_frequency_spectrum() + noise, channel.get_sampling_rate())
 
                     channelSignalReconstructor.run(evt, station, self._det)
                     # save RMS and bandwidth to channel object
-                    evt.set_generator_info(genattrs.Vrms, self._Vrms)
-                    evt.set_generator_info(genattrs.dt, 1. / self._config['sampling_rate'])
-                    evt.set_generator_info(genattrs.Tnoise, self._noise_temp)
-                    evt.set_generator_info(genattrs.bandwidth, next(iter(next(iter(self._integrated_channel_response.values())).values())))
+                    evt.set_parameter(genattrs.Vrms, self._Vrms)
+                    evt.set_parameter(genattrs.dt, 1. / self._config['sampling_rate'])
+                    evt.set_parameter(genattrs.Tnoise, self._noise_temp)
+                    evt.set_parameter(genattrs.bandwidth, next(iter(next(iter(self._integrated_channel_response.values())).values())))
                     for channel in station.iter_channels():
                         channel[chp.Vrms_NuRadioMC_simulation] = self._Vrms_per_channel[station_id][channel.get_id()]
                         channel[chp.bandwidth_NuRadioMC_simulation] = self._integrated_channel_response[station_id][channel.get_id()]
 
+                        if self.__trigger_channel_ids is not None and channel.get_id() in self.__trigger_channel_ids and channel.get_id() in self._Vrms_per_trigger_channel[station_id]:
+                            channel[chp.Vrms_trigger_NuRadioMC_simulation] = self._Vrms_per_trigger_channel[station_id][channel.get_id()]
+
                     if self._outputfilenameNuRadioReco is not None:
                         # downsample traces to detector sampling rate to save file size
                         sampling_rate_detector = self._det.get_sampling_frequency(station_id, self._det.get_channel_ids(station_id)[0])
diff --git a/NuRadioMC/test/SingleEvents/T02RunSimulation.py b/NuRadioMC/test/SingleEvents/T02RunSimulation.py
index 9f23adde77..8e4d67b018 100755
--- a/NuRadioMC/test/SingleEvents/T02RunSimulation.py
+++ b/NuRadioMC/test/SingleEvents/T02RunSimulation.py
@@ -7,12 +7,12 @@
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.channelResampler
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 from NuRadioReco.utilities.logging import LOGGING_STATUS
 from NuRadioMC.simulation import simulation
 import logging
 
-
 # initialize detector sim modules
 efieldToVoltageConverter = NuRadioReco.modules.efieldToVoltageConverter.efieldToVoltageConverter()
 efieldToVoltageConverter.begin()
@@ -82,4 +82,3 @@ def _detector_simulation_trigger(self, evt, station, det):
                             log_level=LOGGING_STATUS,
                             log_level_propagation=LOGGING_STATUS)
 sim.run()
-
diff --git a/NuRadioMC/test/Veff/1e18eV/D05phased_array_deep.py b/NuRadioMC/test/Veff/1e18eV/D05phased_array_deep.py
index a64c7a5bb7..c798136ae0 100644
--- a/NuRadioMC/test/Veff/1e18eV/D05phased_array_deep.py
+++ b/NuRadioMC/test/Veff/1e18eV/D05phased_array_deep.py
@@ -4,6 +4,7 @@
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.phasedarray.triggerSimulator
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 import numpy as np
 import scipy
@@ -52,7 +53,6 @@
 class mySimulation(simulation.simulation):
 
     def _detector_simulation_filter_amp(self, evt, station, det):
-
         channelBandPassFilter.run(evt, station, det,
                                   passband=passband_low, filter_type=filter_type, order=order_low, rp=0.1)
         channelBandPassFilter.run(evt, station, det,
diff --git a/NuRadioMC/test/Veff/1e18eV/T02RunSimulation.py b/NuRadioMC/test/Veff/1e18eV/T02RunSimulation.py
index 11f5a6d276..d663a125ef 100755
--- a/NuRadioMC/test/Veff/1e18eV/T02RunSimulation.py
+++ b/NuRadioMC/test/Veff/1e18eV/T02RunSimulation.py
@@ -5,6 +5,7 @@
 import NuRadioReco.modules.trigger.highLowThreshold
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 from NuRadioMC.simulation import simulation
 import os
@@ -62,4 +63,3 @@ def _detector_simulation_trigger(self, evt, station, det):
                             config_file=os.path.join(path, args.config),
                             file_overwrite=True)
 sim.run()
-
diff --git a/NuRadioMC/test/atmospheric_Aeff/1e18eV/T02RunSimulation.py b/NuRadioMC/test/atmospheric_Aeff/1e18eV/T02RunSimulation.py
index f64a923414..c78afc03bb 100755
--- a/NuRadioMC/test/atmospheric_Aeff/1e18eV/T02RunSimulation.py
+++ b/NuRadioMC/test/atmospheric_Aeff/1e18eV/T02RunSimulation.py
@@ -4,6 +4,7 @@
 # import detector simulation modules
 import NuRadioReco.modules.trigger.highLowThreshold
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioReco.utilities import units
 from NuRadioMC.simulation import simulation
 import os
@@ -59,4 +60,3 @@ def _detector_simulation_trigger(self, evt, station, det):
                             config_file=os.path.join(path, args.config),
                             file_overwrite=True)
 sim.run()
-
diff --git a/NuRadioMC/utilities/merge_hdf5.py b/NuRadioMC/utilities/merge_hdf5.py
index 0ba07a64df..e79ee67069 100644
--- a/NuRadioMC/utilities/merge_hdf5.py
+++ b/NuRadioMC/utilities/merge_hdf5.py
@@ -218,14 +218,16 @@ def merge2(filenames, output_filename):
 
 if __name__ == "__main__":
     """
-    merges multiple hdf5 output files into one single files.
-    The merger module automatically keeps track of the total number
-    of simulated events (which are needed to correctly calculate the effective volume).
+    Merges multiple hdf5 files into one single file. Keeps automatically track of the total number
+    of simulated events across all input files (which is necessary to correctly calculate the effective volume).
 
-    The script expects that the folder structure is
+    If a single path is passed as argument the script interprets it as root directory and expects the following folder structure:
     ../output/energy/*.hdf5.part????
+    The output file path is automatically determined.
 
-    Optional log level setting to either set DEBUG, INFO, or WARNING to the readout. Example: add --loglevel DEBUG when calling script to set loglevel to DEBUG. 
+    If multiple paths are passed, the first one is taken as output file path. The following paths are taken as input.
+
+    Optional log level setting to either set DEBUG, INFO, or WARNING to the readout. Example: add --loglevel DEBUG when calling script to set loglevel to DEBUG.
     """
     parser = argparse.ArgumentParser(description='Merge hdf5 files')
     parser.add_argument('files', nargs='+', help='input file or files')
@@ -237,23 +239,14 @@ def merge2(filenames, output_filename):
         log_val = eval(f'logging.{args.loglevel}')
         logger.setLevel(log_val)
 
-    if(len(args.files) < 1):
-        print("usage: python merge_hdf5.py /path/to/simulation/output/folder\nor python merge_hdf5.py outputfilename input1 input2 ...")
-    elif(len(args.files) == 1):
-        filenames = glob.glob("{}/*/*.hdf5.part????".format(args.files[0]))
-        filenames = np.append(filenames, glob.glob("{}/*/*.hdf5.part??????".format(args.files[0])))
-        filenames = sorted(filenames)
-        filenames2 = []
-        for i, filename in enumerate(filenames):
-            filename, ext = os.path.splitext(filename)
-            if(ext != '.hdf5'):
-                if(filename not in filenames2):
-                    d = os.path.split(filename)
-                    a, b = os.path.split(d[0])
-                    filenames2.append(filename)
-
+    if len(args.files) == 1:
+        root_directory = args.files[0]
+        if not os.path.isdir(root_directory):
+            sys.exit(f"{root_directory} is not a directory.")
+        
+        filenames = np.unique(glob.glob(f"{root_directory}/*/*.hdf5.part*"))
         input_args = []
-        for filename in sorted(filenames2):
+        for filename in sorted(filenames):
             if(os.path.splitext(filename)[1] == '.hdf5'):
                 d = os.path.split(filename)
                 a, b = os.path.split(d[0])
@@ -261,13 +254,12 @@ def merge2(filenames, output_filename):
                 if(os.path.exists(output_filename)):
                     logger.error('file {} already exists, skipping'.format(output_filename))
                 else:
-                    #                 try:
-                    input_files = np.array(sorted(glob.glob(filename + '.part????')))
-                    input_files = np.append(input_files, np.array(sorted(glob.glob(filename + '.part??????'))))
+                    input_files = np.array(sorted(glob.glob(filename + '.part*')))
                     mask = np.array([os.path.getsize(x) > 1000 for x in input_files], dtype=bool)
                     if(np.sum(~mask)):
                         logger.warning("{:d} files were deselected because their filesize was to small".format(np.sum(~mask)))
                     input_args.append({'filenames': input_files[mask], 'output_filename': output_filename})
+        
         if(args.cores == 1):
             for i in range(len(input_args)):
                 merge2(input_args[i]['filenames'], input_args[i]['output_filename'])
@@ -281,7 +273,7 @@ def tmp(kwargs):
             with Pool(args.cores) as p:
                 p.map(tmp, input_args)
 
-    elif(len(args.files) > 1):
+    else:
         output_filename = args.files[0]
         if(os.path.exists(output_filename)):
             logger.error('file {} already exists, skipping'.format(output_filename))
diff --git a/NuRadioMC/utilities/runner_example.py b/NuRadioMC/utilities/runner_example.py
index 8dbdac1c61..129bbce7f6 100644
--- a/NuRadioMC/utilities/runner_example.py
+++ b/NuRadioMC/utilities/runner_example.py
@@ -2,6 +2,7 @@
 from NuRadioReco.utilities import units
 import NuRadioReco.modules.trigger.simpleThreshold
 import NuRadioReco.modules.channelBandPassFilter
+
 from NuRadioMC.EvtGen import generator
 from NuRadioMC.simulation import simulation
 import os
diff --git a/NuRadioReco/detector/LOFAR/LOFAR.json b/NuRadioReco/detector/LOFAR/LOFAR.json
new file mode 100644
index 0000000000..650d880d80
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/LOFAR.json
@@ -0,0 +1,101622 @@
+{
+    "stations": {
+        "2": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.0,
+            "pos_easting": -0.2333,
+            "pos_northing": -0.4208,
+            "pos_site": "lofar",
+            "station_id": 2,
+            "station_type": "LBA"
+        },
+        "1": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.7974,
+            "pos_easting": -147.2564,
+            "pos_northing": -415.4214,
+            "pos_site": "lofar",
+            "station_id": 1,
+            "station_type": "LBA"
+        },
+        "101": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.4811,
+            "pos_easting": 763.7232,
+            "pos_northing": 811.0889,
+            "pos_site": "lofar",
+            "station_id": 101,
+            "station_type": "LBA"
+        },
+        "103": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.3254,
+            "pos_easting": 1819.4905,
+            "pos_northing": 70.1636,
+            "pos_site": "lofar",
+            "station_id": 103,
+            "station_type": "LBA"
+        },
+        "11": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.0949,
+            "pos_easting": 250.5663,
+            "pos_northing": -150.7838,
+            "pos_site": "lofar",
+            "station_id": 11,
+            "station_type": "LBA"
+        },
+        "13": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.2185,
+            "pos_easting": -201.233,
+            "pos_northing": 321.4535,
+            "pos_site": "lofar",
+            "station_id": 13,
+            "station_type": "LBA"
+        },
+        "17": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.0307,
+            "pos_easting": 490.5134,
+            "pos_northing": 71.8218,
+            "pos_site": "lofar",
+            "station_id": 17,
+            "station_type": "LBA"
+        },
+        "201": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.4384,
+            "pos_easting": 868.9973,
+            "pos_northing": -296.7548,
+            "pos_site": "lofar",
+            "station_id": 201,
+            "station_type": "LBA"
+        },
+        "21": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.2578,
+            "pos_easting": -461.7465,
+            "pos_northing": 284.7255,
+            "pos_site": "lofar",
+            "station_id": 21,
+            "station_type": "LBA"
+        },
+        "24": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 1.2697,
+            "pos_easting": 313.0336,
+            "pos_northing": -783.9285,
+            "pos_site": "lofar",
+            "station_id": 24,
+            "station_type": "LBA"
+        },
+        "26": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.0017,
+            "pos_easting": 862.6187,
+            "pos_northing": 105.0734,
+            "pos_site": "lofar",
+            "station_id": 26,
+            "station_type": "LBA"
+        },
+        "28": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.0939,
+            "pos_easting": 353.7128,
+            "pos_northing": 1178.9788,
+            "pos_site": "lofar",
+            "station_id": 28,
+            "station_type": "LBA"
+        },
+        "3": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.0059,
+            "pos_easting": -84.969,
+            "pos_northing": 89.5594,
+            "pos_site": "lofar",
+            "station_id": 3,
+            "station_type": "LBA"
+        },
+        "30": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.2201,
+            "pos_easting": -564.7054,
+            "pos_northing": 796.2982,
+            "pos_site": "lofar",
+            "station_id": 30,
+            "station_type": "LBA"
+        },
+        "301": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 1.2909,
+            "pos_easting": -130.5855,
+            "pos_northing": -1035.8994,
+            "pos_site": "lofar",
+            "station_id": 301,
+            "station_type": "LBA"
+        },
+        "302": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 10.57,
+            "pos_easting": -1386.1001,
+            "pos_northing": -1510.7296,
+            "pos_site": "lofar",
+            "station_id": 302,
+            "station_type": "LBA"
+        },
+        "31": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.1701,
+            "pos_easting": -726.7476,
+            "pos_northing": 283.1579,
+            "pos_site": "lofar",
+            "station_id": 31,
+            "station_type": "LBA"
+        },
+        "32": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 0.8873,
+            "pos_easting": -667.7756,
+            "pos_northing": -296.2379,
+            "pos_site": "lofar",
+            "station_id": 32,
+            "station_type": "LBA"
+        },
+        "4": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.0119,
+            "pos_easting": -92.3159,
+            "pos_northing": -83.1656,
+            "pos_site": "lofar",
+            "station_id": 4,
+            "station_type": "LBA"
+        },
+        "401": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": 1.705,
+            "pos_easting": -938.4986,
+            "pos_northing": -96.3785,
+            "pos_site": "lofar",
+            "station_id": 401,
+            "station_type": "LBA"
+        },
+        "5": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.0109,
+            "pos_easting": 34.2279,
+            "pos_northing": -120.7053,
+            "pos_site": "lofar",
+            "station_id": 5,
+            "station_type": "LBA"
+        },
+        "501": {
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "pos_altitude": -0.2733,
+            "pos_easting": -263.7096,
+            "pos_northing": 1241.2157,
+            "pos_site": "lofar",
+            "station_id": 501,
+            "station_type": "LBA"
+        },
+        "6": {
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+            "ant_position_x": -38.9667,
+            "ant_position_y": 12.0053,
+            "ant_position_z": -0.0002,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_outer",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181010086,
+            "channel_id": 181010086,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181910086": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_orientation_theta": 90.0,
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+            "ant_position_z": -0.0014,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_inner",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181910086,
+            "channel_id": 181910086,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181010087": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
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+            "ant_position_y": 12.0053,
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+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_outer",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181010086,
+            "channel_id": 181010087,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181910087": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_inner",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181910086,
+            "channel_id": 181910087,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011088": {
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+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_position_y": 40.6081,
+            "ant_position_z": 0.0044,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_outer",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011088,
+            "channel_id": 181011088,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911088": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 1.1466,
+            "ant_position_y": -14.2123,
+            "ant_position_z": -0.0012,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_inner",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911088,
+            "channel_id": 181911088,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011089": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": -4.1387,
+            "ant_position_y": 40.6081,
+            "ant_position_z": 0.0044,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_outer",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011088,
+            "channel_id": 181011089,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911089": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 1.1466,
+            "ant_position_y": -14.2123,
+            "ant_position_z": -0.0012,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_inner",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911088,
+            "channel_id": 181911089,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011090": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_position_x": 16.4788,
+            "ant_position_y": 31.9143,
+            "ant_position_z": 0.0036,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_outer",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011090,
+            "channel_id": 181011090,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911090": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_position_y": -14.4025,
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+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_inner",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911090,
+            "channel_id": 181911090,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011091": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
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+            "ant_position_y": 31.9143,
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+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_outer",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011090,
+            "channel_id": 181011091,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911091": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 135.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": -4.3338,
+            "ant_position_y": -14.4025,
+            "ant_position_z": -0.0016,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_inner",
+            "cab_length": 50.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911090,
+            "channel_id": 181911091,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011092": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 135.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 31.6113,
+            "ant_position_y": 25.6669,
+            "ant_position_z": 0.0037,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_outer",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011092,
+            "channel_id": 181011092,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911092": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 31.9201,
+            "ant_position_y": 55.9301,
+            "ant_position_z": 0.0067,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_inner",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911092,
+            "channel_id": 181911092,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011093": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 31.6113,
+            "ant_position_y": 25.6669,
+            "ant_position_z": 0.0037,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_outer",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011092,
+            "channel_id": 181011093,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911093": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 135.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 31.9201,
+            "ant_position_y": 55.9301,
+            "ant_position_z": 0.0067,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_inner",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911092,
+            "channel_id": 181911093,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011094": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 135.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 34.2445,
+            "ant_position_y": -17.4165,
+            "ant_position_z": -0.001,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_outer",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011094,
+            "channel_id": 181011094,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911094": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 55.7311,
+            "ant_position_y": -32.934,
+            "ant_position_z": -0.001,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_inner",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911094,
+            "channel_id": 181911094,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181011095": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 225.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 34.2445,
+            "ant_position_y": -17.4165,
+            "ant_position_z": -0.001,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_X",
+            "ant_mode": "LBA_outer",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181011094,
+            "channel_id": 181011095,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        },
+        "181911095": {
+            "adc_n_samples": 256,
+            "adc_sampling_frequency": 0.2,
+            "amp_type": "100",
+            "ant_deployment_time": "{TinyDate}:2010-06-12T00:00:00",
+            "ant_orientation_phi": 135.0,
+            "ant_orientation_theta": 90.0,
+            "ant_position_x": 55.7311,
+            "ant_position_y": -32.934,
+            "ant_position_z": -0.001,
+            "ant_rotation_phi": 0.0,
+            "ant_rotation_theta": 0.0,
+            "ant_type": "LOFAR_LBA_Y",
+            "ant_mode": "LBA_inner",
+            "cab_length": 80.0,
+            "cab_type": "RG58",
+            "channel_group_id": 181911094,
+            "channel_id": 181911095,
+            "commission_time": "{TinyDate}:2010-06-12T00:00:00",
+            "decommission_time": "{TinyDate}:2038-01-01T00:00:00",
+            "station_id": 501
+        }
+    }
+}
\ No newline at end of file
diff --git a/NuRadioReco/detector/LOFAR/README.md b/NuRadioReco/detector/LOFAR/README.md
new file mode 100644
index 0000000000..b2fdcdd548
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/README.md
@@ -0,0 +1,3 @@
+The signalchain folder contains cable attenuation and RCU gains. The RCU gain contains the gain of the RCU in dB, one value for every frequency in the 30-80MHz band.
+The cable attenuation files (one for each cable length) contain the frequency in MHz and the attenuation in dB (careful with the sign).
+
diff --git a/NuRadioReco/detector/LOFAR/analog_components.py b/NuRadioReco/detector/LOFAR/analog_components.py
new file mode 100644
index 0000000000..47dafd6feb
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/analog_components.py
@@ -0,0 +1,132 @@
+import numpy as np
+import os
+import logging
+from NuRadioReco.utilities import units
+from scipy.interpolate import interp1d
+from radiotools import helper as hp
+
+logger = logging.getLogger("NuRadioReco.LOFAR.analog_components")
+
+
+def load_cable_response(cable_length):
+    """
+    Parameters
+    ----------
+    cable_length: int or float
+        length of the coax cable of the corresponding channel
+
+    Returns
+    -------
+    default: dict
+        A dictionary containing the cable attenuation values for the specified cable length.
+        The dictionary has the following keys:
+
+        - 'frequencies': An array of frequency values ranging from 30 MHz to 80 MHz.
+        - 'attenuation': An array of cable attenuation values corresponding to the frequencies.
+    """
+    module_dir = os.path.dirname(__file__)
+    file_path = os.path.join(module_dir, "signalchain", f"attenuation_RG58_{int(cable_length)}m.txt")
+
+    data = np.loadtxt(file_path)
+
+    default = {
+        'frequencies': np.arange(30, 81) * units.MHz,
+        'attenuation': -1 * data
+    }
+    return default
+
+def get_cable_response(frequencies, cable_length):
+    """
+    Calculate the cable response for given frequencies and cable length.
+
+    This function loads the LOFAR cable response based on the provided cable length,
+    interpolates the attenuation values for the specified frequencies, and returns
+    the interpolated cable response.
+
+    Parameters
+    ----------
+    frequencies: array-like
+        An array of frequency values for which the cable response is to be calculated.
+    cable_length: int or float
+        The length of the cable for which the response is to be loaded.
+
+    Returns
+    -------
+    cable: dict
+        A dictionary containing the interpolated cable attenuation values for the specified frequencies.
+        The dictionary has the following key:
+
+        - 'attenuation': An array of interpolated attenuation values corresponding to the input frequencies.
+    """
+
+    cable_response = load_cable_response(cable_length=cable_length)
+    orig_frequencies = cable_response['frequencies']
+    gain = cable_response['attenuation']
+
+    interp_gain = interp1d(orig_frequencies, gain[:,1], bounds_error=False, fill_value=0)
+
+    cable = {
+        'attenuation': interp_gain(frequencies)
+    }
+    return cable
+
+
+def load_RCU_response():
+    """
+    Load the RCU (Receiver Control Unit) response data.
+
+    This function reads the RCU gain data from a text file located in the "signalchain" directory
+    relative to the current module's directory. It then constructs a dictionary containing the
+    frequency range and the corresponding gain values.
+
+    Returns
+    -------
+    rcu_response: dict
+        A dictionary containing the RCU response data with the following keys:
+
+        - 'frequencies': An array of frequency values ranging from 30 MHz to 80 MHz.
+        - 'gain': An array of gain values in dB corresponding to the frequencies.
+    """
+
+    module_dir = os.path.dirname(__file__)
+    file_path = os.path.join(module_dir, "signalchain/RCU_gain.txt")
+
+    data = np.loadtxt(file_path)
+
+    rcu_response = {
+        'frequencies': np.arange(30, 81) * units.MHz,
+        'gain': data
+    }
+
+    return rcu_response
+
+def get_RCU_response(frequencies):
+    """
+    Fetches the RCU response for given frequencies.
+
+    This function loads the LOFAR RCU response, interpolates the gain values for the specified
+    frequencies, and returns the interpolated RCU response.
+
+    Parameters
+    ----------
+    frequencies: array-like
+        An array of frequency values for which the RCU response is to be calculated.
+
+    Returns
+    -------
+    system: dict
+        A dictionary containing the interpolated RCU gain values for the specified frequencies.
+        The dictionary has the following key:
+
+        - 'gain': An array of interpolated gain values corresponding to the input frequencies.
+    """
+
+    RCU_response = load_RCU_response()
+    orig_frequencies = RCU_response['frequencies']
+    gain = RCU_response['gain']
+
+    interp_gain = interp1d(orig_frequencies, gain, bounds_error=False, fill_value=0)
+
+    system = {'gain': interp_gain(frequencies)}
+
+    return system
diff --git a/NuRadioReco/detector/LOFAR/signalchain/RCU_gain.txt b/NuRadioReco/detector/LOFAR/signalchain/RCU_gain.txt
new file mode 100644
index 0000000000..6f5042c5ab
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/signalchain/RCU_gain.txt
@@ -0,0 +1,51 @@
+0.571199977723
+0.721199977723
+0.799800695235
+0.825966036527
+0.815430246143
+0.783927568626
+0.746950424766
+0.712983105812
+0.682589587584
+0.655908728285
+0.633079386122
+0.614226566278
+0.599111687582
+0.587105693931
+0.577560289905
+0.569827180085
+0.563258069051
+0.557204661382
+0.55101866166
+0.544051774464
+0.535655704375
+0.525182155973
+0.511982833838
+0.495409442551
+0.474813686691
+0.44954727084
+0.418961899576
+0.382409277481
+0.339241109135
+0.288809099118
+0.23046495201
+0.163560372391
+0.0874470648419
+0.00147673394283
+-0.0949989157261
+-0.202628179585
+-0.322059353052
+-0.453940731549
+-0.598920610495
+-0.757647285309
+-0.930769051412
+-1.11893420422
+-1.32279103916
+-1.54298785165
+-2.0801729371
+-2.38499459094
+-2.71223456883
+-3.23061184212
+-4.13436889849
+-5.92420576122
+-8.50082245357
diff --git a/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_115m.txt b/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_115m.txt
new file mode 100644
index 0000000000..b32887f171
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_115m.txt
@@ -0,0 +1,51 @@
+30 9.37884232408
+31 9.53487220268
+32 9.68842092213
+33 9.83960368097
+34 9.98852702327
+35 10.1352897227
+36 10.2799835538
+37 10.4226939673
+38 10.5635006844
+39 10.7024782204
+40 10.839696349
+41 10.9752205142
+42 11.1091121976
+43 11.2414292464
+44 11.3722261673
+45 11.5015543907
+46 11.6294625085
+47 11.7559964886
+48 11.8811998694
+49 12.0051139356
+50 12.1277778787
+51 12.2492289422
+52 12.3695025551
+53 12.4886324526
+54 12.6066507884
+55 12.7235882355
+56 12.8394740809
+57 12.9543363114
+58 13.0682016927
+59 13.1810958433
+60 13.293043302
+61 13.4040675904
+62 13.5141912711
+63 13.6234360018
+64 13.7318225849
+65 13.8393710144
+66 13.9461005192
+67 14.0520296034
+68 14.1571760837
+69 14.2615571255
+70 14.3651892746
+71 14.4680884892
+72 14.5702701679
+73 14.6717491772
+74 14.7725398768
+75 14.8726561435
+76 14.9721113935
+77 15.0709186038
+78 15.1690903319
+79 15.2666387345
+80 15.3635755852
diff --git a/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_50m.txt b/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_50m.txt
new file mode 100644
index 0000000000..6c536a8760
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_50m.txt
@@ -0,0 +1,51 @@
+30 4.07775753221
+31 4.14559660986
+32 4.21235692266
+33 4.27808855694
+34 4.3428378362
+35 4.40664770552
+36 4.46955806685
+37 4.53160607273
+38 4.59282638451
+39 4.65325140018
+40 4.7129114561
+41 4.77183500619
+42 4.83004878157
+43 4.8875779332
+44 4.94444615968
+45 5.00067582203
+46 5.05628804716
+47 5.11130282113
+48 5.16573907364
+49 5.21961475461
+50 5.27294690378
+51 5.32575171402
+52 5.37804458916
+53 5.4298401968
+54 5.48115251668
+55 5.531994885
+56 5.58238003519
+57 5.63232013538
+58 5.6818268229
+59 5.73091123623
+60 5.77958404435
+61 5.82785547408
+62 5.87573533527
+63 5.92323304425
+64 5.9703576456
+65 6.01711783236
+66 6.06352196489
+67 6.10957808842
+68 6.15529394945
+69 6.20067701107
+70 6.24573446722
+71 6.29047325617
+72 6.334900073
+73 6.37902138139
+74 6.4228434247
+75 6.46637223629
+76 6.50961364935
+77 6.55257330602
+78 6.59525666605
+79 6.63766901499
+80 6.67981547185
diff --git a/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_80m.txt b/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_80m.txt
new file mode 100644
index 0000000000..9587e5d16e
--- /dev/null
+++ b/NuRadioReco/detector/LOFAR/signalchain/attenuation_RG58_80m.txt
@@ -0,0 +1,51 @@
+30 6.52441205153
+31 6.63295457577
+32 6.73977107626
+33 6.84494169111
+34 6.94854053793
+35 7.05063632883
+36 7.15129290696
+37 7.25056971637
+38 7.34852221522
+39 7.44520224029
+40 7.54065832975
+41 7.6349360099
+42 7.72807805051
+43 7.82012469312
+44 7.91111385548
+45 8.00108131525
+46 8.09006087545
+47 8.1780845138
+48 8.26518251782
+49 8.35138360738
+50 8.43671504605
+51 8.52120274243
+52 8.60487134266
+53 8.68774431488
+54 8.76984402669
+55 8.851191816
+56 8.93180805631
+57 9.0117122166
+58 9.09092291664
+59 9.16945797796
+60 9.24733447096
+61 9.32456875853
+62 9.40117653643
+63 9.47717287081
+64 9.55257223297
+65 9.62738853178
+66 9.70163514382
+67 9.77532494147
+68 9.84847031912
+69 9.92108321771
+70 9.99317514756
+71 10.0647572099
+72 10.1358401168
+73 10.2064342102
+74 10.2765494795
+75 10.3461955781
+76 10.415381839
+77 10.4841172896
+78 10.5524106657
+79 10.620270424
+80 10.687704755
diff --git a/NuRadioReco/detector/RNO_G/db_mongo_read.py b/NuRadioReco/detector/RNO_G/db_mongo_read.py
index 7921981597..187fa83512 100644
--- a/NuRadioReco/detector/RNO_G/db_mongo_read.py
+++ b/NuRadioReco/detector/RNO_G/db_mongo_read.py
@@ -1,3 +1,18 @@
+"""
+Interface to the MongoDB that contains RNO-G hardware and calibration information
+
+The :mod:`NuRadioReco.detector.RNO_G.db_mongo_read` module and the `Database` class herein mostly serve as the
+backend of the `NuRadioReco.detector.RNO_G.rnog_detector.Detector` class. Most users
+will want to use that class to obtain information about deployed RNO-G stations and hardware.
+`NuRadioReco.detector.RNO_G.rnog_detector.Detector` class has an interface similar to that of
+other detector descriptions in NuRadioMC, and is documented there.
+
+However, for some specific use cases (e.g. finding measurements for individual hardware components
+that have not been deployed to the field), one can use the `Database` class directly, using the
+`Database.get_component_data` method.
+
+"""
+
 import six
 import os
 import urllib.parse
@@ -110,8 +125,9 @@ def __init__(self, database_connection="RNOG_public", database_name=None, mongo_
             logger.error(f'Could not find database "{database_name}" in mongo client.')
             raise KeyError
 
+        logger.info("Attempting to connect to the database ...")
         self.db = self.__mongo_client[database_name]
-        logger.info("database connection to {} established".format(self.db.name))
+        logger.info(f"... connection to {self.db.name} established")
 
         # Set timestamp of database. This is used to determine which primary measurement is used
         self.__database_time = datetime.datetime.utcnow()
@@ -719,8 +735,42 @@ def get_channel_signal_chain_measurement(self, station_id=None, channel_id=None,
             return {k:collection_info[0]['measurements'][k] for k in ('VEL', 'response_chain', 'primary_components')}
 
 
-    def get_component_data(self, component_type, component_id, supplementary_info, primary_time, verbose=True, sparameter='S21'):
-        """ returns the current primary measurement of the component, reads in the component collection"""
+    def get_component_data(self, component_type, component_id, supplementary_info={}, primary_time=None, verbose=True, sparameter='S21'):
+        """
+        returns the current primary measurement of the component, reads in the component collection
+
+        Returns a single measurement (e.g. gain of an IGLU)
+
+        Examples
+        --------
+
+        .. code-block::
+
+            import NuRadioReco.detector.RNO_G.db_mongo_read
+            import datetime
+
+            db = NuRadioReco.detector.RNO_G.db_mongo_read.Database()
+
+            # gives you the entry in the database
+            database_entry = db.get_component_data(
+                component_type='iglu_board',
+                component_id='C0069',
+                supplementary_info={}, # if you want a DRAB you have to specify the channel: {'channel_id':0}
+                verbose=True,
+                sparameter='S21', # you can also read the other S parameters
+                primary_time=datetime.datetime.now())
+
+
+            # extract the gain + phase data
+            y_axis_units = database_entry['y-axis_units']
+            frequencies = database_entry['frequencies']
+            gain_data = database_entry['mag']
+            phase_data = database_entry['phase']
+
+        """
+
+        if primary_time is None:
+            primary_time = self.get_database_time()
 
         # define a search filter
         search_filter = [{'$match': {'name': component_id}}, {'$unwind': '$measurements'}, {'$match': {}}]
@@ -857,50 +907,56 @@ def get_channel_signal_chain(self, channel_signal_id, measurement_name=None, ver
         channel_sig_info = self.get_channel_signal_chain_measurement(
             channel_signal_id=channel_signal_id, measurement_name=measurement_name, verbose=verbose)
 
-        # extract the information about the used components
-        component_dict = channel_sig_info.pop('response_chain')
-
-        # Certain keys in the response chain only carry additional information of other components
-        # and do not describe own components on their own ("channel", "breakout", "weight")
-        # extract the information about the components, the additional information and the weights from the response chain dict
-        filtered_component_dict = {}
-        additional_information = {}
-        weight_dict = {}
-
-        for key, ele in component_dict.items():
-            if re.search("(channel|breakout|weight)", key) is None:
-                filtered_component_dict[key] = ele
-            elif re.search("weight", key) is not None:
-                weight_dict[key.replace("_weight", "")] = ele
-            else:
-                additional_information[key] = ele
-
-        # go through all components and load the s parameter measurements for each used component
-        components_data = {}
-        for component, component_id in filtered_component_dict.items():
-            # Add the additional informatio which were filtered out above to the correct components
-            supp_info = {k.replace(component + "_", ""): additional_information[k] for k in additional_information
-                         if re.search(component, k)}
-
-            if re.search("_[0-9]+", component, re.IGNORECASE):
-                collection_suffix = re.findall("(_[0-9]+)", component, re.IGNORECASE)[0]
-                collection_component = component.replace(collection_suffix, "")
-            else:
-                collection_component = component
-
-            # load the s21 parameter measurement
-            component_data = self.get_component_data(
-                collection_component, component_id, supp_info, primary_time=self.__database_time, verbose=verbose)
-
-            # add the component name, the weight of the s21 measurement and the actual s21 measurement (component_data) to a combined dictionary
-            components_data[component] = {'name': component_id}
-            if component in weight_dict:
-                components_data[component].update({'weight': weight_dict[component]})
-
-            components_data[component].update(component_data)
-
-        # add/update the signal chain to the channel data
-        channel_sig_info['response_chain'] = components_data
+        for chain_key in ['response_chain', 'trigger_response_chain']:
+
+            # Not every channel has a trigger response chain
+            if chain_key not in channel_sig_info:
+                continue
+
+            # extract the information about the used components
+            component_dict = channel_sig_info.pop(chain_key)
+
+            # Certain keys in the response chain only carry additional information of other components
+            # and do not describe own components on their own ("channel", "breakout", "weight")
+            # extract the information about the components, the additional information and the weights from the response chain dict
+            filtered_component_dict = {}
+            additional_information = {}
+            weight_dict = {}
+
+            for key, ele in component_dict.items():
+                if re.search("(channel|breakout|weight)", key) is None:
+                    filtered_component_dict[key] = ele
+                elif re.search("weight", key) is not None:
+                    weight_dict[key.replace("_weight", "")] = ele
+                else:
+                    additional_information[key] = ele
+
+            # go through all components and load the s parameter measurements for each used component
+            components_data = {}
+            for component, component_id in filtered_component_dict.items():
+                # Add the additional informatio which were filtered out above to the correct components
+                supp_info = {k.replace(component + "_", ""): additional_information[k] for k in additional_information
+                            if re.search(component, k)}
+
+                if re.search("_[0-9]+", component, re.IGNORECASE):
+                    collection_suffix = re.findall("(_[0-9]+)", component, re.IGNORECASE)[0]
+                    collection_component = component.replace(collection_suffix, "")
+                else:
+                    collection_component = component
+
+                # load the s21 parameter measurement
+                component_data = self.get_component_data(
+                    collection_component, component_id, supp_info, primary_time=self.__database_time, verbose=verbose)
+
+                # add the component name, the weight of the s21 measurement and the actual s21 measurement (component_data) to a combined dictionary
+                components_data[component] = {'name': component_id}
+                if component in weight_dict:
+                    components_data[component].update({'weight': weight_dict[component]})
+
+                components_data[component].update(component_data)
+
+            # add/update the signal chain to the channel data
+            channel_sig_info[chain_key] = components_data
 
         return channel_sig_info
 
diff --git a/NuRadioReco/detector/RNO_G/rnog_detector.py b/NuRadioReco/detector/RNO_G/rnog_detector.py
index 9d4380cbe5..b0a21a5127 100644
--- a/NuRadioReco/detector/RNO_G/rnog_detector.py
+++ b/NuRadioReco/detector/RNO_G/rnog_detector.py
@@ -121,12 +121,14 @@ def __init__(self, database_connection='RNOG_public', log_level=logging.NOTSET,
             "is_noiseless": False,
         }
 
+        self.additional_data = {}
+
         if select_stations is not None and not isinstance(select_stations, list):
             select_stations = [select_stations]
 
         self.selected_stations = select_stations
         self.logger.info(f"Select the following stations (if possible): {select_stations}")
-
+        self.__db = None
         if detector_file is None:
             self._det_imported_from_file = False
 
@@ -828,7 +830,7 @@ def get_amplifier_response(self, station_id, channel_id, frequencies):
         return response_func(frequencies)
 
     @_check_detector_time
-    def get_signal_chain_response(self, station_id, channel_id):
+    def get_signal_chain_response(self, station_id, channel_id, trigger=False):
         """
         Returns a `detector.response.Response` object which describes the complex response of the
         entire signal chain, i.e., the combined reponse of all components of one
@@ -843,6 +845,9 @@ def get_signal_chain_response(self, station_id, channel_id):
         channel_id: int
             The channel id
 
+        trigger: bool
+            If True, the trigger channel resonse is returned. An error is raised if no trigger response exists. (Default: False)
+
         Returns
         -------
 
@@ -852,9 +857,22 @@ def get_signal_chain_response(self, station_id, channel_id):
         signal_chain_dict = self.get_channel_signal_chain(
             station_id, channel_id)
 
+        if trigger:
+            response_chain_key = "trigger_response_chain"
+            response_key = "total_trigger_response"
+
+            if response_chain_key not in signal_chain_dict or "is_trigger_chain_absolute" not in signal_chain_dict:
+                raise KeyError(f"No trigger response for station.channel {station_id}.{channel_id}")
+
+            if not signal_chain_dict["is_trigger_chain_absolute"]:
+                raise NotImplementedError("Relative trigger chains are not implemented yet.")
+        else:
+            response_chain_key = "response_chain"
+            response_key = "total_response"
+
         # total_response can be None if imported from file
-        if "total_response" not in signal_chain_dict or signal_chain_dict["total_response"] is None:
-            measurement_components_dic = signal_chain_dict["response_chain"]
+        if response_key not in signal_chain_dict or signal_chain_dict[response_key] is None:
+            measurement_components_dic = signal_chain_dict[response_chain_key]
 
             # Here comes a HACK
             components = list(measurement_components_dic.keys())
@@ -904,9 +922,9 @@ def get_signal_chain_response(self, station_id, channel_id):
                 responses.append(response)
 
             # Buffer object
-            signal_chain_dict["total_response"] = np.prod(responses)
+            signal_chain_dict[response_key] = np.prod(responses)
 
-        return signal_chain_dict["total_response"]
+        return signal_chain_dict[response_key]
 
     @_check_detector_time
     def get_signal_chain_components(self, station_id, channel_id):
@@ -1180,72 +1198,57 @@ def is_channel_noiseless(self, station_id, channel_id):
         return is_noiseless
 
 
-    def get_cable_delay(self, station_id, channel_id, use_stored=True):
-        """
-        Return the cable delay of a signal chain as stored in the detector description.
-
-        This interface is required by simulation.py. See `get_time_delay` for description of
-        arguments.
-        """
+    def get_cable_delay(self, station_id, channel_id, use_stored=True, trigger=False):
+        """ Same as `get_time_delay`. Only here to keep the same interface as the other detector classes. """
         # FS: For the RNO-G detector description it is not easy to determine the cable delay alone
         # because it is not clear which reference components may need to be substraced.
         # However, having the cable delay without amplifiers is anyway weird.
-        return self.get_time_delay(station_id, channel_id, cable_only=False, use_stored=use_stored)
+        return self.get_time_delay(station_id, channel_id, use_stored=use_stored, trigger=trigger)
 
-    def get_time_delay(self, station_id, channel_id, cable_only=False, use_stored=True):
-        """
-        Return the sum of the time delay of all components in the signal chain calculated from the phase
+    def get_time_delay(self, station_id, channel_id, use_stored=True, trigger=False):
+        """ Return the sum of the time delay of all components in the signal chain calculated from the phase.
 
         Parameters
         ----------
-
         station_id: int
             The station id
 
         channel_id: int
             The channel id
 
-        cable_only: bool
-            If True: Consider only cables to calculate delay. (Default: False)
-
         use_stored: bool
             If True, take time delay as stored in DB rather than calculated from response. (Default: True)
 
+        trigger: bool
+            If True, the trigger channel resonse is returned. An error is raised if no trigger response exists.
+            (Default: False)
+
         Returns
         -------
-
         time_delay: float
             Sum of the time delays of all components in the signal chain for one channel
         """
-
         signal_chain_dict = self.get_channel_signal_chain(
             station_id, channel_id)
 
-        time_delay = 0
-        for key, value in signal_chain_dict["response_chain"].items():
-
-            if re.search("cable", key) is None and re.search("fiber", key) and cable_only:
-                continue
-
-            weight = value.get("weight", 1)
-            if use_stored:
-                if "time_delay" not in value and "cable_delay" not in value:
-                    self.logger.warning(
-                        f"The signal chain component \"{key}\" of station.channel "
-                        f"{station_id}.{channel_id} has no cable/time delay stored... (hence return time delay without it)")
-                    continue
-
-                try:
-                    time_delay += weight * value["time_delay"]
-                except KeyError:
-                    time_delay += weight * value["cable_delay"]
+        if use_stored:
+            resp = self.get_signal_chain_response(station_id, channel_id, trigger=trigger)
+            return resp.get_time_delay()
+        else:
+            time_delay = 0
+            if trigger and "trigger_response_chain" not in signal_chain_dict:
+                raise KeyError(f"No trigger response for station.channel {station_id}.{channel_id}")
 
-            else:
+            prefix = "trigger_" if trigger else ""
+            for key, value in signal_chain_dict[f"{prefix}response_chain"].items():
                 ydata = [value["mag"], value["phase"]]
+                # This is different from within `get_signal_chain_response` because we do set the time delay here
+                # and thus we do not remove it from the response.
                 response = Response(value["frequencies"], ydata, value["y-axis_units"],
                                     name=key, station_id=station_id, channel_id=channel_id,
                                     log_level=self.__log_level)
 
+                weight = value.get("weight", 1)
                 time_delay += weight * response._calculate_time_delay()
 
         return time_delay
@@ -1275,17 +1278,75 @@ def get_site_coordinates(self, station_id=None):
         return (72.57, -38.46)
 
 
+    def get_database(self):
+        """
+        Returns the database connection
+
+        Returns
+        -------
+
+        db: MongoClient
+            Returns the database connection
+        """
+        if self.__db is None:
+            self.logger.error("No database connection available. Return None")
+            return None
+
+        return self.__db
+
+
+    def get_component(self, collection="coax_cable", component="daq_drab_flower_2024_avg"):
+        """ Get the response of a component from the database.
+
+        This function is a wrapper around the same named function in the database class.
+
+        Parameters
+        ----------
+        collection: str (default: "coax_cable")
+            The collection name in the database.
+        component: str (default: "daq_drab_flower_2024_avg")
+            The component name in the collection.
+
+        Returns
+        -------
+        resp: `NuRadioReco.detector.response.Response`
+            Returns the response of a component.
+
+        See Also
+        --------
+        NuRadioReco.detector.RNO_G.db_mongo_read.Database.get_component_data
+        """
+        if component in self.additional_data and 'y-axis_units' in self.additional_data[component]:
+            component_data = self.additional_data[component]
+        else:
+            db = self.get_database()
+            if db is None:
+                raise ValueError(
+                    "No database connection. You probably imported the detector from a file. "
+                    "Please use the DB connection to load the component data.")
+
+            # load the s21 parameter measurement
+            component_data = db.get_component_data(
+                collection, component)
+            self.additional_data[component] = component_data
+
+        resp = Response(
+            component_data["frequencies"], [component_data["mag"], component_data["phase"]], component_data['y-axis_units'],
+            name=component, station_id=-1, channel_id=None)
+
+        return resp
+
+
 if __name__ == "__main__":
 
     from NuRadioReco.detector import detector
 
     det = detector.Detector(source="rnog_mongo", log_level=logging.DEBUG, always_query_entire_description=False,
-                            database_connection='RNOG_public', select_stations=24)
-
-    det.update(datetime.datetime(2023, 8, 2, 0, 0))
+                            database_connection='RNOG_public', select_stations=13)
 
+    det.update(datetime.datetime(2023, 7, 2, 0, 0))
 
-    response = det.get_signal_chain_response(station_id=24, channel_id=0)
+    response = det.get_signal_chain_response(station_id=13, channel_id=0)
 
     from NuRadioReco.framework import electric_field
     ef = electric_field.ElectricField(channel_ids=[0])
diff --git a/NuRadioReco/detector/antennapattern.py b/NuRadioReco/detector/antennapattern.py
index 9a4bc68a10..17f94c1d5b 100644
--- a/NuRadioReco/detector/antennapattern.py
+++ b/NuRadioReco/detector/antennapattern.py
@@ -1008,24 +1008,31 @@ def parse_LOFAR_txt_file(path_theta, path_phi):
     return freq, theta, phi, real_theta, imaginary_theta, real_phi, imaginary_phi
 
 
-def preprocess_LOFAR_txt(directory, ant='LBA'):
+def preprocess_LOFAR_txt(directory, ant='LBA', orientation=None):
     """
-    Function to parse the LOFAR antenna model simulation files in TXT format. It extracts the
-    vector effective length for all simulated frequencies, azimuth and zenith angles and dumps
-    them into a pickle file according to the NuRadioReco specification.
+    Function to process the TXT files from the old LOFAR antenna model (only tested for LBA). The paths to these
+    files is currently hardcoded. Because of a weird issue which requires minus signs to be added for the X and Y
+    dipoles separately, the orientation can be specified to create separate antenna models for each. If the
+    orientation is not set, the values for the Y dipole are returned.
 
     Parameters
     ----------
     directory : str
-        The path to the directory where the TXT files are stored
+        Path to where the text files are stored
     ant : str, default='LBA'
-        The antenna type
+        The antenna type, either LBA or HBA (not tested)
+    orientation : str, default=None
+        If set, must be either X or Y.
     """
     path_theta = os.path.join(directory, f'{ant}_Vout_theta.txt')
     path_phi = os.path.join(directory, f'{ant}_Vout_phi.txt')
 
     frequencies, thetas, phis, theta_real, theta_imag, phi_real, phi_imag = parse_LOFAR_txt_file(path_theta, path_phi)
 
+    if orientation == 'X':
+        for ar in [theta_real, theta_imag, phi_real, phi_imag]:
+            ar *= -1
+
     VEL_thetas = theta_real + 1j * theta_imag
     VEL_phis = phi_real + 1j * phi_imag
 
@@ -1046,7 +1053,10 @@ def preprocess_LOFAR_txt(directory, ant='LBA'):
     orientation_theta, orientation_phi, rotation_theta, rotation_phi = \
         90 * units.deg, 0 * units.deg, 0 * units.deg, 0 * units.deg
 
-    fname = f'LOFAR_{ant}'
+    if orientation is not None:
+        fname = f'LOFAR_{ant}_{orientation}'
+    else:
+        fname = f'LOFAR_{ant}'
     output_filename = '{}.pkl'.format(os.path.join(path_to_antennamodels, fname, fname))
 
     directory = os.path.dirname(output_filename)
diff --git a/NuRadioReco/detector/detector_base.py b/NuRadioReco/detector/detector_base.py
index f4cb81386e..2dd9564af6 100644
--- a/NuRadioReco/detector/detector_base.py
+++ b/NuRadioReco/detector/detector_base.py
@@ -13,7 +13,10 @@
 from tinydb_serialization import Serializer
 import six  # # used for compatibility between py2 and py3
 import warnings
-from erfa import ErfaWarning
+try:
+    from erfa import ErfaWarning
+except ImportError: # users with astropy < 4.2 may not have pyerfa installed
+    from astropy.utils.exceptions import ErfaWarning
 import NuRadioReco.utilities.metaclasses
 
 logger = logging.getLogger('NuRadioReco.detector')
@@ -974,6 +977,32 @@ def get_channel_group_id(self, station_id, channel_id):
             return channel_id
         else:
             return res['channel_group_id']
+        
+    def get_antenna_mode(self, station_id, channel_id):
+        """
+        returns the antenna mode of a given channel - this is specific to LOFAR antennas, as they operate in either inner or outer mode.
+
+        Parameters
+        ----------
+        station_id: int
+            the station id
+        channel_id: int
+            the channel id
+
+        Returns
+        -------
+        ant_mode : str
+            the antenna mode (LBA inner/outer)
+        """
+
+        res = self.__get_channel(station_id, channel_id)
+        if 'ant_mode' not in res.keys():
+            logger.warning(
+                'Antenna mode not set for channel {} in station {}, returning None'.format(
+                    channel_id, station_id))
+            return None
+        else:
+            return res['ant_mode']
 
     def get_noise_RMS(self, station_id, channel_id, stage='amp'):
         """
diff --git a/NuRadioReco/detector/response.py b/NuRadioReco/detector/response.py
index 9c906bd268..74dadc8ffd 100644
--- a/NuRadioReco/detector/response.py
+++ b/NuRadioReco/detector/response.py
@@ -31,10 +31,11 @@ class Response:
 
     """
 
-    def __init__(self, frequency, y, y_unit, time_delay=0, weight=1,
-                 name="default", station_id=None, channel_id=None,
-                 remove_time_delay=True, debug_plot=False,
-                 log_level=logging.NOTSET, attenuator_in_dB=0):
+    def __init__(
+            self, frequency, y, y_unit, time_delay=0, weight=1,
+            name="default", station_id=None, channel_id=None,
+            remove_time_delay=True, debug_plot=False,
+            log_level=logging.NOTSET, attenuator_in_dB=0):
         """
         Parameters
         ----------
@@ -109,10 +110,14 @@ def __init__(self, frequency, y, y_unit, time_delay=0, weight=1,
         if y_unit[1].lower() == "deg":
             if np.max(np.abs(y_phase)) < 2 * np.pi:
                 self.logger.warning("Is the phase really in deg? Does not look like it... "
-                                    f"Do not convert to rad. Phase: {y_phase}")
+                                    f"Do not convert {name} to rad: {y_phase}")
             else:
                 y_phase = np.deg2rad(y_phase)
         elif y_unit[1].lower() == "rad":
+            # We can not make this test because the phase might be already unwrapped
+            # if np.amax(y_phase) - np.amin(y_phase) > 2 * np.pi:
+            #     self.logger.warning("Is the phase really in rad? Does not look like it... "
+            #                         f"Do convert {name} to rad: {y_phase}")
             y_phase = y_phase
         else:
             raise KeyError
@@ -209,6 +214,8 @@ def __call__(self, freq, component_names=None, blacklist=True):
         """
         response = np.ones_like(freq, dtype=np.complex128)
 
+        freq = np.asarray(freq)
+
         if component_names is not None:
             if isinstance(component_names, str):
                 component_names = [component_names]
@@ -268,6 +275,29 @@ def remove(self, name):
         self.__weights.pop(idx)
         self.__time_delays.pop(idx)
 
+    def get(self, name):
+        """
+        Get a component response from the response object.
+
+        Parameters
+        ----------
+
+        name: str
+            Name of the component to get
+        """
+        if name not in self.get_names():
+            raise ValueError(f"Component {name} not found in response. Options are: {self.get_names()}")
+
+        idx = self.__names.index(name)
+        single_response = copy.deepcopy(self)
+        single_response.__gains = [self.__names[idx]]
+        single_response.__names = [self.__gains[idx]]
+        single_response.__phases = [self.__phases[idx]]
+        single_response.__weights = [self.__weights[idx]]
+        single_response.__time_delays = [self.__time_delays[idx]]
+
+        return single_response
+
     def __mul__(self, other):
         """
         Define multiplication operator for
@@ -277,12 +307,13 @@ def __mul__(self, other):
 
         if isinstance(other, Response):
             self = copy.deepcopy(self)
-            if (self._station_id != other._station_id or
-                self._channel_id != other._channel_id) and (other._station_id != -1 and self._station_id != -1):
-                # station_id == -1 is a special case to all non-station specific responses
+            # station_id == -1 is a special case to all non-station specific responses
+            if ((self._station_id != other._station_id or self._channel_id != other._channel_id) and
+                (other._station_id != -1 and self._station_id != -1)):
                 self.logger.error("It looks like you are combining responses from "
                                   f"two different channels: {self._station_id}.{self._channel_id} "
                                   f" vs {other._station_id}.{other._channel_id} (station_id.channel_id)")
+
             # Store each response individually: append/concatenate lists of gains and phases.
             # The multiplication happens in __call__.
             self.__names += other.__names
@@ -321,6 +352,58 @@ def __rmul__(self, other):
         """ Same as mul """
         return self.__mul__(other)
 
+    def __truediv__(self, other):
+        """
+        Define multiplication operator for
+            - Other objects of the same class
+            - Objects of type NuRadioReco.framework.base_trace
+        """
+
+        if isinstance(other, Response):
+            self = copy.deepcopy(self)
+            # station_id == -1 is a special case to all non-station specific responses
+            if ((self._station_id != other._station_id or self._channel_id != other._channel_id) and
+                (other._station_id != -1 and self._station_id != -1)):
+                self.logger.error(
+                    "It looks like you are combining responses from "
+                    f"two different channels: {self._station_id}.{self._channel_id} "
+                    f" vs {other._station_id}.{other._channel_id} (station_id.channel_id)")
+
+            # Store each response individually: append/concatenate lists of gains and phases.
+            # The multiplication happens in __call__.
+            self.__names += other.__names
+            self.__gains += other.__gains
+            self.__phases += other.__phases
+
+            self.__weights += [-1 * ele for ele in other.__weights]
+            self.__time_delays += [-1 * ele for ele in other.__time_delays]
+            return self
+
+        elif isinstance(other, NuRadioReco.framework.base_trace.BaseTrace):
+            other = copy.copy(other)
+            if self._sanity_check:
+                trace_length = other.get_number_of_samples() / other.get_sampling_rate()
+                time_delay = self._calculate_time_delay()
+                if time_delay > trace_length / 2:
+                    self.logger.warning("The time shift appiled by the response is larger than half the trace length:\n\t"
+                                        f"{time_delay:.2f} vs {trace_length:.2f}")
+
+            spec = other.get_frequency_spectrum()
+            freqs = other.get_frequencies()
+            spec *= 1 / self(freqs)  # __call__
+            other.add_trace_start_time(-np.sum(self.__time_delays))
+            other.set_frequency_spectrum(spec, sampling_rate="same")
+
+            return other
+
+        elif isinstance(other, np.ndarray):
+            raise TypeError("You try to multiply a `Response` object with a numpy array, "
+                            "only `Response` or `BaseTrace` is allowed. "
+                            "Did you call `get_trace()` or `get_frequency_spectrum()` on `BaseTrace`?")
+
+        else:
+            raise TypeError(f"Response multiplied with unknown type: {type(other)}")
+
     def __str__(self):
         ampl = 20 * np.log10(np.abs(self(np.array([0.15, 0.5]) * units.GHz)))
         return "Response of " + ", ".join([f"{name} ({weight})" for name, weight in zip(self.get_names(), self.__weights)]) \
@@ -384,6 +467,10 @@ def get_time_delay(self):
         """ Get time delay from DB """
         return np.sum(self.__time_delays)
 
+    def get_time_delays(self):
+        """ Get time delay from DB """
+        return self.__time_delays
+
     def _calculate_time_delay(self):
         """
         Calculate time delay from phase of the stored complex response function.
@@ -400,25 +487,24 @@ def _calculate_time_delay(self):
             The time delay at ~ 200 MHz
         """
 
-        freqs = np.arange(0.05, 1.2, 0.001) * units.GHz
+        freqs = np.arange(50, 1200, 0.5) * units.MHz
 
         response = self(freqs)
-
         delta_freq = np.diff(freqs)
-
         phase = np.angle(response)
-        time_delay = -np.diff(np.unwrap(phase)) / delta_freq / 2 / np.pi
 
+        time_delay = -np.diff(np.unwrap(phase)) / delta_freq / 2 / np.pi
         mask = np.all([195 * units.MHz < freqs, freqs < 250 * units.MHz], axis=0)[:-1]
         time_delay1 = np.mean(time_delay[mask])
 
-        # fit the unwrapped phase with a linear function
-        popt = np.polyfit(freqs, np.unwrap(phase), 1)
-        time_delay2 = -popt[0] / (2 * np.pi)
+        # This alternative calculation is only meaningful if group delay is ~ constant over the whole frequency range (which is the case for most cables)
+        # # fit the unwrapped phase with a linear function
+        # popt = np.polyfit(freqs, np.unwrap(phase), 1)
+        # time_delay2 = -popt[0] / (2 * np.pi)
 
-        if np.abs(time_delay1 - time_delay2) > 0.1 * units.ns:
-            self.logger.warning("Calculation of time delay. The two methods yield different results: "
-                                f"{time_delay1:.1f} ns / {time_delay2:.1f} ns for {self.get_names()}. Return the former...")
+        # if np.abs(time_delay1 - time_delay2) > 0.1 * units.ns:
+        #     self.logger.warning("Calculation of time delay. The two methods yield different results: "
+        #                         f"{time_delay1:.1f} ns / {time_delay2:.1f} ns for {self.get_names()}. Return the former...")
 
         return time_delay1
 
diff --git a/NuRadioReco/examples/AliasPhasedArray/SNR_study/T02SNRNyquist.py b/NuRadioReco/examples/AliasPhasedArray/SNR_study/T02SNRNyquist.py
index be5949c9dd..70345fe496 100644
--- a/NuRadioReco/examples/AliasPhasedArray/SNR_study/T02SNRNyquist.py
+++ b/NuRadioReco/examples/AliasPhasedArray/SNR_study/T02SNRNyquist.py
@@ -50,6 +50,8 @@
 import NuRadioReco.modules.channelBandPassFilter
 import NuRadioReco.modules.channelGenericNoiseAdder
 import NuRadioReco.utilities.diodeSimulator
+
+
 from NuRadioReco.utilities.traceWindows import get_window_around_maximum
 from NuRadioReco.utilities import units
 from NuRadioMC.simulation import simulation
diff --git a/NuRadioReco/examples/PhasedArray/Effective_volume/T02RunPhasedRNO.py b/NuRadioReco/examples/PhasedArray/Effective_volume/T02RunPhasedRNO.py
index 251f88fcdc..7aaeb36909 100644
--- a/NuRadioReco/examples/PhasedArray/Effective_volume/T02RunPhasedRNO.py
+++ b/NuRadioReco/examples/PhasedArray/Effective_volume/T02RunPhasedRNO.py
@@ -28,6 +28,7 @@
 import NuRadioReco.modules.channelResampler
 import NuRadioReco.modules.channelBandPassFilter
 import NuRadioReco.modules.channelGenericNoiseAdder
+
 from NuRadioReco.utilities import units
 
 # 4 channel, 2x sampling, fft upsampling, 16 ns window
@@ -59,7 +60,6 @@
 class mySimulation(simulation.simulation):
 
     def _detector_simulation_filter_amp(self, evt, station, det):
-
         channelBandPassFilter.run(evt, station, det, passband=[0.0 * units.MHz, 220.0 * units.MHz],
                                   filter_type='cheby1', order=7, rp=.1)
         channelBandPassFilter.run(evt, station, det, passband=[96.0 * units.MHz, 100.0 * units.GHz],
diff --git a/NuRadioReco/examples/PhasedArray/SNR_curves/T02RunSNR.py b/NuRadioReco/examples/PhasedArray/SNR_curves/T02RunSNR.py
index c29774cf16..5452b5694f 100644
--- a/NuRadioReco/examples/PhasedArray/SNR_curves/T02RunSNR.py
+++ b/NuRadioReco/examples/PhasedArray/SNR_curves/T02RunSNR.py
@@ -35,6 +35,7 @@
 import NuRadioReco.modules.channelResampler
 import NuRadioReco.modules.channelBandPassFilter
 import NuRadioReco.modules.channelGenericNoiseAdder
+
 from NuRadioReco.utilities import units
 
 
@@ -127,7 +128,6 @@ def count_events():
 class mySimulation(simulation.simulation):
 
     def _detector_simulation_filter_amp(self, evt, station, det):
-
         channelBandPassFilter.run(evt, station, det, passband=[0.0 * units.MHz, 220.0 * units.MHz],
                                   filter_type='cheby1', order=9, rp=.1)
         channelBandPassFilter.run(evt, station, det, passband=[96.0 * units.MHz, 100.0 * units.GHz],
diff --git a/NuRadioReco/examples/RNO_data/read_data_example/run_reconstruction.py b/NuRadioReco/examples/RNO_data/read_data_example/run_reconstruction.py
index 10fe88283b..f676bd9a93 100644
--- a/NuRadioReco/examples/RNO_data/read_data_example/run_reconstruction.py
+++ b/NuRadioReco/examples/RNO_data/read_data_example/run_reconstruction.py
@@ -61,8 +61,3 @@
 
 		fig.tight_layout()
 		fig.savefig("trace_{}.pdf".format(i_event))
-
-
-
-
-
diff --git a/NuRadioReco/examples/RNO_data/run_reconstruction.py b/NuRadioReco/examples/RNO_data/run_reconstruction.py
index 9dde744152..32b02071ef 100644
--- a/NuRadioReco/examples/RNO_data/run_reconstruction.py
+++ b/NuRadioReco/examples/RNO_data/run_reconstruction.py
@@ -64,8 +64,3 @@
 
     fig.tight_layout()
     fig.savefig("trace_{}.pdf".format(i_event))
-    
-
-
-
-                    
diff --git a/NuRadioReco/examples/RNO_data/station_simulation/RNO_config.yaml b/NuRadioReco/examples/RNO_data/station_simulation/RNO_config.yaml
deleted file mode 100644
index a0dde7deaf..0000000000
--- a/NuRadioReco/examples/RNO_data/station_simulation/RNO_config.yaml
+++ /dev/null
@@ -1,5 +0,0 @@
-propagation:
-  ice_model: greenland_simple
-  attenuation_model: GL1
-
-noise: True
\ No newline at end of file
diff --git a/NuRadioReco/examples/RNO_data/station_simulation/RNO_single_station.json b/NuRadioReco/examples/RNO_data/station_simulation/RNO_single_station.json
deleted file mode 120000
index c2baab8abc..0000000000
--- a/NuRadioReco/examples/RNO_data/station_simulation/RNO_single_station.json
+++ /dev/null
@@ -1 +0,0 @@
-../../../detector/RNO_G/RNO_single_station.json
\ No newline at end of file
diff --git a/NuRadioReco/examples/RNO_data/station_simulation/simulate_rnog_2021.py b/NuRadioReco/examples/RNO_data/station_simulation/simulate_rnog_2021.py
deleted file mode 100644
index 427da97073..0000000000
--- a/NuRadioReco/examples/RNO_data/station_simulation/simulate_rnog_2021.py
+++ /dev/null
@@ -1,298 +0,0 @@
-#!/bin/env python3
-
-import argparse
-import copy
-import logging
-import numpy as np
-import os
-import scipy
-from scipy import constants
-import secrets
-import time
-
-from NuRadioMC.EvtGen import generator
-from NuRadioMC.simulation import simulation
-from NuRadioMC.utilities import runner
-from NuRadioReco.utilities import units
-
-from NuRadioReco.detector import detector
-import NuRadioReco.modules.trigger.highLowThreshold
-from NuRadioReco.modules import triggerTimeAdjuster
-from NuRadioReco.modules.RNO_G import hardwareResponseIncorporator, triggerBoardResponse
-
-root_seed = secrets.randbits(128)
-
-highLowThreshold = NuRadioReco.modules.trigger.highLowThreshold.triggerSimulator()
-triggerTimeAdjuster = triggerTimeAdjuster.triggerTimeAdjuster(log_level=logging.WARNING)
-rnogHarwareResponse = hardwareResponseIncorporator.hardwareResponseIncorporator()
-rnogADCResponse = triggerBoardResponse.triggerBoardResponse(log_level=logging.WARNING)
-rnogADCResponse.begin(adc_input_range=2 * units.volt, clock_offset=0.0, adc_output="voltage")
-
-#############################
-## Set up trigger definitions
-#############################
-
-
-def RNO_G_HighLow_Thresh(lgRate_per_hz):
-    # Thresholds calculated using the RNO-G hardware (iglu + flower_lp)
-    # This applies for the VPol antennas
-    return (-859 + np.sqrt(39392706 - 3602500 * lgRate_per_hz)) / 1441.0
-
-
-deep_trigger_channels = [0, 1, 2, 3]
-high_low_trigger_thresholds = {}
-high_low_trigger_thresholds["10mHz"] = RNO_G_HighLow_Thresh(-2)
-high_low_trigger_thresholds["100mHz"] = RNO_G_HighLow_Thresh(-1)
-high_low_trigger_thresholds["1Hz"] = RNO_G_HighLow_Thresh(0)
-high_low_trigger_thresholds["3Hz"] = RNO_G_HighLow_Thresh(np.log10(3))
-
-
-def task(q, iSim, energy_min, energy_max, detectordescription, config, output_filename, flavor, interaction_type, **kwargs):
-    def get_max_radius_shallow(
-        E,
-    ):  # estimated radius from max distances from noiseless 1 sigma triggers (for PA trigger), E = shower energy
-        if E <= 10**16.25 * units.eV:
-            return 1.5 * units.km
-        elif E <= 10**16.5 * units.eV:
-            return 2.1 * units.km
-        elif E <= 10**16.75 * units.eV:
-            return 2.7 * units.km
-        elif E <= 10**17.0 * units.eV:
-            return 3.1 * units.km
-        elif E <= 10**17.25 * units.eV:
-            return 3.7 * units.km
-        elif E <= 10**17.5 * units.eV:
-            return 3.9 * units.km
-        elif E <= 10**17.75 * units.eV:
-            return 4.4 * units.km
-        elif E <= 10**18.00 * units.eV:
-            return 4.8 * units.km
-        elif E <= 10**18.25 * units.eV:
-            return 5.1 * units.km
-        elif E <= 10**18.50 * units.eV:
-            return 5.25 * units.km
-        elif E <= 10**18.75 * units.eV:
-            return 5.3 * units.km
-        elif E <= 10**19.0 * units.eV:
-            return 5.6 * units.km
-        else:
-            return 6.1 * units.km
-
-    def get_min_z_shallow(E):  # estimated min z-pos from noiseless 1 sigma triggers (for PA trigger), E = shower energy
-        if E <= 10**16.25 * units.eV:
-            return -0.65 * units.km
-        elif E <= 10**16.50 * units.eV:
-            return -0.8 * units.km
-        elif E <= 10**16.75 * units.eV:
-            return -1.2 * units.km
-        elif E <= 10**17.00 * units.eV:
-            return -1.5 * units.km
-        elif E <= 10**17.25 * units.eV:
-            return -1.7 * units.km
-        elif E <= 10**17.50 * units.eV:
-            return -2.0 * units.km
-        elif E <= 10**17.75 * units.eV:
-            return -2.1 * units.km
-        elif E <= 10**18.00 * units.eV:
-            return -2.3 * units.km
-        elif E <= 10**18.25 * units.eV:
-            return -2.4 * units.km
-        elif E <= 10**18.50 * units.eV:
-            return -2.55 * units.km
-        else:
-            return -2.7 * units.km
-
-    class mySimulation(simulation.simulation):
-        def _detector_simulation_filter_amp(self, evt, station, det):
-            # apply the amplifiers and filters to get to RADIANT-level
-            rnogHarwareResponse.run(evt, station, det, temp=293.15, sim_to_data=True)
-
-        def GetTriggerBandVrmsRatio(self, channel_id, evt, station, det):
-            # Because there isn't a good way to track all changes that have been made to a
-            # channel, have to recalculate all filters that have been applied so far
-            # to get the theoretical VRMS
-
-            station_copy = copy.deepcopy(station)
-
-            # Calculate the effective bandwidth of the "full band"
-            channel = station_copy.get_channel(channel_id)
-            spectrum = channel.get_frequency_spectrum()
-            spectrum = np.ones_like(spectrum).astype(complex)
-            channel.set_frequency_spectrum(spectrum, channel.get_sampling_rate())
-            self._detector_simulation_filter_amp(evt, station_copy, det)
-            channel = station_copy.get_channel(channel_id)
-            filt_fullband = channel.get_frequency_spectrum()
-            freqs_fullband = channel.get_frequencies()
-            eff_bandwitdth_fullband = np.trapz(np.abs(filt_fullband) ** 2, freqs_fullband)
-
-            # Calculate the effective bandwidth on the trigger board
-            det_channel = det.get_channel(station_copy.get_id(), channel_id)
-            sampling_rate = det_channel["trigger_adc_sampling_frequency"]
-            mask = freqs_fullband <= sampling_rate
-            channel.resample(sampling_rate)
-            _, trigger_filter = rnogADCResponse.get_trigger_values(station_copy, det, requested_channels=deep_trigger_channels)
-            freqs_trigband = channel.get_frequencies()
-            filt_trigband = channel.get_frequency_spectrum() * trigger_filter(freqs_trigband)
-            eff_bandwitdth_trigband = np.trapz(np.abs(filt_trigband) ** 2, freqs_trigband)
-
-            # V^2 is prop. to the bandwidth
-            vrms_scaling_ratio = (eff_bandwitdth_trigband / eff_bandwitdth_fullband) ** 0.5
-
-            return vrms_scaling_ratio
-
-        def _detector_simulation_trigger(self, evt, station, det):
-
-            # Make a copy so that the "RADIANT waveforms" for the PA channels will be retained
-            station_copy = copy.deepcopy(station)
-
-            ratio = self.GetTriggerBandVrmsRatio(deep_trigger_channels[0], evt, station_copy, det)
-            vrms_input_to_adc = self._Vrms_per_channel[station_copy.get_id()][deep_trigger_channels[0]] * ratio
-
-            # Runs the FLOWER board response
-            vrms_after_gain = rnogADCResponse.run(
-                evt, station_copy, det, requested_channels=deep_trigger_channels, vrms=vrms_input_to_adc, digitize_trace=True
-            )
-
-            pa_channel = det.get_channel(station_copy.get_id(), deep_trigger_channels[0])
-            pa_sampling_rate = pa_channel["trigger_adc_sampling_frequency"] * units.GHz
-
-            for thresh_key in high_low_trigger_thresholds.keys():
-                threshold = high_low_trigger_thresholds[thresh_key]
-
-                highLowThreshold.run(
-                    evt,
-                    station_copy,
-                    det,
-                    threshold_high=threshold * vrms_after_gain,
-                    threshold_low=-threshold * vrms_after_gain,
-                    high_low_window=6 / pa_sampling_rate,
-                    coinc_window=20 / pa_sampling_rate,
-                    number_concidences=2,
-                    triggered_channels=deep_trigger_channels,
-                    trigger_name=f"deep_high_low_{thresh_key}",
-                )
-
-            # write the triggers back into the original copy of the station
-            for trigger in station_copy.get_triggers().values():
-                station.set_trigger(trigger)
-
-            # run the adjustment on the full-band waveforms
-            triggerTimeAdjuster.begin(pre_trigger_time=100 * units.ns)
-            triggerTimeAdjuster.run(evt, station, det)
-
-    flavor_ids = {"e": [12, -12], "mu": [14, -14], "tau": [16, -16]}
-    r_max = get_max_radius_shallow(energy_max)
-    z_min = get_min_z_shallow(energy_max)
-    volume = {
-        "fiducial_rmax": r_max,
-        "fiducial_rmin": 0 * units.km,
-        "fiducial_zmin": z_min,
-        "fiducial_zmax": 0,
-        "x0": kwargs["x0"],
-        "y0": kwargs["y0"],
-    }
-
-    input_data = generator.generate_eventlist_cylinder(
-        "on-the-fly",
-        kwargs["n_events_per_file"],
-        energy_min,
-        energy_max,
-        volume,
-        thetamin=0.0 * units.rad,
-        thetamax=np.pi * units.rad,
-        phimin=0.0 * units.rad,
-        phimax=2 * np.pi * units.rad,
-        start_event_id=1,
-        flavor=flavor_ids[flavor],
-        n_events_per_file=None,
-        spectrum="log_uniform",
-        deposited=True,
-        proposal=False,
-        proposal_config="SouthPole",
-        start_file_id=0,
-        log_level=None,
-        proposal_kwargs={},
-        max_n_events_batch=1e3,
-        write_events=False,
-        seed=root_seed + iSim,
-        interaction_type=interaction_type,
-    )
-
-    sim = mySimulation(
-        inputfilename=input_data,
-        outputfilename=output_filename,
-        detectorfile=detectordescription,
-        outputfilenameNuRadioReco=output_filename.replace(".hdf5", ".nur"),
-        config_file=config,
-    )
-    n_trig = sim.run()
-
-    print(f"simulation pass {iSim} with {n_trig} events", flush=True)
-    q.put(n_trig)
-
-
-if __name__ == "__main__":
-
-    ABS_PATH_HERE = str(os.path.dirname(os.path.realpath(__file__)))
-    def_data_dir = os.path.join(ABS_PATH_HERE, "data")
-
-    parser = argparse.ArgumentParser(description="Run NuRadioMC simulation")
-    # Sim steering arguments
-    parser.add_argument("detectordescription", type=str, help="path to file containing the detector description")
-    parser.add_argument("config", type=str, help="NuRadioMC yaml config file")
-
-    # Neutrino arguments
-    parser.add_argument("energy_min", type=float, help="Minimum range of neutrino energies [eV]")
-    parser.add_argument("energy_max", type=float, help="Maximum range of neutrino energies [eV]")
-    parser.add_argument("flavor", type=str, help="the flavor")
-    parser.add_argument("interaction_type", type=str, help="interaction type cc, nc or ccnc")
-
-    # File meta-variables
-    parser.add_argument("index", type=int, help="counter to create a unique data-set identifier")
-    parser.add_argument("--n_cpus", type=int, default=1, help="Number of cores for parallel running")
-    parser.add_argument("--n_triggers", type=int, default=3e4, help="Number of total events to generate")
-    parser.add_argument("--n_events_per_file", type=int, default=1e3, help="Number of nu-interactions per file")
-    parser.add_argument("--data_dir", type=str, default=def_data_dir, help="directory name where the library will be created")
-    parser.add_argument("--x0", type=float, default=0.0, help="x-coord center of the cylinder over which to throw events")
-    parser.add_argument("--y0", type=float, default=0.0, help="y-coord center of the cylinder over which to throw events")
-    parser.add_argument(
-        "--center_around_station_id",
-        type=int,
-        default=None,
-        help="If set, will center the cylinder around this station, overwrites x0, y0 commands",
-    )
-
-    args = parser.parse_args()
-    kwargs = args.__dict__
-
-    if args.center_around_station_id is not None:
-        temp_det = detector.Detector(json_filename=args.detectordescription, antenna_by_depth=False)
-        if args.center_around_station_id not in temp_det.get_station_ids():
-            raise RuntimeError(f"Cannot find station {args.center_around_station_id} in detector config")
-        kwargs["x0"] = temp_det.get_absolute_position(args.center_around_station_id)[0]
-        kwargs["y0"] = temp_det.get_absolute_position(args.center_around_station_id)[1]
-
-    filename = (os.path.splitext(os.path.basename(__file__))[0]).split(".")[0]
-    output_path = os.path.join(
-        args.data_dir,
-        os.path.splitext(os.path.basename(args.detectordescription))[0],
-        os.path.splitext(os.path.basename(args.config))[0],
-        filename,
-        f"nu_{args.flavor}_{args.interaction_type}",
-        f"{args.index:06}",
-        f"{np.log10(args.energy_min):.2f}eV",
-    )
-
-    if not os.path.exists(output_path):
-        print("Making dirs", output_path)
-        os.makedirs(output_path)
-
-    class myrunner(runner.NuRadioMCRunner):
-        def get_outputfilename(self):
-            return os.path.join(
-                self.output_path, f"{np.log10(args.energy_min):.2f}_{self.kwargs['index']:06d}_{self.i_task:06d}.hdf5"
-            )
-
-    # start simulating a library across the chosen number of cpus. Each CPU will only run for 1 day
-    r = myrunner(args.n_cpus, task, output_path, max_runtime=3600 * 24, n_triggers_max=args.n_triggers, kwargs=kwargs)
-    r.run()
diff --git a/NuRadioReco/examples/RNO_data/station_simulation/simulate_rnog_nominal.py b/NuRadioReco/examples/RNO_data/station_simulation/simulate_rnog_nominal.py
deleted file mode 100755
index 5a85d2835d..0000000000
--- a/NuRadioReco/examples/RNO_data/station_simulation/simulate_rnog_nominal.py
+++ /dev/null
@@ -1,291 +0,0 @@
-#!/bin/env python3
-
-import argparse
-import copy
-import logging
-import numpy as np
-import os
-import scipy
-from scipy import constants
-import secrets
-import time
-
-from NuRadioMC.EvtGen import generator
-from NuRadioMC.simulation import simulation
-from NuRadioMC.utilities import runner
-from NuRadioReco.utilities import units
-
-from NuRadioReco.modules import triggerTimeAdjuster
-from NuRadioReco.modules.phasedarray.triggerSimulator import triggerSimulator as phasedArrayTrigger
-from NuRadioReco.modules.RNO_G import hardwareResponseIncorporator, triggerBoardResponse
-
-root_seed = secrets.randbits(128)
-
-triggerTimeAdjuster = triggerTimeAdjuster.triggerTimeAdjuster(log_level=logging.WARNING)
-rnogHarwareResponse = hardwareResponseIncorporator.hardwareResponseIncorporator()
-rnogADCResponse = triggerBoardResponse.triggerBoardResponse(log_level=logging.WARNING)
-rnogADCResponse.begin(adc_input_range=2 * units.volt, clock_offset=0.0, adc_output="voltage")
-
-#############################
-## Set up trigger definitions
-#############################
-
-
-def RNO_G_PA_Thresh(lgRate_per_hz):
-    # Thresholds calculated using the RNO-G hardware (iglu + flower_lp)
-    return 0.0282 * lgRate_per_hz**2 - 4.607 * lgRate_per_hz + 46.24
-
-
-pa_channels = [0, 1, 2, 3]
-phasedArrayTrigger = phasedArrayTrigger(log_level=logging.WARNING)
-main_low_angle = np.deg2rad(-59.54968597864437)
-main_high_angle = np.deg2rad(59.54968597864437)
-phasing_angles = np.arcsin(np.linspace(np.sin(main_low_angle), np.sin(main_high_angle), 11))
-
-pa_thresholds = {}
-pa_thresholds["1Hz"] = RNO_G_PA_Thresh(0)
-pa_thresholds["10Hz"] = RNO_G_PA_Thresh(1)
-pa_thresholds["100Hz"] = RNO_G_PA_Thresh(2)
-pa_thresholds["1kHz"] = RNO_G_PA_Thresh(3)
-
-
-def task(q, iSim, energy_min, energy_max, detectordescription, config, output_filename, flavor, interaction_type, **kwargs):
-    def get_max_radius_shallow(
-        E,
-    ):  # estimated radius from max distances from noiseless 1 sigma triggers (for PA trigger), E = shower energy
-        if E <= 10**16.25 * units.eV:
-            return 1.5 * units.km
-        elif E <= 10**16.5 * units.eV:
-            return 2.1 * units.km
-        elif E <= 10**16.75 * units.eV:
-            return 2.7 * units.km
-        elif E <= 10**17.0 * units.eV:
-            return 3.1 * units.km
-        elif E <= 10**17.25 * units.eV:
-            return 3.7 * units.km
-        elif E <= 10**17.5 * units.eV:
-            return 3.9 * units.km
-        elif E <= 10**17.75 * units.eV:
-            return 4.4 * units.km
-        elif E <= 10**18.00 * units.eV:
-            return 4.8 * units.km
-        elif E <= 10**18.25 * units.eV:
-            return 5.1 * units.km
-        elif E <= 10**18.50 * units.eV:
-            return 5.25 * units.km
-        elif E <= 10**18.75 * units.eV:
-            return 5.3 * units.km
-        elif E <= 10**19.0 * units.eV:
-            return 5.6 * units.km
-        else:
-            return 6.1 * units.km
-
-    def get_min_z_shallow(E):  # estimated min z-pos from noiseless 1 sigma triggers (for PA trigger), E = shower energy
-        if E <= 10**16.25 * units.eV:
-            return -0.65 * units.km
-        elif E <= 10**16.50 * units.eV:
-            return -0.8 * units.km
-        elif E <= 10**16.75 * units.eV:
-            return -1.2 * units.km
-        elif E <= 10**17.00 * units.eV:
-            return -1.5 * units.km
-        elif E <= 10**17.25 * units.eV:
-            return -1.7 * units.km
-        elif E <= 10**17.50 * units.eV:
-            return -2.0 * units.km
-        elif E <= 10**17.75 * units.eV:
-            return -2.1 * units.km
-        elif E <= 10**18.00 * units.eV:
-            return -2.3 * units.km
-        elif E <= 10**18.25 * units.eV:
-            return -2.4 * units.km
-        elif E <= 10**18.50 * units.eV:
-            return -2.55 * units.km
-        else:
-            return -2.7 * units.km
-
-    class mySimulation(simulation.simulation):
-        def _detector_simulation_filter_amp(self, evt, station, det):
-            # apply the amplifiers and filters to get to RADIANT-level
-            rnogHarwareResponse.run(evt, station, det, temp=293.15, sim_to_data=True)
-
-        def GetTriggerBandVrmsRatio(self, channel_id, evt, station, det):
-            # Because there isn't a good way to track all changes that have been made to a
-            # channel, have to recalculate all filters that have been applied so far
-            # to get the theoretical VRMS
-
-            station_copy = copy.deepcopy(station)
-
-            # Calculate the effective bandwidth of the "full band"
-            channel = station_copy.get_channel(channel_id)
-            spectrum = channel.get_frequency_spectrum()
-            spectrum = np.ones_like(spectrum).astype(complex)
-            channel.set_frequency_spectrum(spectrum, channel.get_sampling_rate())
-            self._detector_simulation_filter_amp(evt, station_copy, det)
-            channel = station_copy.get_channel(channel_id)
-            filt_fullband = channel.get_frequency_spectrum()
-            freqs_fullband = channel.get_frequencies()
-            eff_bandwitdth_fullband = np.trapz(np.abs(filt_fullband) ** 2, freqs_fullband)
-
-            # Calculate the effective bandwidth on the trigger board
-            det_channel = det.get_channel(station_copy.get_id(), channel_id)
-            sampling_rate = det_channel["trigger_adc_sampling_frequency"]
-            mask = freqs_fullband <= sampling_rate
-            channel.resample(sampling_rate)
-            _, trigger_filter = rnogADCResponse.get_trigger_values(station_copy, det, requested_channels=pa_channels)
-            freqs_trigband = channel.get_frequencies()
-            filt_trigband = channel.get_frequency_spectrum() * trigger_filter(freqs_trigband)
-            eff_bandwitdth_trigband = np.trapz(np.abs(filt_trigband) ** 2, freqs_trigband)
-
-            # V^2 is prop. to the bandwidth
-            vrms_scaling_ratio = (eff_bandwitdth_trigband / eff_bandwitdth_fullband) ** 0.5
-
-            return vrms_scaling_ratio
-
-        def _detector_simulation_trigger(self, evt, station, det):
-
-            # Make a copy so that the "RADIANT waveforms" for the PA channels will be retained
-            station_copy = copy.deepcopy(station)
-
-            ratio = self.GetTriggerBandVrmsRatio(pa_channels[0], evt, station_copy, det)
-            vrms_input_to_adc = self._Vrms_per_channel[station_copy.get_id()][pa_channels[0]] * ratio
-
-            # Runs the FLOWER board response
-            vrms_after_gain = rnogADCResponse.run(
-                evt, station_copy, det, requested_channels=pa_channels, vrms=vrms_input_to_adc, digitize_trace=True
-            )  # Digitization will be done in the trigger module
-
-            pa_channel = det.get_channel(station_copy.get_id(), pa_channels[0])
-            pa_sampling_rate = pa_channel["trigger_adc_sampling_frequency"] * units.GHz
-
-            # integration window size and stride
-            # Warning: if you change these, you must also recalculate the thresholds!
-            upsampling_factor = 2
-            pa_window = int(16 * units.ns * pa_sampling_rate * upsampling_factor)
-            pa_step = int(8 * units.ns * pa_sampling_rate * upsampling_factor)
-
-            # Run the phased array trigger
-            for thresh_key in pa_thresholds.keys():
-                threshold = pa_thresholds[thresh_key]
-
-                phasedArrayTrigger.run(
-                    evt,
-                    station_copy,
-                    det,
-                    Vrms=vrms_after_gain,
-                    threshold=threshold * np.power(vrms_after_gain, 2.0),
-                    triggered_channels=pa_channels,
-                    phasing_angles=phasing_angles,
-                    ref_index=1.75,
-                    trigger_name=f"PA_{thresh_key}",  # the name of the trigger
-                    trigger_adc=False,
-                    adc_output=f"voltage",  # output in volts
-                    trigger_filter=None,  # already applied
-                    upsampling_factor=upsampling_factor,
-                    window=pa_window,
-                    step=pa_step,
-                    apply_digitization=False,
-                )
-
-            # write the triggers back into the original copy of the station
-            for trigger in station_copy.get_triggers().values():
-                station.set_trigger(trigger)
-
-            # run the adjustment on the full-band waveforms
-            triggerTimeAdjuster.begin(pre_trigger_time=100 * units.ns)
-            triggerTimeAdjuster.run(evt, station, det)
-
-    flavor_ids = {"e": [12, -12], "mu": [14, -14], "tau": [16, -16]}
-    r_max = get_max_radius_shallow(energy_max)
-    z_min = get_min_z_shallow(energy_max)
-    volume = {"fiducial_rmax": r_max, "fiducial_rmin": 0 * units.km, "fiducial_zmin": z_min, "fiducial_zmax": 0}
-
-    input_data = generator.generate_eventlist_cylinder(
-        "on-the-fly",
-        kwargs["n_events_per_file"],
-        energy_min,
-        energy_max,
-        volume,
-        thetamin=0.0 * units.rad,
-        thetamax=np.pi * units.rad,
-        phimin=0.0 * units.rad,
-        phimax=2 * np.pi * units.rad,
-        start_event_id=1,
-        flavor=flavor_ids[flavor],
-        n_events_per_file=None,
-        spectrum="log_uniform",
-        deposited=True,
-        proposal=False,
-        proposal_config="SouthPole",
-        start_file_id=0,
-        log_level=None,
-        proposal_kwargs={},
-        max_n_events_batch=1e3,
-        write_events=False,
-        seed=root_seed + iSim,
-        interaction_type=interaction_type,
-    )
-
-    sim = mySimulation(
-        inputfilename=input_data,
-        outputfilename=output_filename,
-        detectorfile=detectordescription,
-        outputfilenameNuRadioReco=output_filename.replace(".hdf5", ".nur"),
-        config_file=config,
-    )
-    n_trig = sim.run()
-
-    print(f"simulation pass {iSim} with {n_trig} events", flush=True)
-    q.put(n_trig)
-
-
-if __name__ == "__main__":
-
-    ABS_PATH_HERE = str(os.path.dirname(os.path.realpath(__file__)))
-    def_data_dir = os.path.join(ABS_PATH_HERE, "data")
-
-    parser = argparse.ArgumentParser(description="Run NuRadioMC simulation")
-    # Sim steering arguments
-    parser.add_argument("detectordescription", type=str, help="path to file containing the detector description")
-    parser.add_argument("config", type=str, help="NuRadioMC yaml config file")
-
-    # Neutrino arguments
-    parser.add_argument("energy_min", type=float, help="Minimum range of neutrino energies [eV]")
-    parser.add_argument("energy_max", type=float, help="Maximum range of neutrino energies [eV]")
-    parser.add_argument("flavor", type=str, help="the flavor")
-    parser.add_argument("interaction_type", type=str, help="interaction type cc, nc or ccnc")
-
-    # File meta-variables
-    parser.add_argument("index", type=int, help="counter to create a unique data-set identifier")
-    parser.add_argument("--n_cpus", type=int, default=1, help="Number of cores for parallel running")
-    parser.add_argument("--n_triggers", type=int, default=3e4, help="Number of total events to generate")
-    parser.add_argument("--n_events_per_file", type=int, default=1e3, help="Number of nu-interactions per file")
-    parser.add_argument("--data_dir", type=str, default=def_data_dir, help="directory name where the library will be created")
-
-    args = parser.parse_args()
-    kwargs = args.__dict__
-
-    filename = (os.path.splitext(os.path.basename(__file__))[0]).split(".")[0]
-    output_path = os.path.join(
-        args.data_dir,
-        os.path.splitext(os.path.basename(args.detectordescription))[0],
-        os.path.splitext(os.path.basename(args.config))[0],
-        filename,
-        f"nu_{args.flavor}_{args.interaction_type}",
-        f"{args.index:06}",
-        f"{np.log10(args.energy_min):.2f}eV",
-    )
-
-    if not os.path.exists(output_path):
-        print("Making dirs", output_path)
-        os.makedirs(output_path)
-
-    class myrunner(runner.NuRadioMCRunner):
-        def get_outputfilename(self):
-            return os.path.join(
-                self.output_path, f"{np.log10(args.energy_min):.2f}_{self.kwargs['index']:06d}_{self.i_task:06d}.hdf5"
-            )
-
-    # start simulating a library across the chosen number of cpus. Each CPU will only run for 1 day
-    r = myrunner(args.n_cpus, task, output_path, max_runtime=3600 * 24, n_triggers_max=args.n_triggers, kwargs=kwargs)
-    r.run()
diff --git a/NuRadioReco/examples/StandAloneScripts/B01galacticnoiseadder.py b/NuRadioReco/examples/StandAloneScripts/B01galacticnoiseadder.py
new file mode 100644
index 0000000000..5bd1484a22
--- /dev/null
+++ b/NuRadioReco/examples/StandAloneScripts/B01galacticnoiseadder.py
@@ -0,0 +1,67 @@
+"""
+Script that tests the `channelGalacticNoiseAdder` module
+"""
+from NuRadioReco.modules import channelGalacticNoiseAdder
+import NuRadioReco.framework.event
+import NuRadioReco.framework.station
+import NuRadioReco.framework.channel
+from NuRadioReco.detector.detector import Detector
+from NuRadioReco.utilities import units
+import numpy as np
+import astropy.time
+import logging
+import argparse
+import time
+
+logger = logging.getLogger('NuRadioReco.channelGalacticNoiseAdder')
+
+if __name__ == "__main__":
+    argparser = argparse.ArgumentParser(description="Script to test the channelGalacticNoiseAdder module.")
+    argparser.add_argument('--skymodel', default='gsm2008', help='Which skymodel to test')
+    argparser.add_argument('--n_trials', '-n', default=10, help='Number of runs')
+    args = argparser.parse_args()
+
+
+    noiseadder = channelGalacticNoiseAdder.channelGalacticNoiseAdder()
+    noiseadder.begin(skymodel=args.skymodel, seed=1234)
+    det = Detector(json_filename='RNO_G/RNO_season_2023.json', antenna_by_depth=False)
+    det.update(astropy.time.Time.now())
+
+    evt = NuRadioReco.framework.event.Event(0, 0)
+    evt.set_station(NuRadioReco.framework.station.Station(11))
+    station = evt.get_station(11)
+    station.set_station_time(astropy.time.Time.now())
+
+    n_trials = args.n_trials
+    channel_ids = [13,16,19] # only look at upward-facing LPDAs
+
+    for i in channel_ids: # only look at upward-facing LPDAs
+        channel = NuRadioReco.framework.channel.Channel(i)
+        channel.set_trace(np.zeros(2048), sampling_rate=2.4)
+        station.add_channel(channel)
+
+    logger.status(f"Simulating galactic noise for {len(channel_ids)} channels, {n_trials} trials...")
+    vrms = np.zeros((len(channel_ids), n_trials))
+    for i in range(n_trials):
+        if i==1:
+            t0 = time.time() # we start after the first trial to exclude the antenna loading time
+
+        # reset channel trace
+        for channel_id in channel_ids:
+            channel = station.get_channel(channel_id)
+            channel.set_frequency_spectrum(0*channel.get_frequency_spectrum(), sampling_rate='same')
+
+        noiseadder.run(evt, station, det)
+        for ichannel, channel_id in enumerate(channel_ids):
+            vrms[ichannel, i] = np.std(station.get_channel(channel_id).get_trace())
+
+    dt_per_trial = (time.time() - t0) / (len(channel_ids) * (n_trials - 1)) * units.s
+
+    logger.status(
+        "median RMS noise voltage at antenna outputs: "
+        f"{np.round(np.median(vrms, axis=1) / units.microvolt, 3)} muV "
+        "(Expected ~6 muV)"
+        )
+    logger.status(
+        f"Simulating galactic noise took {dt_per_trial/units.ms:.0f} ms per channel and trial."
+    )
\ No newline at end of file
diff --git a/NuRadioReco/framework/base_shower.py b/NuRadioReco/framework/base_shower.py
index 16b89da586..d7c7e3f76b 100644
--- a/NuRadioReco/framework/base_shower.py
+++ b/NuRadioReco/framework/base_shower.py
@@ -1,6 +1,6 @@
 from __future__ import absolute_import, division, print_function
-import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.framework.parameter_serialization
+from NuRadioReco.framework.parameters import showerParameters
+import NuRadioReco.framework.parameter_storage
 from radiotools import helper as hp, coordinatesystems
 import pickle
 
@@ -8,41 +8,14 @@
 logger = logging.getLogger('NuRadioReco.Shower')
 
 
-class BaseShower:
+class BaseShower(NuRadioReco.framework.parameter_storage.ParameterStorage):
 
     def __init__(self, shower_id=0):
+        super().__init__(showerParameters)
         self._id = shower_id
-        self._parameters = {}
-
-    def __setitem__(self, key, value):
-        self.set_parameter(key, value)
-
-    def __getitem__(self, key):
-        return self.get_parameter(key)
 
     def get_id(self):
         return self._id
-    
-    def get_parameters(self):
-        return self._parameters
-
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.showerParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.showerParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.showerParameters")
-        return self._parameters[key]
-
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.showerParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.showerParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.showerParameters")
-        self._parameters[key] = value
-
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.showerParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.showerParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.showerParameters")
-        return key in self._parameters
 
     def get_axis(self):
         """
@@ -58,15 +31,15 @@ def get_axis(self):
             Shower axis
 
         """
-        if not self.has_parameter(parameters.showerParameters.azimuth) or \
-           not self.has_parameter(parameters.showerParameters.zenith):
+        if not self.has_parameter(showerParameters.azimuth) or \
+           not self.has_parameter(showerParameters.zenith):
             logger.error(
                 "Azimuth or zenith angle not set! Can not return shower axis.")
             raise ValueError(
                 "Azimuth or zenith angle not set! Can not return shower axis.")
 
-        return hp.spherical_to_cartesian(self.get_parameter(parameters.showerParameters.zenith),
-                                         self.get_parameter(parameters.showerParameters.azimuth))
+        return hp.spherical_to_cartesian(self.get_parameter(showerParameters.zenith),
+                                         self.get_parameter(showerParameters.azimuth))
 
     def get_coordinatesystem(self):
         """
@@ -74,7 +47,7 @@ def get_coordinatesystem(self):
 
         Can be used to transform the radio pulses or the observer coordiates
         in the shower frame. Requieres the shower arrival direction
-        (azimuth and zenith angle) and magnetic field vector (parameters.showerParameters).
+        (azimuth and zenith angle) and magnetic field vector (showerParameters).
 
         Returns
         -------
@@ -82,30 +55,24 @@ def get_coordinatesystem(self):
         radiotools.coordinatesystem.cstrafo
 
         """
-        if not self.has_parameter(parameters.showerParameters.azimuth) or \
-           not self.has_parameter(parameters.showerParameters.zenith) or \
-           not self.has_parameter(parameters.showerParameters.magnetic_field_vector):
+        if not self.has_parameter(showerParameters.azimuth) or \
+           not self.has_parameter(showerParameters.zenith) or \
+           not self.has_parameter(showerParameters.magnetic_field_vector):
             logger.error(
                 "Magnetic field vector, azimuth or zenith angle not set! Can not return shower coordinatesystem.")
             raise ValueError(
                 "Magnetic field vector, azimuth or zenith angle not set! Can not return shower coordinatesystem.")
 
-        return coordinatesystems.cstrafo(self.get_parameter(parameters.showerParameters.zenith),
-                                         self.get_parameter(parameters.showerParameters.azimuth),
-                                         self.get_parameter(parameters.showerParameters.magnetic_field_vector))
+        return coordinatesystems.cstrafo(self.get_parameter(showerParameters.zenith),
+                                         self.get_parameter(showerParameters.azimuth),
+                                         self.get_parameter(showerParameters.magnetic_field_vector))
 
     def serialize(self):
-        data = {'_parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                '_id': self._id}
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+        data['_id'] = self._id
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
-        if '_id' in data.keys():
-            self._id = data['_id']
-        else:
-            self._id = None
-        self._parameters = NuRadioReco.framework.parameter_serialization.deserialize(
-            data['_parameters'],
-            parameters.showerParameters
-        )
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
+        self._id = data.get('_id', None)
diff --git a/NuRadioReco/framework/base_station.py b/NuRadioReco/framework/base_station.py
index 6bb95bfe3d..8d611c9563 100644
--- a/NuRadioReco/framework/base_station.py
+++ b/NuRadioReco/framework/base_station.py
@@ -3,26 +3,26 @@
 import NuRadioReco.framework.trigger
 import NuRadioReco.framework.electric_field
 import NuRadioReco.framework.parameters as parameters
+import NuRadioReco.framework.parameter_storage
+from NuRadioReco.utilities import io_utilities
+
 import datetime
 import astropy.time
-import NuRadioReco.framework.parameter_serialization
+import logging
+import collections
 
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
-import logging
-import collections
 
 logger = logging.getLogger('NuRadioReco.BaseStation')
 
 
-class BaseStation():
+class BaseStation(NuRadioReco.framework.parameter_storage.ParameterStorage):
 
     def __init__(self, station_id):
-        self._parameters = {}
-        self._ARIANNA_parameters = {}
-        self._parameter_covariances = {}
+        super().__init__(parameters.stationParameters)
         self._station_id = station_id
         self._station_time = None
         self._triggers = collections.OrderedDict()
@@ -30,59 +30,12 @@ def __init__(self, station_id):
         self._electric_fields = []
         self._particle_type = ''
 
-    def __setitem__(self, key, value):
-        self.set_parameter(key, value)
-
-    def __getitem__(self, key):
-        return self.get_parameter(key)
-
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.stationParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-        return self._parameters[key]
-
-    def get_parameters(self):
-        return self._parameters
-
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.stationParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-        return key in self._parameters.keys()
-
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.stationParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-        self._parameters[key] = value
-
-    def set_parameter_error(self, key, value):
-        if not isinstance(key, parameters.stationParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-        self._parameter_covariances[(key, key)] = value ** 2
-
-    def get_parameter_error(self, key):
-        if not isinstance(key, parameters.stationParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-        return self._parameter_covariances[(key, key)] ** 0.5
-
-    def remove_parameter(self, key):
-        if not isinstance(key, parameters.stationParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.stationParameters")
-        self._parameters.pop(key, None)
-
     def set_station_time(self, time, format=None):
         """
         Set the (absolute) time for the station (stored as astropy.time.Time).
-        Not related to the event._event_time.
 
         Parameters
         ----------
-
         time: astropy.time.Time or datetime.datetime or float
             If "time" is a float, you have to specify its format.
 
@@ -97,22 +50,28 @@ def set_station_time(self, time, format=None):
         elif time is None:
             self._station_time = None
         else:
+            if format is None:
+                logger.error("If you provide a float for the time, you have to specify the format.")
+                raise ValueError("If you provide a float for the time, you have to specify the format.")
             self._station_time = astropy.time.Time(time, format=format)
 
     def get_station_time(self, format='isot'):
         """
-        Returns a astropy.time.Time object
+        Returns the station time as an astropy.time.Time object
+
+        The station time corresponds to the absolute time at which the event
+        starts, i.e. all times in Channel, Trigger and ElectricField objects
+        are measured relative to this time.
 
         Parameters
         ----------
-
         format: str
             Format in which the time object is displayed. (Default: isot)
 
         Returns
         -------
 
-        _station_time: astropy.time.Time
+        station_time: astropy.time.Time
         """
         if self._station_time is None:
             return None
@@ -120,13 +79,6 @@ def get_station_time(self, format='isot'):
         self._station_time.format = format
         return self._station_time
 
-    def get_station_time_dict(self):
-        """ Return the station time as dict {value, format}. Used for reading and writing """
-        if self._station_time is None:
-            return None
-        else:
-            return {'value': self._station_time.value, 'format': self._station_time.format}
-
     def get_id(self):
         return self._station_id
 
@@ -317,28 +269,6 @@ def set_is_cosmic_ray(self):
         """
         self._particle_type = 'cr'
 
-    # provide interface to ARIANNA specific parameters
-    def get_ARIANNA_parameter(self, key):
-        if not isinstance(key, parameters.ARIANNAParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.ARIANNAParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.ARIANNAParameters")
-        return self._ARIANNA_parameters[key]
-
-    def get_ARIANNA_parameters(self):
-        return self._ARIANNA_parameters
-
-    def has_ARIANNA_parameter(self, key):
-        if not isinstance(key, parameters.ARIANNAParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.ARIANNAParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.ARIANNAParameters")
-        return key in self._ARIANNA_parameters.keys()
-
-    def set_ARIANNA_parameter(self, key, value):
-        if not isinstance(key, parameters.ARIANNAParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.ARIANNAParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.ARIANNAParameters")
-        self._ARIANNA_parameters[key] = value
-
     def serialize(self, save_efield_traces):
         trigger_pkls = []
         for trigger in self._triggers.values():
@@ -348,22 +278,23 @@ def serialize(self, save_efield_traces):
         for efield in self.get_electric_fields():
             efield_pkls.append(efield.serialize(save_trace=save_efield_traces))
 
-        station_time_dict = self.get_station_time_dict()
+        station_time_dict = io_utilities._astropy_to_dict(self.get_station_time())
 
-        data = {'_parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                '_parameter_covariances': NuRadioReco.framework.parameter_serialization.serialize_covariances(self._parameter_covariances),
-                '_ARIANNA_parameters': self._ARIANNA_parameters,
-                '_station_id': self._station_id,
-                '_station_time': station_time_dict,
-                '_particle_type': self._particle_type,
-                'triggers': trigger_pkls,
-                '_triggered': self._triggered,
-                'electric_fields': efield_pkls}
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+        data.update({
+            '_station_id': self._station_id,
+            '_station_time': station_time_dict,
+            '_particle_type': self._particle_type,
+            'triggers': trigger_pkls,
+            '_triggered': self._triggered,
+            'electric_fields': efield_pkls
+        })
 
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
 
         if 'triggers' in data:
             self._triggers = NuRadioReco.framework.trigger.deserialize(data['triggers'])
@@ -376,23 +307,15 @@ def deserialize(self, data_pkl):
             efield.deserialize(electric_field)
             self.add_electric_field(efield)
 
-        self._parameters = NuRadioReco.framework.parameter_serialization.deserialize(data['_parameters'],
-                                                                                     parameters.stationParameters)
-
-        self._parameter_covariances = NuRadioReco.framework.parameter_serialization.deserialize_covariances(
-            data['_parameter_covariances'], parameters.stationParameters)
-
+        # For backward compatibility, now ARIANNA parameters are stored in `_parameters`.
         if '_ARIANNA_parameters' in data:
-            self._ARIANNA_parameters = data['_ARIANNA_parameters']
+            for key in data['_ARIANNA_parameters']:
+                self.set_parameter(key, data['_ARIANNA_parameters'][key])
 
         self._station_id = data['_station_id']
         if data['_station_time'] is not None:
-            if isinstance(data['_station_time'], dict):
-                station_time = astropy.time.Time(data['_station_time']['value'], format=data['_station_time']['format'])
-                self.set_station_time(station_time)
-            # For backward compatibility, we also keep supporting station times stored as astropy.time objects
-            else:
-                self.set_station_time(data['_station_time'])
+            station_time = io_utilities._time_object_to_astropy(data['_station_time'])
+            self.set_station_time(station_time)
 
         self._particle_type = data['_particle_type']
 
@@ -409,14 +332,36 @@ def __add__(self, x):
         """
         if not isinstance(x, BaseStation):
             raise AttributeError("Can only add BaseStation to BaseStation")
+
         if self.get_id() != x.get_id():
             raise AttributeError("Can only add BaseStations with the same ID")
+
         for trigger in x.get_triggers().values():
             self.set_trigger(trigger)
+
         for efield in x.get_electric_fields():
             self.add_electric_field(efield)
+
         for key, value in x.get_parameters().items():
             self.set_parameter(key, value)
-        for key, value in x.get_ARIANNA_parameters().items():
-            self.set_ARIANNA_parameter(key, value)
-        return self
\ No newline at end of file
+
+        return self
+
+
+    ######## Deprecated functions ########
+
+    def get_ARIANNA_parameter(self, key):
+        logger.warning("`get_ARIANNA_parameter` is deprecated. Use `get_parameter` instead.")
+        raise NotImplementedError("`get_ARIANNA_parameter` is deprecated. Use `get_parameter` instead.")
+
+    def get_ARIANNA_parameters(self):
+        logger.warning("`get_ARIANNA_parameters` is deprecated. Use `get_parameters` instead.")
+        raise NotImplementedError("`get_ARIANNA_parameters` is deprecated. Use `get_parameters` instead.")
+
+    def has_ARIANNA_parameter(self, key):
+        logger.warning("`has_ARIANNA_parameter` is deprecated. Use `has_parameter` instead.")
+        raise NotImplementedError("`has_ARIANNA_parameter` is deprecated. Use `has_parameter` instead.")
+
+    def set_ARIANNA_parameter(self, key, value):
+        logger.warning("`set_ARIANNA_parameter` is deprecated. Use `set_parameter` instead.")
+        raise NotImplementedError("`set_ARIANNA_parameter` is deprecated. Use `set_parameter` instead.")
diff --git a/NuRadioReco/framework/base_trace.py b/NuRadioReco/framework/base_trace.py
index 020a98167c..3894a5d465 100644
--- a/NuRadioReco/framework/base_trace.py
+++ b/NuRadioReco/framework/base_trace.py
@@ -1,12 +1,14 @@
 from __future__ import absolute_import, division, print_function
+
+from NuRadioReco.utilities import fft, bandpass_filter
+import NuRadioReco.detector.response
+
 import numpy as np
 import logging
 import fractions
 import decimal
 import numbers
 import functools
-from NuRadioReco.utilities import fft, bandpass_filter
-import NuRadioReco.detector.response
 import scipy.signal
 import copy
 try:
@@ -299,9 +301,9 @@ def deserialize(self, data_pkl):
 
     def add_to_trace(self, channel):
         """
-        Adds the trace of another channel to the trace of this channel. The trace is only added within the 
+        Adds the trace of another channel to the trace of this channel. The trace is only added within the
         time window of "this" channel.
-        If this channel is an empty trace with a defined _sampling_rate and _trace_start_time, and a 
+        If this channel is an empty trace with a defined _sampling_rate and _trace_start_time, and a
         _time_trace containing zeros, this function can be seen as recording a channel in the specified
         readout window.
 
@@ -354,7 +356,7 @@ def add_to_trace(self, channel):
             t_end_readout = tt_readout[i_end_readout]
             i_end_channel = n_samples_channel - 1
             t_end_channel = t1_channel
-        
+
         # Determine the remaining time between the binning of the two traces and use time shift as interpolation:
         residual_time_offset = t_start_channel - t_start_readout
         tmp_channel = copy.deepcopy(channel)
diff --git a/NuRadioReco/framework/channel.py b/NuRadioReco/framework/channel.py
index bde211799c..ab0682539b 100644
--- a/NuRadioReco/framework/channel.py
+++ b/NuRadioReco/framework/channel.py
@@ -1,7 +1,7 @@
 from __future__ import absolute_import, division, print_function
 import NuRadioReco.framework.base_trace
 import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.framework.parameter_serialization
+import NuRadioReco.framework.parameter_storage
 try:
     import cPickle as pickle
 except ImportError:
@@ -10,8 +10,8 @@
 logger = logging.getLogger('NuRadioReco.Channel')
 
 
-class Channel(NuRadioReco.framework.base_trace.BaseTrace):
-
+class Channel(NuRadioReco.framework.base_trace.BaseTrace,
+              NuRadioReco.framework.parameter_storage.ParameterStorage):
     def __init__(self, channel_id, channel_group_id=None):
         """
         Parameters
@@ -20,44 +20,49 @@ def __init__(self, channel_id, channel_group_id=None):
             the id of the channel
         channel_group_id: int (default None)
             optionally, several channels can belong to a "channel group". Use case is to identify
-            the channels of a single dual or triple polarized antenna as common in air shower arrays. 
-        
+            the channels of a single dual or triple polarized antenna as common in air shower arrays.
+
         """
         NuRadioReco.framework.base_trace.BaseTrace.__init__(self)
-        self._parameters = {}
+        NuRadioReco.framework.parameter_storage.ParameterStorage.__init__(
+            self, parameters.channelParameters)
+
         self._id = channel_id
         self._group_id = channel_group_id
+        self._trigger_channel = None
 
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.channelParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.channelParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.channelParameters")
-        return self._parameters[key]
+    def set_trigger_channel(self, trigger_channel):
+        """ Sets an extra trigger channel of this channel. """
+        if not type(trigger_channel) == Channel:
+            logger.error("trigger_channel needs to be of type NuRadioReco.framework.Channel")
+            raise ValueError("trigger_channel needs to be of type NuRadioReco.framework.Channel")
 
-    def get_parameters(self):
-        return self._parameters
+        if trigger_channel.get_id() != self.get_id():
+            msg = (f"channel id of trigger channel {trigger_channel.get_id()} is different "
+                f"from the channel id {self.get_id()}")
+            logger.error(msg)
+            raise ValueError(msg)
 
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.channelParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.channelParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.channelParameters")
-        self._parameters[key] = value
+        self._trigger_channel = trigger_channel
 
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.channelParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.channelParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.channelParameters")
-        return key in self._parameters
+    def get_trigger_channel(self):
+        """ Returns the trigger channel of this channel.
 
-    def __setitem__(self, key, value):
-        self.set_parameter(key, value)
+        Not all channels/detectors make use of additional trigger channels.
+        If no trigger channel is set, this just returns ``self``, i.e. the Channel object itself.
+        """
+        if self._trigger_channel is None:
+            return self
+
+        return self._trigger_channel
 
-    def __getitem__(self, key):
-        return self.get_parameter(key)
+    def has_extra_trigger_channel(self):
+        """ Returns True if an extra trigger channel is set, i.e., if `self._trigger_channel` is not None. """
+        return self._trigger_channel is not None
 
     def get_id(self):
         return self._id
-    
+
     def get_group_id(self):
         """
         channel group id
@@ -70,21 +75,39 @@ def get_group_id(self):
             return self._group_id
 
     def serialize(self, save_trace):
+        base_trace_pkl = None
         if save_trace:
             base_trace_pkl = NuRadioReco.framework.base_trace.BaseTrace.serialize(self)
+
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+
+        if self._trigger_channel is not None:
+            trigger_channel_pkl = self._trigger_channel.serialize(save_trace)
         else:
-            base_trace_pkl = None
-        data = {'parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                'id': self.get_id(),
-                'group_id': self._group_id,
-                'base_trace': base_trace_pkl}
+            trigger_channel_pkl = None
+
+        data.update({
+            'id': self.get_id(),
+            'group_id': self._group_id,
+            'base_trace': base_trace_pkl,
+            'trigger_channel_pkl': trigger_channel_pkl,
+        })
 
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
-        if(data['base_trace'] is not None):
+        if data['base_trace'] is not None:
             NuRadioReco.framework.base_trace.BaseTrace.deserialize(self, data['base_trace'])
-        self._parameters = NuRadioReco.framework.parameter_serialization.deserialize(data['parameters'], parameters.channelParameters)
+
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
         self._id = data['id']
         self._group_id = data.get('group_id')  # Attempts to load group_id, returns None if not found
+
+        trigger_channel_pkl = data.get("trigger_channel_pkl", None)
+        if trigger_channel_pkl is not None:
+            trigger_channel = Channel(None)
+            trigger_channel.deserialize(trigger_channel_pkl)
+            self._trigger_channel = trigger_channel
+        else:
+            self._trigger_channel = None
\ No newline at end of file
diff --git a/NuRadioReco/framework/electric_field.py b/NuRadioReco/framework/electric_field.py
index e0f3bed489..53871aa911 100644
--- a/NuRadioReco/framework/electric_field.py
+++ b/NuRadioReco/framework/electric_field.py
@@ -1,7 +1,7 @@
 from __future__ import absolute_import, division, print_function
 import NuRadioReco.framework.base_trace
 import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.framework.parameter_serialization
+import NuRadioReco.framework.parameter_storage
 import radiotools.coordinatesystems
 from NuRadioReco.utilities.trace_utilities import get_stokes
 try:
@@ -12,8 +12,8 @@
 logger = logging.getLogger('NuRadioReco.ElectricField')
 
 
-class ElectricField(NuRadioReco.framework.base_trace.BaseTrace):
-
+class ElectricField(NuRadioReco.framework.base_trace.BaseTrace,
+                    NuRadioReco.framework.parameter_storage.ParameterStorage):
     def __init__(self, channel_ids, position=None,
                  shower_id=None, ray_tracing_id=None):
         """
@@ -37,10 +37,10 @@ def __init__(self, channel_ids, position=None,
             the id of the corresponding ray tracing solution
         """
         NuRadioReco.framework.base_trace.BaseTrace.__init__(self)
-        self._channel_ids = channel_ids
-        self._parameters = {}
-        self._parameter_covariances = {}
+        NuRadioReco.framework.parameter_storage.ParameterStorage.__init__(
+            self, parameters.electricFieldParameters)
 
+        self._channel_ids = channel_ids
         self._position = position
         if position is None:
             self._position = [0, 0, 0]
@@ -54,51 +54,6 @@ def get_unique_identifier(self):
         """
         return (self._channel_ids, self._shower_id, self._ray_tracing_id)
 
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.electricFieldParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-        return self._parameters[key]
-
-    def get_parameters(self):
-        return self._parameters
-
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.electricFieldParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-        self._parameters[key] = value
-
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.electricFieldParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-        return key in self._parameters
-
-    def has_parameter_error(self, key):
-        if not isinstance(key, parameters.electricFieldParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-        return (key, key) in self._parameter_covariances
-
-    def set_parameter_error(self, key, value):
-        if not isinstance(key, parameters.electricFieldParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-        self._parameter_covariances[(key, key)] = value ** 2
-
-    def get_parameter_error(self, key):
-        if not isinstance(key, parameters.electricFieldParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.electricFieldParameters")
-        return self._parameter_covariances[(key, key)] ** 0.5
-
-    def __setitem__(self, key, value):
-        self.set_parameter(key, value)
-
-    def __getitem__(self, key):
-        return self.get_parameter(key)
-
     def set_channel_ids(self, channel_ids):
         self._channel_ids = channel_ids
 
@@ -200,19 +155,19 @@ def get_stokes_parameters(
 
 
     def serialize(self, save_trace):
+        base_trace_pkl = None
         if save_trace:
             base_trace_pkl = NuRadioReco.framework.base_trace.BaseTrace.serialize(self)
-        else:
-            base_trace_pkl = None
 
-        data = {'parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                'parameter_covariances': NuRadioReco.framework.parameter_serialization.serialize_covariances(
-                    self._parameter_covariances),
-                'channel_ids': self._channel_ids,
-                '_shower_id': self._shower_id,
-                '_ray_tracing_id': self._ray_tracing_id,
-                'position': self._position,
-                'base_trace': base_trace_pkl}
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+
+        data.update({
+            'channel_ids': self._channel_ids,
+            '_shower_id': self._shower_id,
+            '_ray_tracing_id': self._ray_tracing_id,
+            'position': self._position,
+            'base_trace': base_trace_pkl
+        })
 
         return pickle.dumps(data, protocol=4)
 
@@ -221,16 +176,11 @@ def deserialize(self, data_pkl):
         if data['base_trace'] is not None:
             NuRadioReco.framework.base_trace.BaseTrace.deserialize(self, data['base_trace'])
 
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
+
         if 'position' in data:  # for backward compatibility
             self._position = data['position']
 
-        self._parameters = NuRadioReco.framework.parameter_serialization.deserialize(
-            data['parameters'], parameters.electricFieldParameters)
-
-        if 'parameter_covariances' in data:
-            self._parameter_covariances = NuRadioReco.framework.parameter_serialization.deserialize_covariances(
-                data['parameter_covariances'], parameters.electricFieldParameters)
-
         self._channel_ids = data['channel_ids']
         self._shower_id = data.get('_shower_id', None)
         self._ray_tracing_id = data.get('_ray_tracing_id', None)
diff --git a/NuRadioReco/framework/emitter.py b/NuRadioReco/framework/emitter.py
index d868fe57a7..f02f63267a 100644
--- a/NuRadioReco/framework/emitter.py
+++ b/NuRadioReco/framework/emitter.py
@@ -1,30 +1,21 @@
 import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.framework.parameter_serialization
+import NuRadioReco.framework.parameter_storage
 import pickle
 
 import logging
 logger = logging.getLogger('NuRadioReco.Emitter')
 
 
-class Emitter:
+class Emitter(NuRadioReco.framework.parameter_storage.ParameterStorage):
 
     def __init__(self, emitter_id=0, station_ids=None):
+        super().__init__(parameters.emitterParameters)
         self._id = emitter_id
         self.__station_ids = station_ids
-        self._parameters = {}
-
-    def __setitem__(self, key, value):
-        self.set_parameter(key, value)
-
-    def __getitem__(self, key):
-        return self.get_parameter(key)
 
     def get_id(self):
         return self._id
 
-    def get_parameters(self):
-        return self._parameters
-
     def get_station_ids(self):
         return self.__station_ids
 
@@ -32,40 +23,20 @@ def has_station_ids(self, ids):
         for station_id in ids:
             if station_id not in self.__station_ids:
                 return False
-        return True
 
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.emitterParameters):
-            logger.error(f"parameter key needs to be of type NuRadioReco.framework.parameters.emitterParameters but is {type(key)}")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.emitterParameters")
-        return self._parameters[key]
-
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.emitterParameters):
-            logger.error(f"parameter key needs to be of type NuRadioReco.framework.parameters.emitterParameters but is {type(key)}")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.emitterParameters")
-        self._parameters[key] = value
-
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.emitterParameters):
-            logger.error(f"parameter key needs to be of type NuRadioReco.framework.parameters.emitterParameters but is {type(key)}")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.emitterParameters")
-        return key in self._parameters
+        return True
 
     def serialize(self):
-        data = {'_parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                'station_ids': self.__station_ids,
-                '_id': self._id}
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+        data.update({
+            'station_ids': self.__station_ids,
+            '_id': self._id
+        })
+
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
-        if '_id' in data.keys():
-            self._id = data['_id']
-        else:
-            self._id = None
-        self._parameters = NuRadioReco.framework.parameter_serialization.deserialize(
-            data['_parameters'],
-            parameters.emitterParameters
-        )
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
+        self._id = data.get('_id', None)
         self.__station_ids = data['station_ids']
diff --git a/NuRadioReco/framework/event.py b/NuRadioReco/framework/event.py
index 00fd163de8..bb601e0c3f 100644
--- a/NuRadioReco/framework/event.py
+++ b/NuRadioReco/framework/event.py
@@ -1,5 +1,4 @@
 from __future__ import absolute_import, division, print_function
-import pickle
 import NuRadioReco.framework.station
 import NuRadioReco.framework.radio_shower
 import NuRadioReco.framework.emitter
@@ -7,26 +6,34 @@
 import NuRadioReco.framework.hybrid_information
 import NuRadioReco.framework.particle
 import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.utilities.version
+import NuRadioReco.framework.parameter_storage
+
+from NuRadioReco.utilities import io_utilities, version
+
+import astropy.time
+import datetime
 from six import itervalues
+import numpy as np
 import collections
+import pickle
+
 import logging
 logger = logging.getLogger('NuRadioReco.Event')
 
 
-class Event:
+class Event(NuRadioReco.framework.parameter_storage.ParameterStorage):
 
     def __init__(self, run_number, event_id):
-        self._parameters = {}
+        super().__init__([parameters.eventParameters, parameters.generatorAttributes])
+
         self.__run_number = run_number
         self._id = event_id
         self.__stations = collections.OrderedDict()
         self.__radio_showers = collections.OrderedDict()
         self.__sim_showers = collections.OrderedDict()
         self.__sim_emitters = collections.OrderedDict()
-        self.__event_time = 0
+        self.__event_time = None
         self.__particles = collections.OrderedDict() # stores a dictionary of simulated MC particles in an event
-        self._generator_info = {} # copies over the relevant information on event generation from the input file attributes
         self.__hybrid_information = NuRadioReco.framework.hybrid_information.HybridInformation()
         self.__modules_event = []  # saves which modules were executed with what parameters on event level
         self.__modules_station = {}  # saves which modules were executed with what parameters on station level
@@ -87,41 +94,17 @@ def iter_modules(self, station_id=None):
                 iE += 1
                 yield self.__modules_event[iE - 1]
 
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.eventParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.eventParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.eventParameters")
-        return self._parameters[key]
-
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.eventParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.eventParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.eventParameters")
-        self._parameters[key] = value
-
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.eventParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.eventParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.eventParameters")
-        return key in self._parameters
-
     def get_generator_info(self, key):
-        if not isinstance(key, parameters.generatorAttributes):
-            logger.error("generator information key needs to be of type NuRadioReco.framework.parameters.generatorAttributes")
-            raise ValueError("generator information key needs to be of type NuRadioReco.framework.parameters.generatorAttributes")
-        return self._generator_info[key]
+        logger.warning("`get_generator_info` is deprecated. Use `get_parameter` instead.")
+        return self.get_parameter(key)
 
     def set_generator_info(self, key, value):
-        if not isinstance(key, parameters.generatorAttributes):
-            logger.error("generator information key needs to be of type NuRadioReco.framework.parameters.generatorAttributes")
-            raise ValueError("generator information key needs to be of type NuRadioReco.framework.parameters.generatorAttributes")
-        self._generator_info[key] = value
+        logger.warning("`set_generator_info` is deprecated. Use `set_parameter` instead.")
+        self.set_parameter(key, value)
 
     def has_generator_info(self, key):
-        if not isinstance(key, parameters.generatorAttributes):
-            logger.error("generator information key needs to be of type NuRadioReco.framework.parameters.generatorAttributes")
-            raise ValueError("generator information key needs to be of type NuRadioReco.framework.parameters.generatorAttributes")
-        return key in self._generator_info
+        logger.warning("`has_generator_info` is deprecated. Use `has_parameter` instead.")
+        return self.has_parameter(key)
 
     def get_id(self):
         return self._id
@@ -132,6 +115,52 @@ def set_id(self, evt_id):
     def get_run_number(self):
         return self.__run_number
 
+    def get_waveforms(self, station_id=None, channel_id=None):
+        """
+        Returns the waveforms stored within the event.
+
+        You can specify the station and channel id to get specific waveforms.
+        If you do not specify anything you will get all waveforms.
+
+        Parameters
+        ----------
+        station_id: int (Default: None)
+            The station id of the station for which the waveforms should be returned.
+            If `None`, the waveforms of all stations are returned.
+        channel_id: int or list of ints (Default: None)
+            The channel id(s) of the channel(s) for which the waveforms should be returned.
+            If `None`, the waveforms of all channels are returned.
+
+        Returns
+        -------
+        times: np.ndarray(nr_stations, nr_channels, nr_samples)
+            A numpy array containing the times of the waveforms.
+            The returned array is squeezed:
+            (1, 10, 2048) -> (10, 2048) or (2, 1, 2048) -> (2, 2048).
+        waveforms: np.ndarray(nr_stations, nr_channels, nr_samples)
+            A numpy array containing the waveforms.
+            The returned array is squeezed (see example for `times`).
+        """
+        times = []
+        waveforms = []
+
+        if isinstance(channel_id, int):
+            channel_id = [channel_id]
+
+        for station in self.get_stations():
+            tmp_times = []
+            tmp_waveforms = []
+            if station_id is not None and station.get_id() != station_id:
+                continue
+            for channel in station.iter_channels(use_channels=channel_id, sorted=True):
+                tmp_times.append(channel.get_times())
+                tmp_waveforms.append(channel.get_trace())
+
+            times.append(tmp_times)
+            waveforms.append(tmp_waveforms)
+
+        return np.squeeze(times), np.squeeze(waveforms)
+
     def get_station(self, station_id=None):
         """
         Returns the station for a given station id.
@@ -158,10 +187,47 @@ def get_station(self, station_id=None):
 
         return self.__stations[station_id]
 
-    def set_event_time(self, event_time):
-        self.__event_time = event_time
+    def set_event_time(self, time, format=None):
+        """
+        Set the (absolute) event time (will be stored as astropy.time.Time).
+
+        Parameters
+        ----------
+        time: astropy.time.Time or datetime.datetime or float
+            If "time" is a float, you have to specify its format.
+
+        format: str (Default: None)
+            Only used when "time" is a float. Format to interpret "time".
+        """
+
+        if isinstance(time, datetime.datetime):
+            self.__event_time = astropy.time.Time(time)
+        elif isinstance(time, astropy.time.Time):
+            self.__event_time = time
+        elif time is None:
+            self.__event_time = None
+        else:
+            if format is None:
+                logger.error("If you provide a float for the time, you have to specify the format.")
+                raise ValueError("If you provide a float for the time, you have to specify the format.")
+            self.__event_time = astropy.time.Time(time, format=format)
 
     def get_event_time(self):
+        """
+        Returns the event time (as astropy.time.Time object).
+
+        If the event time is not set, an error is raised. The event time is often only used in simulations
+        and typically the same a `station.get_station_time()`.
+
+        Returns
+        -------
+        event_time : astropy.time.Time
+            The event time.
+        """
+        if self.__event_time is None:
+            logger.error("Event time is not set. You either have to set it or use `station.get_station_time()`")
+            raise ValueError("Event time is not set. You either have to set it or use `station.get_station_time()`")
+
         return self.__event_time
 
     def get_stations(self):
@@ -290,8 +356,6 @@ def get_interaction_products(self, parent_particle, showers=True, particles=True
                 if particle[parameters.particleParameters.parent_id] == parent_id:
                     yield particle
 
-
-
     def add_shower(self, shower):
         """
         Adds a radio shower to the event
@@ -501,7 +565,7 @@ def get_hybrid_information(self):
     def serialize(self, mode):
         stations_pkl = []
         try:
-            commit_hash = NuRadioReco.utilities.version.get_NuRadioMC_commit_hash()
+            commit_hash = version.get_NuRadioMC_commit_hash()
             self.set_parameter(parameters.eventParameters.hash_NuRadioMC, commit_hash)
         except:
             logger.warning("Event is serialized without commit hash!")
@@ -522,7 +586,8 @@ def serialize(self, mode):
             modules_out_event.append([value[0], None, value[2]])
             invalid_keys = [key for key,val in value[2].items() if isinstance(val, BaseException)]
             if len(invalid_keys):
-                logger.warning(f"The following arguments to module {value[0]} could not be serialized and will not be stored: {invalid_keys}")
+                logger.warning(f"The following arguments to module {value[0]} could not be "
+                               f"serialized and will not be stored: {invalid_keys}")
 
         modules_out_station = {}
         for key in self.__modules_station:  # remove module instances (this will just blow up the file size)
@@ -531,22 +596,26 @@ def serialize(self, mode):
                 modules_out_station[key].append([value[0], value[1], None, value[3]])
                 invalid_keys = [key for key,val in value[3].items() if isinstance(val, BaseException)]
                 if len(invalid_keys):
-                    logger.warning(f"The following arguments to module {value[0]} could not be serialized and will not be stored: {invalid_keys}")
-
-        data = {'_parameters': self._parameters,
-                '__run_number': self.__run_number,
-                '_id': self._id,
-                '__event_time': self.__event_time,
-                'stations': stations_pkl,
-                'showers': showers_pkl,
-                'sim_showers': sim_showers_pkl,
-                'sim_emitters': sim_emitters_pkl,
-                'particles': particles_pkl,
-                'hybrid_info': hybrid_info,
-                'generator_info': self._generator_info,
-                '__modules_event': modules_out_event,
-                '__modules_station': modules_out_station
-                }
+                    logger.warning(f"The following arguments to module {value[0]} could not be "
+                                   f"serialized and will not be stored: {invalid_keys}")
+
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+
+        event_time_dict = io_utilities._astropy_to_dict(self.__event_time)
+        data.update({
+            '__run_number': self.__run_number,
+            '_id': self._id,
+            '__event_time': event_time_dict,
+            'stations': stations_pkl,
+            'showers': showers_pkl,
+            'sim_showers': sim_showers_pkl,
+            'sim_emitters': sim_emitters_pkl,
+            'particles': particles_pkl,
+            'hybrid_info': hybrid_info,
+            '__modules_event': modules_out_event,
+            '__modules_station': modules_out_station
+        })
+
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
@@ -581,13 +650,16 @@ def deserialize(self, data_pkl):
         if 'hybrid_info' in data.keys():
             self.__hybrid_information.deserialize(data['hybrid_info'])
 
-        self._parameters = data['_parameters']
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
+
         self.__run_number = data['__run_number']
         self._id = data['_id']
-        self.__event_time = data['__event_time']
+        self.__event_time = io_utilities._time_object_to_astropy(data['__event_time'])
 
-        if 'generator_info' in data.keys():
-            self._generator_info = data['generator_info']
+        # For backward compatibility, now generator_info are stored in `_parameters`.
+        if 'generator_info' in data:
+            for key in data['generator_info']:
+                self.set_parameter(key, data['generator_info'][key])
 
         if "__modules_event" in data:
             self.__modules_event = data['__modules_event']
diff --git a/NuRadioReco/framework/parameter_serialization.py b/NuRadioReco/framework/parameter_serialization.py
deleted file mode 100644
index 63a69b53e6..0000000000
--- a/NuRadioReco/framework/parameter_serialization.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from __future__ import absolute_import, division, print_function, unicode_literals
-
-
-def serialize(target_object):
-    reply = {}
-    for entry in target_object:
-        reply[str(entry)] = target_object[entry]
-    return reply
-
-
-def deserialize(target_object, parameter_enum):
-    reply = {}
-    for entry in parameter_enum:
-        if str(entry) in target_object:
-            reply[entry] = target_object[str(entry)]
-    return reply
-
-
-def serialize_covariances(target_object):
-    reply = {}
-    for entry in target_object:
-        reply[(str(entry[0]), str(entry[1]))] = target_object[entry]
-    return reply
-
-
-def deserialize_covariances(target_object, parameter_enum):
-    reply = {}
-    for entry in target_object:
-        first_key = None
-        second_key = None
-        for enum in parameter_enum:
-            if str(enum) == entry[0]:
-                first_key = enum
-            if str(enum) == entry[1]:
-                second_key = enum
-        if first_key is not None and second_key is not None:
-            reply[(first_key, second_key)] = target_object[entry]
-    return reply
diff --git a/NuRadioReco/framework/parameter_storage.py b/NuRadioReco/framework/parameter_storage.py
new file mode 100644
index 0000000000..601ca2111a
--- /dev/null
+++ b/NuRadioReco/framework/parameter_storage.py
@@ -0,0 +1,102 @@
+import NuRadioReco.framework.parameters
+
+import copy
+import itertools
+import logging
+logger = logging.getLogger('NuRadioReco.framework.parameter_storage')
+
+
+class ParameterStorage:
+
+    def __init__(self, parameter_types):
+        self._parameters = {}
+        self._parameter_covariances = {}
+        if not isinstance(parameter_types, list):
+            parameter_types = [parameter_types]
+        self._parameter_types = parameter_types
+
+    def add_parameter_type(self, parameter_type):
+        self._parameter_types.append(parameter_type)
+
+    def __setitem__(self, key, value):
+        self.set_parameter(key, value)
+
+    def __getitem__(self, key):
+        return self.get_parameter(key)
+
+    def _check_key(self, key):
+        if not isinstance(key, tuple(self._parameter_types)):
+            logger.error(f"Parameter {key} needs to be of type {self._parameter_types}")
+            raise ValueError(f"Parameter {key} needs to be of type {self._parameter_types}")
+
+    def get_parameter(self, key):
+        self._check_key(key)
+        return self._parameters[key]
+
+    def has_parameter(self, key):
+        self._check_key(key)
+        return key in self._parameters
+
+    def set_parameter(self, key, value):
+        self._check_key(key)
+        self._parameters[key] = value
+
+    def set_parameter_error(self, key, value):
+        self._check_key(key)
+        self._parameter_covariances[(key, key)] = value ** 2
+
+    def get_parameter_error(self, key):
+        self._check_key(key)
+        return self._parameter_covariances[(key, key)] ** 0.5
+
+    def has_parameter_error(self, key):
+        self._check_key(key)
+        return (key, key) in self._parameter_covariances
+
+    def remove_parameter(self, key):
+        self._check_key(key)
+        self._parameters.pop(key, None)
+
+    def get_parameters(self):
+        return copy.copy(self._parameters)
+
+    def serialize(self):
+        parameters = {str(key): self._parameters[key] for key in self._parameters}
+        parameter_covariances = {
+            (str(key[0]), str(key[1])): self._parameter_covariances[key]
+            for key in self._parameter_covariances}
+
+        data = {
+            "_parameters": parameters,
+            "_parameter_covariances": parameter_covariances,
+            "_parameter_types": [parameter_type.__name__ for parameter_type in self._parameter_types]
+        }
+
+        return data
+
+
+    def deserialize(self, data):
+        # for backward compatibility
+        if 'parameters' in data:
+            data['_parameters'] = data['parameters']
+
+        if 'parameter_covariances' in data:
+            data['_parameter_covariances'] = data['parameter_covariances']
+
+        parameters = data["_parameters"]
+        parameter_covariances = data.get("_parameter_covariances", {})
+        if "_parameter_types" in data:
+            parameter_types = [NuRadioReco.framework.parameters.__dict__[parameter_type]
+                               for parameter_type in data["_parameter_types"]]
+        else:
+            parameter_types = self._parameter_types
+
+        for parameter_type in parameter_types:
+            for key in parameter_type:
+                if str(key) in parameters:
+                    self._parameters[key] = parameters[str(key)]
+
+            if len(parameter_covariances):
+                for key in itertools.product(parameter_type, parameter_type):
+                    if (str(key[0]), str(key[1])) in parameter_covariances:
+                        self._parameter_covariances[key] = parameter_covariances[(str(key[0]), str(key[1]))]
diff --git a/NuRadioReco/framework/parameters.py b/NuRadioReco/framework/parameters.py
index 37cba485a2..ee5f5d781c 100644
--- a/NuRadioReco/framework/parameters.py
+++ b/NuRadioReco/framework/parameters.py
@@ -62,7 +62,7 @@ class channelParameters(Enum):
     block_offsets = 18 #: 'block' or pedestal offsets. See `NuRadioReco.modules.RNO_G.channelBlockOffsetFitter`
     Vrms_NuRadioMC_simulation = 19  #: the noise rms used in the MC simulation
     bandwidth_NuRadioMC_simulation = 20  #: the integrated channel response (=bandwidth for signal chains without amplification) used in the MC simulation
-
+    Vrms_trigger_NuRadioMC_simulation = 21  #: the noise rms of the trigger channels (optional) used in the MC simulation
 
 class electricFieldParameters(Enum):
     ray_path_type = 1  #: the type of the ray tracing solution ('direct', 'refracted' or 'reflected')
diff --git a/NuRadioReco/framework/particle.py b/NuRadioReco/framework/particle.py
index 149192f849..11311a9a49 100644
--- a/NuRadioReco/framework/particle.py
+++ b/NuRadioReco/framework/particle.py
@@ -1,6 +1,6 @@
 from __future__ import absolute_import, division, print_function
-import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.framework.parameter_serialization
+from NuRadioReco.framework.parameters import particleParameters as partp
+import NuRadioReco.framework.parameter_storage
 import pickle
 import collections
 import math
@@ -8,19 +8,13 @@
 logger = logging.getLogger('NuRadioReco.Particle')
 
 
-class Particle:
+class Particle(NuRadioReco.framework.parameter_storage.ParameterStorage):
 
     def __init__(self, particle_index):
+        super().__init__(partp)
         # "_id" is not the PDG code but a hierarchical index
         # (PDG code is stored in _parameters["flavor"])
         self._id = particle_index
-        self._parameters = {}
-
-    def __setitem__(self, key, value):
-        self.set_parameter(key, value)
-
-    def __getitem__(self, key):
-        return self.get_parameter(key)
 
     def get_id(self):
         """ Returns hierarchical index """
@@ -31,63 +25,36 @@ def __str__(self):
             "Particle ({}): "
             "Flavor: {: 3}, lgE = {:.1f}, cos(theta) = {:.2f}".format(
                 hex(id(self)),
-                self.get_parameter(parameters.particleParameters.flavor),
-                math.log10(self.get_parameter(parameters.particleParameters.energy)),
-                math.cos(self.get_parameter(parameters.particleParameters.zenith)))
+                self.get_parameter(partp.flavor),
+                math.log10(self.get_parameter(partp.energy)),
+                math.cos(self.get_parameter(partp.zenith)))
         )
 
         return msg
 
-    def get_parameters(self):
-        return self._parameters
-
-    def get_parameter(self, key):
-        if not isinstance(key, parameters.particleParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.particleParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.particleParameters")
-        return self._parameters[key]
+    def as_hdf5_dict(self):
+        hdf5_dict = collections.OrderedDict()
 
-    def set_parameter(self, key, value):
-        if not isinstance(key, parameters.particleParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.particleParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.particleParameters")
-        self._parameters[key] = value
+        key_pairs = [
+            (partp.azimuth, 'azimuths'), (partp.energy, 'energies'), (partp.flavor, 'flavors'),
+            (partp.inelasticity, 'inelasticity'), (partp.interaction_type, 'interaction_type'),
+            (partp.n_interaction, 'n_interaction'), (partp.vertex_time, 'vertex_times'),
+            (partp.weight, 'weights'), (partp.vertex[0], 'xx'), (partp.vertex[1], 'yy'),
+            (partp.zenith, 'zeniths'), (partp.vertex[2], 'zz')
+        ]
 
-    def has_parameter(self, key):
-        if not isinstance(key, parameters.particleParameters):
-            logger.error("parameter key needs to be of type NuRadioReco.framework.parameters.particleParameters")
-            raise ValueError("parameter key needs to be of type NuRadioReco.framework.parameters.particleParameters")
-        return key in self._parameters
+        for key, name in key_pairs:
+            hdf5_dict[name] = self.get_parameter(key)
 
-    def as_hdf5_dict(self):
-        hdf5_dict = collections.OrderedDict()
-        hdf5_dict['azimuths'] = self.get_parameter(parameters.particleParameters.azimuth)
-        hdf5_dict['energies'] = self.get_parameter(parameters.particleParameters.energy)
         hdf5_dict['event_group_ids'] = self.get_id()
-        hdf5_dict['flavors'] = self.get_parameter(parameters.particleParameters.flavor)
-        hdf5_dict['inelasticity'] = self.get_parameter(parameters.particleParameters.inelasticity)
-        hdf5_dict['interaction_type'] = self.get_parameter(parameters.particleParameters.interaction_type)
-        hdf5_dict['n_interaction'] = self.get_parameter(parameters.particleParameters.n_interaction)
-        hdf5_dict['vertex_times'] = self.get_parameter(parameters.particleParameters.vertex_time)
-        hdf5_dict['weights'] = self.get_parameter(parameters.particleParameters.weight)
-        hdf5_dict['xx'] = self.get_parameter(parameters.particleParameters.vertex[0])
-        hdf5_dict['yy'] = self.get_parameter(parameters.particleParameters.vertex[1])
-        hdf5_dict['zeniths'] = self.get_parameter(parameters.particleParameters.zenith)
-        hdf5_dict['zz'] = self.get_parameter(parameters.particleParameters.vertex[2])
         return hdf5_dict
 
     def serialize(self):
-        data = {'_parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                '_id': self._id}
+        data = NuRadioReco.framework.parameter_storage.ParameterStorage.serialize(self)
+        data['_id'] = self._id
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
-        if '_id' in data.keys():
-            self._id = data['_id']
-        else:
-            self._id = None
-        self._parameters = NuRadioReco.framework.parameter_serialization.deserialize(
-            data['_parameters'],
-            parameters.particleParameters
-        )
+        NuRadioReco.framework.parameter_storage.ParameterStorage.deserialize(self, data)
+        self._id = data.get('_id', None)
diff --git a/NuRadioReco/framework/sim_channel.py b/NuRadioReco/framework/sim_channel.py
index ef37d0573b..527fbb8786 100644
--- a/NuRadioReco/framework/sim_channel.py
+++ b/NuRadioReco/framework/sim_channel.py
@@ -1,7 +1,6 @@
 from __future__ import absolute_import, division, print_function
 import NuRadioReco.framework.base_trace
 import NuRadioReco.framework.channel
-import NuRadioReco.framework.parameter_serialization
 try:
     import cPickle as pickle
 except ImportError:
@@ -32,7 +31,7 @@ def __init__(self, channel_id, shower_id, ray_tracing_id, channel_group_id=None)
             the id of the corresponding ray tracing solution
         channel_group_id: int (default: None)
             optionally, several channels can belong to a "channel group". Use case is to identify
-            the channels of a single dual or triple polarized antenna as common in air shower arrays. 
+            the channels of a single dual or triple polarized antenna as common in air shower arrays.
         """
         NuRadioReco.framework.channel.Channel.__init__(self, channel_id, channel_group_id=channel_group_id)
         self._shower_id = shower_id
@@ -52,15 +51,17 @@ def get_unique_identifier(self):
 
     def serialize(self, save_trace):
         channel_pkl = NuRadioReco.framework.channel.Channel.serialize(self, save_trace)
-        data = {'parameters': NuRadioReco.framework.parameter_serialization.serialize(self._parameters),
-                'shower_id': self.get_shower_id(),
-                'ray_tracing_id': self.get_ray_tracing_solution_id(),
-                'channel': channel_pkl}
+        data = {
+            'shower_id': self.get_shower_id(),
+            'ray_tracing_id': self.get_ray_tracing_solution_id(),
+            'channel': channel_pkl
+        }
 
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
         NuRadioReco.framework.channel.Channel.deserialize(self, data['channel'])
+
         self._shower_id = data['shower_id']
         self._ray_tracing_id = data['ray_tracing_id']
diff --git a/NuRadioReco/framework/sim_emitter.py b/NuRadioReco/framework/sim_emitter.py
index cd83745917..b677aac567 100644
--- a/NuRadioReco/framework/sim_emitter.py
+++ b/NuRadioReco/framework/sim_emitter.py
@@ -1,6 +1,4 @@
-import NuRadioReco.framework.parameters as parameters
-import NuRadioReco.framework.parameter_serialization
-import pickle
+import NuRadioReco.framework.emitter
 
 import logging
 logger = logging.getLogger('NuRadioReco.SimEmitter')
diff --git a/NuRadioReco/framework/sim_station.py b/NuRadioReco/framework/sim_station.py
index c5e14bca93..68e191b111 100644
--- a/NuRadioReco/framework/sim_station.py
+++ b/NuRadioReco/framework/sim_station.py
@@ -66,14 +66,25 @@ def iter_channels(self):
         for channel in self.__channels.values():
             yield channel
 
-    def add_channel(self, channel):
+    def add_channel(self, channel, overwrite=False):
         """
-        adds a NuRadioReco.framework.sim_channel to the SimStation object
+        Add a `SimChannel` to the `SimStation`.
+
+        Parameters
+        ----------
+        channel: `NuRadioReco.framework.sim_channel.SimChannel`
+            Channel to be added.
+        overwrite: bool (Default: False)
+            If True, allow to overwrite a existing channel (i.e., a channel with the same unique identifier).
+            If False, raise AttributeError if a channel with the same identifier is being added
         """
         if not isinstance(channel, NuRadioReco.framework.sim_channel.SimChannel):
-            raise AttributeError("channel needs to be of type NuRadioReco.framework.sim_channel")
-        if(channel.get_unique_identifier() in self.__channels):
-            raise AttributeError(f"channel with the unique identifier {channel.get_unique_identifier()} is already present in SimStation")
+            raise AttributeError("`Channel` needs to be of type `NuRadioReco.framework.sim_channel.SimChannel`")
+
+        if not overwrite and channel.get_unique_identifier() in self.__channels:
+            raise AttributeError(
+                f"Channel with the unique identifier {channel.get_unique_identifier()} is already present in SimStation")
+
         self.__channels[channel.get_unique_identifier()] = channel
 
     def get_channel(self, unique_identifier):
diff --git a/NuRadioReco/framework/station.py b/NuRadioReco/framework/station.py
index 869759f6ba..262c1043ca 100644
--- a/NuRadioReco/framework/station.py
+++ b/NuRadioReco/framework/station.py
@@ -30,8 +30,8 @@ def set_sim_station(self, sim_station):
 
     def add_sim_station(self, sim_station):
         """
-        Adds a SimStation to the Station. If a SimStation is already present, the new SimStation is merged to the existing
-        one.
+        Adds a SimStation to the Station. If a SimStation is already present,
+        the new SimStation is merged to the existing one.
 
         Parameters
         ----------
@@ -66,10 +66,11 @@ def has_sim_station(self):
         return self.__sim_station is not None
 
     def iter_channels(self, use_channels=None, sorted=False):
-        """
-        Iterates over all channels of the station. If `use_channels` is not None, only the channels with the ids in
-        `use_channels` are iterated over. If `sorted` is True, the channels are iterated over in ascending order of their
-        ids.
+        """ Iterates over all channels of the station.
+
+        If `use_channels` is not None, only the channels with the ids in `use_channels`
+        are iterated over. If `sorted` is True, the channels are iterated over in
+        ascending order of their ids.
 
         Parameters
         ----------
@@ -94,9 +95,56 @@ def iter_channels(self, use_channels=None, sorted=False):
         for channel_id in channel_ids:
             yield self.get_channel(channel_id)
 
+    def iter_trigger_channels(self, use_channels=None):
+        """ Iterates over all channels of the station and yields `channel.get_trigger_channel()` for each.
+
+        If `use_channels` is not None, only the channels with the ids in `use_channels` are iterated over.
+
+        Parameters
+        ----------
+        use_channels : list of int, optional
+            List of channel ids to iterate over. If None, all channels are iterated over.
+
+        Yields
+        ------
+        NuRadioReco.framework.channel.Channel
+            The next (trigger) channel in the iteration.
+
+        See Also
+        --------
+        NuRadioReco.framework.channel.Channel.get_trigger_channel
+        NuRadioReco.framework.station.Station.iter_channels
+        """
+
+        for channel_id, channel in iteritems(self.__channels):
+            if use_channels is None:
+                yield channel.get_trigger_channel()
+            else:
+                if channel_id in use_channels:
+                    yield channel.get_trigger_channel()
+
     def get_channel(self, channel_id):
         return self.__channels[channel_id]
 
+    def get_trigger_channel(self, channel_id):
+        """
+        Returns the trigger channel of channel with id `channel_id`.
+
+        If the trigger channel is not set, the channel itself is returned (i.e. this is equivalent to `get_channel`)
+
+        Parameters
+        ----------
+        channel_id : int
+            The id of the channel for which to get the trigger channel.
+
+        Returns
+        -------
+        channel: `NuRadioReco.framework.channel.Channel`
+            The trigger channel of the channel with id `channel_id`.
+        """
+        channel = self.get_channel(channel_id)
+        return channel.get_trigger_channel()
+
     def iter_channel_group(self, channel_group_id):
         found_channel_group = False
         for channel_id, channel in iteritems(self.__channels):
@@ -135,7 +183,26 @@ def get_channel_ids(self, return_group_ids=False):
 
         return list(channel_ids)
 
-    def add_channel(self, channel):
+    def add_channel(self, channel, overwrite=False):
+        """
+        Adds a channel to the station. If a channel with the same id is already present, it is overwritten.
+
+        Parameters
+        ----------
+        channel : `NuRadioReco.framework.channel.Channel`
+            The channel to add to the station.
+        overwrite : bool, (Default: True)
+            If True, allow to overwrite an existing channel (i.e., a channel with the same id).
+            If False, raise AttributeError if a channel with the same id is being added.
+        """
+        if not isinstance(channel, NuRadioReco.framework.channel.Channel):
+            raise AttributeError("`Channel` needs to be of type `NuRadioReco.framework.channel.Channel`")
+
+        if not overwrite and channel.get_id() in self.__channels:
+            raise AttributeError(
+                f"Channel with the id {channel.get_id()} is already present in Station. "
+                "If you want to add this channel nonetheless please pass `overwrite=True` as argument")
+
         self.__channels[channel.get_id()] = channel
 
     def has_channel(self, channel_id):
@@ -158,57 +225,73 @@ def remove_channel(self, channel_id):
 
     def set_reference_reconstruction(self, reference):
         if reference not in ['RD', 'MC']:
-            import sys
-            logger.error("reference reconstructions other than RD and MC are not supported")
-            sys.exit(-1)
+            logger.error(f"Reference reconstructions other than 'RD' and 'MC' are not supported. Used value: '{reference}'")
+            raise ValueError(f"Reference reconstructions other than 'RD' and 'MC' are not supported. Used value: '{reference}'")
+
         self.__reference_reconstruction = reference
 
     def get_reference_reconstruction(self):
         return self.__reference_reconstruction
 
     def get_reference_direction(self):
-        if(self.__reference_reconstruction == 'RD'):
+        if self.__reference_reconstruction == 'RD':
             return self.get_parameter('zenith'), self.get_parameter('azimuth')
-        if(self.__reference_reconstruction == 'MC'):
-            return self.get_sim_station().get_parameter('zenith'), self.get_sim_station().get_parameter('azimuth')
+        elif self.__reference_reconstruction == 'MC':
+            return (
+                self.get_sim_station().get_parameter('zenith'),
+                self.get_sim_station().get_parameter('azimuth')
+            )
+        else:
+            logger.error(f"Reference reconstruction not set / unknown: {self.__reference_reconstruction}")
+            raise ValueError(f"Reference reconstruction not set / unknown: {self.__reference_reconstruction}")
+
 
     def get_magnetic_field_vector(self, time=None):
-        if(self.__reference_reconstruction == 'MC'):
+        if self.__reference_reconstruction == 'MC':
             return self.get_sim_station().get_magnetic_field_vector()
-        if(self.__reference_reconstruction == 'RD'):
-            if time is not None:
-                logger.warning("time dependent magnetic field model not yet implemented, returning static magnetic field for the ARIANNA site")
-            from radiotools import helper
-            return helper.get_magnetic_field_vector('arianna')
+        elif self.__reference_reconstruction == 'RD':
+            logger.error(
+                "Magnetic field for `self.__reference_reconstruction == 'RD'` not implemented yet. "
+                "Please use `radiotools.helper.get_magnetic_field_vector(site)` for the site you are interested in.")
+            raise NotImplementedError(
+                "Magnetic field for `self.__reference_reconstruction == 'RD'` not implemented yet. "
+                "Please use `radiotools.helper.get_magnetic_field_vector(site)` for the site you are interested in."
+            )
+        else:
+            logger.error(f"Reference reconstruction not set / unknown: {self.__reference_reconstruction}")
+            raise ValueError(f"Reference reconstruction not set / unknown: {self.__reference_reconstruction}")
 
     def serialize(self, mode):
         save_efield_traces = 'ElectricFields' in mode and mode['ElectricFields'] is True
-        base_station_pkl = NuRadioReco.framework.base_station.BaseStation.serialize(self, save_efield_traces=save_efield_traces)
-        channels_pkl = []
-        save_channel_trace = 'Channels' in mode and mode['Channels'] is True
-        for channel in self.iter_channels():
-            channels_pkl.append(channel.serialize(save_channel_trace))
-        save_sim_channel_trace = 'SimChannels' in mode and mode['SimChannels'] is True
-        save_sim_efield_trace = 'SimElectricFields' in mode and mode['SimElectricFields'] is True
+        base_station_pkl = NuRadioReco.framework.base_station.BaseStation.serialize(
+            self, save_efield_traces=save_efield_traces)
+
+        save_channel_trace = mode.get('Channels', False)
+        channels_pkl = [channel.serialize(save_channel_trace) for channel in self.iter_channels()]
+
         sim_station_pkl = None
-        if(self.has_sim_station()):
-            sim_station_pkl = self.get_sim_station().serialize(save_channel_traces=save_sim_channel_trace,
-                                                               save_efield_traces=save_sim_efield_trace)
+        if self.has_sim_station():
+            sim_station_pkl = self.get_sim_station().serialize(
+                save_channel_traces=mode.get('SimChannels', False),
+                save_efield_traces=mode.get('SimElectricFields', False))
 
         data = {'__reference_reconstruction': self.__reference_reconstruction,
                 'channels': channels_pkl,
                 'base_station': base_station_pkl,
                 'sim_station': sim_station_pkl}
+
         return pickle.dumps(data, protocol=4)
 
     def deserialize(self, data_pkl):
         data = pickle.loads(data_pkl)
         NuRadioReco.framework.base_station.BaseStation.deserialize(self, data['base_station'])
-        if(data['sim_station'] is None):
+
+        if data['sim_station'] is None:
             self.__sim_station = None
         else:
             self.__sim_station = NuRadioReco.framework.sim_station.SimStation(None)
             self.__sim_station.deserialize(data['sim_station'])
+
         for channel_pkl in data['channels']:
             channel = NuRadioReco.framework.channel.Channel(0)
             channel.deserialize(channel_pkl)
diff --git a/NuRadioReco/framework/trigger.py b/NuRadioReco/framework/trigger.py
index fb5b801fa6..2bf4845878 100644
--- a/NuRadioReco/framework/trigger.py
+++ b/NuRadioReco/framework/trigger.py
@@ -103,7 +103,19 @@ def set_trigger_time(self, time):
 
     def get_trigger_time(self):
         """
-        get the trigger time (absolute time with respect to the beginning of the event)
+        Get the trigger time.
+
+        Returns trigger time, i.e. the first time in the event where the trigger condition was fulfilled.
+        This is defined relative to the `station_time <NuRadioReco.framework.station.Station.get_station_time>`.
+
+        Returns
+        -------
+        trigger_time : float
+            The trigger time
+
+        See Also
+        --------
+        get_trigger_times : function to return all times where the trigger condition was fulfilled
         """
         return self._trigger_time
 
@@ -119,7 +131,25 @@ def set_trigger_times(self, times):
 
     def get_trigger_times(self):
         """
-        get the trigger times (time with respect to beginning of trace)
+        Get the trigger times
+
+        For some triggers, in addition to the time of the first trigger,
+        also all subsequent times where the trigger condition were fulfilled are
+        stored. For triggers that do not store this information, this method
+        is equivalent to `get_trigger_time` with the exception that it returns
+        an array (of shape (1,)) instead of a scalar.
+        Note that the trigger times are defined relative to the
+        `station_time <NuRadioReco.framework.station.Station.get_station_time>`.
+
+
+        Returns
+        -------
+        trigger_times : np.ndarray
+            Array of all times where the trigger condition was satisfied
+
+        See Also
+        --------
+        get_trigger_time : method to return the (first) trigger time
         """
         if self._trigger_times is None and not np.isnan(self._trigger_time):
             return np.array(self._trigger_time)
@@ -231,6 +261,12 @@ def __init__(self, name, threshold, channels=None, number_of_coincidences=1,
             default: 1
         channel_coincidence_window: float or None (default)
             the coincidence time between triggers of different channels
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, channels, 'simple_threshold', pre_trigger_times=pre_trigger_times)
         self._threshold = threshold
@@ -269,6 +305,12 @@ def __init__(self, name, threshold_factor, power_mean, power_std,
         output_passband: (float, float) tuple
             Frequencies for a 6th-order Butterworth filter to be applied after
             the diode filtering.
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, triggered_channels, 'envelope_phased', pre_trigger_times=pre_trigger_times)
         self._triggered_channels = triggered_channels
@@ -318,6 +360,12 @@ def __init__(self, name, threshold, channels=None, secondary_channels=None,
             the size of the stride between calculating the phasing (units of ADC time ticks)
         maximum_amps: list of floats (length equal to that of `phasing_angles`)
             the maximum value of all the integration windows for each of the phased waveforms
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, channels, 'simple_phased', pre_trigger_times=pre_trigger_times)
         self._primary_channels = channels
@@ -357,6 +405,12 @@ def __init__(self, name, threshold_high, threshold_low, high_low_window,
         number_of_coincidences: int
             the number of channels that need to fulfill the trigger condition
             default: 1
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, channels, 'high_low', pre_trigger_times=pre_trigger_times)
         self._number_of_coincidences = number_of_coincidences
@@ -387,6 +441,12 @@ def __init__(self, name, threshold, channel_coincidence_window, channels=None, n
         number_of_coincidences: int
             the number of channels that need to fulfill the trigger condition
             default: 1
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, channels, 'int_power', pre_trigger_times=pre_trigger_times)
         self._number_of_coincidences = number_of_coincidences
@@ -422,6 +482,12 @@ def __init__(self, name, passband, order, threshold, number_of_coincidences=2,
             default: 1
         channel_coincidence_window: float or None (default)
             the coincidence time between triggers of different channels
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, channels, 'envelope_trigger', pre_trigger_times=pre_trigger_times)
         self._passband = passband
@@ -433,7 +499,8 @@ def __init__(self, name, passband, order, threshold, number_of_coincidences=2,
 class RNOGSurfaceTrigger(Trigger):
     from NuRadioReco.utilities import units
     def __init__(self, name, threshold, number_of_coincidences=1,
-                 channel_coincidence_window=60*units.ns, channels=[13, 16, 19], temperature=250*units.kelvin, Vbias=2*units.volt, pre_trigger_times=55 * units.ns):
+                 channel_coincidence_window=60*units.ns, channels=[13, 16, 19],
+                 temperature=250*units.kelvin, Vbias=2*units.volt, pre_trigger_times=55 * units.ns):
         """
         initialize trigger class
 
@@ -455,6 +522,12 @@ def __init__(self, name, threshold, number_of_coincidences=1,
             temperature of the trigger board
         Vbias: float
             bias voltage on the trigger board
+        pre_trigger_times: float or dict of floats
+            the time before the trigger time that should be read out
+            if a dict is given, the keys are the channel_ids, and the value is the pre_trigger_time between the
+            start of the trace and the trigger time.
+            if only a float is given, the same pre_trigger_time is used for all channels
+
         """
         Trigger.__init__(self, name, channels, 'rnog_surface_trigger', pre_trigger_times=pre_trigger_times)
         self._threshold = threshold
diff --git a/NuRadioReco/modules/ARA/hardwareResponseIncorporator.py b/NuRadioReco/modules/ARA/hardwareResponseIncorporator.py
index c6d34640ec..5db4ae82b8 100644
--- a/NuRadioReco/modules/ARA/hardwareResponseIncorporator.py
+++ b/NuRadioReco/modules/ARA/hardwareResponseIncorporator.py
@@ -1,5 +1,6 @@
 from NuRadioReco.detector.ARA import analog_components
 from NuRadioReco.modules.base.module import register_run
+from NuRadioReco.utilities import units, signal_processing
 import numpy as np
 import time
 import logging
@@ -46,9 +47,18 @@ def run(self, evt, station, det, sim_to_data=False):
 
             else:
                 trace_before_system_fft = np.zeros_like(trace_fft)
-                trace_before_system_fft[np.abs(system_response['gain']) > 0] = trace_fft[np.abs(system_response['gain']) > 0] / (system_response['gain'] * system_response['phase'])[np.abs(system_response['gain']) > 0]
+                trace_before_system_fft[np.abs(system_response['gain']) > 0] = (
+                    trace_fft[np.abs(system_response['gain']) > 0] /
+                    (system_response['gain'] * system_response['phase'])[np.abs(system_response['gain']) > 0]
+                )
                 channel.set_frequency_spectrum(trace_before_system_fft, channel.get_sampling_rate())
 
+        if not sim_to_data:
+            # Subtraces the cable delay. For `sim_to_data=True`, the cable delay is added
+            # in the efieldToVoltageConverter or with the channelCableDelayAdder
+            # (if efieldToVoltageConverterPerEfield was used).
+            signal_processing.add_cable_delay(station, det, sim_to_data=False, logger=self.logger)
+
         self.__t += time.time() - t
 
     def end(self):
diff --git a/NuRadioReco/modules/ARIANNA/hardwareResponseIncorporator.py b/NuRadioReco/modules/ARIANNA/hardwareResponseIncorporator.py
index 3bac7090ff..2882063781 100644
--- a/NuRadioReco/modules/ARIANNA/hardwareResponseIncorporator.py
+++ b/NuRadioReco/modules/ARIANNA/hardwareResponseIncorporator.py
@@ -1,7 +1,7 @@
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.detector.ARIANNA import analog_components
 import numpy as np
-from NuRadioReco.utilities import units, fft
+from NuRadioReco.utilities import units, fft, signal_processing
 import time
 import logging
 
@@ -110,14 +110,14 @@ def run(self, evt, station, det, sim_to_data=False, phase_only=False, mode=None,
             if True, only the phases response is applied but not the amplitude response
         mode: string or None, default None
             Options:
-            
+
             * 'phase_only': only the phases response is applied but not the amplitude response
                 (identical to phase_only=True )
             * 'relativ': gain of amp is divided by maximum of the gain, i.e. at the maximum of the
                 filter response is 1 (before applying cable response). This makes it easier
                 to compare the filtered to unfiltered signal
             * None : default, gain and phase effects are applied 'normally'
-            
+
         mingainlin: float
             In frequency ranges where the gain gets very small, the reconstruction of the original signal (obtained by
             dividing the measured signal by the gain) leads to excessively high values, due to the effect of
@@ -147,11 +147,11 @@ def run(self, evt, station, det, sim_to_data=False, phase_only=False, mode=None,
             # otherwise, noise will be blown up
             channel.set_frequency_spectrum(trace_fft, channel.get_sampling_rate())
 
-            if not sim_to_data:
-                # Include cable delays
-                cable_delay = det.get_cable_delay(station.get_id(), channel.get_id())
-                self.logger.debug("cable delay of channel {} is {}ns".format(channel.get_id(), cable_delay / units.ns))
-                channel.add_trace_start_time(-cable_delay)
+        if not sim_to_data:
+            # Subtraces the cable delay. For `sim_to_data=True`, the cable delay is added
+            # in the efieldToVoltageConverter or with the channelCableDelayAdder
+            # (if efieldToVoltageConverterPerEfield was used).
+            signal_processing.add_cable_delay(station, det, sim_to_data=False, logger=self.logger)
 
         self.__t += time.time() - t
 
diff --git a/NuRadioReco/modules/LOFAR/beamformingDirectionFitter_LOFAR.py b/NuRadioReco/modules/LOFAR/beamformingDirectionFitter_LOFAR.py
index 6cb1a9e58b..cae55126c7 100644
--- a/NuRadioReco/modules/LOFAR/beamformingDirectionFitter_LOFAR.py
+++ b/NuRadioReco/modules/LOFAR/beamformingDirectionFitter_LOFAR.py
@@ -5,39 +5,18 @@
 
 from scipy import constants
 from scipy.signal import hilbert
-from scipy.optimize import fmin_powell
+from scipy.optimize import minimize, fmin_powell, Bounds
 
 from NuRadioReco.utilities import fft
 from NuRadioReco.utilities import units
 from NuRadioReco.framework.parameters import stationParameters, channelParameters, showerParameters
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.modules.voltageToEfieldConverter import voltageToEfieldConverter
-from NuRadioReco.modules.LOFAR.beamforming_utilities import beamformer
-
+from NuRadioReco.modules.LOFAR.beamforming_utilities import mini_beamformer
 
 lightspeed = constants.c * units.m / units.s
 
 
-def geometric_delays(ant_positions, sky):
-    """
-    Returns geometric delays in a matrix.
-
-    Parameters
-    ----------
-    ant_positions : np.ndarray
-        The antenna positions to use, formatted as a (nr_of_ant, 3) shaped array.
-    sky : np.ndarray
-        The unit vector pointing to the arrival direction, in cartesian coordinates.
-
-    Returns
-    -------
-    delays : np.ndarray
-    """
-    delays = np.dot(ant_positions, sky)
-    delays /= -1 * lightspeed
-    return delays
-
-
 class beamformingDirectionFitter:
     """
     Fits the direction per station using interferometry between all the channels with a good enough signal.
@@ -89,40 +68,51 @@ def _direction_fit(self, station, ant_positions):
         dominant_pol = np.argmax(np.bincount(dominant_pol_traces))
         self.logger.debug(f"Dominant polarisation is {dominant_pol}")
 
-        # Collect the Efield traces - the e-field from the converter has eR as [0] component -> do +1 in index
-        fft_traces = np.array([trace.get_frequency_spectrum()[dominant_pol + 1]
+        # Collect the Efield traces
+        fft_traces = np.array([trace.get_frequency_spectrum()[dominant_pol]
                                for trace in station.get_electric_fields()])
 
         def negative_beamed_signal(direction):
             theta = direction[0]
             phi = direction[1]
-            direction_cartesian = hp.spherical_to_cartesian(theta, phi)
-
-            delays = geometric_delays(ant_positions, direction_cartesian)
-
-            out = beamformer(fft_traces, freq, delays)
-            timeseries = fft.freq2time(out, 200 * units.MHz)  # TODO: is this really necessary?
+            my_direction_cartesian = hp.spherical_to_cartesian(theta, phi)
+            my_out = mini_beamformer(fft_traces, freq, ant_positions, my_direction_cartesian)
+            timeseries = fft.freq2time(my_out, 200 * units.MHz)
 
             return -100 * np.max(timeseries ** 2)
 
         start_direction = np.array([station.get_parameter(stationParameters.zenith) / units.rad,
                                     station.get_parameter(stationParameters.azimuth) / units.rad])
-        fit_direction = fmin_powell(negative_beamed_signal, start_direction,
-                                    maxiter=self.__max_iter, xtol=1.0, disp=False)
+        self.logger.debug(f"Initial guess for fit routine is {start_direction}")
+
+        # Using "minimize" with the "Powell" method allows to use the same minimization routine as before,
+        # but also add bounds to the fit. However, this changes the result of the fitting routine, even if
+        # without bounds the fit converges within the bounds. So currently we stick to the "fmin_powell" method.
+        # fit_result = minimize(negative_beamed_signal, start_direction,
+        #                       method='Powell', bounds=Bounds((0, -np.pi), (np.pi / 2, np.pi), (True, True)),
+        #                       options={'maxiter': self.__max_iter, 'xtol': 1.0, 'disp': False,
+        #                                'direc': np.array([[0.1, 0], [0, 0.1]])})
+        #
+        # self.logger.debug(f"Fit returned the following message: {fit_result.message}")
+        # if not fit_result.success:
+        #     self.logger.warning(f"The fit failed with the following message: {fit_result.message}")
+        #     raise RuntimeError
+        #
+        # fit_direction = fit_result.x
+
+        fit_result = fmin_powell(negative_beamed_signal, start_direction,
+                          maxiter=self.__max_iter, xtol=1.0 * units.deg,
+                          direc=np.array([[2.0 * units.deg, 0], [0, 2.0 * units.deg]]),
+                          disp=False, full_output=True)
+        fit_direction = fit_result[0]
+
+        self.logger.debug(f"Fit finished with following direc vector: {fit_result[2]}")
 
         direction_cartesian = hp.spherical_to_cartesian(*fit_direction)
 
-        delays = geometric_delays(ant_positions, direction_cartesian)
-        out = beamformer(fft_traces, freq, delays)
-
-        fit_direction = hp.cartesian_to_spherical(*direction_cartesian)
-        theta = fit_direction[0]
-        phi = fit_direction[1]
-        if phi < 0:
-            # Radiotools uses np.arctan2, which returns phi in the interval [-pi, pi] -> map to [0, 2*pi]
-            phi += 360 * units.deg
+        out = mini_beamformer(fft_traces, freq, ant_positions, direction_cartesian)
 
-        return [theta, phi], out
+        return fit_direction, out
 
     @register_run()
     def run(self, event, detector):
@@ -149,7 +139,7 @@ def run(self, event, detector):
             zenith = event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.zenith)
             azimuth = event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.azimuth)
 
-            # TODO: get this from Detector description
+            # Get all group IDs which are still present in the Station
             station_channel_group_ids = set([channel.get_group_id() for channel in station.iter_channels()])
 
             position_array = []
@@ -174,8 +164,8 @@ def run(self, event, detector):
             station.set_parameter(stationParameters.zenith, zenith)
             station.set_parameter(stationParameters.azimuth, azimuth)
 
-            direction_difference = np.asarray([100, 100])
-            while direction_difference[0] > 0.5 * units.deg and direction_difference[1] > 0.5 * units.deg:
+            direction_difference = np.asarray([90, 90]) * units.deg
+            while direction_difference[0] > 0.5 * units.deg or direction_difference[1] > 0.5 * units.deg:
                 # Make sure all the previously calculated Efields are removed
                 station.set_electric_fields([])
 
@@ -184,9 +174,13 @@ def run(self, event, detector):
                     converter.run(event, station, detector, use_channels=ant)
 
                 # Perform the direction fit on the station
-                direction_fit, freq_spectrum = self._direction_fit(
-                    station, position_array
-                )
+                try:
+                    direction_fit, freq_spectrum = self._direction_fit(
+                        station, position_array
+                    )
+                except RuntimeError:
+                    self.logger.error(f"Direction fit could not be completed for station  CS{station.get_id():03d}")
+                    break
 
                 # Check if fit produced unphysical results
                 if direction_fit[0] > 90 * units.deg:
diff --git a/NuRadioReco/modules/LOFAR/beamforming_utilities.py b/NuRadioReco/modules/LOFAR/beamforming_utilities.py
index 9273c0b0f4..24229282a2 100644
--- a/NuRadioReco/modules/LOFAR/beamforming_utilities.py
+++ b/NuRadioReco/modules/LOFAR/beamforming_utilities.py
@@ -1,7 +1,5 @@
 import numpy as np
-import radiotools.helper as hp
 
-from scipy.optimize import fmin_powell
 from scipy import constants
 
 from NuRadioReco.utilities import units
@@ -12,56 +10,104 @@
 
 def mini_beamformer(fft_data, frequencies, positions, direction):
     """
+    Beamforms the spectra given the arrival direction and antenna positions.
+    This function is a wrapper around the `beamformer()` function, which beamforms the
+    spectra given the timedelay. Here we first calculate the geometric delays in the far
+    field, based on the arrival direction.
+
+    Parameters
+    ----------
+    fft_data : np.ndarray
+        The Fourier transformed time traces of all antennae, shaped as (nr_of_ant, nr_of_freq_samples)
+    frequencies : np.ndarray
+        The values of the frequencies samples, shaped as (nr_of_freq_samples,)
+    positions : np.ndarray
+        The position of antenna, shaped as (nr_of_ant, 3)
+    direction : np.ndarray
+        The arrival direction in the sky, in cartesian coordinates, shape (3,)
+
+    Returns
+    -------
+    beamformed : np.ndarray
+        The beamformed (ie summed) frequency spectrum
+
+    Notes
+    -----
     Adapted from PyCrTools hBeamformBlock
-
-    Beamform the spectra given the arrival direction. Based on the arrival direction,
-    the geometric delays are calculated.
     """
-    n_antennas = len(positions)
-
-    output = np.zeros([len(frequencies)], dtype=complex)
+    delays = geometric_delay_far_field(positions, direction)
 
-    norm = np.sqrt(direction[0] * direction[0] + direction[1] * direction[1] + direction[2] * direction[2])
+    return beamformer(fft_data, frequencies, delays)
 
-    for a in np.arange(n_antennas):
-        delay = GeometricDelayFarField(positions[a], direction, norm)
 
-        real = 1.0 * np.cos(2 * np.pi * frequencies * delay)
-        imag = 1.0 * np.sin(2 * np.pi * frequencies * delay)
+def beamformer(fft_data, frequencies, delays):
+    """
+    Beamform the spectra according to the given delays, by phase shifting them according
+    to the time delays and summing up the resulting spectra.
+
+    Parameters
+    ----------
+    fft_data : np.ndarray
+        The Fourier transformed time traces of all antennae, shaped as (nr_of_ant, nr_of_freq_samples)
+    frequencies : np.ndarray
+        The values of the frequencies samples, shaped as (nr_of_freq_samples,)
+    delays : np.ndarray
+        The delay per antenna, shaped as (nr_of_ant,)
+
+    Returns
+    -------
+    beamformed : np.ndarray
+        The beamformed (ie summed) frequency spectrum
+    """
+    real = 1.0 * np.cos(2 * np.pi * frequencies[np.newaxis, :] * delays[:, np.newaxis])
+    imag = 1.0 * np.sin(2 * np.pi * frequencies[np.newaxis, :] * delays[:, np.newaxis])
 
-        de = real + 1j * imag
-        output = output + fft_data[a] * de
+    de = real + 1j * imag
+    output = fft_data * de
 
-    return output
+    return np.sum(output, axis=0)  # sum all the antennas together to beamform
 
 
-def beamformer(fft_data, frequencies, delay):
+def geometric_delays_near_field(ant_pos, sky):
     """
-    Beamform the spectra according to the given delays.
+    Calculate the geometric delays of given antenna position, given a source location in the sky.
+
+    Parameters
+    ----------
+    ant_pos : array_like
+        Antenna positions
+    sky : array_like
+        Source location in the sky
+
+    Returns
+    -------
+    delays : array_like
+        The geometric delays for all antenna positions.
     """
-    n_antennas = len(delay)
-    output = np.zeros([len(frequencies)], dtype=complex)
-
-    for a in np.arange(n_antennas):
-        real = 1.0 * np.cos(2 * np.pi * frequencies * delay[a])
-        imag = 1.0 * np.sin(2 * np.pi * frequencies * delay[a])
+    delays = np.linalg.norm(ant_pos - sky, axis=1) / lightspeed
 
-        de = real + 1j * imag
-        output = output + fft_data[a] * de
-
-    return output
+    return delays
 
 
-def geometric_delays(ant_pos, sky):
+def geometric_delay_far_field(ant_positions, direction):
     """
-    Calculate the geometric delays of given antenna position, given an arrival direction in the sky.
+    Calculate the geometric delays in the far field approximation, by projecting the positions onto
+    the arrival direction vector and dividing by the lightspeed.
+
+    If the `direction` vector points towards the incoming direction (as usually calculated using
+    zenith and azimuth), then positive delays correspond to later times compared to the [0, 0, 0]
+    location.
+
+    Parameters
+    ----------
+    ant_positions : array_like
+        The positions of the antennae, with shape (nr_of_ant, 3)
+    direction : array_like
+        The arrival direction, in cartesian coordinates
     """
-    distance = np.sqrt(sky[0] ** 2 + sky[1] ** 2 + sky[2] ** 2)
-    delays = (np.sqrt(
-        (sky[0] - ant_pos[0]) ** 2 + (sky[1] - ant_pos[1]) ** 2 + (sky[2] - ant_pos[2]) ** 2) - distance) / lightspeed
-    return delays
+    direction_normalised = np.asarray(direction) / np.linalg.norm(direction)
 
+    delays = np.dot(ant_positions, direction_normalised)
+    delays /= -1 * lightspeed
 
-def GeometricDelayFarField(position, direction, length):
-    delay = np.sum(np.asarray(direction) * np.asarray(position)) / length / lightspeed
-    return delay
+    return delays
diff --git a/NuRadioReco/modules/LOFAR/hardwareResponseIncorporator.py b/NuRadioReco/modules/LOFAR/hardwareResponseIncorporator.py
new file mode 100644
index 0000000000..f29494070b
--- /dev/null
+++ b/NuRadioReco/modules/LOFAR/hardwareResponseIncorporator.py
@@ -0,0 +1,139 @@
+from NuRadioReco.detector.LOFAR import analog_components
+from NuRadioReco.modules.base.module import register_run
+import numpy as np
+import time
+import logging
+import matplotlib.pyplot as plt
+
+logger = logging.getLogger("LOFAR_hardwareResponseIncorporator")
+
+
+class hardwareResponseIncorporator:
+    """
+    Incorporates the gains and losses induced by the LOFAR 
+    hardware. This is partially taken from the ARA 
+    hardwareResponseIncorporator.
+
+    author: Karen Terveer
+
+    """
+
+    def __init__(self):
+        self.__debug = False
+        self.begin()
+
+    def begin(self, debug=False):
+        self.__debug = debug
+
+    @register_run()
+    def run(self, station, det, sim_to_data=False):
+        """
+        
+        Incorporates the LOFAR signal chain (cable loss and RCU
+        gain)
+
+        Parameters
+        ----------
+        station: Station object
+            The station whose channels noise shall be added to
+        det: Detector object
+            The detector description
+        sim_to_data: bool
+            set to True if working with simulated data to add the 
+            signal chain to it. Set to False if working with
+            measured data.
+
+        """
+
+        channels = station.iter_channels()
+
+        for channel in channels:
+
+            #get cable length of channel to use correct attenuation file
+            cab_len = det.get_cable_type_and_length(station.get_id(),channel.get_id())[1]
+
+            # fetch component responses
+            frequencies = channel.get_frequencies()
+            cable_response = analog_components.get_cable_response(frequencies,cable_length=int(cab_len))
+            RCU_response = analog_components.get_RCU_response(frequencies)
+
+            # calculate total system response. component responses are in dB, convert to linear scale
+            system_response = np.power(10.0,(cable_response['attenuation']/10.0)) * np.power(10.0,(RCU_response['gain']/10.0))
+            trace_fft = channel.get_frequency_spectrum()
+                
+            if sim_to_data:
+
+                trace_after_system_fft = trace_fft * system_response
+                # zero first bins to avoid DC offset
+                trace_after_system_fft[0] = 0
+                channel.set_frequency_spectrum(trace_after_system_fft, channel.get_sampling_rate())
+
+            else:
+                trace_before_system_fft = np.zeros_like(trace_fft)
+                trace_before_system_fft[np.abs(system_response) > 0] = trace_fft[np.abs(system_response) > 0] / system_response[np.abs(system_response) > 0]
+                channel.set_frequency_spectrum(trace_before_system_fft, channel.get_sampling_rate())
+
+
+            if self.__debug == True:
+
+                system_response_spectrum = np.abs(system_response)
+                original_signal_spectrum = np.abs(trace_fft)
+
+                if sim_to_data:
+
+                    applied_signal_spectrum = np.abs(trace_after_system_fft)
+
+                    # Plotting
+                    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
+
+                    # Plot system response
+                    ax1.plot(frequencies, system_response_spectrum)
+                    ax1.set_xlim(0.03,0.08)
+                    ax1.set_yscale("log")
+                    ax1.set_xlabel('frequency (GHz)')
+                    ax1.set_ylabel('amplitude')
+                    ax1.set_title('system response')
+
+                    # Plot original and applied signal spectra
+
+                    ax2.plot(frequencies, original_signal_spectrum, label='original simulated signal')
+                    ax2.plot(frequencies, applied_signal_spectrum, label='system response applied')
+                    ax2.set_yscale("log")
+                    ax2.set_xlim(0.029,0.081)
+                    ax2.set_xlabel('frequency (GHz)')
+                    ax2.set_ylabel('amplitude')
+                    ax2.set_title('signal')
+                    ax2.legend()
+                    plt.tight_layout()
+
+                    plt.show()
+
+                else:
+
+                    applied_signal_spectrum = np.abs(trace_before_system_fft)
+
+                    # Plotting
+                    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))
+
+                    # Plot system response
+                    ax1.plot(frequencies, system_response_spectrum)
+                    ax1.set_xlabel('frequency (GHz)')
+                    ax1.set_ylabel('amplitude')
+                    ax1.set_title('system response')
+                    ax1.set_xlim(0.03,0.08)
+                    ax1.set_yscale("log")
+
+                    # Plot original and applied signal spectra
+                    ax2.plot(frequencies, original_signal_spectrum, label='original signal')
+                    ax2.plot(frequencies, applied_signal_spectrum, label='system response applied')
+                    ax2.set_xlabel('frequency (GHz)')
+                    ax2.set_ylabel('amplitude')
+                    ax2.set_xlim(0.029,0.081)
+                    ax2.set_yscale("log")
+                    ax2.set_title('signal')
+                    ax2.legend()
+                    plt.tight_layout()
+
+                    plt.show()                 
+
+
diff --git a/NuRadioReco/modules/LOFAR/pipelineVisualizer_LOFAR.py b/NuRadioReco/modules/LOFAR/pipelineVisualizer_LOFAR.py
new file mode 100644
index 0000000000..435a0408fe
--- /dev/null
+++ b/NuRadioReco/modules/LOFAR/pipelineVisualizer_LOFAR.py
@@ -0,0 +1,431 @@
+"""
+This module contains the pipelineVisualizer class for LOFAR.
+
+.. moduleauthor:: Karen Terveer <karen.terveer@fau.de>
+"""
+
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+import radiotools
+from matplotlib.cm import get_cmap
+from matplotlib.colors import Normalize
+
+from NuRadioReco.utilities import units
+from NuRadioReco.framework.parameters import stationParameters, channelParameters, showerParameters
+from NuRadioReco.modules.base.module import register_run
+
+
+def check_for_good_ant(event, detector):
+    """
+    Create a dictionary which for every station in the event contains a list of antennas which have not
+    been flagged.
+    """
+    good_antennas_dict = {}
+    for station in event.get_stations():
+        if station.get_parameter(stationParameters.triggered):
+            good_antennas_dict[station.get_id()] = []
+            flagged_channels = station.get_parameter(stationParameters.flagged_channels)
+            # Get all group IDs which are still present in the station
+            station_channel_group_ids = set([channel.get_group_id() for channel in station.iter_channels()])
+
+            # Get the dominant polarisation orientation as calculated by stationPulseFinder
+            dominant_orientation = station.get_parameter(stationParameters.cr_dominant_polarisation)
+
+            good_channel_pair_ids = np.zeros((len(station_channel_group_ids), 2), dtype=int)
+            for ind, channel_group_id in enumerate(station_channel_group_ids):
+                for channel in station.iter_channel_group(channel_group_id):
+                    if np.all(detector.get_antenna_orientation(station.get_id(), channel.get_id()) == dominant_orientation):
+                        good_channel_pair_ids[ind, 0] = channel.get_id()
+                    else:
+                        good_channel_pair_ids[ind, 1] = channel.get_id()
+
+                # Check if dominant channel has been flagged
+                channel = station.get_channel(good_channel_pair_ids[ind, 0])
+                if channel.get_id() not in flagged_channels:
+                    good_antennas_dict[station.get_id()].append(channel.get_id())
+
+    return good_antennas_dict
+
+
+class pipelineVisualizer:
+    """
+    Creates debug plots from the LOFAR pipeline - 
+    This is the pipelineVisualizerTM for LOFAR.
+
+    Any significant plots resulting from the pipeline
+    should be added here by creating a function for them,
+    and calling all functions sequentially.
+    """
+
+    def __init__(self):
+        self.plots = None
+        self.logger = logging.getLogger("NuRadioReco.pipelineVisualizer")
+
+
+    def begin(self, logger_level=logging.NOTSET):
+        self.logger.setLevel(logger_level)
+
+
+    def plot_polarization(self, event, detector):
+        """
+        Plot the polarization of the electric field.
+        This method calculates the stokes parameters of the pulse
+        using get_stokes from framework.electric_field, and
+        determines the polarization angle and degree, plotting
+        them as arrows in the vxB and vxvxB plane.
+        It estimates uncertainties by picking a pure noise value of
+        stokes parameters, propagating through the angle and degree
+        formulas and plotting them as arrows with reduced opacity.
+        Author: Karen Terveer
+
+        Parameters
+        ----------
+        event : Event object
+            The event containing the stations and electric fields.
+        detector : Detector object
+            The detector object containing information about the detector.
+
+        Returns
+        -------
+        fig_pol : matplotlib Figure object
+            The generated figure object containing the polarization plot.
+        """
+
+        from NuRadioReco.framework.electric_field import get_stokes
+
+        fig_pol, ax = plt.subplots(figsize=(8,7))
+
+        triggered_station_ids = [
+            station.get_id() for station in event.get_stations() if station.get_parameter(stationParameters.triggered)
+        ]
+        num_stations = len(triggered_station_ids)
+
+        cmap = get_cmap('jet')  
+        norm = Normalize(vmin=0, vmax=num_stations-1) 
+
+        lora_core = event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.core)
+
+        try:
+            core = event.get_first_shower().get_parameter(showerParameters.core)
+        except KeyError:
+            self.logger.warning("No radio core found, using LORA core instead")
+            core = lora_core
+
+        for i, station in enumerate(event.get_stations()):
+            if station.get_parameter(stationParameters.triggered):
+
+                zenith = station.get_parameter(stationParameters.cr_zenith) / units.rad
+                azimuth = station.get_parameter(stationParameters.cr_azimuth) / units.rad
+
+                cs = radiotools.coordinatesystems.cstrafo(
+                    zenith, azimuth, magnetic_field_vector=None, site="lofar"
+                )
+
+                efields = station.get_electric_fields()
+
+                station_pos = detector.get_absolute_position(station.get_id())
+                station_pos_vB = cs.transform_to_vxB_vxvxB(station_pos, core=core)[0]
+                station_pos_vvB = cs.transform_to_vxB_vxvxB(station_pos, core=core)[1]
+
+                ax.scatter(
+                    station_pos_vB, station_pos_vvB,
+                    color=cmap(norm(i)), s=20, label=f'Station CS{station.get_id():03d}'
+                )
+
+                for field in efields:
+
+                    ids = field.get_channel_ids()
+                    pos = station_pos + detector.get_relative_position(station.get_id(), ids[0])
+
+                    pos_vB = cs.transform_to_vxB_vxvxB(pos, core=core)[0]
+                    pos_vvB = cs.transform_to_vxB_vxvxB(pos, core=core)[1]
+
+                    pulse_window_start, pulse_window_end = station.get_channel(ids[0]).get_parameter(channelParameters.signal_regions)
+                    pulse_window_len = pulse_window_end - pulse_window_start
+
+                    trace = field.get_trace()[:,pulse_window_start:pulse_window_end]
+
+                    efield_trace_vxB_vxvxB = cs.transform_to_vxB_vxvxB(
+                        cs.transform_from_onsky_to_ground(trace)
+                    )
+
+                    #get stokes parameters
+                    stokes = get_stokes(*efield_trace_vxB_vxvxB[:2], window_samples=64)
+
+                    stokes_max = np.argmax(stokes[0])
+
+                    I = stokes[0,stokes_max]
+                    Q = stokes[1,stokes_max]
+                    U = stokes[2,stokes_max]
+                    V = stokes[3,stokes_max]
+
+                    # get stokes uncertainties by picking a pure noise value
+                    I_sigma = stokes[0, stokes_max-pulse_window_len//4]
+                    Q_sigma = stokes[1, stokes_max-pulse_window_len//4]
+                    U_sigma = stokes[2, stokes_max-pulse_window_len//4]
+                    V_sigma = stokes[3, stokes_max-pulse_window_len//4]
+
+                    pol_angle = 0.5 * np.arctan2(U,Q)
+                    pol_angle_sigma= np.sqrt((U_sigma**2*(0.5*Q/(U**2+Q**2))**2 + Q_sigma**2*(0.5*U/(U**2+Q**2))**2))
+
+                    # if the polarization deviates from the vxB direction by more than 80 degrees,
+                    # this could indicate something wrong with the antenna. Show a warning including
+                    # the channel ids
+                    if np.abs(0.5 * np.arctan2(U,Q)) > 80*np.pi/180:
+                        self.logger.warning("strange polarization direction in channel group %s" % ids)
+
+                    pol_degree= np.sqrt(U**2 + Q**2 + V**2) / I
+                    pol_degree *= 7 # scale for better visibility
+
+                    dx = pol_degree * np.cos(pol_angle)
+                    dy = pol_degree * np.sin(pol_angle)
+
+                    dx_sigma_plus = pol_degree * np.cos(pol_angle + pol_angle_sigma)
+                    dy_sigma_plus = pol_degree * np.sin(pol_angle + pol_angle_sigma)
+
+                    dx_sigma_minus = pol_degree * np.cos(pol_angle - pol_angle_sigma)
+                    dy_sigma_minus = pol_degree * np.sin(pol_angle - pol_angle_sigma)
+
+                    ax.arrow(
+                        pos_vB, pos_vvB, dx_sigma_plus, dy_sigma_plus,
+                        head_width=2, head_length=5,
+                        fc=cmap(norm(i)), ec = cmap(norm(i)), alpha=0.5
+                    )
+                    ax.arrow(
+                        pos_vB, pos_vvB, dx_sigma_minus, dy_sigma_minus,
+                        head_width=2, head_length=5,
+                        fc=cmap(norm(i)), ec = cmap(norm(i)), alpha=0.5
+                    )
+                    ax.arrow(
+                        pos_vB, pos_vvB, dx, dy,
+                        head_width=2, head_length=6,
+                        fc=cmap(norm(i)), ec = cmap(norm(i))
+                    )
+
+        if np.any(core != lora_core):
+            lora_vB = cs.transform_to_vxB_vxvxB(lora_core, core=core)[0]
+            lora_vvB = cs.transform_to_vxB_vxvxB(lora_core, core=core)[1]
+            ax.scatter(lora_vB, lora_vvB, color='tab:red', s=50, label='LORA core', marker = 'x')
+            label = 'radio core'
+        else:
+            label = 'LORA core'
+
+        ax.scatter([0], [0], color='black', s=50, label=label, marker = 'x')
+        ax.legend()
+        ax.set_xlabel('Direction along $v \\times B$ [m]')
+        ax.set_ylabel('Direction along $v \\times (v \\times B)$ [m]')   
+
+        return fig_pol
+    
+    def show_direction_plot(self, event):
+        """
+        Make a comparison of the reconstructed direction per station with the LORA direction.
+
+        Author: Philipp Laub
+
+        Parameters
+        ----------
+        event : Event object
+            The event for which to show the final plots.
+        
+        Returns
+        -------
+        fig_dir : matplotlib Figure object
+            The generated figure object containing the direction plot.
+        """
+        
+        # plot reconstructed directions of all stations and compare to LORA in polar plot:
+        fig_dir, ax = plt.subplots(subplot_kw={'projection': 'polar'})
+
+        ax.set_theta_zero_location('E')
+        ax.set_theta_direction(1)
+
+        triggered_station_ids = [
+            station.get_id() for station in event.get_stations() if station.get_parameter(stationParameters.triggered)
+        ]
+        num_stations = len(triggered_station_ids)
+
+        cmap = get_cmap('jet')
+        norm = Normalize(vmin=0, vmax=num_stations-1) 
+
+        for i, station in enumerate(event.get_stations()):
+            if station.get_parameter(stationParameters.triggered):
+                try:
+                    zenith = station.get_parameter(stationParameters.cr_zenith)
+                    azimuth = station.get_parameter(stationParameters.cr_azimuth)
+                except KeyError:
+                    self.logger.info(
+                        f"Station CS{station.get_id():03d} does not have a reconstructed direction, "
+                        f"so I am not plotting this one."
+                    )
+                    continue
+                ax.plot(
+                    azimuth, zenith,
+                    label=f'Station CS{station.get_id():03d}',
+                    marker='P',
+                    markersize=7,
+                    linestyle='',
+                    color=cmap(norm(i))
+                )
+
+        ax.plot(
+            event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.azimuth),
+            event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.zenith),
+            label='LORA',
+            marker="X",
+            markersize=7,
+            linestyle='',
+            color='black'
+        )
+
+        ax.legend()
+        ax.set_title("Reconstructed arrival directions")
+
+        return fig_dir
+
+    def show_time_fluence_plot(self, event, detector, min_number_good_antennas=4):
+
+        """
+        Plot the antenna positions, marking arrival time by color and pseudofluence by markersize.
+        The reconstructed arrival direction per station is indicated with an arrow.
+
+        Author: Philipp Laub
+
+        Parameters
+        ----------
+        event : Event object
+            The event for which to show the final plots.
+        detector : Detector object
+            The detector for which to show the final plots.
+        min_number_good_antennas : int, default=4
+            The minimum number of good antennas that should be 
+            present in a station to consider it for the fit.
+
+        Returns
+        -------
+        fig_pol : matplotlib Figure object
+            The generated figure object containing the polarization plot.
+        """
+        time = detector.get_detector_time().utc
+
+        if time.mjd < 56266:
+            self.logger.warning("Event was before Dec 1, 2012. The non-core station clocks might be off.")
+
+        good_antennas_dict = check_for_good_ant(event, detector)
+
+        fig_time, ax = plt.subplots(dpi=150, figsize=(8, 5))
+
+        triggered_station_ids = [
+            station.get_id() for station in event.get_stations() if station.get_parameter(stationParameters.triggered)
+        ]
+        num_stations = len(triggered_station_ids)
+
+        cmap = get_cmap('jet')
+        norm = Normalize(vmin=0, vmax=num_stations-1) 
+        
+        fluences = []
+        positions = []
+        SNRs = []
+        for station in event.get_stations():
+            if station.get_parameter(stationParameters.triggered):
+                try:
+                    azimuth = station.get_parameter(stationParameters.cr_azimuth)
+                except KeyError:
+                    self.logger.info(
+                        f"Station CS{station.get_id():03d} does not have a reconstructed direction, "
+                        f"so I am not plotting this one."
+                    )
+                    continue
+                good_antennas = good_antennas_dict[station.get_id()]
+                if len(good_antennas) >= min_number_good_antennas:
+                    for antenna in good_antennas:
+                        positions.append(
+                            detector.get_relative_position(station.get_id(), antenna) + detector.get_absolute_position(station.get_id())
+                        )
+                        channel = station.get_channel(antenna)
+                        SNRs.append(channel.get_parameter(channelParameters.SNR))
+                        fluences.append(np.sum(np.square(channel.get_trace())))
+                    station_pos = detector.get_absolute_position(station.get_id())
+                    ax.quiver(station_pos[0], station_pos[1], 
+                            np.cos(azimuth), np.sin(azimuth), 
+                            color='black', 
+                            scale=0.02, 
+                            scale_units='xy', 
+                            angles='uv',
+                            width=0.005)
+        
+        timelags = []
+        for station in event.get_stations():
+            if station.get_parameter(stationParameters.triggered):
+                good_antennas = good_antennas_dict[station.get_id()]
+                if len(good_antennas) >= min_number_good_antennas:
+                    for channel_id in good_antennas:
+                        timelags.append(station.get_channel(channel_id).get_parameter(channelParameters.signal_time))
+
+        for i, station in enumerate(event.get_stations()):
+            # plot absolute station positions
+            if station.get_parameter(stationParameters.triggered):
+                station_pos = detector.get_absolute_position(station.get_id())
+                ax.scatter(station_pos[0], station_pos[1], color=cmap(norm(i)), s=20, label=f'Station CS{station.get_id():03d}')
+
+        timelags = np.array(timelags)
+        timelags -= timelags[0]  # get timelags wrt 1st antenna
+        # plot all locations and use arrival time for color and fluence for marker size and add a colorbar
+        positions = np.array(positions)
+        fluences = np.array(fluences)
+        SNRs = np.array(SNRs)
+        fluence_norm = Normalize(vmin=np.min(fluences), vmax=np.max(fluences))
+        sc = ax.scatter(
+            positions[:,0], 
+            positions[:,1], 
+            c=timelags, 
+            s=15 * fluence_norm(fluences), 
+            cmap='viridis',
+            zorder=-1)
+        
+        ax.set_aspect('equal')
+        plt.colorbar(sc, label='Relative arrival time [ns]', shrink=0.7)
+        ax.set_xlabel('Meters east [m]')
+        ax.set_ylabel('Meters north [m]')
+        plt.legend()
+        plt.title("Antenna positions and arrival time")
+
+        return fig_time
+
+
+    @register_run()
+    def run(self, event, detector, save_dir='.', polarization=False, direction=False):
+        """
+        Produce pipeline plots for the given event.
+
+        Parameters
+        ----------
+        event : Event object
+            The event for which to visualize the pipeline.
+        detector : Detector object
+            The detector for which to visualize the pipeline.
+        save_dir : str, optional
+            The directory to save the plots to. Default is the 
+            current directory.
+        """
+
+        plots = []
+        if polarization:
+            pol_plot = self.plot_polarization(event, detector)
+            plots.append(pol_plot)
+            pol_plot.savefig(f'{save_dir}/polarization_plot_{event.get_id()}.png')
+
+        if direction:
+            dir_plot = self.show_direction_plot(event)
+            plots.append(dir_plot)
+            dir_plot.savefig(f'{save_dir}/direction_plot_{event.get_id()}.png')
+
+            time_fluence_plot = self.show_time_fluence_plot(event, detector)
+            plots.append(time_fluence_plot)
+            time_fluence_plot.savefig(f'{save_dir}/time_fluence_plot_{event.get_id()}.png')
+
+        self.plots = [plot for plot in plots]
+
+    def end(self):
+        pass
diff --git a/NuRadioReco/modules/LOFAR/planeWaveDirectionFitter_LOFAR.py b/NuRadioReco/modules/LOFAR/planeWaveDirectionFitter_LOFAR.py
new file mode 100644
index 0000000000..764345e671
--- /dev/null
+++ b/NuRadioReco/modules/LOFAR/planeWaveDirectionFitter_LOFAR.py
@@ -0,0 +1,521 @@
+"""
+This module has been adapted from pycrtools.modules.tasks.directionfitplanewave and
+NuRadioReco.modules.LOFAR.beamformingDirectionFitter_LOFAR
+
+.. moduleauthor:: Philipp Laub <philipp.laub@fau.de>
+"""
+
+import logging
+import numpy as np
+import matplotlib.pyplot as plt
+import radiotools.helper as hp
+
+
+from NuRadioReco.utilities import units
+from NuRadioReco.framework.parameters import stationParameters, channelParameters, showerParameters
+from NuRadioReco.modules.base.module import register_run
+from NuRadioReco.modules.LOFAR.beamforming_utilities import geometric_delay_far_field, lightspeed
+
+
+def average_direction(event, detector, mode='normal'):
+    """
+    Calculate the average direction for an event based on the plane wave directions of the individual stations.
+
+    Parameters
+    ----------
+    event : Event object
+        The event for which to calculate the average direction.
+    detector : Detector object
+        The detector for which to calculate the average direction.
+    mode : str, default='normal'
+        The mode to use for the calculation. Can be 'normal' (just raw mean) or 'weighted'
+        (with number of good antennas as weight per station).
+
+    Returns
+    -------
+    avg_zenith : float
+        The average zenith angle for the event.
+    avg_azimuth : float
+        The average azimuth angle for the event.
+    """
+    zeniths = []
+    azimuths = []
+    num_good_antennas = []
+    for station in event.get_stations():
+        if station.get_parameter(stationParameters.triggered):
+            flagged_channels = station.get_parameter(stationParameters.flagged_channels)
+            num_good_antennas.append(
+                detector.get_number_of_channels(station.get_id()) - len(flagged_channels)
+            )
+            zeniths.append(station.get_parameter(stationParameters.cr_zenith))
+            azimuths.append(station.get_parameter(stationParameters.cr_azimuth))
+
+    zeniths = np.array(zeniths)
+    azimuths = np.array(azimuths)
+    num_good_antennas = np.array(num_good_antennas)
+
+    # Calculate the average direction: 
+    if mode == 'normal':
+        avg_zenith = np.mean(zeniths)
+        avg_azimuth = np.mean(azimuths)
+    elif mode == 'weighted':
+        avg_zenith = np.sum(zeniths * num_good_antennas) / np.sum(num_good_antennas)
+        avg_azimuth = np.sum(azimuths * num_good_antennas) / np.sum(num_good_antennas)
+    else:
+        raise ValueError(f"Unknown mode: {mode}")
+
+    return avg_zenith, avg_azimuth
+
+
+class planeWaveDirectionFitter:
+    """
+    Fits the direction per station using timing differences of channels under the assumption of an incoming plane wave.
+    """
+
+    def __init__(self):
+        self.logger = logging.getLogger("NuRadioReco.planeWaveDirectionFitter")
+
+        self.__cr_snr = None
+        self.__logger_level = None
+        self.__debug = None
+        self.__window_size = None
+        self.__ignore_non_horizontal_array = None
+        self.__rmsfactor = None
+        self.__min_amp = None
+        self.__max_iter = None
+        self.__min_number_good_antennas = None
+
+    def begin(self, max_iter=10, cr_snr=6.5, min_amp=None, rmsfactor=2.0, force_horizontal_array=True,
+              debug=False, logger_level=logging.NOTSET, min_number_good_antennas=4):
+        """
+        Set the parameters for the plane wave fit.
+
+        Parameters
+        ----------
+        max_iter : int, default=10
+            The maximum number of iterations to use during the fitting procedure.
+        cr_snr : float, default=3
+            The minimum SNR a channel should have to be considered having a cosmic ray signal.
+            Ignored if min_amp is not None.
+        min_amp : float, default=0.001
+            The minimum amplitude a channel should have to be considered having a cosmic ray signal.
+            Set to None if you want to use the SNR instead.
+        rmsfactor : float, default=2.0
+            How many sigma (times RMS) above the average can a delay deviate from the expected timelag
+            (from latest fit iteration) before it is considered bad and removed as outlier.
+        force_horizontal_array : bool, default=True
+            Set to True when you know the array is non-horizontal (z > 0.5) but want to use the
+            horizontal approximation anyway. Recommended to set to True.
+        debug : bool, default=False
+            Set to True to enable debug plots.
+        logger_level : int, default=logging.WARNING
+            The logging level to use for the module.
+        min_number_good_antennas : int, default=4
+            The minimum number of good antennas that should be present in a station to consider it for the fit.
+        """
+        self.__max_iter = max_iter
+        self.__cr_snr = cr_snr
+        self.__min_amp = min_amp
+        self.__rmsfactor = rmsfactor
+        self.__ignore_non_horizontal_array = force_horizontal_array
+        self.__debug = debug
+        self.__logger_level = logger_level
+        self.logger.setLevel(logger_level)
+        self.__min_number_good_antennas = min_number_good_antennas
+
+    @staticmethod
+    def _get_timelags(station, channel_ids_dominant_pol):
+        """
+        Get timing differences between signals in antennas with respect to some reference antenna (the first one
+        in the list of ids). The peak is determined using the Hilbert envelope after resampling the trace with
+        `resample_factor`.
+
+        Parameters
+        ----------
+        station : Station object
+            The station for which to get the time lags
+        channel_ids_dominant_pol : list of int
+            The list of channel ids to return the time lags for (usually the dominant polarisation)
+
+        Returns
+        -------
+        timelags : np.ndarray
+            The timelags (in internal units) for each channel in the list, with respect to the first one
+        """
+        # Get the signal time found by stationPulseFinder
+        timelags = []
+        for channel_id in channel_ids_dominant_pol:
+            timelags.append(station.get_channel(channel_id).get_parameter(channelParameters.signal_time))
+
+        timelags -= timelags[0]  # get timelags wrt 1st antenna
+
+        return np.asarray(timelags)
+
+    @staticmethod
+    def _direction_horizontal_array(positions: np.ndarray, times: np.ndarray, ignore_z_coordinate=False):
+        r"""
+        --- adapted from pycrtools.modules.scrfind ---
+        Given N antenna positions, and (pulse) arrival times for each antenna,
+        get a direction of arrival (azimuth, zenith) assuming a source at infinity (plane wave).
+
+        Here, we find the direction assuming all antennas are placed in the z=0 plane.
+        If all antennas are co-planar, the best-fitting solution can be found using a 2D-linear fit.
+        We find the best-fitting A and B in:
+
+        .. math::
+
+            t = A x + B y + C
+
+        where t is the array of times; x and y are arrays of coordinates of the antennas.
+        The C is the overall time offset in the data, that has to be subtracted out.
+        The optimal value of C has to be determined in the fit process (it's not just the average time,
+        nor the time at antenna 0).
+
+        This is done using :mod:`numpy.linalg.lstsq`.
+
+        The (azimuth, zenith) follows from:
+
+        .. math::
+
+            A = \sin(\mathrm{zenith}) \cos(\mathrm{azimuth})
+
+            B = \sin(\mathrm{zenith}) \sin(\mathrm{azimuth})
+
+        
+        Parameters
+        ----------
+        positions : np.ndarray
+            Positions (x,y,z) of the antennas (shape: (N_antennas, 3))
+        times : array, float
+            Measured pulse arrival times for all antennas
+
+        Returns
+        -------
+        zenith : float
+            Zenith in the [0, 2pi] interval (given in internal units)
+        azimuth : float
+            Azimuth in the [0, 2pi] interval (given in internal units)
+        """
+
+        # make x, y arrays out of the input position array
+        x = positions[:, 0]
+        y = positions[:, 1]
+
+        # now a crude test for nonzero z-input, |z| > 0.5
+        z = positions[:, 2]
+        if not ignore_z_coordinate and max(abs(z)) > 0.5:
+            raise ValueError("Input values of z are nonzero ( > 0.5) !")
+
+        M = np.vstack([x, y, np.ones(len(x))]).T  # says the linalg.lstsq doc
+
+        A, B, C = np.linalg.lstsq(M, lightspeed * times, rcond=None)[0]
+
+        zenith = np.arcsin(np.sqrt(A**2 + B**2))  # TODO: this can result in RuntimeWarning - why?
+        azimuth = np.arctan2(-B, -A)  # note minus sign as we want the direction of the _incoming_ vector (from the sky, not towards it)
+        
+        return np.mod(zenith * units.rad, 360 * units.deg), np.mod(azimuth * units.rad, 360 * units.deg)
+
+    @register_run()
+    def run(self, event, detector):
+        """
+        Run the plane wave fit for the given event and detector.
+
+        Parameters
+        ----------
+        event : Event object
+            The event for which to run the plane wave fit.
+        detector : Detector object
+            The detector for which to run the plane wave fit.
+        """
+        for station in event.get_stations():
+            if not station.get_parameter(stationParameters.triggered):
+                self.logger.debug(f"Station CS{station.get_id():03d} did not trigger, skipping...")
+                continue
+            self.logger.debug(f"Running over station CS{station.get_id():03d}")
+
+            # get LORA initial guess for the direction
+            lora_zenith = event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.zenith)
+            lora_azimuth = event.get_hybrid_information().get_hybrid_shower("LORA").get_parameter(showerParameters.azimuth)
+
+            # Get all group IDs which are still present in the station
+            station_channel_group_ids = set([channel.get_group_id() for channel in station.iter_channels()])
+
+            # Get the dominant polarisation orientation as calculated by stationPulseFinder
+            dominant_orientation = station.get_parameter(stationParameters.cr_dominant_polarisation)
+
+            # Collect the positions of 'good' antennas
+            good_channel_pair_ids = np.zeros((len(station_channel_group_ids), 2), dtype=int)
+            relative_position_array = np.zeros((len(station_channel_group_ids), 3))
+            good_amp_or_snr = np.zeros(len(station_channel_group_ids), dtype=bool)
+            for ind, channel_group_id in enumerate(station_channel_group_ids):
+                relative_position_array[ind] = detector.get_relative_position(station.get_id(), channel_group_id)
+                for channel in station.iter_channel_group(channel_group_id):
+                    if np.all(detector.get_antenna_orientation(station.get_id(), channel.get_id()) == dominant_orientation):
+                        good_channel_pair_ids[ind, 0] = channel.get_id()
+                    else:
+                        good_channel_pair_ids[ind, 1] = channel.get_id()
+
+                # Check if dominant channel has acceptable SNR or acceptable amplitude (if desired)
+                channel = station.get_channel(good_channel_pair_ids[ind, 0])
+                if self.__min_amp is None:
+                    if channel.get_parameter(channelParameters.SNR) > self.__cr_snr:
+                        good_amp_or_snr[ind] = True
+                else:
+                    if np.max(np.abs(channel.get_trace())) >= self.__min_amp:
+                        good_amp_or_snr[ind] = True
+
+            num_good_antennas = np.sum(good_amp_or_snr)
+            mask_good_antennas = np.full(num_good_antennas, True)
+
+            # the dominant antennas are good_antennas[:, 0]
+            good_antennas = good_channel_pair_ids[good_amp_or_snr]
+            position_array = relative_position_array[good_amp_or_snr]
+
+            # iteratively do the plane wave fit and remove outliers (controlled by rmsfactor)
+            # until the number of good antennas remains constant
+            niter = 0
+            zenith, azimuth = lora_zenith, lora_azimuth
+            while niter < self.__max_iter:  # TODO: maybe add additional condition?
+                niter += 1
+                # if only three antennas (or less) remain, fit should not be trusted as it always has a solution (fails)
+                if num_good_antennas < self.__min_number_good_antennas:
+                    self.logger.warning(f"Only {num_good_antennas:d} good antennas remaining!")
+                    self.logger.error(f"Too few good antennas for direction fit!")
+                    break
+
+                # update arrays to use only previously found "good" antennas:
+                position_array = position_array[mask_good_antennas]
+                good_antennas = good_antennas[mask_good_antennas]
+
+                # get time lags from the dominant antennas only
+                times = self._get_timelags(station, good_antennas[:, 0])
+
+                goodpositions = position_array
+                goodtimes = times
+
+                zenith, azimuth = self._direction_horizontal_array(goodpositions, goodtimes,
+                                                                   self.__ignore_non_horizontal_array)
+
+                # get residuals
+                expected_delays = geometric_delay_far_field(
+                    goodpositions, hp.spherical_to_cartesian(zenith / units.rad, azimuth / units.rad)
+                )
+                expected_delays -= expected_delays[0]  # get delays wrt 1st antenna
+
+                residual_delays = goodtimes - expected_delays
+
+                # Debug plots if required
+                if self.__debug:
+                    self.debug_plots(
+                        event, expected_delays, good_antennas, niter, position_array, residual_delays, station, times
+                    )
+
+                if np.isnan(zenith) or np.isnan(azimuth):
+                    self.logger.error(
+                        'Plane wave fit returns NaN. I will try to recover by setting zenith and azimuth '
+                        'to the LORA estimate and recalculating the residual delays.'
+                    )
+
+                    zenith = lora_zenith
+                    azimuth = lora_azimuth
+
+                    expected_delays = geometric_delay_far_field(
+                        goodpositions, hp.spherical_to_cartesian(zenith / units.rad, azimuth / units.rad)
+                    )
+                    expected_delays -= expected_delays[0]
+                    residual_delays = goodtimes - expected_delays
+
+                    bins = int(
+                        (residual_delays.max() - residual_delays.min()) * lightspeed /
+                        (position_array[:, 0].max() - position_array[:, 0].min())
+                    )
+                    hist, edges = np.histogram(residual_delays, bins=max(bins, 1))
+
+                    max_time = np.argmax(hist)
+                    self.logger.debug(f"histogram filled: {hist}")
+                    self.logger.debug(f"edges: {edges}")
+                    self.logger.debug(f"maximum at: {max_time}")
+
+                    upper = edges[min(max_time + 2, len(edges) - 1)]
+                    lower = edges[max(max_time - 1, 0)]
+
+                    self.logger.debug(f"Selecting between lower {lower} and upper {upper}")
+                    mask_good_antennas = (residual_delays > lower) & (residual_delays < upper)
+                else:
+                    # remove > k-sigma outliers and iterate
+                    spread = np.std(residual_delays)
+                    k = self.__rmsfactor
+                    mask_good_antennas = abs(residual_delays - np.mean(residual_delays)) < k * spread
+                    # gives subset of 'good_antennas' that is 'good' after this iteration
+
+                self.logger.debug(f"station {station.get_id()}:")
+                self.logger.debug(f"iteration {niter:d}:")
+                self.logger.debug(f'azimuth = {np.rad2deg(azimuth):.3f}, zenith = {np.rad2deg(zenith):.3f}')
+                self.logger.debug(f'number of good antennas = {num_good_antennas:d}')
+
+                # if the next iteration has the same number of good antennae the while loop will be terminated
+                if len(good_antennas[mask_good_antennas]) == num_good_antennas:
+                    break
+                else:
+                    num_good_antennas = len(good_antennas[mask_good_antennas])
+
+            self.logger.status(
+                f"Azimuth (counterclockwise wrt to East) and zenith for station CS{station.get_id():03d}:"
+            )
+            self.logger.status(f"{azimuth / units.deg}, {zenith / units.deg}")
+
+            self.logger.status(
+                f"Azimuth (clockwise wrt to North) and elevation for station CS{station.get_id():03d}:"
+            )
+            self.logger.status(f"{90 - azimuth / units.deg}, {90 - zenith / units.deg}")
+
+            # Set stationParameters.zenith/azimuth because voltageToEfieldConverter uses these to convert
+            # NOTE: these can be the LORA direction (in case the fit failed)
+            station.set_parameter(stationParameters.zenith, zenith)
+            station.set_parameter(stationParameters.azimuth, azimuth)
+
+            # Only set reconstructed direction if it is not identical to the LORA direction
+            if not (zenith == lora_zenith and azimuth == lora_azimuth):
+                self.logger.info(
+                    f"The fit for station CS{station.get_id():03d} seems to have failed."
+                    f"I will not set the cr_zenith and cr_azimuth station parameters, but you can"
+                    f"still unfold the voltages to electric fields with the LORA direction as this"
+                    f"is saved in the zenith and azimuth station parameters."
+                )
+                station.set_parameter(stationParameters.cr_zenith, zenith)
+                station.set_parameter(stationParameters.cr_azimuth, azimuth)
+
+            # flag channels that were not used in the fit
+            station_flagged_channels = station.get_parameter(stationParameters.flagged_channels)
+
+            for channel_id in good_channel_pair_ids.flatten().tolist():
+                if channel_id not in good_antennas.flatten():
+                    # TODO: this flag is not always correct, as channels excluded by SNR are also flagged
+                    station_flagged_channels[channel_id].append("planewavefit_timing_outlier")
+
+            station.set_parameter(stationParameters.flagged_channels, station_flagged_channels)
+
+    @staticmethod
+    def debug_plots(
+            event, expected_delays, good_antennas, niter, position_array, residual_delays, station, times
+    ):
+        """
+        Create debug plots for the plane wave fit.
+        """
+        planeWaveDirectionFitter.__debug_mosaic(
+            event, expected_delays, good_antennas, niter, position_array, residual_delays, station, times
+        )
+        planeWaveDirectionFitter.__debug_residuals(
+            event, good_antennas, residual_delays, station, niter
+        )
+
+    @staticmethod
+    def __debug_residuals(event, good_antennas, residual_delays, station, niter):
+        """
+        Show the residuals per antenna and mark SNR
+        """
+        fig, ax = plt.subplots()
+
+        antenna_SNRs = np.zeros(len(good_antennas))
+        for i, antenna in enumerate(good_antennas[:, 0]):
+            channel = station.get_channel(antenna)
+            antenna_SNRs[i] = channel.get_parameter(channelParameters.SNR)
+
+        plt.scatter(np.arange(len(residual_delays)), residual_delays, marker='o', c=antenna_SNRs)
+
+        # add colorbar
+        plt.colorbar(label='SNR')
+
+        ax.set_xlabel('Antenna')
+        ax.set_ylabel('Residual time [ns]')
+        ax.set_title(f'Residuals for station {station.get_id()}')
+
+        fig.savefig(
+            f"pipeline_planewavefit_residuals_CS{station.get_id():03d}_iteration{niter}_{event.get_id()}.png",
+            dpi=250, bbox_inches='tight'
+        )
+        # fig.savefig(
+        #     f"pipeline_planewavefit_residuals_CS{station.get_id():03d}_iteration{niter}_{event.get_id()}.svg",
+        #     dpi=250, bbox_inches='tight'
+        # )
+        plt.close(fig)
+
+    @staticmethod
+    def __debug_mosaic(event, expected_delays, good_antennas, niter, position_array, residual_delays, station, times):
+        """
+        Plot the timings, as well as the residuals and the traces used for the fit.
+        """
+        import matplotlib as mpl
+
+        inner = [['times'],
+                 ['expected']]
+        outer = [[inner, 'residuals'],
+                 ['traces', 'traces']]
+
+        fig, axd = plt.subplot_mosaic(outer, layout="constrained", figsize=(10, 12))
+
+        for channel in station.iter_channels(use_channels=good_antennas[:, 0]):
+            axd['traces'].plot(channel.get_trace() / units.mV)
+
+        # mark the signal window:
+        channel = station.get_channel(good_antennas[0, 0])  # should be all the same
+        pulse_window_start, pulse_window_end = channel.get_parameter(channelParameters.signal_regions)
+
+        axd['traces'].axvline(pulse_window_start, color='r')
+        axd['traces'].axvline(pulse_window_end, color='r')
+        axd['traces'].set_xlim(pulse_window_start - 500, pulse_window_end + 500)
+        axd['traces'].set_xlabel('Sample index')
+        axd['traces'].set_ylabel('Amplitude [mV]')
+        axd['traces'].set_title(f'Good traces used in iteration {niter}')
+
+        # Plot the timing residuals
+        datasets = [times, expected_delays]
+        norm1 = mpl.colors.Normalize(vmin=np.min(datasets), vmax=np.max(datasets))
+        norm2 = mpl.colors.Normalize(vmin=np.min(residual_delays), vmax=np.max(residual_delays))
+        cmap1 = mpl.colormaps.get_cmap('viridis')
+        cmap2 = mpl.colormaps.get_cmap('seismic')
+
+        axd['times'].scatter(position_array[:, 0], position_array[:, 1], c=times,
+                             norm=norm1, cmap=cmap1,
+                             label='Measured')
+        axd['times'].set_title("Time delays")
+        axd['times'].legend()
+
+        axd['expected'].scatter(position_array[:, 0], position_array[:, 1], c=expected_delays,
+                                norm=norm1, cmap=cmap1,
+                                label='Expected')
+        axd['expected'].legend()
+
+        axd['residuals'].scatter(position_array[:, 0], position_array[:, 1], c=residual_delays,
+                                 norm=norm2, cmap=cmap2)
+        axd['residuals'].set_title("Residual time delays")
+
+        fig.colorbar(mpl.cm.ScalarMappable(norm=norm1, cmap=cmap1),
+                     ax=axd['expected'], orientation='horizontal',
+                     location='bottom',
+                     label='Time [ns]')
+        fig.colorbar(mpl.cm.ScalarMappable(norm=norm2, cmap=cmap2),
+                     ax=axd['residuals'], orientation='horizontal',
+                     location='bottom',
+                     label='Time [ns]')
+        fig.suptitle(f"Station {station.get_id()}")
+
+        for ax in ['expected', 'residuals']:
+            axd[ax].set_xlabel("Easting [m]")
+        axd['residuals'].yaxis.set_label_position("right")
+        axd['residuals'].set_ylabel("Northing [m]")
+        axd['residuals'].set_aspect('equal')
+
+        fig.savefig(
+            f"pipeline_planewavefit_debug_CS{station.get_id():03d}_iteration{niter}_{event.get_id()}.png",
+            dpi=250, bbox_inches='tight'
+        )
+        # fig.savefig(
+        #     f"pipeline_planewavefit_debug_CS{station.get_id():03d}_iteration{niter}_{event.get_id()}.svg",
+        #     dpi=250, bbox_inches='tight'
+        # )
+
+        plt.close(fig)
+
+    def end(self):
+        pass
diff --git a/NuRadioReco/modules/LOFAR/stationGalacticCalibrator.py b/NuRadioReco/modules/LOFAR/stationGalacticCalibrator.py
index 8750f39473..3b0201cd30 100644
--- a/NuRadioReco/modules/LOFAR/stationGalacticCalibrator.py
+++ b/NuRadioReco/modules/LOFAR/stationGalacticCalibrator.py
@@ -1,6 +1,5 @@
 import os
 import logging
-import datetime
 
 import numpy as np
 
@@ -11,6 +10,7 @@
 
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.utilities import units
+from NuRadioReco.modules.io.LOFAR.readLOFARData import LOFAR_event_id_to_unix
 
 
 def fourier_series(x, p):
@@ -55,17 +55,15 @@ class stationGalacticCalibrator:
     Further details are described in this `overview <https://arxiv.org/pdf/1311.1399.pdf>`_, and also this
     `paper <https://arxiv.org/pdf/1903.05988.pdf>`_ .
     """
+
     def __init__(self, experiment='LOFAR_LBA'):
         self.logger = logging.getLogger('NuRadioReco.LOFAR.stationGalacticCalibrator')
 
         self.__experiment = experiment
 
-        self.__experiment_parameters = None
         self.__abs_calibration_curve = None
         self.__rel_calibration_coefficients = None
 
-        self.begin()
-
     def begin(self, logger_level=logging.NOTSET):
         """
         Loads the experimental parameters (such as longitude and latitude) as well as the Galactic calibration
@@ -81,23 +79,15 @@ def begin(self, logger_level=logging.NOTSET):
         # The files are stored in the data folder of the utilities module, which sits 3 folders up
         data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'utilities', 'data')
 
-        # Get the experiment parameters such as latitude and longitude
-        with open(os.path.join(data_dir, "experiment_parameters.txt"), "r") as f:
-            all_experiment_parameters = f.readlines()
-
-        for line in all_experiment_parameters:
-            if line.startswith(self.__experiment):
-                self.__experiment_parameters = line.split(", ")
-
         # Get absolute calibration curve
         self.__abs_calibration_curve = np.genfromtxt(
             os.path.join(
                 data_dir, "galactic_calibration",
-                f"{self.__experiment}_galactic_{self.__experiment_parameters[6]}_{self.__experiment_parameters[7]}.txt"
+                f"{self.__experiment}_abs_calibration_curve.txt"
             ),
         )
 
-        # Get fitted Fourier coefficients for relative calibration and store them based on polarisation group ID
+        # Get fitted Fourier coefficients for relative calibration and store them based on polarisation
         rel_calibration_file = np.genfromtxt(
             os.path.join(
                 data_dir, "galactic_calibration",
@@ -109,7 +99,7 @@ def begin(self, logger_level=logging.NOTSET):
 
         self.__rel_calibration_coefficients = {}
         for col in rel_calibration_file.T:
-            group_id = int(col[0].split(" ")[1])
+            group_id = str(col[0].split(" ")[1])
             coefficients = col[1:].astype('f8')
 
             self.__rel_calibration_coefficients[group_id] = coefficients
@@ -139,7 +129,7 @@ def _get_absolute_calibration(self, frequencies):
         # Apply the interpolation to the sampled frequencies and return
         return f(frequencies)
 
-    def _get_relative_calibration(self, local_sidereal_time, channel):
+    def _get_relative_calibration(self, local_sidereal_time, channel, channel_polarisation):
         """
         Calculate the relative calibration correction factor for a channel, given the Fourier coefficients for the curve
         of the galactic noise power they observe as a function of the local sidereal time. This makes sure
@@ -152,6 +142,8 @@ def _get_relative_calibration(self, local_sidereal_time, channel):
             The local sidereal time of the observation.
         channel : Channel object
             The channel for which to calculate the correction factor.
+        channel_polarisation : str
+            The key of the channel polarisation, as used in the `self.__rel_calibration_coefficients` dictionary.
 
         Returns
         -------
@@ -166,11 +158,16 @@ def _get_relative_calibration(self, local_sidereal_time, channel):
         calibrated to observe the same value of the Galactic noise.
         """
 
-        # Get channel parameters -> group_id = a(even_id), odd_id = even_id + 1
-        channel_polarisation = channel.get_id() - int(channel.get_group_id()[1:])  # TODO: need clear function?
-        channel_bandwidth = channel.get_sampling_rate() / channel.get_number_of_samples() / units.s  # 1 / period
+        # Get channel parameters
+        channel_bandwidth = channel.get_sampling_rate() / channel.get_number_of_samples()
         channel_power = np.sum(np.abs(channel.get_frequency_spectrum()) ** 2) * channel_bandwidth
 
+        # The NRR frequency spectrum has a factor of sampling rate in the denominator, which after squaring and
+        # multiplying with the channel_bandwidth leaves a 1 / sampling_rate in the channel_power calculation.
+        # To properly match the Fourier series evaluation, the sampling_rate should be in Hz, so we multiply
+        # (as the sampling rate is in the denominator) with the unit here.
+        channel_power *= units.Hz
+
         self.logger.debug(f"Channel power of channel {channel.get_id()} is {channel_power}")
 
         # Calculate Galactic power noise
@@ -184,7 +181,7 @@ def _get_relative_calibration(self, local_sidereal_time, channel):
             scale = 0.0  # A channel without a signal will have 0 channel power, and result in np.inf
         return np.sqrt(scale)  # Correction is applied in time domain
 
-    def _calibrate_channel(self, channel, timestamp):
+    def _calibrate_channel(self, channel, polarisation, timestamp, observing_location):
         """
         Convenience function to apply the absolute and relative calibration to a single channel.
 
@@ -192,14 +189,15 @@ def _calibrate_channel(self, channel, timestamp):
         ----------
         channel : Channel object
             The channel to be calibrated.
+        polarisation : str
+            The polarisation of the channel, as used in the Fourier coefficients file.
         timestamp : int
             The UNIX timestamp corresponding to the observation.
+        observing_location : EarthLocation
+            The location of the observation, used to calculate the local sidereal time.
         """
         # Find the sidereal time for the experiment
-        dt_object = datetime.datetime.utcfromtimestamp(timestamp)
-        observing_location = EarthLocation(lat=float(self.__experiment_parameters[4]) * u.deg,
-                                           lon=float(self.__experiment_parameters[5]) * u.deg)
-        observing_time = Time(dt_object, scale="utc", location=observing_location)
+        observing_time = Time(timestamp, format="unix", location=observing_location)
         local_time = observing_time.sidereal_time("apparent").hour
 
         # Load the trace from the channel
@@ -209,13 +207,43 @@ def _calibrate_channel(self, channel, timestamp):
 
         # Calibrate
         trace_fft *= self._get_absolute_calibration(trace_frequencies)
-        trace_fft *= self._get_relative_calibration(local_time, channel)
+        trace_fft *= self._get_relative_calibration(local_time, channel, polarisation)
 
         # Set the calibrated trace back in the channel
         channel.set_frequency_spectrum(trace_fft, trace_sampling_rate)
 
+    def __get_channel_polarisation(self, detector, station, channel):
+        """
+        Check the channel orientation in the Detector and return the polarisation key to retrieve the
+        corresponding Fourier coefficients.
+
+        Parameters
+        ----------
+        detector : Detector object
+        station : Station object
+        channel : Channel object
+
+        Returns
+        -------
+        str
+            The channel polarisation key
+        """
+        orientation_rad = detector.get_antenna_orientation(
+            station.get_id(), channel.get_id()
+        )[1]  # takes the phi orientation in rad of the specific channel
+        orientation = orientation_rad / units.deg  # get value in degrees
+        if orientation == 225.0:
+            channel_polarisation = 1  # for X dipoles, channel_polarisation is set to 1
+        elif orientation == 135.0:
+            channel_polarisation = 0  # for Y dipoles, channel_polarisation is set to 0
+        else:
+            self.logger.error(f"Antenna orientation of {orientation} does not correspond to either X or Y dipole.")
+            raise ValueError
+
+        return str(channel_polarisation)
+
     @register_run()
-    def run(self, event):
+    def run(self, event, det):
         """
         Run the calibration on all stations in `event`.
 
@@ -223,11 +251,16 @@ def run(self, event):
         ----------
         event : Event object
             The event on which to apply the Galactic calibration.
+        det : Detector object
+            The Detector related to the `event`
         """
-        timestamp = event.get_id() + 1262304000
+        timestamp = LOFAR_event_id_to_unix(event.get_id())
         for station in event.get_stations():
+            site_latitude, site_longitude = det.get_site_coordinates(station.get_id())
+            site_location = EarthLocation(lat=site_latitude * u.deg, lon=site_longitude * u.deg)
             for channel in station.iter_channels():
-                self._calibrate_channel(channel, timestamp)
+                channel_pol = self.__get_channel_polarisation(det, station, channel)
+                self._calibrate_channel(channel, channel_pol, timestamp, site_location)
 
     def end(self):
         pass
diff --git a/NuRadioReco/modules/LOFAR/stationPulseFinder.py b/NuRadioReco/modules/LOFAR/stationPulseFinder.py
index 50ce408e27..e7d92deda0 100644
--- a/NuRadioReco/modules/LOFAR/stationPulseFinder.py
+++ b/NuRadioReco/modules/LOFAR/stationPulseFinder.py
@@ -2,7 +2,7 @@
 import numpy as np
 import radiotools.helper as hp
 
-from scipy.signal import hilbert
+from scipy.signal import hilbert, resample
 
 from NuRadioReco.utilities import fft
 from NuRadioReco.modules.base.module import register_run
@@ -10,38 +10,72 @@
 from NuRadioReco.modules.LOFAR.beamforming_utilities import mini_beamformer
 
 
-def find_snr_of_timeseries(timeseries, window_start=0, window_end=-1, noise_start=0, noise_end=-1):
-    """
-    Return the signal-to-noise ratio (SNR) of a given time trace. The additional parameters allow to
-    select a window where the signal is and a separate window where there is only noise.
+def find_snr_of_timeseries(timeseries, sampling_rate=None, window_start=0, window_end=-1, noise_start=0, noise_end=-1,
+                           resample_factor=1, full_output=False):
+    r"""
+    Return the signal-to-noise ratio (SNR) of a given time trace, defined as
+
+    ..math ::
+        \frac{max( | Hilbert(timeseries[window]) | )}{ STD( Hilbert(timeseries[noise]) ) }
+
+    The signal window and noise window are controlled through the extra parameters to the function.
 
     Parameters
     ----------
-    timeseries: list or np.ndarray
+    timeseries: array-like
         The time trace
+    sampling_rate : float
+        The sampling rate of the time trace (only needed if full_output=True)
     window_start : int
     window_end : int
-        We look for the peak inside the array `timeseries[window_start:window_end]`
+        We look for the peak inside the resampled array `timeseries[window_start:window_end]`
     noise_start : int
     noise_end : int
         The array `timeseries[noise_start:noise_end]` is used to calculate the noise level
+    resample_factor : int, default=1
+        Factor with which the timeseries will be resampled, needs to be integer > 0
+    full_output : bool, default=False
+        If True, also the peak of the envelope, RMS and signal time are returned
 
     Returns
     -------
     The SNR of the time trace
     """
-    analytic_signal = hilbert(timeseries)
+    resampled_window = resample(timeseries[window_start:window_end], (window_end - window_start) * resample_factor)
+    analytic_signal = hilbert(resampled_window)
     amplitude_envelope = np.abs(analytic_signal)
 
-    rms = np.sqrt(
-        np.mean(amplitude_envelope[noise_start:noise_end] ** 2)
+    peak = np.max(
+        amplitude_envelope
     )
 
-    peak = np.max(
-        amplitude_envelope[window_start:window_end]
+    if full_output:
+        resampled_max = np.argmax(analytic_signal)
+        resampled_max_time = resampled_max / sampling_rate / resample_factor
+        window_start_time = window_start / sampling_rate
+        signal_time = window_start_time + resampled_max_time
+
+    # (AC) The Hilbert envelope has a mean that is nonzero 
+    # the stddev only takes the variations around the mean
+    # whereas the (real) RMS takes the square of all values (incl the mean), then sqrt
+    # TEST if this SNR definition gives good results! 
+    # This is what seems to have been done in PyCRTools. See pulseenvelope.py:233
+    # and mMath.cc function hMaxSNR  
+
+    std = np.std(
+        np.abs(hilbert(timeseries[noise_start:noise_end]))
     )
 
-    return peak / rms
+    rms = np.sqrt(
+       np.mean(
+           np.abs(hilbert(timeseries[noise_start:noise_end])) ** 2
+       )
+    )
+
+    if full_output:
+        return peak / std, peak, rms, signal_time
+
+    return peak / std
 
 
 class stationPulseFinder:
@@ -52,16 +86,16 @@ class stationPulseFinder:
     """
 
     def __init__(self):
-        self.logger = logging.getLogger('NuRadioReco.LOFAR.stationPulseFinder')
+        self.logger = logging.getLogger('NuRadioReco.stationPulseFinder')
 
         self.__window_size = None
-        self.__noise_away_from_pulse = None
+        self.__noise_window_size = None
         self.__snr_cr = None
         self.__min_good_channels = None
 
         self.direction_cartesian = None  # The zenith and azimuth pointing towards where to beamform.
 
-    def begin(self, window=500, noise_window=10000, cr_snr=3, good_channels=6, logger_level=logging.NOTSET):
+    def begin(self, window=256, noise_window=10000, cr_snr=6.5, good_channels=6, logger_level=logging.NOTSET):
         """
         Sets the window size to use for pulse finding, as well as the number of samples away from the pulse
         to use for noise measurements. The function also defines what an acceptable SNR is to consider a
@@ -70,7 +104,7 @@ def begin(self, window=500, noise_window=10000, cr_snr=3, good_channels=6, logge
 
         Parameters
         ----------
-        window : int, default=500
+        window : int, default=256
             Size of the window to look for pulse
         noise_window : int, default=10000
             The trace used for noise characterisation goes from sample 0 to the start of the pulse searching
@@ -84,7 +118,7 @@ def begin(self, window=500, noise_window=10000, cr_snr=3, good_channels=6, logge
         """
         # TODO: find window size used in PyCRTools
         self.__window_size = window
-        self.__noise_away_from_pulse = noise_window
+        self.__noise_window_size = noise_window
         self.__snr_cr = cr_snr
         self.__min_good_channels = good_channels
 
@@ -94,8 +128,8 @@ def _signal_windows_polarisation(self, station, channel_positions, channel_ids_p
         """
         Considers the channel groups given by `channel_ids_per_pol` one by one and beamforms the traces
         in the direction of `stationPulseFinder.direction_cartesian`. It then calculates the maximum of the
-        amplitude envelope, and saves the corresponding index with the indices for pulse finding in a tuple,
-        which it returns.
+        amplitude envelope without resampling the signal, and saves the corresponding index with the indices
+        for pulse finding in a tuple, which it returns.
 
         Parameters
         ----------
@@ -120,15 +154,18 @@ def _signal_windows_polarisation(self, station, channel_positions, channel_ids_p
 
         values_per_pol = []
 
-        for channel_ids in channel_ids_per_pol:
-            all_traces = np.array([station.get_channel(channel).get_frequency_spectrum() for channel in channel_ids])
+        # the first few samples are tapered with half-Hann, which would blow up the SNR
+        noise_window_start = 10000
+        noise_window_end = noise_window_start + self.__noise_window_size
 
-            beamed_fft = mini_beamformer(all_traces, frequencies, channel_positions, self.direction_cartesian)
-            beamed_timeseries = fft.freq2time(beamed_fft, sampling_rate, n=all_traces.shape[1])
+        for i, channel_ids in enumerate(channel_ids_per_pol):
+            all_spectra = np.array([station.get_channel(channel).get_frequency_spectrum() for channel in channel_ids])
+            beamed_fft = mini_beamformer(all_spectra, frequencies, channel_positions, self.direction_cartesian)
+            beamed_timeseries = fft.freq2time(beamed_fft, sampling_rate,
+                                              n=station.get_channel(channel_ids[0]).get_trace().shape[0])
 
             analytic_signal = hilbert(beamed_timeseries)
             amplitude_envelope = np.abs(analytic_signal)
-
             signal_window_start = int(
                 np.argmax(amplitude_envelope) - self.__window_size / 2
             )
@@ -136,57 +173,29 @@ def _signal_windows_polarisation(self, station, channel_positions, channel_ids_p
                 np.argmax(amplitude_envelope) + self.__window_size / 2
             )
 
-            values_per_pol.append([np.max(amplitude_envelope), signal_window_start, signal_window_end])
+            snr = find_snr_of_timeseries(beamed_timeseries,
+                                         window_start=signal_window_start, window_end=signal_window_end,
+                                         noise_start=noise_window_start, noise_end=noise_window_end)
 
+            if snr > self.__snr_cr:
+                station.set_parameter(stationParameters.triggered, True)
+            else:
+                station.set_parameter(stationParameters.triggered, False)
+
+            values_per_pol.append([snr, signal_window_start, signal_window_end])
+            # SNR is technically better than just the max(envelope) as a measure for strongest polarization
         values_per_pol = np.asarray(values_per_pol)
         dominant = np.argmax(values_per_pol[:, 0])
         window_start, window_end = values_per_pol[dominant][1:]
 
-        return dominant, window_start, window_end
-
-    def _station_has_cr(self, station, channel_positions, channel_ids_per_pol, signal_window=None, noise_window=None):
-        """
-        Beamform the station towards the direction given by `stationPulseFinder.direction_cartesian`
-        and check if there is any significant signal in the trace. If this is the case,
-        the `stationParameter.triggered` value is set to `True`.
-
-        Parameters
-        ----------
-        station : Station object
-            The station to process
-        channel_positions : np.ndarray
-            The array of channels positions, to be extracted from the detector description
-        channel_ids_per_pol : list[list]
-            A list of channel IDs grouped per polarisation.
-        signal_window : array-like, default=[0, -1]
-            A list containing the first and last index of the trace where to look for a pulse
-        noise_window : array-like, default=[0, -1]
-            A list containing the first and last index of the trace to use for noise characterisation
-        """
-        if signal_window is None:
-            signal_window = [0, -1]
-        if noise_window is None:
-            noise_window = [0, -1]
-
-        frequencies = station.get_channel(channel_ids_per_pol[0][0]).get_frequencies()
-        sampling_rate = station.get_channel(channel_ids_per_pol[0][0]).get_sampling_rate()
-
-        for channel_ids in channel_ids_per_pol:
-            all_traces = np.array([station.get_channel(channel).get_frequency_spectrum() for channel in channel_ids])
-
-            beamed_fft = mini_beamformer(all_traces, frequencies, channel_positions, self.direction_cartesian)
-            beamed_timeseries = fft.freq2time(beamed_fft, sampling_rate, n=all_traces.shape[1])
-
-            snr = find_snr_of_timeseries(beamed_timeseries,
-                                         window_start=signal_window[0], window_end=signal_window[1],
-                                         noise_start=noise_window[0], noise_end=noise_window[1])
-            if snr > self.__snr_cr:
-                station.set_parameter(stationParameters.triggered, True)
-                return True  # no need to check second polarisation if CR found
+        # Save window of strongest polarisation in all channels
+        for channel in station.iter_channels():
+            channel.set_parameter(channelParameters.signal_regions, [int(window_start), int(window_end)])
+            channel.set_parameter(channelParameters.noise_regions, [int(noise_window_start), int(noise_window_end)])
 
-        return False
+        return dominant
 
-    def _find_good_channels(self, station, signal_window=None, noise_window=None):
+    def _find_good_channels(self, station):
         """
         Loop over all channels in the station and return an array which contains booleans indicating whether
         the channel at that index has an SNR higher than the minimum required one
@@ -196,29 +205,33 @@ def _find_good_channels(self, station, signal_window=None, noise_window=None):
         ----------
         station : Station object
             The station to process
-        signal_window : array-like, default=[0, -1]
-            A list containing the first and last index of the trace where to look for a pulse
-        noise_window : array-like, default=[0, -1]
-            A list containing the first and last index of the trace to use for noise characterisation
         """
-        if signal_window is None:
-            signal_window = [0, -1]
-        if noise_window is None:
-            noise_window = [0, -1]
-
         good_channels = []
         for channel in station.iter_channels():
-            snr = find_snr_of_timeseries(channel.get_trace(),
-                                         window_start=signal_window[0], window_end=signal_window[1],
-                                         noise_start=noise_window[0], noise_end=noise_window[1])
+            signal_window = channel.get_parameter(channelParameters.signal_regions)
+            noise_window = channel.get_parameter(channelParameters.noise_regions)
+
+            self.logger.debug(f'Channel {channel.get_id()}: looking for signal in indices {signal_window}')
+            self.logger.debug(f'Channel {channel.get_id()}: using {noise_window} as noise trace')
+
+            snr, peak, rms, signal_time = find_snr_of_timeseries(channel.get_trace(),
+                                                    sampling_rate=channel.get_sampling_rate(),
+                                                    window_start=signal_window[0], window_end=signal_window[1],
+                                                    noise_start=noise_window[0], noise_end=noise_window[1],
+                                                    resample_factor=16, full_output=True)
 
             channel.set_parameter(channelParameters.SNR, snr)
+            channel.set_parameter(channelParameters.noise_rms, rms)
+            channel.set_parameter(channelParameters.signal_time, signal_time)
+            channel.set_parameter(channelParameters.maximum_amplitude_envelope, peak)
+            channel.set_parameter(channelParameters.maximum_amplitude, np.max(channel.get_trace()))
+
             if snr > self.__snr_cr:
                 good_channels.append(channel.get_id())
 
         return good_channels
 
-    def _check_station_triggered(self, station, channel_positions, channel_ids_per_pol, signal_window, noise_window):
+    def _check_station_triggered(self, station):
         """
         Check if the station has been triggered by a radio signal. This functions verifies there is a significant
         pulse in the radio signal after beamforming, and also checks if there enough channels (i.e. LOFAR dipoles)
@@ -229,21 +242,14 @@ def _check_station_triggered(self, station, channel_positions, channel_ids_per_p
         station : Station object
             The station to analyse
         """
-        self.logger.debug(f'Looking for signal in indices {signal_window}')
-        self.logger.debug(f'Using {noise_window} as noise trace')
-
-        cr_found = self._station_has_cr(
-            station, channel_positions, channel_ids_per_pol, signal_window=signal_window, noise_window=noise_window
-        )
-
-        if cr_found:
+        if station.get_parameter(stationParameters.triggered):
             good_channels_station = self._find_good_channels(
-                station, signal_window=signal_window, noise_window=noise_window
+                station
             )
 
             self.logger.debug(f'Station {station.get_id()} has {len(good_channels_station)} good antennas')
             if len(good_channels_station) < self.__min_good_channels:
-                self.logger.warning(f'There are only {len(good_channels_station)} antennas '
+                self.logger.warning(f'Station {station.get_id()} has only {len(good_channels_station)} antennas '
                                     f'with an SNR higher than {self.__snr_cr}, while there '
                                     f'are at least {self.__min_good_channels} required')
                 station.set_parameter(stationParameters.triggered, False)  # stop from further processing
@@ -271,30 +277,44 @@ def run(self, event, detector):
             station_id = station.get_id()
 
             # Get the channel IDs grouped per polarisation (i.e. dipole orientation)
-            ant_same_orientation = detector.get_parallel_channels(station_id)
+            # -> take these from Event, cause some might already be thrown out!
+            station_even_list = []
+            station_odd_list = []
+            for channel in station.iter_channels():
+                if channel.get_id() == channel.get_group_id():
+                    station_even_list.append(channel.get_id())
+                else:
+                    station_odd_list.append(channel.get_id())
 
             # Find the antenna positions by only looking at the channels from a given polarisation
             position_array = [
-                detector.get_absolute_position(station_id) +
+                # detector.get_absolute_position(station_id) +    
+                # only use the relative position since the absolute position would introduce a time shift 
+                # in the beamformed timeseries which would lead to a time shift in the signal window.
                 detector.get_relative_position(station_id, channel_id)
-                for channel_id in ant_same_orientation[0]
+                for channel_id in station_even_list
             ]
             position_array = np.asarray(position_array)
 
             # Find polarisation with max envelope amplitude and calculate pulse search window from it
-            dominant_pol, pulse_window_start, pulse_window_end = self._signal_windows_polarisation(
-                station, position_array, ant_same_orientation
+            dominant_pol = self._signal_windows_polarisation(
+                station, position_array, [station_even_list, station_odd_list]
             )
 
             # Save the antenna orientation which contains the strongest pulse
-            station.set_parameter(stationParameters.cr_dominant_polarisation,
-                                  detector.get_antenna_orientation(station_id, ant_same_orientation[dominant_pol][0]))
-
-            # Check if the station has a strong enough signal
-            signal_window = [int(pulse_window_start), int(pulse_window_end)]
-            noise_window = [0, int(pulse_window_start - self.__noise_away_from_pulse)]
-
-            self._check_station_triggered(station, position_array, ant_same_orientation, signal_window, noise_window)
+            if dominant_pol == 0:
+                dominant_orientation = detector.get_antenna_orientation(station_id, station_even_list[0])
+            elif dominant_pol == 1:
+                dominant_orientation = detector.get_antenna_orientation(station_id, station_odd_list[0])
+            else:
+                raise ValueError(f"Dominant polarisation {dominant_pol} not recognised")
+
+            station.set_parameter(stationParameters.cr_dominant_polarisation, dominant_orientation)
+
+            # Go over all the channels to check if individual SNR is strong enough
+            self._check_station_triggered(
+                station
+            )
 
     def end(self):
         pass
diff --git a/NuRadioReco/modules/LOFAR/stationRFIFilter.py b/NuRadioReco/modules/LOFAR/stationRFIFilter.py
index 182483edb8..ee023e9a16 100644
--- a/NuRadioReco/modules/LOFAR/stationRFIFilter.py
+++ b/NuRadioReco/modules/LOFAR/stationRFIFilter.py
@@ -5,13 +5,13 @@
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.utilities.signal_processing import half_hann_window
 from NuRadioReco.modules.io.LOFAR.rawTBBio import MultiFile_Dal1
+from NuRadioReco.modules.io.LOFAR.readLOFARData import tbbID_to_nrrID, nrrID_to_tbbID
 
 from NuRadioReco.framework.parameters import stationParameters
 from NuRadioReco.utilities import units
 
 logger = logging.getLogger('NuRadioReco.LOFAR.stationRFIFilter')
 
-
 def num_double_zeros(data, threshold=None, ave_shift=False):
     """if data is a numpy array, give number of points that have  zero preceded by a zero"""
 
@@ -76,6 +76,7 @@ def FindRFI_LOFAR(
         rfi_cleaning_trace_length=8192,
         flagged_antenna_ids=None,
         num_dbl_z=1000,
+        pol=None
 ):
     """
     A code that basically reads given LOFAR TBB H5 file and returns an array of dirty channels.
@@ -91,7 +92,7 @@ def FindRFI_LOFAR(
         Size of total block of trace to be evaluated for finding dirty rfi channels.
     rfi_cleaning_trace_length : int
         Size of one chunk of trace to be evaluated at a time for calculating spectrum from.
-    flagged_antenna_ids : list, default=[]
+    flagged_antenna_ids : list[str] or set[str], default=[]
         List of antennas which are already flagged. These will not be considered for the RFI detection process.
     num_dbl_z : int, default=100
         The number of double zeros allowed in a block, if there are too many, then there could be data loss.
@@ -158,12 +159,16 @@ def FindRFI_LOFAR(
     max_blocks = num_blocks
 
     window_function = half_hann_window(rfi_cleaning_trace_length, 0.1)
+
     antenna_ids = tbb_file.get_antenna_names()
     antenna_ids = [id for id in antenna_ids if id not in flagged_antenna_ids]
+    if pol is not None:
+        antenna_ids = [id for id in antenna_ids if int(id) % 2 == pol] # do one polarization
     num_antennas = len(antenna_ids)
+    #logger.info(antenna_ids)
 
     # step one: find which blocks are good, and find average power
-    oneAnt_data = np.zeros(rfi_cleaning_trace_length, dtype=np.double) # initialize at zero
+    oneAnt_data = np.zeros(rfi_cleaning_trace_length, dtype=np.double)  # initialize at zero
 
     logger.info("finding good blocks")
     blocks_good = np.zeros((num_antennas, max_blocks), dtype=bool)
@@ -177,12 +182,11 @@ def FindRFI_LOFAR(
                 oneAnt_data[:] = tbb_file.get_data(
                     rfi_cleaning_trace_length * block, rfi_cleaning_trace_length, antenna_ID=antenna_ids[ant_i]
                 )
-            except: # TODO: more specific exception
+            except IndexError:
                 logger.warning('Could not read data for antenna %s block %d' % (antenna_ids[ant_i], block_i))
                 # proceed with zeros in the block
-            #oneAnt_data[:] = tbb_file.get_data(
-            #    rfi_cleaning_trace_length * block, rfi_cleaning_trace_length, antenna_index=ant_i
-            #)
+                blocks_good[ant_i, block_i] = False
+                continue
             if (
                     num_double_zeros(oneAnt_data) < num_dbl_z
             ):  # this antenna on this block is good
@@ -270,7 +274,7 @@ def FindRFI_LOFAR(
             ):
                 continue
             oneAnt_data[:] = tbb_file.get_data(
-                rfi_cleaning_trace_length * block, rfi_cleaning_trace_length, antenna_index=ant_i
+                rfi_cleaning_trace_length * block, rfi_cleaning_trace_length, antenna_ID=antenna_ids[ant_i]
             )
 
             # Window the data
@@ -306,13 +310,7 @@ def FindRFI_LOFAR(
         if np.min(num_processed_blocks[antenna_is_good]) == num_blocks:
             break
 
-    logger.info(
-        num_blocks,
-        "analyzed blocks",
-        np.sum(antenna_is_good),
-        "analyzed antennas out of",
-        len(antenna_is_good),
-    )
+    logger.info(f"{num_blocks} analyzed blocks, {np.sum(antenna_is_good)} analyzed antennas out of {len(antenna_is_good)}")
 
     # Get only good antennas
     antenna_is_good[
@@ -328,14 +326,12 @@ def FindRFI_LOFAR(
 
     # Get median of stability by channel, across each antenna
     median_phase_spread_byChannel = np.median(phase_stability[antenna_is_good], axis=0)
-
     # Get median across all channels
     median_spread = np.median(median_phase_spread_byChannel)
     # Create a noise cutoff
     sorted_phase_spreads = np.sort(median_phase_spread_byChannel)
     N = len(median_phase_spread_byChannel)
     noise = sorted_phase_spreads[int(N * 0.95)] - sorted_phase_spreads[int(N / 2)]
-
     # Get channels contaminated by RFI, where phase stability is smaller than noise
     dirty_channels = np.where(
         median_phase_spread_byChannel < (median_spread - 3 * noise)
@@ -412,12 +408,15 @@ class stationRFIFilter:
     other antenna in the station. If the phase is stable, this indicates a constant source contaminating the data.
     More information can be found in Section 3.2.2 of `this paper <https://arxiv.org/pdf/1311.1399.pdf>`_ .
     """
+
     def __init__(self):
         self.logger = logger  # logging.getLogger('NuRadioReco.channelRFIFilter')
 
         self.__rfi_trace_length = None
         self.__station_list = None
         self.__metadata_dir = None
+        self.__median_spectrum = None
+        self.__do_polarizations_apart = None
 
     @property
     def station_list(self):
@@ -435,7 +434,8 @@ def metadata_dir(self):
     def metadata_dir(self, new_dir):
         self.__metadata_dir = new_dir
 
-    def begin(self, rfi_cleaning_trace_length=65536, reader=None, logger_level=logging.NOTSET):
+    def begin(self, rfi_cleaning_trace_length=65536, reader=None, do_polarizations_apart=False,
+              logger_level=logging.NOTSET):
         """
         Set the variables used for RFI detection. The `reader` object can be used to retrieve the filenames associated
         with the loaded stations, as well as the metadata directory.
@@ -446,6 +446,8 @@ def begin(self, rfi_cleaning_trace_length=65536, reader=None, logger_level=loggi
             The number of samples to use per block to construct the frequency spectrum.
         reader : readLOFARData object, default=None
             If provided, the reader will be used to set the metadata directory and find the TBB files paths.
+        do_polarizations_apart : bool, default=False
+            If True, the X and Y polarisations will be processed separately.
         logger_level : int, default=logging.NOTSET
             Use this parameter to override the logging level for this module.
 
@@ -455,7 +457,7 @@ def begin(self, rfi_cleaning_trace_length=65536, reader=None, logger_level=loggi
         manually before attempting to execute the `stationRFIFilter.run()` function.
         """
         self.__rfi_trace_length = rfi_cleaning_trace_length
-
+        self.__do_polarizations_apart = do_polarizations_apart
         if reader is not None:
             self.station_list = reader.get_stations()
             self.metadata_dir = reader.meta_dir
@@ -474,11 +476,13 @@ def run(self, event):
             The event on which to run the filter.
         """
         stations_dict = self.station_list
+        self.__median_spectrum = {}
 
         for station in event.get_stations():
             station_name = f'CS{station.get_id():03}'
             station_files = stations_dict[station_name]['files']
-            flagged_channel_ids = station.get_parameter(stationParameters.flagged_channels)
+            antenna_set = stations_dict[station_name]['metadata'][1]
+            flagged_channel_ids: dict[int, list[str]] = station.get_parameter(stationParameters.flagged_channels)  # this is a defaultdict
 
             # Find the length of a trace in the station (assume all channels have been loaded with same length)
             station_trace_length = station.get_channel(station.get_channel_ids()[0]).get_number_of_samples()
@@ -493,16 +497,45 @@ def run(self, event):
                                   f'length ({self.__rfi_trace_length}) as well as a multiple of it.')
                 raise ValueError
 
-            # TODO: replace this with FindRFI() as to allow other experiments to use the same code
-            packet = FindRFI_LOFAR(station_files,
-                                   self.metadata_dir,
-                                   station_trace_length,
-                                   self.__rfi_trace_length,
-                                   flagged_antenna_ids=flagged_channel_ids
-                                   )
+            flagged_tbb_channel_ids = set()
+            for ind in flagged_channel_ids:
+                flagged_tbb_channel_ids.add(nrrID_to_tbbID(ind))  # in rawTBBio, antenna IDs are str
+
+            if not self.__do_polarizations_apart:
+                packet = FindRFI_LOFAR(station_files,
+                                    self.metadata_dir,
+                                    station_trace_length,
+                                    self.__rfi_trace_length,
+                                    flagged_antenna_ids=flagged_tbb_channel_ids
+                                    )
+                dirty_channels = packet['dirty_channels']
+
+            else:
+                packet0 = FindRFI_LOFAR(station_files,
+                                    self.metadata_dir,
+                                    station_trace_length,
+                                    self.__rfi_trace_length,
+                                    flagged_antenna_ids=flagged_tbb_channel_ids, pol=0
+                                    )
+
+                packet1 = FindRFI_LOFAR(station_files,
+                                    self.metadata_dir,
+                                    station_trace_length,
+                                    self.__rfi_trace_length,
+                                    flagged_antenna_ids=flagged_tbb_channel_ids, pol=1
+                                    )
+
+
+                # Extract the necessary information from FindRFI
+                dirty_channels_0 = packet0['dirty_channels']
+                dirty_channels_1 = packet1['dirty_channels']
+
+                dirty_channels = list(set(dirty_channels_0) | set(dirty_channels_1))
+                packet = packet0
+                packet['antenna_names'].extend(packet1['antenna_names'])
+                packet['cleaned_power'] = np.concatenate( (packet['cleaned_power'], packet1['cleaned_power'])  )
+
 
-            # Extract the necessary information from FindRFI
-            dirty_channels = packet['dirty_channels']
             station.set_parameter(stationParameters.dirty_fft_channels, dirty_channels)
 
             # implement outlier detection in cleaned power
@@ -511,21 +544,44 @@ def run(self, event):
             median_dipole_power = np.median(cleaned_power)
             bad_dipole_indices = np.where(
                 np.logical_or(
-                    cleaned_power < 0.5 * median_dipole_power, cleaned_power > 2.0 * median_dipole_power))[0]
+                    cleaned_power < 0.5 * median_dipole_power, cleaned_power > 2.0 * median_dipole_power
+                )
+            )[0]
             # which dipole ids are these
             self.logger.info(
-                'There are %d outliers in cleaned power (dipole ids): %s' % (len(bad_dipole_indices), antenna_ids[bad_dipole_indices])
+                f'There are {len(bad_dipole_indices)} outliers in cleaned power \n'
+                f'Dipole ids: {antenna_ids[bad_dipole_indices]}'
             )
-            # remove from station, add to flagged list
-            for id in antenna_ids[bad_dipole_indices]:
-                station.remove_channel(int(id))  # are channel ids integers?
 
-            flagged_channel_ids.update([id for id in antenna_ids[bad_dipole_indices]]) # is set, not list
+            # Construct flagged set of NRR channel IDs
+            channel_ids_to_remove = set()
+            for ind in antenna_ids[bad_dipole_indices]:
+                # convert TBB IDs to nrr IDs
+                nrr_id = tbbID_to_nrrID(ind, antenna_set)
+
+                # Ensure both channels from same antenna are removed
+                group_id_to_remove = station.get_channel(int(nrr_id)).get_group_id()
+                for channel_id in station.iter_channel_group(group_id_to_remove):
+                    channel_ids_to_remove.add(channel_id.get_id())
+            self.logger.info(
+                f'Removing the following channels from station {station_name}: \n'
+                f'{channel_ids_to_remove}'
+            )
+
+            # Remove bad antennas (= both channels!) from station, if it exists
+            for nrr_id in channel_ids_to_remove:
+                station.remove_channel(nrr_id)
+
+            for channel_ind in channel_ids_to_remove:
+                flagged_channel_ids[channel_ind].append('rfi_outliers_cleaned_power')
+
             station.set_parameter(stationParameters.flagged_channels, flagged_channel_ids)
 
             # Set spectral amplitude to zero for channels with RFI
+            spectra_before_flag = []
             for channel in station.iter_channels():
                 trace_fft = channel.get_frequency_spectrum()
+                spectra_before_flag.append(np.copy(trace_fft))
                 sample_rate = channel.get_sampling_rate()
 
                 # Reject DC and first harmonic
@@ -536,6 +592,46 @@ def run(self, event):
                 trace_fft[dirty_channels] *= 0.0
 
                 channel.set_frequency_spectrum(trace_fft, sample_rate)
+            self.__median_spectrum[station.get_id()] = np.median(np.abs(np.asarray(spectra_before_flag)), axis=0)
+
+    def end(self, event=None):
+        if event is not None:
+            for station in event.get_stations():
+                self.plot_median_freq_spectrum(event, station, rfi_cleaned=False, flagging=True)
+                self.plot_median_freq_spectrum(event, station, rfi_cleaned=True, flagging=False)
+
+    def plot_median_freq_spectrum(self, event, station, rfi_cleaned: bool = False, flagging: bool = False):
+        import matplotlib.pyplot as plt
+        if flagging and rfi_cleaned:
+            logger.warning("plot_median_freq_spectrum flagging the rfi_cleaned channels in a clean trace is weird, but ok")
 
-    def end(self):
-        pass
+        station_name = f'CS{station.get_id():03d}'
+
+        if rfi_cleaned:
+            # median spectrum from channels in the station. Since this function is expected to run in the .end() after .run(), the traces there are cleaned
+            spectra = []
+            for channel in station.iter_channels():
+                spectrum = channel.get_frequency_spectrum()
+                spectra.append(np.abs(spectrum))
+            median_spectrum = np.median(np.array(spectra), axis=0)
+            plot_filename = f"{event.get_id()}-{station_name}-rfi_cleaning_flags.pdf"
+        else:
+            # pre rfi cleaned spectrum stored
+            median_spectrum = self.__median_spectrum[station.get_id()]
+            plot_filename = f"{event.get_id()}-{station_name}-median_spectrum_after_rfi_cleaning.pdf"
+
+        fig = plt.figure()
+        ax = fig.add_subplot()
+        log_median_spectrum = np.log10(median_spectrum)
+        channel = station.get_channel(station.get_channel_ids()[0])
+        freq_MHz = channel.get_frequencies() / units.MHz
+        ax.plot(freq_MHz, log_median_spectrum,zorder=1)
+        if flagging:
+            dirty_channels = station[stationParameters.dirty_fft_channels]
+            ax.scatter(freq_MHz[dirty_channels], log_median_spectrum[dirty_channels], marker="x", color="red", zorder=2)
+        ax.set_xlabel("Frequency [MHz]")
+        ax.set_ylabel("Log-Spectral Power [ADU]")
+        ax.set_title(f"{station_name} Median frequency spectrum")
+        import os 
+        os.makedirs(str(event.get_id()), exist_ok=True)
+        plt.savefig(f"{event.get_id()}/" + plot_filename)
diff --git a/NuRadioReco/modules/RNO_G/channelBlockOffsetFitter.py b/NuRadioReco/modules/RNO_G/channelBlockOffsetFitter.py
index d306bba247..c1d28fa931 100644
--- a/NuRadioReco/modules/RNO_G/channelBlockOffsetFitter.py
+++ b/NuRadioReco/modules/RNO_G/channelBlockOffsetFitter.py
@@ -12,8 +12,13 @@
 from NuRadioReco.utilities import units, fft
 from NuRadioReco.framework.base_trace import BaseTrace
 from NuRadioReco.framework.parameters import channelParameters
+from NuRadioReco.modules.base.module import register_run
+
 import numpy as np
 import scipy.optimize
+import logging
+
+logger = logging.getLogger('NuRadioReco.RNO_G.channelBlockOffsetFitter')
 
 class channelBlockOffsets:
 
@@ -48,8 +53,8 @@ def add_offsets(self, event, station, offsets=1*units.mV, channel_ids=None):
 
         Parameters
         ----------
-        event: Event object | None
-        station: Station
+        event : `NuRadioReco.framework.event.Event` | None
+        station : `NuRadioReco.framework.station.Station`
             The station to add block offsets to
         offsets: float | array | dict
             offsets to add to the event. Default: 1 mV
@@ -86,16 +91,16 @@ def add_offsets(self, event, station, offsets=1*units.mV, channel_ids=None):
             # save the added offsets as a channelParameter
             if channel.has_parameter(channelParameters.block_offsets):
                 block_offsets_old = channel.get_parameter(channelParameters.block_offsets)
-                channel.set_parameter(channelParameters.block_offsets, block_offsets_old + offsets)
+                channel.set_parameter(channelParameters.block_offsets, block_offsets_old + add_offsets)
             else:
-                channel.set_parameter(channelParameters.block_offsets, offsets)
+                channel.set_parameter(channelParameters.block_offsets, add_offsets)
 
             channel.set_trace(
                 channel.get_trace() + np.repeat(add_offsets, self.block_size),
                 channel.get_sampling_rate()
             )
 
-    def remove_offsets(self, event, station, mode='fit', channel_ids=None, maxiter=5):
+    def remove_offsets(self, event, station, mode='auto', channel_ids=None, maxiter=5):
         """
         Remove block offsets from an event
 
@@ -105,27 +110,33 @@ def remove_offsets(self, event, station, mode='fit', channel_ids=None, maxiter=5
 
         Parameters
         ----------
-        event: NuRadioReco.framework.event.Event | None
-        station: NuRadioReco.framework.station.Station
+        event : `NuRadioReco.framework.event.Event` | None
+        station : `NuRadioReco.framework.station.Station`
             The station to remove the block offsets from
-        mode: 'fit' | 'approximate' | 'stored'
+        mode: str {'auto', 'fit', 'approximate', 'stored'}, optional
             
-            - 'fit' (default): fit the block offsets with a minimizer
+            - 'fit': fit the block offsets with a minimizer
             - 'approximate' : use the first guess from the out-of-band component,
               without any fitting (slightly faster)
+            - 'auto' (default): decide automatically between 'approximate' and 'fit'
+              based on the estimated size of the block offsets.
             - 'stored': use the block offsets already stored in the 
               ``channelParameters.block_offsets`` parameter. Will raise an error 
               if this parameter is not present.
 
         channel_ids: list | None
             List of channel ids to remove offsets from. If None (default),
-            remove offsets from all channels in ``station``
+            remove offsets from all channels in `station`
         maxiter: int, default 5
             (Only if mode=='fit') The maximum number of fit iterations.
             This can be increased to more accurately remove the block offsets
             at the cost of performance. (The default value removes 'most' offsets
             to about 1%)
 
+        See Also
+        --------
+        run : alias of this method
+
         """
         if channel_ids  is None:
             channel_ids = station.get_channel_ids()
@@ -149,10 +160,63 @@ def remove_offsets(self, event, station, mode='fit', channel_ids=None, maxiter=5
         
         self.add_offsets(event, station, offsets, channel_ids)
 
+    def begin(self):
+        """(Unused)"""
+        pass
+
+    @register_run()
+    def run(self, event, station, det=None, mode='auto', channel_ids=None, **kwargs):
+        """
+        Remove the block offsets from all channels of a station.
+
+        Fits and removes the block offsets from an event. The removed offsets
+        are stored in the ``channelParameters.block_offsets``
+        parameter.
+
+        This method is an alias of `remove_offsets`, with the only difference the inclusion
+        of the (unused) `det` parameter, to be consistent with the `run` methods of other
+        NuRadio classes.
+
+        Parameters
+        ----------
+        event : `NuRadioReco.framework.event.Event` | None
+        station : `NuRadioReco.framework.station.Station`
+            The station to remove the block offsets from
+        det : Detector object, optional
+            Detector object (not used in this method,
+            included to have the same signature as other NuRadio classes)
+        mode : str {'auto', 'fit', 'approximate', 'stored'}, optional
+
+            - 'fit': fit the block offsets with a minimizer
+            - 'approximate' : use the first guess from the out-of-band component,
+              without any fitting (slightly faster)
+            - 'auto' (default): decide automatically between 'approximate' and 'fit'
+              based on the estimated size of the block offsets.
+            - 'stored': use the block offsets already stored in the
+              ``channelParameters.block_offsets`` parameter. Will raise an error
+              if this parameter is not present.
+
+        channel_ids : list | None
+            List of channel ids to remove offsets from. If None (default),
+            remove offsets from all channels in `station`.
+        **kwargs : keyword arguments
+            Other keyword arguments to be passed to the `remove_offsets` function.
+
+        See Also
+        --------
+        remove_offsets : alias of this method without the (unused) `det` parameter
+        """
+        self.remove_offsets(event, station, mode=mode, channel_ids=channel_ids, **kwargs)
+
+
+    def end(self):
+        """(Unused)"""
+        pass
+
 
 def fit_block_offsets(
         trace, block_size=128, sampling_rate=3.2*units.GHz,
-        max_frequency=50*units.MHz, mode='fit', return_trace = False,
+        max_frequency=50*units.MHz, mode='auto', return_trace = False,
         maxiter=5, tol=1e-6):
     """
     Fit 'block' offsets for a voltage trace
@@ -172,10 +236,12 @@ def fit_block_offsets(
         the fit to the block offsets is performed
         in the frequency domain, in the band up to
         max_frequency
-    mode: 'fit' | 'approximate'
-        Whether to fit the block offsets (default)
+    mode : str {'auto', 'fit', 'approximate'}, optional
+        Whether to fit the block offsets
         or just use the first guess from the out-of-band
-        component (faster)
+        component (faster). By default ('auto'), decide
+        automatically based on the size of the block offsets
+        (only fit if the largest block offset exceeds 50% of the Vrms).
     return_trace: bool (default: False)
         if True, return the tuple (offsets, output_trace)
         where the output_trace is the input trace with
@@ -198,6 +264,13 @@ def fit_block_offsets(
     ----------------
     tol: float (default: 1e-6)
         tolerance parameter passed on to scipy.optimize.minimize
+
+    See Also
+    --------
+    channelBlockOffsets :
+        Class that uses this function to automatically remove the block offsets for all
+        channels in a station.
+
     """
     dt = 1. / sampling_rate
     spectrum = fft.time2freq(trace, sampling_rate)
@@ -216,10 +289,24 @@ def fit_block_offsets(
 
     # obtain guesses for block offsets
     a_guess = np.mean(np.split(filtered_trace, n_blocks), axis=1)
+
     if mode == 'approximate':
-        block_offsets = a_guess + np.mean(trace)
+        perform_fit = False
     elif mode == 'fit':
-        # self._offset_guess[channel_id] = a_guess
+        perform_fit = True
+    elif mode == 'auto':
+        # continue to fitting step only if the largest block offset is more than 20% of the Vrms
+        max_offset = np.max(np.abs(a_guess))
+        vrms = np.std(trace)
+        perform_fit = max_offset > 0.5 * vrms
+        if perform_fit:
+            logger.warning("Trace has large block offsets (>{:.0f}% of Vrms), removing by fitting.".format(100 * max_offset / vrms))
+    else:
+        raise ValueError(f'Invalid value for mode={mode}. Accepted values are {{"fit", "approximate"}}')
+
+    if not perform_fit: # just return the first guess
+        block_offsets = a_guess + np.mean(trace)
+    else:
         # we can get rid of one parameter through a global shift
         a_guess = a_guess[:-1] - a_guess[-1]
 
@@ -246,15 +333,16 @@ def pedestal_fit(a):
             return chi2
 
         res = scipy.optimize.minimize(pedestal_fit, a_guess, tol=tol, options=dict(maxiter=maxiter)).x
-
-        block_offsets = np.zeros(len(res) + 1)
-        block_offsets[:-1] = res
+        logger.debug(
+            "Fit shifted estimated block offsets by {:.2f} ({:.0f}%)".format(
+                np.median(res-a_guess), 100*np.median((res-a_guess)/res)))
 
         # the fit is not sensitive to an overall shift,
         # so we include the zero-meaning here
+        block_offsets = np.zeros(len(res) + 1)
+        block_offsets[:-1] = res
         block_offsets += np.mean(trace) - np.mean(block_offsets)
-    else:
-        raise ValueError(f'Invalid value for mode={mode}. Accepted values are {{"fit", "approximate"}}')
+
 
     if return_trace:
         output_trace = trace - np.repeat(block_offsets, block_size)
diff --git a/NuRadioReco/modules/RNO_G/crRNOGTemplateCreator.py b/NuRadioReco/modules/RNO_G/crRNOGTemplateCreator.py
new file mode 100644
index 0000000000..5e56531d39
--- /dev/null
+++ b/NuRadioReco/modules/RNO_G/crRNOGTemplateCreator.py
@@ -0,0 +1,290 @@
+from __future__ import annotations
+from typing import Any
+import matplotlib.pyplot as plt
+from NuRadioReco.utilities import units
+import astropy
+import numpy as np
+import NuRadioReco.detector.detector as detector
+import NuRadioReco.modules.efieldToVoltageConverter
+import logging
+import NuRadioReco.modules.RNO_G.hardwareResponseIncorporator
+import datetime
+from NuRadioReco.framework.event import Event
+from NuRadioReco.framework.station import Station
+from NuRadioReco.framework.channel import Channel
+from NuRadioReco.framework.sim_station import SimStation
+from NuRadioReco.framework.sim_channel import SimChannel
+from NuRadioReco.framework.parameters import stationParameters
+from NuRadioReco.framework.parameters import electricFieldParameters
+from NuRadioReco.framework.electric_field import ElectricField
+import pickle
+import os
+import NuRadioReco.modules.channelBandPassFilter
+
+
+class crRNOGTemplateCreator:
+    """
+        Creates CR templates by assuming a gaussian function for the electric field
+
+    """
+
+    def __init__(self):
+        self.logger = logging.getLogger("NuRadioReco.crRNOGTemplateCreator")
+        self.__detector_file = None
+
+        self.__template_run_id = None
+        self.__template_channel_id = None
+        self.__template_station_id = None
+
+        self.__sampling_rate = None
+        self.__template_sample_number = None
+
+        self.__antenna_rotation = None
+        self.__efield_width = None
+        self.__efield_amplitudes = None
+        self.__template_event_id = None
+
+        self.__cr_zenith = None
+        self.__cr_azimuth = None
+
+        self.__debug = None
+
+        self.__template_save_path = None
+
+        self.__efieldToVoltageConverter = NuRadioReco.modules.efieldToVoltageConverter.efieldToVoltageConverter()
+        self.__hardwareResponseIncorporator = NuRadioReco.modules.RNO_G.hardwareResponseIncorporator.hardwareResponseIncorporator()
+        self.__channelBandPassFilter = NuRadioReco.modules.channelBandPassFilter.channelBandPassFilter()
+
+
+    def begin(self, detector_file:str, template_save_path:str, debug:bool=False, logger_level:logging.Logger=logging.NOTSET) -> None:
+        """
+                begin method
+
+                Parameters
+                ----------
+                detector_file: str
+                    path to the detector file used for the template set
+                template_save_path: str
+                    path to a folder where the templates are stored
+                debug: bool, default: False
+                    enable/disable debug mode
+                logger_level: str or int, optional
+                    Set verbosity level for logger (default: logging.NOTSET)
+                """
+
+        self.__detector_file = detector_file
+
+        self.logger.setLevel(logger_level)
+
+        # set up the efield to voltage converter
+        self.__efieldToVoltageConverter.begin(debug=debug)
+
+        self.__debug = debug
+
+        self.__template_save_path = template_save_path
+
+
+    def set_template_parameter(self, template_run_id:list[int]=[0, 0, 0], template_event_id:list[int]=[0, 1, 2], template_station_id:list[int]=[101, 101, 101], 
+                               template_channel_id:list[int]=[0, 0, 0], efield_width:list[float]=[5, 4, 2], antenna_rotation:list[float]=[160, 160, 160], 
+                               efield_amplitudes:list[float]=[-0.2, 0.8], cr_zenith:list[float]=[55, 55, 55], cr_azimuth:list[float]=[0, 0, 0],
+                               sampling_rate:float=3.2 * units.GHz, number_of_samples:int=2048) ->None:
+        """
+        set_parameter_templates method
+
+        sets the parameter to create the template set
+
+        Parameters
+        ----------
+        template_run_id: list of int, default: [0,0,0]
+            run ids of the artificial templates
+        template_event_id: list of int, default: [0,1,2]
+            event ids of the artificial templates
+        template_station_id: list of int, default: [101,101,101]
+            station ids of the artificial templates
+        template_channel_id: list of int, default: [0,0,0]
+            channel ids of the artificial templates
+        efield_width: list of int, default: [5,4,2]
+            width (in samples) of the gaussian function used to create the Efield
+        antenna_rotation: list of int, default: [160,160,160]
+            rotation angle of the LPDA
+        efield_amplitudes: list of float, default:[-0.2,0.8]
+            array with the amplitudes of the Efield components [E_theta, E_phi]
+        cr_zenith: list of int, default: [55,55,55]
+            zenith angle of the cr for the template
+        cr_azimuth: list of int, default: [0,0,0]
+            azimuth angle of the cr for the template
+        sampling_rate: float, default: 3.2
+            sampling rate used to build the template
+        number_of_samples: int, default: 2048
+            number of samples used for the trace
+        """
+
+        self.__template_run_id = template_run_id
+        self.__template_event_id = template_event_id
+        self.__template_station_id = template_station_id
+        self.__template_channel_id = template_channel_id
+
+        self.__efield_width = efield_width
+        self.__efield_amplitudes = efield_amplitudes
+        self.__antenna_rotation = antenna_rotation
+
+        self.__sampling_rate = sampling_rate
+        self.__template_sample_number = number_of_samples
+
+        self.__cr_zenith = cr_zenith
+        self.__cr_azimuth = cr_azimuth
+
+
+    def run(self, template_filename:str='templates_cr_station_101.pickle', include_hardware_response:bool=True, hardware_response_source:str='json', 
+            return_templates:bool=False, bandpass_filter:None|dict[str,Any]=None) -> None|list[Event]:
+        """
+        run method
+
+        creates a pickle file with the Efield trace of the artificial templates
+
+        Parameters
+        ----------
+        template_filename: str, default: 'templates_cr_station_101.pickle'
+            filename of the pickle file that will be used to store the templates
+        include_hardware_response: bool, default: True
+            if true, the hardware response of the surface amps (hardwareResponseIncorporator) is applied
+        hardware_response_source: str, default: "json"
+            define if the hardware response is loaded from the json ('json') or from the database ('database')
+        return_templates: bool, default: False
+            if true, the template traces are returned in an addition to saving them in a pickle file
+        bandpass_filter: dict, optional
+            If a dictionary is given, a bandpass filter will be applied to the templates. The dictionary should hold all arguments that are needed for the channelBandPassFilter.
+        
+        Returns
+        -------
+        template_event: list of `NuRadioReco.framework.event.Event` or None
+            If return templates is True, a list with the templates is returned.
+        """
+
+        # if no parameters are set, the standard parameters are used
+        if self.__efield_width is None:
+            self.logger.info("The default parameters are used for template creation.")
+            self.set_template_parameter()
+
+        template_events = []
+        save_dic = {}
+        for crz in list(set(self.__cr_zenith)):
+            save_dic_help = {}
+            for cra in list(set(self.__cr_azimuth)):
+                # loop over the different antenna rotation angles:
+                templates = {}
+                for rid, eid, sid, cid, e_width, antrot, cr_zen, cr_az in zip(self.__template_run_id, self.__template_event_id, self.__template_station_id, self.__template_channel_id, self.__efield_width, self.__antenna_rotation, self.__cr_zenith, self.__cr_azimuth):
+                    if cr_zen == crz and cr_az == cra:
+                        # create the detector
+                        det_temp = detector.generic_detector.GenericDetector(json_filename=self.__detector_file, antenna_by_depth=False, create_new=True, log_level='ERROR')
+                        det_temp.update(datetime.datetime(2025, 10, 1))
+                        det_temp.get_channel(sid, cid)['ant_rotation_phi'] = antrot
+
+                        station_time = datetime.datetime(2025, 10, 1)
+
+                        temp_evt = _create_Efield(det_temp, rid, eid, cid, sid, station_time, self.__template_sample_number, e_width,
+                                                 self.__efield_amplitudes[1], self.__efield_amplitudes[0], cr_zen, cr_az, self.__sampling_rate, self.__debug)
+
+                        self.__efieldToVoltageConverter.run(temp_evt, temp_evt.get_station(sid), det_temp)
+
+                        if include_hardware_response:
+                            if hardware_response_source == 'json':
+                                self.logger.info("The placeholder hardware response from NuRadioMC is applied to the templates.")
+                                self.__hardwareResponseIncorporator.run(temp_evt, temp_evt.get_station(sid), det_temp, sim_to_data=True)
+                            elif hardware_response_source == 'database':
+                                self.logger.info("The hardware response from the database is applied to the templates.")
+                                # create a rno-g detetcor to load the hardware response
+                                rnog_det = detector.Detector(source="rnog_mongo", log_level=logging.WARNING, always_query_entire_description=False,
+                                                             database_connection='RNOG_public', select_stations=sid)
+                                rnog_det.update(datetime.datetime(2023, 3, 4, 0, 0))
+                                self.__hardwareResponseIncorporator.run(temp_evt, temp_evt.get_station(sid), rnog_det, sim_to_data=True)
+
+
+                        if bandpass_filter is not None:
+                            # apply the channelBandPassFilter
+                            self.logger.info("The channelBandPassFilter is applied.")
+                            self.__channelBandPassFilter.run(temp_evt, temp_evt.get_station(sid), det_temp, **bandpass_filter)
+
+                        if self.__debug:
+                            plt.plot(temp_evt.get_station(sid).get_channel(cid).get_times() / units.ns, temp_evt.get_station(sid).get_channel(cid).get_trace())
+                            plt.xlabel('times [ns]')
+                            plt.ylabel('amplitudes')
+                            plt.show()
+                            plt.plot(temp_evt.get_station(sid).get_channel(cid).get_frequencies() / units.MHz, np.abs(temp_evt.get_station(sid).get_channel(cid).get_frequency_spectrum()))
+                            plt.xlabel('frequency [MHz]')
+                            plt.ylabel('amplitudes')
+                            plt.show()
+                        template_events.append(temp_evt)
+                        templates[e_width] = temp_evt.get_station(sid).get_channel(cid).get_trace()
+                if templates != {}:
+                    save_dic_help[np.deg2rad(cra)] = templates
+            if save_dic_help != {}:
+                save_dic[np.deg2rad(crz)] = save_dic_help
+
+        # write as pickle file
+        with open(os.path.join(self.__template_save_path, template_filename), "wb") as pickle_file:
+            pickle.dump([save_dic], pickle_file)
+            self.logger.info(f"The templates are saved to {os.path.join(self.__template_save_path, template_filename)}")
+        if return_templates:
+            return template_events
+
+
+def _gaussian_func(x, A, mu, sigma):
+    return A * np.exp(-(x - mu) ** 2 / (2 * sigma ** 2))
+
+
+def _create_Efield(detector:detector.generic_detector.GenericDetector, run_id:int, event_id:int, channel_id:int, station_id:int, 
+                   station_time:datetime.datetime, trace_samples:int, gaussian_width:float, e_phi:float, e_theta:float, cr_zenith:float, 
+                   cr_azimuth:float, sampling_rate:float, debug:bool) -> Event:
+    """ function that creates an event with a gaussian electric field """
+    event = Event(run_id, event_id)
+
+    station = Station(station_id)
+    event.set_station(station)
+
+    station.set_station_time(astropy.time.Time(station_time))
+
+    channel = Channel(channel_id)
+    station.add_channel(channel)
+
+    sim_station = SimStation(station_id)
+    station.set_sim_station(sim_station)
+
+    sim_channel = SimChannel(channel_id, 0, 0)
+    sim_station.add_channel(sim_channel)
+
+    detector.set_event(run_id, event_id)
+
+    electric_field = ElectricField([channel_id])
+
+    e_field = [np.zeros(trace_samples), np.zeros(trace_samples), np.zeros(trace_samples)]
+    x_data = np.arange(0, trace_samples, 1)
+    for ii, x in enumerate(x_data):
+        e_field[1][ii] = _gaussian_func(x, e_theta, 1000, gaussian_width)
+        e_field[2][ii] = _gaussian_func(x, e_phi, 1000, gaussian_width)
+
+    e_field = np.asarray(e_field)
+    electric_field.set_trace(e_field, sampling_rate=sampling_rate)
+    sim_station.add_electric_field(electric_field)
+
+    if debug:
+        # plot the electric field
+        plt.plot(electric_field.get_times() / units.ns, electric_field.get_trace()[0])
+        plt.plot(electric_field.get_times() / units.ns, electric_field.get_trace()[1])
+        plt.plot(electric_field.get_times() / units.ns, electric_field.get_trace()[2])
+        plt.xlabel('time [ns]')
+        plt.ylabel('electric field')
+        plt.show()
+
+    sim_station.set_is_cosmic_ray()
+
+    val_zenith = cr_zenith * (np.pi / 180)
+    val_azimuth = cr_azimuth * (np.pi / 180)
+
+    sim_station.set_parameter(stationParameters.zenith, val_zenith)
+    sim_station.set_parameter(stationParameters.azimuth, val_azimuth)
+    electric_field.set_parameter(electricFieldParameters.ray_path_type, 'direct')
+    electric_field.set_parameter(electricFieldParameters.zenith, val_zenith)
+    electric_field.set_parameter(electricFieldParameters.azimuth, val_azimuth)
+
+    return event
diff --git a/NuRadioReco/modules/RNO_G/hardwareResponseIncorporator.py b/NuRadioReco/modules/RNO_G/hardwareResponseIncorporator.py
index 8fdae8e4ad..80a22f7066 100644
--- a/NuRadioReco/modules/RNO_G/hardwareResponseIncorporator.py
+++ b/NuRadioReco/modules/RNO_G/hardwareResponseIncorporator.py
@@ -1,17 +1,20 @@
 from NuRadioReco.modules.base.module import register_run
-from NuRadioReco.utilities import units, fft
+from NuRadioReco.utilities import units, fft, signal_processing
+import NuRadioReco.framework.station
 
 from NuRadioReco.detector.RNO_G import analog_components
-import NuRadioReco.detector.detector as detector
+from NuRadioReco.detector import detector
 
 import numpy as np
+import copy
 import time
 import logging
 
 
 class hardwareResponseIncorporator:
     """
-    Incorporates the gain and phase induced by the RNO-G hardware.
+    Incorporates the compex response of the RNO-G hardware. The response is obtained from the detector description.
+    The response is applied in the frequency domain.
     """
 
     def __init__(self):
@@ -20,15 +23,20 @@ def __init__(self):
         self.__time_delays = {}
         self.__t = 0
         self.__mingainlin = None
-        self.__debug = None
-        self.begin()
 
-    def begin(self, debug=False):
-        self.__debug = debug
+    def begin(self, trigger_channels=None):
+        """
+        Parameters
+        ----------
+        trigger_channels: list of int
+            List of channels for which an extra trigger channel with a different response is used. (Default: None)
+        """
+        self.trigger_channels = trigger_channels
+
 
     def get_filter(self, frequencies, station_id, channel_id, det,
                    temp=293.15, sim_to_data=False, phase_only=False,
-                   mode=None, mingainlin=None):
+                   mode=None, mingainlin=None, is_trigger=False):
         """
         Helper function to return the filter that the module applies.
 
@@ -63,8 +71,8 @@ def get_filter(self, frequencies, station_id, channel_id, det,
             * 'phase_only': only the phases response is applied but not the amplitude response
               (identical to phase_only=True )
             * 'relative': gain of amp is divided by maximum of the gain, i.e. at the maximum of the
-              filter response is 1 (before applying cable response). This makes it easier
-              to compare the filtered to unfiltered signal
+              filter response is 1 (before applying cable response). This makes it easier to compare
+              the filtered to unfiltered signal
             * None : default, gain and phase effects are applied 'normally'
 
         mingainlin: float
@@ -76,6 +84,9 @@ def get_filter(self, frequencies, station_id, channel_id, det,
             Note: The adjustment to the minimal gain is NOT visible when getting the amp response from
             ``analog_components.get_amplifier_response()``
 
+        is_trigger: bool
+            Use trigger channel response instead. Only relevant for RNO-G. (Default: False)
+
         Returns
         -------
         array of complex floats
@@ -83,8 +94,8 @@ def get_filter(self, frequencies, station_id, channel_id, det,
         """
 
         if isinstance(det, detector.rnog_detector.Detector):
-            amp_response = det.get_signal_chain_response(
-                station_id, channel_id)(frequencies)
+            resp = det.get_signal_chain_response(station_id, channel_id, is_trigger)
+            amp_response = resp(frequencies)
         elif isinstance(det, detector.detector_base.DetectorBase):
             amp_type = det.get_amplifier_type(station_id, channel_id)
             # it reads the log file. change this to load_amp_measurement if you want the RI file
@@ -107,7 +118,7 @@ def get_filter(self, frequencies, station_id, channel_id, det,
             pass
         elif mode == 'phase_only':
             cable_response = np.ones_like(cable_response) * np.exp(1j * np.angle(cable_response))
-            amp_response = np.ones_like(amp_response) * np.angle(amp_response)
+            amp_response = np.ones_like(amp_response) * np.exp(1j * np.angle(amp_response))
         elif mode == 'relative':
             ampmax = np.max(np.abs(amp_response))
             amp_response /= ampmax
@@ -150,10 +161,10 @@ def run(self, evt, station, det, temp=293.15, sim_to_data=False, phase_only=Fals
             Options:
 
             * 'phase_only': only the phases response is applied but not the amplitude response
-              (identical to phase_only=True )
+              (identical to phase_only=True)
             * 'relative': gain of amp is divided by maximum of the gain, i.e. at the maximum of the
-              filter response is 1 (before applying cable response). This makes it easier
-              to compare the filtered to unfiltered signal
+              filter response is 1 (before applying cable response). This makes it easier to compare
+              the filtered to unfiltered signal
             * None: default, gain and phase effects are applied 'normally'
 
         mingainlin: float
@@ -174,12 +185,47 @@ def run(self, evt, station, det, temp=293.15, sim_to_data=False, phase_only=Fals
 
         t = time.time()
 
+        if self.trigger_channels is not None and not isinstance(det, detector.rnog_detector.Detector):
+            raise ValueError("Simulating extra trigger channels is only possible with the `rnog_detector.Detector` class.")
+
+        has_trigger_channels = False
         for channel in station.iter_channels():
             frequencies = channel.get_frequencies()
             trace_fft = channel.get_frequency_spectrum()
 
-            trace_fft *= self.get_filter(
+            filter = self.get_filter(
                 frequencies, station.get_id(), channel.get_id(), det, temp, sim_to_data, phase_only, mode, mingainlin)
+
+            if (self.trigger_channels is not None and
+                channel.get_id() in self.trigger_channels and
+                isinstance(station, NuRadioReco.framework.station.Station)):
+                """
+                Create a copy of the channel and apply the readout and trigger channel response respectively.
+                We do this here under the assumption that up to this point no difference between the two channels
+                had to be made. This is acutally not strictly true. The cable delay is already added in the
+                efieldToVoltageConverter module. I.e., the assumption is made that the cable delay is no different
+                between the two. While this might be true/a good approximation for the moment it is not given that
+                this holds for the future. You have been warned!
+
+                See also: https://nu-radio.github.io/NuRadioMC/NuRadioReco/pages/event_structure.html#channel for a bit more context.
+                """
+                trig_filter = self.get_filter(
+                    frequencies, station.get_id(), channel.get_id(), det, temp, sim_to_data,
+                    phase_only, mode, mingainlin, is_trigger=True)
+
+                trig_trace_fft = trace_fft * trig_filter
+                # zero first bins to avoid DC offset
+                trig_trace_fft[0] = 0
+
+                # Add trigger channel
+                trig_channel = copy.deepcopy(channel)
+                trig_channel.set_frequency_spectrum(
+                    trig_trace_fft, channel.get_sampling_rate())
+
+                channel.set_trigger_channel(trig_channel)
+                has_trigger_channels = True
+
+            trace_fft *= filter
             # zero first bins to avoid DC offset
             trace_fft[0] = 0
 
@@ -188,13 +234,14 @@ def run(self, evt, station, det, temp=293.15, sim_to_data=False, phase_only=Fals
             channel.set_frequency_spectrum(
                 trace_fft, channel.get_sampling_rate())
 
-            if not sim_to_data:
-                # Include cable delays
-                cable_delay = det.get_cable_delay(
-                    station.get_id(), channel.get_id())
-                self.logger.debug("cable delay of channel {} is {}ns".format(
-                    channel.get_id(), cable_delay / units.ns))
-                channel.add_trace_start_time(-cable_delay)
+        if not sim_to_data:
+            # Subtraces the cable delay. For `sim_to_data=True`, the cable delay is added
+            # in the efieldToVoltageConverter or with the channelCableDelayAdder
+            # (if efieldToVoltageConverterPerEfield was used).
+            signal_processing.add_cable_delay(station, det, sim_to_data, trigger=False, logger=self.logger)
+            if has_trigger_channels:
+                signal_processing.add_cable_delay(
+                        station, det, sim_to_data, trigger=True, logger=self.logger)
 
         self.__t += time.time() - t
 
@@ -222,8 +269,8 @@ def __calculate_time_delays_amp(self, amp_type):
         amp_response_phase = amp_response_f['phase'](ff)
         mask = (ff < 70 * units.MHz) & (ff > 40 * units.MHz)
         spec[~mask] = 0
-        trace2 = fft.freq2time(spec * amp_response_gain *
-                               amp_response_phase, sampling_rate)
+        trace2 = fft.freq2time(
+            spec * amp_response_gain * amp_response_phase, sampling_rate)
         max_time2 = np.abs(trace2).argmax() / sampling_rate
         return max_time2 - max_time
 
diff --git a/NuRadioReco/modules/RNO_G/triggerBoardResponse.py b/NuRadioReco/modules/RNO_G/triggerBoardResponse.py
index e20978bddf..6e2eea31df 100644
--- a/NuRadioReco/modules/RNO_G/triggerBoardResponse.py
+++ b/NuRadioReco/modules/RNO_G/triggerBoardResponse.py
@@ -1,11 +1,9 @@
 import logging
 import numpy as np
-import time
 
-from NuRadioReco.detector.RNO_G import analog_components
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.modules.analogToDigitalConverter import analogToDigitalConverter
-from NuRadioReco.utilities import units, fft
+from NuRadioReco.utilities import units
 
 logger = logging.getLogger("NuRadioReco.triggerBoardResponse")
 
@@ -46,7 +44,7 @@ def begin(self, adc_input_range=2 * units.volt, clock_offset=0.0, adc_output="vo
         self._adc_output = adc_output
 
         # the fields that need to exist in the detector description for this module to work
-        self._mandatory_fields = ["trigger_amp_type", "trigger_adc_nbits", "trigger_adc_noise_nbits"]
+        self._mandatory_fields = ["trigger_adc_nbits", "trigger_adc_noise_nbits"]
 
         # Table 21 in https://www.analog.com/media/en/technical-documentation/data-sheets/hmcad1511.pdf
         self._triggerBoardAmplifications = np.array([1, 1.25, 2, 2.5, 4, 5, 8, 10, 12.5, 16, 20, 25, 32, 50])
@@ -69,7 +67,7 @@ def apply_trigger_filter(self, station, trigger_channels, trigger_filter):
         """
 
         for channel_id in trigger_channels:
-            channel = station.get_channel(channel_id)
+            channel = station.get_trigger_channel(channel_id)
 
             # calculate and apply trigger filters
             freqs = channel.get_frequencies()
@@ -100,7 +98,7 @@ def get_avg_vrms(self, station, trigger_channels, trace_split=20):
 
         avg_vrms = 0
         for channel_id in trigger_channels:
-            channel = station.get_channel(channel_id)
+            channel = station.get_trigger_channel(channel_id)
             trace = np.array(channel.get_trace())
             trace = trace[: int(trace_split * int(len(trace) / trace_split))].reshape((trace_split, -1))
             approx_vrms = np.median(np.std(trace, axis=1))
@@ -153,8 +151,8 @@ def apply_adc_gain(self, station, det, trigger_channels, avg_vrms=None):
 
             noise_bits = det_channel["trigger_adc_noise_nbits"]
             total_bits = det_channel["trigger_adc_nbits"]
-            volts_per_adc = self._adc_input_range / 2 ** total_bits
-            ideal_vrms = volts_per_adc * (2 ** (noise_bits - 1) - 1)
+            volts_per_adc = self._adc_input_range / (2 ** total_bits - 1)
+            ideal_vrms = volts_per_adc * (2 ** (noise_bits - 1))
 
             msg = f"\t Ch: {channel_id}\t Target Vrms: {ideal_vrms / units.mV:0.3f} mV"
             msg += f"\t V/ADC: {volts_per_adc / units.mV:0.3f} mV"
@@ -171,7 +169,7 @@ def apply_adc_gain(self, station, det, trigger_channels, avg_vrms=None):
                 gain_to_use = self._triggerBoardAmplifications[-1]
                 vrms_after_gain.append(amplified_vrms_values[-1])
 
-            channel = station.get_channel(channel_id)
+            channel = station.get_trigger_channel(channel_id)
             self.logger.debug(f"\t Ch: {channel_id}\t Actuall Vrms: {np.std(channel.get_trace() * gain_to_use) / units.mV:0.3f} mV")
             channel.set_trace(channel.get_trace() * gain_to_use, channel.get_sampling_rate())
             self.logger.debug(f"\t Used Vrms: {vrms_after_gain[-1] / units.mV:0.3f} mV" + f"\tADC Gain {gain_to_use}")
@@ -180,92 +178,10 @@ def apply_adc_gain(self, station, det, trigger_channels, avg_vrms=None):
 
         return np.array(vrms_after_gain), ideal_vrms
 
-    def get_trigger_values(self, station, det, requested_channels=[]):
-        """
-        Extracts information about the trigger channels from the detector description
-
-        Parameters
-        ----------
-        station : Station
-            Station to use
-        det : Detector
-            The detector description
-        requested_channels : list (default: [])
-            Channels that this function should consider when extracting data
-
-        Returns
-        -------
-        found_trigger_channels : list
-            channel ids for the trigger channels
-
-        trigger_filter : function
-            set of interpolations describing the `gain` and `phase` of the filter
-            (see function `load_amp_response` in file `./detector/RNO_G/analog_components.py`)
-
-        """
-
-        station_id = station.get_id()
-
-        found_trigger_channels = []
-        trigger_amp_response = None
-        trigger_amp_response_name = None
-
-        for det_channel in station.iter_channels():
-            channel_id = det_channel.get_id()
-
-            # If specifying the exact channels to consider, demand
-            # that this ID is in the list
-            if len(requested_channels) and channel_id not in requested_channels:
-                continue
-
-            det_channel = det.get_channel(station_id, channel_id)
-
-            keep = True
-            for field in self._mandatory_fields:
-                if field not in det_channel:
-                    keep = False
-                    break
-
-            if not keep:
-                continue
-
-            found_trigger_channels.append(channel_id)
-
-            # Check for mixed amplifiers (required for get_avg_vrms to work correctly)
-            if trigger_amp_response_name is not None and trigger_amp_response_name != det_channel["trigger_amp_type"]:
-                name = det_channel["trigger_amp_type"]
-                msg = f'Channel {channel_id} has `trigger_amp_type` of "{name}", but a `trigger_amp_type` of'
-                msg += f' "{trigger_amp_response_name}" was already found in the detector file. This module is not meant'
-                msg += " to work with mixed values of `trigger_amp_type`. Either unify the `trigger_amp_type` or supply"
-                msg += ' a group of channels to consider using the "requested_channels" option of this module'
-                raise RuntimeError(msg)
-
-            trigger_amp_response_name = det_channel["trigger_amp_type"]
-            trigger_amp_response = analog_components.load_amp_response(trigger_amp_response_name)
-
-        if not len(found_trigger_channels):
-            msg = f"No triggering channels found in station {station_id}. Make sure that trigger channels have the following"
-            msg += f" fields defined {self._mandatory_fields}"
-            raise RuntimeError(msg)
-
-        # Sanity check for the expected channels
-        for channel_id in requested_channels:
-            if channel_id not in found_trigger_channels:
-                msg = f"Channel {channel_id} was requested to participlate in the trigger but was not found with the proper the mandatory"
-                msg += f" fields set. Ensure that the following settings are in your Detector configuration {self._mandatory_fields}"
-                self.logger.warn(msg)
-
-        self.logger.debug(f"Found trigger channels {found_trigger_channels}")
-        self.logger.debug(f"Found trigger amps {trigger_amp_response_name}")
-
-        def trigger_filter(freqs):
-            return trigger_amp_response["gain"](freqs) * trigger_amp_response["phase"](freqs)
-
-        return found_trigger_channels, trigger_filter
 
     def digitize_trace(self, station, det, trigger_channels, vrms):
         for channel_id in trigger_channels:
-            channel = station.get_channel(channel_id)
+            channel = station.get_trigger_channel(channel_id)
 
             digitized_trace, adc_sampling_frequency = self._adc.get_digital_trace(
                 station,
@@ -284,14 +200,11 @@ def digitize_trace(self, station, det, trigger_channels, vrms):
             channel.set_trace(digitized_trace, adc_sampling_frequency)
 
     @register_run()
-    def run(self, evt, station, det, requested_channels=[], vrms=None, apply_adc_gain=True,
-            digitize_trace=True, do_apply_trigger_filter=True):
+    def run(self, evt, station, det, trigger_channels, vrms=None, apply_adc_gain=True,
+            digitize_trace=True):
         """
         Applies the additional filters on the trigger board and performs a gain amplification
         to get the correct number of trigger bits.
-        This module expects that all of the trigger channels (either defined by the existance of
-        `trigger_XXX` fields in the detector .json file or via the `requested_channels`
-        argument) are identical.
 
         Parameters
         ----------
@@ -301,8 +214,8 @@ def run(self, evt, station, det, requested_channels=[], vrms=None, apply_adc_gai
             Station to run the module on
         det : Detector
             The detector description
-        requested_channels : list (default: [])
-            Channels that this module should consider when applying the filter
+        trigger_channels : list
+            Channels that this module should consider applying the filter/board response
         vrms : float (default: None)
             The Vrms of the trigger channels including the trigger board filters
             If set to `None`, this will be estimated using the waveforms
@@ -310,24 +223,14 @@ def run(self, evt, station, det, requested_channels=[], vrms=None, apply_adc_gai
             Apply the gain shift to achieve the specified level of noise bits
         digitize_trace : bool (default: True)
             Apply the quantization to the voltages (uses `NuRadioReco.modules.analogToDigitalConverter` to do so)
-        do_apply_trigger_filter : bool (default: True)
-            Apply the trigger filter to the waveforms. The response might already be ablied as part of the
-            detector simulation, in which case this should be set to `False`.
 
         Returns
         -------
         trigger_board_vrms : float
             the RMS voltage of the waveforms on the trigger board after applying the ADC gain
-
         """
         self.logger.debug("Applying the RNO-G trigger board response")
 
-        if do_apply_trigger_filter:
-            trigger_channels, trigger_amp_response = self.get_trigger_values(station, det, requested_channels)
-            self.apply_trigger_filter(station, trigger_channels, trigger_amp_response)
-        else:
-            trigger_channels = requested_channels
-
         if vrms is None:
             vrms = self.get_avg_vrms(station, trigger_channels)
 
diff --git a/NuRadioReco/modules/base/module.py b/NuRadioReco/modules/base/module.py
index ed4c8de00d..0323bc8f18 100644
--- a/NuRadioReco/modules/base/module.py
+++ b/NuRadioReco/modules/base/module.py
@@ -2,7 +2,7 @@
 from timeit import default_timer as timer
 import NuRadioReco.framework.event
 import NuRadioReco.framework.base_station
-import NuRadioReco.detector.detector_base
+import NuRadioReco.detector.detector as detectors
 import inspect
 import pickle
 
@@ -74,7 +74,7 @@ def register_run_method(self, *args, **kwargs):
                 # station should be second argument
                 elif isinstance(value, NuRadioReco.framework.base_station.BaseStation) and idx == 1:
                     station = value
-                elif isinstance(value, (NuRadioReco.detector.detector_base.DetectorBase, NuRadioReco.detector.RNO_G.rnog_detector.Detector)):
+                elif isinstance(value, (detectors.detector_base.DetectorBase, detectors.rnog_detector.Detector)):
                     pass # we don't try to store detectors
                 else: # we try to store other arguments IF they are pickleable
                     try:
diff --git a/NuRadioReco/modules/channelAddCableDelay.py b/NuRadioReco/modules/channelAddCableDelay.py
index 4c7509de8d..bcc8b13f91 100644
--- a/NuRadioReco/modules/channelAddCableDelay.py
+++ b/NuRadioReco/modules/channelAddCableDelay.py
@@ -1,16 +1,19 @@
 from NuRadioReco.modules.base.module import register_run
-from NuRadioReco.utilities import units
+from NuRadioReco.utilities import units, signal_processing
 import logging
 
 
 class channelAddCableDelay:
     """
-    Adds the cable delay to channels
+    Adds the cable delay to channels.
     """
 
     def __init__(self):
         self.logger = logging.getLogger("NuRadioReco.channelApplyCableDelay")
 
+    def begin(self):
+        pass
+
     @register_run()
     def run(self, evt, station, det, mode='add'):
         """
@@ -24,14 +27,9 @@ def run(self, evt, station, det, mode='add'):
         mode : str (default: "add")
             options: 'add' or 'subtract'.
         """
-        for channel in station.iter_channels():
-            cable_delay = det.get_cable_delay(station.get_id(), channel.get_id())
-            self.logger.debug(f"Channel {channel.get_id()}: {mode} {cable_delay / units.ns:.2f} ns")
+        if mode not in ['add', 'subtract']:
+            raise ValueError(f"Unknown mode '{mode}' for channelAddCableDelay. "
+                            "Valid options are 'add' or 'subtract'.")
 
-            if mode == 'add':
-                channel.add_trace_start_time(cable_delay)
-            elif mode == 'subtract':
-                channel.add_trace_start_time(-1 * cable_delay)
-            else:
-                raise ValueError(f"Unknown mode '{mode}' for channelAddCableDelay. "
-                                 "Valid options are 'add' or 'subtract'.")
+        sim_to_data = mode == "add"
+        signal_processing.add_cable_delay(station, det, sim_to_data, trigger=False, logger=self.logger)
\ No newline at end of file
diff --git a/NuRadioReco/modules/channelGalacticNoiseAdder.py b/NuRadioReco/modules/channelGalacticNoiseAdder.py
index 0d30d2958e..b673f67082 100644
--- a/NuRadioReco/modules/channelGalacticNoiseAdder.py
+++ b/NuRadioReco/modules/channelGalacticNoiseAdder.py
@@ -9,14 +9,8 @@
 import scipy.constants
 import scipy.interpolate
 import functools
-
-from pygdsm import (
-    GlobalSkyModel16,
-    GlobalSkyModel,
-    LowFrequencySkyModel,
-    HaslamSkyModel,
-)
-
+from contextlib import redirect_stdout
+from numpy.random import Generator, Philox
 import healpy
 import astropy.coordinates
 import astropy.units
@@ -24,20 +18,35 @@
 logger = logging.getLogger('NuRadioReco.channelGalacticNoiseAdder')
 
 try:
-    from radiocalibrationtoolkit import *  # Documentation: https://github.com/F-Tomas/radiocalibrationtoolkit/tree/main contains SSM, GMOSS, ULSA
-except Exception as _:
-    logger.info("Import of `radiocalibrationtoolkit` failed. Consider installing it to use more sky models. "
-                "See documentation at https://github.com/F-Tomas/radiocalibrationtoolkit/tree/main")
+    from pygdsm import (
+        GlobalSkyModel16,
+        GlobalSkyModel,
+        LowFrequencySkyModel,
+        HaslamSkyModel,
+    )
+except ImportError as e:
+    logger.error(
+        "To use the channelGalacticNoiseAdder, 'pygdsm' needs to be installed:\n\n"
+        "\t pip install git+https://github.com/telegraphic/pygdsm\n"
+        )
+    raise(e)
 
 try:
-    from pylfmap import LFmap  # Documentation: https://github.com/F-Tomas/pylfmap needs cfitsio installation
+    with redirect_stdout(None): # suppress (usually irrelevant) print statements from pylfmap
+        from pylfmap import LFmap  # Documentation: https://github.com/F-Tomas/pylfmap needs cfitsio installation
 except ImportError:
-    logger.info("LFmap import failed. Consider installing it to use LFmap as sky model.")
-
+    logger.info(
+        "pylfmap import failed. Consider installing it from "
+        "https://github.com/F-Tomas/pylfmap to use LFmap as sky model.")
+except IndexError: # this is a common error if cfitsio is not found... there are probably others
+    logger.error(
+        "pylfmap import failed. This might be because you do not have a working "
+        "installation of cfitsio. See https://github.com/F-Tomas/pylfmap/issues/2 for potential tips")
 
 class channelGalacticNoiseAdder:
     """
     Class that simulates the noise produced by galactic radio emission
+
     Uses the pydgsm package (https://github.com/telegraphic/pygdsm), which provides
     radio background data based on Oliveira-Costa et al. (2008) (https://arxiv.org/abs/0802.1525)
     and Zheng et al. (2016) (https://arxiv.org/abs/1605.04920)
@@ -49,11 +58,8 @@ class channelGalacticNoiseAdder:
     """
 
     def __init__(self):
-        self.__zenith_sample = None
-        self.__azimuth_sample = None
         self.__n_side = None
         self.__interpolation_frequencies = None
-        self.__gdsm = None
         self.__radio_sky = None
         self.__noise_temperatures = None
         self.__antenna_pattern_provider = NuRadioReco.detector.antennapattern.AntennaPatternProvider()
@@ -64,14 +70,15 @@ def begin(
             debug=False,
             n_side=4,
             freq_range=None,
-            interpolation_frequencies=None
+            interpolation_frequencies=None,
+            seed=None
     ):
         """
         Set up important parameters for the module
 
         Parameters
         ----------
-        skymodel: {'lfmap', 'lfss', 'gsm2016', 'haslam', 'ssm', 'gmoss', 'ulsa_fdi', 'ulsa_dpi', 'ulsa_ci'}, optional
+        skymodel: {'gsm2008', 'lfmap', 'lfss', 'gsm2016', 'haslam'}, optional
             Choose the sky model to use. If none is provided, the Global Sky Model (2008) is used as a default.
         debug: bool, default: False
             Deprecated. Will be removed in future versions.
@@ -82,7 +89,7 @@ def begin(
             from that direction is calculated. The number of pixels used is
             12 * n_side ** 2, so a larger value for n_side will result better accuracy
             but also greatly increase computing time.
-        freq_range: array of len=2, default: [10,1100] * units.MHZ
+        freq_range: array of len=2, default: [10, 1000] * units.MHZ
             The sky brightness temperature will be evaluated for the frequencies
             within this limit. Brightness temperature for frequencies in between are
             calculated by interpolation the log10 of the temperature
@@ -90,20 +97,24 @@ def begin(
             specified in the run method.
         interpolation_frequencies: array of frequencies to interpolate to.
             Kept for historic purposes with intention to deprecate in the future.
+        seed : {None, int, array_like[ints], SeedSequence}, optional
+            The seed that is passed on to the `numpy.random.Philox` bitgenerator used for random
+            number generation.
         """
         if debug:
             warnings.warn("This argument is deprecated and will be removed in future versions.", DeprecationWarning)
 
+        self.__random_generator = Generator(Philox(seed))
         self.__n_side = n_side
         self.solid_angle = healpy.pixelfunc.nside2pixarea(self.__n_side, degrees=False)
 
         if interpolation_frequencies is None:
             if freq_range is None:
-                freq_range = np.array([10, 1100]) * units.MHz
+                freq_range = np.array([10, 1000]) * units.MHz
 
             # define interpolation frequencies. Set in logarithmic range from freq_range[0] to freq_range[1],
-            # rounded to 0 decimal places to avoid import errors from LFmap and tabulated models.
-            self.__interpolation_frequencies = np.around(np.logspace(*np.log10(freq_range), num=15), 0)
+            # rounded to MHz to avoid import errors from LFmap and tabulated models.
+            self.__interpolation_frequencies = np.around(np.logspace(*np.log10(freq_range), num=15), 3)
         else:
             self.__interpolation_frequencies = interpolation_frequencies
             logger.warning("DeprecationWarning: Optional argument 'interpolation_frequencies' was replaced by 'freq_range'.")
@@ -113,42 +124,27 @@ def begin(
             if skymodel is None:
                 sky_model = GlobalSkyModel(freq_unit="MHz")
                 logger.info("No sky model specified. Using standard: Global Sky Model (2008). Available models: "
-                            "lfmap, lfss, gsm2016, haslam, ssm, gmoss, ulsa_fdi, ulsa_dpi, ulsa_ci")
-            elif skymodel == 'lfss':
+                            "gsm2008, lfmap, lfss, gsm2016, haslam")
+            elif skymodel.lower() == 'lfss':
                 sky_model = LowFrequencySkyModel(freq_unit="MHz")
                 logger.info("Using LFSS as sky model")
-            elif skymodel == 'gsm2008':
+            elif skymodel.lower() == 'gsm2008':
                 sky_model = GlobalSkyModel(freq_unit="MHz")
                 logger.info("Using GSM2008 as sky model")
-            elif skymodel == 'gsm2016':
+            elif skymodel.lower() == 'gsm2016':
                 sky_model = GlobalSkyModel16(freq_unit="MHz")
                 logger.info("Using GSM2016 as sky model")
-            elif skymodel == 'haslam':
+            elif skymodel.lower() == 'haslam':
                 sky_model = HaslamSkyModel(freq_unit="MHz", spectral_index=-2.53)
                 logger.info("Using Haslam as sky model")
-            elif skymodel == 'lfmap':
+            elif skymodel.lower() == 'lfmap':
                 sky_model = LFmap()
                 logger.info("Using LFmap as sky model")
-            elif skymodel == 'ssm':
-                sky_model = SSM()
-                logger.info("Using SSM as sky model")
-            elif skymodel == 'gmoss':
-                sky_model = GMOSS()
-                logger.info("Using GMOSS as sky model")
-            elif skymodel == 'ulsa_fdi':
-                sky_model = ULSA(index_type='freq_dependent_index')
-                logger.info("Using ULSA_fdi as sky model")
-            elif skymodel == 'ulsa_ci':
-                sky_model = ULSA(index_type='constant_index')
-                logger.info("Using ULSA_ci as sky model")
-            elif skymodel == 'ulsa_dpi':
-                sky_model = ULSA(index_type='direction_dependent_index')
-                logger.info("Using ULSA_dpi as sky model")
             else:
                 logger.error(f"Sky model {skymodel} unknown. Defaulting to Global Sky Model (2008).")
                 sky_model = GlobalSkyModel(freq_unit="MHz")
 
-        except ImportError:
+        except NameError:
             logger.error(f"Could not find {skymodel} skymodel. Do you have the correct package installed? \n"
                         f"Defaulting to Global Sky Model (2008) as sky model.")
             sky_model = GlobalSkyModel(freq_unit="MHz")
@@ -184,11 +180,16 @@ def run(
             The station whose channels noise shall be added to
         detector: Detector object
             The detector description
-        passband: list of float
+        passband: list of float, optional
             Lower and upper bound of the frequency range in which noise shall be
-            added
+            added. The default (no passband specified) is [10, 1000] MHz
         """
 
+        if self.__noise_temperatures is None: # check if .begin has been called, give helpful error message if not
+            msg = "channelGalacticNoiseAdder was not initialized correctly. Maybe you forgot to call `.begin()`?"
+            logger.error(msg)
+            raise ValueError(msg)
+
         # check that or all channels channel.get_frequencies() is identical
         last_freqs = None
         for channel in station.iter_channels():
@@ -203,7 +204,7 @@ def run(
         d_f = freqs[2] - freqs[1]
 
         if passband is None:
-            passband = [10 * units.MHz, 1100 * units.MHz]
+            passband = [10 * units.MHz, 1000 * units.MHz]
 
         passband_filter = (freqs > passband[0]) & (freqs < passband[1])
 
@@ -213,9 +214,8 @@ def run(
         local_coordinates = get_local_coordinates((site_latitude, site_longitude), station_time, self.__n_side)
 
         n_ice = ice.get_refractive_index(-0.01, detector.get_site(station.get_id()))
-        n_air = 1.000292  # TODO: This value applies under standard conditions. Does it hold for Greenland?
+        n_air = ice.get_refractive_index(depth=1, site=detector.get_site(station.get_id()))
         c_vac = scipy.constants.c * units.m / units.s
-        c_air = c_vac / n_air
 
         channel_spectra = {}
         for channel in station.iter_channels():
@@ -223,7 +223,7 @@ def run(
 
         for i_pixel in range(healpy.pixelfunc.nside2npix(self.__n_side)):
             azimuth = local_coordinates[i_pixel].az.rad
-            zenith = np.pi / 2. - local_coordinates[i_pixel].alt.rad
+            zenith = np.pi / 2. - local_coordinates[i_pixel].alt.rad # this is the in-air zenith
 
             if zenith > 90. * units.deg:
                 continue
@@ -250,12 +250,13 @@ def run(
             spectral_radiance_per_bin = spectral_radiance * d_f
 
             # calculate electric field per frequency bin from the radiance per bin
-            efield_amplitude = np.sqrt(spectral_radiance_per_bin / (c_vac * scipy.constants.epsilon_0 * (
+            efield_amplitude = np.sqrt(
+                spectral_radiance_per_bin / (c_vac * scipy.constants.epsilon_0 * (
                         units.coulomb / units.V / units.m))) / d_f
 
             # assign random phases to electric field
-            noise_spectrum = np.zeros((3, freqs.shape[0]), dtype=np.complex128)
-            phases = np.random.uniform(0, 2. * np.pi, len(spectral_radiance))
+            noise_spectrum = np.zeros((3, freqs.shape[0]), dtype=complex)
+            phases = self.__random_generator.uniform(0, 2. * np.pi, len(spectral_radiance))
 
             noise_spectrum[1][passband_filter] = np.exp(1j * phases) * efield_amplitude
             noise_spectrum[2][passband_filter] = np.exp(1j * phases) * efield_amplitude
@@ -263,14 +264,17 @@ def run(
             channel_noise_spec = np.zeros_like(noise_spectrum)
 
             for channel in station.iter_channels():
-                if detector.get_relative_position(station.get_id(), channel.get_id())[2] < 0:
+                channel_pos = detector.get_relative_position(station.get_id(), channel.get_id())
+                if channel_pos[2] < 0:
                     curr_t_theta = t_theta
                     curr_t_phi = t_phi
                     curr_fresnel_zenith = fresnel_zenith
-                else:
+                    curr_n = n_ice
+                else: # we are in air
                     curr_t_theta = 1
                     curr_t_phi = 1
                     curr_fresnel_zenith = zenith
+                    curr_n = n_air
 
                 antenna_pattern = self.__antenna_pattern_provider.load_antenna_pattern(
                     detector.get_antenna_model(station.get_id(), channel.get_id()))
@@ -279,23 +283,17 @@ def run(
                 # calculate the phase offset in comparison to station center
                 # consider additional distance in air & ice
                 # assume for air & ice constant index of refraction
-                channel_pos_x, channel_pos_y, _ = detector.get_relative_position(station.get_id(), channel.get_id())
-                # positive value, 0 for above surface antennas
-                channel_depth = abs(min(detector.get_relative_position(station.get_id(), channel.get_id())[2], 0))
-                sin_zenith = np.sin(zenith)
-                delta_phases = (
-                        2 * np.pi * freqs[passband_filter] / c_air *
-                        (
-                                sin_zenith *
-                                (np.cos(azimuth) * channel_pos_x + channel_pos_y * np.sin(azimuth)) +
-                                channel_depth *
-                                ((n_ice / n_air) ** 2 + sin_zenith ** 2) /
-                                np.sqrt((n_ice / n_air) ** 2 - sin_zenith ** 2)
-                        )
-                )
+                dt = geometryUtilities.get_time_delay_from_direction(
+                    curr_fresnel_zenith, azimuth, channel_pos, n=curr_n)
+                if channel_pos[2] < -5:
+                    logger.warning(
+                        "Galactic noise cannot be simulated accurately for deep in-ice channels. "
+                        "Coherence and arrival direction of noise are probably inaccurate.")
+
+                delta_phases = -2 * np.pi * freqs[passband_filter] * dt
 
                 # add random polarizations and phase to electric field
-                polarizations = np.random.uniform(0, 2. * np.pi, len(spectral_radiance))
+                polarizations = self.__random_generator.uniform(0, 2. * np.pi, len(spectral_radiance))
 
                 channel_noise_spec[1][passband_filter] = noise_spectrum[1][passband_filter] * np.exp(
                     1j * delta_phases) * np.cos(polarizations)
@@ -305,8 +303,10 @@ def run(
                 # fold electric field with antenna response
                 antenna_response = antenna_pattern.get_antenna_response_vectorized(freqs, curr_fresnel_zenith, azimuth,
                                                                                    *antenna_orientation)
-                channel_noise_spectrum = antenna_response['theta'] * channel_noise_spec[1] * curr_t_theta + \
-                                         antenna_response['phi'] * channel_noise_spec[2] * curr_t_phi
+                channel_noise_spectrum = (
+                    antenna_response['theta'] * channel_noise_spec[1] * curr_t_theta
+                    + antenna_response['phi'] * channel_noise_spec[2] * curr_t_phi
+                )
 
                 # add noise spectrum from pixel in the sky to channel spectrum
                 channel_spectra[channel.get_id()] += channel_noise_spectrum
diff --git a/NuRadioReco/modules/channelStopFilter.py b/NuRadioReco/modules/channelStopFilter.py
index a38a8a583e..3a25808ecf 100644
--- a/NuRadioReco/modules/channelStopFilter.py
+++ b/NuRadioReco/modules/channelStopFilter.py
@@ -3,13 +3,20 @@
 import logging
 from NuRadioReco.utilities import units
 import scipy.signal.windows
-logger = logging.getLogger('channelStopFilter')
+logger = logging.getLogger('NuRadioReco.channelStopFilter')
 
 
 class channelStopFilter:
     """
-    at the beginning and end of the trace (around the 'stop') our electronic produces a glitch
-    this modules smoothly filters the beginning and the end of the trace
+    Apply tapering towards zero to channel traces
+
+    This class applies a Tukey window to the ends of the traces
+    to gradually taper them towards zero, and appends zeros at either end.
+    This prevents unphysical features that can result from applying transformations
+    to the data in the frequency domain (see :doc:`additional explanation here </NuRadioReco/pages/times>`)
+
+    For ARIANNA data, this module should always be used to remove the glitch around
+    the beginning and end of the trace (around the 'stop').
     """
 
     def begin(self):
@@ -39,9 +46,11 @@ def run(self, evt, station, det, filter_size=0.1, prepend=128 * units.ns, append
             sampling_rate = channel.get_sampling_rate()
             window = scipy.signal.windows.tukey(len(trace), filter_size)
             trace *= window
-            trace = np.append(np.zeros(int(np.round(prepend * sampling_rate))), trace)
+            prepend_samples = int(np.round(prepend * sampling_rate))
+            trace = np.append(np.zeros(prepend_samples), trace)
             trace = np.append(trace, np.zeros(int(np.round(append * sampling_rate))))
             channel.set_trace(trace, sampling_rate)
+            channel.add_trace_start_time(-prepend_samples / sampling_rate)
 
     def end(self):
         pass
diff --git a/NuRadioReco/modules/cosmicRayEnergyReconstructor.py b/NuRadioReco/modules/cosmicRayEnergyReconstructor.py
index 083104b3c4..05c70a70cd 100644
--- a/NuRadioReco/modules/cosmicRayEnergyReconstructor.py
+++ b/NuRadioReco/modules/cosmicRayEnergyReconstructor.py
@@ -1,3 +1,16 @@
+"""
+Module to reconstruct the energy of an air shower from its radio signal
+
+To reconstruct the air-shower energy, the radio signals should be bandpass filtered
+between 80 to 300 MHz with a 10th order Butterworth filter, and both the cosmic ray
+direction and the unfolded electric field should be present. The latter can be obtained
+(after the signal direction has been reconstructed) by running the
+`NuRadioReco.modules.voltageToAnalyticEfieldConverter` module.
+
+For more details on this algorithm, see https://dx.doi.org/10.1088/1475-7516/2019/10/075
+
+"""
+
 import numpy as np
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.framework.parameters import stationParameters as stnp
@@ -13,6 +26,7 @@
 class cosmicRayEnergyReconstructor:
     """
     Reconstructs the energy of an air shower from its radio signal
+
     Requires the following modules to be run beforehand:
     
     * a 10th order Butterworth bandpass filter with passband 80-300 MHz
@@ -36,27 +50,66 @@ def __init__(self):
             'auger': {
                 'scale': np.array([(229.96, -123.75, 110.51), (214.46, -111.01, 119.18)]),
                 'falloff': np.array([(-.1445, -.09820), (.5936, -1.1763)])
+            },
+            'summit': {
+                'scale': np.array([[ 404.5 , -131.56,   11.7 ], [ 428.97,  -92.11,    5.94]]),
+                'falloff': np.array([[-0.3391,  0.1738], [ 0.9543, -1.6967]])
             }
         }
         self.__elevations = {  # TODO: This should be changed once we have implemented a proper coordinate system
             'mooresbay': 30.,
             'southpole': 2800.,
-            'auger': 1560.
+            'auger': 1560.,
+            'summit': 3216.
         }
         self.__site = None
-        self.__parametrization_for_site = None
-        self.__elevation = None
 
-    def begin(self, site='mooresbay'):
+    def begin(self, site=None):
+        """
+        Initialize the cosmicRayEnergyReconstructor (optional)
+
+        Parameters
+        ----------
+        site : string | None (default: None)
+            Specifies the site of the station. The parameterization of
+            the cosmic ray energy depends on the site of the detector.
+
+            If None, the site will be determined from the detector
+            passed to the `run` function.
+
+        """
         self.__site = site
         if site not in self.__parametrizations.keys():
             self.logger.error('Unsupported site. Please select one of the following: {}'.format(self.__parametrizations.keys()))
             raise ValueError
-        self.__parametrization_for_site = self.__parametrizations[site]
-        self.__elevation = self.__elevations[site]
 
     @register_run()
-    def run(self, event, station, detector):
+    def run(self, event, station, detector, electric_field=None):
+        """
+        Determine the cosmic ray energy from the electric field fluence.
+
+        The reconstructed cosmic ray energy will be stored in the
+        station in the :obj:`cr_energy_em <NuRadioReco.framework.parameters.stationParameters.cr_energy_em>` parameter.
+
+        Parameters
+        ----------
+        event : Event
+        station : Station
+            The station containing the reconstructed electric field.
+            If it contains multiple electric fields, only the last electric field
+            will be used, unless another electric field is passed as a keyword-argument
+        detector : Detector
+        electric_field : ElectricField | None (default: None)
+            If not None, reconstruct the energy for this electric field.
+            Otherwise, reconstruct the last electric field in the station.
+            Useful if a station contains multiple reconstructed electric fields.
+
+        Returns
+        -------
+        rec_energy : float
+            The reconstructed cosmic ray energy
+
+        """
 
         if not station.is_cosmic_ray():
             self.logger.warning('Event is not a cosmic ray!')
@@ -65,30 +118,52 @@ def run(self, event, station, detector):
             return
         zenith = station.get_parameter(stnp.zenith)
         azimuth = station.get_parameter(stnp.azimuth)
+        site = self.__site
+        if site is None:
+            site = detector.get_site(station.get_id())
+            if site not in self.__parametrizations.keys():
+                self.logger.error('Unsupported site. Please select one of the following: {}'.format(self.__parametrizations.keys()))
+                raise ValueError
+        parametrization_for_site = self.__parametrizations[site]
+        elevation = self.__elevations[site]
+
         if zenith < 30. * units.deg:
             self.logger.warning('Zenith angle is smaller than 30deg. Energy reconstruction is likely to be inaccurate!')
-        n_efields = len(station.get_electric_fields())
-        if n_efields == 0:
-            self.logger.error('No E-field found. Please run the voltageToAnalyticEfieldConverter beforehand!')
-            return
-        if n_efields > 1:
-            self.logger.warning('Multiple E-fields were found. Only the first E-field will be used.')
-        electric_field = station.get_electric_fields()[0]
+        if electric_field is None:
+            n_efields = len(station.get_electric_fields())
+            if n_efields == 0:
+                self.logger.error('No E-field found. Please run the voltageToAnalyticEfieldConverter beforehand!')
+                return
+            if n_efields > 1:
+                self.logger.warning('Multiple E-fields were found. Only the last E-field will be used.')
+            electric_field = station.get_electric_fields()[-1]
+
         spectrum_slope = electric_field.get_parameter(efp.cr_spectrum_slope)
-        alpha = hp.get_angle_to_magnetic_field_vector(zenith, azimuth, self.__site)
-        cs = radiotools.coordinatesystems.cstrafo(zenith, azimuth, site=self.__site)
+        alpha = hp.get_angle_to_magnetic_field_vector(zenith, azimuth, site)
+        cs = radiotools.coordinatesystems.cstrafo(zenith, azimuth, site=site)
         efield_trace_vxB_vxvxB = cs.transform_to_vxB_vxvxB(cs.transform_from_onsky_to_ground(electric_field.get_trace()))
         efield_trace_vxB_vxvxB[0] /= np.sin(alpha)  # correct energy fluence for effect of angle to magnetic field
         energy_fluence = NuRadioReco.utilities.trace_utilities.get_electric_field_energy_fluence(efield_trace_vxB_vxvxB, electric_field.get_times())
         energy_fluence = np.abs(energy_fluence[0]) + np.abs(energy_fluence[1])
-        xmax_distance = self.__atmosphere.get_distance_xmax_geometric(zenith, 750., self.__elevation)  # parametrization is for Xmax of 750g/cm^2
+        xmax_distance = self.__atmosphere.get_distance_xmax_geometric(zenith, 750., elevation)  # parametrization is for Xmax of 750g/cm^2
+        if np.any(xmax_distance) < 0:
+            self.logger.warning(
+                f"Estimated distance to Xmax is negative for zenith {zenith/units.deg:.0f} and elevation {elevation}. "
+                "This means Xmax may be below the detector elevation. The absolute value "
+                "of the distance will be used instead, but note that the resulting energy estimates may be inaccurate"
+            )
+
+            xmax_distance = np.abs(xmax_distance) # for some zeniths and altitudes the parameterization can become negative
+
         # find out if we are inside or outside of the Cherenkov ring
         second_order_spectrum_parameter = electric_field.get_parameter(efp.cr_spectrum_quadratic_term)
         if second_order_spectrum_parameter <= spectrum_slope * .1:
-            scale_parameter = self.__parametrization_for_site['scale'][0][0] * zenith ** 2 + self.__parametrization_for_site['scale'][0][1] * zenith + self.__parametrization_for_site['scale'][0][0]
-            falloff_parameter = self.__parametrization_for_site['falloff'][0][0] * zenith + self.__parametrization_for_site['falloff'][0][1]
+            scale_parameter = parametrization_for_site['scale'][0][0] * zenith ** 2 + parametrization_for_site['scale'][0][1] * zenith + parametrization_for_site['scale'][0][2]
+            falloff_parameter = parametrization_for_site['falloff'][0][0] * zenith + parametrization_for_site['falloff'][0][1]
         else:
-            scale_parameter = self.__parametrization_for_site['scale'][1][0] * zenith ** 2 + self.__parametrization_for_site['scale'][1][1] * zenith + self.__parametrization_for_site['scale'][1][0]
-            falloff_parameter = self.__parametrization_for_site['falloff'][1][0] * zenith + self.__parametrization_for_site['falloff'][1][1]
+            scale_parameter = parametrization_for_site['scale'][1][0] * zenith ** 2 + parametrization_for_site['scale'][1][1] * zenith + parametrization_for_site['scale'][1][2]
+            falloff_parameter = parametrization_for_site['falloff'][1][0] * zenith + parametrization_for_site['falloff'][1][1]
         rec_energy = 1.e18 * np.sqrt(energy_fluence) * (xmax_distance / units.km) / (scale_parameter * np.exp(falloff_parameter * np.abs(spectrum_slope) ** 0.8))
         station.set_parameter(stnp.cr_energy_em, rec_energy)
+
+        return rec_energy
diff --git a/NuRadioReco/modules/efieldToVoltageConverter.py b/NuRadioReco/modules/efieldToVoltageConverter.py
index 53508e73cc..6e14b6ae17 100644
--- a/NuRadioReco/modules/efieldToVoltageConverter.py
+++ b/NuRadioReco/modules/efieldToVoltageConverter.py
@@ -1,17 +1,16 @@
 import numpy as np
 import time
 import logging
+import copy
+
 import NuRadioReco.framework.channel
 import NuRadioReco.framework.base_trace
-from NuRadioReco.modules.base.module import register_run
-from NuRadioReco.detector import antennapattern
-from NuRadioReco.utilities import geometryUtilities as geo_utl
-from NuRadioReco.utilities import units, fft
-from NuRadioReco.utilities import ice
-from NuRadioReco.utilities import trace_utilities
 from NuRadioReco.framework.parameters import electricFieldParameters as efp
 from NuRadioReco.framework.parameters import stationParameters as stnp
-import copy
+
+from NuRadioReco.modules.base.module import register_run
+from NuRadioReco.detector import antennapattern
+from NuRadioReco.utilities import units, fft, ice, trace_utilities, geometryUtilities as geo_utl
 
 
 class efieldToVoltageConverter():
@@ -30,17 +29,17 @@ def __init__(self, log_level=logging.NOTSET):
         self.__t = 0
         self.__uncertainty = None
         self.__debug = None
-        self.__time_resolution = None
         self.__pre_pulse_time = None
         self.__post_pulse_time = None
         self.__max_upsampling_factor = None
         self.antenna_provider = None
-        self.begin()
         self.logger = logging.getLogger('NuRadioReco.efieldToVoltageConverter')
         self.logger.setLevel(log_level)
+        self.begin()
+
 
     def begin(self, debug=False, uncertainty=None,
-              time_resolution=0.1 * units.ns,
+              time_resolution=None,
               pre_pulse_time=200 * units.ns,
               post_pulse_time=200 * units.ns
               ):
@@ -62,32 +61,31 @@ def begin(self, debug=False, uncertainty=None,
              * 'amp': statistical uncertainty of the amplifier aplification,
                 specify value as relative difference of linear gain
 
-        time_resolution: float
-            time resolution of shifting pulse times
         pre_pulse_time: float
             length of empty samples that is added before the first pulse
         post_pulse_time: float
             length of empty samples that is added after the simulated trace
         """
+
+        if time_resolution is not None:
+            self.logger.warning("`time_resolution` is deprecated and will be removed in the future. "
+                                "The argument is ignored.")
+
         self.__debug = debug
-        self.__time_resolution = time_resolution
         self.__pre_pulse_time = pre_pulse_time
         self.__post_pulse_time = post_pulse_time
         self.__max_upsampling_factor = 5000
-        if uncertainty is None:
-            self.__uncertainty = {}
-        else:
-            self.__uncertainty = uncertainty
+
         # some uncertainties are systematic, fix them here
-        if('sys_dx' in self.__uncertainty):
-            self.__uncertainty['sys_dx'] = np.random.normal(0, self.__uncertainty['sys_dx'])
-        if('sys_dy' in self.__uncertainty):
-            self.__uncertainty['sys_dy'] = np.random.normal(0, self.__uncertainty['sys_dy'])
-        if('sys_dz' in self.__uncertainty):
-            self.__uncertainty['sys_dz'] = np.random.normal(0, self.__uncertainty['sys_dz'])
-        if('sys_amp' in self.__uncertainty):
-            for iCh in self.__uncertainty['sys_amp'].keys():
+        self.__uncertainty = uncertainty or {}
+        for key in ['sys_dx', 'sys_dy', 'sys_dz']:
+            if key in self.__uncertainty:
+                self.__uncertainty[key] = np.random.normal(0, self.__uncertainty[key])
+
+        if 'sys_amp' in self.__uncertainty:
+            for iCh in self.__uncertainty['sys_amp']:
                 self.__uncertainty['sys_amp'][iCh] = np.random.normal(1, self.__uncertainty['sys_amp'][iCh])
+
         self.antenna_provider = antennapattern.AntennaPatternProvider()
 
     @register_run()
@@ -96,53 +94,52 @@ def run(self, evt, station, det, channel_ids=None):
 
         # access simulated efield and high level parameters
         sim_station = station.get_sim_station()
-        if(len(sim_station.get_electric_fields()) == 0):
-            raise LookupError(f"station {station.get_id()} has no efields")
         sim_station_id = sim_station.get_id()
+        if len(sim_station.get_electric_fields()) == 0:
+            raise LookupError(f"station {station.get_id()} has no efields")
 
         # first we determine the trace start time of all channels and correct
-        # for different cable delays
         times_min = []
         times_max = []
         if channel_ids is None:
             channel_ids = det.get_channel_ids(sim_station_id)
+
         for channel_id in channel_ids:
             for electric_field in sim_station.get_electric_fields_for_channels([channel_id]):
-                time_resolution = 1. / electric_field.get_sampling_rate()
                 cab_delay = det.get_cable_delay(sim_station_id, channel_id)
                 t0 = electric_field.get_trace_start_time() + cab_delay
+
                 # if we have a cosmic ray event, the different signal travel time to the antennas has to be taken into account
                 if sim_station.is_cosmic_ray():
-                    site = det.get_site(sim_station_id)
-                    antenna_position = det.get_relative_position(sim_station_id, channel_id) - electric_field.get_position()
-                    if sim_station.get_parameter(stnp.zenith) > 90 * units.deg:  # signal is coming from below, so we take IOR of ice
-                        index_of_refraction = ice.get_refractive_index(antenna_position[2], site)
-                    else:  # signal is coming from above, so we take IOR of air
-                        index_of_refraction = ice.get_refractive_index(1, site)
-                    # For cosmic ray events, we only have one electric field for all channels, so we have to account
-                    # for the difference in signal travel between channels. IMPORTANT: This is only accurate
-                    # if all channels have the same z coordinate
-                    travel_time_shift = geo_utl.get_time_delay_from_direction(
-                        sim_station.get_parameter(stnp.zenith),
-                        sim_station.get_parameter(stnp.azimuth),
-                        antenna_position,
-                        index_of_refraction
-                    )
+                    travel_time_shift = calculate_time_shift_for_cosmic_ray(det, sim_station, electric_field, channel_id)
                     t0 += travel_time_shift
-                if(not np.isnan(t0)):  # trace start time is None if no ray tracing solution was found and channel contains only zeros
+
+                if not np.isnan(t0):
+                    # trace start time is None if no ray tracing solution was found and channel contains only zeros
                     times_min.append(t0)
                     times_max.append(t0 + electric_field.get_number_of_samples() / electric_field.get_sampling_rate())
-                    self.logger.debug("trace start time {}, cab_delty {}, tracelength {}".format(electric_field.get_trace_start_time(), cab_delay, electric_field.get_number_of_samples() / electric_field.get_sampling_rate()))
+                    self.logger.debug("trace start time {}, cable delay {}, tracelength {}".format(
+                        electric_field.get_trace_start_time(), cab_delay,
+                        electric_field.get_number_of_samples() / electric_field.get_sampling_rate()))
+
+        times_min = np.min(times_min)
+        times_max = np.max(times_max)
 
         # pad event times by pre/post pulse time
-        times_min = np.array(times_min) - self.__pre_pulse_time
-        times_max = np.array(times_max) + self.__post_pulse_time
+        times_min -= self.__pre_pulse_time
+        times_max += self.__post_pulse_time
 
-        trace_length = times_max.max() - times_min.min()
+        # assumes that all electric fields have the same sampling rate
+        time_resolution = 1. / electric_field.get_sampling_rate()
+
+        trace_length = times_max - times_min
         trace_length_samples = int(round(trace_length / time_resolution))
         if trace_length_samples % 2 != 0:
             trace_length_samples += 1
-        self.logger.debug("smallest trace start time {:.1f}, largest trace time {:.1f} -> n_samples = {:d} {:.0f}ns)".format(times_min.min(), times_max.max(), trace_length_samples, trace_length / units.ns))
+
+        self.logger.debug(
+            "smallest trace start time {:.1f}, largest trace time {:.1f} -> n_samples = {:d} {:.0f}ns)".format(
+                times_min, times_max, trace_length_samples, trace_length / units.ns))
 
         # loop over all channels
         for channel_id in channel_ids:
@@ -153,44 +150,40 @@ def run(self, evt, station, det, channel_ids=None):
             # and everything up in the time domain
             self.logger.debug('channel id {}'.format(channel_id))
             channel = NuRadioReco.framework.channel.Channel(channel_id)
-            channel_spectrum = None
-            trace_object = None
-            if(self.__debug):
+
+            if self.__debug:
                 from matplotlib import pyplot as plt
                 fig, axes = plt.subplots(2, 1)
+
+            channel_spectrum = None
+            trace_object = None
             for electric_field in sim_station.get_electric_fields_for_channels([channel_id]):
 
                 # all simulated channels have a different trace start time
                 # in a measurement, all channels have the same physical start time
                 # so we need to create one long trace that can hold all the different channel times
                 # to achieve a good time resolution, we upsample the trace first.
-                new_efield = NuRadioReco.framework.base_trace.BaseTrace()  # create new data structure with new efield length
-                new_efield.set_trace(copy.copy(electric_field.get_trace()), electric_field.get_sampling_rate())
                 new_trace = np.zeros((3, trace_length_samples))
+
                 # calculate the start bin
-                if(not np.isnan(electric_field.get_trace_start_time())):
+                if not np.isnan(electric_field.get_trace_start_time()):
                     cab_delay = det.get_cable_delay(sim_station_id, channel_id)
                     if sim_station.is_cosmic_ray():
-                        site = det.get_site(sim_station_id)
-                        antenna_position = det.get_relative_position(sim_station_id, channel_id) - electric_field.get_position()
-                        if sim_station.get_parameter(stnp.zenith) > 90 * units.deg:  # signal is coming from below, so we take IOR of ice
-                            index_of_refraction = ice.get_refractive_index(antenna_position[2], site)
-                        else:  # signal is coming from above, so we take IOR of air
-                            index_of_refraction = ice.get_refractive_index(1, site)
-                        travel_time_shift = geo_utl.get_time_delay_from_direction(
-                            sim_station.get_parameter(stnp.zenith),
-                            sim_station.get_parameter(stnp.azimuth),
-                            antenna_position,
-                            index_of_refraction
-                        )
-                        start_time = electric_field.get_trace_start_time() + cab_delay - times_min.min() + travel_time_shift
-                        start_bin = int(round(start_time / time_resolution))
-                        time_remainder = start_time - start_bin * time_resolution
+                        travel_time_shift = calculate_time_shift_for_cosmic_ray(
+                            det, sim_station, electric_field, channel_id)
                     else:
-                        start_time = electric_field.get_trace_start_time() + cab_delay - times_min.min()
-                        start_bin = int(round(start_time / time_resolution))
-                        time_remainder = start_time - start_bin * time_resolution
-                    self.logger.debug('channel {}, start time {:.1f} = bin {:d}, ray solution {}'.format(channel_id, electric_field.get_trace_start_time() + cab_delay, start_bin, electric_field[efp.ray_path_type]))
+                        travel_time_shift = 0
+
+                    start_time = electric_field.get_trace_start_time() - times_min + cab_delay + travel_time_shift
+                    start_bin = int(round(start_time / time_resolution))
+
+                    # calculate error by using discret bins
+                    time_remainder = start_time - start_bin * time_resolution
+                    self.logger.debug('channel {}, start time {:.1f} = bin {:d}, ray solution {}'.format(
+                        channel_id, electric_field.get_trace_start_time() + cab_delay, start_bin, electric_field[efp.ray_path_type]))
+
+                    new_efield = NuRadioReco.framework.base_trace.BaseTrace()  # create new data structure with new efield length
+                    new_efield.set_trace(copy.copy(electric_field.get_trace()), electric_field.get_sampling_rate())
                     new_efield.apply_time_shift(time_remainder)
 
                     tr = new_efield.get_trace()
@@ -201,20 +194,25 @@ def run(self, evt, station, det, channel_ids=None):
                         # ensure new efield does not extend beyond end of trace although this should not happen
                         self.logger.warning("electric field trace extends beyond the end of the trace and will be cut.")
                         stop_bin = np.shape(new_trace)[-1]
-                        tr = np.atleast_2d(tr)[:,:stop_bin-start_bin]
+                        tr = np.atleast_2d(tr)[:, :stop_bin-start_bin]
+
                     if start_bin < 0:
                         # ensure new efield does not extend beyond start of trace although this should not happen
                         self.logger.warning("electric field trace extends beyond the beginning of the trace and will be cut.")
-                        tr = np.atleast_2d(tr)[:,-start_bin:]
+                        tr = np.atleast_2d(tr)[:, -start_bin:]
                         start_bin = 0
+
                     new_trace[:, start_bin:stop_bin] = tr
+
                 trace_object = NuRadioReco.framework.base_trace.BaseTrace()
                 trace_object.set_trace(new_trace, 1. / time_resolution)
-                if(self.__debug):
+
+                if self.__debug:
                     axes[0].plot(trace_object.get_times(), new_trace[1], label="eTheta {}".format(electric_field[efp.ray_path_type]), c='C0')
                     axes[0].plot(trace_object.get_times(), new_trace[2], label="ePhi {}".format(electric_field[efp.ray_path_type]), c='C0', linestyle=':')
                     axes[0].plot(electric_field.get_times(), electric_field.get_trace()[1], c='C1', linestyle='-', alpha=.5)
                     axes[0].plot(electric_field.get_times(), electric_field.get_trace()[2], c='C1', linestyle=':', alpha=.5)
+
                 ff = trace_object.get_frequencies()
                 efield_fft = trace_object.get_frequency_spectrum()
 
@@ -222,7 +220,8 @@ def run(self, evt, station, det, channel_ids=None):
                 azimuth = electric_field[efp.azimuth]
 
                 # get antenna pattern for current channel
-                VEL = trace_utilities.get_efield_antenna_factor(sim_station, ff, [channel_id], det, zenith, azimuth, self.antenna_provider)
+                VEL = trace_utilities.get_efield_antenna_factor(
+                    sim_station, ff, [channel_id], det, zenith, azimuth, self.antenna_provider)
 
                 if VEL is None:  # this can happen if there is not signal path to the antenna
                     voltage_fft = np.zeros_like(efield_fft[1])  # set voltage trace to zeros
@@ -234,31 +233,36 @@ def run(self, evt, station, det, channel_ids=None):
                 # Remove DC offset
                 voltage_fft[np.where(ff < 5 * units.MHz)] = 0.
 
-                if(self.__debug):
-                    axes[1].plot(trace_object.get_times(), fft.freq2time(voltage_fft, electric_field.get_sampling_rate()), label="{}, zen = {:.0f}deg".format(electric_field[efp.ray_path_type], zenith / units.deg))
+                if self.__debug:
+                    axes[1].plot(
+                        trace_object.get_times(), fft.freq2time(voltage_fft, electric_field.get_sampling_rate()),
+                        label="{}, zen = {:.0f}deg".format(electric_field[efp.ray_path_type], zenith / units.deg))
 
-                if('amp' in self.__uncertainty):
+                if 'amp' in self.__uncertainty:
                     voltage_fft *= np.random.normal(1, self.__uncertainty['amp'][channel_id])
-                if('sys_amp' in self.__uncertainty):
+
+                if 'sys_amp' in self.__uncertainty:
                     voltage_fft *= self.__uncertainty['sys_amp'][channel_id]
 
-                if(channel_spectrum is None):
+                if channel_spectrum is None:
                     channel_spectrum = voltage_fft
                 else:
                     channel_spectrum += voltage_fft
 
-            if(self.__debug):
+            if self.__debug:
                 axes[0].legend(loc='upper left')
                 axes[1].legend(loc='upper left')
                 plt.show()
+
             if trace_object is None:  # this happens if don't have any efield for this channel
                 # set the trace to zeros
                 channel.set_trace(np.zeros(trace_length_samples), 1. / time_resolution)
             else:
                 channel.set_frequency_spectrum(channel_spectrum, trace_object.get_sampling_rate())
-            channel.set_trace_start_time(times_min.min())
 
+            channel.set_trace_start_time(times_min)
             station.add_channel(channel)
+
         self.__t += time.time() - t
 
     def end(self):
@@ -267,3 +271,39 @@ def end(self):
         dt = timedelta(seconds=self.__t)
         self.logger.info("total time used by this module is {}".format(dt))
         return dt
+
+
+def calculate_time_shift_for_cosmic_ray(det, station, efield, channel_id):
+    """
+    Calculate the time shift for a cosmic ray event
+
+    Parameters
+    ----------
+    det : Detector
+    station : Station
+    efield : ElectricField
+
+    Returns
+    -------
+    float
+        time shift in ns
+    """
+    station_id = station.get_id()
+    site = det.get_site(station_id)
+    antenna_position = det.get_relative_position(station_id, channel_id) - efield.get_position()
+    if station.get_parameter(stnp.zenith) > 90 * units.deg:  # signal is coming from below, so we take IOR of ice
+        index_of_refraction = ice.get_refractive_index(antenna_position[2], site)
+    else:  # signal is coming from above, so we take IOR of air
+        index_of_refraction = ice.get_refractive_index(1, site)
+
+    # For cosmic ray events, we only have one electric field for all channels, so we have to account
+    # for the difference in signal travel between channels. IMPORTANT: This is only accurate
+    # if all channels have the same z coordinate
+    travel_time_shift = geo_utl.get_time_delay_from_direction(
+        station.get_parameter(stnp.zenith),
+        station.get_parameter(stnp.azimuth),
+        antenna_position,
+        index_of_refraction
+    )
+
+    return travel_time_shift
diff --git a/NuRadioReco/modules/io/LOFAR/rawTBBio.py b/NuRadioReco/modules/io/LOFAR/rawTBBio.py
index 8a1444142d..bc87d57dc7 100644
--- a/NuRadioReco/modules/io/LOFAR/rawTBBio.py
+++ b/NuRadioReco/modules/io/LOFAR/rawTBBio.py
@@ -281,8 +281,8 @@ def __init__(
                 dif = np.linalg.norm(
                     TBB_ITRF_dipole_positions[i] - self.ITRF_dipole_positions[i]
                 )
-                if dif > 1:
-                    logger.warning(
+                if dif > 1 and use_TBB_positions:
+                    logger.status(
                         f"Station {self.StationName} has suspicious antenna locations. Using metadata instead",
                     )
                     use_TBB_positions = False
@@ -426,6 +426,12 @@ def get_data(self, start_index, num_points, antenna_index=None, antenna_ID=None)
         initial_point = self.sample_offsets[antenna_index] + start_index
         final_point = initial_point + num_points
 
+        if final_point > len(self.file[self.stationKey][antenna_ID]):
+            raise IndexError(
+                "Data point", final_point,
+                "is off end of file with length", len(self.file[self.stationKey][antenna_ID]),
+            )
+
         return self.file[self.stationKey][antenna_ID][initial_point:final_point]
 
 
@@ -697,19 +703,19 @@ def find_and_set_polarization_delay(self, verbose=False, tolerance=1e-9):
         odd_delays = all_antenna_calibrations[1::2]
         odd_offset = odd_delays - even_delays
         median_odd_offset = np.median(odd_offset)
-        if verbose:
-            print("median offset is:", median_odd_offset)
+        logger.info("median offset is:", median_odd_offset)
+
         below_tolerance = np.abs(odd_offset - median_odd_offset) < tolerance
-        if verbose:
-            print(
-                np.sum(below_tolerance),
-                "antennas below tolerance.",
-                len(below_tolerance) - np.sum(below_tolerance),
-                "above.",
-            )
+        logger.info(
+            np.sum(below_tolerance),
+            "antennas below tolerance.",
+            len(below_tolerance) - np.sum(below_tolerance),
+            "above.",
+        )
+
         ave_best_offset = np.average(odd_offset[below_tolerance])
-        if verbose:
-            print("average of below-tolerance offset is:", ave_best_offset)
+        logger.info("average of below-tolerance offset is:", ave_best_offset)
+
         self.set_odd_polarization_delay(-ave_best_offset)
 
         above_tolerance = np.zeros(len(all_antenna_calibrations), dtype=bool)
@@ -960,11 +966,9 @@ def get_data(self, start_index, num_points, antenna_index=None, antenna_ID=None)
         antenna_ID = self.dipoleNames[antenna_index]
 
         if final_point > len(TBB_file.file[TBB_file.stationKey][antenna_ID]):
-            print(
-                "WARNING! data point",
-                final_point,
-                "is off end of file",
-                len(TBB_file.file[TBB_file.stationKey][antenna_ID]),
+            raise IndexError(
+                "Data point", final_point,
+                "is off end of file with length", len(TBB_file.file[TBB_file.stationKey][antenna_ID]),
             )
 
         return TBB_file.file[TBB_file.stationKey][antenna_ID][initial_point:final_point]
diff --git a/NuRadioReco/modules/io/LOFAR/rawTBBio_metadata.py b/NuRadioReco/modules/io/LOFAR/rawTBBio_metadata.py
index 95e5bf64a1..6d025b1560 100644
--- a/NuRadioReco/modules/io/LOFAR/rawTBBio_metadata.py
+++ b/NuRadioReco/modules/io/LOFAR/rawTBBio_metadata.py
@@ -158,14 +158,14 @@ def getItrfAntennaPosition(station, antenna_set, metadata_dir):
         # h for lbh
         # l for lbl
         feed = {"CS": {}, "RS": {}, "DE": {}}
-        feed["CS"]["LBA_SPARSE_EVEN"] = "24llhh"
-        feed["CS"]["LBA_SPARSE_ODD"] = "24hhll"
+        feed["CS"]["LBA_SPARSE_EVEN"] = "24hhll"
+        feed["CS"]["LBA_SPARSE_ODD"] = "24llhh"
         feed["CS"]["LBA_X"] = "48hl"
         feed["CS"]["LBA_Y"] = "48lh"
         feed["CS"]["LBA_INNER"] = "96h"
         feed["CS"]["LBA_OUTER"] = "96l"
-        feed["RS"]["LBA_SPARSE_EVEN"] = "24llhh"
-        feed["RS"]["LBA_SPARSE_ODD"] = "24hhll"
+        feed["RS"]["LBA_SPARSE_EVEN"] = "24hhll"
+        feed["RS"]["LBA_SPARSE_ODD"] = "24llhh"
         feed["RS"]["LBA_X"] = "48hl"
         feed["RS"]["LBA_Y"] = "48lh"
         feed["RS"]["LBA_INNER"] = "96h"
diff --git a/NuRadioReco/modules/io/LOFAR/readLOFARData.py b/NuRadioReco/modules/io/LOFAR/readLOFARData.py
index f185507e23..d2d9ff3b95 100644
--- a/NuRadioReco/modules/io/LOFAR/readLOFARData.py
+++ b/NuRadioReco/modules/io/LOFAR/readLOFARData.py
@@ -2,10 +2,14 @@
 import os
 import glob
 import json
+import math
 import logging
+from collections import defaultdict
+
 import numpy as np
 
-from datetime import datetime
+# from datetime import datetime
+from astropy.time import Time
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.utilities import units
 
@@ -67,13 +71,13 @@ def get_metadata(filenames, metadata_dir):
 
 
 def lora_timestamp_to_blocknumber(
-    lora_seconds,
-    lora_nanoseconds,
-    start_time,
-    sample_number,
-    clock_offset=1e4 * units.ns,
-    block_size=2**16,
-    sampling_frequency=200 * units.MHz,
+        lora_seconds,
+        lora_nanoseconds,
+        start_time,
+        sample_number,
+        clock_offset=1e4 * units.ns,
+        block_size=2 ** 16,
+        sampling_frequency=200 * units.MHz,
 ):
     """
     Calculates block number corresponding to LORA timestamp and the sample number within that block
@@ -81,9 +85,9 @@ def lora_timestamp_to_blocknumber(
     Parameters
     ----------
     lora_seconds : int
-        LORA timestamp in seconds (UTC timestamp, second after 1st January 1970)
+        LORA timestamp in seconds (UNIX timestamp, second after 1st January 1970)
     lora_nanoseconds : int
-        LORA timestamp in nanoseconds
+        The number of nanoseconds after `lora_seconds` at which LORA triggered
     start_time : int
         LOFAR TBB timestamp
     sample_number : int
@@ -113,23 +117,43 @@ def lora_timestamp_to_blocknumber(
     return int(value / block_size), int(value % block_size)
 
 
-def tbb_filetag_from_utc(timestamp):
+def LOFAR_event_id_to_unix(event_id):
+    # 1262304000 = Unix timestamp on Jan 1, 2010 (date -u --date "Jan 1, 2010 00:00:00" +"%s")
+    return event_id + 1262304000
+
+
+def tbb_filetag_from_unix(timestamp):
     """
-    Returns TBB filename based on UTC timestamp of an event.
+    Returns TBB filename based on UNIX timestamp of an event.
 
     Parameters
     ----------
     timestamp: int
-        UTC timestamp from GPS
+        UNIX timestamp from GPS
 
     Returns
     -------
     filename: str
         The tag in the TBB filename identifying the files of the event.
+
+    Notes
+    -----
+    Technically speaking there is no such thing as an "UTC timestamp". The Coordinated Universal Time (UTC)
+    is a time standard , which defines a reference for current time. It uses hours, minutes and seconds to
+    divide a day. Crucially, it allows for the introduction of leap seconds.
+
+    The UNIX timestamp on the other hand is defined as the number of **non-leap** seconds passed since
+    00:00:00 UTC on 1 January 1970. However, when a leap second occurs the UNIX timestamp is actually reset
+    (i.e. that same timestamp refers to two moments in time). As such, the UNIX timestamp remains
+    synchronised with the UTC time (except for the one second that is a leap second).
+
+    Note that astropy has support for leap seconds. During the day a leap seconds occurs, UNIX timestamps
+    are reported as floating point values (instead of the usual integers). Conversely, converting a UNIX
+    timestamp to a datetime will give a millisecond contribution. Though the seconds are still accounted
+    for as one would expect.
     """
-    # utc_timestamp_base = 1262304000  # Unix timestamp on Jan 1, 2010 (date -u --date "Jan 1, 2010 00:00:00" +"%s")
 
-    dt_object = datetime.utcfromtimestamp(timestamp)
+    dt_object = Time(timestamp, format='unix').to_datetime()
     year = dt_object.year
     month = dt_object.month
     day = dt_object.day
@@ -143,9 +167,87 @@ def tbb_filetag_from_utc(timestamp):
     return radio_file_tag
 
 
+def tbbID_to_nrrID(channel_id, mode):
+    """
+    Converts a TBB channel ID to the corresponding NRR channel ID given the antenna mode. This simply adds a "9" as
+    the fourth element of the channelID, if the antenna mode is "LBA_inner". The function :func:`nrrID_to_tbbID`
+    can be used to do the opposite.
+
+    As of February 2024, this function only supports "LBA_INNER" and "LBA_OUTER" as possible antenna modes.
+    Note that the antenna mode is always converted to lowercase, so the comparison is case-insensitive (i.e.
+    "LBA_inner" and "LBA_INNER" are both recognised as the same antenna set).
+
+    Parameters
+    ----------
+    channel_id: str or int
+        TBB channel ID
+    mode: {"LBA_inner", "LBA_outer"}
+        The antenna set for which to convert (case-insensitive).
+
+    Returns
+    -------
+    nrr_channel_id: str
+        The NuRadioReco channelID corresponding to the TBB channelID depending on whether the antenna mode
+        is "lba_inner" or "lba_outer"
+
+    Notes
+    -----
+    This function encodes the convention used in the `LOFAR.json` detector description. For the inner LBA antennas,
+    a "9" was added as the fourth element of the channelID. However, the TBB files always use the same set of
+    channel IDs (i.e. the same channel ID refers to different physical antennas depending on the antenna set).
+    Given a channel ID from the TBB file and the antenna set, this function returns the channel ID of the
+    corresponding channel in the NRR Detector description.
+    """
+
+    # if channelID is integer, convert to string and fill it with zeroes at the beginning to get a string length of 9
+    if type(channel_id) == int:
+        channel_id = str(channel_id).zfill(9)
+
+    if mode.lower() == "lba_outer":  # for LBA_outer, keep the zero on 4th position of string
+        # For safety, the string is overwritten here. But it should not be necessary
+        nrr_channel_id = channel_id[:3] + '0' + channel_id[4:]
+    elif mode.lower() == "lba_inner":  # for LBA_inner, replace the fourth digit (zero) in the string with a 9
+        nrr_channel_id = channel_id[:3] + '9' + channel_id[4:]
+    else:
+        logger.warning("%s is not a valid antenna mode - valid modes are LBA_inner and LBA_outer" % mode)
+        nrr_channel_id = channel_id  # return the input channel ID if mode is invalid.
+
+    return nrr_channel_id
+
+
+def nrrID_to_tbbID(channel_id):
+    """
+    This function does the opposite of :func:`tbbID_to_nrrID` . It returns the TBB channel ID given a NRR channel ID.
+    Following the convention used in the LOFAR detector description as of February 2024, this simply replaces the
+    fourth element of the channelID with a "0".
+
+    Parameters
+    ------------
+    channel_id: str or int
+        Channel ID
+
+    Returns
+    -------
+    tbb_channel_id: str
+        The TBB channelID corresponding to the NuRadioReco channelID
+
+    See Also
+    --------
+    tbbID_to_nrrID : convert TBB channel ID to NRR channel ID
+    """
+
+    # if channelID is integer, convert to string and fill it with zeroes at the beginning to get a string length of 9
+    if type(channel_id) == int:
+        channel_id = str(channel_id).zfill(9)
+
+    tbb_channel_id = channel_id[:3] + '0' + channel_id[4:]  # replace fourth element with a 0
+
+    return tbb_channel_id
+
+
 class getLOFARtraces:
     def __init__(
-        self, tbb_h5_filename, metadata_dir, time_s, time_ns, trace_length_nbins
+            self, tbb_h5_filename, metadata_dir, time_s, time_ns, trace_length_nbins
     ):
         """
         A Class to facilitate getting traces from LOFAR TBB HDF5 Files
@@ -153,7 +255,7 @@ def __init__(
         Parameters
         ----------
         time_s: int
-            Event trigger timestamp in UTC seconds
+            Event trigger timestamp in seconds
         time_ns: int
             Event trigger timestamp in ns past UTC second
         trace_length_nbins : int
@@ -181,7 +283,9 @@ def setup_trace_loading(self):
         self.tbb_file = rawTBBio.MultiFile_Dal1(self.data_filename, metadata_dir=self.metadata_dir)
         sample_number = self.tbb_file.get_nominal_sample_number()
         timestamp = self.tbb_file.get_timestamp()
-        station_clock_offsets = rawTBBio_metadata.getClockCorrections(metadata_dir=self.metadata_dir)
+        station_clock_offsets = rawTBBio_metadata.getClockCorrections(
+            metadata_dir=self.metadata_dir, time=timestamp
+        )
         this_station_name = self.tbb_file.get_station_name()
 
         logger.info("Getting clock offset for station %s" % this_station_name)
@@ -194,7 +298,7 @@ def setup_trace_loading(self):
         self.block_number, self.sample_number_in_block = packet
 
         self.alignment_shift = -(
-            self.trace_length_nbins // 2 - self.sample_number_in_block
+                self.trace_length_nbins // 2 - self.sample_number_in_block
         )  # minus sign, apparently...
 
         logger.info(
@@ -205,6 +309,11 @@ def setup_trace_loading(self):
     def check_trace_quality(self):
         """
         Check all traces recorded from the TBB against quality requirements.
+
+        Returns
+        -------
+        deviating_dipoles : set of str
+        dipoles_missing_counterpart : set of str
         """
         dipole_names = np.array(self.tbb_file.get_antenna_names())
 
@@ -271,7 +380,6 @@ def close_file(self):
         return
 
 
-# TODO: make reader only read certain stations
 class readLOFARData:
     """
     This class reads in the data from the TBB files and puts them into an Event structure. It relies on the KRATOS
@@ -286,10 +394,11 @@ class readLOFARData:
     metadata_directory: Path-like str, default="/vol/astro7/lofar/vhecr/kratos/data/"
         The path to the directory containing the LOFAR metadata (antenna positions and timing calibrations).
     """
+
     def __init__(self, restricted_station_set=None, tbb_directory=None, json_directory=None, metadata_directory=None):
         self.logger = logger  # logging.getLogger('NuRadioReco.readLOFARData')
 
-        self.tbb_dir = '/vol/astro3/lofar/vhecr/lora_triggered/data/' if tbb_directory is None else tbb_directory
+        self.tbb_dir = '/vol/astro5/lofar/astro3/vhecr/lora_triggered/data/' if tbb_directory is None else tbb_directory
         self.json_dir = '/vol/astro7/lofar/kratos_files/json' if json_directory is None else json_directory
         self.meta_dir = '/vol/astro7/lofar/vhecr/kratos/data/' if metadata_directory is None else metadata_directory
 
@@ -360,7 +469,7 @@ def get_stations(self):
             1. station name
             2. antenna set
             3. tbb timestamp (seconds)
-            4. tbb timestamp (nanoseconds)
+            4. tbb timestamp (nanoseconds after last second)
             5. station clock frequency (Hz)
             6. positions of antennas
             7. dipole IDs
@@ -368,6 +477,36 @@ def get_stations(self):
         """
         return self.__stations.copy()
 
+    def get_station_calibration_delays(self, station_id):
+        """
+        Make a dictionary of channel ids and their corresponding calibration delays,
+        to avoid misapplying the delays to the wrong channel. Also converts the list
+        of channel IDs pulled from the TBB metadata to their NRR channel ID counterpart.
+
+        Parameters
+        ----------
+        station_id : int
+            The station ID for which to get the calibration delays
+
+        Returns
+        -------
+        station_calibration_delays : dict
+            Dictionary containing the NRR channel IDs as keys and the calibration delays as values
+        """
+        station_name = f"CS{station_id:03}"
+        station_calibration_delays = dict(
+            zip(
+                map(
+                    int,
+                    [tbbID_to_nrrID(channel_id, self.__stations[station_name]['metadata'][1])
+                     for channel_id in self.__stations[station_name]['metadata'][-2]]
+                ),
+                self.__stations[station_name]['metadata'][-1]
+            )
+        )
+
+        return station_calibration_delays
+
     def begin(self, event_id, logger_level=logging.NOTSET):
         """
         Prepare the reader to ingest the event with ID `event_id`. This resets the internal representation of the
@@ -395,17 +534,35 @@ def begin(self, event_id, logger_level=logging.NOTSET):
         self.__lora_timestamp = lora_dict["LORA"]["utc_time_stamp"]
         self.__lora_timestamp_ns = lora_dict["LORA"]["time_stamp_ns"]
 
+        if self.__lora_timestamp != LOFAR_event_id_to_unix(self.__event_id):
+            self.logger.error(f"LORA timestamp {self.__lora_timestamp} does not match event ID {self.__event_id}")
+
         # Read in data from LORA file and save it in a HybridShower
         self.__hybrid_shower = NuRadioReco.framework.hybrid_shower.HybridShower("LORA")
 
+        # Read in zenith and azimuth -> make sure they are in range [-pi, pi]
+        zenith = math.remainder(lora_dict["LORA"]["zenith_rad"], 2 * np.pi)
+        azimuth = math.remainder(lora_dict["LORA"]["azimuth_rad"], 2 * np.pi)
+
+        # Read in core position reconstruction from LORA
+        core_pos_x = lora_dict["LORA"]["core_x_m"]
+        core_pos_y = lora_dict["LORA"]["core_y_m"]
+
+        # Read in energy estimate from LORA
+        energy = lora_dict["LORA"]["energy_GeV"]
+        
         # The LORA coordinate system has x pointing East -> set this through magnetic field vector (values from 2015)
         self.__hybrid_shower.set_parameter(showerParameters.magnetic_field_vector,
                                            np.array([0.004675, 0.186270, -0.456412]))
-        self.__hybrid_shower.set_parameter(showerParameters.zenith, lora_dict["LORA"]["zenith_rad"] * units.radian)
-        self.__hybrid_shower.set_parameter(showerParameters.azimuth, lora_dict["LORA"]["azimuth_rad"] * units.radian)
+        self.__hybrid_shower.set_parameter(showerParameters.zenith, zenith * units.radian)
+        self.__hybrid_shower.set_parameter(showerParameters.azimuth, azimuth * units.radian)
 
+        # Add LORA core and energy to parameters. The z-Position of the core is always at 7.6m for LOFAR
+        self.__hybrid_shower.set_parameter(showerParameters.core, np.array([core_pos_x * units.m, core_pos_y * units.m, 7.6 * units.m]))
+        self.__hybrid_shower.set_parameter(showerParameters.energy, energy * units.GeV)
+        
         # Go through TBB directory and identify all files for this event
-        tbb_filename_pattern = tbb_filetag_from_utc(self.__event_id + 1262304000)  # event id is based on timestamp
+        tbb_filename_pattern = tbb_filetag_from_unix(self.__lora_timestamp)
 
         tbb_filename_pattern = self.tbb_dir + "/*" + tbb_filename_pattern + "*.h5"
         self.logger.debug(f'Looking for files with {tbb_filename_pattern}...')
@@ -415,23 +572,32 @@ def begin(self, event_id, logger_level=logging.NOTSET):
         # TODO: save paths of files per event in some kind of database
 
         for tbb_filename in all_tbb_files:
-            station_name = re.findall(r"CS\d\d\d", tbb_filename)[0]
+            station_name = re.findall(r"CS\d\d\d", tbb_filename)
+            station_name = next(iter(station_name), None)  # Get the first entry, if the list is not empty -> defaults to None
+            if station_name is None:
+                logger.status(f'TBB file {tbb_filename} is for remote station, skipping...')
+                continue
             if (self.__restricted_station_set is not None) and (station_name not in self.__restricted_station_set):
-                continue # only process stations in the given set
-            self.logger.info(f'Found file {tbb_filename} for station {station_name}...')
+                continue  # only process stations in the given set
+
             self.__stations[station_name]['files'].append(tbb_filename)
 
-            # Save the metadata only once (in case there are multiple files for a station)
-            if 'metadata' not in self.__stations[station_name]:
-                self.__stations[station_name]['metadata'] = get_metadata([tbb_filename], self.meta_dir)
+        # Save the metadata after all files for a station have been found
+        # TODO: make metadata a dictionary
+        for station_name in self.__stations:
+            station_files = self.__stations[station_name]['files']
+            if len(station_files) > 0:
+                self.logger.info(f'Found files {station_files} for station {station_name}...')
+                self.__stations[station_name]['metadata'] = get_metadata(station_files, self.meta_dir)
 
     @register_run()
     def run(self, detector, trace_length=65536):
         """
         Runs the reader with the provided detector. For every station that has files associated with it, a Station
-        object is created together with its channels (pulled from the detector description). Every channel also gets
-        a group ID which corresponds to the polarisation (i.e. 0 for even and 1 for odd), as to be able to retrieve
-        all channels per polarisation during processing.
+        object is created together with its channels (pulled from the detector description, depending on the antenna
+        set (LBA_OUTER/INNER)). Every channel also gets a group ID which is retrieved from the Detector description.
+        For LOFAR we use the integer value of the even dipole number, so channels '001000000' and '001000001',
+        which are the two dipoles composing one physical antenna, both get group ID 1000000.
 
         Parameters
         ----------
@@ -451,6 +617,10 @@ def run(self, detector, trace_length=65536):
         # Add HybridShower to HybridInformation
         evt.get_hybrid_information().add_hybrid_shower(self.__hybrid_shower)
 
+        # update the detector to the event time
+        time = Time(self.__lora_timestamp, format='unix')
+        detector.update(time)
+
         # Add all Detector stations to Event
         for station_name, station_dict in self.__stations.items():
             station_id = int(station_name[2:])
@@ -459,9 +629,11 @@ def run(self, detector, trace_length=65536):
             if len(station_files) == 0:
                 continue
 
+            # The metadata is only defined if there are files in the station
+            antenna_set = station_dict['metadata'][1]
+
             station = NuRadioReco.framework.station.Station(station_id)
-            radio_shower = NuRadioReco.framework.radio_shower.RadioShower(shower_id=station_id,
-                                                                          station_ids=[station_id])
+            station.set_station_time(time)
 
             # Use KRATOS io functions to access trace
             lofar_trace_access = getLOFARtraces(
@@ -476,38 +648,84 @@ def run(self, detector, trace_length=65536):
             self.logger.debug("Channels deviating: %s" % channels_deviating)
             self.logger.debug("Channels no counterpart: %s" % channels_missing_counterpart)
 
-            # done here as it needs median timing values over all traces in the station
-            flagged_channel_ids = channels_deviating.union(channels_missing_counterpart)
-            for channel_id in detector.get_channel_ids(station_id):
-                if channel_id in flagged_channel_ids:
-                    continue                 
+            # empty set to add the NRR flagged channel IDs to
+            flagged_nrr_channel_ids: dict = defaultdict(list)
+            flagged_nrr_channel_group_ids: set[int] = set()  # keep track of channel group IDs to remove
+
+            # Get the list of all dipole names which are present in the TTB file
+            # This avoids issues when a TBB file would not contain all channels
+            channel_tbb_ids: list[str] = station_dict['metadata'][6]
+
+            self.logger.debug(f"These channels are present in the TBB file: {channel_tbb_ids}")
+
+            for TBB_channel_id in channel_tbb_ids:
+                # convert TBB ID to NRR equivalent based on antenna set to be able to access trace
+                channel_id = int(tbbID_to_nrrID(TBB_channel_id, antenna_set))
+
+                if TBB_channel_id in channels_deviating:
+                    self.logger.status(f"Channel {channel_id} was flagged at read-in, "
+                                       f"not adding to station {station_name}")
+                    flagged_nrr_channel_ids[channel_id].append("reader_deviating_channel")
+                    flagged_nrr_channel_group_ids.add(detector.get_channel_group_id(station_id, channel_id))
+                    continue
+                elif TBB_channel_id in channels_missing_counterpart:
+                    self.logger.status(f"Channel {channel_id} was flagged at read-in, "
+                                       f"not adding to station {station_name}")
+                    flagged_nrr_channel_ids[channel_id].append("reader_channel_missing_counterpart")
+                    flagged_nrr_channel_group_ids.add(detector.get_channel_group_id(station_id, channel_id))
+                    continue
 
                 # read in trace, see if that works. Needed or overly careful?
                 try:
-                    this_trace = lofar_trace_access.get_trace(str(channel_id).zfill(9))  # channel ID is 9 digits
-                except:  # FIXME: Too general except statement
-                    flagged_channel_ids.add(channel_id)                    
-                    logger.warning("Could not read data for channel id %s" % channel_id)
+                    this_trace = lofar_trace_access.get_trace(TBB_channel_id)  # TBB_channel_id is str of 9 digits
+                except IndexError:
+                    self.logger.warning(f"Could not read data for channel id {channel_id}")
+                    flagged_nrr_channel_ids[channel_id].append("reader_trace_error")
+                    flagged_nrr_channel_group_ids.add(detector.get_channel_group_id(station_id, channel_id))
                     continue
 
-                # The channel_group_id should be interpreted as an antenna index
-                # dipoles '001000000' and '001000001' -> 'a1000000'
-                # TODO: implement channel_group_id as parameter in Detector file
-                channel_group = 'a' + str(channel_id - channel_id % 2)
-                
+                # The channel_group_id should be interpreted as an antenna index (e.g. like 'a1000000' which
+                # was used in PyCRTools). The group ID is pulled from the Detector description.
+                # Example: dipoles '001000000' (NRR ID 1000000) and '001000001' (NRR ID 1000001)
+                # both get group ID 1000000
+                channel_group: int = detector.get_channel_group_id(station_id, channel_id)
+
                 channel = NuRadioReco.framework.channel.Channel(channel_id, channel_group_id=channel_group)
                 channel.set_trace(this_trace, station_dict['metadata'][4] * units.Hz)
                 station.add_channel(channel)
 
-            # store set of flagged channel ids as station parameter
-            station.set_parameter(stationParameters.flagged_channels, flagged_channel_ids)
-
-            # Add station to Event, together with RadioShower to store reconstruction values later on
+            # Check both channels from the flagged group IDs are removed from station
+            # This is needed because when a trace read in fails, the counterpart is not automatically removed
+            self.logger.debug(f"Flagged channel group IDs: {flagged_nrr_channel_group_ids}")
+            channels_to_remove = []  # cannot remove channel in loop, so store them and delete after
+            for channel_group_id in flagged_nrr_channel_group_ids:
+                try:
+                    for channel in station.iter_channel_group(channel_group_id):
+                        self.logger.status(f"Removing channel {channel.get_id()} with group ID {channel_group_id} "
+                                           f"from station {station_name}")
+                        channels_to_remove.append(channel)
+                except ValueError:
+                    # The channel_group_id not longer present in the station
+                    self.logger.debug(f"Both channels of group ID {channel_group_id} were already removed "
+                                      f"from station {station_name}")
+
+            for channel in channels_to_remove:
+                station.remove_channel(channel)
+                flagged_nrr_channel_ids[channel.get_id()].append("reader_removed_group_id")
+
+            # store set of flagged nrr channel ids as station parameter
+            station.set_parameter(stationParameters.flagged_channels, flagged_nrr_channel_ids)
+
+            # Add station to Event
             evt.set_station(station)
-            evt.add_shower(radio_shower)
 
             lofar_trace_access.close_file()
-
+            
+        # Add general event radio shower to event to store reconstruction values later
+        radio_shower = NuRadioReco.framework.radio_shower.RadioShower(
+            shower_id=evt.get_id(), station_ids=evt.get_station_ids()
+        )
+        evt.add_shower(radio_shower)
         yield evt
 
     def end(self):
diff --git a/NuRadioReco/modules/io/NuRadioRecoio.py b/NuRadioReco/modules/io/NuRadioRecoio.py
index fd6e5d696f..a2a3b9e55e 100644
--- a/NuRadioReco/modules/io/NuRadioRecoio.py
+++ b/NuRadioReco/modules/io/NuRadioRecoio.py
@@ -2,6 +2,7 @@
 import NuRadioReco.framework.event
 import NuRadioReco.detector.detector
 import NuRadioReco.modules.io.event_parser_factory
+from NuRadioReco.utilities import io_utilities
 
 import numpy as np
 import astropy.time
@@ -188,18 +189,9 @@ def _parse_event_header(self, evt_header):
                         self.__event_headers[station_id][key] = []
 
                     if key == stnp.station_time:
-                        station_time = None
-                        if value is not None:
-                            if isinstance(value, dict):
-                                station_time = astropy.time.Time(value["value"], format=value["format"])
-                            # For backward compatibility, we also keep supporting station times stored
-                            # as astropy.time objects
-                            elif isinstance(value, astropy.time.Time):
-                                station_time = value
-                            else:
-                                err = f"Station time not stored as dict or astropy.time.Time: ({type(value)})"
-                                self.logger.error(err)
-                                raise ValueError(err)
+                        station_time = io_utilities._time_object_to_astropy(value)
+
+                        if station_time is not None:
                             try:
                                 station_time.format = 'isot'
                             except AttributeError:
diff --git a/NuRadioReco/modules/io/RNO_G/readRNOGDataMattak.py b/NuRadioReco/modules/io/RNO_G/readRNOGDataMattak.py
index 65710d7b16..9f8d56f284 100644
--- a/NuRadioReco/modules/io/RNO_G/readRNOGDataMattak.py
+++ b/NuRadioReco/modules/io/RNO_G/readRNOGDataMattak.py
@@ -213,8 +213,11 @@ def __init__(self, run_table_path=None, load_run_table=True, log_level=logging.N
                         self.logger.warn("No connect to RunTable database could be established. "
                                         "Runs can not be filtered.")
                 except ImportError:
-                    self.logger.warn("Import of run table failed. Runs can not be filtered.! \n"
-                            "You can get the interface from GitHub: git@github.com:RNO-G/rnog-runtable.git")
+                    self.logger.warn(
+                        "import run_table failed. You can still use readRNOGData, but runs can not be filtered. "
+                        "To install the run table, run\n\n"
+                        "\tpip install git+ssh://git@github.com/RNO-G/rnog-runtable.git\n"
+                    )
             else:
                 # some users may mistakenly try to pass the .root files to __init__
                 # we check for this and raise a (hopefully) helpful error message
@@ -237,7 +240,7 @@ def begin(self,
             read_calibrated_data=False,
             select_triggers=None,
             select_runs=False,
-            apply_baseline_correction='approximate',
+            apply_baseline_correction='auto',
             convert_to_voltage=True,
             selectors=[],
             run_types=["physics"],
@@ -269,16 +272,20 @@ def begin(self,
         Other Parameters
         ----------------
 
-        apply_baseline_correction: 'fit' | 'approximate' | 'median' | 'none'
+        apply_baseline_correction: str {'auto', 'fit', 'approximate', 'median', 'none'}, optional
             Removes the DC (baseline) block offsets (pedestals).
             Options are, in order of decreasing precision and increasing performance:
 
             * 'fit' : do a full out-of-band fit to determine the block offsets; for more details,
               see :mod:`NuRadioReco.modules.RNO_G.channelBlockOffsetFitter` (slow)
-            * 'approximate' : estimate block offsets by looking at the low-pass filtered trace (default)
+            * 'approximate' : estimate block offsets by looking at the low-pass filtered trace
             * 'median' : subtract the median of each block (faster)
             * 'none' : do not apply a baseline correction (fastest)
 
+            The default ('auto') first performs the 'approximate' block offset removal, then
+            automatically decides whether to continue with the full 'fit' depending on the estimated
+            block offset size.
+
         convert_to_voltage: bool
             Only applies when non-calibrated data are read. If true, convert ADC to voltage.
             (Default: True)
@@ -317,11 +324,14 @@ def begin(self,
             the data in batches based on this number.
             NOTE: This is only relevant for the mattak uproot backend
         """
-
         t0 = time.time()
 
         self._read_calibrated_data = read_calibrated_data
-        baseline_correction_valid_options = ['approximate', 'fit', 'median', 'none']
+
+        baseline_correction_valid_options = ['auto', 'approximate', 'fit', 'median', 'none']
+        if apply_baseline_correction is None:
+            apply_baseline_correction = 'none'
+
         if apply_baseline_correction.lower() not in baseline_correction_valid_options:
             raise ValueError(
                 f"Value for apply_baseline_correction ({apply_baseline_correction}) not recognized. "
@@ -565,7 +575,8 @@ def _select_events(self, evtinfo):
         skip: bool
             Returns False to skip/reject event, return True to keep/read event
         """
-        self.logger.debug(f"Processing event number {self.__counter} out of total {self._n_events_total}")
+        self.logger.debug(
+            f"(_select_events) Processing event number {self.__counter} out of total {self._n_events_total}")
 
         self.__counter += 1  # for logging
         if self._selectors is not None:
@@ -675,7 +686,7 @@ def get_waveforms(self, apply_baseline_correction=None, max_events=1000):
 
         for dataset in self._datasets:
             dataset.setEntries((0, dataset.N()))
-            if apply_baseline_correction in ['fit', 'approximate']: # we need the sampling rate
+            if apply_baseline_correction in ['auto', 'fit', 'approximate']: # we need the sampling rate
                 try:
                     sampling_rate = dataset.eventInfo()[0].sampleRate
                 except AttributeError:
@@ -697,7 +708,7 @@ def get_waveforms(self, apply_baseline_correction=None, max_events=1000):
 
                 if apply_baseline_correction == 'median':
                     wfs = _baseline_correction(wfs)
-                elif apply_baseline_correction in ['fit', 'approximate']:
+                elif apply_baseline_correction in ['auto', 'fit', 'approximate']:
                     wfs = np.vstack([
                         fit_block_offsets(
                             wf, mode=self._apply_baseline_correction,
@@ -778,13 +789,14 @@ def _get_event(self, event_info, waveforms):
 
         trigger = NuRadioReco.framework.trigger.Trigger(event_info.triggerType)
         trigger.set_triggered()
-        trigger.set_trigger_time(trigger_time)
+        trigger.set_trigger_time(0)  # The trigger time is relative to the event/station time
         station.set_trigger(trigger)
         block_offsets = None
 
         if self._apply_baseline_correction == 'median':
             waveforms, block_offsets = _baseline_correction(waveforms, return_offsets=True)
 
+        readout_delays = event_info.readoutDelay
         for channel_id, wf in enumerate(waveforms):
             channel = NuRadioReco.framework.channel.Channel(channel_id)
             if self._read_calibrated_data:
@@ -797,7 +809,7 @@ def _get_event(self, event_info, waveforms):
 
                 channel.set_trace(wf, sampling_rate * units.GHz)
 
-            time_offset = get_time_offset(event_info.triggerType)
+            time_offset = get_time_offset(event_info.triggerType) + readout_delays[channel_id]
             channel.set_trace_start_time(-time_offset)  # relative to event/trigger time
             if block_offsets is not None:
                 channel.set_parameter(NuRadioReco.framework.parameters.channelParameters.block_offsets, block_offsets.T[channel_id])
@@ -805,7 +817,7 @@ def _get_event(self, event_info, waveforms):
             station.add_channel(channel)
 
         evt.set_station(station)
-        if self._apply_baseline_correction in ['fit', 'approximate']:
+        if self._apply_baseline_correction in ['auto', 'fit', 'approximate']:
             self._blockoffsetfitter.remove_offsets(evt, station, mode=self._apply_baseline_correction)
 
         return evt
@@ -889,7 +901,7 @@ def get_event(self, run_nr, event_id):
         evt: `NuRadioReco.framework.event.Event`
         """
 
-        self.logger.debug(f"Processing event {event_id}")
+        self.logger.debug(f"Getting event {event_id}")
         t0 = time.time()
 
         event_infos = self.get_events_information(keys=["eventNumber", "run"])
diff --git a/NuRadioReco/modules/io/eventWriter.py b/NuRadioReco/modules/io/eventWriter.py
index 31e6fdbb65..d9118b22c7 100644
--- a/NuRadioReco/modules/io/eventWriter.py
+++ b/NuRadioReco/modules/io/eventWriter.py
@@ -1,10 +1,13 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
-import pickle
+
 from NuRadioReco.modules.base.module import register_run
 from NuRadioReco.modules.io.NuRadioRecoio import VERSION, VERSION_MINOR
-import logging
 from NuRadioReco.framework.parameters import stationParameters as stnp
 from NuRadioReco.detector import generic_detector
+from NuRadioReco.utilities import io_utilities
+
+import pickle
+import logging
 logger = logging.getLogger("NuRadioReco.eventWriter")
 
 
@@ -12,12 +15,12 @@ def get_header(evt):
     header = {'stations': {}}
     for iS, station in enumerate(evt.get_stations()):
         header['stations'][station.get_id()] = station.get_parameters().copy()
-        header['stations'][station.get_id()][stnp.station_time] = station.get_station_time_dict()
+        header['stations'][station.get_id()][stnp.station_time] = io_utilities._astropy_to_dict(station.get_station_time())
 
         if station.has_sim_station():
             header['stations'][station.get_id()]['sim_station'] = {}
             header['stations'][station.get_id()]['sim_station'] = station.get_sim_station().get_parameters().copy()
-    
+
     header['event_id'] = (evt.get_run_number(), evt.get_id())
     return header
 
@@ -79,10 +82,10 @@ def begin(self, filename, max_file_size=1024, check_for_duplicates=False, events
             self.__filename = filename[:-4]
         else:
             self.__filename = filename
-        
+
         if filename.endswith('.ari'):
             logger.warning('The file ending .ari for NuRadioReco files is deprecated. Please use .nur instead.')
-        
+
         self.__check_for_duplicates = check_for_duplicates
         self.__number_of_events = 0
         self.__current_file_size = 0
@@ -105,7 +108,7 @@ def run(self, evt, det=None, mode=None):
         det: detector object
             If a detector object is passed, the detector description for the
             events is written in the file as well
-        mode: dictionary, optional 
+        mode: dictionary, optional
             Specifies what will be saved into the `*.nur` output file.
             Can contain the following keys:
 
diff --git a/NuRadioReco/modules/neutrinoDirectionReconstructor/voltageToEfieldAnalyticConverterForNeutrinos.py b/NuRadioReco/modules/neutrinoDirectionReconstructor/voltageToEfieldAnalyticConverterForNeutrinos.py
index 07f7daa191..79499e2289 100644
--- a/NuRadioReco/modules/neutrinoDirectionReconstructor/voltageToEfieldAnalyticConverterForNeutrinos.py
+++ b/NuRadioReco/modules/neutrinoDirectionReconstructor/voltageToEfieldAnalyticConverterForNeutrinos.py
@@ -1,46 +1,24 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
-from NuRadioReco.modules.base.module import register_run
-import os
-import time
-import random
+
 import numpy as np
 from scipy import signal
-from scipy.signal import correlate
+
 from scipy import optimize as opt
 import matplotlib.pyplot as plt
-from scipy.optimize import Bounds
-from types import SimpleNamespace
-import collections
 
-from radiotools import helper as hp
-from radiotools import plthelpers as php
-from radiotools import coordinatesystems as cs
-
-from NuRadioReco.detector import detector
 from NuRadioReco.detector import antennapattern
-from NuRadioReco.utilities import units, fft, trace_utilities
-
+from NuRadioReco.utilities import units, fft, geometryUtilities as geo_utl
+from NuRadioMC.SignalGen import askaryan as ask
+from NuRadioMC.SignalProp import analyticraytracing as ray
+from NuRadioReco.framework.parameters import stationParameters as stnp, showerParameters as shp
 
-from NuRadioReco.framework.parameters import stationParameters as stnp
-from NuRadioReco.framework.parameters import electricFieldParameters as efp
-from NuRadioReco.framework.parameters import showerParameters as shp
-from NuRadioReco.modules import channelResampler as CchannelResampler
+import NuRadioReco.modules.channelResampler
 import NuRadioReco.framework.electric_field
-from NuRadioReco.utilities.geometryUtilities import get_time_delay_from_direction
 
-from NuRadioMC.SignalProp import propagation
-from NuRadioMC.SignalGen.parametrizations import get_time_trace
+from radiotools import plthelpers as php, coordinatesystems as cstrans, helper as hp
 
-from NuRadioMC.utilities import medium
-from radiotools import coordinatesystems as cstrans
-from NuRadioMC.SignalProp import analyticraytracing as ray
-from radiotools import plthelpers as php
-from NuRadioMC.SignalGen import askaryan as ask
-from NuRadioReco.utilities import geometryUtilities as geo_utl
-import time
 
-channelResampler = CchannelResampler.channelResampler()
-channelResampler.begin(debug=False)
+channelResampler = NuRadioReco.modules.channelResampler.channelResampler()
 
 class voltageToAnalyticEfieldConverterNeutrinos:
     """
diff --git a/NuRadioReco/modules/trigger/envelopeTrigger.py b/NuRadioReco/modules/trigger/envelopeTrigger.py
index af9e143b5f..fd98f77f37 100644
--- a/NuRadioReco/modules/trigger/envelopeTrigger.py
+++ b/NuRadioReco/modules/trigger/envelopeTrigger.py
@@ -22,7 +22,7 @@ def get_envelope_triggers(trace, threshold):  # define trigger constraint for ea
         the signal trace
     threshold: float
         the threshold
-        
+
     Returns
     -------
     triggered bins: array of bools
@@ -75,20 +75,20 @@ def run(self, evt, station, det, passband, order, threshold, coinc_window, numbe
         """
         t = time.time()  # absolute time of system
 
-        sampling_rate = station.get_channel(det.get_channel_ids(station.get_id())[0]).get_sampling_rate()
+        if triggered_channels is None:
+            tmp_channel = station.get_trigger_channel(station.get_channel_ids()[0])
+        else:
+            tmp_channel = station.get_trigger_channel(triggered_channels[0])
+
+        channel_trace_start_time = tmp_channel.get_trace_start_time()
+        sampling_rate = tmp_channel.get_sampling_rate()
+
         dt = 1. / sampling_rate
 
         triggered_bins_channels = []
         channels_that_passed_trigger = []
 
-        if triggered_channels is None:  # caveat: all channels start at the same time
-            for channel in station.iter_channels():
-                channel_trace_start_time = channel.get_trace_start_time()
-                break
-        else:
-            channel_trace_start_time = station.get_channel(triggered_channels[0]).get_trace_start_time()
-
-        for channel in station.iter_channels():
+        for channel in station.iter_trigger_channels():
             channel_id = channel.get_id()
             if triggered_channels is not None and channel_id not in triggered_channels:
                 logger.debug("skipping channel {}".format(channel_id))
diff --git a/NuRadioReco/modules/trigger/highLowThreshold.py b/NuRadioReco/modules/trigger/highLowThreshold.py
index 7ad6d408d6..aed46d4d0f 100644
--- a/NuRadioReco/modules/trigger/highLowThreshold.py
+++ b/NuRadioReco/modules/trigger/highLowThreshold.py
@@ -166,13 +166,13 @@ def run(self, evt, station, det,
             triggerd_bins_channels = []
 
             if triggered_channels is None:
-                for channel in station.iter_channels():
+                for channel in station.iter_trigger_channels():
                     channel_trace_start_time = channel.get_trace_start_time()
                     break
             else:
-                channel_trace_start_time = station.get_channel(triggered_channels[0]).get_trace_start_time()
+                channel_trace_start_time = station.get_trigger_channel(triggered_channels[0]).get_trace_start_time()
 
-            for channel in station.iter_channels():
+            for channel in station.iter_trigger_channels():
                 channel_id = channel.get_id()
                 if triggered_channels is not None and channel_id not in triggered_channels:
                     continue
@@ -226,7 +226,7 @@ def run(self, evt, station, det,
             max_signal = 0
 
             if has_triggered:
-                for channel in station.iter_channels():
+                for channel in station.iter_trigger_channels():
                     max_signal = max(max_signal, np.abs(channel.get_trace()[triggered_bins]).max())
 
                 station.set_parameter(stnp.channels_max_amplitude, max_signal)
diff --git a/NuRadioReco/modules/trigger/multiHighLowThreshold.py b/NuRadioReco/modules/trigger/multiHighLowThreshold.py
index 35170e5a00..7bb743dbee 100644
--- a/NuRadioReco/modules/trigger/multiHighLowThreshold.py
+++ b/NuRadioReco/modules/trigger/multiHighLowThreshold.py
@@ -123,18 +123,18 @@ def run(self, evt, station, det,
             logger.error("Impossible trigger configuration, high {0} low {1}.".format(threshold_high, threshold_low))
             raise NotImplementedError
 
-        sampling_rate = station.get_channel(station.get_channel_ids()[0]).get_sampling_rate()
+        sampling_rate = station.get_trigger_channel(station.get_channel_ids()[0]).get_sampling_rate()
         if not set_not_triggered:
             triggerd_bins_channels = []
             dt = 1. / sampling_rate
             if triggered_channels is None:
-                for channel in station.iter_channels():
+                for channel in station.iter_trigger_channels():
                     channel_trace_start_time = channel.get_trace_start_time()
                     break
             else:
-                channel_trace_start_time = station.get_channel(triggered_channels[0]).get_trace_start_time()
+                channel_trace_start_time = station.get_trigger_channel(triggered_channels[0]).get_trace_start_time()
             channels_that_passed_trigger = []
-            for channel in station.iter_channels():
+            for channel in station.iter_trigger_channels():
                 channel_id = channel.get_id()
                 if triggered_channels is not None and channel_id not in triggered_channels:
                     continue
@@ -159,7 +159,7 @@ def run(self, evt, station, det,
             # set maximum signal aplitude
             max_signal = 0
             if(has_triggered):
-                for channel in station.iter_channels():
+                for channel in station.iter_trigger_channels():
                     max_signal = max(max_signal, np.abs(channel.get_trace()[triggered_bins]).max())
                 station.set_parameter(stnp.channels_max_amplitude, max_signal)
         else:
diff --git a/NuRadioReco/modules/trigger/powerIntegration.py b/NuRadioReco/modules/trigger/powerIntegration.py
index cf4c466df4..7c8fb694f3 100644
--- a/NuRadioReco/modules/trigger/powerIntegration.py
+++ b/NuRadioReco/modules/trigger/powerIntegration.py
@@ -25,7 +25,7 @@ def get_power_int_triggers(trace, threshold, window=10 * units.ns, dt=1 * units.
         the time binning of the trace
     full_output: bool (default False)
         if True, the integrated power is returned as second argument
-    
+
     Returns
     -------
     triggered bins: array of bools
@@ -87,17 +87,19 @@ def run(self, evt, station, det,
         """
         t = time.time()
 
-        sampling_rate = station.get_channel(station.get_channel_ids()[0]).get_sampling_rate()
-        dt = 1. / sampling_rate
-        triggerd_bins_channels = []
         if triggered_channels is None:
-            for channel in station.iter_channels():
-                channel_trace_start_time = channel.get_trace_start_time()
-                break
+            tmp_channel = station.get_trigger_channel(station.get_channel_ids()[0])
         else:
-            channel_trace_start_time = station.get_channel(triggered_channels[0]).get_trace_start_time()
+            tmp_channel = station.get_trigger_channel(triggered_channels[0])
+
+        channel_trace_start_time = tmp_channel.get_trace_start_time()
+        sampling_rate = tmp_channel.get_sampling_rate()
+
+        dt = 1. / sampling_rate
+
+        triggerd_bins_channels = []
         channels_that_passed_trigger = []
-        for channel in station.iter_channels():
+        for channel in station.iter_trigger_channels():
             channel_id = channel.get_id()
             if triggered_channels is not None and channel_id not in triggered_channels:
                 logger.debug("skipping channel {}".format(channel_id))
@@ -119,7 +121,7 @@ def run(self, evt, station, det,
         # set maximum signal aplitude
         max_signal = 0
         if(has_triggered):
-            for channel in station.iter_channels():
+            for channel in station.iter_trigger_channels():
                 max_signal = max(max_signal, np.abs(channel.get_trace()[triggered_bins]).max())
             station.set_parameter(stnp.channels_max_amplitude, max_signal)
         trigger = IntegratedPowerTrigger(trigger_name, threshold, triggered_channels,
diff --git a/NuRadioReco/modules/trigger/rnog_surface_trigger.py b/NuRadioReco/modules/trigger/rnog_surface_trigger.py
index 0e38a0e8ed..c089bee645 100644
--- a/NuRadioReco/modules/trigger/rnog_surface_trigger.py
+++ b/NuRadioReco/modules/trigger/rnog_surface_trigger.py
@@ -127,20 +127,20 @@ def run(self, evt, station, det, threshold, coinc_window=60*units.ns, number_coi
 
         t = time.time()  # absolute time of system
 
-        sampling_rate = station.get_channel(det.get_channel_ids(station.get_id())[0]).get_sampling_rate()
+        if triggered_channels is None:
+            tmp_channel = station.get_trigger_channel(station.get_channel_ids()[0])
+        else:
+            tmp_channel = station.get_trigger_channel(triggered_channels[0])
+
+        channel_trace_start_time = tmp_channel.get_trace_start_time()
+        sampling_rate = tmp_channel.get_sampling_rate()
+
         dt = 1. / sampling_rate
 
         triggered_bins_channels = []
         channels_that_passed_trigger = []
 
-        if triggered_channels is None:  # caveat: all channels start at the same time
-            for channel in station.iter_channels():
-                channel_trace_start_time = channel.get_trace_start_time()
-                break
-        else:
-            channel_trace_start_time = station.get_channel(triggered_channels[0]).get_trace_start_time()
-
-        for channel in station.iter_channels():
+        for channel in station.iter_trigger_channels():
             channel_id = channel.get_id()
             logger.debug(f'channel id {channel_id}')
             if triggered_channels is not None and channel_id not in triggered_channels:
diff --git a/NuRadioReco/modules/trigger/simpleThreshold.py b/NuRadioReco/modules/trigger/simpleThreshold.py
index f896bf2bc3..f30d4b0f4b 100644
--- a/NuRadioReco/modules/trigger/simpleThreshold.py
+++ b/NuRadioReco/modules/trigger/simpleThreshold.py
@@ -76,19 +76,19 @@ def run(self, evt, station, det,
         """
         t = time.time()
 
-        sampling_rate = station.get_channel(station.get_channel_ids()[0]).get_sampling_rate()
-        dt = 1. / sampling_rate
-
         if triggered_channels is None:
-            for channel in station.iter_channels():
-                channel_trace_start_time = channel.get_trace_start_time()
-                break
+            tmp_channel = station.get_trigger_channel(station.get_channel_ids()[0])
         else:
-            channel_trace_start_time = station.get_channel(triggered_channels[0]).get_trace_start_time()
+            tmp_channel = station.get_trigger_channel(triggered_channels[0])
+
+        channel_trace_start_time = tmp_channel.get_trace_start_time()
+        sampling_rate = tmp_channel.get_sampling_rate()
+
+        dt = 1. / sampling_rate
 
         triggerd_bins_channels = []
         channels_that_passed_trigger = []
-        for channel in station.iter_channels():
+        for channel in station.iter_trigger_channels():
             channel_id = channel.get_id()
             if triggered_channels is not None and channel_id not in triggered_channels:
                 self.logger.debug("skipping channel {}".format(channel_id))
@@ -116,7 +116,7 @@ def run(self, evt, station, det,
         # set maximum signal aplitude
         max_signal = 0
         if has_triggered:
-            for channel in station.iter_channels():
+            for channel in station.iter_trigger_channels():
                 max_signal = max(max_signal, np.abs(channel.get_trace()[triggered_bins]).max())
             station.set_parameter(stnp.channels_max_amplitude, max_signal)
 
diff --git a/NuRadioReco/test/tiny_reconstruction/reference.json b/NuRadioReco/test/tiny_reconstruction/reference.json
index 968d3584b6..cb11e1da43 100644
--- a/NuRadioReco/test/tiny_reconstruction/reference.json
+++ b/NuRadioReco/test/tiny_reconstruction/reference.json
@@ -1 +1 @@
-{"0": {"station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": null, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": 0.09219726935932945, "zenith": 0.76585400390625, "azimuth": 3.94125048828125, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": null, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": null, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "sim_station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": 1.58489319246e+18, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": null, "zenith": 0.7853981633974483, "azimuth": 3.957853280977215, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": 1.3918747974426e+18, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": 646.2024663, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "channel_parameters": {"zenith": [null, null, null, null], "azimuth": [null, null, null, null], "maximum_amplitude": [0.0741114148547873, 0.07822416425569524, 0.08019365868532256, 0.09219726935932945], "SNR": [{"integrated_power": 0.0, "peak_2_peak_amplitude": 0.004780092071917627, "peak_amplitude": 0.00485887816779479, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.0029781482057526206, "peak_amplitude": 0.0030899574970230446, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.002963497087732319, "peak_amplitude": 0.0029912274254951183, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.003315202825440897, "peak_amplitude": 0.0035027540130119802, "Seckel_2_noise": 5}], "maximum_amplitude_envelope": [0.0789854151249416, 0.0850534211758587, 0.08035990802734218, 0.09425391321371974], "P2P_amplitude": [0.13819712244096005, 0.15451260952068696, 0.15292399225172826, 0.17650992826729145], "cr_xcorrelations": [null, null, null, null], "nu_xcorrelations": [null, null, null, null], "signal_time": [-165.45597857145674, -169.65597857145679, -182.85597857145672, -181.05597857145676], "noise_rms": [0.009497729224153949, 0.008938569434903116, 0.008083395580028708, 0.009418626437748765], "signal_regions": [null, null, null, null], "noise_regions": [null, null, null, null], "signal_time_offset": [null, null, null, null], "signal_receiving_zenith": [null, null, null, null], "signal_ray_type": [null, null, null, null], "signal_receiving_azimuth": [null, null, null, null]}, "electric_field_parameters": {"ray_path_type": [null, null], "polarization_angle": [1.4654396095601694, 1.4895721505795514], "polarization_angle_expectation": [1.3193420263512765, -1.8222506272385168], "signal_energy_fluence": [[0.0, 0.1499228726724943, 13.406681586865194], [0.0, 0.1138089722425281, 17.17484362157034]], "cr_spectrum_slope": [null, -5.857288373436567], "zenith": [0.76585400390625, 0.76585400390625], "azimuth": [3.94125048828125, 3.94125048828125], "signal_time": [-256.0418188450112, null], "nu_vertex_distance": [null, null], "nu_viewing_angle": [null, null], "max_amp_antenna": [null, null], "max_amp_antenna_envelope": [null, null], "reflection_coefficient_theta": [null, null], "reflection_coefficient_phi": [null, null], "cr_spectrum_quadratic_term": [null, 5.585796124606195], "energy_fluence_ratios": [null, null]}}, "1": {"station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": null, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": 0.34265971445728505, "zenith": 0.79, "azimuth": 3.96, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": null, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": null, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "sim_station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": 1.58489319246e+18, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": null, "zenith": 0.7853981633974483, "azimuth": 3.957853280977215, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": 1.3918747974426e+18, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": 646.2024663, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "channel_parameters": {"zenith": [null, null, null, null], "azimuth": [null, null, null, null], "maximum_amplitude": [0.29720895105426465, 0.34265971445728505, 0.28631950709258547, 0.3355886336552229], "SNR": [{"integrated_power": 0.0, "peak_2_peak_amplitude": 0.006919176246584566, "peak_amplitude": 0.007147260712361315, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.0047795421977155994, "peak_amplitude": 0.00483475221965112, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.007958346991753162, "peak_amplitude": 0.00808818541204719, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 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\ No newline at end of file
+{"0": {"station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": null, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": 0.09219726935932945, "zenith": 0.76585400390625, "azimuth": 3.94125048828125, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": null, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": null, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "sim_station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": 1.58489319246e+18, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": null, 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null, null, null]}, "electric_field_parameters": {"ray_path_type": [null, null], "polarization_angle": [1.4654396095601694, 1.4895721505795514], "polarization_angle_expectation": [1.3193420263512765, -1.8222506272385168], "signal_energy_fluence": [[0.0, 0.1499228726724943, 13.406681586865194], [0.0, 0.1138089722425281, 17.17484362157034]], "cr_spectrum_slope": [null, -5.857288373436567], "zenith": [0.76585400390625, 0.76585400390625], "azimuth": [3.94125048828125, 3.94125048828125], "signal_time": [-384.0418188450112, null], "nu_vertex_distance": [null, null], "nu_viewing_angle": [null, null], "max_amp_antenna": [null, null], "max_amp_antenna_envelope": [null, null], "reflection_coefficient_theta": [null, null], "reflection_coefficient_phi": [null, null], "cr_spectrum_quadratic_term": [null, 5.585796124606195], "energy_fluence_ratios": [null, null]}}, "1": {"station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": null, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": 0.34265971445728505, "zenith": 0.79, "azimuth": 3.96, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": null, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": null, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "sim_station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": 1.58489319246e+18, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": null, "zenith": 0.7853981633974483, "azimuth": 3.957853280977215, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": 1.3918747974426e+18, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": 646.2024663, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "channel_parameters": {"zenith": [null, null, null, null], "azimuth": [null, null, null, null], "maximum_amplitude": [0.29720895105426465, 0.34265971445728505, 0.28631950709258547, 0.3355886336552229], "SNR": [{"integrated_power": 0.0, "peak_2_peak_amplitude": 0.006919176246584566, "peak_amplitude": 0.007147260712361315, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.0047795421977155994, "peak_amplitude": 0.00483475221965112, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.007958346991753162, "peak_amplitude": 0.00808818541204719, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.006171263679930979, "peak_amplitude": 0.00634102083568216, "Seckel_2_noise": 5}], "maximum_amplitude_envelope": [0.30136927893948445, 0.3430368419418388, 0.2883085667945778, 0.3430413736731565], "P2P_amplitude": [0.5781561921741288, 0.6591005848543372, 0.5651166372487595, 0.6691483403791307], "cr_xcorrelations": [null, null, null, null], "nu_xcorrelations": [null, null, null, null], "signal_time": [455.9440214285433, 457.5440214285433, 442.5440214285433, 447.1440214285432], "noise_rms": [0.009359269057858015, 0.009003414652873262, 0.008341757854147532, 0.009479378300635246], "signal_regions": [null, null, null, null], "noise_regions": [null, null, null, null], "signal_time_offset": [null, null, null, null], "signal_receiving_zenith": [null, null, null, null], "signal_ray_type": [null, null, null, null], "signal_receiving_azimuth": [null, null, null, null]}, "electric_field_parameters": {"ray_path_type": [null, null], "polarization_angle": [1.4164838522395478, 1.4309598632203113], "polarization_angle_expectation": [1.3259385237455839, -1.8156541298442093], "signal_energy_fluence": [[0.0, 7.388558320124647, 305.36881995591784], [0.0, 6.420446226373949, 324.0685176647595]], "cr_spectrum_slope": [null, -6.704957977720079], "zenith": [0.79, 0.79], "azimuth": [3.96, 3.96], "signal_time": [372.04549368634525, null], "nu_vertex_distance": [null, null], "nu_viewing_angle": [null, null], "max_amp_antenna": [null, null], "max_amp_antenna_envelope": [null, null], "reflection_coefficient_theta": [null, null], "reflection_coefficient_phi": [null, null], "cr_spectrum_quadratic_term": [null, -2.2958549567008015], "energy_fluence_ratios": [null, null]}}, "2": {"station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": null, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": 0.29293556379400426, "zenith": 0.78, "azimuth": 3.95, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": null, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": null, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "sim_station_parameters": {"nu_zenith": null, "nu_azimuth": null, "nu_energy": null, "nu_flavor": null, "ccnc": null, "nu_vertex": null, "inelasticity": null, "triggered": null, "cr_energy": 1.58489319246e+18, "cr_zenith": null, "cr_azimuth": null, "channels_max_amplitude": null, "zenith": 0.7853981633974483, "azimuth": 3.957853280977215, "zenith_cr_templatefit": null, "zenith_nu_templatefit": null, "cr_xcorrelations": null, "nu_xcorrelations": null, "station_time": null, "cr_energy_em": 1.3918747974426e+18, "nu_inttype": null, "chi2_efield_time_direction_fit": null, "ndf_efield_time_direction_fit": null, "cr_xmax": 646.2024663, "vertex_2D_fit": null, "distance_correlations": null, "shower_energy": null, "viewing_angles": null}, "channel_parameters": {"zenith": [null, null, null, null], "azimuth": [null, null, null, null], "maximum_amplitude": [0.2565625777544711, 0.2778654985637024, 0.2672414043783895, 0.29293556379400426], "SNR": [{"integrated_power": 0.0, "peak_2_peak_amplitude": 0.007948759818769018, "peak_amplitude": 0.008209754518559166, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.002601223824751355, "peak_amplitude": 0.0026817225129317146, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.008923287811149969, "peak_amplitude": 0.009007262903406938, "Seckel_2_noise": 5}, {"integrated_power": 0.0, "peak_2_peak_amplitude": 0.0068025654072232655, "peak_amplitude": 0.007054720375806741, "Seckel_2_noise": 5}], "maximum_amplitude_envelope": [0.2618281394747253, 0.28224029243565324, 0.26779467480180347, 0.2932426286212969], "P2P_amplitude": [0.5044222411860875, 0.547608219972491, 0.5318703949237792, 0.5851999240317709], "cr_xcorrelations": [null, null, null, null], "nu_xcorrelations": [null, null, null, null], "signal_time": [-230.65597857145667, -261.45597857145674, -276.2559785714567, -275.45597857145674], "noise_rms": [0.008359098917345579, 0.00848077598024109, 0.008989153845587915, 0.008201090181413125], "signal_regions": [null, null, null, null], "noise_regions": [null, null, null, null], "signal_time_offset": [null, null, null, null], "signal_receiving_zenith": [null, null, null, null], "signal_ray_type": [null, null, null, null], "signal_receiving_azimuth": [null, null, null, null]}, "electric_field_parameters": {"ray_path_type": [null, null], "polarization_angle": [1.4786245054415663, 1.5037562254029073], "polarization_angle_expectation": [1.3232105351636871, -1.818382118426106], "signal_energy_fluence": [[0.0, 2.8633662095356187, 335.1320229092165], [0.0, 1.9443851451542769, 431.3306782776532]], "cr_spectrum_slope": [null, -1.715922037957672], "zenith": [0.78, 0.78], "azimuth": [3.95, 3.95], "signal_time": [-321.88791795047746, null], "nu_vertex_distance": [null, null], "nu_viewing_angle": [null, null], "max_amp_antenna": [null, null], "max_amp_antenna_envelope": [null, null], "reflection_coefficient_theta": [null, null], "reflection_coefficient_phi": [null, null], "cr_spectrum_quadratic_term": [null, 0.5209156977104409], "energy_fluence_ratios": [null, null]}}}
\ No newline at end of file
diff --git a/NuRadioReco/test/trigger_tests/trigger_tests.py b/NuRadioReco/test/trigger_tests/trigger_tests.py
index d6a9003ca2..d75fc79f21 100644
--- a/NuRadioReco/test/trigger_tests/trigger_tests.py
+++ b/NuRadioReco/test/trigger_tests/trigger_tests.py
@@ -30,6 +30,11 @@
 
 for event in event_reader.run():
     station = event.get_station(1)
+
+    # First remove channels to reproduce them with the efieldToVoltageConverter
+    for chid in station.get_channel_ids():
+        station.remove_channel(chid)
+
     efield_to_voltage_converter.run(event, station, det)
     hardware_response_incorporator.run(event, station, det, True)
     high_low_trigger.run(event, station, det, threshold_high=40 * units.mV, threshold_low=-40 * units.mV)
diff --git a/NuRadioReco/utilities/data/galactic_calibration/LOFAR_LBA_Fourier_coefficients.txt b/NuRadioReco/utilities/data/galactic_calibration/LOFAR_LBA_Fourier_coefficients.txt
new file mode 100644
index 0000000000..9c1810c929
--- /dev/null
+++ b/NuRadioReco/utilities/data/galactic_calibration/LOFAR_LBA_Fourier_coefficients.txt
@@ -0,0 +1,8 @@
+# Galactic noise LST time variation curve Fourier coefficients for LOFAR LBA (30-80 MHz)
+# - see stationGalacticCalibrator.py for details
+polarization 1, polarization 0
+1.489468000000000042e-02, 1.347391000000000040e-02
+-1.293050000000000015e-03, -8.876500000000000275e-04
+8.947700000000000271e-04, 5.982200000000000017e-04
+-2.072200000000000116e-04, 1.167800000000000034e-04
+-4.650699999999999908e-04, -3.978699999999999837e-04
diff --git a/NuRadioReco/utilities/data/galactic_calibration/LOFAR_LBA_abs_calibration_curve.txt b/NuRadioReco/utilities/data/galactic_calibration/LOFAR_LBA_abs_calibration_curve.txt
new file mode 100644
index 0000000000..4afefddb25
--- /dev/null
+++ b/NuRadioReco/utilities/data/galactic_calibration/LOFAR_LBA_abs_calibration_curve.txt
@@ -0,0 +1,104 @@
+# Measured absolute calibration curve for LOFAR
+# This file contains the calibration factor per frequency going from 0 to 100 MHz in steps of 1 MHz
+# - see stationGalacticCalibrator.py for details
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
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+0.000000000000000000e+00
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+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+1.373214519609999927e-05
+1.398463322390000028e-05
+1.487489938209999919e-05
+1.544021703539999866e-05
+1.606845682250000000e-05
+1.662419427410000006e-05
+1.670390660469999998e-05
+1.744809318480000114e-05
+1.805257364859999905e-05
+1.870668550539999968e-05
+1.885190998310000090e-05
+1.996250513859999885e-05
+2.018785665840000071e-05
+2.115736807970000074e-05
+2.158294555279999946e-05
+2.201338248660000024e-05
+2.237363191249999857e-05
+2.244844196970000006e-05
+2.378024838910000141e-05
+2.405815431109999930e-05
+2.420203834770000157e-05
+2.453058691869999862e-05
+2.493999059649999958e-05
+2.637740238040000096e-05
+2.703342534140000002e-05
+2.780348576779999932e-05
+3.071479913909999794e-05
+3.407557058920000168e-05
+3.673118498510000325e-05
+3.899874400280000313e-05
+3.722579134649999676e-05
+3.542935109340000148e-05
+3.355523709419999917e-05
+2.965298159290000028e-05
+2.792712523519999971e-05
+2.881854497299999985e-05
+2.924788438090000092e-05
+2.984547687060000099e-05
+3.070454621030000152e-05
+3.072105535339999757e-05
+3.164428712059999776e-05
+3.230463883799999778e-05
+3.332038820460000289e-05
+3.466510609350000049e-05
+3.551931370769999778e-05
+3.739192759369999974e-05
+3.973970379140000274e-05
+4.306250487269999786e-05
+4.746120819939999978e-05
+5.023458661239999971e-05
+5.536218483039999794e-05
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
+0.000000000000000000e+00
diff --git a/NuRadioReco/utilities/ice.py b/NuRadioReco/utilities/ice.py
index b8affc79c0..6af9b3afd4 100644
--- a/NuRadioReco/utilities/ice.py
+++ b/NuRadioReco/utilities/ice.py
@@ -44,9 +44,9 @@ def get_refractive_index(depth, site='southpole'):
     if site.lower() in ['lofar', 'auger', 'ska']:
         return 1.000293
     else:
-        if not site.lower() in ['southpole', 'mooresbay', 'summit']:
-            logger.warning(f"site '{site}' unknown, assuming in-ice detector")
-        if(depth <= 0):
+        if not site.lower() in ['southpole', 'mooresbay', 'summit', 'greenland', 'sp']:
+            logger.warning(f"Site '{site}' unknown, assuming in-ice detector")
+        if depth <= 0:
             return 1.3
         else:
             return 1.000293
\ No newline at end of file
diff --git a/NuRadioReco/utilities/io_utilities.py b/NuRadioReco/utilities/io_utilities.py
index 9ed3146dbd..af46b46c3c 100644
--- a/NuRadioReco/utilities/io_utilities.py
+++ b/NuRadioReco/utilities/io_utilities.py
@@ -1,4 +1,10 @@
 import pickle
+import logging
+
+import datetime
+import astropy.time
+
+logger = logging.getLogger('NuRadioReco.utilities.io_utilities')
 
 
 def read_pickle(filename, encoding='latin1'):
@@ -21,3 +27,94 @@ def read_pickle(filename, encoding='latin1'):
     except:
         with open(filename, 'rb') as file:
             return pickle.load(file, encoding=encoding)
+
+
+def _astropy_to_dict(time):
+    """
+    Convert an astropy object to a dictionary.
+
+    Parameters
+    ----------
+    time: astropy.time.Time
+        Time object to be converted to a dictionary
+    """
+    if time is None:
+        return None
+
+    if not isinstance(time, astropy.time.Time):
+        logger.error(f'Input is not an astropy object: {time}')
+        raise ValueError(f'Input is not an astropy object: {time}')
+
+    # Internally, astropy stores the time in the julian date (jd) fornat with a tuple of two double-precision floats.
+    # The first float has an integer value and represents the number of days since the epoch (12:00 at January 1, 4713 BC)
+    # and the second float gives the fraction of the day. That means we can reach a precision of (number of nanoseconds in a day) / 2^52:
+    # 3600 * 24 * 1e9 / 2^52 = 0.02 ns. We choose to store the time object in its native format.
+
+    data = {
+        "val": time.jd1,
+        "val2": time.jd2,
+        "scale": time.scale,
+        "format": "jd",
+    }
+
+    return data
+
+
+def _time_object_to_astropy(time_object):
+    """
+    Convert a time_object to an astropy object.
+
+    This function tries to encompases all the different possible ways
+    a time object might have been stored inside a nur file.
+
+    Parameters
+    ----------
+    time_object: dict or float or datetime.datetime or astropy.time.Time
+        The time object to be converted to an astropy object
+
+    Returns
+    -------
+    time: astropy.time.Time
+        The time object
+    """
+    if time_object is None:
+        return None
+
+    if isinstance(time_object, (int, float)) and time_object == 0:
+        # 0 was an old default value for the event time. It was replaced by None.
+        return None
+
+    if isinstance(time_object, astropy.time.Time):
+        # For backward compatibility, we also keep supporting station times stored as astropy.time objects
+        return time_object
+
+    if isinstance(time_object, datetime.datetime):
+        # For backward compatibility, we also keep supporting station times stored as datetime objects
+        logger.warning(
+            "Time object created from a `datetime` object. "
+            "Nanosecond accuracy is not ensured.")
+
+        return astropy.time.Time(time_object)
+
+    if isinstance(time_object, dict):
+
+        if 'value' in time_object and 'format' in time_object:
+            logger.warning(
+                "Time object created from a dictionary which does not store the nano second separately. "
+                "Nanosecond accuracy is not ensured.")
+
+            return astropy.time.Time(time_object['value'], format=time_object['format'])
+
+        elif 'val' in time_object and 'val2' in time_object:
+            if "format" not in time_object or time_object["format"] != "jd":
+                logger.error(f"Time object is a dictionary but the format is wrong: {time_object}")
+                raise ValueError(f"Time object is a dictionary but the format is wrong: {time_object}")
+
+            return astropy.time.Time(**time_object)
+
+        else:
+            logger.error(f"Time object dictionary not recognized: {time_object}")
+            raise ValueError(f"Time object dictionary not recognized: {time_object}")
+
+    logger.error(f"Time object not recognized: {time_object}")
+    raise ValueError(f"Time object not recognized: {time_object}")
\ No newline at end of file
diff --git a/NuRadioReco/utilities/noise.py b/NuRadioReco/utilities/noise.py
index f3ec9e7b7a..3218c7d966 100644
--- a/NuRadioReco/utilities/noise.py
+++ b/NuRadioReco/utilities/noise.py
@@ -41,7 +41,7 @@ def rolled_sum_roll(traces, rolling):
 def rolling_indices(traces, rolling):
     """
     pre calculates rolling index array for rolled sum via take
-  
+
     Parameters
     ----------
     traces: list
@@ -197,16 +197,18 @@ def generate_noise(self):
 
                 spec *= self.filt
                 trace = fft.freq2time(spec, self.sampling_rate)
-                if(np.any(trace > self.threshold) and np.any(trace < -self.threshold)):
+
+                if np.any(trace > self.threshold) and np.any(trace < -self.threshold):
                     triggered_bins = get_high_low_triggers(trace, self.threshold, -self.threshold, self.time_coincidence, self.dt)
-                    if(True in triggered_bins):
+                    if np.any(triggered_bins):
                         t_bins[iCh] = triggered_bins
                         trace_to_keep = trace if not self.keep_full_band else trace_copy
-                        if(iCh == 0):
+                        if iCh == 0:
                             n_traces[iCh] = np.roll(trace_to_keep, self.trigger_bin - np.argwhere(triggered_bins == True)[0])
                         else:
                             tmp = np.random.randint(self.trigger_bin_low, self.trigger_bin)
                             n_traces[iCh] = np.roll(trace_to_keep, tmp - np.argwhere(triggered_bins == True)[0])
+
         traces = np.zeros((self.n_channels, self.n_samples))
         rnd_iterator = list(range(self.n_channels))
         np.random.shuffle(rnd_iterator)
@@ -234,7 +236,7 @@ def __init__(self, detector_filename, station_id, triggered_channels,
                  noise_type="rayleigh", log_level=logging.NOTSET,
                  pre_trigger_time=100 * units.ns, trace_length=512 * units.ns, filt=None,
                  upsampling=2, window_length=16 * units.ns, step_size=8 * units.ns,
-                 main_low_angle=np.deg2rad(-59.54968597864437), 
+                 main_low_angle=np.deg2rad(-59.54968597864437),
                  main_high_angle=np.deg2rad(59.54968597864437),
                  n_beams=11, quantize=True):
         """
@@ -261,7 +263,7 @@ def __init__(self, detector_filename, station_id, triggered_channels,
             the type of the noise, can be
             * "rayleigh" (default)
             * "noise"
-        log_level: logging enum, default warn 
+        log_level: logging enum, default warn
             the print level for this module
         pre_trigger_time: float, default 100 ns
             the time in the trace before the trigger happens
@@ -557,7 +559,7 @@ def generate_noise(self, phasing_mode="slice", trigger_mode="binned_sum", debug=
             dt_triggering += time.process_time() - tstart
 
             if is_triggered:
-                triggered_bin = triggered_bin // self.upsampling  # the trace is cut in the downsampled version. Therefore, triggered bin is factor of two smaller. 
+                triggered_bin = triggered_bin // self.upsampling  # the trace is cut in the downsampled version. Therefore, triggered bin is factor of two smaller.
                 i_low = triggered_bin - self.pre_trigger_bins
                 i_high = i_low + self.n_samples_trigger
 
diff --git a/NuRadioReco/utilities/signal_processing.py b/NuRadioReco/utilities/signal_processing.py
index 4d16e31736..e017f477af 100644
--- a/NuRadioReco/utilities/signal_processing.py
+++ b/NuRadioReco/utilities/signal_processing.py
@@ -1,5 +1,9 @@
-import numpy as np
+from NuRadioReco.utilities import units
+from NuRadioReco.detector import detector
+from NuRadioReco.framework.sim_station import SimStation
+
 from scipy.signal.windows import hann
+import numpy as np
 
 
 def half_hann_window(length, half_percent=None, hann_window_length=None):
@@ -27,3 +31,54 @@ def half_hann_window(length, half_percent=None, hann_window_length=None):
     half_hann_widow[-hann_window_length:] = hann_window[hann_window_length:]
 
     return half_hann_widow
+
+
+def add_cable_delay(station, det, sim_to_data=None, trigger=False, logger=None):
+    """
+    Add or subtract cable delay by modifying the ``trace_start_time``.
+
+    Parameters
+    ----------
+    station: Station
+        The station to add the cable delay to.
+
+    det: Detector
+        The detector description
+
+    trigger: bool
+        If True, take the time delay from the trigger channel response.
+        Only possible if ``det`` is of type `rnog_detector.Detector`. (Default: False)
+
+    logger: logging.Logger, default=None
+        If set, use ``logger.debug(..)`` to log the cable delay.
+
+    See Also
+    --------
+    NuRadioReco.modules.channelAddCableDelay.channelAddCableDelay : module that automatically applies / corrects for cable delays.
+    """
+    assert sim_to_data is not None, "``sim_to_data`` is None, please specify."
+
+    add_or_subtract = 1 if sim_to_data else -1
+    msg = "Add" if sim_to_data else "Subtract"
+
+    if trigger and not isinstance(det, detector.rnog_detector.Detector):
+        raise ValueError("Simulating extra trigger channels is only possible with the `rnog_detector.Detector` class.")
+
+    for channel in station.iter_channels():
+
+        if trigger:
+            if not channel.has_extra_trigger_channel():
+                continue
+
+            channel = channel.get_trigger_channel()
+            cable_delay = det.get_cable_delay(station.get_id(), channel.get_id(), trigger=True)
+
+        else:
+            # Only the RNOG detector has the argument `trigger`. Default is false
+            cable_delay = det.get_cable_delay(station.get_id(), channel.get_id())
+
+        if logger is not None:
+            logger.debug(f"{msg} {cable_delay / units.ns:.2f}ns "
+                        f"of cable delay to channel {channel.get_id()}")
+
+        channel.add_trace_start_time(add_or_subtract * cable_delay)
diff --git a/NuRadioReco/utilities/variableWindowSizeCorrelation.py b/NuRadioReco/utilities/variableWindowSizeCorrelation.py
new file mode 100644
index 0000000000..384ff0e25e
--- /dev/null
+++ b/NuRadioReco/utilities/variableWindowSizeCorrelation.py
@@ -0,0 +1,137 @@
+from NuRadioReco.utilities import units
+import numpy as np
+import timeit
+from numpy import linalg as LA
+import logging
+
+
+class variableWindowSizeCorrelation:
+    """
+        Module that calculates the correlation between a data trace and a template trace with variable window size
+    """
+
+    def __init__(self):
+        self.__debug = None
+        self.logger = logging.getLogger('NuRadioReco.utilities.variableWindowSizeCorrelation')
+        self.begin()
+
+    def begin(self, debug=False, logger_level=logging.NOTSET):
+        """
+        begin method
+
+        initialize variableWindowSizeCorrelation
+
+        Parameters
+        ----------
+        debug: boolean
+            if true, debug information and plots will be printed
+        logger_level: string or logging variable
+            Set verbosity level for logger (default: logging.NOTSET)
+        """
+
+        self.__debug = debug
+        if debug:
+            self.logger.setLevel(logging.DEBUG)
+        else:
+            self.logger.setLevel(logger_level)
+
+    def run(self, dataTrace, templateTrace, window_size, sampling_rate=3.2*units.GHz, return_time_difference=False):
+        """
+        run method
+
+        calculate the correlation between to traces using a variable window size and matrix multiplication
+
+        Parameters
+        ----------
+        dataTrace: array
+            full trace of the data event
+        templateTrace: array
+            full trace of the template
+        window_size: int
+            size of the template window, used for the correlation (should be given in units.ns)
+        sampling_rate: float
+            sampling rate of the data and template trace
+        return_time_difference: boolean
+            if true, the time difference (for the maximal correlation value) between the starting of the data trace and the starting of the (cut) template trace is returned (returned time is in units.ns)
+
+        Returns
+        -------
+        correlation : array of floats
+        time_diff : float, optional
+            The time difference of the maximal correlation value. Returned only if ``return_time_difference==True``
+        """
+        if self.__debug:
+            start = timeit.default_timer()
+
+        # preparing the traces
+        dataTrace = np.float32(dataTrace)
+        templateTrace = np.float32(templateTrace)
+
+        # create the template window
+        window_steps = window_size * (sampling_rate * units.GHz)
+
+        max_amp = max(abs(templateTrace))
+        max_amp_i = np.where(abs(templateTrace) == max_amp)[0][0]
+        lower_bound = int(max_amp_i - window_steps / 3)
+        upper_bound = int(max_amp_i + 2 * window_steps / 3)
+        templateTrace = templateTrace[lower_bound:upper_bound]
+
+        # zero padding on the data trace
+        dataTrace = np.append(np.zeros(len(templateTrace) - 1), dataTrace)
+        dataTrace = np.append(dataTrace, np.zeros(len(templateTrace) - 1))
+
+        # only calculate the correlation of the part of the trace where at least 10% of the maximum is visible (fastens the calculation)
+        plot_data_trace = dataTrace
+        max_amp_data = max(abs(dataTrace))
+        help_val = np.where(abs(dataTrace) >= 0.1 * max_amp_data)[0]
+        lower_bound_data = help_val[0] - (len(templateTrace) - 1)
+        upper_bound_data = help_val[len(help_val) - 1] + (len(templateTrace) - 1)
+        dataTrace = dataTrace[lower_bound_data:upper_bound_data]
+
+        # run the correlation using matrix multiplication
+        dataMatrix = np.lib.stride_tricks.sliding_window_view(dataTrace, len(templateTrace))
+        corr_numerator = dataMatrix.dot(templateTrace)
+        norm_dataMatrix = LA.norm(dataMatrix, axis=1)
+        norm_templateTrace = LA.norm(templateTrace)
+        corr_denominator = norm_dataMatrix * norm_templateTrace
+        correlation = corr_numerator / corr_denominator
+
+        max_correlation = max(abs(correlation))
+        max_corr_i = np.where(abs(np.asarray(correlation)) == max_correlation)[0][0]
+
+        if return_time_difference:
+            # calculate the time difference between the beginning of the template and data trace for the largest correlation value
+            # time difference is given in ns
+            time_diff = (max_corr_i + (lower_bound_data - len(templateTrace))) / sampling_rate
+
+        if self.__debug:
+            stop = timeit.default_timer()
+            self.logger.debug(f'total run time: {stop - start} s')
+            self.logger.debug(f'max correlation: {max_correlation}')
+            if return_time_difference:
+                self.logger.debug(f'time difference: {time_diff} ns')
+
+        if self.__debug:
+            import matplotlib.pyplot as plt
+            fig, axs = plt.subplots(2)
+            axs[0].plot(correlation)
+            axs[0].plot(np.array([np.where(abs(correlation) == max(abs(correlation)))[0][0]]), np.array([max_correlation]), marker="x", markersize=12, color='tab:red')
+            axs[0].set_ylim(-1.1, 1.1)
+            axs[0].set_ylabel(r"$\chi$")
+            axs[0].set_xlabel('N')
+            axs[1].plot(plot_data_trace, label='complete data trace')
+            x_data = np.arange(0, len(dataTrace), 1)
+            x_data = x_data + lower_bound_data
+            axs[1].plot(x_data, dataTrace, label='scanned data trace')
+            x_template = np.arange(0, len(templateTrace), 1)
+            x_template = x_template + max_corr_i + lower_bound_data
+            axs[1].plot(x_template, templateTrace, label='template')
+            axs[1].set_xlabel('time')
+            axs[1].set_ylabel('amplitude')
+            plt.legend()
+            plt.show()
+
+        if return_time_difference:
+            return correlation, time_diff
+        else:
+            return correlation
diff --git a/changelog.txt b/changelog.txt
index 5688a21feb..7045df5305 100644
--- a/changelog.txt
+++ b/changelog.txt
@@ -2,6 +2,26 @@ Changelog - to keep track of all relevant changes
 
 please update the categories "new features" and "bugfixes" before a pull request merge!
 
+version 3.0.0-dev
+new features:
+- created module and files to retrieve cable and RCU responses for LOFAR
+- added module to incorporate the LOFAR hardware response into the signals
+- added files containing the coefficients for the LOFAR Galactic calibration module in utilities/data/galactic_calibration
+- added LOFAR modules to fit the arrival direction using a planewave fit and to visualize the results of the pipeline
+- added support for LBA INNER in the LOFAR reader
+- added LOFAR detector description in JSON format
+- cleaned up the LOFAR beamforming utilities module
+- updated logging in all LOFAR modules
+- added support for more Galactic noise models in channelGalacticNoiseAdder
+- added support for "ant_mode" keyword in detector description
+- added option to choose dipole orientation when creating the LOFAR LBA antenna pattern
+- implementation of additional trigger channels. The signals on which the trigger is determined might be passed
+through a different response chain as the signals which are readout. To allow simulating this different response
+we added an optional trigger_channel object which lives within the corresponding channel obejct.
+
+bugfixes:
+-
+
 version 3.0
 NuRadioMC 3.0 is a complete refactor of the NuRadioMC core (simulation.py), enabling long-awaited features such as:
 - exposing functions to calculate voltage traces from showers for stand-alone use (needed e.g. for Likelihood forward folding reconstruction)
@@ -26,6 +46,8 @@ Only one trigger can be the primary.
 
 version 2.3.0-dev
 new features:
+- added module to create CR templates for RNO-G channels
+- added function to calculate correlation between two traces using a sliding window (with variable window size)
 - Enhanced detector_browser to allow display RNO-G database detector
 - Added download utility unifying download of antenna models/proposal tables/shower
 library with possibility to add/change fallback-server(s). Added Chicago
diff --git a/documentation/make_docs.py b/documentation/make_docs.py
index 055d638e19..d6aa254b54 100644
--- a/documentation/make_docs.py
+++ b/documentation/make_docs.py
@@ -54,6 +54,11 @@
             "Do not delete existing html files and build only pages which have changed."
             " Useful if you are only modifying or adding (not moving/removing) pages.")
     )
+    argparser.add_argument(
+        '--skip-apidoc', default=False, const=True, action='store_const', help=(
+            "Skip the compilation of the automatic API documentation. "
+            "Speeds up the compilation of the documentation if no changes were made to the code.")
+    )
     argparser.add_argument(
         '--debug', '-v', default=0, action='count',
         help="Store full debugging output in make_docs.log."
@@ -73,42 +78,42 @@
 
     doc_path = os.path.dirname(os.path.realpath(__file__))
     os.chdir(doc_path)
-
-    # we exclude scripts, examples and tests from the code documentation,
-    # as apidoc doesn't really handle those well
-    exclude_modules = []
-    exclude_modules.append('../**/test')
-    exclude_modules.append('../**/tests')
-    exclude_modules.append('../**/scripts')
-    exclude_modules.append('../**/examples')
-    exclude_modules.append('../**/eventbrowser') 
-    exclude_modules.append('../**/setup.py')
-    exclude_modules.append('../**/CPPAnalyticRayTracing') # C code also doesn't work right now
-    exclude_modules.append('../**/araroot') # this doesn't work because we don't have the .so files to read ARA data
-
-    # create the automatic code documentation with apidoc
-    for module in ['NuRadioReco', 'NuRadioMC']:
-        output_folder = 'source/{}/apidoc'.format(module)
-        if os.path.exists(output_folder):
-            if not parsed_args.no_clean: # remove old apidoc folder
-                logger.info('Removing old apidoc folder: {}'.format(output_folder))
-                subprocess.check_output(['rm', '-rf', output_folder])
-
-        module_path = '../{}/'.format(module)
-
-        logger.info("Creating automatic documentation files with apidoc:")
-        logger.info("excluding modules: {}".format(exclude_modules))
-        subprocess.run(
-            [
-                'sphinx-apidoc', '-efMT', '--ext-autodoc', '--ext-intersphinx',
-                '--ext-coverage', '--ext-githubpages', '-o', output_folder,
-                module_path, *exclude_modules
-            ], stdout=pipe_stdout
-        )
-        # We don't use the top level NuRadioReco.rst / NuRadioMC.rst toctrees,
-        # so we remove them to eliminate a sphinx warning
-        subprocess.check_output([
-            'rm', os.path.join(output_folder, '{}.rst'.format(module))])
+    if not parsed_args.skip_apidoc:
+        # we exclude scripts, examples and tests from the code documentation,
+        # as apidoc doesn't really handle those well
+        exclude_modules = []
+        exclude_modules.append('../**/test')
+        exclude_modules.append('../**/tests')
+        exclude_modules.append('../**/scripts')
+        exclude_modules.append('../**/examples')
+        exclude_modules.append('../**/eventbrowser')
+        exclude_modules.append('../**/setup.py')
+        exclude_modules.append('../**/CPPAnalyticRayTracing') # C code also doesn't work right now
+        exclude_modules.append('../**/araroot') # this doesn't work because we don't have the .so files to read ARA data
+
+        # create the automatic code documentation with apidoc
+        for module in ['NuRadioReco', 'NuRadioMC']:
+            output_folder = 'source/{}/apidoc'.format(module)
+            if os.path.exists(output_folder):
+                if not parsed_args.no_clean: # remove old apidoc folder
+                    logger.info('Removing old apidoc folder: {}'.format(output_folder))
+                    subprocess.check_output(['rm', '-rf', output_folder])
+
+            module_path = '../{}/'.format(module)
+
+            logger.info("Creating automatic documentation files with apidoc:")
+            logger.info("excluding modules: {}".format(exclude_modules))
+            subprocess.run(
+                [
+                    'sphinx-apidoc', '-efMT', '-d', '1', '--ext-autodoc', '--ext-intersphinx',
+                    '--ext-coverage', '--ext-githubpages', '-o', output_folder,
+                    module_path, *exclude_modules
+                ], stdout=pipe_stdout
+            )
+            # We don't use the top level NuRadioReco.rst / NuRadioMC.rst toctrees,
+            # so we remove them to eliminate a sphinx warning
+            subprocess.check_output([
+                'rm', os.path.join(output_folder, '{}.rst'.format(module))])
 
     if not parsed_args.no_clean:
         logger.info('Removing old \'build\' directory...')
diff --git a/documentation/source/Experiments/lofar/overview.rst b/documentation/source/Experiments/lofar/overview.rst
new file mode 100644
index 0000000000..3cf76bebbc
--- /dev/null
+++ b/documentation/source/Experiments/lofar/overview.rst
@@ -0,0 +1,11 @@
+LOFAR
+=====
+
+NuRadioReco is also used for analysis of LOFAR data. To this end, it
+includes some LOFAR-specific modules.
+
+To **read** LOFAR data, use the :py:mod:`readLOFARData <NuRadioReco.modules.io.LOFAR.readLOFARData>`
+module.
+
+For analysis and reconstruction of LOFAR data, we use the modules in
+:py:mod:`NuRadioReco.modules.LOFAR <NuRadioReco.modules.LOFAR>`
\ No newline at end of file
diff --git a/documentation/source/Experiments/welcome_page.rst b/documentation/source/Experiments/welcome_page.rst
new file mode 100644
index 0000000000..70654d624f
--- /dev/null
+++ b/documentation/source/Experiments/welcome_page.rst
@@ -0,0 +1,16 @@
+Experiment-specific features
+============================
+
+NuRadioMC (and NuRadioReco) are used by several different experiments.
+To facilitate this, NuRadioReco contains several experiment-specific
+features. Most of these are included under ``NuRadioReco.modules.<EXPERIMENT>``
+for analysis features, and readers for the raw experiment data under
+``NuRadioReco.modules.io.<EXPERIMENT>``.
+
+More information about the features available for each experiment
+are available on the pages below.
+
+.. toctree::
+    :maxdepth: 1
+
+    lofar/overview
\ No newline at end of file
diff --git a/documentation/source/Introduction/pages/contributing.rst b/documentation/source/Introduction/pages/contributing.rst
index 4c8bfc4a9c..dfc522f9e3 100644
--- a/documentation/source/Introduction/pages/contributing.rst
+++ b/documentation/source/Introduction/pages/contributing.rst
@@ -174,17 +174,17 @@ Dependencies are also maintained in ``pyproject.toml``. To update the dependenci
   under ``[tool.poetry.dependencies]``. Acceptable version specifications are ``"4.1.1"`` (4.1.1 only),
   ``">=4.1.1"`` (4.1.1 or greater), or ``"*"`` (any version). Please do not use poetry-specific version
   specifiers like ``^`` or ``~``.
-* If you are adding an **optional** dependency, add your dependency under ``[tool.poetry.dev-dependencies]``.
+* If you are adding an **optional** dependency, you can specify this by adding ``optional=true``.
   Additionally, please name the feature that requires this dependency, and add it under ``[tool.poetry.extras]``.
   E.g. in order to generate the documentation, we require ``Sphinx``, ``sphinx-rtd-theme`` and ``numpydoc`` to be installed.
   This is specified in ``pyproject.toml`` as follows:
 
   .. code-block::
 
-    [tool.poetry.dev-dependencies]
-    Sphinx = "*"
-    sphinx-rtd-theme = "*"
-    numpydoc = "*"
+    [tool.poetry.dependencies]
+    Sphinx = {version = "*", optional = true}
+    sphinx-rtd-theme = {version = "*", optional = true}
+    numpydoc = {version = "*", optional = true}
 
     [tool.poetry.extras]
     documentation = ["Sphinx", "sphinx-rtd-theme", "numpydoc"]
diff --git a/documentation/source/Introduction/pages/installation.rst b/documentation/source/Introduction/pages/installation.rst
index 7356788e5f..c415ce6b47 100644
--- a/documentation/source/Introduction/pages/installation.rst
+++ b/documentation/source/Introduction/pages/installation.rst
@@ -16,7 +16,8 @@ using ``pip``:
       pip install NuRadioMC
 
 NuRadioMC/NuRadioReco will then be available from Python using ``import NuRadioMC`` and ``import NuRadioReco``, respectively.
-The pip installation will also install all core dependencies. 
+The pip installation will install all core dependencies. Some :ref:`optional dependencies <Introduction/pages/installation:Optional Dependencies>`
+can be installed by appending ``[option]``, i.e. ``pip install NuRadioMC[option]``.
 
 .. Important::
 
@@ -32,7 +33,7 @@ The pip installation will also install all core dependencies.
   instead, or install it manually (see below).
 
 Development version
----------------------------
+-------------------
 The most recent version of ``NuRadioMC`` is available on `github <https://github.com/nu-radio/NuRadioMC.git>`__.
 It can be downloaded manually from the `repository website <https://github.com/nu-radio/NuRadioMC/releases/latest>`__,
 or cloned using ``git``
@@ -80,7 +81,7 @@ To install all (optional and non-optional) dependencies available in pip at once
 
 .. code-block:: Bash
 
-  pip install numpy scipy matplotlib astropy tinydb tinydb-serialization aenum h5py mysql-python pymongo dash plotly toml peakutils
+  pip install numpy scipy matplotlib astropy tinydb tinydb-serialization aenum h5py mysql-connector-python pymongo dash plotly toml peakutils future radiotools filelock mattak git+https://github.com/telegraphic/pygdsm pylfmap MCEq crflux
 
 Note that some optional dependencies are not pip-installable and need to be 
 :ref:`installed manually <Introduction/pages/installation:Not pip-installable packages>`
@@ -136,30 +137,17 @@ Core Dependencies
 
     pip install aenum
 
-Optional Dependencies
-^^^^^^^^^^^^^^^^^^^^^
-
-These packages are recommended to be able to use all of NuRadioMC/NuRadioReco's features:
-
 - h5py to open HDF5 files:
 
-.. code-block:: Bash
-
-  pip install h5py
-
-- uproot to open RNO-G root files:
-
-.. code-block:: bash
-
-  pip install uproot awkward
+  .. code-block:: Bash
 
-- To access some detector databases:
+    pip install h5py
 
-- For SQL datbases install `MySQL <https://www.mysql.com/>`_ and mysql-python:
+- filelock:
 
   .. code-block:: Bash
 
-    pip install mysql-python
+    pip install filelock
 
 - For `MongoDB <https://www.mongodb.com>`_ databases install:
 
@@ -174,24 +162,33 @@ These packages are recommended to be able to use all of NuRadioMC/NuRadioReco's
     pip install dash
     pip install plotly
 
-  If you want templates to show up in the Event Display, you need to set up an environment variable NURADIORECOTEMPLATES and have it point to the template directory.
+Optional Dependencies
+^^^^^^^^^^^^^^^^^^^^^
 
-- The documentation is created using `Sphinx <https://www.sphinx-doc.org>`_. We use the ``readthedocs`` theme, and the ``numpydoc`` format is used in our docstrings.
-  This dependency is needed only if you want to generate the documentation locally - the `online documentation <https://nu-radio.github.io/NuRadioMC/main.html>`_ is generated by a Github action automatically.
-  Note that we use the `sphinx autodoc <https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#module-sphinx.ext.autodoc>`_
-  feature, which tries to import all modules it documents. So if you are missing some optional dependencies, it will not generate correct documentation for all the code.
+These packages are recommended to be able to use all of NuRadioMC/NuRadioReco's features.
+They can be installed by including adding ``[option]`` when installing NuRadioMC. Alternatively,
+use ``pip install nuradiomc[all]`` to install all optional dependencies.
 
-  .. code-block:: Bash
+- ``[RNO-G]``
 
-    pip install sphinx sphinx_rtd_theme numpydoc
+  `mattak <https://github.com/RNO-G/mattak>`__ is required to open RNO-G root files:
 
-- Some debug plots need peakutils:
+  .. code-block:: bash
 
-  .. code-block:: Bash
+    pip install mattak
 
-    pip install peakutils
+- ``[rno-g-extras]``
 
-- Proposal to use :mod:`NuRadioMC.EvtGen.NuRadioProposal` module:
+  Optionally, to filter RNO-G data (during read in) the `RNO-G run table database <https://github.com/RNO-G/rnog-runtable>`__
+  can be used. Note that this requires membership of the RNO-G Github organisation (not public):
+
+  .. code-block:: bash
+
+    pip install git+ssh://git@github.com/RNO-G/rnog-runtable.git
+
+- ``[proposal]``
+
+  ``proposal`` is needed to use :mod:`NuRadioMC.EvtGen.NuRadioProposal` module (simulating secondary particles):
 
   .. code-block:: bash
 
@@ -203,12 +200,46 @@ These packages are recommended to be able to use all of NuRadioMC/NuRadioReco's
 
   - if the linux kernel is too old (eg. on some computing clusters), refer to `this step-by-step guide <https://github.com/tudo-astroparticlephysics/PROPOSAL/wiki/Installing-PROPOSAL-on-a-Linux-kernel---4.11>`_
   
+- ``[galacticnoise]``
+
+  To use the channelGalacticNoiseAdder, you need the `PyGDSM <https://github.com/telegraphic/pygdsm>`_ package.
+  Some additional galactic noise models used by LOFAR for calibration purposes are provided by ``pylfmap``.
+
+  .. code-block:: Bash
+
+    pip install git+https://github.com/telegraphic/pygdsm pylfmap
+
+- ``[muon-flux]``
+
+  Needed for some muon flux calculations
+
+  .. code-block:: bash
+
+    pip install MCEq crflux
+
+- ``[documentation]``
+
+  The documentation is created using `Sphinx <https://www.sphinx-doc.org>`_. We use the ``readthedocs`` theme, and the ``numpydoc`` format is used in our docstrings.
+  This dependency is needed only if you want to generate the documentation locally - the `online documentation <https://nu-radio.github.io/NuRadioMC/main.html>`_ is generated by a Github action automatically.
+  Note that we use the `sphinx autodoc <https://www.sphinx-doc.org/en/master/usage/extensions/autodoc.html#module-sphinx.ext.autodoc>`_
+  feature, which tries to import all modules it documents. So if you are missing some optional dependencies, it will not generate correct documentation for all the code.
+
+  .. code-block:: Bash
 
-- To use the channelGalacticNoiseAdder, you need the `PyGDSM <https://github.com/telegraphic/pygdsm>`_ package.
+    pip install sphinx sphinx_rtd_theme numpydoc
+
+- Some debug plots need peakutils:
 
   .. code-block:: Bash
 
-    pip install git+https://github.com/telegraphic/pygdsm
+    pip install peakutils
+
+- For SQL databases install `MySQL <https://www.mysql.com/>`_ and mysql-python:
+
+  .. code-block:: Bash
+
+    pip install mysql-connector-python
+
 
 Not pip-installable packages
 ____________________________
diff --git a/documentation/source/NuRadioMC/pages/HDF5_structure.rst b/documentation/source/NuRadioMC/pages/HDF5_structure.rst
index 62b3dd1daa..be78f3c0a2 100644
--- a/documentation/source/NuRadioMC/pages/HDF5_structure.rst
+++ b/documentation/source/NuRadioMC/pages/HDF5_structure.rst
@@ -55,28 +55,29 @@ The top-level attributes can be accessed using ``f.attrs``. These contain:
     .. _hdf5-attrs-table:
 
     .. csv-table:: HDF5 attributes
-            :header: "Key", "Description"
-            :widths: auto
-            :delim: |
-
-            ``NuRadioMC_EvtGen_version`` ``NuRadioMC_EvtGen_version_hash`` ``NuRadioMC_version`` ``NuRadioMC_version_hash`` | Versions of the generator/framework as integer as hash
-            ``Emin`` ``Emax`` | Define energy range for neutrino energies
-            ``phimax`` ``phimin`` | Define azimuth range for incoming neutrino directions
-            ``thetamax`` ``thetamin`` | Define zenith range for incoming neutrino directions
-            ``flavors`` | A list of particle flavors that were simulated, using the PDG convention.
-            ``n_events`` | Total number of generated/simulated events(including those that did not trigger)
-            ``fiducial_xmax`` ``fiducial_xmin`` ``fiducial_ymax`` ``fiducial_ymin`` ``fiducial_zmax`` ``fiducial_zmin`` / ``fiducial_rmax`` ``fiducial_rmin`` ``fiducial_zmax`` ``fiducial_zmin`` | Specify the simulated qubic/cylindrical fiducial volume.  An event has to produce an interaction within this volume. However, in case of a muon or tau CC interaction the first interaction can occur outside
-            ``rmax`` ``rmin`` ``zmax`` ``zmin`` / ``xmax`` ``xmin`` ``ymax`` ``ymin`` ``zmax`` ``zmin`` | Specify the qubic/cylindrical volume in which neutrino interactions are generated
-            ``volume`` | Volume of the above specified volume
-            ``area`` | Surface area of the above specified volume
-            ``start_event_id`` | ``event_id`` of the first event in the file
-            ``trigger_names`` | List of the names of the different triggers simulated
-            ``Tnoise`` | (explicit) noise temperature used in simulation
-            ``n_samples`` | Samples of the to-be generated antenna signals
-            ``config`` | The (yaml-style) config file used for the simulation
-            ``deposited`` |
-            ``detector`` | The (json-format) detector description used for the simulation
-            ``dt`` | The time resolution, i.e. the inverse of the sampling rate used for the simulation. This is not necessarily the same as the sampling rate of the simulated channels!
+        :header-rows: 1
+        :widths: auto
+        :delim: |
+
+        Key | Description
+        ``NuRadioMC_EvtGen_version`` ``NuRadioMC_EvtGen_version_hash`` ``NuRadioMC_version`` ``NuRadioMC_version_hash`` | Versions of the generator/framework as integer as hash
+        ``Emin`` ``Emax`` | Define energy range for neutrino energies
+        ``phimax`` ``phimin`` | Define azimuth range for incoming neutrino directions
+        ``thetamax`` ``thetamin`` | Define zenith range for incoming neutrino directions
+        ``flavors`` | A list of particle flavors that were simulated, using the PDG convention.
+        ``n_events`` | Total number of generated/simulated events(including those that did not trigger)
+        ``fiducial_xmax`` ``fiducial_xmin`` ``fiducial_ymax`` ``fiducial_ymin`` ``fiducial_zmax`` ``fiducial_zmin`` / ``fiducial_rmax`` ``fiducial_rmin`` ``fiducial_zmax`` ``fiducial_zmin`` | Specify the simulated qubic/cylindrical fiducial volume.  An event has to produce an interaction within this volume. However, in case of a muon or tau CC interaction the first interaction can occur outside
+        ``rmax`` ``rmin`` ``zmax`` ``zmin`` / ``xmax`` ``xmin`` ``ymax`` ``ymin`` ``zmax`` ``zmin`` | Specify the qubic/cylindrical volume in which neutrino interactions are generated
+        ``volume`` | Volume of the above specified volume
+        ``area`` | Surface area of the above specified volume
+        ``start_event_id`` | ``event_id`` of the first event in the file
+        ``trigger_names`` | List of the names of the different triggers simulated
+        ``Tnoise`` | (explicit) noise temperature used in simulation
+        ``n_samples`` | Samples of the to-be generated antenna signals
+        ``config`` | The (yaml-style) config file used for the simulation
+        ``deposited`` |
+        ``detector`` | The (json-format) detector description used for the simulation
+        ``dt`` | The time resolution, i.e. the inverse of the sampling rate used for the simulation. This is not necessarily the same as the sampling rate of the simulated channels!
 
 
 The station-level attributes can be accessed using ``f[station_<station_id>].attrs``. The first two attributes ``Vrms`` and ``bandwidth`` also exist on the top-level and refer to the corresponding to the first station/channel pair.
@@ -84,13 +85,15 @@ The station-level attributes can be accessed using ``f[station_<station_id>].att
     .. _hdf5-station-attrs-table:
 
     .. csv-table:: HDF5 station attributes
-            :header: "Key", "Description"
-            :widths: auto
-            :delim: |
-
-            ``Vrms`` | RMS of the voltage used as thermal noise floor :math:`v_{n} = (k_{B} \, R \, T \, \Delta f) ^ {0.5}`. See the relevant section "Noise voltage and power" in this `wiki article <https://en.wikipedia.org/wiki/Johnson%E2%80%93Nyquist_noise>`_ (last two equations). Determine from ``Tnoise`` and ``bandwidth`` (see below).
-            ``bandwidth`` | Bandwidth is above equation. Calculated as the integral over the simulated filter response (`filt`) squared: :math:`\Delta f = np.trapz(np.abs(filt) ** 2, ff)`.
-            ``antenna_positions`` | Relative position of all simulated antennas (channels)
+        :header-rows: 1
+        :widths: auto
+        :delim: |
+        
+        Key | Description
+        ``Vrms`` | RMS of the voltage used as thermal noise floor :math:`v_{n} = (k_{B} \, R \, T \, \Delta f) ^ {0.5}`. See the relevant section "Noise voltage and power" in this `wiki article <https://en.wikipedia.org/wiki/Johnson%E2%80%93Nyquist_noise>`_ (last two equations). Determine from ``Tnoise`` and ``bandwidth`` (see below).
+        ``Vrms_trigger`` | (Optional) Same as ``Vrms`` but for the trigger channels if they were simulated with a different response.
+        ``bandwidth`` | Bandwidth is above equation. Calculated as the integral over the simulated filter response (`filt`) squared: :math:`\Delta f = np.trapz(np.abs(filt) ** 2, ff)`.
+        ``antenna_positions`` | Relative position of all simulated antennas (channels)
 
 HDF5 file contents
 __________________
@@ -100,27 +103,28 @@ is the number of showers (which may be larger than the number of events), and ``
     .. _hdf5-items-table:
 
     .. csv-table:: HDF5 items
-            :header: "Key", "Shape", "Description"
-            :widths: auto
-            :delim: |
-
-            ``event_group_ids`` | (``n_showers``) | Specifies the event id to which the corresponding shower belongs (``n_events = len(unique(event_group_ids)))``)
-            ``xx`` ``yy`` ``zz`` | (``n_showers``) | Specifying coordinates of interaction vertices
-            ``vertex_times`` | (``n_showers``) | Time at the interaction vertex. The neutrino interaction (= first interaction) is defined as time 0
-            ``azimuths`` ``zeniths`` | (``n_showers``) | Angle Specifying the neutrino incoming direction (``azimuths = 0`` points east)
-            ``energies`` | (``n_showers``) | Energy of the parent particle of a shower. This is typically the energy of the neutrino (for showers produced at the first interaction: all flavor NC, electron CC interactions) or the energy of a muon or tau lepton when those are producing secondary energy losses
-            ``shower_energies`` | (``n_showers``) | Energy of the shower which is used to determine the radio emission
-            ``flavors`` | (``n_showers``) | Same as above (the parent of an electromagnetic cascade in an electron CC interaction is the neutrino)
-            ``inelasticity`` | (``n_showers``) | Inelasticity of the first interaction
-            ``interaction_type`` | (``n_showers``) | Interaction type producing the shower (for the first interaction that can be "nc" or "cc")
-            ``multiple_triggers`` | (``n_showers``, ``n_triggers``) | Information which exact trigger fired each shower. The different triggers are specified in the attributes (``f.attrs["triggers"]``). The order of ``f.attrs["triggers"]`` matches that in ``multiple_triggers``
-            ``triggered`` | (``n_showers``) | A boolean; ``True`` if any trigger fired for this shower, ``False`` otherwise
-            ``trigger_times`` | (``n_showers``, ``n_triggers``) | The trigger times (relative to the first interaction) at which each shower triggered. If there are multiple stations, this will be the earliest trigger time.
-            ``n_interaction`` | (``n_showers``) | Hierarchical counter for the number of showers per event (also accounts for showers which did not trigger and might not be saved)
-            ``shower_ids`` | (``n_showers``) | Hierarchical counter for the number of triggered showers
-            ``shower_realization_ARZ`` | (``n_showers``) | Which realization from the ARZ shower library was used for each shower (only if ARZ was used for signal generation).
-            ``shower_type`` | (``n_showers``) | Type of the shower (so far we only have "em" and "had")
-            ``weights`` | (``n_showers``) | Weight for the probability that the neutrino reached the interaction vertex taking into account the attenuation from the earth (Does not include interaction probability in the volume)
+        :header-rows: 1
+        :widths: auto
+        :delim: |
+
+        Key | Shape | Description
+        ``event_group_ids`` | (``n_showers``) | Specifies the event id to which the corresponding shower belongs (``n_events = len(unique(event_group_ids)))``)
+        ``xx`` ``yy`` ``zz`` | (``n_showers``) | Specifying coordinates of interaction vertices
+        ``vertex_times`` | (``n_showers``) | Time at the interaction vertex. The neutrino interaction (= first interaction) is defined as time 0
+        ``azimuths`` ``zeniths`` | (``n_showers``) | Angle Specifying the neutrino incoming direction (``azimuths = 0`` points east)
+        ``energies`` | (``n_showers``) | Energy of the parent particle of a shower. This is typically the energy of the neutrino (for showers produced at the first interaction: all flavor NC, electron CC interactions) or the energy of a muon or tau lepton when those are producing secondary energy losses
+        ``shower_energies`` | (``n_showers``) | Energy of the shower which is used to determine the radio emission
+        ``flavors`` | (``n_showers``) | Same as above (the parent of an electromagnetic cascade in an electron CC interaction is the neutrino)
+        ``inelasticity`` | (``n_showers``) | Inelasticity of the first interaction
+        ``interaction_type`` | (``n_showers``) | Interaction type producing the shower (for the first interaction that can be "nc" or "cc")
+        ``multiple_triggers`` | (``n_showers``, ``n_triggers``) | Information which exact trigger fired each shower. The different triggers are specified in the attributes (``f.attrs["triggers"]``). The order of ``f.attrs["triggers"]`` matches that in ``multiple_triggers``
+        ``triggered`` | (``n_showers``) | A boolean; ``True`` if any trigger fired for this shower, ``False`` otherwise
+        ``trigger_times`` | (``n_showers``, ``n_triggers``) | The trigger times (relative to the first interaction) at which each shower triggered. If there are multiple stations, this will be the earliest trigger time.
+        ``n_interaction`` | (``n_showers``) | Hierarchical counter for the number of showers per event (also accounts for showers which did not trigger and might not be saved)
+        ``shower_ids`` | (``n_showers``) | Hierarchical counter for the number of triggered showers
+        ``shower_realization_ARZ`` | (``n_showers``) | Which realization from the ARZ shower library was used for each shower (only if ARZ was used for signal generation).
+        ``shower_type`` | (``n_showers``) | Type of the shower (so far we only have "em" and "had")
+        ``weights`` | (``n_showers``) | Weight for the probability that the neutrino reached the interaction vertex taking into account the attenuation from the earth (Does not include interaction probability in the volume)
 
 
 Station data
@@ -139,33 +143,34 @@ station triggered, with which amplitude, etc. The same approach works for ``show
     .. _hdf5-station-table:
 
     .. csv-table:: HDF5 station items
-            :header: "Key", "Shape", "Description"
-            :widths: auto
-            :delim: |
-
-            ``event_group_ids`` | (``m_events``) | The event group ids of the triggered events in the selected station
-            ``event_group_id_per_shower`` | (``m_showers``) | The event group id of every shower that triggered the selected station
-            ``event_ids`` | (``m_events``) | The event ids of each event that triggered in that station for every event group id. These are unique only within each separate event group, and start from 0.
-            ``event_id_per_shower`` | (``m_showers``) | The event ids of each event that triggered in that station. This one is for every shower
-            ``focusing_factor`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) |
-            ``launch_vectors`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``, ``3``) | 3D (Cartesian) coordinates of the launch vector of each ray tracing solution, per shower and channel.
-            ``max_amp_shower_and_ray`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | Maximum amplitude per shower, channel and ray tracing solution.
-            ``maximum_amplitudes`` | (``m_events``, ``n_channels``) | Maximum amplitude per event and channel
-            ``maximum_amplitudes_envelope`` | (``m_events``, ``n_channels``) | Maximum amplitude of the hilbert envelope for each event and channel
-            ``multiple_triggers`` | (``m_showers``, ``n_triggers``) | A boolean array that specifies if a shower contributed to an event that fulfills a certain trigger. The index of the trigger can be translated to the trigger name via the attribute ``trigger_names``.
-            ``multiple_triggers_per_event`` | (``m_events``, ``n_triggers``) | A boolean array that specifies if each event fulfilled a certain trigger. The index of the trigger can be translated to the trigger name via the attribute ``trigger_names``.
-            ``polarization`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``, ``3``) | 3D coordinates of the polarization vector at the antenna in cartesian coordinates. (The receive vector (which is opposite to the propagation direction) was used to rotate from spherical/on-sky coordinates to cartesian coordinates). The polarization vector does not include any propagation effects that could change the polarization, such as different reflectivities at the surface for the p and s polarization component.   
-            ``ray_tracing_C0`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | One of two parameters specifying the **analytic** ray tracing solution. Can be used to retrieve the solutions without having to re-run the ray tracer.
-            ``ray_tracing_C1`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | One of two parameters specifying the **analytic** ray tracing solution. Can be used to retrieve the solutions without having to re-run the ray tracer.
-            ``ray_tracing_reflection`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) |
-            ``ray_tracing_reflection_case`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) |
-            ``ray_tracing_solution_type`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) |
-            ``receive_vectors`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``, ``3``) | 3D (Cartesian) coordinates of the receive vector of each ray tracing solution, per shower and channel.
-            ``shower_id`` | (``m_showers``) | The Shower ids of showers that triggered the selected station
-            ``time_shower_and_ray`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The "signal time" per shower and raytracing solution. I.e., the time of the signal arriving at the DAQ including, e.g., cable delay, ...
-            ``travel_distances`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The distance travelled by each ray tracing solution to a specific channel
-            ``travel_times`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The time travelled by each ray tracing solution to a specific channel
-            ``triggered`` | (``m_showers``) | Whether each shower contributed to an event that satisfied any trigger condition
-            ``triggered_per_event`` | (``m_events``) | Whether each event fulfilled any trigger condition.
-            ``trigger_times`` | (``m_showers``, ``n_triggers``) | The trigger times for each shower and trigger. IMPORTANT: A shower can potentially generate multiple events. Then this field is ambiguous, as only a single trigger time per shower can be saved. In that case, the latest trigger time is saved into this field.
-            ``trigger_times_per_event`` | (``m_events``, ``n_triggers``) | The trigger times per event.
+        :header-rows: 1
+        :widths: auto
+        :delim: |
+
+        Key | Shape | Description
+        ``event_group_ids`` | (``m_events``) | The event group ids of the triggered events in the selected station
+        ``event_group_id_per_shower`` | (``m_showers``) | The event group id of every shower that triggered the selected station
+        ``event_ids`` | (``m_events``) | The event ids of each event that triggered in that station for every event group id. These are unique only within each separate event group, and start from 0.
+        ``event_id_per_shower`` | (``m_showers``) | The event ids of each event that triggered in that station. This one is for every shower
+        ``shower_id`` | (``m_showers``) | The Shower ids of showers that triggered the selected station
+        ``max_amp_shower_and_ray`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | Maximum amplitude per shower, channel and ray tracing solution.
+        ``maximum_amplitudes`` | (``m_events``, ``n_channels``) | Maximum amplitude per event and channel
+        ``maximum_amplitudes_envelope`` | (``m_events``, ``n_channels``) | Maximum amplitude of the hilbert envelope for each event and channel
+        ``multiple_triggers`` | (``m_showers``, ``n_triggers``) | A boolean array that specifies if a shower contributed to an event that fulfills a certain trigger. The index of the trigger can be translated to the trigger name via the attribute ``trigger_names``.
+        ``multiple_triggers_per_event`` | (``m_events``, ``n_triggers``) | A boolean array that specifies if each event fulfilled a certain trigger. The index of the trigger can be translated to the trigger name via the attribute ``trigger_names``.
+        ``polarization`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``, ``3``) | 3D coordinates of the polarization vector at the antenna in cartesian coordinates. (The receive vector (which is opposite to the propagation direction) was used to rotate from spherical/on-sky coordinates to cartesian coordinates). The polarization vector does not include any propagation effects that could change the polarization, such as different reflectivities at the surface for the p and s polarization component.
+        ``ray_tracing_C0`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | One of two parameters specifying the **analytic** ray tracing solution. Can be used to retrieve the solutions without having to re-run the ray tracer.
+        ``ray_tracing_C1`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | One of two parameters specifying the **analytic** ray tracing solution. Can be used to retrieve the solutions without having to re-run the ray tracer.
+        ``ray_tracing_reflection`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The number of bottom reflections (This variable is only non-zero if a reflection layer was defined in the ice model and if 'propagation.n_reflections' was set to a value larger than 0 in the config.yaml file.)
+        ``ray_tracing_reflection_case`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | Only relevant for bottom reflections. 1: rays start upwards, 2: rays start downwards
+        ``ray_tracing_solution_type`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The type of the ray tracing solution. 0: direct, 1: refracted, 2: reflected (off the surface)  (A refracted ray is defined as a ray that has a turning point, i.e. if it transitions from upward going to downward going; a reflected ray is defined if it has a surface reflection.)
+        ``focusing_factor`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) |  The focusing factor calculated by the propagation module.
+        ``launch_vectors`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``, ``3``) | 3D (Cartesian) coordinates of the launch vector of each ray tracing solution, per shower and channel.
+        ``receive_vectors`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``, ``3``) | 3D (Cartesian) coordinates of the receive vector of each ray tracing solution, per shower and channel.
+        ``time_shower_and_ray`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The "signal time" per shower and raytracing solution. I.e., the time of the signal arriving at the DAQ including, e.g., cable delay, ...
+        ``travel_distances`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The distance travelled by each ray tracing solution to a specific channel
+        ``travel_times`` | (``m_showers``, ``n_channels``, ``n_ray_tracing_solutions``) | The time travelled by each ray tracing solution to a specific channel
+        ``triggered`` | (``m_showers``) | Whether each shower contributed to an event that satisfied any trigger condition
+        ``triggered_per_event`` | (``m_events``) | Whether each event fulfilled any trigger condition.
+        ``trigger_times`` | (``m_showers``, ``n_triggers``) | The trigger times for each shower and trigger. IMPORTANT: A shower can potentially generate multiple events. Then this field is ambiguous, as only a single trigger time per shower can be saved. In that case, the latest trigger time is saved into this field.
+        ``trigger_times_per_event`` | (``m_events``, ``n_triggers``) | The trigger times per event.
diff --git a/documentation/source/NuRadioMC/pages/code_documentation.rst b/documentation/source/NuRadioMC/pages/code_documentation.rst
index da394bddb1..67293042f4 100644
--- a/documentation/source/NuRadioMC/pages/code_documentation.rst
+++ b/documentation/source/NuRadioMC/pages/code_documentation.rst
@@ -1,9 +1,9 @@
 
 Code Documentation
-===============================
+==================
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
 
    ../apidoc/NuRadioMC.EvtGen
    ../apidoc/NuRadioMC.SignalGen
diff --git a/documentation/source/NuRadioReco/pages/code_documentation.rst b/documentation/source/NuRadioReco/pages/code_documentation.rst
index d7dc776b66..63640b1130 100644
--- a/documentation/source/NuRadioReco/pages/code_documentation.rst
+++ b/documentation/source/NuRadioReco/pages/code_documentation.rst
@@ -1,8 +1,7 @@
 Code Documentation
-===============================
-
+==================
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
 
    ../apidoc/NuRadioReco.framework
    ../apidoc/NuRadioReco.modules
diff --git a/documentation/source/NuRadioReco/pages/detector/antennamodels.rst b/documentation/source/NuRadioReco/pages/detector/antennamodels.rst
index 775ddbfcd9..c497804213 100644
--- a/documentation/source/NuRadioReco/pages/detector/antennamodels.rst
+++ b/documentation/source/NuRadioReco/pages/detector/antennamodels.rst
@@ -11,6 +11,8 @@ the antenna model so that he can put it on our central server.
 Implemetation of Antenna Models
 ===============================
 
+For the antenna orientation and rotation, the conventions are described in :ref:`Properties of Detector Description <NuRadioReco/pages/detector/detector_database_fields:Properties of Detector Description>`
+
 The antenna models are accessed in the `AntennaPattern` class in the `nuradioreco.detector.antennapattern.py` module.
 Different software packages are used to simulate the antennas, internally, NuRadioReco converts the data to a common pickle format in which they are
 stored.
@@ -36,8 +38,6 @@ The antenna pickle files contains 9 lists of the following data:
     - H_theta: array of floats
         the complex realized vector effective length of the eTheta polarization component as described in (A.13) of the NuRadioReco paper `arxiv:1903.07023 <https://arxiv.org/abs/1903.07023>`__
 
-For the antenna orientation and rotation, the conventions are described in :ref:`Properties of Detector Description <NuRadioReco/pages/detector/detector_database_fields:Properties of Detector Description>`
-
 The calculation of the vector effective length is described in Appendix A.1, A.2 and A.3 of the NuRadioReco paper `arxiv:1903.07023 <https://arxiv.org/abs/1903.07023>`__.
 
 
@@ -429,9 +429,9 @@ RNOG_vpol_v2_5inch_center_n1.75
 -------------------------------
 XFdtd simulations for the RNO-G VPol in an 11.2 inch diameter borehole with index of refraction of ice n=1.75.
 The antenna is placed in the center (x, y) of the borehole. An extra cubic interpolation is performed in frequencies (5 MHz step).
-Theta range [0, 180] Phi range [0, 360] Freq range [0, 700]MHz. 
+Theta range [0, 180] Phi range [0, 360] Freq range [0, 700]MHz.
 
-Note: Simulation ran with Theta range [0, 90] and Phi range [0, 90] due to simulation size constraints and was extended to range noted above using symmetry. 
+Note: Simulation ran with Theta range [0, 90] and Phi range [0, 90] due to simulation size constraints and was extended to range noted above using symmetry.
 
 Last updated: 2024
 
@@ -457,7 +457,7 @@ RNOG_quadslot_v2_rescaled_fineFreq
 RNOG_quadslot_v3_air_rescaled_to_n1.74
 ---------------------------------------
 XFdtd simulations in for the RNO-G Hpol.
-Simulations are done in air, frequencies are rescaled with n=1.74. 
+Simulations are done in air, frequencies are rescaled with n=1.74.
 Theta range [-180, 180] Phi range [0, 360] Freq range [57, 574]MHz
 
 Last updated: 2020
diff --git a/documentation/source/NuRadioReco/pages/detector/detector_database_fields.rst b/documentation/source/NuRadioReco/pages/detector/detector_database_fields.rst
index a5853d0a1f..ee5cc709fd 100644
--- a/documentation/source/NuRadioReco/pages/detector/detector_database_fields.rst
+++ b/documentation/source/NuRadioReco/pages/detector/detector_database_fields.rst
@@ -2,30 +2,20 @@ Properties of Detector Description
 =========================================
 This pages documents and defines the properties that are part of the detector description
 
-Antenna Table
------------------------------
-- position_x: The x position of the antenna feed point relative to the station position
-- position_y: The y position of the antenna feed point relative to the station position
-- position_z: The z position of the antenna feed point relative to the station position
-
-The orientation of the antenna is described with 2 vectors. The first vector (referred to as 'orientation') is the orientation of the antenna, in case
-of an LPDA the boresight direction (pointing into the main sensitivity direction) and in case of dipoles this vector is
-parallel to the rod.
-The second vector describes a rotation around the first vector and needs to be perpendicular to the 'orientation' vector.
-For LPDAs, it is perpendicular to the antenna tines and points into the same direction as the connector of the create LPDAs.
-For dipoles, it can point in any direction that is perpendicular to the first vector because of the radial symmetry of dipoles.
+Antennas
+--------
 
-.. image:: orientation_sketch.png
-   :width: 600
+Position:
+- `ant_position_{x,y,z}``: The x, y, and z position of the antenna feed point relative to the station position
+
+Orientation:
 
-The vectors are each described by two angles:
+The orientation of the antenna is described with 2 unit vectors. The first vector (referred to as 'orientation') defines the orientation of the antenna. The second vector (referred to as 'rotation') defines the rotation around the orientation. The vectors are each described by two angles. The `theta` angle defines the vectors like a zenith angle (0deg is the zenith, 180deg is straight down) and the `phi` angle defines the azimuth angle (counting from East counterclockwise). Please note that the rotation vector has to be perpendicular to the orientation vector.
 
-- orientation_theta: orientation of the antenna, as a zenith angle (0deg is the zenith, 180deg is straight down); for LPDA: outward along boresight; for dipoles: upward along axis of azimuthal symmetry
-- orientation_phi: orientation of the antenna, as an azimuth angle (counting from East counterclockwise); for LPDA: outward along boresight; for dipoles: upward along axis of azimuthal symmetry
-- rotation_theta: rotation of the antenna, is perpendicular to 'orientation', for LPDAs: vector perpendicular to the plane containing the the tines
-- rotation_phi: rotation of the antenna, is perpendicular to 'orientation', for LPDAs: vector perpendicular to the plane containing the the tines
+For LPDAs, the orientation vector (`ant_orientation_theta` and `ant_orientation_phi`) defines the boresight direction (the main sensitivity direction, see sketch below). The rotation vector (`ant_rotation_theta` and `ant_rotation_phi`) is perpendicular to the antenna tines and points into the same direction as the connector of the create LPDAs. For dipoles, the orientation vector is parallel to the rod, the rotation vector can point in any direction that is perpendicular to the first vector because of the radial symmetry of dipoles. The user must specify *both* the orientation vector and the rotation vector. See the illustration of the two vectors for an LPDA and dipole.
 
-The user must specify *both* the orientation vector and the rotation vector. 
+.. image:: orientation_sketch.png
+   :width: 600
 
 Here are a few examples.
 
@@ -38,16 +28,16 @@ Here are a few examples.
 	rotation_phi = 90
 	rotation_theta = 90
 
-For an LPDA, the orientation vector is along the boresight direction, and so to 
+For an LPDA, the orientation vector is along the boresight direction, and so to
 point the antenna downward, we specify `orientation_theta=180`.
 Because the antenna is completely pointed down, `orientation_phi` can be any
 value, so we specify 0 for simplicity.
 Because the orientation vector points *down* (`orientation_theta = 180`),
 and because the rotation vector is perpendicular to the orientation vector,
-we must specify the rotation vector to point *sideways*, and so 
+we must specify the rotation vector to point *sideways*, and so
 we set `rotation_theta = 90`.
-Finally, to orient the tines correctly: the rotation vector is in the plane 
-*perpendicular* to the tines, so we must rotate by 90 (`rotation_phi = 90`) 
+Finally, to orient the tines correctly: the rotation vector is in the plane
+*perpendicular* to the tines, so we must rotate by 90 (`rotation_phi = 90`)
 to have the tines themselves be aligned with the *x*-axis.
 
 2. For a dipole, the following specifies a traditional "upright" dipole.
@@ -65,16 +55,16 @@ Because the antenna is completely vertical, `orientation_phi` can be any
 value, so we specify 0 for simplicity.
 Because the orientation vector points vertically (`orientation_theta = 0`),
 and because the rotation vector is perpendicular to the orientation vector,
-we must specify the rotation vector to point *sideways*, and so 
+we must specify the rotation vector to point *sideways*, and so
 we set `rotation_theta = 90`.
 Because the dipole is azimuthally symmetric, we can fix `rotation_phi` to any
 value, and choose 0 for simplicity.
 
-Antenna positions can be visualized in 3D using the script 
+Antenna positions can be visualized in 3D using the script
 `NuRadioReco/detector/visualize_detector.py my_detector.json`
 
 
-Additionally, for the antenna, the  user must specify 
+Additionally, for the antenna, the  user must specify
 
 - deployment_time: the time of antenna deployment aka the time when the antenna depth was measured (relevant because the depth changes because of snow accumulation)
 - type: the type of antenna. A list of available antenna types and their descriptions is given :doc:`here </NuRadioReco/pages/detector/antennamodels>`
@@ -82,24 +72,24 @@ Additionally, for the antenna, the  user must specify
 Further Discussion on Antenna Coordinates
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 In principle, the orientation of the antenna is uniquely defined by the three
-`Euler angles <https://en.wikipedia.org/wiki/Euler_angles>`_. 
-However, the Euler angles are relatively un-intuitive in everyday use. 
-The solution was instead to specify the antenna orientation with 
+`Euler angles <https://en.wikipedia.org/wiki/Euler_angles>`_.
+However, the Euler angles are relatively un-intuitive in everyday use.
+The solution was instead to specify the antenna orientation with
 two orthogonal vectors that are more intuitive,
 and that together over-determine the three Euler angles, and therefore,
 the antenna orientation.
 
-`orientation_phi` is roughly the equivalent of the first Euler angle, 
+`orientation_phi` is roughly the equivalent of the first Euler angle,
 the rotation around the *z*-axis.
 `orientation_theta` is roughly the equivalent of the second Euler angle,
 the rotation around the *x'*-axis.
-`rotation_theta` and `rotation_phi` together specify the third Euler angle, 
+`rotation_theta` and `rotation_phi` together specify the third Euler angle,
 the rotation around the *z''*-axis.
 (Here referring to the intrinsic Euler angle definition in the z-x'-z'' convention).
 
 
 ADC Table
------------------------------
+---------
 We document here the properties that are part of the analog-to-digital converter (ADC) description.
 
 - `adc_nbits`: the number of bits of the ADC
diff --git a/documentation/source/NuRadioReco/pages/detector/orientation_sketch.png b/documentation/source/NuRadioReco/pages/detector/orientation_sketch.png
index bac25b6bc2..9284b84772 100644
Binary files a/documentation/source/NuRadioReco/pages/detector/orientation_sketch.png and b/documentation/source/NuRadioReco/pages/detector/orientation_sketch.png differ
diff --git a/documentation/source/NuRadioReco/pages/event_display.rst b/documentation/source/NuRadioReco/pages/event_display.rst
index b7cce7c744..07f51e9bad 100644
--- a/documentation/source/NuRadioReco/pages/event_display.rst
+++ b/documentation/source/NuRadioReco/pages/event_display.rst
@@ -21,3 +21,6 @@ file you want to see.
 Alternatively, you can use the file ``NuRadioViewer`` in the ``eventbrowser`` directory.
 Add the directory to your system ``$PATH`` and you can open the eventbrowser by
 typing ``NuRadioViewer filename.nur``.
+
+If you want templates to show up in the Event Display,
+you need to set up an environment variable ``NURADIORECOTEMPLATES`` and have it point to the template directory.
diff --git a/documentation/source/NuRadioReco/pages/event_structure.rst b/documentation/source/NuRadioReco/pages/event_structure.rst
index 27cf488b38..54dde05753 100644
--- a/documentation/source/NuRadioReco/pages/event_structure.rst
+++ b/documentation/source/NuRadioReco/pages/event_structure.rst
@@ -250,8 +250,15 @@ ____________
 The :class:`Channel<NuRadioReco.framework.channel>`
 is used to store information about the voltage traces recorded in a channel,
 which can be accessed via the parameter storage and methods inherited from
-the ``BaseTrace`` class.
-
+the ``BaseTrace`` class. Optionally a ``trigger_channel`` object of the same class can
+be stored within a channel object. The idea is that the voltage traces on which the trigger(s)
+run can be a different from what the system reads out (additional filters may be applied to
+the trigger channels). One can access the ``trigger_channel`` from a channel object via the
+``get_trigger_channel()`` method. This function will always return a channel object. If no
+trigger channel was defined, ``self``, i.e., the channel object itself, is returned.
+The :class:`Station<NuRadioReco.framework.station>` class also has a method to return trigger channels
+(if defined). Trigger modules should typically call these ``get_trigger_channel()`` methods to
+get the channel objects.
 
 Hybrid Information
 ___________________
@@ -281,7 +288,7 @@ _________________
 A ``HybridDetector`` can be used to store more detailed and experiment-specific information
 about a complementary detector. The diversity of hybrid radio detectors makes it
 impractical to provide this functionality inside NuRadioReco itself, but a custom
-``HybridDetector`` class can be impemented inside an independent repository. This class
+``HybridDetector`` class can be implemented inside an independent repository. This class
 can be slotted into the data structure via the ``set_hybrid_detector`` method of the ``HybridShower``
 class and accessed via its ``get_hybrid_detector`` method.
 
diff --git a/documentation/source/NuRadioReco/pages/times.rst b/documentation/source/NuRadioReco/pages/times.rst
index 348486ffb2..779db32a79 100644
--- a/documentation/source/NuRadioReco/pages/times.rst
+++ b/documentation/source/NuRadioReco/pages/times.rst
@@ -1,52 +1,106 @@
-overview of times
+Overview of times
 =================
-Time delays are introduced by several hardware components. These time delays are often corrected for by folding/unfolding the complex transfer function (for an amp e.g. the measurement of the S12 parameter). The unfolding is typically done in the frequency domain where a convolution becomes a simple multiplication. As a consequence of typically short trace length (<~1000 samples) and because a Fourier transform implies implicitly a periodic signal, a pulse being at the beginning of the trace can end up being at the end of the trace. To avoid this behavior we use the following procedure:
+This page provides an overview of the different times defined in different places in NuRadioMC/NuRadioReco.
+Generally speaking, the global time of an `Event <NuRadioReco.framework.event.Event>` is stored as
+a :ref:`station or event time <NuRadioReco/pages/times:Station time (Event time)>`. The times corresponding to the `voltage time trace <NuRadioReco.framework.channel.Channel>`,
+`electric fields <NuRadioReco.framework.electric_field.ElectricField>` or `triggers <NuRadioReco.framework.trigger.Trigger>`
+are then stored as floats relative to this global time inside each object. They can be obtained by the
+`get_times() <NuRadioReco.framework.base_trace.BaseTrace.get_times>` method for trace-like objects (`ElectricField <NuRadioReco.framework.electric_field.ElectricField>` ,
+`Channel <NuRadioReco.framework.channel.Channel>`), or by the `get_trigger_time <NuRadioReco.framework.trigger.Trigger.get_trigger_time>`
+method for `Trigger <NuRadioReco.framework.trigger.Trigger>` objects.
 
-We smoothly filter the first 5% and last 5% of the trace using a Tukey window function. This is a function that goes smoothly from 0 to 1.
-To avoid rollover of the pulse, we add 128ns of zeros to the beginning and end of the trace. Steps 2) and 3) are performed by the channelStopFilter module
-Both electric fields and channels have a trace_start_time variable. The get_times() function will automatically add the trace_start_time to the time array.
+Time delays are introduced by several hardware components. These time delays are often accounted for by folding/unfolding the complex transfer function (for an amplifier e.g. the measurement of the S12 parameter).
+The unfolding is typically done in the frequency domain where a convolution becomes a simple multiplication.
+As a consequence of typically short trace length (<~1000 samples) and because a Fourier transform implies implicitly a periodic signal,
+a pulse being at the beginning of the trace can end up being at the end of the trace.
+This can be avoided by using the `NuRadioReco.modules.channelStopFilter` module, which appends zeros at either end of the trace
+and applies a Tukey window to taper the ends of the trace towards zero.
 
-ARIANNA specific details:
+.. Note::
+  For the **ARIANNA** experiment, the hardware produces an artifact (a glitch) at the STOP position (i.e. the physical beginning of the trace).
+  Because of the way the hardware works, the STOP position is not at the beginning of the trace but can be anywhere.
+  During read in of the snowshovel calibrated data files, the trace is rolled such that the physical beginning (the STOP position) is at sample zero of the trace.
+  This glitch is removed by the `channelStopFilter <NuRadioReco.modules.channelStopFilter>` procedure described in the :ref:`module overview below <NuRadioReco/pages/times:Overview of modules that affect time>` .
+
+Station time (Event time)
 -------------------------
-Our hardware produces an artifact (a glitch) at the STOP position (i.e. the physical beginning of the trace). Because of the way the hardware works, the STOP position is not at the beginning of the trace but can be anywhere. During read in of the snowshovel calibrated data files, the trace is rolled such that the physical beginning (the STOP position) is at sample zero of the trace. This glitch is removed by the procedure described above.
+The global time at which the event takes place is stored as the `event time <NuRadioReco.framework.event.Event.get_event_time>`
+in the `Event <NuRadioReco.framework.event.Event>` object.
+This time usually corresponds to the "vertex time" of the first interaction for simulations,
+and the time at which the data was recorded in the DAQ for data.
+It is stored as an `astropy.time.Time` object to enable sub-ns precision on the absolute time.
+
+In **simulated data**, the `event time <NuRadioReco.framework.event.Event.get_event_time>`
+is generally the same as the `station_time <NuRadioReco.framework.station.Station.get_station_time>` stored
+in the `Station <NuRadioReco.framework.station.Station>` object.
+In **experimental data**, the `station_time <NuRadioReco.framework.station.Station.get_station_time>`
+usually corresponds to the time the data was read out (recorded), and the `event time <NuRadioReco.framework.event.Event.get_event_time>` may not always be defined.
+In this case, because different stations may operate and trigger independently,
+the station_times of different stations are not guaranteed to agree, even if they were triggered by the same source.
+
 
+Times in `Channel <NuRadioReco.framework.channel.Channel>`, `ElectricField <NuRadioReco.framework.electric_field.ElectricField>` and
+`Trigger <NuRadioReco.framework.trigger.Trigger>` objects are all defined relative to the
+`station_time <NuRadioReco.framework.station.Station.get_station_time>` of the `Station <NuRadioReco.framework.station.Station>`
+they are stored in (see the description of the :doc:`NuRadio data structure </NuRadioReco/pages/event_structure>`).
+These times are stored as an array of floats.
+For trace-like objects (`Channels <NuRadioReco.framework.channel.Channel>` and `ElectricField <NuRadioReco.framework.electric_field.ElectricField>`),
+the times can be obtained through the `get_times() <NuRadioReco.framework.base_trace.BaseTrace.get_times>` method of these classes.
+Additionally, the trace start time (the first value of `get_times() <NuRadioReco.framework.base_trace.BaseTrace.get_times>`)
+is accessible as the `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>` .
 
-Station time
-------------
-The trace_start_times are all given relative to the station_time of the station the E-field or channel belongs to. The station_time is stored in an astopy.time.Time object for sub nanosecond precision on absolute times.
-The trace_start_time itself is stored as a float. For simulations, the trace_start_time is relative to the vertex time, i.e., the time of the particle interaction. 
-For data: TODO, describe how current RNO-G data is handled
 
 Trace start times in channels
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Specify when the trace starts relative to the station time. Effects that change the pulse time for all frequencies equally (for example cable delays) are most often taken into account by changing the trace_start_time.
+Specify when the trace starts relative to the station time. Effects that change the pulse time for all frequencies equally (for example cable delays) are most often taken into account by changing the `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>`.
 
 Trace start times in E-fields
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Work similar to trace_start_time in channels, but with a caveat: Since EM-waves move, electric_field objects hold a position (relative to the station they are associated with). The E-field is therefore defined as the field an observer would measure at the given position. Note that this position does not necessarily have to coincide with the position of a channel the E-field is associated with. This is the case for (some) cosmic-ray simulations where the same E-field at the surface is used for all surface LPDAs.
+Work similar to `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>` in channels, but with a caveat: Since EM-waves move, electric_field objects hold a position (relative to the station they are associated with). The E-field is therefore defined as the field an observer would measure at the given position. Note that this position does not necessarily have to coincide with the position of a channel the E-field is associated with. This is the case for (some) cosmic-ray simulations where the same E-field at the surface is used for all surface LPDAs.
 
-overview of modules that alter time
-===================================
-We list all relevant modules that is used for a MC simulation and reconstruction. For a pure data reconstruction, the first few modules are just not used
+Trigger times
+^^^^^^^^^^^^^
+The `trigger_time <NuRadioReco.framework.trigger.Trigger.get_trigger_time>`,
+which is the time at which the trigger fired, is stored in the `Trigger <NuRadioReco.framework.trigger.Trigger>`
+object (which can be obtained using `station.get_trigger() <NuRadioReco.framework.station.Station.get_trigger>`).
+This is the time at which the trigger condition was first fulfilled.
+As for the trace_start_time, the trigger time is defined relative to the
+`station_time <NuRadioReco.framework.station.Station.get_station_time>` .
 
-* readCoREAS: CoREAS reader prepends n samples to the simulated trace. This is done so that the trace does not directly start with the pulse and to have a good frequency resolution.
 
-* efieldToVoltageConverter: the traces are rolled (rotated) according to the time delay due to the geometric separation of the antennas and cable delays.
+Overview of modules that affect time
+------------------------------------
+We list all relevant modules that are used for a MC simulation and reconstruction. For a pure data reconstruction, the first two modules are not used.
 
-* hardwareResponseIncorporator (sim to data):
-    * the channel traces are folded with the amplifier response which also includes some time delay
-    * note that the hardwareResponseIncorporator does not take cable delays into account, as this is done by the efieldToVoltageConverter
+* `NuRadioReco.modules.io.coreas`: CoREAS reader prepends n samples to the simulated trace. This is done so that the trace does not directly start with the pulse and to have a good frequency resolution.
+
+* `NuRadioReco.modules.efieldToVoltageConverter`:
+  the voltage traces are delayed compared to the electric field signal due to the geometric separation of the antennas and cable delays.
+  This is accounted for by shifting the `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>`.
+  Note that the very similar `NuRadioReco.modules.efieldToVoltageConverterPerEfield` (which creates one
+  `SimChannel <NuRadioReco.framework.sim_channel.SimChannel>` per electric field instead of combining the induced voltage traces in a single channel) does **not** include the cable delays!
 
-* triggerTimeAdjuster
-    * 'sim_to_data' mode: This modules cuts the trace to the correct length (as specified in the detector description) around the trigger time with a pre-trigger time as defined by the respective trigger module. In the case of multiple triggers it used the primary trigger. If no primary trigger is defined, it uses the trigger with the earliest trigger time. In the end, the trace_start_time is set to the trigger time. This is done because this reflects how raw experimental data looks like. 
-    * 'data_to_sim' mode: The module determines the trigger that was used to cut the trace to its current length (the 'sim_to_data' step above in case of simulations) and adjusts the trace_start_time according to the different readout delays. The "primary trigger" defines the readout delays. After applying this module in the "data_to_sim" direction, the position in the trace that caused the trigger can be found via `trigger_time` - `trace_start_time`.
+* `NuRadioReco.modules.RNO_G.hardwareResponseIncorporator`, `NuRadioReco.modules.ARIANNA.hardwareResponseIncorporator`, `NuRadioReco.modules.ARA.hardwareResponseIncorporator`:
 
-* channelStopFilter: this module prepends and appends all channels with a fixed length (128ns by default). The 'prepend' time is subtracted from the station start time (because all channels get the same time delay)
+  If ``sim to data=True``:
+
+    * the channel traces are folded with the amplifier response which also includes some time delay.
+      This delay is applied to the trace in the frequency domain (i.e. the signal is shifted within the trace, rather than adjusting the trace_start_time)
+    * note that the hardwareResponseIncorporator does not take cable delays into account, as this is done by the efieldToVoltageConverter
+
+  If ``sim to data=False``:
 
-* hardwareResponseIncorporator (data reconstruction):
     * unfolds amplifier -> also implies a time delay in the channel trace
     * cable delay is subtracted from the trace start time (due to the limited trace length, the trace is not rolled to account for cable delays)
 
-* voltageToEfieldConverter:
-    * the traces from all used channels are cut to the overlapping region (including delays due to geometry and differences in delays due to different hardware components, e.g. cables of different length's)
-    * the E-field trace_start_time is set accordingly
\ No newline at end of file
+* `NuRadioReco.modules.triggerTimeAdjuster`
+    * ``sim_to_data`` mode: This modules cuts the trace to the correct length (as specified in the detector description) around the trigger time with a pre-trigger time as defined by the respective trigger module. In the case of multiple triggers it used the primary trigger. If no primary trigger is defined, it uses the trigger with the earliest trigger time. In the end, the `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>` is set to the trigger time. This is done because this reflects what raw experimental data looks like.
+    * ``data_to_sim`` mode: The module determines the trigger that was used to cut the trace to its current length (the 'sim_to_data' step above in case of simulations) and adjusts the `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>` according to the different readout delays. The "primary trigger" defines the readout delays. **After** applying this module in the "data_to_sim" direction, the position in the trace that caused the trigger can be found via the `trigger_time <NuRadioReco.framework.trigger.Trigger.get_trigger_time>`.
+
+* `NuRadioReco.modules.channelStopFilter`: this module prepends and appends all channels with a fixed length (128ns by default).
+  The 'prepend' time is subtracted from the trace start time (because all channels get the same time delay).
+  It additionally applies a tukey window to taper off the start and end (by default, the first and last 5%) of the trace.
+
+* `NuRadioReco.modules.voltageToEfieldConverter`:
+    * the traces from all used channels are cut to the overlapping region (including delays due to geometry and differences in delays due to different group delays in hardware, e.g. different antenna/amplifier responses)
+    * the E-field `trace_start_time <NuRadioReco.framework.base_trace.BaseTrace.get_trace_start_time>` is set accordingly
\ No newline at end of file
diff --git a/documentation/source/main.rst b/documentation/source/main.rst
index 07d26be2fb..bf46a2d2d8 100644
--- a/documentation/source/main.rst
+++ b/documentation/source/main.rst
@@ -13,6 +13,7 @@ Welcome to NuRadio's documentation!
    Introduction/pages/welcome_page.rst
    NuRadioReco/pages/welcome_page.rst
    NuRadioMC/pages/welcome_page.rst
+   Experiments/welcome_page.rst
 
 Indices and tables
 ==================
diff --git a/install_dev.py b/install_dev.py
index 820a5a73ca..563646a299 100644
--- a/install_dev.py
+++ b/install_dev.py
@@ -160,6 +160,10 @@ def convert_poetry_to_pip(reqs):
             import toml
             toml_dict = toml.load(os.path.join(top_dir, 'pyproject.toml'))
             reqs = toml_dict['tool']['poetry']['dependencies']
+            for req in list(reqs.keys()): # ignore optional requirements
+                if isinstance(reqs[req], dict):
+                    if 'optional' in reqs[req]:
+                        reqs.pop(req)
             reqs_pip = convert_poetry_to_pip(reqs)
 
             # install the requirements using pip
@@ -179,10 +183,10 @@ def convert_poetry_to_pip(reqs):
                 retcode |= subprocess.call([sys.executable, '-m', 'pip', 'install', 'toml'] + pip_install_as_user) # we need toml to read pyproject.toml
                 import toml
             toml_dict = toml.load(os.path.join(top_dir, 'pyproject.toml'))
-            reqs = toml_dict['tool']['poetry']['dev-dependencies']
+            reqs = toml_dict['tool']['poetry']['dependencies']
             extras = toml_dict['tool']['poetry']['extras']
-            header = "{:4s}|{:12s}|{:16s}|{}\n".format("id", "Install?", "extra", "modules")
-            str_format = "{:4s}|{:12s}|{:16s}|{}\n"
+            header = "{:4s}|{:10s}|{:16s}|{}\n".format("id", "Install?", "extra", "modules")
+            str_format = "{:4s}|{:10s}|{:16s}|{}\n"
             selected_for_install = []
             header = str_format.format("id", "Install?", "feature", "modules")
             footer = (
diff --git a/pyproject.toml b/pyproject.toml
index 107d2696c2..3e6f2156f6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,25 +42,25 @@ uproot = "*"
 importlib-metadata = {version = ">=4.8.1", python = "<3.8"}
 numba = "*"
 filelock = "*"
-
-[tool.poetry.dev-dependencies]
-Sphinx = "*"
-sphinx-rtd-theme = "*"
-numpydoc = "*"
-proposal = "7.6.2"
-pygdsm = {git = "https://github.com/telegraphic/pygdsm"}
-nifty5 = {git = "https://gitlab.mpcdf.mpg.de/ift/nifty.git", branch="NIFTy_5"}
-pypocketfft = {git = "https://gitlab.mpcdf.mpg.de/mtr/pypocketfft"}
-MCEq = "*"
-crflux = "*"
-pandas = "*"
-mattak = {git = "https://github.com/RNO-G/mattak"}
-runtable = {git = "ssh://git@github.com/RNO-G/rnog-runtable.git"}
+Sphinx = {version = "*", optional = true}
+sphinx-rtd-theme = {version = "*", optional = true}
+numpydoc = {version = "*", optional = true}
+proposal = {version = "7.6.2", optional = true}
+pygdsm = {git = "https://github.com/telegraphic/pygdsm", optional = true}
+pylfmap = {version = "*", optional = true}
+nifty5 = {git = "https://gitlab.mpcdf.mpg.de/ift/nifty.git", branch="NIFTy_5", optional = true}
+pypocketfft = {git = "https://gitlab.mpcdf.mpg.de/mtr/pypocketfft", optional = true}
+MCEq = {version = "*", optional = true}
+crflux = {version = "*", optional = true}
+mattak = {git = "https://github.com/RNO-G/mattak", optional = true}
+rnog-runtable = {git = "ssh://git@github.com/RNO-G/rnog-runtable.git", optional = true}
 
 [tool.poetry.extras]
 documentation = ["Sphinx", "sphinx-rtd-theme", "numpydoc"]
 proposal = ["proposal"]
-galacticnoise = ['pygdsm']
-ift_reco = ['nifty5', 'pypocketfft']
-muon_flux_calc = ['MCEq', 'crflux']
-RNO_G_DATA = ["mattak", "runtable", "pandas"]
+galacticnoise = ['pygdsm', 'pylfmap']
+ift-reco = ['nifty5', 'pypocketfft']
+muon-flux = ['MCEq', 'crflux']
+RNO-G = ["mattak"]
+RNO-G-extras = ["rnog-runtable"]
+ALL = ["Sphinx", "sphinx-rtd-theme", "numpydoc", "proposal", "pygdsm", 'pylfmap', "nifty5", "pypocketfft", "MCEq", "crflux", "mattak"]