Add unit tests for filterinc capability and automatic spin state splitting

Co-authored-by: bruhn_b <basil.bruhn@psi.ch>
Reviewed-on: #1
Co-authored-by: Artur Glavic <artur.glavic@psi.ch>
Co-committed-by: Artur Glavic <artur.glavic@psi.ch>

.gitattributes

@@ -0,0 +1 @@
+*.hdf filter=lfs diff=lfs merge=lfs -text

.github/workflows/release.yml

@@ -19,50 +19,113 @@ on:
           - all
           - windows
           - linux
-          - mac
+          - all_incl_release
 
 jobs:
-  build-ubuntu-latest:
+  test:
+
     runs-on: ubuntu-latest
-    if: ${{ (github.event_name != 'workflow_dispatch') || (contains(fromJson('["all", "linux"]'), github.event.inputs.build-items)) }}
+    strategy:
+      matrix:
+        python-version: ['3.8', '3.9', '3.10', '3.12']
+      fail-fast: false
+
+    steps:
+      - name: Checkout LFS objects
+        run: git clone https://${{secrets.GITHUB_TOKEN}}@gitea.psi.ch/${{ github.repository }}.git .
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install pytest
+          pip install -r requirements.txt
+
+      - name: Test with pytest
+        run: |
+          python -m pytest tests
+  
+
+  build-ubuntu-latest:
+    needs: [test]
+    runs-on: ubuntu-latest
+    if: ${{ (github.event_name != 'workflow_dispatch') || (contains(fromJson('["all", "linux", "all_incl_release"]'), github.event.inputs.build-items)) }}
+    permissions:
+      id-token: write
     steps:
     - uses: actions/checkout@v4
     - uses: actions/setup-python@v5
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
         pip install build
         pip install -r requirements.txt
+        pip install wheel build twine
     - name: Build PyPI package
       run: |
         python3 -m build
+#    - name: Archive distribution
+#      uses: actions/upload-artifact@v3
+#      with:
+#        name: linux-dist
+#        path: |
+#          dist/*.tar.gz
     - name: Upload to PyPI
-      if: github.event_name != 'workflow_dispatch'
-      uses: pypa/gh-action-pypi-publish@release/v1
-      with:
-        user: __token__
-        password: ${{ secrets.PYPI_TOKEN }}
-        skip-existing: true
-    - name: Archive distribution
-      uses: actions/upload-artifact@v4
-      with:
-        name: linux-dist
-        path: |
-          dist/*.tar.gz
+      run: |
+        twine upload dist/* -u __token__ -p ${{ secrets.PYPI_TOKEN }} --skip-existing
+
+  build-windows:
+    needs: [test]
+    runs-on: windows-latest
+    if: ${{ (github.event_name != 'workflow_dispatch') || (contains(fromJson('["all", "windows", "all_incl_release"]'), github.event.inputs.build-items)) }}
 
-  release:
-    if: github.event_name != 'workflow_dispatch'
-    runs-on: ubuntu-latest
-    needs: [build-ubuntu-latest]
     steps:
     - uses: actions/checkout@v4
-    - uses: actions/download-artifact@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: 3.12
+    - name: Install dependencies
+      run: |
+        C:\Miniconda\condabin\conda.bat env update --file conda_windows.yml --name base
+        C:\Miniconda\condabin\conda.bat init powershell
+    - name: Build with pyinstaller
+      run: |
+        pyinstaller windows_build.spec
+        cd dist\eos
+        Compress-Archive -Path .\* -Destination ..\..\eos.zip
+    - name: Archive distribution
+      uses: actions/upload-artifact@v3
+      with:
+        name: windows-dist
+        path: |
+          eos.zip
+
+  release:
+    if:  ${{ (github.event_name != 'workflow_dispatch') || (contains(fromJson('["all_incl_release"]'), github.event.inputs.build-items)) }}
+    runs-on: ubuntu-latest
+    needs: [build-ubuntu-latest, build-windows]
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+        fetch-tags: true
+    - uses: actions/download-artifact@v3
       with:
         name: linux-dist
+    - uses: actions/download-artifact@v3
+      with:
+        name: windows-dist
+    - name: get latest version tag
+      run: echo "RELEASE_TAG=$(git describe --abbrev=0 --tags)" >> $GITHUB_ENV
     - uses: ncipollo/release-action@v1
       with:
-        artifacts: "amor*.tar.gz"
+        artifacts: "amor*.tar.gz,*.zip"
         token: ${{ secrets.GITHUB_TOKEN }}
         allowUpdates: true
+        tag: ${{ env.RELEASE_TAG }}

.github/workflows/unit_tests.yml

@@ -10,16 +10,18 @@ on:
   workflow_dispatch:
 
 jobs:
-  build:
+  test:
 
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10', '3.11', '3.12']
+        python-version: ['3.8', '3.9', '3.10', '3.12']
       fail-fast: false
 
     steps:
-      - uses: actions/checkout@v4
+      - name: Checkout LFS objects
+        run: git clone https://${{secrets.GITHUB_TOKEN}}@gitea.psi.ch/${{ github.repository }}.git .
+
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v5
         with:
@@ -33,5 +35,4 @@ jobs:
 
       - name: Test with pytest
         run: |
-          cd tests
-          python -m pytest --pyargs .
+          python -m pytest tests

.gitignore

@@ -8,3 +8,5 @@ raw
 test_results
 build
 dist
+profile_test.prof
+amor_eos.log

README.md

@@ -1,12 +0,0 @@
-Software repository for the neutron reflectometer Amor at the Paul Scherrer Institut, Switzerland
-
-reduction of the raw files (.hdf) to reflectivity files in one of the representations of the **ORSO reflectivity file format**:
-
-> `eos.py`  
-> `neos.py` : version for playing and testing
-
-visualisation of the content of a raw file (.hdf):
-
-> `events2histogram.py`
-
-#TODO: real readme for final system needed.

README.rst

@@ -0,0 +1,27 @@
+EOS - The AMOR focusing reflectometry data reduction software
+-------------------------------------------------------------
+
+.. image:: https://img.shields.io/pypi/v/amor-eos.svg
+        :target: https://pypi.python.org/pypi/amor-eos/
+
+
+Software repository for the neutron reflectometer Amor at the Paul Scherrer Institut, Switzerland
+
+Reduction of the raw files (.hdf) to reflectivity files in one of the representations of the **ORSO reflectivity file format**:
+
+  eos --help
+
+visualisation of the content of a raw file (.hdf):
+
+  events2histogram.py
+
+:TODO: real readme for final system needed.
+
+Installation
+------------
+Create a virtual python environment (>3.8) and install the PyPI package:
+
+  pip install amor-eos
+
+On Windows you can also use the binary eos.exe that you find in the
+[GitHub Releases]([https://github.com/jochenstahn/amor/releases/latest) section

conda_windows.yml

@@ -0,0 +1,44 @@
+name: eos_build
+channels:
+  - defaults
+dependencies:
+  - altgraph=0.17.3=py312haa95532_0
+  - blas=1.0=mkl
+  - bzip2=1.0.8=h2bbff1b_6
+  - ca-certificates=2024.11.26=haa95532_0
+  - expat=2.6.3=h5da7b33_0
+  - h5py=3.12.1=py312h3b2c811_0
+  - hdf5=1.12.1=h51c971a_3
+  - icc_rt=2022.1.0=h6049295_2
+  - intel-openmp=2023.1.0=h59b6b97_46320
+  - libffi=3.4.4=hd77b12b_1
+  - llvmlite=0.43.0=py312hf2fb9eb_0
+  - mkl=2023.1.0=h6b88ed4_46358
+  - mkl-service=2.4.0=py312h2bbff1b_1
+  - mkl_fft=1.3.11=py312h827c3e9_0
+  - mkl_random=1.2.8=py312h0158946_0
+  - numba=0.60.0=py312h0158946_0
+  - numpy=1.26.4=py312hfd52020_0
+  - numpy-base=1.26.4=py312h4dde369_0
+  - openssl=3.0.15=h827c3e9_0
+  - packaging=24.1=py312haa95532_0
+  - pefile=2023.2.7=py312haa95532_0
+  - pip=24.2=py312haa95532_0
+  - pyinstaller=6.9.0=py312h0416ee5_0
+  - pyinstaller-hooks-contrib=2024.7=py312haa95532_0
+  - python=3.12.7=h14ffc60_0
+  - pywin32-ctypes=0.2.2=py312haa95532_0
+  - setuptools=75.1.0=py312haa95532_0
+  - sqlite=3.45.3=h2bbff1b_0
+  - tbb=2021.8.0=h59b6b97_0
+  - tk=8.6.14=h0416ee5_0
+  - tzdata=2024b=h04d1e81_0
+  - vc=14.40=h2eaa2aa_1
+  - vs2015_runtime=14.40.33807=h98bb1dd_1
+  - wheel=0.44.0=py312haa95532_0
+  - xz=5.4.6=h8cc25b3_1
+  - zlib=1.2.13=h8cc25b3_1
+  - pip:
+      - orsopy==1.2.1
+      - pyyaml==6.0.2
+      - tzdata

eos.py

@@ -1,46 +0,0 @@
-#!/usr/bin/env python
-"""
-eos reduces measurements performed on Amor@SINQ, PSI
-
-Author: Jochen Stahn (algorithms, python draft),
-        Artur Glavic (structuring and optimisation of code)
-
-conventions (not strictly followed, yet):
-- array names end with the suffix '_x[y]' with the meaning
-    _e  = events
-    _tof
-    _l  = lambda
-    _t  = theta
-    _z  = detector z
-    _lz = (lambda, detector z)
-    _q  = q_z
-"""
-
-import logging
-
-from libeos.command_line import command_line_options
-from libeos.logconfig import setup_logging
-from libeos.reduction import AmorReduction
-
-#=====================================================================================================
-# TODO:
-# - calculate resolution using the chopperPhase
-# - deal with background correction
-# - format of 'call' + add '-Y' if not supplied
-#=====================================================================================================
-
-def main():
-    setup_logging()
-    logging.warning('######## eos - data reduction for Amor ########')
-
-    # read command line arguments and generate classes holding configuration parameters
-    config = command_line_options()
-    # Create reducer with these arguments
-    reducer = AmorReduction(config)
-    # Perform actual reduction
-    reducer.reduce()
-
-    logging.info('######## eos - finished ########')
-
-if __name__ == '__main__':
-    main()

eos/__init__.py

@@ -1,6 +1,6 @@
 """
-Package to handle data redction at AMOR instrument to be used by eos.py script.
+Package to handle data redction at AMOR instrument to be used by __main__.py script.
 """
 
-__version__ = '2.1.1'
-__date__    = '2024-10-30'
+__version__ = '3.2.2'
+__date__    = '2026-02-27'

eos/__main__.py

@@ -0,0 +1,42 @@
+"""
+eos reduces measurements performed on Amor@SINQ, PSI
+
+Author: Jochen Stahn (algorithms, python draft),
+        Artur Glavic (structuring and optimisation of code)
+"""
+
+import logging
+
+# need to do absolute import here as pyinstaller requires it
+from eos.options import ReflectivityConfig, ReaderConfig, ExperimentConfig, ReflectivityReductionConfig, ReflectivityOutputConfig
+from eos.command_line import commandLineArgs
+from eos.logconfig import setup_logging, update_loglevel
+
+
+def main():
+    setup_logging()
+
+    # read command line arguments and generate classes holding configuration parameters
+    clas = commandLineArgs([ReaderConfig, ExperimentConfig, ReflectivityReductionConfig, ReflectivityOutputConfig],
+                           'eos')
+    update_loglevel(clas.verbose)
+
+    reader_config = ReaderConfig.from_args(clas)
+    experiment_config = ExperimentConfig.from_args(clas)
+    reduction_config = ReflectivityReductionConfig.from_args(clas)
+    output_config = ReflectivityOutputConfig.from_args(clas)
+    config = ReflectivityConfig(reader_config, experiment_config, reduction_config, output_config)
+
+    logging.warning('######## eos - data reduction for Amor ########')
+
+    # only import heavy module if sufficient command line parameters were provided
+    from eos.reduction_reflectivity import ReflectivityReduction
+    # Create reducer with these arguments
+    reducer = ReflectivityReduction(config)
+    # Perform actual reduction
+    reducer.reduce()
+
+    logging.info('######## eos - finished ########')
+
+if __name__ == '__main__':
+    main()

eos/command_line.py

@@ -0,0 +1,42 @@
+import argparse
+
+from typing import List, Type
+from .options import ArgParsable
+
+
+def commandLineArgs(config_items: List[Type[ArgParsable]], program_name=None, extra_args=[]):
+    """
+    Process command line argument.
+    The type of the default values is used for conversion and validation.
+    """
+    msg = "eos reads data from (one or several) raw file(s) of the .hdf format, \
+           performs various corrections, conversations and projections and exports\
+           the resulting reflectivity in an orso-compatible format."
+    clas = argparse.ArgumentParser(prog=program_name,
+                                   description = msg, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    clas.add_argument('-v', '--verbose', action='count', default=0)
+
+    clas_groups = {}
+
+    all_arguments = []
+    for cls in config_items:
+        all_arguments += cls.get_commandline_parameters()
+
+    all_arguments.sort() # parameters are sorted alphabetically, unless they have higher priority
+    for cpc in all_arguments:
+        if not cpc.group in clas_groups:
+            clas_groups[cpc.group] = clas.add_argument_group(cpc.group)
+        if cpc.short_form:
+            clas_groups[cpc.group].add_argument(
+                    f'-{cpc.short_form}', f'--{cpc.argument}', **cpc.add_argument_args
+                    )
+        else:
+            clas_groups[cpc.group].add_argument(
+                    f'--{cpc.argument}', **cpc.add_argument_args
+                    )
+
+    for ma in extra_args:
+        clas.add_argument(**ma)
+
+    return clas.parse_args()

eos/const.py

@@ -0,0 +1,11 @@
+"""
+Constants used in data reduction.
+"""
+
+hdm = 6.626176e-34/1.674928e-27  # h / m
+lamdaCut = 2.5  # Aa
+lamdaMax = 15.0 # Aa
+
+polarizationConfigs = ['unpolarized', 'unpolarized', 'po', 'mo', 'op', 'pp', 'mp', 'om', 'pm', 'mm']
+polarizationLabels = ['undetermined', 'unpolarized', 'spin-up', 'spin-down', 'op',
+                      'up-up', 'down-up', 'om', 'up-down', 'down-down']

eos/e2h.py

@@ -0,0 +1,42 @@
+"""
+events2histogram vizualising data from Amor@SINQ, PSI
+
+Author: Jochen Stahn (algorithms, python draft),
+        Artur Glavic (structuring and optimisation of code)
+"""
+import logging
+
+# need to do absolute import here as pyinstaller requires it
+from eos.options import E2HConfig, ReaderConfig, ExperimentConfig, E2HReductionConfig
+from eos.command_line import commandLineArgs
+from eos.logconfig import setup_logging, update_loglevel
+
+
+def main():
+    setup_logging()
+    logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+    # read command line arguments and generate classes holding configuration parameters
+    clas = commandLineArgs([ReaderConfig, ExperimentConfig, E2HReductionConfig],
+                           'events2histogram')
+    update_loglevel(clas.verbose)
+
+    reader_config = ReaderConfig.from_args(clas)
+    experiment_config = ExperimentConfig.from_args(clas)
+    reduction_config = E2HReductionConfig.from_args(clas)
+    config = E2HConfig(reader_config, experiment_config, reduction_config)
+
+    logging.warning('######## events2histogram - data vizualization for Amor ########')
+    from eos.reduction_e2h import E2HReduction
+
+    # only import heavy module if sufficient command line parameters were provided
+    from eos.reduction_reflectivity import ReflectivityReduction
+    # Create reducer with these arguments
+    reducer = E2HReduction(config)
+    # Perform actual reduction
+    reducer.reduce()
+
+    logging.info('######## events2histogram - finished ########')
+
+if __name__ == '__main__':
+    main()

eos/event_analysis.py

@@ -0,0 +1,163 @@
+"""
+Define an event dataformat that performs reduction actions like wavelength calculation on per-event basis.
+With large number of events these actions can be time consuming so they use numba based functions.
+"""
+import numpy as np
+import logging
+
+from typing import Tuple
+
+from . import const
+from .event_data_types import EventDataAction, EventDatasetProtocol, append_fields, EVENT_BITMASKS
+from .helpers import filter_project_x, merge_frames, extract_walltime, add_log_to_pulses
+from .instrument import Detector
+from .options import IncidentAngle
+from .header import Header
+
+class ExtractWalltime(EventDataAction):
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        wallTime = extract_walltime(dataset.data.events.tof,
+                                    dataset.data.packets.start_index,
+                                    dataset.data.packets.time)
+        logging.debug(f'        expending event stream by wallTime')
+        new_events = append_fields(dataset.data.events, [('wallTime', wallTime.dtype)])
+        new_events.wallTime = wallTime
+        dataset.data.events = new_events
+
+class MergeFrames(EventDataAction):
+    def __init__(self, lamdaCut=None):
+        self.lamdaCut=lamdaCut
+
+    def perform_action(self, dataset: EventDatasetProtocol)->None:
+        if self.lamdaCut is None:
+            lamdaCut = const.lamdaCut
+        else:
+            lamdaCut = self.lamdaCut
+        tofCut = lamdaCut*dataset.geometry.chopperDetectorDistance/const.hdm*1e-13
+        total_offset = (tofCut +
+                        dataset.timing.tau * (dataset.timing.ch1TriggerPhase + dataset.timing.chopperPhase/2)/180)
+        dataset.data.events.tof = merge_frames(dataset.data.events.tof, tofCut, dataset.timing.tau, total_offset)
+
+
+class AnalyzePixelIDs(EventDataAction):
+    def __init__(self, yRange: Tuple[int, int]):
+        self.yRange = yRange
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        d = dataset.data
+        (detZ, detXdist, delta, mask) = filter_project_x(
+                Detector.pixelLookUp, d.events.pixelID, self.yRange[0], self.yRange[1]
+                )
+        ana_events = append_fields(d.events, [
+            ('detZ', detZ.dtype), ('detXdist', detXdist.dtype), ('delta', delta.dtype)])
+        # add analysis per event
+        ana_events.detZ = detZ
+        ana_events.detXdist = detXdist
+        ana_events.delta = delta
+        ana_events.mask += np.logical_not(mask)*EVENT_BITMASKS['yRange']
+        d.events = ana_events
+
+class CalculateWavelength(EventDataAction):
+    def __init__(self, lambdaRange: Tuple[float, float]):
+        self.lambdaRange = lambdaRange
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        d = dataset.data
+        if not 'detXdist' in dataset.data.events.dtype.names:
+            raise ValueError("CalculateWavelength requires dataset with analyzed pixels, perform AnalyzePixelIDs first")
+
+        #lamdaMax = const.lamdaCut+1.e13*dataset.timing.tau*const.hdm/(dataset.geometry.chopperDetectorDistance+124.)
+
+        # lambda
+        # TODO: one of the most time consuming actions, could be implemented in numba, instead?
+        lamda = (1.e13*const.hdm)*d.events.tof/(dataset.geometry.chopperDetectorDistance+d.events.detXdist)
+
+        final_events = append_fields(d.events, [('lamda', np.float64)])
+        # add analysis per event
+        final_events.lamda = lamda
+        final_events.mask += EVENT_BITMASKS["LamdaRange"]*(
+                (self.lambdaRange[0]>lamda) | (lamda>self.lambdaRange[1]))
+        d.events = final_events
+
+class CalculateQ(EventDataAction):
+    def __init__(self, incidentAngle: IncidentAngle):
+        self.incidentAngle = incidentAngle
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        d = dataset.data
+        if not 'lamda' in dataset.data.events.dtype.names:
+            raise ValueError("CalculateQ requires dataset with analyzed wavelength, perform CalculateWavelength first")
+
+        lamda = d.events.lamda
+
+        final_events = append_fields(d.events, [('qz', np.float64)])
+
+        # alpha_f
+        # q_z
+        if self.incidentAngle == IncidentAngle.alphaF:
+            alphaF_e  = dataset.geometry.nu - dataset.geometry.mu + d.events.delta
+            final_events.qz = 4*np.pi*(np.sin(np.deg2rad(alphaF_e))/lamda)
+        elif self.incidentAngle == IncidentAngle.nu:
+            alphaF_e  = (dataset.geometry.nu + d.events.delta + dataset.geometry.kap + dataset.geometry.kad) / 2.
+            final_events.qz = 4*np.pi*(np.sin(np.deg2rad(alphaF_e))/lamda)
+        else:
+            alphaF_e  = dataset.geometry.nu - dataset.geometry.mu + d.events.delta
+            alphaI    = dataset.geometry.kap + dataset.geometry.kad + dataset.geometry.mu
+            final_events.qz = 2*np.pi * ((np.sin(np.deg2rad(alphaF_e)) + np.sin(np.deg2rad(alphaI)))/lamda)
+            final_events = append_fields(final_events, [('qx', np.float64)])
+            final_events.qx = 2*np.pi * ((np.cos(np.deg2rad(alphaF_e)) - np.cos(np.deg2rad(alphaI)))/lamda)
+
+        dataset.data.events = final_events
+
+    def update_header(self, header: Header):
+        if self.incidentAngle == IncidentAngle.alphaF:
+            header.measurement_scheme = 'angle- and energy-dispersive'
+        else:
+            header.measurement_scheme = 'energy-dispersive'
+
+class FilterQzRange(EventDataAction):
+    def __init__(self, qzRange: Tuple[float, float]):
+        self.qzRange = qzRange
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        d = dataset.data
+        if not 'qz' in dataset.data.events.dtype.names:
+            raise ValueError("FilterQzRange requires dataset with qz values per events, perform WavelengthAndQ first")
+
+        d.events.mask += EVENT_BITMASKS["qRange"]*((self.qzRange[0]>d.events.qz) | (d.events.qz>self.qzRange[1]))
+
+class FilterByLog(EventDataAction):
+
+    def __init__(self, filter_string, remove_switchpulse=False):
+        if filter_string.startswith('!'):
+            filter_string = filter_string[1:]
+            remove_switchpulse = True
+        self.filter_string = filter_string
+        self.remove_switchpulse = remove_switchpulse
+
+    def perform_action(self, dataset: EventDatasetProtocol) -> None:
+        filter_variable = None
+        # go through existing keys in reverse order of their length to insure a name containing another is used
+        existing_keys = list(dataset.data.device_logs.keys())
+        existing_keys.sort(key=lambda x: -len(x))
+        for key in existing_keys:
+            if key in self.filter_string:
+                filter_variable = key
+                break
+        if filter_variable is None:
+            logging.warning(f'          could not find suitable parameter to filter on in {self.filter_string}, '
+                            f'available parameters are: {list(sorted(existing_keys))}')
+            return
+        logging.debug(f'          using parameter {filter_variable} to apply filter {self.filter_string}')
+        if not filter_variable in EVENT_BITMASKS:
+            EVENT_BITMASKS[filter_variable] = max(EVENT_BITMASKS.values())*2
+        if not filter_variable in dataset.data.pulses.dtype.names:
+            # interpolate the parameter values for all existing pulses
+            add_log_to_pulses(filter_variable, dataset)
+        fltr_pulses = eval(self.filter_string, {filter_variable: dataset.data.pulses[filter_variable]})
+        if self.remove_switchpulse:
+            switched = fltr_pulses[:-1] & ~fltr_pulses[1:]
+            fltr_pulses[:-1] &= ~switched
+        goodTimeS = dataset.data.pulses.time[fltr_pulses]
+        filter_e = np.logical_not(np.isin(dataset.data.events.wallTime, goodTimeS))
+        dataset.data.events.mask += EVENT_BITMASKS[filter_variable]*filter_e

eos/event_data_types.py

@@ -0,0 +1,146 @@
+"""
+Specify the data type and protocol used for event datasets.
+"""
+from typing import Dict, List, Optional, Protocol, Tuple
+from dataclasses import dataclass, field
+from .header import Header
+from abc import ABC, abstractmethod
+from hashlib import sha256
+import  numpy as np
+import logging
+
+@dataclass
+class AmorGeometry:
+    mu:float
+    nu:float
+    kap:float
+    kad:float
+    div:float
+
+    chopperSeparation: float
+    detectorDistance: float
+    chopperDetectorDistance: float
+
+@dataclass
+class AmorTiming:
+    ch1TriggerPhase: float
+    ch2TriggerPhase: float
+    chopperSpeed: float
+    chopperPhase: float
+    tau: float
+
+# Structured datatypes used for event streams
+EVENT_TYPE = np.dtype([('tof', np.float64), ('pixelID', np.uint32), ('mask', np.int32)])
+PACKET_TYPE = np.dtype([('start_index', np.uint32), ('time', np.int64)])
+PULSE_TYPE = np.dtype([('time', np.int64), ('monitor', np.float32)])
+PC_TYPE = np.dtype([('current', np.float32), ('time', np.int64)])
+LOG_TYPE = np.dtype([('value', np.float32), ('time', np.int64)])
+
+# define the bitmask for individual event filters
+EVENT_BITMASKS = {
+    'MonitorThreshold': 1,
+    'StrangeTimes': 2,
+    'yRange': 4,
+    'LamdaRange': 8,
+    'qRange': 16,
+    }
+
+def append_fields(input: np.recarray, new_fields: List[Tuple[str, np.dtype]]):
+    # add one ore more fields to a recarray, numpy functions seems to fail
+    flds = [(name, dtypei[0]) for name, dtypei in input.dtype.fields.items()]
+    flds += new_fields
+    output = np.recarray(len(input), dtype=flds)
+
+    for field in input.dtype.fields.keys():
+        output[field] = input[field]
+    return output
+
+@dataclass
+class AmorEventStream:
+    events: np.recarray # EVENT_TYPE
+    packets: np.recarray # PACKET_TYPE
+    pulses: np.recarray  # PULSE_TYPE
+    proton_current: np.recarray # PC_TYPE
+    device_logs: Dict[str, np.recarray] = field(default_factory=dict) # LOG_TYPE
+
+class EventDatasetProtocol(Protocol):
+    """
+    Minimal attributes a dataset needs to provide to work with EventDataAction
+    """
+    geometry: AmorGeometry
+    timing: AmorTiming
+    data: AmorEventStream
+
+    def append(self, other):
+        # Should define a way to add events from other to own
+        ...
+
+    def update_header(self, header:Header):
+        # update a header with the information read from file
+        ...
+
+class EventDataAction(ABC):
+    """
+    Abstract base class used for actions applied to an EventDatasetProtocol based objects.
+    Each action can optionally modify the header information.
+    Actions can be combined using the pipe operator | (OR).
+    """
+
+    def __call__(self, dataset: EventDatasetProtocol)->None:
+        logging.debug(f"        Enter action {self.__class__.__name__} on {dataset!r}")
+        self.perform_action(dataset)
+
+    @abstractmethod
+    def perform_action(self, dataset: EventDatasetProtocol)->None: ...
+
+    def update_header(self, header:Header)->None:
+        if hasattr(self, 'action_name'):
+            header.reduction.corrections.append(getattr(self, 'action_name'))
+
+    def __or__(self, other:'EventDataAction')->'CombinedAction':
+        return CombinedAction([self, other])
+
+    def __repr__(self):
+        output = self.__class__.__name__+'('
+        for key,value in self.__dict__.items():
+            output += f'{key}={value}, '
+        return output.rstrip(', ')+')'
+
+    def action_hash(self)->bytes:
+        # generate a unique hash that encodes this action with its configuration parameters
+        mh = sha256()
+        mh.update(self.__class__.__name__.encode())
+        for key,value in sorted(self.__dict__.items()):
+            mh.update(repr(value).encode())
+        return mh.hexdigest()
+
+class CombinedAction(EventDataAction):
+    """
+    Used to perform multiple actions in one call. Stores a sequence of actions
+    that are then performed individually one after the other.
+    """
+    def __init__(self, actions: List[EventDataAction]) -> None:
+        self._actions = actions
+
+    def perform_action(self, dataset: EventDatasetProtocol)->None:
+        for action in self._actions:
+            action(dataset)
+
+    def update_header(self, header:Header)->None:
+        for action in self._actions:
+            action.update_header(header)
+
+    def __or__(self, other:'EventDataAction')->'CombinedAction':
+        return CombinedAction(self._actions+[other])
+
+    def __repr__(self):
+        output = repr(self._actions[0])
+        for ai in self._actions[1:]:
+            output += ' | '+repr(ai)
+        return output
+
+    def action_hash(self)->bytes:
+        mh = sha256()
+        for action in self._actions:
+            mh.update(action.action_hash().encode())
+        return mh.hexdigest()

eos/event_handling.py

@@ -0,0 +1,202 @@
+"""
+Calculations performed on AmorEventData.
+This module contains actions that do not need the numba base helper functions. Other actions are in event_analysis
+"""
+import logging
+import os
+import numpy as np
+
+from .header import Header
+from .options import ExperimentConfig, MonitorType
+from .event_data_types import EventDatasetProtocol, EventDataAction, EVENT_BITMASKS
+
+class ApplyPhaseOffset(EventDataAction):
+    def __init__(self, chopperPhaseOffset: float):
+        self.chopperPhaseOffset=chopperPhaseOffset
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        logging.debug(
+            f'        replaced ch1TriggerPhase = {dataset.timing.ch1TriggerPhase} '
+            f'with {self.chopperPhaseOffset}')
+        dataset.timing.ch1TriggerPhase = self.chopperPhaseOffset
+
+class ApplyParameterOverwrites(EventDataAction):
+    def __init__(self, config: ExperimentConfig):
+        self.config=config
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        if self.config.muOffset:
+            logging.debug(f'        set muOffset = {self.config.muOffset}')
+            dataset.geometry.mu += self.config.muOffset
+        if self.config.mu:
+            logging.debug(f'        replaced mu = {dataset.geometry.mu} with {self.config.mu}')
+            dataset.geometry.mu = self.config.mu
+        if self.config.nu:
+            logging.debug(f'        replaced nu = {dataset.geometry.nu} with {self.config.nu}')
+            dataset.geometry.nu = self.config.nu
+        logging.info(f'      mu = {dataset.geometry.mu:6.3f}, '
+                     f'nu = {dataset.geometry.nu:6.3f}, '
+                     f'kap = {dataset.geometry.kap:6.3f}, '
+                     f'kad = {dataset.geometry.kad:6.3f}')
+
+    def update_header(self, header:Header) ->None:
+        if self.config.sampleModel:
+            import yaml
+            from orsopy.fileio.model_language import SampleModel
+            if self.config.sampleModel.endswith('.yml') or self.config.sampleModel.endswith('.yaml'):
+                if os.path.isfile(self.config.sampleModel):
+                    with open(self.config.sampleModel, 'r') as model_yml:
+                        model = yaml.safe_load(model_yml)
+                else:
+                    logging.warning(f'  ! the file {self.config.sampleModel} does not exist. Ignored!')
+                    return
+            else:
+                model = dict(stack=self.config.sampleModel)
+            logging.debug(f'        set sample.model = {self.config.sampleModel}')
+            header.sample.model = SampleModel.from_dict(model)
+
+
+class CorrectChopperPhase(EventDataAction):
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        dataset.data.events.tof += dataset.timing.tau*(dataset.timing.ch1TriggerPhase-dataset.timing.chopperPhase/2)/180
+
+
+class CorrectSeriesTime(EventDataAction):
+    def __init__(self, seriesStartTime):
+        self.seriesStartTime = np.int64(seriesStartTime)
+
+    def perform_action(self, dataset: EventDatasetProtocol)->None:
+        if not 'wallTime' in dataset.data.events.dtype.names:
+            raise ValueError("CorrectTimeSeries requires walltTime to be extracted, please run ExtractWalltime first")
+        dataset.data.pulses.time -= self.seriesStartTime
+        dataset.data.events.wallTime -= self.seriesStartTime
+        dataset.data.proton_current.time -= self.seriesStartTime
+        for value in dataset.data.device_logs.values():
+            value.time -= self.seriesStartTime
+        start, stop = dataset.data.events.wallTime[0], dataset.data.events.wallTime[-1]
+        logging.debug(f'      wall time from {start/1e9:6.1f} s to {stop/1e9:6.1f} s, '
+                      f'series time = {self.seriesStartTime/1e9:6.1f}')
+
+
+class AssociatePulseWithMonitor(EventDataAction):
+    def __init__(self, monitorType:MonitorType):
+        self.monitorType = monitorType
+
+    def perform_action(self, dataset: EventDatasetProtocol)->None:
+        logging.debug(f'      using monitor type {self.monitorType}')
+        if self.monitorType in [MonitorType.proton_charge or MonitorType.debug]:
+            dataset.data.pulses.monitor = self.get_current_per_pulse(dataset.data.pulses.time,
+                                                              dataset.data.proton_current.time,
+                                                              dataset.data.proton_current.current)\
+                                                              * 2*dataset.timing.tau * 1e-3
+        elif self.monitorType==MonitorType.time:
+            dataset.data.pulses.monitor  = 2*dataset.timing.tau
+        else:  # pulses
+            dataset.data.pulses.monitor  = 1
+
+        if self.monitorType == MonitorType.debug:
+            if not 'wallTime' in dataset.data.events.dtype.names:
+                raise ValueError(
+                    "AssociatePulseWithMonitor requires walltTime for debugging, please run ExtractWalltime first")
+            cpp, t_bins = np.histogram(dataset.data.events.wallTime, dataset.data.pulses.time)
+            np.savetxt('tme.hst', np.vstack((dataset.data.pulses.time[:-1], cpp, dataset.data.pulses.monitor[:-1])).T)
+
+    @staticmethod
+    def get_current_per_pulse(pulseTimeS, currentTimeS, currents):
+        # add currents for early pulses and current time value after last pulse (j+1)
+        currentTimeS = np.hstack([[0], currentTimeS, [pulseTimeS[-1]+1]])
+        currents = np.hstack([[0], currents])
+        pulseCurrentS = np.zeros(pulseTimeS.shape[0], dtype=float)
+        j = 0
+        for i, ti in enumerate(pulseTimeS):
+            # find the last current item that was before this pulse
+            while ti >= currentTimeS[j+1]:
+                j += 1
+            pulseCurrentS[i] = currents[j]
+        return pulseCurrentS
+
+class FilterMonitorThreshold(EventDataAction):
+    def __init__(self, lowCurrentThreshold:float):
+        self.lowCurrentThreshold = lowCurrentThreshold
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        if not 'wallTime' in dataset.data.events.dtype.names:
+            raise ValueError(
+                    "FilterMonitorThreshold requires walltTime to be extracted, please run ExtractWalltime first")
+        low_current_filter = dataset.data.pulses.monitor>2*dataset.timing.tau*self.lowCurrentThreshold*1e-3
+        dataset.data.pulses.monitor[np.logical_not(low_current_filter)] = 0.
+        goodTimeS = dataset.data.pulses.time[low_current_filter]
+        filter_e = np.logical_not(np.isin(dataset.data.events.wallTime, goodTimeS))
+
+        dataset.data.events.mask += EVENT_BITMASKS['MonitorThreshold']*filter_e
+        logging.info(f'      low-beam (<{self.lowCurrentThreshold} mC) rejected pulses: '
+                     f'{dataset.data.pulses.monitor.shape[0]-goodTimeS.shape[0]} '
+                     f'out of {dataset.data.pulses.monitor.shape[0]}')
+        logging.info(f'          with {filter_e.sum()} events')
+        if goodTimeS.shape[0]:
+            logging.info(f'      average counts per pulse =  {dataset.data.events.shape[0]/goodTimeS.shape[0]:7.1f}')
+        else:
+            logging.info(f'      average counts per pulse = undefined')
+
+class FilterStrangeTimes(EventDataAction):
+    def perform_action(self, dataset: EventDatasetProtocol)->None:
+        filter_e = np.logical_not(dataset.data.events.tof<=2*dataset.timing.tau)
+        dataset.data.events.mask += EVENT_BITMASKS['StrangeTimes']*filter_e
+        if filter_e.any():
+            logging.warning(f'        strange times: {filter_e.sum()}')
+
+
+class TofTimeCorrection(EventDataAction):
+    def __init__(self, correct_chopper_opening: bool = True):
+        self.correct_chopper_opening = correct_chopper_opening
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        if not 'delta' in dataset.data.events.dtype.names:
+            raise ValueError(
+                    "TofTimeCorrection requires delta to be extracted, please run AnalyzePixelIDs first")
+        d = dataset.data
+        if self.correct_chopper_opening:
+            d.events.tof -= ( d.events.delta / 180. ) * dataset.timing.tau
+        else:
+            d.events.tof -= ( dataset.geometry.kad / 180. ) * dataset.timing.tau
+
+class ApplyMask(EventDataAction):
+    def __init__(self, bitmask_filter=None):
+        self.bitmask_filter = bitmask_filter
+
+    def perform_action(self, dataset: EventDatasetProtocol) ->None:
+        # TODO: why is this action time consuming?
+        d = dataset.data
+        pre_filter = d.events.shape[0]
+        if logging.getLogger().level <= logging.DEBUG:
+            # only run this calculation if debug level is actually active
+            filtered_by_mask = {}
+            for key, value in EVENT_BITMASKS.items():
+                filtered_by_mask[key] = ((d.events.mask & value)!=0).sum()
+            logging.debug(f"        Removed by filters: {filtered_by_mask}")
+        if self.bitmask_filter is None:
+            d.events = d.events[d.events.mask==0]
+        else:
+            # remove the provided bitmask_filter bits from the events
+            # this means that all bits that are set in bitmask_filter will NOT be used to filter events
+            fltr = (d.events.mask & (~self.bitmask_filter)) == 0
+            d.events = d.events[fltr]
+        post_filter = d.events.shape[0]
+        logging.info(f'      number of events: total = {pre_filter:7d}, filtered = {post_filter:7d}')
+        if d.device_logs == {} or not hasattr(dataset, 'update_info_from_logs'):
+            return
+        # filter pulses and logs to allow update of header information
+        from .helpers import add_log_to_pulses
+        times = np.unique(d.events.wallTime)
+        # make sure all log variables are associated with pulses
+        for key, log in d.device_logs.items():
+            if not key in d.pulses.dtype.names:
+                # interpolate the parameter values for all existing pulses
+                add_log_to_pulses(key, dataset)
+        # remove all pulses that have no more events
+        d.pulses = d.pulses[np.isin(d.pulses.time, times)]
+        for key, log in d.device_logs.items():
+            d.device_logs[key] = np.recarray(d.pulses.shape, dtype = log.dtype)
+            d.device_logs[key].time = d.pulses.time
+            d.device_logs[key].value = d.pulses[key]
+        dataset.update_info_from_logs()

eos/file_reader.py

@@ -0,0 +1,517 @@
+"""
+Reading of Amor NeXus data files to extract metadata and event stream.
+"""
+from typing import BinaryIO, List, Union
+
+import sys
+import h5py
+import numpy as np
+import logging
+import subprocess
+
+from datetime import datetime
+
+from orsopy import fileio
+from orsopy.fileio.model_language import SampleModel
+
+from . import const
+from .header import Header
+from .event_data_types import AmorGeometry, AmorTiming, AmorEventStream, LOG_TYPE, PACKET_TYPE, EVENT_TYPE, PULSE_TYPE, \
+    PC_TYPE
+
+try:
+    import zoneinfo
+except ImportError:
+    # for python versions < 3.9 try to use the backports version
+    from backports import zoneinfo
+
+
+# Time zone used to interpret time strings
+AMOR_LOCAL_TIMEZONE = zoneinfo.ZoneInfo(key='Europe/Zurich')
+UTC = zoneinfo.ZoneInfo(key='UTC')
+
+class AmorHeader:
+    """
+    Collects header information from Amor NeXus fiel without reading event data.
+    """
+    # mapping of names to (hdf_path, dtype, nicos_key[, suffix])
+    hdf_paths = dict(
+            title=('entry1/title', str),
+            proposal_id=('entry1/proposal_id', str),
+            user_name=('entry1/user/name', str),
+            user_email=('entry1/user/email', str),
+            sample_name=('entry1/sample/name', str),
+            source_name=('entry1/Amor/source/name', str),
+            sample_model=('entry1/sample/model', str),
+            start_time=('entry1/start_time', str),
+            start_time_fallback=('entry1/Amor/instrument_control_parameters/start_time', str),
+
+            chopper_separation=('entry1/Amor/chopper/pair_separation', float),
+            detector_distance=('entry1/Amor/detector/transformation/distance', float),
+            chopper_distance=('entry1/Amor/chopper/distance', float),
+            sample_temperature=('entry1/sample/temperature', float),
+            sample_magnetic_field=('entry1/sample/magnetic_field', float),
+
+            mu=('entry1/Amor/instrument_control_parameters/mu', float, 'mu'),
+            nu=('entry1/Amor/instrument_control_parameters/nu', float, 'nu'),
+            kap=('entry1/Amor/instrument_control_parameters/kappa', float, 'kappa'),
+            kad=('entry1/Amor/instrument_control_parameters/kappa_offset', float, 'kappa_offset'),
+            div=('entry1/Amor/instrument_control_parameters/div', float, 'div'),
+            ch1_trigger_phase=('entry1/Amor/chopper/ch1_trigger_phase', float, 'ch1_trigger_phase'),
+            ch2_trigger_phase=('entry1/Amor/chopper/ch2_trigger_phase', float, 'ch2_trigger_phase'),
+            chopper_speed=('entry1/Amor/chopper/rotation_speed', float, 'chopper_phase'),
+            chopper_phase=('entry1/Amor/chopper/phase', float, 'chopper_phase'),
+            polarization_config_label=('entry1/Amor/polarization/configuration', int, 'polarization_config_label', '/*'),
+            )
+
+    def __init__(self, fileName:Union[str, h5py.File, BinaryIO]):
+        if type(fileName) is str:
+            logging.warning(f'    {fileName.split("/")[-1]}')
+            self.hdf = h5py.File(fileName, 'r', swmr=True)
+        elif type(fileName) is h5py.File:
+            self.hdf = fileName
+        else:
+            self.hdf = h5py.File(fileName, 'r')
+
+        self._log_keys = []
+
+        self.read_header_info()
+        self.read_instrument_configuration()
+
+        if type(fileName) is str:
+            # close the input file to free memory, only if the file was opened in this object
+            self.hdf.close()
+        del(self.hdf)
+
+    def _replace_if_missing(self, key, nicos_key, dtype=float, suffix=''):
+        from .nicos_interface import lookup_nicos_value
+        year = self.fileDate.strftime('%Y')
+        return lookup_nicos_value(key, nicos_key, dtype, suffix, year)
+
+    def rv(self, key):
+        """
+        Generic read value methos based on key in hdf_paths dictionary.
+        """
+        hdf_path, dtype, *nicos = self.hdf_paths[key]
+        try:
+            hdfgrp = self.hdf[hdf_path]
+            if hdfgrp.attrs.get('NX_class', None) == 'NXlog':
+                # use the last value that was recoreded before the count started
+                time_column = hdfgrp['time'][:]
+                try:
+                    start_index = np.where(time_column<=self._start_time_ns)[0][0]
+                except IndexError:
+                    start_index = 0
+                if hdfgrp['value'].ndim==1:
+                    output = dtype(hdfgrp['value'][start_index])
+                else:
+                    output = dtype(hdfgrp['value'][start_index, 0])
+                # make sure key is only appended if no exception was raised
+                self._log_keys.append(key)
+                return output
+            elif dtype is str:
+                return self.read_string(hdf_path)
+            else:
+                if len(hdfgrp.shape)==1:
+                    return dtype(hdfgrp[0])
+                else:
+                    return dtype(hdfgrp[()])
+        except (KeyError, IndexError):
+            if nicos:
+                nicos_key = nicos[0]
+                suffix = nicos[1] if len(nicos)>1 else ''
+                return self._replace_if_missing(key, nicos_key, dtype, suffix)
+            else:
+                raise
+
+
+    def read_string(self, path):
+        if not np.shape(self.hdf[path]):
+            return self.hdf[path][()].decode('utf-8')
+        else:
+            # format until 2025
+            return self.hdf[path][0].decode('utf-8')
+
+    def read_header_info(self):
+        self._start_time_ns = np.uint64(0)
+        try:
+            start_time = self.rv('start_time')
+        except KeyError:
+            start_time = self.rv('start_time_fallback')
+
+        # extract start time as unix time, adding UTC offset of 1h to time string
+        if start_time.endswith('Z') and sys.version_info.minor<11:
+            # older python versions did not support Z format
+            start_time = start_time[:-1]
+            TZ = UTC
+        else:
+            TZ = AMOR_LOCAL_TIMEZONE
+        start_date = datetime.fromisoformat(start_time)
+        self.fileDate = start_date.replace(tzinfo=TZ)
+        self._start_time_ns = np.uint64(self.fileDate.timestamp()*1e9)
+
+        # read general information and first data set
+        title = self.rv('title')
+        proposal_id = self.rv('proposal_id')
+        user_name = self.rv('user_name')
+        user_affiliation = 'unknown'
+        user_email = self.rv('user_email')
+        user_orcid = None
+        sampleName = self.rv('sample_name')
+        instrumentName = 'Amor'
+        source = self.rv('source_name')
+        sourceProbe = 'neutron'
+        model = self.rv('sample_model')
+        if 'stack' in model:
+            import yaml
+            model = yaml.safe_load(model)
+        else:
+            model = dict(stack=model)
+
+        self.owner = fileio.Person(
+                name=user_name,
+                affiliation=user_affiliation,
+                contact=user_email,
+                )
+        if user_orcid:
+            self.owner.orcid = user_orcid
+
+        self.experiment = fileio.Experiment(
+                title=title,
+                instrument=instrumentName,
+                start_date=start_date,
+                probe=sourceProbe,
+                facility=source,
+                proposalID=proposal_id
+                )
+        if model['stack'] == '':
+            om = None
+        else:
+            om = SampleModel.from_dict(model)
+        self.sample = fileio.Sample(
+                name=sampleName,
+                model=om,
+                sample_parameters={},
+                )
+        # while event times are not evaluated, use average_value reported in file for SEE
+        if self.hdf['entry1/sample'].get('temperature', None) is not None:
+            try:
+                sample_temperature = self.rv('sample_temperature')
+            except IndexError: pass
+            else:
+                self.sample.sample_parameters['temperature'] = fileio.Value(sample_temperature, unit='K')
+        if self.hdf['entry1/sample'].get('magnetic_field', None) is not None:
+            try:
+                sample_magnetic_field = self.rv('sample_magnetic_field')
+            except IndexError: pass
+            else:
+                self.sample.sample_parameters['magnetic_field'] = fileio.Value(sample_magnetic_field, unit='T')
+
+    def read_instrument_configuration(self):
+        chopperSeparation = self.rv('chopper_separation')
+        detectorDistance = self.rv('detector_distance')
+        chopperDistance = self.rv('chopper_distance')
+        chopperDetectorDistance = detectorDistance - chopperDistance
+
+        mu = self.rv('mu')
+        nu = self.rv('nu')
+        kap = self.rv('kap')
+        kad = self.rv('kad')
+        div = self.rv('div')
+        ch1TriggerPhase = self.rv('ch1_trigger_phase')
+        ch2TriggerPhase = self.rv('ch2_trigger_phase')
+        try:
+            chopperTriggerTime = (float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][7]) \
+                                  -float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][0])) \
+                                 /7
+            chopperTriggerTimeDiff = float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_offset'][2])
+        except (KeyError, IndexError):
+            logging.debug('      chopper speed and phase taken from .hdf file')
+            chopperSpeed = self.rv('chopper_speed')
+            chopperPhase = self.rv('chopper_phase')
+            tau = 30/chopperSpeed
+        else:
+            tau = int(1e-6*chopperTriggerTime/2+0.5)*(1e-3)
+            chopperTriggerPhase = 180e-9*chopperTriggerTimeDiff/tau
+            chopperSpeed = 30/tau
+            chopperPhase = chopperTriggerPhase+ch1TriggerPhase-ch2TriggerPhase
+
+        self.geometry = AmorGeometry(mu, nu, kap, kad, div,
+                                     chopperSeparation, detectorDistance, chopperDetectorDistance)
+        self.timing = AmorTiming(ch1TriggerPhase, ch2TriggerPhase, chopperSpeed, chopperPhase, tau)
+
+        polarizationConfigLabel = self.rv('polarization_config_label')
+        polarizationConfig = fileio.Polarization(const.polarizationConfigs[polarizationConfigLabel])
+        logging.debug(f'      polarization configuration: {polarizationConfig} (index {polarizationConfigLabel})')
+
+
+        self.instrument_settings = fileio.InstrumentSettings(
+            incident_angle = fileio.ValueRange(round(mu+kap+kad-0.5*div, 3),
+                                               round(mu+kap+kad+0.5*div, 3),
+                                               'deg'),
+            wavelength = fileio.ValueRange(const.lamdaCut, const.lamdaMax, 'angstrom'),
+            polarization = fileio.Polarization(polarizationConfig)
+            )
+        self.instrument_settings.qz = fileio.ValueRange(round(4*np.pi*np.sin(np.deg2rad(mu+kap+kad-0.5*div))/const.lamdaMax, 3),
+                                                        round(4*np.pi*np.sin(np.deg2rad(mu+kap+kad+0.5*div))/const.lamdaCut, 3),
+                                                        '1/angstrom')
+        self.instrument_settings.mu = fileio.Value(
+                round(mu, 3),
+                'deg',
+                comment='sample angle to horizon')
+        self.instrument_settings.nu = fileio.Value(
+                round(nu, 3),
+                'deg',
+                comment='detector angle to horizon')
+        self.instrument_settings.div = fileio.Value(
+                round(div, 3),
+                'deg',
+                comment='incoming beam divergence')
+        self.instrument_settings.kap = fileio.Value(
+                round(kap, 3),
+                'deg',
+                comment='incoming beam inclination')
+        if abs(kad)>0.02:
+            self.instrument_settings.kad = fileio.Value(
+                    round(kad, 3),
+                    'deg',
+                    comment='incoming beam angular offset')
+
+
+    def update_header(self, header:Header):
+        """
+        Add dataset information into an existing header.
+        """
+        logging.info(f'    meta data from: {self.file_list[0]}')
+        header.owner = self.owner
+        header.experiment = self.experiment
+        header.sample = self.sample
+        header.measurement_instrument_settings = self.instrument_settings
+
+
+class AmorEventData(AmorHeader):
+    """
+    Read one amor NeXus datafile and extract relevant header information.
+
+    Implements EventDatasetProtocol
+    """
+    file_list: List[str]
+    first_index: int
+    last_index: int = -1
+    EOF: bool = False
+    max_events: int
+    owner: fileio.Person
+    experiment: fileio.Experiment
+    sample: fileio.Sample
+    instrument_settings: fileio.InstrumentSettings
+    geometry: AmorGeometry
+    timing: AmorTiming
+    data: AmorEventStream
+
+    eventStartTime: np.int64
+
+    def __init__(self, fileName:Union[str, h5py.File, BinaryIO], first_index:int=0, max_events:int=100_000_000):
+        if type(fileName) is str:
+            logging.warning(f'    {fileName.split("/")[-1]}')
+            self.file_list = [fileName]
+            hdf = h5py.File(fileName, 'r', swmr=True)
+        elif type(fileName) is h5py.File:
+            self.file_list = [fileName.filename]
+            hdf = fileName
+        else:
+            self.file_list = [repr(fileName)]
+            hdf = h5py.File(fileName, 'r')
+        self.first_index = first_index
+        self.max_events = max_events
+
+        super().__init__(hdf)
+        self.hdf = hdf
+        try:
+            self.read_event_stream()
+        except EOFError:
+            self.hdf.close()
+            del(self.hdf)
+            raise
+        self.read_log_streams()
+
+        if type(fileName) is str:
+            # close the input file to free memory, only if the file was opened in this object
+            self.hdf.close()
+        del(self.hdf)
+
+
+    def read_event_stream(self):
+        """
+        Read the actual event data from file. If file is too large, find event index from packets
+        that allow splitting of file smaller than self.max_events.
+        """
+        packets = np.recarray(self.hdf['/entry1/Amor/detector/data/event_index'].shape, dtype=PACKET_TYPE)
+        packets.start_index = self.hdf['/entry1/Amor/detector/data/event_index'][:]
+        packets.time = self.hdf['/entry1/Amor/detector/data/event_time_zero'][:]
+        try:
+            # packet index that matches first event index
+            start_packet = int(np.where(packets.start_index==self.first_index)[0][0])
+        except IndexError:
+            raise EOFError(f'No event packet found starting at event #{self.first_index}, '
+                           f'number of events is {self.hdf["/entry1/Amor/detector/data/event_time_offset"].shape[0]}')
+        packets = packets[start_packet:]
+        if packets.shape[0]==0:
+            raise EOFError(f'No more packets left after start_packet filter')
+
+        nevts = self.hdf['/entry1/Amor/detector/data/event_time_offset'].shape[0]
+        if (nevts-self.first_index)>self.max_events:
+            end_packet = np.where(packets.start_index<=(self.first_index+self.max_events))[0][-1]
+            end_packet = max(1, end_packet)
+            if len(packets)==1:
+                self.last_index = nevts-1
+            else:
+                self.last_index = packets.start_index[end_packet]-1
+            packets = packets[:end_packet]
+        else:
+            self.last_index = nevts-1
+            self.EOF = True
+
+        if packets.shape[0]==0:
+            raise EOFError(f'No more packets left after end_packet filter, first_index={self.first_index}, '
+                           f'max_events={self.max_events}, nevts={nevts}')
+
+        nevts = self.last_index+1-self.first_index
+
+        # adapte packet to event index relation
+        packets.start_index -= self.first_index
+
+        events = np.recarray(nevts, dtype=EVENT_TYPE)
+        events.tof = np.array(self.hdf['/entry1/Amor/detector/data/event_time_offset'][self.first_index:self.last_index+1])/1.e9
+        events.pixelID = self.hdf['/entry1/Amor/detector/data/event_id'][self.first_index:self.last_index+1]
+        events.mask = 0
+
+        pulses = self.read_chopper_trigger_stream(packets)
+        current = self.read_proton_current_stream(packets)
+        self.data = AmorEventStream(events, packets, pulses, current)
+
+        if self.first_index>0 and not self.EOF:
+            # label the file name if not all events were used
+            self.file_list[0] += f'[{self.first_index}:{self.last_index}]'
+
+    def read_log_streams(self):
+        """
+        Read the individual NXlog datasets that can later be used for filtering etc.
+        """
+        for key in self._log_keys:
+            hdf_path, dtype, *_ = self.hdf_paths[key]
+            hdfgroup = self.hdf[hdf_path]
+            shape = hdfgroup['time'].shape
+            data = np.recarray(shape, dtype=np.dtype([('value', self.hdf_paths[key][1]), ('time', np.int64)]))
+            data.time = hdfgroup['time'][:]
+            if len(hdfgroup['value'].shape)==1:
+                data.value = hdfgroup['value'][:]
+            else:
+                data.value = hdfgroup['value'][:, 0]
+            self.data.device_logs[key] = data
+
+    def update_info_from_logs(self):
+        RELEVANT_ITEMS = ['sample_temperature', 'sample_magnetic_field', 'polarization_config_label']
+        for key, log in self.data.device_logs.items():
+            if key not in RELEVANT_ITEMS:
+                continue
+            if log.value.dtype in [np.int8, np.int16, np.int32, np.int64]:
+                # for integer items (flags) report the most common one
+                value = np.bincount(log.value).argmax()
+                if logging.getLogger().getEffectiveLevel() <= logging.DEBUG \
+                        and np.unique(log.value).shape[0]>1:
+                    logging.debug(f'        filtered values for {key} not unique, '
+                                  f'has {np.unique(log.value).shape[0]} values')
+            else:
+                value = log.value.mean()
+            if key == 'polarization_config_label':
+                self.instrument_settings.polarization = fileio.Polarization(const.polarizationConfigs[value])
+            elif key == 'sample_temperature':
+                self.sample.sample_parameters['temperature'].magnitue = value
+            elif key == 'sample_magnetic_field':
+                self.sample.sample_parameters['magnetic_field'].magnitue = value
+
+
+
+    def read_chopper_trigger_stream(self, packets):
+        chopper1TriggerTime = np.array(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][:-2], dtype=np.int64)
+        #self.chopper2TriggerTime = self.chopper1TriggerTime + np.array(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time'][:-2], dtype=np.int64)
+        #                           + np.array(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_offset'][:], dtype=np.int64)
+        if np.shape(chopper1TriggerTime)[0] > 2:
+            startTime = chopper1TriggerTime[0]
+            pulseTimeS = chopper1TriggerTime
+        else:
+            logging.critical('     No chopper trigger data available, using event steram instead, pulse filtering will fail!')
+            startTime = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][0], dtype=np.int64)
+            stopTime = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][-2], dtype=np.int64)
+            pulseTimeS = np.arange(startTime, stopTime, self.timing.tau*1e9, dtype=np.int64)
+        pulses = np.recarray(pulseTimeS.shape, dtype=PULSE_TYPE)
+        pulses.time = pulseTimeS
+        pulses.monitor = 1. # default is monitor pulses as it requires no calculation
+        # apply filter in case the events were filtered
+        if (self.first_index>0 or not self.EOF):
+            pulses = pulses[(pulses.time>=packets.time[0])&(pulses.time<=packets.time[-1])]
+        self.eventStartTime = startTime
+        return pulses
+
+    def read_proton_current_stream(self, packets):
+        proton_current = np.recarray(self.hdf['entry1/Amor/detector/proton_current/time'].shape, dtype=PC_TYPE)
+        proton_current.time = self.hdf['entry1/Amor/detector/proton_current/time'][:]
+        if self.hdf['entry1/Amor/detector/proton_current/value'].ndim==1:
+            proton_current.current = self.hdf['entry1/Amor/detector/proton_current/value'][:]
+        else:
+            proton_current.current = self.hdf['entry1/Amor/detector/proton_current/value'][:,0]
+
+        if self.first_index>0 or not self.EOF:
+            proton_current = proton_current[(proton_current.time>=packets.time[0])&
+                                            (proton_current.time<=packets.time[-1])]
+        return proton_current
+
+    def info(self):
+        output = ""
+        for key in ['owner', 'experiment', 'sample', 'instrument_settings']:
+            value = repr(getattr(self, key)).replace("\n","\n      ")
+            output += f'\n{key}={value},'
+        output += '\n'
+        return output
+
+    def append(self, other):
+        """
+        Append event streams from another file to this one. Adjusts the event indices in the
+        packets to stay valid.
+        """
+        new_events = np.concatenate([self.data.events, other.data.events]).view(np.recarray)
+        new_pulses = np.concatenate([self.data.pulses, other.data.pulses]).view(np.recarray)
+        new_proton_current = np.concatenate([self.data.proton_current, other.data.proton_current]).view(np.recarray)
+        new_packets = np.concatenate([self.data.packets, other.data.packets]).view(np.recarray)
+        new_packets.start_index[self.data.packets.shape[0]:] += self.data.events.shape[0]
+        self.data = AmorEventStream(new_events, new_packets, new_pulses, new_proton_current)
+        # Indicate that this is amodified dataset, basically counts number of appends as negative indices
+        self.last_index = min(self.last_index-1, -1)
+        self.file_list += other.file_list
+
+    def __repr__(self):
+        output = (f"AmorEventData({self.file_list!r}) # {self.data.events.shape[0]} events, "
+                  f"{self.data.pulses.shape[0]} pulses")
+
+        return output
+
+    def get_timeslice(self, start, end)->'AmorEventData':
+        # return a new dataset with just events that occured in given time slice
+        if not 'wallTime' in self.data.events.dtype.names:
+            raise ValueError("This dataset is missing a wallTime that is required for time slicing")
+        # convert from seconds to epoch integer values
+        start , end = start*1e9, end*1e9
+        event_filter = self.data.events.wallTime>=start
+        event_filter &= self.data.events.wallTime<end
+        pulse_filter = self.data.pulses.time>=start
+        pulse_filter &= self.data.pulses.time<end
+        output = super().__new__(AmorEventData)
+        for key, value in self.__dict__.items():
+            if key == 'data':
+                continue
+            else:
+                setattr(output, key, value)
+        # TODO: this is not strictly correct, as the packet/event relationship is lost
+        output.data = AmorEventStream(self.data.events[event_filter], self.data.packets,
+                                      self.data.pulses[pulse_filter], self.data.proton_current)
+        return output

eos/header.py

@@ -5,6 +5,7 @@ Class to handle Orso header information that changes gradually during the reduct
 import platform
 import sys
 from datetime import datetime
+from typing import List, Literal
 
 from orsopy import fileio
 
@@ -13,13 +14,16 @@ from . import __version__
 
 class Header:
     """orso compatible output file header content"""
+    owner: fileio.Person
+    experiment: fileio.Experiment
+    sample: fileio.Sample
+    measurement_instrument_settings: fileio.InstrumentSettings
+    measurement_scheme: Literal["angle- and energy-dispersive", "angle-dispersive", "energy-dispersive"]
+    measurement_data_files: List[fileio.File]
+    measurement_additional_files: List[fileio.File]
+
 
     def __init__(self):
-        self.owner                           = None
-        self.experiment                      = None
-        self.sample                          = None
-        self.measurement_instrument_settings = None
-        self.measurement_scheme              = None
         self.measurement_data_files          = []
         self.measurement_additional_files    = []
 
@@ -64,10 +68,3 @@ class Header:
         if columns is None:
             columns = self.columns()
         return fileio.Orso(ds, red, columns+extra_columns)
-    #-------------------------------------------------------------------------------------------------
-    def create_call_string(self):
-        callString = ' '.join(sys.argv)
-        if '-Y' not in callString:
-            callString += f' -Y {datetime.now().year}'
-        return callString
-    #-------------------------------------------------------------------------------------------------

eos/helpers.py

@@ -0,0 +1,35 @@
+"""
+Helper functions used during calculations. Uses numba enhanced functions if available, otherwise numpy based
+fallback is imported.
+"""
+import numpy as np
+from .event_data_types import EventDatasetProtocol, append_fields
+
+try:
+    from .helpers_numba import merge_frames, extract_walltime, filter_project_x, calculate_derived_properties_focussing
+except ImportError:
+    from .helpers_fallback import merge_frames, extract_walltime, filter_project_x, calculate_derived_properties_focussing
+
+def add_log_to_pulses(key, dataset: EventDatasetProtocol):
+    """
+    Add a log value for each pulse to the pulses array.
+    """
+    pulses = dataset.data.pulses
+    log_data = dataset.data.device_logs[key]
+    if log_data.time[0]>0:
+        logTimeS = np.hstack([[0], log_data.time, [pulses.time[-1]+1]])
+        logValues = np.hstack([[log_data.value[0]], log_data.value])
+    else:
+        logTimeS = np.hstack([log_data.time, [pulses.time[-1]+1]])
+        logValues = log_data.value
+    pulseLogS = np.zeros(pulses.time.shape[0], dtype=log_data.value.dtype)
+    j = 0
+    for i, ti in enumerate(pulses.time):
+        # find the last current item that was before this pulse
+        while ti>=logTimeS[j+1]:
+            j += 1
+        pulseLogS[i] = logValues[j]
+    pulses = append_fields(pulses, [(key, pulseLogS.dtype)])
+    pulses[key] = pulseLogS
+    dataset.data.pulses = pulses
+

eos/helpers_fallback.py

@@ -0,0 +1,26 @@
+"""
+Equivalent function as in helpers_numba.py but using just numpy functionality.
+"""
+
+import numpy as np
+
+def merge_frames(tof_e, tofCut, tau, total_offset):
+    # tof shifted to 1 frame
+    return np.remainder(tof_e-(tofCut-tau), tau)+total_offset
+
+def extract_walltime(tof_e, dataPacket_p, dataPacketTime_p):
+    output = np.empty(np.shape(tof_e)[0], dtype=np.int64)
+    for i in range(len(dataPacket_p)-1):
+        output[dataPacket_p[i]:dataPacket_p[i+1]] = dataPacketTime_p[i]
+    output[dataPacket_p[-1]:] = dataPacketTime_p[-1]
+    return output
+
+def filter_project_x(pixelLookUp, pixelID_e, ymin, ymax):
+    (detY_e, detZ_e, detXdist_e, delta_e) = pixelLookUp[np.int_(pixelID_e)-1, :].T
+    # define mask and filter y range
+    mask_e = (ymin<=detY_e) & (detY_e<=ymax)
+    return (detZ_e, detXdist_e, delta_e, mask_e)
+
+def calculate_derived_properties_focussing(tof_e, detXdist_e, delta_e, mask_e,
+                                           lmin, lmax, nu, mu, chopperDetectorDistance, hdm):
+    raise NotImplementedError("Only exists in numba implementation so far.")

eos/helpers_numba.py

@@ -11,7 +11,7 @@ def merge_frames(tof_e, tofCut, tau, total_offset):
         tof_e_out[ti] = ((tof_e[ti]-dt)%tau)+total_offset  # tof shifted to 1 frame
     return tof_e_out
 
-@nb.jit(nb.float64[:](nb.float64[:], nb.uint64[:], nb.int64[:]),
+@nb.jit(nb.int64[:](nb.float64[:], nb.uint32[:], nb.int64[:]),
         nopython=True, parallel=True, cache=True)
 def extract_walltime(tof_e, dataPacket_p, dataPacketTime_p):
     # assigning every event the wall time of the event packet (absolute time of pulse ?start?)
@@ -25,7 +25,7 @@ def extract_walltime(tof_e, dataPacket_p, dataPacketTime_p):
     return wallTime_e
 
 @nb.jit(nb.types.Tuple((nb.int64[:], nb.float64[:], nb.float64[:], nb.boolean[:]))
-                (nb.float64[:, :], nb.int64[:], nb.int64, nb.int64),
+                (nb.float64[:, :], nb.uint32[:], nb.int64, nb.int64),
         nopython=True, parallel=True, cache=True)
 def filter_project_x(pixelLookUp, pixelID_e, ymin, ymax):
     # project events on z-axis and create filter for events outside of y-range

eos/instrument.py

@@ -0,0 +1,130 @@
+"""
+Classes describing the AMOR instrument configuration used during reduction.
+"""
+
+import logging
+import numpy as np
+
+from . import const
+
+try:
+    from functools import cache
+except ImportError:
+    # python <3.9
+    def cache(func): return func
+
+
+class Detector:
+    nBlades  = 14  # number of active blades in the detector
+    nWires   = 32  # number of wires per blade
+    nStripes = 64  # number of stipes per blade
+    angle    = np.deg2rad(5.1)  # deg  angle of incidence of the beam on the blades (def: 5.1)
+    dZ       = 4.0*np.sin(angle)  # mm  height-distance of neighboring pixels on one blade
+    dX       = 4.0*np.cos(angle)  # mm  depth-distance of neighboring pixels on one blace
+    bladeZ   = 10.455  # mm  distance between detector blades
+    zero     = 0.5*nBlades*bladeZ  # mm  vertical center of the detector
+    distance = 4000.  # mm  distance from focal point to leading blade edge
+
+    delta_z: np.ndarray
+    pixelLookUp: np.ndarray
+
+    @staticmethod
+    def resolve_pixels():
+        """
+        Determine spatial coordinats and angles from pixel number,
+        does only have to be computed once for the detector
+        """
+        if hasattr(Detector, 'pixelLookUp'):
+            return
+        nPixel = Detector.nWires * Detector.nStripes * Detector.nBlades
+        pixelID = np.arange(nPixel)
+        (bladeNr, bPixel) = np.divmod(pixelID, Detector.nWires * Detector.nStripes)
+        (bZi, detYi)      = np.divmod(bPixel, Detector.nStripes)                     # z index on blade, y index on detector
+        detZi             = bladeNr * Detector.nWires + bZi                          # z index on detector
+        detX              = bZi * Detector.dX                                        # x position in detector
+        # detZ              = Detector.zero - bladeNr * Detector.bladeZ - bZi * Detector.dZ      # z position on detector
+        bladeAngle        = np.rad2deg( 2. * np.arcsin(0.5*Detector.bladeZ / Detector.distance) )
+        delta             = (Detector.nBlades/2. - bladeNr) * bladeAngle \
+                            - np.rad2deg( np.arctan(bZi*Detector.dZ / ( Detector.distance + bZi * Detector.dX) ) )
+        delta_z      = delta[detYi==1]
+        pixel_lookup=np.vstack((detYi.T, detZi.T, detX.T, delta.T)).T
+        Detector.delta_z = delta_z
+        Detector.pixelLookUp = pixel_lookup
+
+# guarantee that pixelLookUp has been computed
+Detector.resolve_pixels()
+
+class LZGrid:
+    dldl = 0.005  # Delta lambda / lambda
+
+    # as using cahced results, make sure the object is not modified
+    @property
+    def qResolution(self):
+        return self._qResolution
+    @property
+    def qzRange(self):
+        return self._qzRange
+
+    def __init__(self, qResolution, qzRange, lambda_overwrite=None):
+        self._qResolution = qResolution
+        self._qzRange = qzRange
+        if lambda_overwrite is None:
+            self.lamdaMax = const.lamdaMax
+            self.lamdaCut = const.lamdaCut
+        else:
+            self.lamdaCut, self.lamdaMax = lambda_overwrite
+
+    @property
+    @cache
+    def shape(self):
+        # gives the shape of the grid, not of the bin-edges
+        return (self.lamda().shape[0]-1, self.z().shape[0]-1)
+
+    @cache
+    def q(self):
+        resolutions = [0.005, 0.01, 0.02, 0.025, 0.04, 0.05, 0.1, 1]
+        a, b = np.histogram([self.qResolution], bins = resolutions)
+        dqdq = np.matmul(b[:-1],a)
+        if dqdq != self.qResolution:
+            logging.info(f'#   changed resolution to {dqdq}')
+        qq = 0.01
+        # linear up to qq
+        q_grid = np.arange(0, qq, qq*dqdq)
+        # exponential from qq on
+        q_grid = np.append(q_grid, qq*(1.+dqdq)**np.arange(int(np.log(self.qzRange[1]/qq)/np.log(1+dqdq))))
+        q_grid = q_grid[q_grid>=self.qzRange[0]]
+        return q_grid
+
+    @cache
+    def lamda(self):
+        lamdaMax = self.lamdaMax
+        lamdaMin = self.lamdaCut
+        lamda_grid = lamdaMin*(1+self.dldl)**np.arange(int(np.log(lamdaMax/lamdaMin)/np.log(1+self.dldl)+1))
+        return lamda_grid
+
+    @cache
+    def z(self):
+        # TODO: shouldn't this be -0.5 to be the edges of each pixel?
+        return np.arange(Detector.nBlades*Detector.nWires+1)
+
+    @cache
+    def lz(self):
+        return np.ones(( self.lamda().shape[0]-1, self.z().shape[0]-1))
+
+    @cache
+    def delta(self, detectorDistance):
+        # unused for now
+        bladeAngle = np.rad2deg( 2. * np.arcsin(0.5*Detector.bladeZ / detectorDistance) )
+        blade_grid = np.arctan( np.arange(33) * Detector.dZ / ( detectorDistance + np.arange(33) * Detector.dX) )
+        blade_grid = np.rad2deg(blade_grid)
+        stepWidth  = blade_grid[1] - blade_grid[0]
+        blade_grid = blade_grid - 0.2 * stepWidth
+
+        delta_grid = []
+        for b in np.arange(Detector.nBlades-1):
+            delta_grid = np.concatenate((delta_grid, blade_grid), axis=None)
+            blade_grid = blade_grid + bladeAngle
+            delta_grid = delta_grid[delta_grid<blade_grid[0]-0.5*stepWidth]
+        delta_grid = np.concatenate((delta_grid, blade_grid), axis=None)
+
+        return -np.flip(delta_grid) + 0.5*Detector.nBlades * bladeAngle

eos/kafka_events.py

@@ -0,0 +1,165 @@
+"""
+Collect AMOR detector events send via Kafka.
+"""
+
+import logging
+import numpy as np
+from threading import Thread, Event
+from time import time
+
+from .event_data_types import AmorGeometry, AmorTiming, AmorEventStream, PACKET_TYPE, EVENT_TYPE, PULSE_TYPE, PC_TYPE
+
+from uuid import uuid4
+from streaming_data_types.eventdata_ev44 import EventData
+from streaming_data_types.logdata_f144 import ExtractedLogData
+from streaming_data_types import deserialise_f144, deserialise_ev44
+from confluent_kafka import Consumer
+
+from .header import Header
+
+
+try:
+    from streaming_data_types.utils import get_schema
+except ImportError:
+    from streaming_data_types.utils import _get_schema as get_schema
+
+
+KAFKA_BROKER = 'linkafka01.psi.ch:9092'
+AMOR_EVENTS = 'amor_detector'
+AMOR_NICOS = 'amor_nicosForwarder'
+
+class KafkaFrozenData:
+    """
+    Represents event stream data from Kafka at a given time.
+    Will be returned by KafkaEventData to be use in conjunction
+    with data processing and projections.
+
+    Implements EventDatasetProtocol
+    """
+    geometry: AmorGeometry
+    timing: AmorTiming
+    data: AmorEventStream
+
+    def __init__(self, geometry, timing, data, monitor=1.):
+        self.geometry = geometry
+        self.timing = timing
+        self.data = data
+        self.monitor = monitor
+
+    def append(self, other):
+        raise NotImplementedError("can't append live datastream to other event data")
+
+    def update_header(self, header:Header):
+        # maybe makes sense later, but for now just used for live vizualization
+        ...
+
+class KafkaEventData(Thread):
+    """
+    Read Nicos information and events from Kafka. Creates a background
+    thread that listens to Kafka events and converts them to eos compatible information.
+    """
+    geometry: AmorGeometry
+    timing: AmorTiming
+    events: np.recarray
+
+    def __init__(self):
+        self.stop_event = Event()
+        self.stop_counting = Event()
+        self.new_events = Event()
+        self.last_read = 0
+        self.last_read_time = 0.
+        self.start_time = time()
+        self.consumer = Consumer(
+                {'bootstrap.servers': 'linkafka01.psi.ch:9092',
+                 'group.id': uuid4()})
+        self.consumer.subscribe([AMOR_EVENTS, AMOR_NICOS])
+        self.geometry = AmorGeometry(1.0, 2.0, 0., 0., 1.5, 10.0, 4.0, 10.0)
+        self.timing = AmorTiming(0., 0., 500., 0., 30./500.)
+        # create empty dataset
+        self.events = np.recarray(0, dtype=EVENT_TYPE)
+        super().__init__()
+
+    def run(self):
+        while not self.stop_event.is_set():
+            messages = self.consumer.consume(10, timeout=1)
+            for message in messages:
+                self.process_message(message)
+
+    def process_message(self, message):
+        if message.error():
+            logging.info(f"  received Kafka message with error: {message.error()}")
+            return
+        schema = get_schema(message.value())
+        if message.topic()==AMOR_EVENTS and schema=='ev44':
+            events:EventData = deserialise_ev44(message.value())
+            self.add_events(events)
+            self.new_events.set()
+            logging.debug(f'  new events {events}')
+        elif message.topic()==AMOR_NICOS and schema=='f144':
+            nicos_data:ExtractedLogData = deserialise_f144(message.value())
+            if nicos_data.source_name in self.nicos_mapping.keys():
+                logging.debug(f'  {nicos_data.source_name} = {nicos_data.value}')
+                self.update_instrument(nicos_data)
+
+    def add_events(self, events:EventData):
+        """
+        Add new events to the Dataset. The object keeps raw events
+        and only copies the latest set to the self.data object,
+        this allows to run the event processing to be performed on a "clean"
+        evnet stream each time.
+        """
+        if self.stop_counting.is_set():
+            return
+        prev_size = self.events.shape[0]
+        new_events = events.pixel_id.shape[0]
+        self.events.resize(prev_size+new_events, refcheck=False)
+        self.events.pixelID[prev_size:] = events.pixel_id
+        self.events.mask[prev_size:] = 0
+        self.events.tof[prev_size:] = events.time_of_flight/1.e9
+
+    nicos_mapping = {
+        'mu': ('geometry', 'mu'),
+        'nu':  ('geometry', 'nu'),
+        'kappa':  ('geometry', 'kap'),
+        'kappa_offset':  ('geometry', 'kad'),
+        'ch1_trigger_phase':  ('timing', 'ch1TriggerPhase'),
+        'ch2_trigger_phase':  ('timing', 'ch2TriggerPhase'),
+        'ch2_speed':  ('timing', 'chopperSpeed'),
+        'chopper_phase':  ('timing', 'chopperPhase'),
+        }
+
+    def update_instrument(self, nicos_data:ExtractedLogData):
+        if nicos_data.source_name in self.nicos_mapping:
+            attr, subattr = self.nicos_mapping[nicos_data.source_name]
+            setattr(getattr(self, attr), subattr, nicos_data.value)
+            if nicos_data.source_name=='ch2_speed':
+                self.timing.tau = 30./self.timing.chopperSpeed
+
+    def monitor(self):
+        return time()-self.start_time
+
+    def restart(self):
+        # empty event buffer
+        self.events = np.recarray(0, dtype=EVENT_TYPE)
+        self.stop_counting.clear()
+        self.last_read = 0
+        self.start_time = time()
+        self.new_events.clear()
+
+    def get_events(self, total_counts=False):
+        packets = np.recarray(0, dtype=PACKET_TYPE)
+        pulses = np.recarray(0, dtype=PULSE_TYPE)
+        pc = np.recarray(0, dtype=PC_TYPE)
+        if total_counts:
+            last_read = 0
+        else:
+            last_read = self.last_read
+        if last_read>=self.events.shape[0]:
+            raise EOFError("No new events arrived")
+        data = AmorEventStream(self.events[last_read:].copy(), packets, pulses, pc)
+        self.last_read = self.events.shape[0]
+        self.new_events.clear()
+        t_now = time()
+        monitor = t_now-self.last_read_time
+        self.last_read_time = t_now
+        return KafkaFrozenData(self.geometry, self.timing, data, monitor=monitor)

eos/kafka_serializer.py

@@ -0,0 +1,283 @@
+"""
+Allows to send eos projections to Kafka using ESS histogram serialization.
+
+For histogram_h01 the message is build using:
+
+hist = {
+    "source": "some_source",
+    "timestamp": 123456,
+    "current_shape": [2, 5],
+    "dim_metadata": [
+        {
+            "length": 2,
+            "unit": "a",
+            "label": "x",
+            "bin_boundaries": np.array([10, 11, 12]),
+        },
+        {
+            "length": 5,
+            "unit": "b",
+            "label": "y",
+            "bin_boundaries": np.array([0, 1, 2, 3, 4, 5]),
+        },
+    ],
+    "last_metadata_timestamp": 123456,
+    "data": np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]),
+    "errors": np.array([[5, 4, 3, 2, 1], [10, 9, 8, 7, 6]]),
+    "info": "info_string",
+}
+"""
+import logging
+from typing import List, Tuple, Union
+from threading import Thread, Event
+
+import numpy as np
+import json
+from time import time
+from dataclasses import dataclass, asdict
+from streaming_data_types import histogram_hs01
+from confluent_kafka import Producer, Consumer
+
+from uuid import uuid4
+
+from .projection import TofZProjection, YZProjection
+
+KAFKA_BROKER = 'linkafka01.psi.ch:9092'
+KAFKA_TOPICS = {
+    'histogram': 'amor_histograms',
+    'response': 'amor_histResponse',
+    'command':  'amor_histCommands'
+    }
+
+def ktime():
+    return int(time()*1_000)
+
+@dataclass
+class DimMetadata:
+    length: int
+    unit: str
+    label: str
+    bin_boundaries: np.ndarray
+
+@dataclass
+class HistogramMessage:
+    source: str
+    timestamp: int
+    current_shape: Tuple[int, int]
+    dim_metadata: Tuple[DimMetadata, DimMetadata]
+    last_metadata_timestamp: int
+    data: np.ndarray
+    errors: np.ndarray
+    info: str
+
+    def serialize(self):
+        return histogram_hs01.serialise_hs01(asdict(self))
+
+@dataclass
+class CommandMessage:
+    msg_id: str
+
+    cmd=None
+
+    @classmethod
+    def get_message(cls, data):
+        """
+        Uses the sub-class cmd attribute to select which message to retugn
+        """
+        msg = dict([(ci.cmd, ci) for ci in cls.__subclasses__()])
+        return msg[data['cmd']](**data)
+
+
+@dataclass
+class Stop(CommandMessage):
+    hist_id: str
+    id: str
+    cmd:str = 'stop'
+
+@dataclass
+class HistogramConfig:
+    id: str
+    type: str
+    data_brokers: List[str]
+    topic: str
+    data_topics: List[str]
+    tof_range: Tuple[float, float]
+    det_range: Tuple[int, int]
+    num_bins: int
+    width: int
+    height: int
+    left_edges: list
+    source: str
+
+@dataclass
+class ConfigureHistogram(CommandMessage):
+    histograms: List[HistogramConfig]
+    start: int
+    cmd:str = 'config'
+
+    def __post_init__(self):
+        self.histograms = [HistogramConfig(**cfg) for cfg in self.histograms]
+
+
+class ESSSerializer:
+
+    def __init__(self):
+        self.producer = Producer({
+            'bootstrap.servers': KAFKA_BROKER,
+            'message.max.bytes': 4_000_000,
+            })
+        self.consumer = Consumer({
+            'bootstrap.servers': KAFKA_BROKER,
+            "group.id": uuid4(),
+            "default.topic.config": {"auto.offset.reset": "latest"},
+            })
+        self._active_histogram_yz = None
+        self._active_histogram_tofz = None
+        self.new_count_started = Event()
+        self.count_stopped = Event()
+
+        self.consumer.subscribe([KAFKA_TOPICS['command']])
+
+    def process_message(self, message):
+        if message.error():
+            logging.error("Command Consumer Error: %s", message.error())
+        else:
+            command = json.loads(message.value().decode())
+            try:
+                command = CommandMessage.get_message(command)
+            except Exception:
+                logging.error(f'Could not interpret message: \n{command}', exc_info=True)
+                return
+            logging.info(command)
+            resp = json.dumps({
+                "msg_id":   getattr(command, "id", None) or command.msg_id,
+                "response": "ACK",
+                "message":  ""
+                })
+            self.producer.produce(
+                    topic=KAFKA_TOPICS['response'],
+                    value=resp
+                    )
+            self.producer.flush()
+            if isinstance(command, Stop):
+                if command.hist_id in [self._active_histogram_yz, self._active_histogram_tofz]:
+                    self.count_stopped.set()
+                else:
+                    return
+            elif isinstance(command, ConfigureHistogram):
+                for hist in command.histograms:
+                    if hist.topic == KAFKA_TOPICS['histogram']+'_YZ':
+                        self._active_histogram_yz = hist.id
+                        logging.debug(f"   histogram data_topic: {hist.data_topics}")
+                        self._start = command.start
+                        self.count_stopped.clear()
+                        self.new_count_started.set()
+                    if hist.topic == KAFKA_TOPICS['histogram']+'_TofZ':
+                        self._active_histogram_tofz = hist.id
+
+    def receive(self, timeout=5):
+        rec = self.consumer.poll(timeout)
+        if rec is not None:
+            self.process_message(rec)
+            return True
+        else:
+            return False
+
+    def receive_loop(self):
+        while not self._stop_receiving.is_set():
+            try:
+                self.receive()
+            except Exception:
+                logging.error("Exception while receiving", exc_info=True)
+
+    def start_command_thread(self):
+        self._stop_receiving = Event()
+        self._command_thread = Thread(target=self.receive_loop)
+        self._command_thread.start()
+
+    def end_command_thread(self, event=None):
+        self._stop_receiving.set()
+        self._command_thread.join()
+
+    def acked(self, err, msg):
+        # We need to have callback to produce-method to catch server errors
+        if err is not None:
+            logging.warning("Failed to deliver message: %s: %s" % (str(msg), str(err)))
+        else:
+            logging.debug("Message produced: %s" % (str(msg)))
+
+    def send(self, proj: Union[YZProjection, TofZProjection], final=False):
+        if final:
+            state = 'FINISHED'
+        else:
+            state = 'COUNTING'
+        if isinstance(proj, YZProjection):
+            if self._active_histogram_yz is None:
+                return
+            suffix = 'YZ'
+            message = HistogramMessage(
+                source='amor-eos',
+                timestamp=ktime(),
+                current_shape=(proj.y.shape[0]-1, proj.z.shape[0]-1),
+                dim_metadata=(
+                    DimMetadata(
+                            length=proj.y.shape[0]-1,
+                            unit="pixel",
+                            label="Y",
+                            bin_boundaries=proj.y,
+                            ),
+                    DimMetadata(
+                            length=proj.z.shape[0]-1,
+                            unit="pixel",
+                            label="Z",
+                            bin_boundaries=proj.z,
+                            )
+                ),
+                last_metadata_timestamp=0,
+                data=proj.data.cts,
+                errors=np.sqrt(proj.data.cts),
+                info=json.dumps({
+                    "start": self._start,
+                    "state": state,
+                    "num events": proj.data.cts.sum()
+                })
+                )
+            logging.info(f"   {state}: Sending {proj.data.cts.sum()} events to Nicos")
+        elif isinstance(proj, TofZProjection):
+            if self._active_histogram_tofz is None:
+                return
+            suffix = 'TofZ'
+            message = HistogramMessage(
+                source='amor-eos',
+                timestamp=ktime(),
+                current_shape=(proj.tof.shape[0]-1, proj.z.shape[0]-1),
+                dim_metadata=(
+                    DimMetadata(
+                            length=proj.tof.shape[0]-1,
+                            unit="ms",
+                            label="ToF",
+                            bin_boundaries=proj.tof,
+                            ),
+                    DimMetadata(
+                            length=proj.z.shape[0]-1,
+                            unit="pixel",
+                            label="Z",
+                            bin_boundaries=proj.z,
+                            ),
+                ),
+                last_metadata_timestamp=0,
+                data=proj.data.cts,
+                errors=np.sqrt(proj.data.cts),
+                info=json.dumps({
+                    "start": self._start,
+                    "state": state,
+                    "num events": proj.data.I.sum()
+                })
+                )
+        else:
+            raise NotImplementedError(f"Histogram for {proj.__class__.__name__} not implemented")
+
+        self.producer.produce(value=message.serialize(),
+                              topic=KAFKA_TOPICS['histogram']+'_'+suffix,
+                              callback=self.acked)
+        self.producer.flush()

eos/logconfig.py

@@ -33,10 +33,10 @@ def setup_logging():
     logfile.setLevel(logging.DEBUG)
     logger.addHandler(logfile)
 
-def update_loglevel(verbose=False, debug=False):
-    if verbose:
+def update_loglevel(verbose=0):
+    if verbose==1:
         logging.getLogger().handlers[0].setLevel(logging.INFO)
-    if debug:
+    if verbose>1:
         console = logging.getLogger().handlers[0]
         console.setLevel(logging.DEBUG)
         formatter = logging.Formatter('%(levelname).1s %(message)s')

eos/ls.py

@@ -0,0 +1,54 @@
+"""
+eosls executable script to list available datafiles in current folder with some metadata information.
+
+Author: Jochen Stahn (algorithms, python draft),
+        Artur Glavic (structuring and optimisation of code)
+"""
+import os
+import logging
+
+from eos.command_line import commandLineArgs
+
+def main():
+    logging.getLogger().setLevel(logging.CRITICAL)
+    clas = commandLineArgs([], 'eosls', extra_args=[
+        dict(dest='path', nargs='*', default=['.'], help='paths to list file in')])
+
+    from glob import glob
+    import tabulate
+    from eos.file_reader import AmorHeader
+
+    files = []
+    for path in clas.path:
+        files+=glob(os.path.join(path, 'amor*.hdf'))
+    files.sort()
+
+    data = {
+        'File name': [],
+        'Start Time': [],
+        'mu': [],
+        'nu': [],
+        'div': [],
+        'Sample': [],
+        'T [K]': [],
+        'H [T]': [],
+        }
+    for fi in files:
+        data['File name'].append(os.path.basename(fi))
+        ah = AmorHeader(fi)
+        data['Sample'].append(ah.sample.name)
+        data['Start Time'].append(ah.fileDate.strftime('%y %m-%d %H:%M:%S'))
+        data['mu'].append('%.3f' % ah.geometry.mu)
+        data['nu'].append('%.3f' % ah.geometry.nu)
+        data['div'].append('%.3f' % ah.geometry.div)
+
+        T = ah.sample.sample_parameters.get('temperature', None)
+        data['T [K]'].append(T.magnitude if T is not None else '-')
+
+        H = ah.sample.sample_parameters.get('magnetic_field', None)
+        data['H [T]'].append(H.magnitude if H is not None else '-')
+
+    print(tabulate.tabulate(data, headers="keys"))
+
+if __name__ == '__main__':
+    main()

eos/nicos.py

@@ -0,0 +1,46 @@
+"""
+amor-nicos vizualising data from Amor@SINQ, PSI
+
+Author: Jochen Stahn (algorithms, python draft),
+        Artur Glavic (structuring and optimisation of code)
+"""
+import logging
+
+# need to do absolute import here as pyinstaller requires it
+from eos.options import E2HConfig, ReaderConfig, ExperimentConfig, E2HReductionConfig
+from eos.command_line import commandLineArgs
+from eos.logconfig import setup_logging, update_loglevel
+
+
+def main():
+    setup_logging()
+    logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+    # read command line arguments and generate classes holding configuration parameters
+    clas = commandLineArgs([ReaderConfig, ExperimentConfig, E2HReductionConfig],
+                           'amor-nicos')
+    update_loglevel(clas.verbose)
+    if clas.verbose<2:
+        # only log info level in logfile
+        logger = logging.getLogger()  # logging.getLogger('quicknxs')
+        logger.setLevel(logging.INFO)
+
+    reader_config = ReaderConfig.from_args(clas)
+    experiment_config = ExperimentConfig.from_args(clas)
+    reduction_config = E2HReductionConfig.from_args(clas)
+    config = E2HConfig(reader_config, experiment_config, reduction_config)
+
+    logging.warning('######## amor-nicos - Nicos histogram for Amor ########')
+    from eos.reduction_kafka import KafkaReduction
+
+    # only import heavy module if sufficient command line parameters were provided
+    from eos.reduction_reflectivity import ReflectivityReduction
+    # Create reducer with these arguments
+    reducer = KafkaReduction(config)
+    # Perform actual reduction
+    reducer.reduce()
+
+    logging.info('######## amor-nicos - finished ########')
+
+if __name__ == '__main__':
+    main()

eos/nicos_interface.py

@@ -0,0 +1,60 @@
+"""
+Functions used to directly access information from nicos.
+"""
+
+import socket
+import platform
+import logging
+import subprocess
+
+ON_AMOR = platform.node().startswith('amor')
+NICOS_CACHE_DIR = '/home/data/nicosdata/cache/'
+GREP = '/usr/bin/grep "value"'
+
+
+def lookup_nicos_value(key, nicos_key, dtype=float, suffix='', year="2026"):
+    # TODO: Implement direct communication to nicos to read the cache
+    if nicos_key=='ignore':
+        return dtype(0)
+    try:
+        logging.debug(f'     trying socket request for device {nicos_key}')
+        response = nicos_single_request(nicos_key)
+        logging.info(f"     using parameter {nicos_key} from nicos cache via socket")
+        return dtype(response)
+    except Exception as e:
+        logging.debug(f'       socket request failed with {e!r}')
+    if ON_AMOR:
+        logging.debug(f"     trying to extract {nicos_key} from nicos cache files")
+        call = f'{GREP} {NICOS_CACHE_DIR}nicos-{nicos_key}/{year}{suffix}'
+        try:
+            value = str(subprocess.getoutput(call)).split('\t')[-1]
+            logging.info(f"     using parameter {nicos_key} from nicos cache file")
+            return dtype(value)
+        except Exception:
+            logging.error(f"       couldn't get value from nicos cache {nicos_key}, {call}")
+            return dtype(0)
+    else:
+        logging.warning(f"     parameter {key} not found, relpace by zero")
+        return dtype(0)
+
+def nicos_single_request(device):
+    sentinel = b'\n'
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.settimeout(1.0)
+        s.connect(('amor', 14869))
+
+        tosend = f'@nicos/{device}/value?\n'
+
+        # write request
+        # self.log.debug("get_explicit: sending %r", tosend)
+        s.sendall(tosend.encode())
+
+        # read response
+        data = b''
+        while not data.endswith(sentinel):
+            newdata = s.recv(8192)  # blocking read
+            if not newdata:
+                raise IOError('cache closed connection')
+            data += newdata
+        s.shutdown(socket.SHUT_RDWR)
+    return data.decode('utf-8').split('=')[-1]

eos/normalization.py

@@ -0,0 +1,132 @@
+"""
+Defines how to normalize a focusing reflectometry dataset by a reference measurement.
+"""
+import logging
+import os
+import numpy as np
+from typing import List, Optional
+
+from orsopy import fileio
+
+from .event_data_types import EventDatasetProtocol
+from .header import Header
+from .options import NormalisationMethod
+from .instrument import Detector, LZGrid
+
+
+class LZNormalisation:
+    file_list = List[str]
+    angle: float
+    monitor: float
+    norm: np.ndarray
+
+    def __init__(self, reference:EventDatasetProtocol, normalisationMethod: NormalisationMethod, grid: LZGrid):
+        self.angle = reference.geometry.nu-reference.geometry.mu
+        lamda_e = reference.data.events.lamda
+        detZ_e = reference.data.events.detZ
+        self.monitor = np.sum(reference.data.pulses.monitor)
+        norm_lz, _, _ = np.histogram2d(lamda_e, detZ_e, bins=(grid.lamda(), grid.z()))
+        if normalisationMethod==NormalisationMethod.direct_beam:
+            self.norm = np.flip(norm_lz, 1)
+        else:
+            # correct for reference sm reflectivity
+            lamda_l = grid.lamda()
+            theta_z = self.angle+Detector.delta_z
+            lamda_lz = (grid.lz().T*lamda_l[:-1]).T
+            theta_lz = grid.lz()*theta_z
+            qz_lz = 4.0*np.pi*np.sin(np.deg2rad(theta_lz))/lamda_lz
+            # TODO: introduce variable for `m` and propably for the slope
+            # Correct reflectivity of m=5 supermirror
+            Rsm_lz = np.ones(np.shape(qz_lz))
+            Rsm_lz = np.where(qz_lz>0.0217, 1-(qz_lz-0.0217)*(0.0625/0.0217), Rsm_lz)
+            Rsm_lz = np.where(qz_lz>0.0217*5, np.nan, Rsm_lz)
+            self.norm = norm_lz/Rsm_lz
+        self.file_list = [os.path.basename(entry) for entry in reference.file_list]
+
+    @classmethod
+    def from_file(cls, filename, check_hash=None) -> Optional['LZNormalisation']:
+        self = super().__new__(cls)
+        with open(filename, 'rb') as fh:
+            hash = str(np.load(fh, allow_pickle=True))
+            self.file_list = np.load(fh, allow_pickle=True).tolist()
+            self.angle = np.load(fh, allow_pickle=True)
+            self.norm = np.load(fh, allow_pickle=True)
+            self.monitor = np.load(fh, allow_pickle=True)
+        if check_hash is not None and hash != check_hash:
+            logging.info('    file hash does not match this reduction configuration')
+            raise ValueError('file hash does not match this reduction configuration')
+        return self
+
+    @classmethod
+    def unity(cls, grid:LZGrid) -> 'LZNormalisation':
+        logging.warning(f'normalisation is unity')
+        self = super().__new__(cls)
+        self.norm = grid.lz()
+        self.file_list = []
+        self.angle = 1.
+        self.monitor = 1.
+        return self
+
+    @classmethod
+    def model(cls, grid:LZGrid) -> 'LZNormalisation':
+        # generate a normalization based on angular and wavelength distribution model
+        # TODO: add options for sample size for better absolute normalization
+        logging.warning(f'normalisation is model')
+        self = super().__new__(cls)
+        self.angle = 1.0
+        self.monitor = 4e6
+
+        lamda_l  = grid.lamda()
+        lamda_c = (lamda_l[:-1]+lamda_l[1:])/2
+
+        delta = np.rad2deg(np.arctan2(grid.z(), Detector.distance))/2.0
+        delta_c = (delta[:-1]+delta[1:])/2-delta.mean()
+
+        # approximate spectrum by Maxwell-Boltzmann and intensity by linear footprint
+        a = 3.8
+        Ilambda = np.sqrt(2./np.pi)*lamda_c**2/a**3*np.exp(-lamda_c**2/(2.*a**2))
+        Idelta = np.where(abs(delta_c)<0.75, (self.angle-delta_c), np.nan)
+
+        self.norm = 1e6*Ilambda[:, np.newaxis]*Idelta[np.newaxis, :]
+
+        return self
+
+    def safe(self, filename, hash):
+        with open(filename, 'wb') as fh:
+            np.save(fh, hash, allow_pickle=False)
+            np.save(fh, np.array(self.file_list), allow_pickle=False)
+            np.save(fh, np.array(self.angle), allow_pickle=False)
+            np.save(fh, self.norm, allow_pickle=False)
+            np.save(fh, self.monitor, allow_pickle=False)
+
+    def update_header(self, header:Header):
+        header.measurement_additional_files = [fileio.File(file=os.path.basename(entry)) for entry in self.file_list]
+
+    def smooth(self, width):
+        logging.debug(f'apply convolution with gaussian along lambda axis to smooth norm, sigma={width}')
+        try:
+            from scipy.signal import fftconvolve
+        except ImportError:
+            self._smooth_slow(width)
+        kx = np.arange(self.norm.shape[0])-self.norm.shape[0]/2.
+        kernel = np.exp(-0.5*kx**2/width**2)
+        kernel/=kernel.sum()
+        kernel = kernel[:, np.newaxis]*np.ones(self.norm.shape[1])[np.newaxis, :]
+        unorm = np.where(np.isnan(self.norm), 0., self.norm)
+        nnorm = fftconvolve(unorm, kernel, mode='same', axes=0)
+        nnorm[np.isnan(self.norm)] = np.nan
+        self.norm = nnorm
+
+    def _smooth_slow(self, width):
+        # like smooth but using numpy buildin slow convolve
+        nnorm = np.zeros_like(self.norm)
+
+        kx = np.arange(self.norm.shape[0])-self.norm.shape[0]/2.
+        kernel = np.exp(-0.5*kx**2/width**2)
+        kernel/=kernel.sum()
+        unorm = np.where(np.isnan(self.norm), 0., self.norm)
+
+        for row in range(self.norm.shape[1]):
+            nnorm[:, row] = np.convolve(unorm[:, row], kernel, mode='same')
+        nnorm[np.isnan(self.norm)] = np.nan
+        self.norm = nnorm

eos/options.py

@@ -0,0 +1,752 @@
+"""
+Classes for stroing various configurations needed for reduction.
+"""
+import argparse
+from dataclasses import dataclass, field, Field, fields, MISSING
+from typing import get_args, get_origin, List, Optional, Tuple, Union
+from datetime import datetime
+from os import path
+import numpy as np
+
+import logging
+
+
+try:
+    from enum import StrEnum
+except ImportError:
+    try:
+        # python <3.11 try to use backports
+        from backports.strenum import StrEnum
+    except ImportError:
+        # python <3.10 use Enum instead
+        from enum import Enum as StrEnum
+
+class InCallString(StrEnum):
+    auto='auto'
+    always='always'
+    never='never'
+
+@dataclass
+class CommandlineParameterConfig:
+    argument: str # default parameter for command line resutign ins "--argument"
+    add_argument_args: dict # all arguments that will be passed to add_argument method
+    short_form: Optional[str] = None
+    group: str = 'misc'
+    priority: int = 0
+    in_call_string: InCallString = InCallString.auto
+
+    def __gt__(self, other):
+        """
+        Sort required arguments first, then use priority, then name
+        """
+        return (not self.add_argument_args.get('required', False), -self.priority, self.argument)>(
+            not other.add_argument_args.get('required', False), -other.priority, other.argument)
+
+class ArgParsable:
+    def __init_subclass__(cls):
+        # create a nice documentation string that takes help into account
+        cls.__doc__ = cls.__name__ + " Parameters:\n"
+        for key, typ in cls.__annotations__.items():
+            if get_origin(typ) is Union and type(None) in get_args(typ):
+                optional = True
+                typ = get_args(typ)[0]
+            else:
+                optional = False
+
+            value = getattr(cls, key, None)
+            try:
+                cls.__doc__ += f"    {key} ({typ.__name__})"
+            except AttributeError:
+                cls.__doc__ += f"    {key}"
+            if isinstance(value, Field):
+                if value.default is not MISSING:
+                    cls.__doc__ += f" = {value.default}"
+                if 'help' in value.metadata:
+                    cls.__doc__ += f" - {value.metadata['help']}"
+            elif value is not None:
+                    cls.__doc__ += f" = {value}"
+            if optional:
+                cls.__doc__ += " [Optional]"
+            cls.__doc__ += "\n"
+        return cls
+
+    @classmethod
+    def get_commandline_parameters(cls) -> List[CommandlineParameterConfig]:
+        """
+        Return a list of arguments used in building the command line parameters.
+
+        Union types besides Optional are not supported.
+        """
+        output = []
+        for field in fields(cls):
+            args={}
+            if field.default is not MISSING:
+                args['default'] = field.default
+                args['required'] = False
+            elif field.default_factory is not MISSING:
+                args['default'] = field.default_factory()
+                args['required'] = False
+            else:
+                args['required'] = True
+            if get_origin(field.type) is Union and type(None) in get_args(field.type):
+                # optional argument
+                typ = get_args(field.type)[0]
+                del(args['default'])
+            else:
+                typ = field.type
+            if get_origin(typ) is list:
+                args['nargs'] = '+'
+                args['action'] = 'extend'
+                typ = get_args(typ)[0]
+                if get_origin(typ) is tuple:
+                    # tuple of items are put together during evaluation
+                    typ = get_args(typ)[0]
+            elif get_origin(typ) is tuple:
+                args['nargs'] = len(get_args(typ))
+                typ = get_args(typ)[0]
+            if issubclass(typ, StrEnum):
+                args['choices'] = [ci.value for ci in typ]
+                if field.default is not MISSING:
+                    args['default'] = field.default.value
+                typ = str
+
+            if typ is bool:
+                args['action'] = 'store_false' if field.default else 'store_true'
+            else:
+                args['type'] = typ
+
+            if 'help' in field.metadata:
+                args['help'] = field.metadata['help']
+
+            output.append(CommandlineParameterConfig(
+                    field.name,
+                    add_argument_args=args,
+                    group=field.metadata.get('group', 'misc'),
+                    short_form=field.metadata.get('short', None),
+                    priority=field.metadata.get('priority', 0),
+                    in_call_string=field.metadata.get('in_call_string', InCallString.auto),
+                    ))
+        return output
+
+    @classmethod
+    def get_default(cls, key):
+        """
+        Return the default argument for an attribute, None if it doesn't exist.
+        """
+        for field in fields(cls):
+            if field.name != key:
+                continue
+            if field.default is not MISSING:
+                return field.default
+            elif field.default_factory is not MISSING:
+                return field.default_factory()
+        return None
+
+    def is_default(self, key):
+        value = getattr(self, key)
+        return value == self.get_default(key)
+
+    @classmethod
+    def from_args(cls, args: argparse.Namespace):
+        """
+        Create the child class from the command line argument Namespace object.
+        All attributes that are not needed for this class are ignored.
+        """
+        inpargs = {}
+        for field in fields(cls):
+            value = getattr(args, field.name)
+            typ = field.type
+            if get_origin(field.type) is Union and type(None) in get_args(field.type):
+                # optional argument
+                typ = get_args(field.type)[0]
+            if get_origin(typ) is list:
+                item_typ = get_args(typ)[0]
+                if get_origin(item_typ) is tuple:
+                    # tuple of items are put together during evaluation
+                    tuple_length = len(get_args(item_typ))
+                    value = [tuple(value[i*tuple_length+j] for j in range(tuple_length)) for i in range(len(value)//tuple_length)]
+            if isinstance(typ, type) and issubclass(typ, StrEnum):
+                # convert str to enum
+                try:
+                    value = typ(value)
+                except ValueError:
+                    choices = [ci.value for ci in typ]
+                    raise ValueError(f"Parameter --{field.name} has to be one of {choices}")
+
+            inpargs[field.name] = value
+        return cls(**inpargs)
+
+    def get_call_parameters(self, abbrv=True):
+        """
+        Return a list of command line arguments that reproduce this config, do not add default parameters.
+        """
+        output = []
+        for arg in sorted(self.get_commandline_parameters()):
+            if ((arg.in_call_string==InCallString.auto and self.is_default(arg.argument)) or
+                    arg.in_call_string==InCallString.never):
+                # skip default arguments or arguments defined to never appear in call string
+                continue
+            if arg.short_form and abbrv:
+                item = '-' + arg.short_form + ' '
+            else:
+                item = '--' + arg.argument + ' '
+            if arg.add_argument_args.get('type', None) in [str, float, int]:
+                nargs = arg.add_argument_args.get('nargs', None)
+                if nargs is None:
+                    item += str(getattr(self, arg.argument))
+                elif nargs=='+':
+                    # remove the default parameters, only show added ones
+                    ignore = len(arg.add_argument_args.get('default', []))
+                    item += ' '.join([str(pi) for pi in getattr(self, arg.argument)[ignore:]])
+                else:
+                    item += ' '.join([str(pi) for pi in getattr(self, arg.argument)])
+            # boolean flags only reach this point if they are non-default
+            output.append((arg, item))
+        return output
+
+# definition of command line arguments
+
+@dataclass
+class ReaderConfig(ArgParsable):
+    year: int = field(
+            default=datetime.now().year,
+            metadata={
+                'short': 'Y', 
+                'group': 'input data', 
+                'help': 'year the measurement was performed',
+                'in_call_string': InCallString.always,
+                },
+            )
+    rawPath: List[str] = field(
+            default_factory=lambda: ['.', path.join('.','raw'), path.join('..','raw'), path.join('..','..','raw')],
+            metadata={
+                'short': 'rp',
+                'group': 'input data',
+                'help': 'search paths for hdf files',
+                },
+            )
+    startTime: Optional[float] = field(
+            default = None,
+            metadata={         
+                'short': 'st',     
+                'group': 'data manicure',
+                'help': 'set time zero other than the start of the data aquisition',
+                },
+            )
+
+class IncidentAngle(StrEnum):
+    alphaF = 'alphaF'
+    mu = 'mu'
+    nu = 'nu'
+
+class MonitorType(StrEnum):
+    auto = 'a'
+    proton_charge = 'p'
+    time = 't'
+    neutron_monitor = 'n'
+    debug = 'x'
+
+MONITOR_UNITS = {
+    MonitorType.neutron_monitor: 'cnts',
+    MonitorType.proton_charge: 'mC',
+    MonitorType.time: 's',
+    MonitorType.auto: 'various',
+    MonitorType.debug: 'mC',
+    }
+
+@dataclass
+class ExperimentConfig(ArgParsable):
+    chopperPhase: float = field(
+            default=0,
+            metadata={
+                'short': 'cp',
+                'group': 'instrument settings',
+                'help': 'phase between opening of chopper 1 and closing of chopper 2 window',
+                },
+            ) 
+    chopperPhaseOffset: float = field(
+            default=-5,
+            metadata={
+                'short': 'co',
+                'group': 'instrument settings',  
+                'help': 'phase between chopper 1 index pulse and closing edge',
+                },                               
+            )                          
+    chopperSpeed: float = field(
+            default=500,
+            metadata={
+                'short': 'cs',
+                'group': 'instrument settings',
+                'help': 'rotation speed of the chopper disks in rpm',
+                },
+            )
+    yRange: Tuple[int, int] = field(
+            default=(18, 48),
+            metadata={
+                'short': 'y',
+                'group': 'region of interest',
+                'help': 'horizontal pixel range on the detector to be used',
+                },
+            )
+    lambdaRange: Tuple[float, float] = field(
+            default_factory=lambda: [3, 12.5],
+            metadata={
+                'short': 'l',
+                'group': 'region of interest',
+                'help': 'wavelength range to be used (in angstrom)',
+                },
+            )
+    lowCurrentThreshold: float = field(
+            default=50,
+            metadata={
+                'short': 'pt',
+                'group': 'instrument settings',
+                'help': 'proton current below which the events are ignored (per chopper pulse)',
+                },
+            )
+
+    incidentAngle: IncidentAngle = field(
+            default=IncidentAngle.alphaF,
+            metadata={
+                'short': 'ai',
+                'group': 'instrument settings',
+                'help': 'calculate alphaI = [alphaF], [mu]+kappa+delta_kappa or ([nu]+kappa+delta_kappa)/2',
+                },
+            )
+    alphaF = 'alphaF'
+    sampleModel: Optional[str] = field(
+            default=None,
+            metadata={
+                'short': 'sm',
+                'group': 'sample',  
+                'help': 'orso type string to describe the sample in one line',
+                },                               
+            )                          
+    mu: Optional[float] = field(
+            default=None,
+            metadata={
+                'short': 'mu',
+                'group': 'sample',  
+                'help': 'inclination of the sample surface w.r.t. the instrument horizon',
+                },                               
+            )                          
+    nu: Optional[float] = field(
+            default=None,
+            metadata={
+                'short': 'nu',
+                'group': 'sample',  
+                'help': 'inclination of the detector w.r.t. the instrument horizon',
+                },                               
+            )                          
+    muOffset: Optional[float] = field(
+            default=None,
+            metadata={
+                'short': 'm',
+                'group': 'sample',  
+                'help': 'correction offset for mu misalignment (mu_real = mu_file + mu_offset)',
+                },                               
+            )                          
+    monitorType: MonitorType = field(
+            default=MonitorType.proton_charge,
+            metadata={
+                'short': 'mt',
+                'group': 'instrument settings',
+                'help': 'one of [a]uto, [p]rotonCurrent, [t]ime or [n]eutronMonitor',
+                },                               
+            )                          
+
+class NormalisationMethod(StrEnum):
+    direct_beam = 'd'
+    over_illuminated = 'o'
+    under_illuminated = 'u'
+
+@dataclass
+class ReflectivityReductionConfig(ArgParsable):
+    fileIdentifier: List[str] = field(
+            metadata={
+                'short':    'f',
+                'priority': 100,
+                'group':    'input data',
+                'help':     'file number(s) or offset (if < 1)',
+                },
+            )
+
+    qResolution: float = field(
+            default=0.01,
+            metadata={
+                'short': 'r',
+                'group': 'data manicure',
+                'help': 'output resolution of q-scale Delta q / q',
+                },
+            )
+    qzRange: Tuple[float, float] = field(
+            default_factory=lambda: [0.005, 0.51],
+            metadata={
+                'short': 'q',
+                'group': 'region of interest',
+                'help': '?',
+                },
+            )
+    thetaRange: Tuple[float, float] = field(
+            default_factory=lambda: [-12., 12.],
+            metadata={
+                'short': 't',
+                'group': 'region of interest',
+                'help': 'absolute theta region of interest',
+                },
+            )
+    thetaRangeR: Tuple[float, float] = field(
+            default_factory=lambda: [-0.75, 0.75],
+            metadata={
+                'short': 'T',
+                'group': 'region of interest',
+                'help': 'theta region of interest w.r.t. beam center',
+                },
+            )
+    thetaFilters: List[Tuple[float, float]] = field(
+            default_factory=lambda: [],
+            metadata={
+                'short': 'TF',
+                'group': 'region of interest',
+                'help': 'add one or more theta ranges that will be filtered in reduction',
+                },
+            )
+    normalisationMethod: NormalisationMethod = field(
+            default=NormalisationMethod.over_illuminated,
+            metadata={
+                'short': 'nm', 
+                'priority': 90,
+                'group': 'input data',
+                'help': 'normalisation method: [o]verillumination, [u]nderillumination, [d]irect_beam'})
+    normalizationFilter: float = field(
+            default=-1,
+            metadata={
+                'group': 'input data',
+                'help': 'minimum normalization counts in lambda-theta bin to use, else filter'})
+    normAngleFilter: float = field(
+            default=-1,
+            metadata={
+                'group': 'input data',
+                'help': 'minimum normalization counts total thetat bin to use, else filter'})
+    normalizationSmoothing: float = field(
+            default=0,
+            metadata={
+                'group': 'input data',
+                'help': 'apply convolution on lambda axes to smooth the normalization data, useful for low statistics'})
+    scale: List[float] = field(
+            default_factory=lambda: [1.],
+            metadata={
+                'short': 's',
+                'group': 'data manicure',
+                'help': '(list of) scaling factors, if less elements than files use the last one',
+                },
+            ) 
+    autoscale: Tuple[float, float] = field(
+           default=None,
+           metadata={
+               'short': 'S',
+               'group': 'data manicure',
+               'help': '',
+               },
+           )
+    subtract: Optional[str] = field(
+            default=None, 
+            metadata={
+                'short': 'sub',
+                'group': 'input data', 
+                'help': 'File with R(q_z) curve to be subtracted (in .Rqz.ort format)'})
+    normalisationFileIdentifier: Optional[List[str]] = field(
+            default=None,
+            metadata={
+                'short': 'n', 
+                'priority': 90,
+                'group': 'input data', 
+                'help': 'file number(s) of normalisation measurement'})
+    timeSlize: Optional[List[float]] = field(
+            default= None,
+            metadata={
+                'short': 'ts',
+                'group': 'region of interest',
+                'help': 'time slizing <interval> ,[<start> [,stop]]',
+                },
+            )
+
+    logfilter: List[str] = field(
+            default_factory=lambda: [],
+            metadata={
+                'short': 'lf',
+                'group': 'region of interest',
+                'help':  'filter using comparison to a log values, multpiple allowd (example "sample_temperature<150")',
+                },
+            )
+
+
+class OutputFomatOption(StrEnum):
+    Rqz_ort = "Rqz.ort"
+    Rqz_orb = "Rqz.orb"
+    Rlt_ort = "Rlt.ort"
+    Rlt_orb = "Rlt.orb"
+    ort = "ort"
+    orb = "orb"
+    Rqz = "Rqz"
+    Rlt = "Rlt"
+
+
+class PlotColormaps(StrEnum):
+    gist_ncar = "gist_ncar"
+    viridis = "viridis"
+    inferno = "inferno"
+    gist_rainbow = "gist_rainbow"
+    nipy_spectral = "nipy_spectral"
+    jochen_deluxe = "jochen_deluxe"
+
+@dataclass
+class ReflectivityOutputConfig(ArgParsable):
+    outputFormats: List[OutputFomatOption] = field(
+            default_factory=lambda: ['Rqz.ort'],
+            metadata={
+                'short': 'of',
+                'group': 'output',
+                'help': 'one of "Rqz[.ort]", "Rlt[.ort]" or both with "ort"',
+                },
+            )
+    outputName: str = field(
+            default='fromEOS',
+            metadata={
+                'short': 'o',
+                'group': 'output',
+                'help': '?',
+                },
+            )
+    outputPath: str = field(
+            default='.',
+            metadata={
+                'short': 'op',
+                'group': 'output',
+                'help': '?',
+                },
+            )
+    plot: bool = field(
+            default=False,
+            metadata={
+                'group': 'output',
+                'help': 'show matplotlib graphs of results',
+                },
+            )
+
+    plot_colormap: PlotColormaps = field(
+            default=PlotColormaps.gist_ncar,
+            metadata={
+                'short': 'pcmap',
+                'group': 'output',
+                'help': 'matplotlib colormap used in lambda-theta graphs when plotting',
+                },
+            )
+
+    append: bool = field(
+            default=False,
+            metadata={
+                'group': 'output',
+                'help': 'if file already exists, append result as additional ORSO dataset (only Rqz.ort)',
+                },
+            )
+
+    def _output_format_list(self, outputFormat):
+        format_list = []
+        if OutputFomatOption.ort in outputFormat\
+                or OutputFomatOption.Rqz_ort in outputFormat\
+                or OutputFomatOption.Rqz in outputFormat:
+            format_list.append(OutputFomatOption.Rqz_ort)
+        if OutputFomatOption.ort in outputFormat\
+                or OutputFomatOption.Rlt_ort in outputFormat\
+                or OutputFomatOption.Rlt in outputFormat:
+            format_list.append(OutputFomatOption.Rlt_ort)
+        if OutputFomatOption.orb in outputFormat\
+                or OutputFomatOption.Rqz_orb in outputFormat\
+                or OutputFomatOption.Rqz in outputFormat:
+            format_list.append(OutputFomatOption.Rqz_orb)
+        if OutputFomatOption.orb in outputFormat\
+                or OutputFomatOption.Rlt_orb in outputFormat\
+                or OutputFomatOption.Rlt in outputFormat:
+            format_list.append(OutputFomatOption.Rlt_orb)
+        return sorted(format_list, reverse=True)
+
+    def __post_init__(self):
+        self.outputFormats = self._output_format_list(self.outputFormats)
+
+
+# ===================================
+
+@dataclass
+class ReflectivityConfig:
+    reader: ReaderConfig
+    experiment: ExperimentConfig
+    reduction: ReflectivityReductionConfig
+    output: ReflectivityOutputConfig
+    
+    _call_string_overwrite=None
+    
+    #@property
+    #def call_string(self)->str:
+    #    if self._call_string_overwrite:
+    #        return self._call_string_overwrite
+    #    else:
+    #        return self.calculate_call_string()
+    
+    def call_string(self):
+        base = 'eos'
+
+        call_parameters = self.reader.get_call_parameters()
+        call_parameters += self.output.get_call_parameters()
+        call_parameters += self.reduction.get_call_parameters()
+        call_parameters += self.experiment.get_call_parameters()
+
+        call_parameters.sort()
+
+        cpout = f'{base} ' + ' '.join([cp[1] for cp in call_parameters])
+
+
+        logging.debug(f'Argument list build in EOSConfig.call_string: {cpout}')
+        return  cpout
+
+class E2HPlotSelection(StrEnum):
+    All = 'all'
+    Raw = 'raw'
+    YZ = 'Iyz'
+    LT = 'Ilt'
+    YT = 'Iyt'
+    TZ = 'Itz'
+    Q = 'Iq'
+    L = 'Il'
+    T = 'It'
+    ToF = 'tof'
+
+
+class E2HPlotArguments(StrEnum):
+    Default = 'def'
+    OutputFile = 'file'
+    Logarithmic = 'log'
+    Linear = 'lin'
+
+@dataclass
+class E2HReductionConfig(ArgParsable):
+    fileIdentifier: str = field(
+            default='0',
+            metadata={
+                'short':    'f',
+                'priority': 100,
+                'group':    'input data',
+                'help':     'file number(s) or offset (if < 1), events2histogram only accepts one short code',
+                },
+            )
+
+    show_plot: bool = field(
+            default=False,
+            metadata={
+                'short': 'sp',
+                'group': 'output',
+                'help': 'show matplotlib graphs of results',
+                },
+            )
+
+    plot: E2HPlotSelection = field(
+            default=E2HPlotSelection.All,
+            metadata={
+                'short': 'p',
+                'group': 'output',
+                'help': 'select what to plot or write',
+                },
+            )
+
+    kafka: bool = field(
+            default=False,
+            metadata={
+                'group': 'output',
+                'help': 'send result to kafka for Nicos',
+                },
+            )
+
+    plotArgs: E2HPlotArguments = field(
+            default=E2HPlotArguments.Default,
+            metadata={
+                'short': 'pa',
+                'group': 'output',
+                'help': 'select configuration for plot',
+                },
+            )
+
+    update: bool = field(
+            default=False,
+            metadata={
+                'short': 'u',
+                'group': 'output',
+                'help': 'keep running in the background and update plot when file is modified, implies --plot',
+                },
+            )
+
+    fast: bool = field(
+            default=False,
+            metadata={
+                'group': 'input data',
+                'help': 'skip some reduction steps to speed up calculations',
+                },
+            )
+
+    normalizationModel: bool = field(
+            default=False,
+            metadata={
+                'short': 'nm',
+                'group': 'input data',
+                'help': 'use model for incoming spectrum and divergence to normalize for reflectivity',
+                },
+            )
+
+    plot_colormap: PlotColormaps = field(
+            default=PlotColormaps.jochen_deluxe,
+            metadata={
+                'short': 'pcmap',
+                'group': 'output',
+                'help': 'matplotlib colormap used in lambda-theta graphs when plotting',
+                },
+            )
+
+    max_events: int = field(
+            default = 10_000_000,
+            metadata={
+                'group': 'input data',
+                'help':  'maximum number of events read at once',
+                },
+            )
+
+    thetaRangeR: Tuple[float, float] = field(
+            default_factory=lambda: [-0.75, 0.75],
+            metadata={
+                'short': 'T',
+                'group': 'region of interest',
+                'help': 'theta region of interest w.r.t. beam center',
+                },
+            )
+
+    thetaFilters: List[Tuple[float, float]] = field(
+            default_factory=lambda: [],
+            metadata={
+                'short': 'TF',
+                'group': 'region of interest',
+                'help': 'add one or more theta ranges that will be filtered in reduction',
+                },
+            )
+
+    fontsize: float = field(
+            default=8.,
+            metadata={
+                'short': 'pf',
+                'group': 'output',
+                'help': 'font size for graphs',
+                },
+            )
+
+@dataclass
+class E2HConfig:
+    reader: ReaderConfig
+    experiment: ExperimentConfig
+    reduction: E2HReductionConfig

eos/path_handling.py

@@ -0,0 +1,82 @@
+"""
+Defines how file paths are resolved from short_notation, year and number to filename.
+"""
+import logging
+import os
+from typing import List
+
+
+class PathResolver:
+    def __init__(self, year, rawPath):
+        self.year = year
+        self.rawPath = rawPath
+
+    def resolve(self, short_notation):
+        return list(map(self.get_path, self.expand_file_list(short_notation)))
+
+    @staticmethod
+    def expand_file_list(short_notation)->List[int]:
+        """Evaluate string entry for file number lists"""
+        file_list = []
+        for i in short_notation.split(','):
+            if '-' in i and not i.startswith('-'):
+                if ':' in i:
+                    step = i.split(':', 1)[1]
+                    file_list += range(int(i.split('-', 1)[0]),
+                                       int((i.rsplit('-', 1)[1]).split(':', 1)[0])+1,
+                                       int(step))
+                else:
+                    step = 1
+                    file_list += range(int(i.split('-', 1)[0]),
+                                       int(i.split('-', 1)[1])+1,
+                                       int(step))
+            else:
+                file_list += [int(i)]
+        return list(sorted(file_list))
+
+    def get_path(self, number):
+        if number<=0:
+            number = self.search_latest(number)
+        fileName = f'amor{self.year}n{number:06d}.hdf'
+        path = ''
+        for rawd in self.rawPath:
+            if os.path.exists(os.path.join(rawd, fileName)):
+                path = rawd
+                break
+        if not path:
+            from glob import glob
+            potential_file = glob(f'/home/amor/data/{self.year}/*/{fileName}')
+            if len(potential_file)>0:
+                path = os.path.dirname(potential_file[0])
+            else:
+                raise FileNotFoundError(f'# ERROR: the file {fileName} can not be found '
+                                        f'in {self.rawPath+["/home/amor/data"]}')
+        return os.path.join(path, fileName)
+
+    def search_latest(self, number):
+        if number>0:
+            raise ValueError('number needs to be relative index (negative)')
+        if os.path.exists(f'/home/amor/data/{self.year}/DataNumber'):
+            try:
+                with open(f'/home/amor/data/{self.year}/DataNumber', 'r') as fh:
+                    current_index = int(fh.readline())-1
+            except Exception:
+                logging.error('Can not access DataNumber', exc_info=True)
+            else:
+                return current_index+number
+        # find all files from the given year, convert to number and return latest
+        from glob import glob
+        possible_files = []
+        for rawd in self.rawPath:
+            possible_files += glob(os.path.join(rawd, f'amor{self.year}n??????.hdf'))
+
+        possible_files += glob(f'/home/amor/data/{self.year}/*/amor{self.year}n??????.hdf')
+        possible_indices = list(set([int(os.path.basename(fi)[9:15]) for fi in possible_files]))
+        possible_indices.sort()
+        try:
+            return possible_indices[number-1]
+        except IndexError:
+            raise FileNotFoundError(f'# Could not find suitable file for relative index {number} '
+                                    f'in {self.rawPath+["/home/amor/data"]}, '
+                                    f'possible indices {possible_indices}')
+

eos/projection.py

@@ -0,0 +1,832 @@
+"""
+Classes used to calculate projections/binnings from event data onto given grids.
+"""
+
+import logging
+import warnings
+from abc import ABC, abstractmethod
+from typing import List, Tuple, Union
+
+import numpy as np
+from dataclasses import dataclass
+
+from .event_data_types import EventDatasetProtocol
+from .instrument import Detector, LZGrid
+from .normalization import LZNormalisation
+
+class ProjectionInterface(ABC):
+    @abstractmethod
+    def project(self, dataset: EventDatasetProtocol, monitor: float): ...
+
+    @abstractmethod
+    def clear(self): ...
+
+    @abstractmethod
+    def plot(self, **kwargs): ...
+
+    @abstractmethod
+    def update_plot(self): ...
+
+@dataclass
+class ProjectedReflectivity:
+    R: np.ndarray
+    dR: np.ndarray
+    Q: np.ndarray
+    dQ: np.ndarray
+
+    @property
+    def data(self):
+        """
+        Return combined data compatible with storing as columns in orso file.
+            Q, R, dR, dQ
+        """
+        return np.array([self.Q, self.R, self.dR, self.dQ]).T
+
+    def data_for_time(self, time):
+        tme = np.ones(np.shape(self.Q))*time
+        return np.array([self.Q, self.R, self.dR, self.dQ, tme]).T
+
+    def scale(self, factor):
+        self.R *= factor
+        self.dR *= factor
+
+    def autoscale(self, range):
+        filter_q = (range[0]<=self.Q) & (self.Q<=range[1])
+        filter_q &= self.dR>0
+        if filter_q.sum()>0:
+            scale = (self.R[filter_q]/self.dR[filter_q]).sum()/(self.R[filter_q]**2/self.dR[filter_q]).sum()
+            self.scale(scale)
+            logging.info(f'      scaling factor = {scale}')
+            return scale
+        else:
+            logging.warning('      automatic scaling not possible')
+            return 1.0
+
+    def stitch(self, other: 'ProjectedReflectivity'):
+        # find scaling factor between two reflectivities at points both are not zero
+        filter_q = np.logical_not(np.isnan(other.R*self.R))
+        filter_q &= self.R>0
+        filter_q &= other.R>0
+        R1 = self.R[filter_q]
+        dR1 = self.dR[filter_q]
+        R2 = other.R[filter_q]
+        dR2 = other.dR[filter_q]
+        if len(R1)>0:
+            scale = (R1**2*R2**2/(dR1**2*dR2**2)).sum() / (R1**3*R2/(dR1**2*dR2**2)).sum()
+            self.scale(scale)
+            logging.info(f'      scaling factor = {scale}')
+            return scale
+        else:
+            logging.warning('      automatic scaling not possible')
+            return 1.0
+
+    def subtract(self, R, dR):
+        # subtract another dataset with same q-points
+        self.R -= R
+        self.dR = np.sqrt(self.dR**2+dR**2)
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        plt.errorbar(self.Q, self.R, yerr=self.dR, **kwargs)
+        plt.yscale('log')
+        plt.xlabel('Q / Å$^{-1}$')
+        plt.ylabel('R')
+
+class LZProjection(ProjectionInterface):
+    grid: LZGrid
+    lamda: np.ndarray
+    alphaF: np.ndarray
+    is_normalized: bool
+    angle: float
+
+    data: np.recarray
+    _dtype = np.dtype([
+            ('I', np.float64),
+            ('mask', bool),
+            ('ref', np.float64),
+            ('err', np.float64),
+            ('res', np.float64),
+            ('qz', np.float64),
+            ('qx', np.float64),
+            ('norm', np.float64),
+            ])
+
+    def __init__(self, tthh: float, grid: LZGrid):
+        self.grid = grid
+        self.is_normalized = False
+        self.angle = tthh
+
+        alphaF_z  = tthh + Detector.delta_z
+        lamda_l  = self.grid.lamda()
+        lamda_c = (lamda_l[:-1]+lamda_l[1:])/2
+
+        lz_shape = self.grid.lz()
+
+        self.lamda  = lz_shape*lamda_c[:, np.newaxis]
+        self.alphaF = lz_shape*alphaF_z[np.newaxis, :]
+        self.data = np.zeros(self.alphaF.shape, dtype=self._dtype).view(np.recarray)
+        self.data.mask = True
+        self.monitor = 0.
+
+    @classmethod
+    def from_dataset(cls, dataset: EventDatasetProtocol, grid: LZGrid, has_offspecular=False):
+        tthh  = dataset.geometry.nu - dataset.geometry.mu
+        output = cls(tthh, grid)
+        output.correct_gravity(dataset.geometry.detectorDistance)
+        if has_offspecular:
+            alphaI_lz = grid.lz()*(dataset.geometry.mu+dataset.geometry.kap+dataset.geometry.kad)
+            output.calculate_q(alphaI_lz)
+        else:
+            output.calculate_q()
+        return output
+
+    def correct_gravity(self, detector_distance):
+        self.alphaF += np.rad2deg( np.arctan( 3.07e-10 * detector_distance * self.lamda**2 ) )
+
+    def calculate_q(self, alphaI=None):
+        if alphaI is None:
+            self.data.qz = 4.0*np.pi*np.sin(np.deg2rad(self.alphaF))/self.lamda
+            self.data.qx = 0.*self.data.qz
+        else:
+            self.data.qz = 2.0*np.pi*(np.sin(np.deg2rad(self.alphaF))+np.sin(np.deg2rad(alphaI)))/self.lamda
+            self.data.qx = 2.0*np.pi*(np.cos(np.deg2rad(self.alphaF))-np.cos(np.deg2rad(alphaI)))/self.lamda
+
+        if self.data.qz[0,self.data.qz.shape[1]//2]  < 0:
+            # assuming a 'measurement from below' when center of detector at negative qz
+            self.data.qz *= -1
+
+        self.calculate_q_resolution()
+
+    def calculate_q_resolution(self):
+        res_lz    = self.grid.lz() * 0.022**2
+        res_lz    = res_lz + (0.008/self.alphaF)**2
+        self.data.res    = self.data.qz * np.sqrt(res_lz)
+
+    def apply_theta_filter(self, theta_range):
+        # Filters points within theta range
+        self.data.mask &= (self.alphaF<theta_range[0])|(self.alphaF>theta_range[1])
+
+    def apply_theta_mask(self, theta_range):
+        # Mask points outside theta range
+        self.data.mask &= self.alphaF>=theta_range[0]
+        self.data.mask &= self.alphaF<=theta_range[1]
+
+    def apply_lamda_mask(self, lamda_range):
+        # Mask points outside lambda range
+        self.data.mask &= self.lamda>=lamda_range[0]
+        self.data.mask &= self.lamda<=lamda_range[1]
+
+    def apply_norm_mask(self, norm: LZNormalisation, min_norm=-1, min_theta=-1):
+        # Mask points where normliazation is nan
+        self.data.mask &= np.logical_not(np.isnan(norm.norm))&(norm.norm>min_norm)
+        if min_theta>0:
+            thsum = np.nansum(norm.norm, axis=0)
+            self.data.mask &= (thsum>min_theta)[np.newaxis, :]
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        """
+            Project dataset on grid and add to intensity.
+            Can be called multiple times to sequentially add events.
+        """
+        # TODO: maybe move monitor calculation in here instead of reduction?
+        e = dataset.data.events
+        int_lz, *_  = np.histogram2d(e.lamda, e.detZ, bins = (self.grid.lamda(), self.grid.z()))
+        self.data.I += int_lz
+        self.monitor += monitor
+        # in case the intensity changed one needs to normalize again
+        self.is_normalized = False
+
+    def clear(self):
+        # empty data
+        self.data[:] = 0
+        self.data.mask = True
+        self.monitor = 0.
+        self.norm_monitor = 1.
+
+    @property
+    def I(self):
+        output = self.data.I[:]
+        output[np.logical_not(self.data.mask)] = np.nan
+        return output / self.monitor
+
+    def calc_error(self):
+        # calculate error bars for resulting intensity after normalization
+        self.data.err = self.data.ref * np.sqrt( 1./(self.data.I+.1) + 1./(self.data.norm+0.1) )
+
+    def normalize_over_illuminated(self, norm: LZNormalisation):
+        """
+        Normalize the dataaset and take into account a difference in
+        detector angle for measurement and reference.
+        """
+        logging.debug(f'        correcting for incident angle difference from norm {norm.angle} to data {self.angle}')
+        norm_lz = norm.norm
+        delta_lz = np.ones_like(norm_lz)*Detector.delta_z
+        # do not perform gravity correction for footprint, would require norm detector distance that is unknown here
+        fp_corr_lz = np.where(np.absolute(delta_lz+norm.angle)>5e-3,
+                              (delta_lz+self.angle)/(delta_lz+norm.angle), np.nan)
+        fp_corr_lz[fp_corr_lz<0] = np.nan
+        self.data.mask &=  np.logical_not(np.isnan(fp_corr_lz))
+        self.data.norm = norm_lz*fp_corr_lz
+        self.norm_monitor = norm.monitor
+        ref_lz = self.data.I/np.where(self.data.norm>0, self.data.norm, np.nan)
+        ref_lz *= norm.monitor/self.monitor
+        ref_lz[np.logical_not(self.data.mask)] = np.nan
+        self.data.ref = ref_lz
+        self.calc_error()
+        self.is_normalized = True
+
+    def normalize_no_footprint(self, norm: LZNormalisation):
+        norm_lz = norm.norm
+        ref_lz = (self.data.I/norm_lz)
+        ref_lz *= norm.monitor/self.monitor
+        ref_lz[np.logical_not(self.data.mask)] = np.nan
+        self.data.norm = norm_lz
+        self.data.ref = ref_lz
+        self.calc_error()
+        self.is_normalized = True
+
+    def scale(self, factor: float):
+        if not self.is_normalized:
+            raise ValueError("Dataset needs to be normalized, first")
+        self.data.ref *= factor
+        self.data.err *= factor
+        self.norm_monitor /= factor
+
+    def project_on_qz(self):
+        if not self.is_normalized:
+            raise ValueError("Dataset needs to be normalized, first")
+        q_q       = self.grid.q()
+        weights_lzf = self.data.norm[self.data.mask]
+        q_lzf = self.data.qz[self.data.mask]
+        I_lzf = self.data.I[self.data.mask]
+        dq_lzf = self.data.res[self.data.mask]
+
+        # get number of grid points contributing to a bin, filter points with no contribution
+        N_q       = np.histogram(q_lzf, bins = q_q)[0]
+        N_q       = np.where(N_q > 0, N_q, np.nan)
+        fltr      = N_q>0
+
+        # calculate sum of all normalization weights per bin
+        W_q       = np.maximum(np.histogram(q_lzf, bins = q_q, weights = weights_lzf)[0], 1e-10)
+        # calculate sum of all dataset counts per bin
+        I_q       = np.histogram(q_lzf, bins = q_q, weights = I_lzf)[0]
+        # normlaize dataaset by normalization counts and scale by monitor
+        R_q       = np.where(fltr, I_q*self.norm_monitor/self.monitor / W_q, np.nan)
+        # error as squar-root of counts and sqrt from normalization (dR/R = sqrt( (dI/I)² + (dW/W)²)
+        dR_q      = np.where(fltr, R_q*(np.sqrt(1./(I_q+0.1)+ 1./(W_q+0.1))), np.nan)
+        # q-resolution is the weighted sum of individual points q-resolutions
+        dq_q      = np.histogram(q_lzf, bins = q_q, weights = weights_lzf * dq_lzf )[0]
+        dq_q      = np.where(fltr, dq_q/W_q, np.nan)
+        return ProjectedReflectivity(R_q, dR_q, (q_q[1:]+q_q[:-1])/2., dq_q)
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        from matplotlib.colors import LogNorm
+
+        if 'colorbar' in kwargs:
+            cmap=True
+            del(kwargs['colorbar'])
+        else:
+            cmap=False
+
+        if self.is_normalized:
+            I = self.data.ref
+        else:
+            I = self.data.I
+
+
+        if not 'norm' in kwargs:
+            vmin = I[(I>0)].min()
+            vmax = np.nanmax(I)
+            kwargs['norm'] = LogNorm(vmin, vmax, clip=True)
+
+
+        # suppress warning for wrongly sorted y-axis pixels (blades overlap)
+        with warnings.catch_warnings(action='ignore', category=UserWarning):
+            self._graph = plt.pcolormesh(self.lamda, self.alphaF, I, **kwargs)
+        if cmap:
+            if self.is_normalized:
+                plt.colorbar(label='R')
+            else:
+                plt.colorbar(label='I / cpm')
+        plt.xlabel('$\\lambda$ / $\\AA$')
+        plt.ylabel('$\\Theta$ / °')
+        plt.xlim(self.lamda[0,0], self.lamda[-1,0])
+        af = self.alphaF[self.data.mask]
+        plt.ylim(af.min(), af.max())
+        plt.title('Wavelength vs. Reflection Angle')
+
+        self._graph_axis = plt.gca()
+        plt.connect('button_press_event', self.draw_qline)
+
+    def update_plot(self):
+        """
+        Inline update of previous plot by just updating the data.
+        """
+        from matplotlib.colors import LogNorm
+        if self.is_normalized:
+            I = self.data.ref
+        else:
+            I = self.data.I
+
+        if isinstance(self._graph.norm, LogNorm):
+            vmin = I[(I>0)].min()*0.5
+        else:
+            vmin = 0
+        vmax = np.nanmax(I)
+        self._graph.set_array(I)
+        self._graph.norm.vmin = vmin
+        self._graph.norm.vmax = vmax
+
+        if self.is_normalized:
+            self._graph.set_array(self.data.ref)
+        else:
+            self._graph.set_array(self.data.I)
+
+    def draw_qline(self, event):
+        if event.inaxes is not self._graph_axis:
+            return
+        from matplotlib import pyplot as plt
+        tbm = self._graph_axis.figure.canvas.manager.toolbar.mode
+        if event.button is plt.MouseButton.LEFT and tbm=='':
+            slope = event.ydata/event.xdata
+            xmax = 12.5
+            self._graph_axis.plot([0, xmax], [0, slope*xmax], '-', color='grey')
+            self._graph_axis.text(event.xdata, event.ydata, f'q={np.deg2rad(slope)*4.*np.pi:.3f}', backgroundcolor='white')
+            plt.draw()
+        if event.button is plt.MouseButton.RIGHT and tbm=='':
+            for art in list(self._graph_axis.lines)+list(self._graph_axis.texts):
+                art.remove()
+            plt.draw()
+
+ONLY_MAP = ['colorbar', 'cmap', 'norm']
+
+class ReflectivityProjector(ProjectionInterface):
+    lzprojection: LZProjection
+    data: ProjectedReflectivity
+    # TODO: maybe implement direct 1d projection in here
+
+    def __init__(self, lzprojection, norm):
+        self.lzprojection = lzprojection
+        self.norm = norm
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        self.lzprojection.project(dataset, monitor)
+        self.lzprojection.normalize_over_illuminated(self.norm)
+        self.data = self.lzprojection.project_on_qz()
+
+    def clear(self):
+        self.lzprojection.clear()
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        for key in ONLY_MAP:
+            if key in kwargs: del(kwargs[key])
+
+        self._graph = plt.errorbar(self.data.Q, self.data.R, xerr=self.data.dQ, yerr=self.data.dR, **kwargs)
+        self._graph_axis = plt.gca()
+        plt.title('Reflectivity (might be improperly normalized)')
+        plt.yscale('log')
+        plt.xlabel('Q / $\\AA^{-1}$')
+        plt.ylabel('R')
+
+    def update_plot(self):
+        ln, _, (barsx, barsy) = self._graph
+
+        yerr_top = self.data.R+self.data.dR
+        yerr_bot = self.data.R-self.data.dR
+        xerr_top = self.data.Q+self.data.dQ
+        xerr_bot = self.data.Q-self.data.dQ
+
+        new_segments_x = [np.array([[xt, y], [xb, y]]) for xt, xb, y in zip(xerr_top, xerr_bot, self.data.R)]
+        new_segments_y = [np.array([[x, yt], [x, yb]]) for x, yt, yb in zip(self.data.Q, yerr_top, yerr_bot)]
+        barsx.set_segments(new_segments_x)
+        barsy.set_segments(new_segments_y)
+
+        ln.set_ydata(self.data.R)
+
+
+class YZProjection(ProjectionInterface):
+    y: np.ndarray
+    z: np.ndarray
+
+    data: np.recarray
+    _dtype = np.dtype([
+            ('cts', np.float64),
+            ('I', np.float64),
+            ('err', np.float64),
+            ])
+
+    def __init__(self):
+        self.z = np.arange(Detector.nBlades*Detector.nWires+1)-0.5
+        self.y = np.arange(Detector.nStripes+1)-0.5
+        self.data = np.zeros((self.y.shape[0]-1, self.z.shape[0]-1), dtype=self._dtype).view(np.recarray)
+        self.monitor = 0.
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        detYi, detZi, detX, delta = Detector.pixelLookUp[dataset.data.events.pixelID-1].T
+
+        cts , *_ = np.histogram2d(detYi, detZi, bins=(self.y, self.z))
+        self.data.cts += cts
+        self.monitor += monitor
+
+        self.data.I = self.data.cts / self.monitor
+        self.data.err = np.sqrt(self.data.cts) / self.monitor
+
+    def clear(self):
+        self.data[:] = 0
+        self.monitor = 0.
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        from matplotlib.colors import LogNorm
+
+        if 'colorbar' in kwargs:
+            cmap=True
+            del(kwargs['colorbar'])
+        else:
+            cmap=False
+
+        vmax = self.data.I.max()
+
+        if not 'norm' in kwargs:
+            vmin = self.data.I[(self.data.I>0)].min()*0.5
+            kwargs['norm'] = LogNorm(vmin, vmax)
+
+        self._graph = plt.pcolormesh(self.y, self.z, self.data.I.T, **kwargs)
+        if cmap:
+            plt.colorbar(label='I / cpm')
+
+        plt.xlabel('Y')
+        plt.ylabel('Z')
+        plt.xlim(self.y[0], self.y[-1])
+        plt.ylim(self.z[-1], self.z[0])
+        plt.title('Horizontal Pixel vs. Vertical Pixel')
+
+        self._graph_axis = plt.gca()
+        plt.connect('button_press_event', self.draw_yzcross)
+
+    def update_plot(self):
+        """
+        Inline update of previous plot by just updating the data.
+        """
+        from matplotlib.colors import LogNorm
+        if isinstance(self._graph.norm, LogNorm):
+            vmin = self.data.I[(self.data.I>0)].min()*0.5
+        else:
+            vmin = 0
+        vmax = self.data.I.max()
+        self._graph.set_array(self.data.I.T)
+        self._graph.norm.vmin = vmin
+        self._graph.norm.vmax = vmax
+
+    def draw_yzcross(self, event):
+        if event.inaxes is not self._graph_axis:
+            return
+        from matplotlib import pyplot as plt
+        tbm = self._graph_axis.figure.canvas.manager.toolbar.mode
+        if event.button is plt.MouseButton.LEFT and tbm=='':
+            self._graph_axis.plot([event.xdata, event.xdata], [self.z[0], self.z[-1]], '-', color='grey')
+            self._graph_axis.plot([self.y[0], self.y[-1]], [event.ydata, event.ydata], '-', color='grey')
+            self._graph_axis.text(event.xdata, event.ydata, f'({event.xdata:.1f}, {event.ydata:.1f})', backgroundcolor='white')
+            plt.draw()
+        if event.button is plt.MouseButton.RIGHT and tbm=='':
+            for art in list(self._graph_axis.lines)+list(self._graph_axis.texts):
+                art.remove()
+            plt.draw()
+
+class YTProjection(YZProjection):
+    theta: np.ndarray
+
+    def __init__(self, tthh: float):
+        dd = Detector.delta_z[1]-Detector.delta_z[0]
+        delta = np.hstack([Detector.delta_z, Detector.delta_z[-1]+dd])-dd/2.
+        self.theta  = tthh + delta
+        super().__init__()
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        from matplotlib.colors import LogNorm
+
+        if 'colorbar' in kwargs:
+            cmap=True
+            del(kwargs['colorbar'])
+        else:
+            cmap=False
+
+        if not 'norm' in kwargs:
+            kwargs['norm'] = LogNorm()
+
+        self._graph = plt.pcolormesh(self.y, self.theta, self.data.I.T, **kwargs)
+        if cmap:
+            plt.colorbar(label='I / cpm')
+
+        plt.xlabel('Y')
+        plt.ylabel('Theta / °')
+        plt.xlim(self.y[0], self.y[-1])
+        plt.ylim(self.theta[-1], self.theta[0])
+        plt.title('Horizontal Pixel vs. Angle')
+
+        self._graph_axis = plt.gca()
+        plt.connect('button_press_event', self.draw_tzcross)
+
+    def draw_tzcross(self, event):
+        if event.inaxes is not self._graph_axis:
+            return
+        from matplotlib import pyplot as plt
+        tbm = self._graph_axis.figure.canvas.manager.toolbar.mode
+        if event.button is plt.MouseButton.LEFT and tbm=='':
+            self._graph_axis.plot([event.xdata, event.xdata], [self.theta[0], self.theta[-1]], '-', color='grey')
+            self._graph_axis.plot([self.y[0], self.y[-1]], [event.ydata, event.ydata], '-', color='grey')
+            self._graph_axis.text(event.xdata, event.ydata, f'({event.xdata:.1f}, {event.ydata:.1f})', backgroundcolor='white')
+            plt.draw()
+        if event.button is plt.MouseButton.RIGHT and tbm=='':
+            for art in list(self._graph_axis.lines)+list(self._graph_axis.texts):
+                art.remove()
+            plt.draw()
+
+
+class TofZProjection(ProjectionInterface):
+    tof: np.ndarray
+    z: np.ndarray
+
+    data: np.recarray
+    _dtype = np.dtype([
+            ('cts', np.float64),
+            ('I', np.float64),
+            ('err', np.float64),
+            ])
+
+    def __init__(self, tau, foldback=False, combine=1):
+        self.z = np.arange(Detector.nBlades*Detector.nWires+1)-0.5
+        if foldback:
+            self.tof = np.arange(0, tau, 0.0005*combine)
+        else:
+            self.tof = np.arange(0, 2*tau, 0.0005*combine)
+        self.data = np.zeros((self.tof.shape[0]-1, self.z.shape[0]-1), dtype=self._dtype).view(np.recarray)
+        self.monitor = 0.
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        detYi, detZi, detX, delta = Detector.pixelLookUp[dataset.data.events.pixelID-1].T
+
+        cts , *_ = np.histogram2d(dataset.data.events.tof, detZi, bins=(self.tof, self.z))
+        self.data.cts += cts
+        self.monitor += monitor
+
+        self.data.I = self.data.cts / self.monitor
+        self.data.err = np.sqrt(self.data.cts) / self.monitor
+
+    def clear(self):
+        self.data[:] = 0
+        self.monitor = 0.
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        from matplotlib.colors import LogNorm
+
+        if 'colorbar' in kwargs:
+            cmap=True
+            del(kwargs['colorbar'])
+        else:
+            cmap=False
+
+        if not 'norm' in kwargs:
+            kwargs['norm'] = LogNorm()
+
+        self._graph = plt.pcolormesh(self.tof*1e3, self.z, self.data.I.T, **kwargs)
+        if cmap:
+            plt.colorbar(label='I / cpm')
+
+        plt.xlabel('Time of Flight / ms')
+        plt.ylabel('Z')
+        plt.xlim(self.tof[0]*1e3, self.tof[-1]*1e3)
+        plt.ylim(self.z[-1], self.z[0])
+        plt.title('Time of Flight vs. Vertical Pixel')
+
+        self._graph_axis = plt.gca()
+        plt.connect('button_press_event', self.draw_tzcross)
+
+    def update_plot(self):
+        """
+        Inline update of previous plot by just updating the data.
+        """
+        from matplotlib.colors import LogNorm
+        if isinstance(self._graph.norm, LogNorm):
+            vmin = self.data.I[(self.data.I>0)].min()*0.5
+        else:
+            vmin = 0
+        vmax = self.data.I.max()
+        self._graph.set_array(self.data.I.T)
+        self._graph.norm.vmin = vmin
+        self._graph.norm.vmax = vmax
+
+    def draw_tzcross(self, event):
+        if event.inaxes is not self._graph_axis:
+            return
+        from matplotlib import pyplot as plt
+        tbm = self._graph_axis.figure.canvas.manager.toolbar.mode
+        if event.button is plt.MouseButton.LEFT and tbm=='':
+            self._graph_axis.plot([event.xdata, event.xdata], [self.z[0], self.z[-1]], '-', color='grey')
+            self._graph_axis.plot([self.tof[0]*1e3, self.tof[-1]*1e3], [event.ydata, event.ydata], '-', color='grey')
+            self._graph_axis.text(event.xdata, event.ydata, f'({event.xdata:.2f}, {event.ydata:.1f})', backgroundcolor='white')
+            plt.draw()
+        if event.button is plt.MouseButton.RIGHT and tbm=='':
+            for art in list(self._graph_axis.lines)+list(self._graph_axis.texts):
+                art.remove()
+            plt.draw()
+
+class TofProjection(ProjectionInterface):
+    tof: np.ndarray
+
+    data: np.recarray
+    _dtype = np.dtype([
+            ('cts', np.float64),
+            ('I', np.float64),
+            ('err', np.float64),
+            ])
+
+    def __init__(self, tau, foldback=False):
+        if foldback:
+            self.tof = np.arange(0, tau, 0.0005)
+        else:
+            self.tof = np.arange(0, 2*tau, 0.0005)
+        self.data = np.zeros(self.tof.shape[0]-1, dtype=self._dtype).view(np.recarray)
+        self.monitor = 0.
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        cts , *_ = np.histogram(dataset.data.events.tof, bins=self.tof)
+        self.data.cts += cts
+        self.monitor += monitor
+
+        self.data.I = self.data.cts / self.monitor
+        self.data.err = np.sqrt(self.data.cts) / self.monitor
+
+    def clear(self):
+        self.data[:] = 0
+        self.monitor = 0.
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        for key in ONLY_MAP:
+            if key in kwargs: del(kwargs[key])
+
+
+        self._graph = plt.plot(self.tof[:-1]*1e3, self.data.I, **kwargs)
+
+        plt.xlabel('Time of Flight / ms')
+        plt.ylabel('I / cpm')
+        plt.xlim(self.tof[0]*1e3, self.tof[-1]*1e3)
+        plt.title('Time of Flight')
+
+    def update_plot(self):
+        """
+        Inline update of previous plot by just updating the data.
+        """
+        self._graph[0].set_ydata(self.data.I.T)
+
+class LProjection(ProjectionInterface):
+    lamda: np.ndarray
+
+    data: np.recarray
+    _dtype = np.dtype([
+            ('cts', np.float64),
+            ('I', np.float64),
+            ('err', np.float64),
+            ])
+
+    def __init__(self):
+        self.lamda = np.linspace(3.0, 12.0, 91)
+        self.data = np.zeros(self.lamda.shape[0]-1, dtype=self._dtype).view(np.recarray)
+        self.monitor = 0.
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        cts , *_ = np.histogram(dataset.data.events.lamda, bins=self.lamda)
+        self.data.cts += cts
+        self.monitor += monitor
+
+        self.data.I = self.data.cts / self.monitor
+        self.data.err = np.sqrt(self.data.cts) / self.monitor
+
+    def clear(self):
+        self.data[:] = 0
+        self.monitor = 0.
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        for key in ONLY_MAP:
+            if key in kwargs: del(kwargs[key])
+
+
+        self._graph = plt.plot(self.lamda[:-1], self.data.I, **kwargs)
+
+        plt.xlabel('Wavelength / Angstrom')
+        plt.ylabel('I / cpm')
+        plt.xlim(self.lamda[0], self.lamda[-1])
+        plt.title('Wavelength')
+
+    def update_plot(self):
+        """
+        Inline update of previous plot by just updating the data.
+        """
+        self._graph[0].set_ydata(self.data.I.T)
+
+class TProjection(ProjectionInterface):
+    theta: np.ndarray
+    z: np.ndarray
+
+    data: np.recarray
+    _dtype = np.dtype([
+            ('cts', np.float64),
+            ('I', np.float64),
+            ('err', np.float64),
+            ])
+
+    def __init__(self, tthh):
+        self.z = np.arange(Detector.nBlades*Detector.nWires+1)-0.5
+        dd = Detector.delta_z[1]-Detector.delta_z[0]
+        delta = np.hstack([Detector.delta_z, Detector.delta_z[-1]+dd])-dd/2.
+        self.theta = tthh+delta
+        self.data = np.zeros(self.theta.shape[0]-1, dtype=self._dtype).view(np.recarray)
+        self.monitor = 0.
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        detYi, detZi, detX, delta = Detector.pixelLookUp[dataset.data.events.pixelID-1].T
+
+        cts , *_ = np.histogram(detZi, bins=self.z)
+        self.data.cts += cts
+        self.monitor += monitor
+
+        self.data.I = self.data.cts / self.monitor
+        self.data.err = np.sqrt(self.data.cts) / self.monitor
+
+    def clear(self):
+        self.data[:] = 0
+        self.monitor = 0.
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        for key in ONLY_MAP:
+            if key in kwargs: del(kwargs[key])
+
+
+        self._graph = plt.plot(self.theta[:-1], self.data.I, **kwargs)
+
+        plt.xlabel('Reflection Angle / °')
+        plt.ylabel('I / cpm')
+        plt.xlim(self.theta[-1], self.theta[0])
+        plt.title('Theta')
+
+    def update_plot(self):
+        """
+        Inline update of previous plot by just updating the data.
+        """
+        self._graph[0].set_ydata(self.data.I.T)
+
+
+class CombinedProjection(ProjectionInterface):
+    """
+    Allows to put multiple projections together to conveniently generate combined graphs.
+    """
+    projections: List[ProjectionInterface]
+    projection_placements: List[Union[Tuple[int, int], Tuple[int, int, int, int]]]
+    grid_size: Tuple[int, int]
+
+
+    def __init__(self, grid_rows, grid_cols, projections, projection_placements):
+        self.projections = projections
+        self.projection_placements = projection_placements
+        self.grid_size = grid_rows, grid_cols
+
+    def project(self, dataset: EventDatasetProtocol, monitor: float):
+        for pi in self.projections:
+            pi.project(dataset, monitor)
+
+    def clear(self):
+        for pi in self.projections:
+            pi.clear()
+
+    def plot(self, **kwargs):
+        from matplotlib import pyplot as plt
+        fig = plt.gcf()
+        axs = fig.add_gridspec(self.grid_size[0], self.grid_size[1])
+        # axs = fig.add_gridspec(self.grid_size[0]+1, self.grid_size[1],
+        #                        height_ratios=[1.0 for i in range(self.grid_size[0])]+[0.2])
+        self._axes = []
+        for pi, placement in zip(self.projections, self.projection_placements):
+            if len(placement) == 2:
+                ax = fig.add_subplot(axs[placement[0], placement[1]])
+            else:
+                ax = fig.add_subplot(axs[placement[0]:placement[1], placement[2]:placement[3]])
+            pi.plot(**dict(kwargs))
+        # Create the RangeSlider
+        # from matplotlib.widgets import RangeSlider
+        # slider_ax = fig.add_subplot(axs[self.grid_size[0], :])
+        # self._slider = RangeSlider(slider_ax, "Plot Range", 0., 1., valinit=(0., 1.))
+        # self._slider.on_changed(self.update_range)
+
+    def update_plot(self):
+        for pi in self.projections:
+            pi.update_plot()
+
+    # def update_range(self, event):
+    #     ...

eos/reduction_e2h.py

@@ -0,0 +1,318 @@
+"""
+Events 2 histogram, quick reduction of single file to display during experiment.
+Can be used as a live preview with automatic update when files are modified.
+"""
+
+import logging
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.colors import ListedColormap
+
+from orsopy import fileio
+from datetime import datetime
+
+from .file_reader import AmorEventData, AmorHeader
+from .header import Header
+from .instrument import LZGrid
+from .normalization import LZNormalisation
+from .options import E2HConfig, E2HPlotArguments, IncidentAngle, MonitorType, E2HPlotSelection
+from . import event_handling as eh
+from .path_handling import PathResolver
+from .projection import CombinedProjection, LProjection, LZProjection, ProjectionInterface, ReflectivityProjector, \
+    TofProjection,  TofZProjection,  TProjection, YTProjection, YZProjection
+
+NEEDS_LAMDA = (E2HPlotSelection.All, E2HPlotSelection.LT, E2HPlotSelection.Q, E2HPlotSelection.L)
+
+class E2HReduction:
+    config: E2HConfig
+    header: Header
+    event_actions: eh.EventDataAction
+
+    _last_mtime = 0.
+    projection: ProjectionInterface
+
+    def __init__(self, config: E2HConfig):
+        self.config = config
+
+        self.header = Header()
+
+        self.fig = plt.figure()
+        self.register_colormap()
+        self.prepare_actions()
+
+    def prepare_actions(self):
+        """
+        Does not do any actual reduction.
+        """
+        self.path_resolver = PathResolver(self.config.reader.year, self.config.reader.rawPath)
+        self.file_list = self.path_resolver.resolve(self.config.reduction.fileIdentifier)
+        self.file_index = 0
+        self.plot_kwds = {}
+        plt.rcParams.update({'font.size': self.config.reduction.fontsize})
+
+        self.overwrite = eh.ApplyParameterOverwrites(self.config.experiment) # some actions use instrument parameters, change before that
+        if self.config.reduction.update:
+            # live update implies plotting
+            self.config.reduction.show_plot = True
+
+        if self.config.reduction.plot==E2HPlotSelection.Raw:
+            # Raw implies fast caculations
+            self.config.reduction.fast = True
+        if not self.config.experiment.is_default('lambdaRange'):
+            # filtering wavelength requires frame analysis
+            self.config.reduction.fast = False
+
+        if not self.config.reduction.fast or self.config.reduction.plot in NEEDS_LAMDA:
+            from . import event_analysis as ea
+
+        # Actions on datasets not used for normalization
+        self.event_actions = eh.ApplyPhaseOffset(self.config.experiment.chopperPhaseOffset)
+        if not self.config.reduction.fast:
+            self.event_actions |= self.overwrite
+            self.event_actions |= eh.CorrectChopperPhase()
+            self.event_actions |= ea.ExtractWalltime()
+        else:
+            logging.info('    Fast reduction always uses time normalization')
+            self.config.experiment.monitorType = MonitorType.time
+        self.event_actions |= eh.AssociatePulseWithMonitor(self.config.experiment.monitorType)
+        if self.config.experiment.monitorType in [MonitorType.proton_charge, MonitorType.debug]:
+            # the filtering only makes sense if using actual monitor data, not time
+            self.event_actions |= eh.FilterMonitorThreshold(self.config.experiment.lowCurrentThreshold)
+        if not self.config.reduction.fast:
+            self.event_actions |= eh.FilterStrangeTimes()
+            if self.config.reduction.plot in [E2HPlotSelection.YT, E2HPlotSelection.YZ]:
+                # perform time fold-back and apply yRange filter if not fast mode
+                self.event_actions |= ea.MergeFrames()
+                self.event_actions |= ea.AnalyzePixelIDs(self.config.experiment.yRange)
+            if self.config.reduction.plot==E2HPlotSelection.YT:
+                # perform corrections for tof if not fast mode
+                self.event_actions |= eh.TofTimeCorrection(self.config.experiment.incidentAngle==IncidentAngle.alphaF)
+        # select needed actions in depenence of plots
+        if self.config.reduction.plot in NEEDS_LAMDA or not self.config.experiment.is_default('lambdaRange'):
+            self.event_actions |= ea.MergeFrames(lamdaCut=self.config.experiment.lambdaRange[0])
+            self.event_actions |= ea.AnalyzePixelIDs(self.config.experiment.yRange)
+            self.event_actions |= eh.TofTimeCorrection(self.config.experiment.incidentAngle==IncidentAngle.alphaF)
+            self.event_actions |= ea.CalculateWavelength(self.config.experiment.lambdaRange)
+        self.event_actions |= eh.ApplyMask()
+
+        # plot dependant options
+        if self.config.reduction.plot in [E2HPlotSelection.All, E2HPlotSelection.LT, E2HPlotSelection.Q]:
+            self.grid = LZGrid(0.05, [0.0, 0.25], lambda_overwrite=self.config.experiment.lambdaRange)
+            self.grid.dldl = 0.01
+
+        if self.config.reduction.plot in [E2HPlotSelection.All, E2HPlotSelection.Raw,
+                                          E2HPlotSelection.LT, E2HPlotSelection.YT,
+                                          E2HPlotSelection.YZ, E2HPlotSelection.TZ]:
+            self.plot_kwds['colorbar'] = True
+            self.plot_kwds['cmap'] = str(self.config.reduction.plot_colormap)
+            if self.config.reduction.plotArgs==E2HPlotArguments.Linear:
+                self.plot_kwds['norm'] = None
+
+    def reduce(self):
+        if self.config.reduction.plot in [E2HPlotSelection.All, E2HPlotSelection.LT, E2HPlotSelection.Q]:
+            if self.config.reduction.normalizationModel:
+                self.norm = LZNormalisation.model(self.grid)
+            else:
+                self.norm = LZNormalisation.unity(self.grid)
+
+        self.prepare_graphs()
+
+        while self.file_index < len(self.file_list):
+            self.read_data()
+            self.add_data()
+
+        if self.config.reduction.plotArgs==E2HPlotArguments.OutputFile:
+            self.create_file_output()
+        if self.config.reduction.plotArgs!=E2HPlotArguments.OutputFile or self.config.reduction.show_plot:
+            self.create_graph()
+
+        if self.config.reduction.plotArgs==E2HPlotArguments.Default and not self.config.reduction.update:
+            # safe to image file if not auto-updating graph
+            plt.savefig(f'e2h_{self.config.reduction.plot}.png', dpi=300)
+        if self.config.reduction.kafka:
+            from .kafka_serializer import ESSSerializer
+            self.serializer = ESSSerializer()
+            self.fig.canvas.mpl_connect('close_event', self.serializer.end_command_thread)
+            self.serializer.start_command_thread()
+            self.serializer.send(self.projection)
+        if self.config.reduction.update:
+            self.timer = self.fig.canvas.new_timer(1000)
+            self.timer.add_callback(self.update)
+            self.timer.start()
+        if self.config.reduction.show_plot:
+            plt.show()
+
+
+    def register_colormap(self):
+        cmap = plt.colormaps['turbo'](np.arange(256))
+        cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
+        cmap = ListedColormap(cmap, name='jochen_deluxe', N=cmap.shape[0])
+        #cmap.set_bad((1.,1.,0.9))
+        plt.colormaps.register(cmap)
+
+    def prepare_graphs(self):
+        last_file_header = AmorHeader(self.file_list[-1])
+        self.overwrite.perform_action(last_file_header)
+        tthh  = last_file_header.geometry.nu - last_file_header.geometry.mu
+
+        if not self.config.reduction.is_default('thetaRangeR'):
+            # adjust range based on detector center
+            thetaRange = [ti+tthh for ti in self.config.reduction.thetaRangeR]
+        else:
+            thetaRange = [tthh - last_file_header.geometry.div/2, tthh + last_file_header.geometry.div/2]
+
+        if self.config.reduction.plot==E2HPlotSelection.LT:
+            self.projection = LZProjection(tthh, self.grid)
+            if not self.config.reduction.fast:
+                self.projection.correct_gravity(last_file_header.geometry.detectorDistance)
+            self.projection.apply_lamda_mask(self.config.experiment.lambdaRange)
+            self.projection.apply_theta_mask(thetaRange)
+            for thi in self.config.reduction.thetaFilters:
+                self.projection.apply_theta_filter((thi[0]+tthh, thi[1]+tthh))
+            self.projection.apply_norm_mask(self.norm)
+
+        if self.config.reduction.plot==E2HPlotSelection.Q:
+            plz = LZProjection(tthh, self.grid)
+            if not self.config.reduction.fast:
+                plz.correct_gravity(last_file_header.geometry.detectorDistance)
+            plz.calculate_q()
+            plz.apply_lamda_mask(self.config.experiment.lambdaRange)
+            plz.apply_theta_mask(thetaRange)
+            for thi in self.config.reduction.thetaFilters:
+                self.projection.apply_theta_filter((thi[0]+tthh, thi[1]+tthh))
+            plz.apply_norm_mask(self.norm)
+            self.projection = ReflectivityProjector(plz, self.norm)
+
+        if self.config.reduction.plot==E2HPlotSelection.YZ:
+            self.projection = YZProjection()
+
+        if self.config.reduction.plot==E2HPlotSelection.YT:
+            self.projection = YTProjection(tthh)
+
+        if self.config.reduction.plot==E2HPlotSelection.T:
+            self.projection = TProjection(tthh)
+
+        if self.config.reduction.plot==E2HPlotSelection.L:
+            self.projection = LProjection()
+
+        if self.config.reduction.plot==E2HPlotSelection.TZ:
+            self.projection = TofZProjection(last_file_header.timing.tau, foldback=not self.config.reduction.fast)
+
+        if self.config.reduction.plot==E2HPlotSelection.ToF:
+            self.projection = TofProjection(last_file_header.timing.tau, foldback=not self.config.reduction.fast)
+
+        if self.config.reduction.plot==E2HPlotSelection.All:
+            plz = LZProjection(tthh, self.grid)
+            if not self.config.reduction.fast:
+                plz.correct_gravity(last_file_header.geometry.detectorDistance)
+            plz.calculate_q()
+            plz.apply_lamda_mask(self.config.experiment.lambdaRange)
+            plz.apply_theta_mask(thetaRange)
+            for thi in self.config.reduction.thetaFilters:
+                plz.apply_theta_filter((thi[0]+tthh, thi[1]+tthh))
+            plz.apply_norm_mask(self.norm)
+            pr = ReflectivityProjector(plz, self.norm)
+            pyz = YZProjection()
+            self.projection = CombinedProjection(3, 2, [plz, pyz, pr],
+                                                 [(0, 2, 0, 1), (0, 2, 1, 2), (2, 3, 0, 2)])
+
+        if self.config.reduction.plot==E2HPlotSelection.Raw:
+            del(self.plot_kwds['colorbar'])
+            # A combined graph that does not require longer calculations
+            plyt = YTProjection(tthh)
+            pltofz = TofZProjection(last_file_header.timing.tau, foldback=not self.config.reduction.fast)
+            pltof =  TofProjection(last_file_header.timing.tau, foldback=not self.config.reduction.fast)
+            plt = TProjection(tthh)
+
+            self.projection = CombinedProjection(3, 3, [plyt, pltofz, plt, pltof],
+                                                 [(0,2, 0, 1), (0, 2, 1, 3), (2,3, 0,1),(2,3,1,3)])
+
+    def read_data(self):
+        fileName = self.file_list[self.file_index]
+        self.dataset = AmorEventData(fileName, max_events=self.config.reduction.max_events)
+        if self.dataset.EOF or fileName==self.file_list[-1]:
+            self.file_index += 1
+        self.event_actions(self.dataset)
+        self.dataset.update_header(self.header)
+
+        self.header.measurement_data_files.append(fileio.File(file=os.path.basename(fileName),
+                                                              timestamp=self.dataset.fileDate))
+
+    def add_data(self):
+        self.monitor = self.dataset.data.pulses.monitor.sum()
+        self.projection.project(self.dataset, monitor=self.monitor)
+        if self.config.reduction.plot==E2HPlotSelection.LT:
+            self.projection.normalize_over_illuminated(self.norm)
+
+    def create_file_output(self):
+        raise NotImplementedError("Export to text output not yet implemented")
+
+    def create_title(self):
+        output = "Events to Histogram - "
+        output += ",".join(["#"+os.path.basename(fi)[9:15].lstrip('0') for fi in self.file_list])
+        output += f" ($\\mu$={self.dataset.geometry.mu:.2f} ;"
+        output += f" $\\nu$={self.dataset.geometry.nu:.2f})"
+        if self.config.reduction.update:
+            output += f"\n at "+datetime.now().strftime("%m/%d/%Y %H:%M:%S")
+        return output
+
+    def create_graph(self):
+        plt.suptitle(self.create_title())
+        self.projection.plot(**self.plot_kwds)
+        plt.tight_layout(pad=0.5)
+
+    def replace_dataset(self, latest):
+        new_files = self.path_resolver.resolve(f'{latest}')
+        if not os.path.exists(new_files[-1]):
+            return
+        try:
+            # check that events exist in the new file
+            AmorEventData(new_files[-1], 0, max_events=1_000)
+        except Exception:
+            logging.debug("Problem when trying to load new dataset", exc_info=True)
+            return
+
+        logging.warning(f"Preceding to next file {latest}")
+        self.file_list = new_files
+        self.file_index = 0
+        self.prepare_actions()
+        self.prepare_graphs()
+        self.read_data()
+        self.add_data()
+        self.fig.clear()
+        self.create_graph()
+        plt.draw()
+
+    def update(self):
+        logging.debug("    check for update")
+        if self.config.reduction.fileIdentifier=='0':
+            # if latest file was choosen, check if new one available and switch to it
+            current = int(os.path.basename(self.file_list[-1])[9:15])
+            latest = self.path_resolver.search_latest(0)
+            if latest>current:
+                self.replace_dataset(latest)
+                return
+        # if all events were read last time, only load more if file was modified
+        if self.dataset.EOF and os.path.getmtime(self.file_list[-1])<=self._last_mtime:
+            return
+
+        self._last_mtime = os.path.getmtime(self.file_list[-1])
+        try:
+            update_data = AmorEventData(self.file_list[-1], self.dataset.last_index+1,
+                                        max_events=self.config.reduction.max_events)
+        except EOFError:
+            return
+        logging.info("    updating with new data")
+
+        self.event_actions(update_data)
+        self.dataset=update_data
+        self.monitor = self.dataset.data.pulses.monitor.sum()
+        self.projection.project(update_data, self.monitor)
+
+        self.projection.update_plot()
+        plt.suptitle(self.create_title())
+        plt.draw()
+
+        if self.config.reduction.kafka:
+            self.serializer.send(self.projection)

eos/reduction_kafka.py

@@ -0,0 +1,128 @@
+"""
+Events 2 histogram, quick reduction of single file to display during experiment.
+Can be used as a live preview with automatic update when files are modified.
+"""
+
+import logging
+import os
+
+from time import sleep
+from .kafka_events import KafkaEventData
+from .header import Header
+from .options import E2HConfig
+from . import event_handling as eh, event_analysis as ea
+from .projection import  TofZProjection,  YZProjection
+from .kafka_serializer import ESSSerializer
+
+
+class KafkaReduction:
+    config: E2HConfig
+    header: Header
+    event_actions: eh.EventDataAction
+
+    _last_mtime = 0.
+    proj_yz: YZProjection
+    proj_tofz = TofZProjection
+
+    def __init__(self, config: E2HConfig):
+        self.config = config
+
+        self.header = Header()
+        self.event_data = KafkaEventData()
+        self.event_data.start()
+
+        self.prepare_actions()
+
+    def prepare_actions(self):
+        """
+        Does not do any actual reduction.
+        """
+        # Actions on datasets not used for normalization
+        self.event_actions = eh.ApplyPhaseOffset(self.config.experiment.chopperPhaseOffset)
+        self.event_actions |= eh.CorrectChopperPhase()
+        self.event_actions |= ea.MergeFrames()
+        self.event_actions |= eh.ApplyMask()
+
+    def reduce(self):
+        self.create_projections()
+        self.read_data()
+        self.add_data()
+
+        self.serializer = ESSSerializer()
+        self.serializer.start_command_thread()
+
+        self.loop()
+
+    def create_projections(self):
+        self.proj_yz = YZProjection()
+        self.proj_tofz = TofZProjection(self.event_data.timing.tau, foldback=True, combine=2)
+
+    def read_data(self):
+        # make sure the first events have arrived before starting analysis
+        self.event_data.new_events.wait()
+        self.dataset = self.event_data.get_events()
+        self.event_actions(self.dataset)
+
+
+    def add_data(self):
+        self.monitor = self.dataset.monitor
+        self.proj_yz.project(self.dataset, monitor=self.monitor)
+        self.proj_tofz.project(self.dataset, monitor=self.monitor)
+
+    def loop(self):
+        self.wait_for = self.serializer.new_count_started
+        while True:
+            try:
+                self.update()
+                self.wait_for.wait(1.0)
+            except KeyboardInterrupt:
+                self.event_data.stop_event.set()
+                self.event_data.join()
+                self.serializer.end_command_thread()
+                return
+
+    def update(self):
+        if self.serializer.new_count_started.is_set():
+            logging.warning('Start new count, clearing event data')
+            self.wait_for = self.serializer.count_stopped
+            self.event_data.restart()
+            self.serializer.new_count_started.clear()
+            self.create_projections()
+            return
+        elif self.serializer.count_stopped.is_set() and not self.event_data.stop_counting.is_set():
+            return self.finish_count()
+        try:
+            update_data = self.event_data.get_events()
+        except EOFError:
+            return
+        logging.info("    updating with new data")
+
+        self.event_actions(update_data)
+        self.dataset=update_data
+        self.monitor = self.dataset.monitor
+        self.proj_yz.project(update_data, self.monitor)
+        self.proj_tofz.project(update_data, self.monitor)
+
+        self.serializer.send(self.proj_yz)
+        self.serializer.send(self.proj_tofz)
+
+    def finish_count(self):
+        logging.debug("    stop event set, hold event collection and send final results")
+        self.wait_for = self.serializer.new_count_started
+        self.event_data.stop_counting.set()
+
+        try:
+            update_data = self.event_data.get_events()
+        except EOFError:
+            pass
+        else:
+            self.event_actions(update_data)
+            self.dataset = update_data
+            self.monitor = self.dataset.monitor
+            self.proj_yz.project(update_data, self.monitor)
+            self.proj_tofz.project(update_data, self.monitor)
+
+        logging.warning(f'  stop counting, total events {int(self.proj_tofz.data.cts.sum())}')
+
+        self.serializer.send(self.proj_yz, final=True)
+        self.serializer.send(self.proj_tofz, final=True)

eos/reduction_reflectivity.py

@@ -0,0 +1,471 @@
+import logging
+import os
+import sys
+
+import numpy as np
+from orsopy import fileio
+
+from .event_analysis import FilterByLog
+from .event_handling import ApplyMask
+from .file_reader import AmorEventData
+from .header import Header
+from .path_handling import PathResolver
+from .options import ReflectivityConfig, IncidentAngle, MonitorType, NormalisationMethod, MONITOR_UNITS
+from .instrument import LZGrid
+from .normalization import LZNormalisation
+from . import event_handling as eh, event_analysis as ea
+from .projection import LZProjection
+
+
+class ReflectivityReduction:
+    config: ReflectivityConfig
+    header: Header
+    normevent_actions: eh.EventDataAction
+    dataevent_actions: eh.EventDataAction
+    
+    def __init__(self, config: ReflectivityConfig):
+        self.config = config
+
+        self.header = Header()
+        self.header.reduction.call = config.call_string()
+
+        self.prepare_actions()
+
+    def prepare_actions(self):
+        """
+        Prepare the actions applied to each event dataset, does not do any actual reduction.
+        """
+        self.path_resolver = PathResolver(self.config.reader.year, self.config.reader.rawPath)
+
+        # setup all actions performed on event datasets before projection on the grid
+        # The order of these corrections matter as some rely on parameters modified before
+        if self.config.reduction.normalisationFileIdentifier:
+            # explicit steps performed on AmorEventDataset for normalization files
+            self.normevent_actions = eh.ApplyPhaseOffset(self.config.experiment.chopperPhaseOffset)
+            self.normevent_actions |= eh.CorrectChopperPhase()
+            self.normevent_actions |= eh.AssociatePulseWithMonitor(self.config.experiment.monitorType)
+            if self.config.experiment.monitorType in [MonitorType.proton_charge, MonitorType.debug]:
+                self.normevent_actions |= ea.ExtractWalltime()
+                self.normevent_actions |= eh.FilterMonitorThreshold(self.config.experiment.lowCurrentThreshold)
+            self.normevent_actions |= eh.FilterStrangeTimes()
+            self.normevent_actions |= ea.MergeFrames()
+            self.normevent_actions |= ea.AnalyzePixelIDs(self.config.experiment.yRange)
+            self.normevent_actions |= eh.TofTimeCorrection(self.config.experiment.incidentAngle==IncidentAngle.alphaF)
+            self.normevent_actions |= ea.CalculateWavelength(self.config.experiment.lambdaRange)
+            self.normevent_actions |= eh.ApplyMask()
+        # Actions on datasets not used for normalization
+        self.dataevent_actions = eh.ApplyPhaseOffset(self.config.experiment.chopperPhaseOffset)
+        self.dataevent_actions |= eh.ApplyParameterOverwrites(self.config.experiment) # some actions use instrument parameters, change before that
+        self.dataevent_actions |= eh.CorrectChopperPhase()
+        self.dataevent_actions |= ea.ExtractWalltime()
+        self.dataevent_time_correction = eh.CorrectSeriesTime(0) # will be set from first dataset
+        self.dataevent_actions |= self.dataevent_time_correction
+        self.dataevent_actions |= eh.AssociatePulseWithMonitor(self.config.experiment.monitorType)
+        if self.config.experiment.monitorType in [MonitorType.proton_charge or MonitorType.debug]:
+            # the filtering only makes sense if using actual monitor data, not time
+            self.dataevent_actions |= eh.FilterMonitorThreshold(self.config.experiment.lowCurrentThreshold)
+        self.dataevent_actions |= eh.FilterStrangeTimes()
+        self.dataevent_actions |= ea.MergeFrames()
+        self.dataevent_actions |= ea.AnalyzePixelIDs(self.config.experiment.yRange)
+        self.dataevent_actions |= eh.TofTimeCorrection(self.config.experiment.incidentAngle==IncidentAngle.alphaF)
+        self.dataevent_actions |= ea.CalculateWavelength(self.config.experiment.lambdaRange)
+        self.dataevent_actions |= ea.CalculateQ(self.config.experiment.incidentAngle)
+        if not self.config.reduction.is_default('qzRange'):
+            self.dataevent_actions |= ea.FilterQzRange(self.config.reduction.qzRange)
+        for lf in self.config.reduction.logfilter:
+            self.dataevent_actions |= ea.FilterByLog(lf)
+        self.dataevent_actions |= eh.ApplyMask()
+
+        self.grid = LZGrid(self.config.reduction.qResolution, self.config.reduction.qzRange)
+
+    def reduce(self):
+        if not os.path.exists(f'{self.config.output.outputPath}'):
+            logging.debug(f'Creating destination path {self.config.output.outputPath}')
+            os.system(f'mkdir {self.config.output.outputPath}')
+
+        # load or create normalisation matrix
+        if self.config.reduction.normalisationFileIdentifier:
+            # TODO: change option definition to single normalization short_code
+            self.create_normalisation_map(self.config.reduction.normalisationFileIdentifier[0])
+        else:
+            self.norm = LZNormalisation.unity(self.grid)
+
+        if self.config.reduction.normalizationSmoothing:
+            self.norm.smooth(self.config.reduction.normalizationSmoothing)
+
+        # load R(q_z) curve to be subtracted:
+        if self.config.reduction.subtract:
+            self.sq_q, self.sR_q, self.sdR_q, self.sFileName = self.loadRqz(self.config.reduction.subtract)
+            logging.warning(f'loaded background file: {self.sFileName}')
+            self.header.reduction.corrections.append(f'background from \'{self.sFileName}\' subtracted')
+            self.subtract = True
+        else:
+            self.subtract = False
+
+        # load measurement data and do the reduction
+        self.datasetsRqz = []
+        self.datasetsRlt = []
+        for i, short_notation in enumerate(self.config.reduction.fileIdentifier):
+            self.read_file_block(i, short_notation)
+
+        # output
+        if self.config.output.is_default('outputName'):
+            import datetime
+            _date = datetime.datetime.now().replace(microsecond=0).isoformat().replace(':', '-')
+            if self.header.sample.name:
+                _sampleName = self.header.sample.name.replace(' ', '_')
+            else:
+                _sampleName = 'unknown'
+            _mu = int(self.dataset.geometry.mu * 3)
+            self.config.output.outputName = f'{_sampleName}_{_mu:03}_{_date}'
+
+        logging.warning('output:')
+
+        if 'Rqz.ort' in self.config.output.outputFormats:
+            self.save_Rqz()
+
+        if 'Rlt.ort' in self.config.output.outputFormats:
+            self.save_Rtl()
+
+        if self.config.output.plot:
+            import matplotlib.pyplot as plt
+            if 'Rqz.ort' in self.config.output.outputFormats:
+                plt.figure(num=99)
+                plt.legend()
+            plt.show()
+
+    def read_file_block(self, i, short_notation):
+        logging.warning('input:')
+        file_list = self.path_resolver.resolve(short_notation)
+
+        self.header.measurement_data_files = []
+
+        self.dataset = AmorEventData(file_list[0])
+        if self.config.experiment.monitorType==MonitorType.auto:
+            if self.dataset.data.proton_current.current.sum()>1:
+                self.config.experiment.monitorType = MonitorType.proton_charge
+                logging.debug('      monitor type set to "proton current"')
+            else:
+                self.config.experiment.monitorType = MonitorType.time
+                logging.debug('      monitor type set to "time"')
+            # update actions to sue selected monitor
+            self.prepare_actions()
+            # reload normalization to make sure the monitor matches
+            if self.config.reduction.normalisationFileIdentifier:
+                self.create_normalisation_map(self.config.reduction.normalisationFileIdentifier[0])
+
+        self.dataevent_time_correction.seriesStartTime = self.dataset.eventStartTime
+        self.dataevent_actions(self.dataset)
+        self.dataset.update_header(self.header)
+        self.dataevent_actions.update_header(self.header)
+        for fi in file_list[1:]:
+            di = AmorEventData(fi)
+            self.dataevent_actions(di)
+            self.dataset.append(di)
+
+        for fileName in file_list:
+            self.header.measurement_data_files.append(fileio.File( file=os.path.basename(fileName),
+                                                                   timestamp=self.dataset.fileDate))
+
+        if 'polarization_config_label' in self.dataset.data.device_logs:
+            pols = np.unique(self.dataset.data.device_logs['polarization_config_label'].value)
+            pols = pols[pols>0]
+            if len(pols)>1:
+                logging.warning(f'    found {len(pols)} polarization configurations, splitting dataset accordingly')
+                from copy import deepcopy
+                from . import const
+                full_ds = deepcopy(self.dataset)
+                for pi in pols:
+                    plabel = const.polarizationLabels[pi]
+                    pol_filter = FilterByLog(f'polarization_config_label=={pi}',
+                                             remove_switchpulse=True) | ApplyMask()
+                    logging.info(f'    filter {plabel} using polarization_config_label=={pi}')
+                    pol_filter(self.dataset)
+                    self.dataset.update_header(self.header)
+                    pol_filter.update_header(self.header)
+                    if self.config.reduction.timeSlize:
+                        if i>0:
+                            logging.warning(
+                                "    time slizing should only be used for one set of datafiles, check parameters")
+                        self.analyze_timeslices(i, polstr=f' : polarization = {plabel}')
+                    else:
+                        self.analyze_unsliced(i, polstr=f' : polarization = {plabel}')
+                    self.dataset = deepcopy(full_ds)
+                return
+        if self.config.reduction.timeSlize:
+            if i>0:
+                logging.warning("    time slizing should only be used for one set of datafiles, check parameters")
+            self.analyze_timeslices(i)
+        else:
+            self.analyze_unsliced(i)
+
+    def analyze_unsliced(self, i, polstr=''):
+        self.monitor = self.dataset.data.pulses.monitor.sum()
+        logging.info(f'    monitor = {self.monitor:8.2f} {MONITOR_UNITS[self.config.experiment.monitorType]}')
+
+        proj:LZProjection = self.project_on_lz()
+        try:
+            scale = self.config.reduction.scale[i]
+        except IndexError:
+            scale = self.config.reduction.scale[-1]
+        proj.scale(scale)
+
+        if 'Rqz.ort' in self.config.output.outputFormats:
+            headerRqz = self.header.orso_header()
+            headerRqz.data_set = f'Nr {i} : mu = {self.dataset.geometry.mu:6.3f} deg{polstr}'
+
+            # projection on qz-grid
+            result = proj.project_on_qz()
+
+            if self.config.reduction.autoscale:
+                if i==0:
+                    result.autoscale(self.config.reduction.autoscale)
+                else:
+                    result.stitch(self.last_result)
+
+            if self.subtract:
+                if len(result.Q)==len(self.sq_q):
+                    result.subtract(self.sR_q, self.sdR_q)
+                else:
+                    logging.warning(
+                            f'backgroung file {self.sFileName} not compatible with q_z scale ({len(self.sq_q)} vs. {len(result.Q)})')
+
+            orso_data = fileio.OrsoDataset(headerRqz, result.data)
+            self.last_result = result
+            self.datasetsRqz.append(orso_data)
+
+            if self.config.output.plot:
+                import matplotlib.pyplot as plt
+                # plot all reflectivity results in same graph
+                plt.figure(num=99)
+                result.plot(label=f'{self.config.reduction.fileIdentifier[i]}')
+        if 'Rlt.ort' in self.config.output.outputFormats:
+            columns = [
+                fileio.Column('Qz', '1/angstrom', 'normal momentum transfer'),
+                fileio.Column('R', '', 'specular reflectivity'),
+                fileio.ErrorColumn(error_of='R', error_type='uncertainty', value_is='sigma'),
+                fileio.ErrorColumn(error_of='Qz', error_type='resolution', value_is='sigma'),
+                fileio.Column('lambda', 'angstrom', 'wavelength'),
+                fileio.Column('alpha_f', 'deg', 'final angle'),
+                fileio.Column('l', '', 'index of lambda-bin'),
+                fileio.Column('t', '', 'index of theta bin'),
+                fileio.Column('intensity', '', 'filtered neutron events per pixel'),
+                fileio.Column('norm', '', 'normalisation matrix'),
+                fileio.Column('mask', '', 'pixels used for calculating R(q_z)'),
+                fileio.Column('Qx', '1/angstrom', 'parallel momentum transfer'),
+                ]
+
+            ts, zs = proj.data.shape
+            lindex_lz = np.tile(np.arange(1, ts+1), (zs, 1)).T
+            tindex_lz = np.tile(np.arange(1, zs+1), (ts, 1))
+
+            j = 0
+            for item in zip(
+                    proj.data.qz.T,
+                    proj.data.ref.T,
+                    proj.data.err.T,
+                    proj.data.res.T,
+                    proj.lamda.T,
+                    proj.alphaF.T,
+                    lindex_lz.T,
+                    tindex_lz.T,
+                    proj.data.I.T,
+                    proj.data.norm.T,
+                    proj.data.mask.T,
+                    proj.data.qx.T,
+                    ):
+                data = np.array(list(item)).T
+                headerRlt = self.header.orso_header(columns=columns)
+                headerRlt.data_set = f'dataset_{i}_{j+1} : alpha_f = {proj.alphaF[0, j]:6.3f} deg'
+                orso_data = fileio.OrsoDataset(headerRlt, data)
+                self.datasetsRlt.append(orso_data)
+                j += 1
+
+            if self.config.output.plot:
+                import matplotlib.pyplot as plt
+                plt.figure()
+                proj.plot(colorbar=True, cmap=str(self.config.output.plot_colormap))
+                plt.title(f'{self.config.reduction.fileIdentifier[i]}')
+
+    def analyze_timeslices(self, i, polstr=''):
+        wallTime_e = np.float64(self.dataset.data.events.wallTime)/1e9
+        pulseTimeS = np.float64(self.dataset.data.pulses.time)/1e9
+        interval = self.config.reduction.timeSlize[0]
+        try:
+            start = self.config.reduction.timeSlize[1]
+        except IndexError:
+            start = 0
+        try:
+            stop = self.config.reduction.timeSlize[2]
+        except IndexError:
+            stop = wallTime_e[-1]
+        # make overwriting log lines possible by removing newline at the end
+        #logging.StreamHandler.terminator = "\r"
+        logging.warning(f'    time slizing')
+        logging.info('      slize  time  monitor')
+        for ti, time in enumerate(np.arange(start, stop, interval)):
+            slice = self.dataset.get_timeslice(time, time+interval)
+            self.monitor = np.sum(slice.data.pulses.monitor)
+            logging.info(f'      {ti:<4d}  {time:6.0f}  {self.monitor:7.2f} {MONITOR_UNITS[self.config.experiment.monitorType]}')
+
+            proj: LZProjection = self.project_on_lz(slice)
+            try:
+                scale = self.config.reduction.scale[i]
+            except IndexError:
+                scale = self.config.reduction.scale[-1]
+            proj.scale(scale)
+
+            # projection on qz-grid
+            result = proj.project_on_qz()
+
+            if self.config.reduction.autoscale:
+                # scale every slice the same
+                if ti==0:
+                    if i==0:
+                        atscale = result.autoscale(self.config.reduction.autoscale)
+                    else:
+                        atscale = result.stitch(self.last_result)
+                else:
+                    result.scale(atscale)
+
+            if self.subtract:
+                if len(result.Q)==len(self.sq_q):
+                    result.subtract(self.sR_q, self.sdR_q)
+                else:
+                    logging.warning(
+                            f'backgroung file {self.sFileName} not compatible with q_z scale ({len(self.sq_q)} vs. {len(result.Q)})')
+
+            headerRqz = self.header.orso_header(
+                    extra_columns=[fileio.Column('time', 's', 'time relative to start of measurement series')])
+            headerRqz.data_set = f'{i}_{ti}: time = {time:8.1f} s  to {time+interval:8.1f} s{polstr}'
+            orso_data = fileio.OrsoDataset(headerRqz, result.data_for_time(time))
+            self.datasetsRqz.append(orso_data)
+
+            if self.config.output.plot:
+                import matplotlib.pyplot as plt
+                # plot all reflectivity results in same graph
+                plt.figure(num=99)
+                result.plot(label=f'{self.config.reduction.fileIdentifier[i]} @ {time:.1f}s')
+
+        self.last_result = result
+        # reset normal logging behavior
+        #logging.StreamHandler.terminator = "\n"
+        logging.info(f'      done  {min(time+interval, pulseTimeS[-1]):5.0f}')
+
+    def save_Rqz(self):
+        fname = os.path.join(self.config.output.outputPath, f'{self.config.output.outputName}.Rqz.ort')
+        logging.warning(f'    {fname}')
+        if os.path.exists(fname) and self.config.output.append:
+            logging.info('        file already exists, append as new dataset')
+            with open(fname, 'r') as f:
+                f.readline()
+                theSecondLine = f.readline()[3:]
+            prev_data = fileio.load_orso(fname)
+            prev_names = [di.info.data_set for di in prev_data]
+            for i, di in enumerate(self.datasetsRqz):
+                while di.info.data_set in prev_names:
+                    if di.info.data_set.startswith('Nr '):
+                        di.info.data_set = f'Nr {i+len(prev_data)} :'+di.info.data_set.split(':', 1)[1]
+                        break
+                    di.info.data_set = di.info.data_set+'_'
+            fileio.save_orso(prev_data+self.datasetsRqz, fname, data_separator='\n', comment=theSecondLine)
+        else:
+            theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(q_z)'
+            fileio.save_orso(self.datasetsRqz, fname, data_separator='\n', comment=theSecondLine)
+
+    def save_Rtl(self):
+        fname = os.path.join(self.config.output.outputPath, f'{self.config.output.outputName}.Rlt.ort')
+        logging.warning(f'    {fname}')
+        theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(lambda, theta)'
+        fileio.save_orso(self.datasetsRlt, fname, data_separator='\n', comment=theSecondLine)
+
+    def loadRqz(self, name):
+        fname = os.path.join(self.config.output.outputPath, name)
+        if os.path.exists(fname):
+            fileName = fname
+        elif os.path.exists(f'{fname}.Rqz.ort'):
+            fileName = f'{fname}.Rqz.ort'
+        else:
+            sys.exit(f'### the background file \'{fname}\' does not exist! => stopping')
+
+        q_q, Sq_q, dS_q = np.loadtxt(fileName, usecols=(0, 1, 2), comments='#', unpack=True)
+
+        return q_q, Sq_q, dS_q, fileName
+
+    def create_normalisation_map(self, short_notation):
+        outputPath = self.config.output.outputPath
+        normalisation_list = self.path_resolver.expand_file_list(short_notation)
+        name = '_'.join(map(str, normalisation_list))
+        n_path = os.path.join(outputPath, f'{name}.norm')
+
+        self.norm = None
+        if os.path.exists(n_path):
+            logging.debug(f'trying to load matrix from file {n_path}')
+            try:
+                self.norm = LZNormalisation.from_file(n_path, self.normevent_actions.action_hash())
+            except (ValueError, EOFError):
+                self.norm =None
+            else:
+                logging.warning(f'normalisation matrix: found and using {n_path}')
+        if self.norm is None:
+            # in case file does not exist or the action hash doesn't match, create new normalization
+            logging.warning(f'normalisation matrix: using the files {normalisation_list}')
+            normalization_files = list(map(self.path_resolver.get_path, normalisation_list))
+            reference = AmorEventData(normalization_files[0])
+            self.normevent_actions(reference)
+            for nfi in normalization_files[1:]:
+                toadd = AmorEventData(nfi)
+                self.normevent_actions(toadd)
+                reference.append(toadd)
+            self.norm = LZNormalisation(reference, self.config.reduction.normalisationMethod, self.grid)
+            if reference.data.events.shape[0] > 1e6:
+                self.norm.safe(n_path, self.normevent_actions.action_hash())
+
+        self.norm.update_header(self.header)
+        self.header.reduction.corrections.append('normalisation with \'additional files\'')
+
+    def project_on_lz(self, dataset=None):
+        if dataset is None:
+            dataset=self.dataset
+        proj = LZProjection.from_dataset(dataset, self.grid,
+                                         has_offspecular=(self.config.experiment.incidentAngle!=IncidentAngle.alphaF))
+
+        t0 = dataset.geometry.nu-dataset.geometry.mu
+        if not self.config.reduction.is_default('thetaRangeR'):
+            # adjust range based on detector center
+            thetaRange = [ti+t0 for ti in self.config.reduction.thetaRangeR]
+            proj.apply_theta_mask(thetaRange)
+        elif not self.config.reduction.is_default('thetaRange'):
+            proj.apply_theta_mask(self.config.reduction.thetaRange)
+        else:
+            thetaRange = [dataset.geometry.nu - dataset.geometry.mu - dataset.geometry.div/2,
+                                              dataset.geometry.nu - dataset.geometry.mu + dataset.geometry.div/2]
+            proj.apply_theta_mask(thetaRange)
+        for thi in self.config.reduction.thetaFilters:
+            # apply theta filters relative to angle on detector (issues with parts of the incoming divergence)
+            proj.apply_theta_filter((thi[0]+t0, thi[1]+t0))
+
+        proj.apply_lamda_mask(self.config.experiment.lambdaRange)
+
+        proj.apply_norm_mask(self.norm, min_norm=self.config.reduction.normalizationFilter,
+                             min_theta=self.config.reduction.normAngleFilter)
+
+        proj.project(dataset, self.monitor)
+
+        if self.config.reduction.normalisationMethod == NormalisationMethod.over_illuminated:
+            logging.debug('      assuming an overilluminated sample and correcting for the angle of incidence')
+            proj.normalize_over_illuminated(self.norm)
+        elif self.config.reduction.normalisationMethod==NormalisationMethod.under_illuminated:
+            logging.debug('      assuming an underilluminated sample and ignoring the angle of incidence')
+            proj.normalize_no_footprint(self.norm)
+        elif self.config.reduction.normalisationMethod==NormalisationMethod.direct_beam:
+            logging.debug('      assuming direct beam for normalisation and ignoring the angle of incidence')
+            proj.normalize_no_footprint(self.norm)
+        else:
+            logging.error('unknown normalisation method! Use [u]nder, [o]ver or [d]irect illumination')
+            proj.normalize_no_footprint(self.norm)
+        if self.monitor<=1e-6:
+            logging.info('                               low monitor -> nan output')
+            proj.data.ref *= np.nan
+
+        return proj

libeos/command_line.py

@@ -1,219 +0,0 @@
-import argparse
-
-from .logconfig import update_loglevel
-from .options import ReaderConfig, EOSConfig, ExperimentConfig, OutputConfig, ReductionConfig, Defaults
-
-
-def commandLineArgs():
-    """
-    Process command line argument.
-    The type of the default values is used for conversion and validation.
-    """
-    msg = "eos reads data from (one or several) raw file(s) of the .hdf format, \
-           performs various corrections, conversations and projections and exports\
-           the resulting reflectivity in an orso-compatible format."
-    clas = argparse.ArgumentParser(description = msg)
-
-    input_data = clas.add_argument_group('input data')
-    input_data.add_argument("-f", "--fileIdentifier",
-                            required = True,
-                            nargs = '+',
-                            help = "file number(s) or offset (if < 1)")
-    input_data.add_argument("-n", "--normalisationFileIdentifier",
-                            default = Defaults.normalisationFileIdentifier,
-                            nargs = '+',
-                            help = "file number(s) of normalisation measurement")
-    input_data.add_argument("-rp", "--rawPath", 
-                            type = str,
-                            default = Defaults.rawPath,
-                            help = "ath to directory with .hdf files")
-    input_data.add_argument("-Y", "--year",
-                            default = Defaults.year,
-                            type = int,
-                            help = "year the measurement was performed")
-    input_data.add_argument("-sub", "--subtract",
-                            help = "R(q_z) curve to be subtracted (in .Rqz.ort format)")
-    input_data.add_argument("-nm", "--normalisationMethod",
-                            default = Defaults.normalisationMethod,
-                            help = "normalisation method: [o]verillumination, [u]nderillumination, [d]irect_beam")
-    input_data.add_argument("-mt", "--monitorType",
-                            type = str,
-                            default = Defaults.monitorType,
-                            help = "one of [p]rotonCurrent, [t]ime or [n]eutronMonitor")
-
-    output = clas.add_argument_group('output')
-    output.add_argument("-o", "--outputName",
-                            default = Defaults.outputName,
-                            help = "output file name (withot suffix)")
-    output.add_argument("-op", "--outputPath",
-                            type = str,
-                            default = Defaults.outputPath,
-                            help = "path for output")
-    output.add_argument("-of", "--outputFormat",
-                            nargs = '+',
-                            default = Defaults.outputFormat,
-                            help = "one of [Rqz.ort, Rlt.ort]")
-    output.add_argument("-ai", "--incidentAngle",
-                            type = str,
-                            default = Defaults.incidentAngle,
-                            help = "calulate alpha_i from [alphaF, mu, nu]",
-                            )
-    output.add_argument("-r", "--qResolution",
-                            default = Defaults.qResolution,
-                            type = float,
-                            help = "q_z resolution")
-    output.add_argument("-ts", "--timeSlize",
-                            nargs = '+',
-                            type = float,
-                            help = "time slizing <interval> ,[<start> [,stop]]")
-    output.add_argument("-s", "--scale",
-                            nargs = '+',
-                            default = Defaults.scale,
-                            type = float,
-                            help = "scaling factor for R(q_z)")
-    output.add_argument("-S", "--autoscale",
-                            nargs = 2,
-                            type = float,
-                            help = "scale to 1 in the given q_z range")
-
-    masks = clas.add_argument_group('masks')
-    masks.add_argument("-l", "--lambdaRange",
-                            default = Defaults.lambdaRange,
-                            nargs = 2,
-                            type = float,
-                            help = "wavelength range")
-    masks.add_argument("-t", "--thetaRange",
-                            default = Defaults.thetaRange,
-                            nargs = 2,
-                            type = float,
-                            help = "absolute theta range")
-    masks.add_argument("-T", "--thetaRangeR",
-                            default = Defaults.thetaRangeR,
-                            nargs = 2,
-                            type = float,
-                            help = "relative theta range")
-    masks.add_argument("-y", "--yRange",
-                            default = Defaults.yRange,
-                            nargs = 2,
-                            type = int,
-                            help = "detector y range")
-    masks.add_argument("-q", "--qzRange",
-                            default = Defaults.qzRange,
-                            nargs = 2,
-                            type = float,
-                            help = "q_z range")
-    masks.add_argument("-ct", "--lowCurrentThreshold",
-                            default = Defaults.lowCurrentThreshold,
-                            type = float,
-                            help = "proton current threshold for discarding neutron pulses")
-
-
-    overwrite = clas.add_argument_group('overwrite')
-    overwrite.add_argument("-cs", "--chopperSpeed",
-                            default = Defaults.chopperSpeed,
-                            type = float,
-                            help = "chopper speed in rpm")
-    overwrite.add_argument("-cp", "--chopperPhase",
-                            default = Defaults.chopperPhase,
-                            type = float,
-                            help = "chopper phase")
-    overwrite.add_argument("-co", "--chopperPhaseOffset",
-                            default = Defaults.chopperPhaseOffset,
-                            type = float,
-                            help = "phase offset between chopper opening and trigger pulse")
-    overwrite.add_argument("-m", "--muOffset",
-                            default = Defaults.muOffset,
-                            type = float,
-                            help = "mu offset")
-    overwrite.add_argument("-mu", "--mu",
-                            default = Defaults.mu,
-                            type = float,
-                            help ="value of mu")
-    overwrite.add_argument("-nu", "--nu",
-                            default = Defaults.nu,
-                            type = float,
-                            help = "value of nu")
-    overwrite.add_argument("-sm", "--sampleModel",
-                            default = Defaults.sampleModel,
-                            type = str,
-                            help = "1-line orso sample model description")
-
-    misc = clas.add_argument_group('misc')
-    misc.add_argument('-v', '--verbose', action='store_true')
-    misc.add_argument('-vv', '--debug', action='store_true')
-
-    return clas.parse_args()
-
-
-def expand_file_list(short_notation):
-    """Evaluate string entry for file number lists"""
-    #log().debug('Executing get_flist')
-    file_list=[]
-    for i in short_notation.split(','):
-        if '-' in i:
-            if ':' in i:
-                step = i.split(':', 1)[1]
-                file_list += range(int(i.split('-', 1)[0]), int((i.rsplit('-', 1)[1]).split(':', 1)[0])+1, int(step))
-            else:
-                step = 1
-                file_list += range(int(i.split('-', 1)[0]), int(i.split('-', 1)[1])+1, int(step))
-        else:
-            file_list += [int(i)]
-
-    return sorted(file_list)
-
-
-def output_format_list(outputFormat):
-    format_list = []
-    if 'ort' in outputFormat or 'Rqz.ort' in outputFormat or 'Rqz' in outputFormat:
-        format_list.append('Rqz.ort')
-    if 'ort' in outputFormat or 'Rlt.ort' in outputFormat or 'Rlt' in outputFormat:
-        format_list.append('Rlt.ort')
-    if 'orb' in outputFormat or 'Rqz.orb' in outputFormat or 'Rqz' in outputFormat:
-        format_list.append('Rqz.orb')
-    if 'orb' in outputFormat or 'Rlt.orb' in outputFormat or 'Rlt' in outputFormat:
-        format_list.append('Rlt.orb')
-    return sorted(format_list, reverse=True)
-
-def command_line_options():
-    clas   = commandLineArgs()
-    update_loglevel(clas.verbose, clas.debug)
-
-    reader_config = ReaderConfig(
-        year                         = clas.year,
-        rawPath                      = clas.rawPath,
-        )
-    experiment_config = ExperimentConfig(
-        sampleModel                  = clas.sampleModel,
-        chopperPhase                 = clas.chopperPhase,
-        chopperPhaseOffset           = clas.chopperPhaseOffset,
-        yRange                       = clas.yRange,
-        lambdaRange                  = clas.lambdaRange,
-        qzRange                      = clas.qzRange,
-        lowCurrentThreshold          = clas.lowCurrentThreshold,
-        incidentAngle                = clas.incidentAngle,
-        mu                           = clas.mu,
-        nu                           = clas.nu,
-        muOffset                     = clas.muOffset,
-        monitorType                  = clas.monitorType,
-        )
-    reduction_config = ReductionConfig(
-        qResolution                  = clas.qResolution,
-        qzRange                      = clas.qzRange,
-        autoscale                    = clas.autoscale,
-        thetaRange                   = clas.thetaRange,
-        thetaRangeR                  = clas.thetaRangeR,
-        fileIdentifier               = clas.fileIdentifier,
-        scale                        = clas.scale,
-        subtract                     = clas.subtract,
-        normalisationFileIdentifier  = clas.normalisationFileIdentifier,
-        normalisationMethod          = clas.normalisationMethod,
-        timeSlize                    = clas.timeSlize,
-        )
-    output_config = OutputConfig(
-        outputFormats                = output_format_list(clas.outputFormat),
-        outputName                   = clas.outputName,
-        outputPath                   = clas.outputPath,
-        )
-
-    return EOSConfig(reader_config, experiment_config, reduction_config, output_config)

libeos/const.py

@@ -1,6 +0,0 @@
-"""
-Constants used in data reduction.
-"""
-
-hdm = 6.626176e-34/1.674928e-27  # h / m
-lamdaCut = 2.5  # Aa

libeos/file_reader.py

@@ -1,473 +0,0 @@
-import logging
-import os
-import subprocess
-import sys
-from datetime import datetime
-from typing import List
-
-import h5py
-import numpy as np
-from orsopy import fileio
-from orsopy.fileio.model_language import SampleModel
-
-from . import const
-from .header import Header
-from .instrument import Detector
-from .options import ExperimentConfig, ReaderConfig
-
-try:
-    from . import nb_helpers
-except Exception:
-    nb_helpers = None
-
-def get_current_per_pulse(pulseTimeS, currentTimeS, currents):
-    # add currents for early pulses and current time value after last pulse (j+1)
-    currentTimeS = np.hstack([[0], currentTimeS, [pulseTimeS[-1]+1]])
-    currents = np.hstack([[0], currents])
-    pulseCurrentS = np.zeros(pulseTimeS.shape[0], dtype=float)
-    j = 0
-    for i, ti in enumerate(pulseTimeS):
-        if ti >= currentTimeS[j+1]: 
-            j += 1
-        pulseCurrentS[i] = currents[j]
-        #print(f' {i}  {pulseTimeS[i]}  {pulseCurrentS[i]}') 
-    return pulseCurrentS
-
-class AmorData:
-    """read meta-data and event streams from .hdf file(s), apply filters and conversions"""
-    chopperDetectorDistance: float
-    chopperDistance: float
-    chopperPhase: float
-    chopperSpeed: float
-    div: float
-    data_file_numbers: List[int]
-    delta_z: np.ndarray
-    detZ_e: np.ndarray
-    lamda_e: np.ndarray
-    wallTime_e: np.ndarray
-    kad: float
-    kap: float
-    lambdaMax: float
-    lambda_e: np.ndarray
-    #monitor: float
-    mu: float
-    nu: float
-    tau: float
-    tofCut: float
-    start_date: str
-    monitorType: str
-
-    seriesStartTime = None
-
-    #-------------------------------------------------------------------------------------------------
-    def __init__(self, header: Header, reader_config: ReaderConfig, config: ExperimentConfig,
-                 short_notation:str, norm=False):
-        #self.startTime = reader_config.startTime
-        self.header = header
-        self.config = config
-        self.reader_config = reader_config
-        self.expand_file_list(short_notation)
-        self.read_data(norm=norm)
-
-    #-------------------------------------------------------------------------------------------------
-    def read_data(self, norm=False):
-        self.file_list = []
-        for number in self.data_file_numbers:
-            self.file_list.append(self.path_generator(number))
-        ## read specific meta data and measurement from first file
-        if norm:
-            self.readHeaderInfo = False
-        else:
-            self.readHeaderInfo = True
-
-        _detZ_e = []
-        _lamda_e = []
-        _wallTime_e = []
-        #_monitor = 0
-        _monitorPerPulse = []
-        _pulseTimeS = []
-        for file in self.file_list:
-            self.read_individual_data(file, norm)
-            _detZ_e = np.append(_detZ_e, self.detZ_e)
-            _lamda_e = np.append(_lamda_e, self.lamda_e)
-            _wallTime_e = np.append(_wallTime_e, self.wallTime_e)
-            _monitorPerPulse = np.append(_monitorPerPulse, self.monitorPerPulse)
-            _pulseTimeS = np.append(_pulseTimeS, self.pulseTimeS)
-            #_monitor += self.monitor
-        self.detZ_e = _detZ_e
-        self.lamda_e = _lamda_e
-        self.wallTime_e = _wallTime_e
-        #self.monitor = _monitor
-        self.monitorPerPulse = _monitorPerPulse   
-        self.pulseTimeS    = _pulseTimeS
-
-    #-------------------------------------------------------------------------------------------------
-    #def path_generator(self, number):
-    #    fileName = f'amor{self.reader_config.year}n{number:06d}.hdf'
-    #    if os.path.exists(os.path.join(self.reader_config.dataPath,fileName)):
-    #        path = self.reader_config.dataPath
-    #    elif os.path.exists(fileName):
-    #        path = '.'
-    #    elif os.path.exists(os.path.join('.','raw', fileName)):
-    #        path = os.path.join('.','raw')
-    #    elif os.path.exists(os.path.join('..','raw', fileName)):
-    #        path = os.path.join('..','raw')
-    #    elif os.path.exists(f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}/{fileName}'):
-    #        path = f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}'
-    #    else:
-    #        sys.exit(f'# ERROR: the file {fileName} is nowhere to be found!')
-    #    return os.path.join(path, fileName)
-    #-------------------------------------------------------------------------------------------------
-    def path_generator(self, number):
-        fileName = f'amor{self.reader_config.year}n{number:06d}.hdf'
-        path = ''
-        for rawd in self.reader_config.rawPath:
-            if os.path.exists(os.path.join(rawd,fileName)):
-                path = rawd
-                break
-        if not path:
-            if os.path.exists(f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}/{fileName}'):
-                path = f'/afs/psi.ch/project/sinqdata/{self.reader_config.year}/amor/{int(number/1000)}'
-            else:
-                sys.exit(f'# ERROR: the file {fileName} can not be found in {self.reader_config.rawPath}')
-        return os.path.join(path, fileName)
-    #-------------------------------------------------------------------------------------------------
-    def expand_file_list(self, short_notation):
-        """Evaluate string entry for file number lists"""
-        #log().debug('Executing get_flist')
-        file_list=[]
-        for i in short_notation.split(','):
-            if '-' in i:
-                if ':' in i:
-                    step = i.split(':', 1)[1]
-                    file_list += range(int(i.split('-', 1)[0]), int((i.rsplit('-', 1)[1]).split(':', 1)[0])+1, int(step))
-                else:
-                    step = 1
-                    file_list += range(int(i.split('-', 1)[0]), int(i.split('-', 1)[1])+1, int(step))
-            else:
-                file_list += [int(i)]
-        self.data_file_numbers=sorted(file_list)
-    #-------------------------------------------------------------------------------------------------
-    def resolve_pixels(self):
-        """determine spatial coordinats and angles from pixel number"""
-        nPixel = Detector.nWires * Detector.nStripes * Detector.nBlades
-        pixelID = np.arange(nPixel)
-        (bladeNr, bPixel) = np.divmod(pixelID, Detector.nWires * Detector.nStripes)
-        (bZi, detYi)      = np.divmod(bPixel, Detector.nStripes)                     # z index on blade, y index on detector
-        detZi             = bladeNr * Detector.nWires + bZi                          # z index on detector
-        detX              = bZi * Detector.dX                                        # x position in detector
-        # detZ              = Detector.zero - bladeNr * Detector.bladeZ - bZi * Detector.dZ      # z position on detector
-        bladeAngle        = np.rad2deg( 2. * np.arcsin(0.5*Detector.bladeZ / Detector.distance) )
-        delta             = (Detector.nBlades/2. - bladeNr) * bladeAngle \
-                            - np.rad2deg( np.arctan(bZi*Detector.dZ / ( Detector.distance + bZi * Detector.dX) ) )
-        self.delta_z      = delta[detYi==1]
-        return np.vstack((detYi.T, detZi.T, detX.T, delta.T)).T
-    #-------------------------------------------------------------------------------------------------
-    def read_individual_data(self, fileName, norm=False):
-        self.hdf = h5py.File(fileName, 'r', swmr=True)
-
-        if self.readHeaderInfo:
-            self.read_header_info()
-
-        logging.warning(f'    from file: {fileName}')
-        self.read_individual_header()
-
-        # add header content
-        if self.readHeaderInfo:
-            self.readHeaderInfo = False
-            self.header.measurement_instrument_settings = fileio.InstrumentSettings(
-                incident_angle = fileio.ValueRange(round(self.mu+self.kap+self.kad-0.5*self.div, 3),
-                                                   round(self.mu+self.kap+self.kad+0.5*self.div, 3),
-                                                   'deg'),
-                wavelength = fileio.ValueRange(const.lamdaCut, self.config.lambdaRange[1], 'angstrom'),
-                polarization = fileio.Polarization.unpolarized,
-                )
-            self.header.measurement_instrument_settings.mu = fileio.Value(round(self.mu, 3), 'deg', comment='sample angle to horizon')
-            self.header.measurement_instrument_settings.nu = fileio.Value(round(self.nu, 3), 'deg', comment='detector angle to horizon')
-            self.header.measurement_instrument_settings.div = fileio.Value(round(self.div, 3), 'deg', comment='incoming beam divergence')
-            self.header.measurement_instrument_settings.kap = fileio.Value(round(self.kap, 3), 'deg', comment='incoming beam inclination')
-            if abs(self.kad)>0.02:
-                self.header.measurement_instrument_settings.kad = fileio.Value(round(self.kad, 3), 'deg', comment='incoming beam angular offset')
-        if norm:
-            self.header.measurement_additional_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=self.fileDate))
-        else:
-            self.header.measurement_data_files.append(fileio.File(file=fileName.split('/')[-1], timestamp=self.fileDate))
-        logging.info(f'      mu = {self.mu:6.3f}, nu = {self.nu:6.3f}, kap = {self.kap:6.3f}, kad = {self.kad:6.3f}')
-
-        self.read_event_stream()
-        totalNumber = np.shape(self.tof_e)[0]
-
-        self.sort_pulses()
-
-        self.associate_pulse_with_monitor()
-
-        self.extract_walltime(norm)
-
-        # following lines: debugging output to trace the time-offset of proton current and neutron pulses
-        if self.config.monitorType == 'p':
-            cpp, t_bins = np.histogram(self.wallTime_e, self.pulseTimeS)
-            np.savetxt('tme.hst', np.vstack((self.pulseTimeS[:-1], cpp, self.monitorPerPulse[:-1])).T)
-
-        self.average_events_per_pulse()
-
-        self.monitor_threshold()
-
-        self.filter_strange_times()
-
-        self.merge_frames()
-
-        self.filter_project_x()
-
-        self.correct_for_chopper_opening()
-
-        self.calculate_derived_properties()
-
-        self.filter_qz_range(norm)
-
-        logging.info(f'      number of events: total = {totalNumber:7d}, filtered = {np.shape(self.lamda_e)[0]:7d}')
-
-    def sort_pulses(self):
-        chopperPeriod = np.int64(2*self.tau*1e9)
-        pulseTime = np.sort(self.dataPacketTime_p)
-        pulseTime = pulseTime[np.abs(pulseTime[:]-np.roll(pulseTime, 1)[:])>5]
-
-        pulseTime -= np.int64(self.seriesStartTime)
-        self.stopTime = pulseTime[-1]
-
-        # fill in missing pulse times 
-        # TODO: check for real end time
-        pulseTime = pulseTime[pulseTime>=0]
-        firstPulse = pulseTime[0] % np.int64(self.tau*2e9)
-        self.pulseTimeS = np.array([], dtype=np.int64)
-        nxt = firstPulse
-        for tt in pulseTime:
-            while tt - nxt > self.tau*1e9:
-                self.pulseTimeS = np.append(self.pulseTimeS, nxt)
-                nxt += chopperPeriod
-            self.pulseTimeS = np.append(self.pulseTimeS, tt)
-            nxt = self.pulseTimeS[-1] + chopperPeriod
-
-    def associate_pulse_with_monitor(self):
-        if self.config.monitorType == 'p': # protonCharge
-            self.currentTime -= np.int64(self.seriesStartTime)
-            self.currentTime -= np.int64(16e9) # time offset of proton current signal
-            self.monitorPerPulse = get_current_per_pulse(self.pulseTimeS, self.currentTime, self.current) * 2*self.tau * 1e-3
-            # filter low-current pulses
-            self.monitorPerPulse = np.where(self.monitorPerPulse > 2*self.tau * self.config.lowCurrentThreshold * 1e-3, self.monitorPerPulse, 0)
-        elif self.config.monitorType == 't': # countingTime
-            self.monitorPerPulse = np.ones(np.shape(self.pulseTimeS)[0])*self.tau
-        else: 
-            self.monitorPerPulse = 1./np.shape(pulseTimeS)[1]
-
-    def extract_walltime(self, norm):
-        if nb_helpers:
-            self.wallTime_e = nb_helpers.extract_walltime(self.tof_e, self.dataPacket_p, self.dataPacketTime_p)
-        else:
-            self.wallTime_e = np.empty(np.shape(self.tof_e)[0], dtype=np.int64)
-            for i in range(len(self.dataPacket_p)-1):
-                self.wallTime_e[self.dataPacket_p[i]:self.dataPacket_p[i+1]] = self.dataPacketTime_p[i]
-            self.wallTime_e[self.dataPacket_p[-1]:] = self.dataPacketTime_p[-1]
-        self.wallTime_e -= np.int64(self.seriesStartTime)
-        logging.debug(f'      wall time from {self.wallTime_e[0]/1e9:6.1f} s to {self.wallTime_e[-1]/1e9:6.1f} s')
-
-    def average_events_per_pulse(self):
-        if self.config.monitorType == 'p':
-            for i, time in enumerate(self.pulseTimeS):
-                events = np.shape(self.wallTime_e[self.wallTime_e == time])[0]
-                #print(f' {i:6.0f} {events:6.0f} {self.monitorPerPulse[i]:6.2f}')
-
-    def monitor_threshold(self):
-        if self.config.monitorType == 'p': # fix to check for file compatibility
-            goodTimeS = self.pulseTimeS[self.monitorPerPulse!=0]
-            filter_e = np.where(np.isin(self.wallTime_e, goodTimeS), True, False)
-            self.tof_e = self.tof_e[filter_e]
-            self.pixelID_e = self.pixelID_e[filter_e]
-            self.wallTime_e = self.wallTime_e[filter_e]
-            logging.info(f'      rejected {np.shape(self.monitorPerPulse)[0]-np.shape(goodTimeS)[0]} out of {np.shape(self.monitorPerPulse)[0]} pulses')
-            logging.info(f'          with {np.shape(filter_e)[0]-np.shape(self.tof_e)[0]} events due to low beam current')
-            logging.info(f'      average counts per pulse =  {np.shape(self.tof_e)[0] / np.shape(goodTimeS[goodTimeS!=0])[0]:7.1f}')
-
-    def filter_qz_range(self, norm):
-        if self.config.qzRange[1]<0.3 and not norm:
-            self.mask_e = np.logical_and(self.mask_e,
-                                         (self.config.qzRange[0]<=self.qz_e) & (self.qz_e<=self.config.qzRange[1]))
-        self.detZ_e = self.detZ_e[self.mask_e]
-        self.lamda_e = self.lamda_e[self.mask_e]
-        self.wallTime_e = self.wallTime_e[self.mask_e]
-
-    def calculate_derived_properties(self):
-        self.lamdaMax = const.lamdaCut+1.e13*self.tau*const.hdm/(self.chopperDetectorDistance+124.)
-        if nb_helpers:
-            self.lamda_e, self.qz_e, self.mask_e = nb_helpers.calculate_derived_properties_focussing(
-                    self.tof_e, self.detXdist_e, self.delta_e, self.mask_e,
-                    self.config.lambdaRange[0], self.config.lambdaRange[1], self.nu, self.mu,
-                    self.chopperDetectorDistance, const.hdm
-                    )
-            return
-        # lambda
-        self.lamda_e = (1.e13*const.hdm)*self.tof_e/(self.chopperDetectorDistance+self.detXdist_e)
-        self.mask_e = np.logical_and(self.mask_e, (self.config.lambdaRange[0]<=self.lamda_e) & (
-                    self.lamda_e<=self.config.lambdaRange[1]))
-        # alpha_f
-        # q_z
-        if self.config.incidentAngle == 'alphaF':
-            alphaF_e  = self.nu - self.mu + self.delta_e
-            self.qz_e = 4*np.pi*(np.sin(np.deg2rad(alphaF_e))/self.lamda_e)
-            # qx_e    = 0.
-            self.header.measurement_scheme = 'angle- and energy-dispersive'
-        elif self.config.incidentAngle == 'nu':
-            alphaF_e  = (self.nu + self.delta_e + self.kap + self.kad) / 2.
-            self.qz_e = 4*np.pi*(np.sin(np.deg2rad(alphaF_e))/self.lamda_e)
-            # qx_e    = 0.
-            self.header.measurement_scheme = 'energy-dispersive'
-        else:
-            alphaF_e  = self.nu - self.mu + self.delta_e
-            alphaI    = self.kap + self.kad + self.mu
-            self.qz_e = 2*np.pi * ((np.sin(np.deg2rad(alphaF_e)) + np.sin(np.deg2rad(alphaI)))/self.lamda_e)
-            self.qx_e = 2*np.pi * ((np.cos(np.deg2rad(alphaF_e)) - np.cos(np.deg2rad(alphaI)))/self.lamda_e)
-            self.header.measurement_scheme = 'energy-dispersive'
-
-    def correct_for_chopper_opening(self):
-        # correct tof for beam size effect at chopper:  t_cor = (delta / 180 deg) * tau
-        if self.config.incidentAngle == 'alphaF':
-            self.tof_e    -= ( self.delta_e / 180. ) * self.tau
-        else:
-            # TODO: check sign of correction
-            self.tof_e    -= ( self.kad / 180. ) * self.tau
-
-    def filter_project_x(self):
-        pixelLookUp = self.resolve_pixels()
-        if nb_helpers:
-            (self.detZ_e, self.detXdist_e, self.delta_e, self.mask_e) = nb_helpers.filter_project_x(
-                    pixelLookUp, self.pixelID_e.astype(np.int64), self.config.yRange[0], self.config.yRange[1]
-                    )
-        else:
-            # resolve pixel ID into y and z indicees, x position and angle
-            (detY_e, self.detZ_e, self.detXdist_e, self.delta_e) = pixelLookUp[np.int_(self.pixelID_e)-1, :].T
-            # define mask and filter y range
-            self.mask_e = (self.config.yRange[0]<=detY_e) & (detY_e<=self.config.yRange[1])
-
-    def merge_frames(self):
-        total_offset = self.tofCut+self.tau*self.config.chopperPhaseOffset/180.
-        if nb_helpers:
-            self.tof_e = nb_helpers.merge_frames(self.tof_e, self.tofCut, self.tau, total_offset)
-        else:
-            self.tof_e = np.remainder(self.tof_e-(self.tofCut-self.tau), self.tau)+total_offset  # tof shifted to 1 frame
-
-    def filter_strange_times(self):
-        # 'strange' tof times are those with t > 2 tau (originating from the efu)
-        filter_e = (self.tof_e<=2*self.tau)
-        self.tof_e = self.tof_e[filter_e]
-        self.pixelID_e = self.pixelID_e[filter_e]
-        self.wallTime_e = self.wallTime_e[filter_e]
-        if np.shape(filter_e)[0]-np.shape(self.tof_e)[0]>0.5:
-            logging.warning(f'#    strange times: {np.shape(filter_e)[0]-np.shape(self.tof_e)[0]}')
-
-    def read_event_stream(self):
-        self.tof_e = np.array(self.hdf['/entry1/Amor/detector/data/event_time_offset'][:])/1.e9
-        self.pixelID_e = np.array(self.hdf['/entry1/Amor/detector/data/event_id'][:], dtype=np.int64)
-        self.dataPacket_p = np.array(self.hdf['/entry1/Amor/detector/data/event_index'][:], dtype=np.uint64)
-        self.dataPacketTime_p = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][:], dtype=np.int64)
-        if self.config.monitorType in ['auto', 'p']:
-            try:
-                self.currentTime = np.array(self.hdf['entry1/Amor/detector/proton_current/time'][:], dtype=np.int64)
-                self.current = np.array(self.hdf['entry1/Amor/detector/proton_current/value'][:,0], dtype=float)
-                if len(self.current)>4:
-                    self.config.monitorType = 'p'
-                else:
-                    self.config.monitorType = 't'
-            except(KeyError, IndexError):
-                self.config.monitorType = 't'
-        else:
-            self.config.monitorType = 't'
-        #TODO: protonMonitor
-
-    def read_individual_header(self):
-        self.chopperDistance = float(np.take(self.hdf['entry1/Amor/chopper/pair_separation'], 0))
-        self.detectorDistance = float(np.take(self.hdf['entry1/Amor/detector/transformation/distance'], 0))
-        self.chopperDetectorDistance = self.detectorDistance-float(np.take(self.hdf['entry1/Amor/chopper/distance'], 0))
-        self.tofCut = const.lamdaCut*self.chopperDetectorDistance/const.hdm*1.e-13
-
-        try:
-            self.mu   = float(np.take(self.hdf['/entry1/Amor/master_parameters/mu/value'], 0))
-            self.nu   = float(np.take(self.hdf['/entry1/Amor/master_parameters/nu/value'], 0))
-            self.kap  = float(np.take(self.hdf['/entry1/Amor/master_parameters/kap/value'], 0))
-            self.kad  = float(np.take(self.hdf['/entry1/Amor/master_parameters/kad/value'], 0))
-            self.div  = float(np.take(self.hdf['/entry1/Amor/master_parameters/div/value'], 0))
-            self.chopperSpeed = float(np.take(self.hdf['/entry1/Amor/chopper/rotation_speed/value'], 0))
-            self.chopperPhase = float(np.take(self.hdf['/entry1/Amor/chopper/phase/value'], 0))
-        except(KeyError, IndexError):
-            logging.warning("     using parameters from nicos cache")
-            year_date = str(self.start_date).replace('-', '/', 1)
-            #cachePath = '/home/amor/nicosdata/amor/cache/'
-            #cachePath = '/home/nicos/amorcache/'
-            cachePath = '/home/amor/cache/'
-            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-mu/{year_date}')).split('\t')[-1]
-            self.mu = float(value)
-            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-nu/{year_date}')).split('\t')[-1]
-            self.nu = float(value)
-            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-kap/{year_date}')).split('\t')[-1]
-            self.kap = float(value)
-            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-kad/{year_date}')).split('\t')[-1]
-            self.kad = float(value)
-            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-div/{year_date}')).split('\t')[-1]
-            self.div = float(value)
-            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-ch1_speed/{year_date}')).split('\t')[-1]
-            self.chopperSpeed = float(value)
-            self.chopperPhase = self.config.chopperPhase
-        self.tau     = 30. / self.chopperSpeed
-
-        if self.config.muOffset:
-            self.mu += self.config.muOffset
-        if self.config.mu:
-            self.mu = self.config.mu
-        if self.config.nu:
-            self.nu = self.config.nu
-
-        self.fileDate = datetime.fromisoformat( self.hdf['/entry1/start_time'][0].decode('utf-8') )
-        self.startTime = np.int64( self.fileDate.timestamp() * 1e9 )
-        if self.seriesStartTime is None:
-            self.seriesStartTime = self.startTime 
-
-    def read_header_info(self):
-        # read general information and first data set
-        logging.info(f'    meta data from: {self.file_list[0]}')
-        self.hdf = h5py.File(self.file_list[0], 'r', swmr=True)
-        title = self.hdf['entry1/title'][0].decode('utf-8')
-        proposal_id = self.hdf['entry1/proposal_id'][0].decode('utf-8')
-        user_name = self.hdf['entry1/user/name'][0].decode('utf-8')
-        user_affiliation = 'unknown'
-        user_email = self.hdf['entry1/user/email'][0].decode('utf-8')
-        user_orcid = None
-        sampleName = self.hdf['entry1/sample/name'][0].decode('utf-8')
-        model = self.hdf['entry1/sample/model'][0].decode('utf-8')
-        instrumentName = 'Amor'
-        source = self.hdf['entry1/Amor/source/name'][0].decode('utf-8')
-        sourceProbe = 'neutron'
-        start_time = self.hdf['entry1/start_time'][0].decode('utf-8')
-        self.start_date = start_time.split(' ')[0]
-        if self.config.sampleModel:
-            model = self.config.sampleModel
-        # assembling orso header information
-        self.header.owner = fileio.Person(
-                name=user_name,
-                affiliation=user_affiliation,
-                contact=user_email,
-                )
-        if user_orcid:
-            self.header.owner.orcid = user_orcid
-        self.header.experiment = fileio.Experiment(
-                title=title,
-                instrument=instrumentName,
-                start_date=self.start_date,
-                probe=sourceProbe,
-                facility=source,
-                proposalID=proposal_id
-                )
-        self.header.sample = fileio.Sample(
-                name=sampleName,
-                model=SampleModel(stack=model),
-                sample_parameters=None,
-                )
-        self.header.measurement_scheme = 'angle- and energy-dispersive'
-

libeos/instrument.py

@@ -1,70 +0,0 @@
-"""
-Classes describing the AMOR instrument configuration used during reduction.
-"""
-
-import logging
-import numpy as np
-
-from . import const
-
-class Detector:
-    nBlades  = 14  # number of active blades in the detector
-    nWires   = 32  # number of wires per blade
-    nStripes = 64  # number of stipes per blade
-    angle    = np.deg2rad(5.1)  # deg  angle of incidence of the beam on the blades (def: 5.1)
-    dZ       = 4.0*np.sin(angle)  # mm  height-distance of neighboring pixels on one blade
-    dX       = 4.0*np.cos(angle)  # mm  depth-distance of neighboring pixels on one blace
-    bladeZ   = 10.455  # mm  distance between detector blades
-    zero     = 0.5*nBlades*bladeZ  # mm  vertical center of the detector
-    distance = 4000.  # mm  distance from focal point to leading blade edge
-
-class Grid:
-
-    def __init__(self, qResolution, qzRange):
-        self.lamdaCut = const.lamdaCut
-        self.dldl = 0.005     # Delta lambda / lambda
-        self.qResolution = qResolution
-        self.qzRange = qzRange
-
-    def q(self):
-        resolutions = [0.005, 0.01, 0.02, 0.025, 0.04, 0.05, 0.1, 1]
-        a, b = np.histogram([self.qResolution], bins = resolutions)
-        dqdq = np.matmul(b[:-1],a)
-        if dqdq != self.qResolution:
-            logging.info(f'#   changed resolution to {dqdq}')
-        qq = 0.01
-        # linear up to qq
-        q_grid = np.arange(0, qq, qq*dqdq)
-        # exponential from qq on
-        q_grid = np.append(q_grid, qq*(1.+dqdq)**np.arange(int(np.log(self.qzRange[1]/qq)/np.log(1+dqdq))))
-        q_grid = q_grid[q_grid>=self.qzRange[0]]
-        return q_grid
-
-    def lamda(self):
-        lamdaMax = 16
-        lamdaMin = self.lamdaCut
-        lamda_grid = lamdaMin*(1+self.dldl)**np.arange(int(np.log(lamdaMax/lamdaMin)/np.log(1+self.dldl)+1))
-        return lamda_grid
-
-    def z(self):
-        return np.arange(Detector.nBlades*Detector.nWires+1)
-
-    def lz(self):
-        return np.ones(( np.shape(self.lamda()[:-1])[0], np.shape(self.z()[:-1])[0] ))
-
-    def delta(self, detectorDistance):
-        # unused for now
-        bladeAngle = np.rad2deg( 2. * np.arcsin(0.5*Detector.bladeZ / detectorDistance) )
-        blade_grid = np.arctan( np.arange(33) * Detector.dZ / ( detectorDistance + np.arange(33) * Detector.dX) )
-        blade_grid = np.rad2deg(blade_grid)
-        stepWidth  = blade_grid[1] - blade_grid[0]
-        blade_grid = blade_grid - 0.2 * stepWidth
-
-        delta_grid = []
-        for b in np.arange(Detector.nBlades-1):
-            delta_grid = np.concatenate((delta_grid, blade_grid), axis=None)
-            blade_grid = blade_grid + bladeAngle
-            delta_grid = delta_grid[delta_grid<blade_grid[0]-0.5*stepWidth]
-        delta_grid = np.concatenate((delta_grid, blade_grid), axis=None)
-
-        return -np.flip(delta_grid) + 0.5*Detector.nBlades * bladeAngle

libeos/options.py

@@ -1,192 +0,0 @@
-"""
-Classes for stroing various configurations needed for reduction.
-"""
-from dataclasses import dataclass, field
-from typing import Optional, Tuple
-from datetime import datetime
-from os import path
-import numpy as np
-
-import logging
-
-class Defaults:
-    # fileIdentifier
-    outputPath                  = '.'
-    rawPath                     = ['.', path.join('.','raw'), path.join('..','raw'), path.join('..','..','raw')]
-    year                        = datetime.now().year
-    normalisationFileIdentifier = []
-    normalisationMethod         = 'o'
-    monitorType                 = 'auto'
-    # subtract
-    outputName                  = "fromEOS"
-    outputFormat                = ['Rqz.ort']
-    incidentAngle               = 'alphaF'
-    qResolution                 = 0.01
-    #timeSlize
-    scale                       = [1]
-    # autoscale
-    lambdaRange                 = [2., 15.]
-    thetaRange                  = [-12., 12.]
-    thetaRangeR                 = [-0.75, 0.75]
-    yRange                      = [11, 41]
-    qzRange                     = [0.005, 0.30]
-    chopperSpeed                = 500
-    chopperPhase                = -13.5
-    chopperPhaseOffset          = 7
-    muOffset                    = 0
-    mu                          = 0
-    nu                          = 0
-    sampleModel                 = None
-    lowCurrentThreshold         = 50
-    #
-    
-    
-
-@dataclass
-class ReaderConfig:
-    year: int
-    rawPath: Tuple[str]
-    startTime: Optional[float] = 0
-
-@dataclass
-class ExperimentConfig:
-    incidentAngle: str 
-    chopperPhase: float
-    yRange: Tuple[float, float]
-    lambdaRange: Tuple[float, float]
-    qzRange: Tuple[float, float]
-    monitorType: str
-    lowCurrentThreshold: float
-
-    sampleModel: Optional[str] = None
-    chopperPhaseOffset: float = 0
-    mu: Optional[float] = None
-    nu: Optional[float] = None
-    muOffset: Optional[float] = None
-
-@dataclass
-class ReductionConfig:
-    normalisationMethod: str
-    qResolution: float
-    qzRange: Tuple[float, float]
-    thetaRange: Tuple[float, float]
-    thetaRangeR: Tuple[float, float]
-
-    fileIdentifier: list = field(default_factory=lambda: ["0"])
-    scale: list = field(default_factory=lambda: [1]) #per file scaling; if less elements than files use the last one
-
-    autoscale: Optional[Tuple[bool, bool]] = None
-    subtract: Optional[str] = None
-    normalisationFileIdentifier: Optional[list] = None
-    timeSlize: Optional[list] = None
-
-@dataclass
-class OutputConfig:
-    outputFormats: list
-    outputName: str
-    outputPath: str
-
-@dataclass
-class EOSConfig:
-    reader: ReaderConfig
-    experiment: ExperimentConfig
-    reduction: ReductionConfig
-    output: OutputConfig
-    
-    _call_string_overwrite=None
-    
-    #@property
-    #def call_string(self)->str:
-    #    if self._call_string_overwrite:
-    #        return self._call_string_overwrite
-    #    else:
-    #        return self.calculate_call_string()
-    
-    def call_string(self):
-        base = 'python eos.py'
-        
-        inpt = ''
-        if self.reader.year:
-            inpt += f' -Y {self.reader.year}'
-        else:
-            inpt += f' -Y {datetime.now().year}'
-        if np.shape(self.reader.rawPath)[0] == 1:
-            inpt += f' --rawPath {self.reader.rawPath}'
-        if self.reduction.subtract:
-            inpt += f' -subtract {self.reduction.subtract}'
-        if self.reduction.normalisationFileIdentifier:
-            inpt += f' -n {" ".join(self.reduction.normalisationFileIdentifier)}'
-        if self.reduction.fileIdentifier:
-            inpt += f' -f {" ".join(self.reduction.fileIdentifier)}'
-
-        otpt = ''
-        if self.reduction.qResolution:
-            otpt += f' -r {self.reduction.qResolution}'
-        if self.output.outputPath != '.':
-            inpt += f' --outputdPath {self.output.outputPath}'
-        if self.output.outputName:
-            otpt += f' -o {self.output.outputName}'
-        if self.output.outputFormats != ['Rqz.ort']:
-            otpt += f' -of {" ".join(self.output.outputFormats)}'
-            
-        mask = ''    
-        if self.experiment.yRange != Defaults.yRange:
-            mask += f' -y {" ".join(str(ii) for ii in self.experiment.yRange)}'
-        if self.experiment.lambdaRange!= Defaults.lambdaRange:
-            mask += f' -l {" ".join(str(ff) for ff in self.experiment.lambdaRange)}'
-        if self.reduction.thetaRange != Defaults.thetaRange:
-            mask += f' -t {" ".join(str(ff) for ff in self.reduction.thetaRange)}'
-        elif self.reduction.thetaRangeR != Defaults.thetaRangeR:
-            mask += f' -T {" ".join(str(ff) for ff in self.reduction.thetaRangeR)}'
-        if self.experiment.qzRange!= Defaults.qzRange:
-            mask += f' -q {" ".join(str(ff) for ff in self.experiment.qzRange)}'
-
-        para = ''
-        if self.experiment.chopperPhase != Defaults.chopperPhase:
-            para += f' --chopperPhase {self.experiment.chopperPhase}'
-        if self.experiment.chopperPhaseOffset != Defaults.chopperPhaseOffset:
-            para += f' --chopperPhaseOffset {self.experiment.chopperPhaseOffset}'
-        if self.experiment.mu:
-            para += f' --mu {self.experiment.mu}'
-        elif self.experiment.muOffset:
-            para += f' --muOffset {self.experiment.muOffset}'
-        if self.experiment.nu:
-            para += f' --nu {self.experiment.nu}'
-
-        modl = ''
-        if self.experiment.sampleModel:
-            modl += f" --sampleModel '{self.experiment.sampleModel}'"
-
-        acts = ''
-        if self.reduction.autoscale:
-            acts += f' --autoscale {" ".join(str(ff) for ff in self.reduction.autoscale)}'
-        if self.reduction.scale != Defaults.scale:
-            acts += f' --scale {self.reduction.scale}'
-        if self.reduction.timeSlize:
-            acts += f' --timeSlize {" ".join(str(ff) for ff in self.reduction.timeSlize)}'
-
-        mlst = base + inpt + otpt 
-        if mask:
-            mlst += mask
-        if para:
-            mlst += para
-        if acts:
-            mlst += acts
-        if modl:
-            mlst += modl
-
-        if len(mlst) > 70:
-            mlst = base + '  ' + inpt + '  ' + otpt 
-            if mask:
-                mlst += '  ' + mask
-            if para:
-                mlst += '  ' + para
-            if acts:
-                mlst += '  ' + acts
-            if modl:
-                mlst += '  ' + modl
-
-        logging.debug(f'Argument list build in EOSConfig.call_string: {mlst}')
-        return  mlst
-
-            

libeos/reduction.py

@@ -1,493 +0,0 @@
-import logging
-import os
-import sys
-
-import numpy as np
-from orsopy import fileio
-
-from .command_line import expand_file_list
-from .file_reader import AmorData
-from .header import Header
-from .options import EOSConfig
-from .instrument import Grid
-
-class AmorReduction:
-    def __init__(self, config: EOSConfig):
-        self.experiment_config = config.experiment
-        self.reader_config = config.reader
-        self.reduction_config = config.reduction
-        self.output_config = config.output
-        self.grid = Grid(config.reduction.qResolution, config.experiment.qzRange)
-
-        self.header = Header()
-        self.header.reduction.call = config.call_string()
-
-        self.monitorUnit = {'n': 'cnts', 'p': 'mC', 't': 's'}
-
-    def reduce(self):
-        if not os.path.exists(f'{self.output_config.outputPath}'):
-            logging.debug(f'Creating destination path {self.output_config.outputPath}')
-            os.system(f'mkdir {self.output_config.outputPath}')
-
-        # load or create normalisation matrix
-        if self.reduction_config.normalisationFileIdentifier:
-            self.create_normalisation_map(self.reduction_config.normalisationFileIdentifier[0])
-        else:
-            self.norm_lz = self.grid.lz()
-            self.normAngle = 1.
-            self.normMonitor = 1.
-
-            logging.warning('normalisation matrix: none requested')
-
-        # load R(q_z) curve to be subtracted:
-        if self.reduction_config.subtract:
-            self.sq_q, self.sR_q, self.sdR_q, self.sFileName = self.loadRqz(self.reduction_config.subtract)
-            logging.warning(f'loaded background file: {self.sFileName}')
-            self.header.reduction.corrections.append(f'background from \'{self.sFileName}\' subtracted')
-            self.subtract = True
-        else:
-            self.subtract = False
-
-        # load measurement data and do the reduction
-        self.datasetsRqz = []
-        self.datasetsRlt = []
-        for i, short_notation in enumerate(self.reduction_config.fileIdentifier):
-            self.read_file_block(i, short_notation)
-
-        # output
-        logging.warning('output:')
-
-        if 'Rqz.ort' in self.output_config.outputFormats:
-            self.save_Rqz()
-
-        if 'Rlt.ort' in self.output_config.outputFormats:
-            self.save_Rtl()
-
-    def read_file_block(self, i, short_notation):
-        logging.warning('reading input:')
-        self.header.measurement_data_files = []
-        self.file_reader = AmorData(header=self.header,
-                                    reader_config=self.reader_config,
-                                    config=self.experiment_config,
-                                    short_notation=short_notation)
-        if self.reduction_config.timeSlize:
-            self.read_timeslices(i)
-        else:
-            self.read_unsliced(i)
-
-    def read_unsliced(self, i):
-        lamda_e = self.file_reader.lamda_e
-        detZ_e  = self.file_reader.detZ_e
-        self.monitor = np.sum(self.file_reader.monitorPerPulse)
-        logging.warning(f'    monitor = {self.monitor:8.2f} {self.monitorUnit[self.experiment_config.monitorType]}')
-        qz_lz, qx_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, self.mask_lz = self.project_on_lz(
-                self.file_reader, self.norm_lz, self.normAngle, lamda_e, detZ_e)
-        #if self.monitor>1 :
-        #    ref_lz /= self.monitor
-        #    err_lz /= self.monitor
-        try:
-            ref_lz *= self.reduction_config.scale[i]
-            err_lz *= self.reduction_config.scale[i]
-        except IndexError:
-            ref_lz *= self.reduction_config.scale[-1]
-            err_lz *= self.reduction_config.scale[-1]
-        if 'Rqz.ort' in self.output_config.outputFormats:
-            headerRqz = self.header.orso_header()
-            headerRqz.data_set = f'Nr {i} : mu = {self.file_reader.mu:6.3f} deg'
-
-            if qz_lz[0,int(np.shape(qz_lz)[1]/2)]  < 0:
-                # assuming a 'measurement from below' when center of detector at negative qz
-                qz_lz *= -1
-
-            # projection on qz-grid
-            q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, self.norm_lz, self.mask_lz)
-
-            # The filtering is now done by restricting the qz-grid
-            #filter_q = np.where((self.experiment_config.qzRange[0]>q_q) & (q_q>self.experiment_config.qzRange[1]),
-            #                    False, True)
-            #q_q = q_q[filter_q]
-            #R_q = R_q[filter_q]
-            #dR_q = dR_q[filter_q]
-            #dq_q = dq_q[filter_q]
-
-            if self.reduction_config.autoscale:
-                if i==0:
-                    R_q, dR_q = self.autoscale(q_q, R_q, dR_q)
-                else:
-                    pRq_z = self.datasetsRqz[i-1].data[:, 1]
-                    pdRq_z = self.datasetsRqz[i-1].data[:, 2]
-                    R_q, dR_q = self.autoscale(q_q, R_q, dR_q, pRq_z, pdRq_z)
-
-            if self.subtract:
-                if len(q_q)==len(self.sq_q):
-                    R_q -= self.sR_q
-                    dR_q = np.sqrt(dR_q**2+self.sdR_q**2)
-                else:
-                    logging.warning(
-                            f'backgroung file {self.sFileName} not compatible with q_z scale ({len(self.sq_q)} vs. {len(q_q)})')
-
-            data = np.array([q_q, R_q, dR_q, dq_q]).T
-            orso_data = fileio.OrsoDataset(headerRqz, data)
-            self.datasetsRqz.append(orso_data)
-        if 'Rlt.ort' in self.output_config.outputFormats:
-            columns = [
-                fileio.Column('Qz', '1/angstrom', 'normal momentum transfer'),
-                fileio.Column('R', '', 'specular reflectivity'),
-                fileio.ErrorColumn(error_of='R', error_type='uncertainty', value_is='sigma'),
-                fileio.ErrorColumn(error_of='Qz', error_type='resolution', value_is='sigma'),
-                fileio.Column('lambda', 'angstrom', 'wavelength'),
-                fileio.Column('alpha_f', 'deg', 'final angle'),
-                fileio.Column('l', '', 'index of lambda-bin'),
-                fileio.Column('t', '', 'index of theta bin'),
-                fileio.Column('intensity', '', 'filtered neutron events per pixel'),
-                fileio.Column('norm', '', 'normalisation matrix'),
-                fileio.Column('mask', '', 'pixels used for calculating R(q_z)'),
-                fileio.Column('Qx', '1/angstrom', 'parallel momentum transfer'),
-                ]
-            # data_source = file_reader.data_source
-
-            ts, zs = ref_lz.shape
-            lindex_lz = np.tile(np.arange(1, ts+1), (zs, 1)).T
-            tindex_lz = np.tile(np.arange(1, zs+1), (ts, 1))
-
-            j = 0
-            for item in zip(
-                    qz_lz.T,
-                    ref_lz.T,
-                    err_lz.T,
-                    res_lz.T,
-                    lamda_lz.T,
-                    theta_lz.T,
-                    lindex_lz.T,
-                    tindex_lz.T,
-                    int_lz.T,
-                    self.norm_lz.T,
-                    np.where(self.mask_lz, 1, 0).T,
-                    qx_lz.T,
-                    ):
-                data = np.array(list(item)).T
-                headerRlt = self.header.orso_header(columns=columns)
-                headerRlt.data_set = f'dataset_{i}_{j+1} : alpha_f = {theta_lz[0, j]:6.3f} deg'
-                orso_data = fileio.OrsoDataset(headerRlt, data)
-                self.datasetsRlt.append(orso_data)
-                j += 1
-
-    def read_timeslices(self, i):
-        wallTime_e = np.float64(self.file_reader.wallTime_e)/1e9
-        pulseTimeS = np.float64(self.file_reader.pulseTimeS)/1e9
-        interval = self.reduction_config.timeSlize[0]
-        try:
-            start = self.reduction_config.timeSlize[1]
-        except:
-            start = 0
-        try:
-            stop = self.reduction_config.timeSlize[2]
-        except:
-            stop = wallTime_e[-1]
-        # make overwriting log lines possible by removing newline at the end
-        #logging.StreamHandler.terminator = "\r"
-        logging.warning(f'    time slizing')
-        logging.info('      slize  time  monitor')
-        for ti, time in enumerate(np.arange(start, stop, interval)):
-
-            filter_e = np.where((time<wallTime_e) & (wallTime_e<time+interval), True, False)
-            lamda_e = self.file_reader.lamda_e[filter_e]
-            detZ_e = self.file_reader.detZ_e[filter_e]
-            filter_m = np.where((time<pulseTimeS) & (pulseTimeS<time+interval), True, False)
-            self.monitor = np.sum(self.file_reader.monitorPerPulse[filter_m])
-            logging.info(f'      {ti:<4d}  {time:5.0f}  {self.monitor:7.2f} {self.monitorUnit[self.experiment_config.monitorType]}')
-
-            qz_lz, qx_lz, ref_lz, err_lz, res_lz, lamda_lz, theta_lz, int_lz, mask_lz = self.project_on_lz(
-                    self.file_reader, self.norm_lz, self.normAngle, lamda_e, detZ_e)
-            q_q, R_q, dR_q, dq_q = self.project_on_qz(qz_lz, ref_lz, err_lz, res_lz, self.norm_lz, mask_lz)
-
-            filter_q = np.where((self.experiment_config.qzRange[0]<q_q) & (q_q<self.experiment_config.qzRange[1]),
-                                True, False)
-            q_q = q_q[filter_q]
-            R_q = R_q[filter_q]
-            dR_q = dR_q[filter_q]
-            dq_q = dq_q[filter_q]
-
-            if self.reduction_config.autoscale:
-                R_q, dR_q = self.autoscale(q_q, R_q, dR_q)
-
-            if self.subtract:
-                if len(q_q)==len(self.sq_q):
-                    R_q -= self.sR_q
-                    dR_q = np.sqrt(dR_q**2+self.sdR_q**2)
-                else:
-                    self.subtract = False
-                    logging.warning(
-                            f'background file {self.sFileName} not compatible with q_z scale ({len(self.sq_q)} vs. {len(q_q)})')
-
-            tme_q = np.ones(np.shape(q_q))*time
-            data = np.array([q_q, R_q, dR_q, dq_q, tme_q]).T
-            headerRqz = self.header.orso_header(
-                    extra_columns=[fileio.Column('time', 's', 'time relative to start of measurement series')])
-            headerRqz.data_set = f'{i}_{ti}: time = {time:8.1f} s  to {time+interval:8.1f} s'
-            orso_data = fileio.OrsoDataset(headerRqz, data)
-            self.datasetsRqz.append(orso_data)
-        # reset normal logging behavior
-        #logging.StreamHandler.terminator = "\n"
-        logging.info(f'      done  {time+interval:5.0f}')
-
-    def save_Rqz(self):
-        fname = os.path.join(self.output_config.outputPath, f'{self.output_config.outputName}.Rqz.ort')
-        logging.warning(f'    {fname}')
-        theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(q_z)'
-        fileio.save_orso(self.datasetsRqz, fname, data_separator='\n', comment=theSecondLine)
-
-    def save_Rtl(self):
-        fname = os.path.join(self.output_config.outputPath, f'{self.output_config.outputName}.Rlt.ort')
-        logging.warning(f'    {fname}')
-        theSecondLine = f' {self.header.experiment.title} | {self.header.experiment.start_date} | sample {self.header.sample.name} | R(lambda, theta)'
-        fileio.save_orso(self.datasetsRlt, fname, data_separator='\n', comment=theSecondLine)
-
-    def autoscale(self, q_q, R_q, dR_q, pR_q=[], pdR_q=[]):
-        autoscale = self.reduction_config.autoscale
-        if len(pR_q) == 0:
-            filter_q  = np.where((autoscale[0]<=q_q)&(q_q<=autoscale[1]), True, False)
-            filter_q  = np.where(dR_q>0, filter_q, False)
-            if len(filter_q[filter_q]) > 0:
-                scale = np.sum(R_q[filter_q]**2/dR_q[filter_q]) / np.sum(R_q[filter_q]/dR_q[filter_q])
-            else:
-                logging.warning('      automatic scaling not possible')
-                scale = 1.
-        else:
-            filter_q  = np.where(np.isnan(pR_q*R_q), False, True)
-            filter_q  = np.where(R_q>0, filter_q, False)
-            filter_q  = np.where(pR_q>0, filter_q, False)
-            if len(filter_q[filter_q]) > 0:
-                scale = np.sum(R_q[filter_q]**3 * pR_q[filter_q] / (dR_q[filter_q]**2 * pdR_q[filter_q]**2)) \
-                      / np.sum(R_q[filter_q]**2 * pR_q[filter_q]**2 / (dR_q[filter_q]**2  * pdR_q[filter_q]**2))
-            else:
-                logging.warning('      automatic scaling not possible')
-                scale = 1.
-        R_q  /= scale
-        dR_q /= scale
-        logging.debug(f'      scaling factor = {scale}')
-
-        return R_q, dR_q
-
-    def project_on_qz(self, q_lz, R_lz, dR_lz, dq_lz, norm_lz, mask_lz):
-        q_q       = self.grid.q()
-        mask_lzf  = mask_lz.flatten()
-        q_lzf     = q_lz.flatten()[mask_lzf]
-        R_lzf     = R_lz.flatten()[mask_lzf]
-        dR_lzf    = dR_lz.flatten()[mask_lzf]
-        dq_lzf    = dq_lz.flatten()[mask_lzf]
-        norm_lzf  = norm_lz.flatten()[mask_lzf]
-
-        weights_lzf = norm_lzf
-        #weights_lzf = np.sqrt(norm_lzf)
-        #weights_lzf = 1 / dR_lzf
-
-        N_q       = np.histogram(q_lzf, bins = q_q, weights = weights_lzf )[0]
-        N_q       = np.where(N_q > 0, N_q, np.nan)
-
-        R_q       = np.histogram(q_lzf, bins = q_q, weights = weights_lzf * R_lzf )[0]
-        R_q       = R_q / N_q
-
-        dR_q      = np.histogram(q_lzf, bins = q_q, weights = (weights_lzf * dR_lzf)**2 )[0]
-        dR_q      = np.sqrt( dR_q ) / N_q
-
-        # TODO: different error propagations for dR and dq!
-        # this is what should work:
-        #dq_q      = np.histogram(q_lzf, bins = q_q, weights = (weights_lzf * dq_lzf)**2 )[0]
-        #dq_q      = np.sqrt( dq_q ) / N_q 
-        # and this actually works:
-        N_q       = np.histogram(q_lzf, bins = q_q, weights = weights_lzf**2 )[0]
-        N_q       = np.where(N_q > 0, N_q, np.nan)
-        dq_q      = np.histogram(q_lzf, bins = q_q, weights = (weights_lzf * dq_lzf)**2 )[0]
-        dq_q      = np.sqrt( dq_q / N_q )
-
-        q_q       = 0.5 * (q_q + np.roll(q_q, 1))
-
-        return q_q[1:], R_q, dR_q, dq_q
-
-    def loadRqz(self, name):
-        fname = os.path.join(self.output_config.outputPath, name)
-        if os.path.exists(fname):
-            fileName = fname
-        elif os.path.exists(f'{fname}.Rqz.ort'):
-            fileName = f'{fname}.Rqz.ort'
-        else:
-            sys.exit(f'### the background file \'{fname}\' does not exist! => stopping')
-
-        q_q, Sq_q, dS_q = np.loadtxt(fileName, usecols=(0, 1, 2), comments='#', unpack=True)
-
-        return q_q, Sq_q, dS_q, fileName
-
-    def create_normalisation_map(self, short_notation):
-        outputPath = self.output_config.outputPath
-        normalisation_list = expand_file_list(short_notation)
-        name = str(normalisation_list[0])
-        for i in range(1, len(normalisation_list), 1):
-            name = f'{name}_{normalisation_list[i]}'
-        n_path = os.path.join(outputPath, f'{name}.norm')
-        if os.path.exists(n_path):
-            logging.warning(f'normalisation matrix: found and using {n_path}')
-            with open(n_path, 'rb') as fh:
-                self.normFileList = np.load(fh, allow_pickle=True)
-                self.normAngle    = np.load(fh, allow_pickle=True)
-                self.norm_lz      = np.load(fh, allow_pickle=True)
-                self.normMonitor  = np.load(fh, allow_pickle=True)
-            for i, entry in enumerate(self.normFileList):
-                 self.normFileList[i] = entry.split('/')[-1]
-            self.header.measurement_additional_files = self.normFileList
-        else:
-            logging.warning(f'normalisation matrix: using the files {normalisation_list}')
-            fromHDF = AmorData(header=self.header,
-                               reader_config=self.reader_config,
-                               config=self.experiment_config,
-                               short_notation=short_notation, norm=True)
-            self.normAngle     = fromHDF.nu - fromHDF.mu
-            lamda_e          = fromHDF.lamda_e
-            detZ_e           = fromHDF.detZ_e
-            self.normMonitor = np.sum(fromHDF.monitorPerPulse)
-            self.norm_lz, bins_l, bins_z = np.histogram2d(lamda_e, detZ_e, bins = (self.grid.lamda(), self.grid.z()))
-            self.norm_lz = np.where(self.norm_lz>2, self.norm_lz, np.nan)
-            # correct for the SM reflectivity
-            lamda_l  = self.grid.lamda()
-            theta_z  = self.normAngle + fromHDF.delta_z
-            lamda_lz = (self.grid.lz().T*lamda_l[:-1]).T
-            theta_lz = self.grid.lz()*theta_z
-            qz_lz    = 4.0*np.pi * np.sin(np.deg2rad(theta_lz)) / lamda_lz
-            Rsm_lz   = np.ones(np.shape(qz_lz))
-            Rsm_lz   = np.where(qz_lz>0.0217, 1-(qz_lz-0.0217)*(0.0625/0.0217), Rsm_lz)
-            # TODO: introduce variable for `m` and propably for the decay
-            Rsm_lz   = np.where(qz_lz>0.0217*5, np.nan, Rsm_lz)
-            self.norm_lz  = self.norm_lz / Rsm_lz
-
-            if len(lamda_e) > 1e6:
-                with open(n_path, 'wb') as fh:
-                    np.save(fh, np.array(fromHDF.file_list), allow_pickle=False)
-                    np.save(fh, np.array(self.normAngle), allow_pickle=False)
-                    np.save(fh, self.norm_lz, allow_pickle=False)
-                    np.save(fh, self.normMonitor, allow_pickle=False)
-            self.normFileList = fromHDF.file_list
-        self.header.reduction.corrections.append('normalisation with \'additional files\'')
-
-    def project_on_lz(self, fromHDF, norm_lz, normAngle, lamda_e, detZ_e):
-        # projection on lambda-z-grid
-        lamda_l  = self.grid.lamda()
-        alphaF_z  = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
-        #if self.experiment_config.incidentAngle == 'alphaF':
-        #  alphaF_z  = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
-        #elif self.experiment_config.incidentAngle == 'nu':
-        #  alphaF_z  = (fromHDF.nu + fromHDF.delta_z + fromHDF.kap + fromHDF.kad) / 2.
-        #else:
-        #  pass
-        lamda_lz  = (self.grid.lz().T*lamda_l[:-1]).T
-        alphaF_lz = self.grid.lz()*alphaF_z
-
-        thetaN_z  = fromHDF.delta_z + normAngle
-        thetaN_lz = np.ones(np.shape(norm_lz))*thetaN_z
-        thetaN_lz = np.where(np.absolute(thetaN_lz)>5e-3, thetaN_lz, np.nan)
-
-        mask_lz   = np.where(np.isnan(norm_lz), False, True)
-        mask_lz   = np.logical_and(mask_lz, np.where(np.absolute(thetaN_lz)>5e-3, True, False))
-        mask_lz   = np.logical_and(mask_lz, np.where(np.absolute(alphaF_lz)>5e-3, True, False))
-        if self.reduction_config.thetaRange[1]<12:
-          mask_lz   = np.logical_and(mask_lz, np.where(alphaF_lz >= self.reduction_config.thetaRange[0], True, False))
-          mask_lz   = np.logical_and(mask_lz, np.where(alphaF_lz <= self.reduction_config.thetaRange[1], True, False))
-        else:
-          self.reduction_config.thetaRange = [fromHDF.nu - fromHDF.mu - fromHDF.div/2, 
-                                              fromHDF.nu - fromHDF.mu + fromHDF.div/2]
-          mask_lz   = np.logical_and(mask_lz, np.where(alphaF_lz >= self.reduction_config.thetaRange[0], True, False))
-          mask_lz   = np.logical_and(mask_lz, np.where(alphaF_lz <= self.reduction_config.thetaRange[1], True, False))
-        if self.reduction_config.thetaRangeR[1]<12:
-          t0 = fromHDF.nu - fromHDF.mu
-          mask_lz   = np.logical_and(mask_lz, np.where(alphaF_lz-t0 >= self.reduction_config.thetaRangeR[0], True, False))
-          mask_lz   = np.logical_and(mask_lz, np.where(alphaF_lz-t0 <= self.reduction_config.thetaRangeR[1], True, False))
-        if self.experiment_config.lambdaRange[1]<15:
-          mask_lz   = np.logical_and(mask_lz, np.where(lamda_lz >= self.experiment_config.lambdaRange[0], True, False))
-          mask_lz   = np.logical_and(mask_lz, np.where(lamda_lz <= self.experiment_config.lambdaRange[1], True, False))
-
-        #           gravity correction
-        #alphaF_lz += np.rad2deg( np.arctan( 3.07e-10 * (fromHDF.detectorDistance + detXdist_e) * lamda_lz**2 ) )
-        alphaF_lz += np.rad2deg( np.arctan( 3.07e-10 * fromHDF.detectorDistance * lamda_lz**2 ) )
-
-        if self.experiment_config.incidentAngle == 'alphaF':
-          #alphaI_lz = alphaF_lz
-          qz_lz     = 4.0*np.pi * np.sin(np.deg2rad(alphaF_lz)) / lamda_lz
-          qx_lz     = self.grid.lz() * 0.
-        else:
-          alphaI_lz = self.grid.lz()*(fromHDF.mu + fromHDF.kap + fromHDF.kad)
-          qz_lz     = 2.0*np.pi * (np.sin(np.deg2rad(alphaF_lz)) + np.sin(np.deg2rad(alphaI_lz))) / lamda_lz
-          qx_lz     = 2.0*np.pi * (np.cos(np.deg2rad(alphaF_lz)) - np.cos(np.deg2rad(alphaI_lz))) / lamda_lz
-
-        int_lz, bins_l, bins_z  = np.histogram2d(lamda_e, detZ_e, bins = (lamda_l, self.grid.z()))
-        #           cut normalisation sample horizon
-        int_lz    = np.where(mask_lz, int_lz, np.nan)
-        thetaF_lz = np.where(mask_lz, alphaF_lz, np.nan)
-
-        if self.reduction_config.normalisationMethod == 'o':
-            logging.debug('      assuming an overilluminated sample and correcting for the angle of incidence')
-            ref_lz    = (int_lz * np.absolute(thetaN_lz)) / (norm_lz * np.absolute(thetaF_lz))
-        elif self.reduction_config.normalisationMethod == 'u':
-            logging.debug('      assuming an underilluminated sample and ignoring the angle of incidence')
-            ref_lz    = (int_lz / norm_lz)
-        elif self.reduction_config.normalisationMethod == 'd':
-            logging.debug('      assuming direct beam for normalisation and ignoring the angle of incidence')
-            norm_lz = np.flip(norm_lz,1)
-            ref_lz    = (int_lz / norm_lz)
-        else:
-            logging.error('unknown normalisation method! Use [u], [o] or [d]')
-            ref_lz    = (int_lz * np.absolute(thetaN_lz)) / (norm_lz * np.absolute(thetaF_lz))
-        if self.monitor > 1e-6 :
-            ref_lz   *= self.normMonitor / self.monitor
-        else:
-            logging.warning('   too small monitor value for normalisation -> ignoring monitors')
-        err_lz    = ref_lz * np.sqrt( 1/(int_lz+.1) + 1/norm_lz ) 
-
-        # TODO: allow for non-ideal Delta lambda / lambda (rather than 2.2%)
-        res_lz    = np.ones((np.shape(lamda_l[:-1])[0], np.shape(alphaF_z)[0])) * 0.022**2
-        res_lz    = res_lz + (0.008/alphaF_lz)**2
-        res_lz    = qz_lz * np.sqrt(res_lz)
-
-        return qz_lz, qx_lz, ref_lz, err_lz, res_lz, lamda_lz, alphaF_lz, int_lz, mask_lz
-
-
-    @staticmethod
-    def histogram2d_lz(lamda_e, detZ_e, bins):
-        """
-        Perform binning operation equivalent to numpy bin2d for the sepcial case
-        of the second dimension using integer positions (pre-defined pixels).
-        Based on the devide_bin algorithm below.
-        """
-        dimension = bins[1].shape[0]-1
-        if not (np.array(bins[1])==np.arange(dimension+1)).all():
-            raise ValueError("histogram2d_lz requires second bin dimension to be contigous integer range")
-        binning = AmorReduction.devide_bin(lamda_e, detZ_e.astype(np.int64), bins[0], dimension)
-        return np.array(binning), bins[0], bins[1]
-
-    @staticmethod
-    def devide_bin(lambda_e, position_e, lamda_edges, dimension):
-        '''
-        Use a divide and conquer strategy to bin the data. For the actual binning the
-        numpy bincount function is used, as it is much faster than histogram for
-        counting of integer values.
-
-        :param lambda_e: Array of wavelength for each event
-        :param position_e: Array of positional indices for each event
-        :param lamda_edges: The edges of bins to be used for the histogram
-        :param dimension: position number of buckets in output arrray
-
-        :return: 2D list of dimensions (lambda, x) of counts
-        '''
-        if len(lambda_e)==0:
-            # no more events in range, return empty bins
-            return [np.zeros(dimension, dtype=np.int64).tolist()]*(len(lamda_edges)-1)
-        if len(lamda_edges)==2:
-            # deepest recursion reached, all items should be within the two ToF edges
-            return [np.bincount(position_e, minlength=dimension).tolist()]
-        # split all events into two time of flight regions
-        split_idx = len(lamda_edges)//2
-        left_region = lambda_e<lamda_edges[split_idx]
-        left_list = AmorReduction.devide_bin(lambda_e[left_region], position_e[left_region],
-                                             lamda_edges[:split_idx+1], dimension)
-        right_region = np.logical_not(left_region)
-        right_list = AmorReduction.devide_bin(lambda_e[right_region], position_e[right_region],
-                                              lamda_edges[split_idx:], dimension)
-        return left_list+right_list

life_histogrammer.py

@@ -1,716 +0,0 @@
-__version__ = '2024-03-30'
-
-import os
-import sys
-import subprocess
-import h5py
-import glob
-import numpy as np
-import argparse
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-import time
-import logging
-from datetime import datetime
-
-#==============================================================================
-#==============================================================================
-class Detector:
-    def __init__(self):
-        self.nBlades = 14                                    # number of active blades in the detector 
-        angle        = np.deg2rad( 5.1 )                     # deg  angle of incidence of the beam on the blades (def: 5.1)
-        self.dZ      =  4.0 * np.sin(angle)                  # mm  height-distance of neighboring pixels on one blade
-        self.dX      =  4.0 * np.cos(angle)                  # mm  depth-distance of neighboring pixels on one blace
-        self.bladeZ  = 10.7                                  # mm  distance between detector blades (consistent with nu!)
-        self.zero    =  0.5 * self.nBlades * self.bladeZ     # mm  vertical center of the detector 
-
-#==============================================================================
-def pixel2quantity():
-    det = Detector()
-    nPixel = 64 * 32 * det.nBlades
-    pixelID = np.arange(nPixel)
-    (bladeNr, bPixel) = np.divmod(pixelID, 64*32)
-    (bZ, bY)          = np.divmod(bPixel, 64)
-    z                 = det.zero - bladeNr * det.bladeZ - bZ * det.dZ
-    x                 = (31 - bZ) * det.dX 
-    bladeAngle        = np.rad2deg( 2. * np.arcsin(0.5*det.bladeZ / detectorDistance) )
-    delta             = (det.nBlades/2. - bladeNr) * bladeAngle - np.rad2deg( np.arctan(bZ*det.dZ / ( detectorDistance + bZ * det.dX) ) )
-    dZ                = bladeNr * 32 + bZ
-    quantity = np.vstack((dZ.T, bY.T, delta.T, x.T)).T
-    
-    return quantity
-
-#==============================================================================
-def analyse_ev(event_e, tof_e, yMin, yMax, thetaMin, thetaMax):
-    
-    data_e = np.zeros((len(event_e), 9), dtype=float)
-    
-    # data_e column description:
-    #    0: wall time / s
-    #    1: pixelID
-    #    2: z on detector
-    #    3: y on detector
-    #    4: delta / deg  = angle on detector
-    #    5: path within detector / mm
-    #    6: lambda / angstrom
-    #    7: theta / deg
-    #    8: q_z / angstrom^-1
-    
-    data_e[:,0]   = tof_e[:]
-    data_e[:,1]   = event_e[:]
-
-    # filter 'strange' tof times > 2 tau
-    if True:
-        filter_e = (data_e[:,0] <= 2*tau)
-        #print(event_e[~filter_e])
-        #print(data_e[~filter_e,0])
-        data_e = data_e[filter_e,:]
-        if np.shape(filter_e)[0]-np.shape(data_e)[0] > 0.5 and verbous: 
-            logging.warning(f'##   strange times: {np.shape(filter_e)[0]-np.shape(data_e)[0]}')
-
-    pixelLookUp = pixel2quantity()
-    data_e[:,2:6] = pixelLookUp[np.int_(data_e[:,1])-1,:]
-
-    #================================
-
-    # filter y range
-    filter_e = (yMin <= data_e[:,3]) & (data_e[:,3] <= yMax) 
-    data_e = data_e[filter_e,:]
-    
-    # correct tof for beam size effect at chopper 
-    data_e[:,0] -= ( data_e[:,4] / 180. ) * tau
-      
-    # effective flight path length
-    #data_e[:,5] = chopperDetectorDistance + data_e[:,5] 
-    
-    # calculate lambda
-    hdm       = 6.626176e-34/1.674928e-27               # h / m
-    data_e[:,6] = 1.e13 * data_e[:,0] * hdm / ( chopperDetectorDistance + data_e[:,5] )
-    
-    # theta 
-    data_e[:,7] = nu - mu + data_e[:,4] 
-
-    # gravity compensation
-    data_e[:,7] += np.rad2deg( np.arctan( 3.07e-10 * ( detectorDistance +  data_e[:,5]) * data_e[:,6] * data_e[:,6] ) )
-
-    # filter theta range
-    filter_l = (thetaMin <= data_e[:,7]) & (data_e[:,7] <= thetaMax) 
-    data_e = data_e[filter_l,:]
-    
-    # q_z
-    data_e[:,8] = 4*np.pi * np.sin( np.deg2rad( data_e[:,7] ) ) / data_e[:,6]
-    
-    # filter q_z range
-    #filter_e = (qMin < data_e[:,6]) & (data_e[:,6] < qMax)
-    #data_e = data_e[filter_e,:]
-
-    return data_e
-
-#==============================================================================
-class Meta:
-  # AMOR hdf dataset with associated properties from metadata
-    def __init__(self, fileName):
-        self.fileName = fileName
-
-        fh = h5py.File(fileName, 'r', swmr=True)
-
-        # for processing
-
-        self.chopperDistance            = float(np.take(fh['/entry1/Amor/chopper/pair_separation'], 0))  # mm
-        # the following is the distance from the sample to the detector entry window, not to the center of rotation
-        self.detectorDistance           = float(np.take(fh['/entry1/Amor/detector/transformation/distance'], 0)) # mm
-        self.chopperDetectorDistance    = self.detectorDistance - float(np.take(fh['entry1/Amor/chopper/distance'], 0))  # mm
-
-        self.lamdaCut                   = 2.5 # Aa
-
-        startDate                       = str(fh['/entry1/start_time'][0].decode('utf-8'))
-        self.startDate                  = datetime.strptime(startDate, '%Y-%m-%d %H:%M:%S')
-        startDate                       = datetime.timestamp(self.startDate)
-        self.countingTime               = float(np.take(fh['/entry1/Amor/detector/data/event_time_zero'], -1))/1e9 - startDate
-                                          # not exact for low rates
-
-        ka0 = 0.245 # given inclination of the beam after the Selene guide
-
-        year_date = str(datetime.today()).split(' ')[0].replace("-", "/", 1)
-
-        # deside from where to take the control paralemters
-        try: 
-            self.mu   = float(np.take(fh['/entry1/Amor/master_parameters/mu/value'], 0))
-            self.nu   = float(np.take(fh['/entry1/Amor/master_parameters/nu/value'], 0))
-            self.kap  = float(np.take(fh['/entry1/Amor/master_parameters/kap/value'], 0))
-            self.kad  = float(np.take(fh['/entry1/Amor/master_parameters/kad/value'], 0))
-            self.div  = float(np.take(fh['/entry1/Amor/master_parameters/div/value'], 0))
-            chSp      = float(np.take(fh['/entry1/Amor/chopper/rotation_speed/value'], 0))
-            self.chPh = float(np.take(fh['/entry1/Amor/chopper/phase/value'], 0))
-        except (KeyError, IndexError):
-            logging.warning(f"     using parameters from nicos cache")
-            #cachePath = '/home/amor/nicosdata/amor/cache/'
-            cachePath = '/home/nicos/amorcache/'
-            value = str(subprocess.getoutput('/usr/bin/grep "value" '+cachePath+'nicos-mu/'+year_date)).split('\t')[-1]
-            self.mu = float(value)
-            value = str(subprocess.getoutput('/usr/bin/grep "value" '+cachePath+'nicos-nu/'+year_date)).split('\t')[-1]
-            self.nu = float(value)
-            value = str(subprocess.getoutput('/usr/bin/grep "value" '+cachePath+'nicos-kap/'+year_date)).split('\t')[-1]
-            self.kap = float(value)
-            value = str(subprocess.getoutput('/usr/bin/grep "value" '+cachePath+'nicos-kad/'+year_date)).split('\t')[-1]
-            self.kad = float(value)
-            value = str(subprocess.getoutput('/usr/bin/grep "value" '+cachePath+'nicos-div/'+year_date)).split('\t')[-1]
-            self.div = float(value)
-            value = str(subprocess.getoutput('/usr/bin/grep "value" '+cachePath+'nicos-ch1_speed/'+year_date)).split('\t')[-1]
-            chSp = float(value)
-            self.chPh = np.nan
-
-        if chSp:
-            self.tau = 30. / chSp
-        else:
-            self.tau = 0
- 
-        try: # not yet correctly implemented in nexus template
-            spin = str(fh['/entry1/polarizer/spin_flipper/spin'][0].decode('utf-8'))
-            if spin == "b'p'":
-                self.spin = 'p'
-            elif spin == "b'm'":
-                self.spin = 'm'
-            elif spin == "b'up'":
-                self.spin = 'p'
-            elif spin == "b'down'":
-                self.spin = 'm'
-            elif spin == '?':
-                self.spin = '?'
-            else:
-                self.spin = 'n'  
-        except (KeyError, IndexError):
-            self.spin = '?'
-
-        fh.close()
-
-#==============================================================================
-def resolveNumber(dataPath, ident):
-    if ident == '0' or '-' in ident[0]:
-        try: 
-            nnr = int(ident)
-        except:
-            logging.error("ERROR: '{}' is no valid file identifier!".format(ident))
-        fileNames = glob.glob(dataPath+'/*.hdf')
-        fileNames.sort()
-        fileName  = fileNames[nnr-1] 
-        fileName = fileName.split('/')[-1]
-        fileNumber = fileName.split('n')[1].split('.')[0].lstrip('0')
-    else:
-        fileNumber = ident
-
-    return fileNumber 
-
-#==============================================================================
-def fileNameCreator(dataPath, ident):
-    clas = commandLineArgs()
-    ident=str(ident)
-    try: 
-        nnr = int(ident)
-    except:
-        logging.error("ERROR: '{}' is no valid file identifier!".format(ident))
-
-    if nnr <= 0 :
-        fileName = glob.glob(dataPath+'/*.hdf')[nnr-1]
-        fileName = fileName.split('/')[-1]
-    else:
-        fileName = f'amor{clas.year}n{ident:>06s}'
-        
-    fileName = fileName.split('.')[0]
-    fileName = fileName+'.hdf'
-    fileName = dataPath+fileName
-    
-    fileNumber = fileName.split('n')[-1].split('.')[0].lstrip('0')
-  
-    return fileName, fileNumber
-
-#==============================================================================
-class PlotSelection:
-
-    def headline(self, fileNumber, totalCounts):
-        headLine = "#{}   \u03bc={:>1.2f} \u03bd={:>1.2f} {:>12,} cts   {:>8.1f} s".format(fileNumber, mu+5e-3, nu+5e-3, totalCounts, countingTime)
-        return headLine
-
-    # grids
-
-    def y_grid(self):
-        y_grid = np.arange(yMin, yMax+1, 1)
-        return y_grid 
-
-    def lamda_grid(self):
-        dldl      = 0.005 # Delta lambda / lambda
-        lMin = max(2, lamdaMin)
-        lamda_grid = lMin*(1+dldl)**np.arange(int(np.log(lamdaMax/lMin)/np.log(1+dldl)+1))
-        return lamda_grid
-    
-    def theta_grid(self):
-        det = Detector()
-
-        bladeAngle = np.rad2deg( 2. * np.arcsin(0.5*det.bladeZ / detectorDistance) )
-        blade_grid = np.arctan( np.arange(33) * det.dZ / ( detectorDistance + np.arange(33) * det.dX) )
-        blade_grid = np.rad2deg(blade_grid)
-        stepWidth = blade_grid[1] - blade_grid[0]
-        blade_grid = blade_grid - 0.2 * stepWidth
-
-        delta_grid = []
-        for b in np.arange(det.nBlades-1):
-            delta_grid = np.concatenate((delta_grid, blade_grid), axis=None)
-            blade_grid = blade_grid + bladeAngle
-            delta_grid = delta_grid[delta_grid<blade_grid[0]-0.5*stepWidth]
-        delta_grid = np.concatenate((delta_grid, blade_grid), axis=None)
-
-        theta_grid = nu - mu - np.flip(delta_grid) + 0.5*det.nBlades * bladeAngle    
-  
-        theta_grid = theta_grid[theta_grid>=thetaMin]
-        theta_grid = theta_grid[theta_grid<=thetaMax]
-
-        return theta_grid
-
-    def q_grid(self):
-        dqdq      = 0.010 # Delta q_z / q_z
-        q_grid = qMin*(1.+dqdq)**np.arange(int(np.log(qMax/qMin)/np.log(1+dqdq)))
-        return q_grid
-
-    # create PNG with several plots
-
-    def all(self, fileNumber, arg, data_e):
-        #cmap='gist_earth'
-        cmap = mpl.cm.gnuplot(np.arange(256))
-        cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
-        cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
-        I_yt, bins_y, bins_t = np.histogram2d(data_e[:,3], data_e[:,7], bins = (self.y_grid(), self.theta_grid()))
-        I_lt, bins_l, bins_t = np.histogram2d(data_e[:,6], data_e[:,7], bins = (self.lamda_grid(), self.theta_grid()))
-        I_q, bins_q = np.histogram(data_e[:,8], bins = self.q_grid())
-        q_lim = 4*np.pi*np.array([ max( np.sin(self.theta_grid()[0]*np.pi/180.)/self.lamda_grid()[-1] , 1e-4 ),
-                                   min( np.sin(self.theta_grid()[-1]*np.pi/180.)/self.lamda_grid()[0] , 0.03 )])
-        if arg == 'lin':
-            #vmin = min(np.min(I_lt), np.min(I_yt))
-            vmin = 0
-            vmax = max(5, np.max(I_lt), np.max(I_yt))
-        else:
-            vmin = 0
-            vmax = max(1, np.log(np.max(I_lt)+.1)/np.log(10)*1.05, np.log(np.max(I_yt)+.1)/np.log(10)*1.05)
-        # I(y, theta)   
-        fig = plt.figure()
-        axs = fig.add_gridspec(2,3)
-        myt = fig.add_subplot(axs[0,0])
-        myt.set_title('detector area')
-        myt.set_xlabel('$y ~/~ \\mathrm{bins}$')
-        myt.set_ylabel('$\\theta ~/~ \\mathrm{deg}$')
-        if arg == 'lin':
-            myt.pcolormesh(bins_y, bins_t, I_yt.T, cmap=cmap, vmin=vmin, vmax=vmax)
-        else:
-            myt.pcolormesh(bins_y, bins_t, (np.log(I_yt + 5.e-1) / np.log(10.)).T, cmap=cmap, vmin=vmin, vmax=vmax)
-        # I(lambda, theta)   
-        mlt = fig.add_subplot(axs[0,1:])
-        mlt.set_title('angle- and energy disperse')
-        mlt.set_xlabel('$\\lambda ~/~ \\mathrm{\\AA}$')
-        mlt.axes.get_yaxis().set_visible(False)
-        if arg == 'lin':
-            cb = mlt.pcolormesh(bins_l, bins_t, I_lt.T, cmap=cmap, vmin=vmin, vmax=vmax)
-        else:
-            cb = mlt.pcolormesh(bins_l, bins_t, (np.log(I_lt + 5.e-1) / np.log(10.)).T, cmap=cmap, vmin=vmin, vmax=vmax)
-        # I(q_z)
-        lqz = fig.add_subplot(axs[1,:])
-        lqz.set_title('$I(q_z)$')
-        lqz.set_ylabel('$\\log_{10}(\\mathrm{cnts})$')
-        lqz.set_xlabel('$q_z~/~\\mathrm{\\AA}^{-1}$')
-        lqz.set_xlim(q_lim)
-        if arg == 'lin':
-            plt.plot(bins_q[:-1], I_q, color='blue', linewidth=0.5)
-        else:
-            err_q = np.sqrt(I_q+1)
-            low_q = np.where(I_q-err_q>0, I_q-err_q, 0.1)
-            plt.fill_between(bins_q[:-1], np.log(low_q)/np.log(10), np.log(I_q+err_q/2)/np.log(10), color='lightgrey')
-            plt.plot(bins_q[:-1], np.log(I_q+5e-1)/np.log(10), color='blue', linewidth=0.5)
-            lw = I_q[ ((q_lim[0] < bins_q[:-1]) & (bins_q[:-1] < q_lim[1])) ].min() 
-            plt.ylim(max(-0.1, np.log(lw+.1)/np.log(10)-0.1), )
-        #
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=2.8, c='r')
-        fig.colorbar(cb, ax=mlt)
-        plt.subplots_adjust(hspace=0.6, wspace=0.1)
-        plt.savefig(output, format='png', dpi=150)
-
-    # create PNG with one plot
-
-    def Iyz(self, fileNumber, arg, data_e):
-        det = Detector()
-        cmap = mpl.cm.gnuplot(np.arange(256))
-        cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
-        cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
-        z_grid = np.arange(det.nBlades*32)
-        I_yz, bins_y, bins_z = np.histogram2d(data_e[:,3], data_e[:,2], bins = (self.y_grid(), z_grid))
-        if arg == 'log':
-            vmin = 0
-            vmax = max(1, np.log(np.max(I_yt)+.1)/np.log(10)*1.05)
-            plt.pcolormesh(bins_y[:],bins_z[:],(np.log(I_yz+6e-1)/np.log(10)).T, cmap=cmap, vmin=vmin, vmax=vmax)
-        else:
-            plt.pcolormesh(bins_y[:],bins_z[:],I_yz.T, cmap=cmap)
-        plt.xlabel('$y ~/~ \\mathrm{bins}$')
-        plt.ylabel('$z ~/~ \\mathrm{bins}$')
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.colorbar()
-        plt.savefig(output, format='png', dpi=150)
-
-    def Ilt(self, fileNumber, arg, data_e) :
-        cmap = mpl.cm.gnuplot(np.arange(256))
-        cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
-        cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
-        I_lt, bins_l, bins_t = np.histogram2d(data_e[:,6], data_e[:,7], bins = (self.lamda_grid(), self.theta_grid()))
-        if arg == 'log':
-            vmax = max(1, np.log(np.max(I_lt)+.1)/np.log(10)*1.05 )
-            plt.pcolormesh(bins_l, bins_t, (np.log(I_lt+I_lt[I_lt>0].min()/2)/np.log(10.)).T, cmap=cmap, vmin=0, vmax=vmax)
-        else :
-            vmax = max(np.max(I_lt), 5)
-            plt.pcolormesh(bins_l, bins_t, I_lt.T, cmap=cmap, vmin=0, vmax=vmax)
-        plt.xlim(0,)
-        #if np.min(bins_t) > 0.01 :
-        #    plt.ylim(bottom=0)
-        #else:
-        #    plt.ylim(bottom=np.min(bins_t))
-        #if np.max(bins_t) < -0.01:
-        #    plt.ylim(top=0)
-        #else:
-        #    plt.ylim(top=np.max(bins_t))
-        plt.xlim(lamdaMin, lamdaMax)
-        plt.ylim(thetaMin, thetaMax)
-        plt.xlabel('$\\lambda ~/~ \\mathrm{\\AA}$')
-        plt.ylabel('$\\theta ~/~ \\mathrm{deg}$')
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.colorbar()
-        plt.savefig(output, format='png', dpi=150)
-    
-    def Itz(self, fileNumber, arg, data_e):
-        det = Detector()
-        cmap = mpl.cm.gnuplot(np.arange(256))
-        cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
-        cmap = mpl.colors.ListedColormap(cmap, name='myColorMap', N=cmap.shape[0])
-        time_grid = np.arange(0, tau, 0.0005)
-        z_grid = np.arange(det.nBlades*32+1)
-     
-        I_tz, bins_t, bins_z = np.histogram2d(data_e[:,0], data_e[:,2], bins = (time_grid, z_grid))
-        if arg == 'log':
-            vmax = max(2., np.log(np.max(I_tz)+.1)/np.log(10)*1.05 )
-            plt.pcolormesh(bins_t, bins_z, (np.log(I_tz+5.e-1)/np.log(10.)).T, cmap=cmap, vmin=0, vmax=vmax)
-        else :
-            vmax = max(np.max(I_tz), 5)
-            plt.pcolormesh(bins_t, bins_z, I_tz.T, cmap=cmap, vmin=0, vmax=vmax)
-        if True:
-            plt.xlim(0,)
-            plt.ylim(0,)
-        plt.xlabel('$t ~/~ \\mathrm{s}$')
-        plt.ylabel('$z$ pixel row')
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.colorbar()
-        plt.savefig(output, format='png', dpi=150)
-    
-    def Iq(self, fileNumber, arg, data_e):
-        I_q, bins_q = np.histogram(data_e[:,8], bins = self.q_grid())
-        err_q = np.sqrt(I_q+1)
-        q_lim = 4*np.pi*np.array([ max( np.sin(self.theta_grid()[0]*np.pi/180.)/self.lamda_grid()[-1] , 1e-4 ),
-                                   min( np.sin(self.theta_grid()[-1]*np.pi/180.)/self.lamda_grid()[0] , 0.03 )])
-        if arg == 'log':
-            low_q = np.where(I_q-err_q>0, I_q-err_q, 0.1)
-            plt.fill_between(bins_q[:-1], np.log(low_q)/np.log(10), np.log(I_q+err_q/2)/np.log(10), color='lightgrey')
-            plt.plot(bins_q[:-1], np.log(I_q+5e-1)/np.log(10), color='blue', linewidth=0.5)
-            lw = I_q[ ((q_lim[0] < bins_q[:-1]) & (bins_q[:-1] < q_lim[1])) ].min() 
-            plt.ylim(max(-0.1, np.log(lw+.1)/np.log(10)-0.1), )
-        else:
-            plt.plot(bins_q[:-1], I_q, color='blue', linewidth=0.5)
-        plt.ylabel('$\\log_{10}(\\mathrm{cnts})$')
-        plt.xlabel('$q_z ~/~ \\mathrm{\\AA}^{-1}$')
-        plt.xlim(q_lim)
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.savefig(output, format='png', dpi=150)
-
-    def Il(self, fileNumber, arg, data_e):
-        I_l, bins_l = np.histogram(data_e[:,6], bins = self.lamda_grid())
-        if arg == 'lin':
-            plt.plot(bins_l[:-1], I_l)
-            plt.ylabel('$I ~/~ \\mathrm{cnts}$')
-        else:
-            plt.plot(bins_l[:-1], np.log(I_l+5.e-1)/np.log(10.))
-            plt.ylabel('$\\log_{10} I ~/~ \\mathrm{cnts}$')
-        plt.xlabel('$\\lambda ~/~ \\mathrm{\\AA}$')
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.savefig(output, format='png', dpi=150)
-    
-    def It(self, fileNumber, arg, data_e):
-        I_t, bins_t = np.histogram(data_e[:,7], bins = self.theta_grid())
-        plt.plot( I_t, bins_t[:-1])
-        plt.xlabel('$\\mathrm{cnts}$')
-        plt.ylabel('$\\theta ~/~ \\mathrm{deg}$')
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.savefig(output, format='png', dpi=150)
-
-    def tof(self, fileNumber, arg, data_e):
-        time_grid = np.arange(0, 1.3*tau, 0.0005)
-        I_t, bins_t = np.histogram(data_e[:,0], bins = time_grid)
-        if arg == 'lin':
-            plt.plot(bins_t[:-1]+tau, I_t)
-            plt.plot(bins_t[:-1], I_t)
-            plt.plot(bins_t[:-1]+2*tau, I_t)
-        else:  
-            lI_t = np.log(I_t+5.e-1)/np.log(10.)
-            plt.plot(bins_t[:-1]+tau, lI_t)
-            plt.plot(bins_t[:-1], lI_t)
-            plt.plot(bins_t[:-1]+2*tau, lI_t)
-        plt.ylabel('log(counts)')
-        plt.xlabel('time / s')
-        headline = self.headline(fileNumber, np.shape(data_e)[0])
-        plt.title(headline, loc='left', y=1.0, c='r')
-        plt.savefig(output, format='png')
-
-#==============================================================================
-def process(dataPath, ident, clas):
-    #================================
-    # constants
-    hdm       = 6.626176e-34/1.674928e-27               # h / m
-    #================================
-    # instrument specific parameters
-    #================================
-    global lamdaMin, lamdaMax, qMin, qMax, thetaMin, thetaMax, yMin, yMax
-    # defaults   
-    lamdaCut  = 2.5     # Aa  used to reshuffle tof
-    # data filtering and folding 
-    
-    #================================
-    if clas.lambdaRange:
-        lamdaMin  = clas.lambdaRange[0]
-        lamdaMax  = clas.lambdaRange[1]
-    else:
-        lamdaMin  = lamdaCut 
-
-    chopperPhase = clas.chopperPhase
-    tofOffset    = clas.TOFOffset
-    thetaMin     = clas.thetaRange[0]
-    thetaMax     = clas.thetaRange[1]
-    yMin         = clas.yRange[0]
-    yMax         = clas.yRange[1]
-    qMin         = clas.qRange[0]
-    qMax         = clas.qRange[1]
-
-    #================================
-    # find and open input file 
-    global ev
-
-    data_eSum = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 0]])
-    sumTime = 0
-
-    number = resolveNumber(dataPath, ident)
-    fileName, fileNumber = fileNameCreator(dataPath, str(number))
-
-    if verbous:
-        logging.info('life_histogrammer processing file ->\033[1m {} \033[0m<-'.format(fileNumber))
-
-    for i in range(6):
-        ev = h5py.File(fileName, 'r', swmr=True)
-        try:
-            ev['/entry1/Amor/detector/data/event_time_zero'][-1]
-            break 
-        except (KeyError, IndexError):
-            ev.close()
-            if i < 5:
-                if verbous:
-                    print("no data yet, retrying ({})    ".format(10-2*i), end='\r')
-                time.sleep(2)
-                continue
-            else:
-                if verbous:
-                    print("# time-out: no longer waiting for data!\a")
-                return
-        
-    # get and process data
-    meta = Meta(fileName)
-
-    global mu, nu, tau
-
-    if clas.mu < 98.: 
-        mu = clas.mu 
-    else:
-        mu = meta.mu + clas.muOffset
-
-    if clas.nu < 98.:
-        nu = clas.nu
-    else:
-        nu = meta.nu
-
-    if clas.chopperSpeed:
-        tau = 30./ clas.chopperSpeed
-    else:
-        tau = meta.tau
-
-    try:
-        chPh
-    except NameError:
-        chPh = meta.chPh
-    spin = meta.spin
-
-    global countingTime, detectorDistance, chopperDetectorDistance
-    detectorDistance = meta.detectorDistance
-    chopperDetectorDistance = meta.chopperDetectorDistance
-    countingTime = meta.countingTime
-
-    if verbous:
-        logging.info("   mu = {:>4.2f} deg,  nu = {:>4.2f} deg".format(mu, nu))
-        if spin == 'u':
-            logging.info('   spin <+|')
-        elif spin == 'd':
-            logging.info('   spin <-|')
-
-    try: lamdaMax
-    except NameError: lamdaMax = lamdaMin + tau * hdm/chopperDetectorDistance * 1e13
-    
-    tofOffset = tau * chopperPhase / 180.                       # mismatch of chopper pulse and time-zero
-    tofCut = lamdaCut * chopperDetectorDistance / hdm * 1.e-13  # tof of frame start
-
-    tof_e  = np.array(ev['/entry1/Amor/detector/data/event_time_offset'][:], dtype=np.uint64)/1.e9 + tofOffset # tof 
-    
-    detPixelID_e = np.array(ev['/entry1/Amor/detector/data/event_id'][:], dtype=np.uint64)  # pixel index
-    
-    dataPacket_p = np.array(ev['/entry1/Amor/detector/data/event_index'][:], dtype=np.uint64)  # data packet index
-
-    tof_e  = np.where(tof_e<tofCut, tof_e+2.*tau, tof_e)
-    tof_e  = np.where(tof_e>tau+tofCut, tof_e-tau, tof_e)
-
-    data_e = analyse_ev(detPixelID_e, tof_e, yMin, yMax, thetaMin, thetaMax)
-
-    ev.close()
-
-    data_eSum = np.append(data_eSum, data_e, axis=0)
-    sumTime += countingTime
-
-    if verbous:
-        logging.info("   total counts = {} in {:6.1f} s".format(np.shape(data_e)[0], sumTime))
-
-    #================================
-    # plotting data
-    plotType = clas.plot[0]
-    try:
-        arg = clas.plot[1]
-    except IndexError:
-        arg = 'def'
-    plott = PlotSelection()
-    try:
-        plotFunction = getattr(plott, plotType)
-        plotFunction(fileNumber, arg, data_e)
-        plt.close()
-    except Exception as e:
-        logging.error(f"ERROR: '{plotType}' is no known output format!")
-        logging.error(f"       original error: {e}")
-
-#==============================================================================   
-def commandLineArgs():    
-    msg = "events2histogram reads the eventstream from an hdf raw file and \
-           creates various histogrammed outputs or plots."
-    clas = argparse.ArgumentParser(description = msg)
-
-    clas.add_argument("-c", "--chopperSpeed",
-                            type=float,
-                            help ="chopper speed in rpm")
-    clas.add_argument("-d", "--dataPath",  
-                            help ="relative path to directory with .hdf files")
-    clas.add_argument("-f", "--fileIdent",       
-                            default='0',                               
-                            help ="file number or offset (if negative)")
-    clas.add_argument("-l", "--lambdaRange",                            
-                            nargs=2, 
-                            type=float,
-                            help ="wavelength range to be used")
-    clas.add_argument("-M", "--muOffset",        
-                            default=0.,                     
-                            type=float,
-                            help ="mu offset")
-    clas.add_argument("-m", "--mu",                                              
-                            default=99.,
-                            type=float,
-                            help ="value of mu")
-    clas.add_argument("-n", "--nu",                                              
-                            default=99.,
-                            type=float,
-                            help ="value of nu")
-    clas.add_argument("-P", "--chopperPhase",    
-                            default=-5.,                     
-                            type=float,
-                            help ="chopper phase offset")
-    clas.add_argument("-p", "--plot",            
-                            default=['all', 'def'], 
-                            nargs='+',         
-                            help ="select what to plot or write")
-    clas.add_argument("-q", "--qRange",          
-                            default=[0.005, 0.30], 
-                            nargs=2, 
-                            type=float,
-                            help ="q_z range")
-    clas.add_argument("-T", "--TOFOffset",       
-                            default=0.0,                    
-                            type=float,
-                            help ="TOF zero offset")
-    clas.add_argument("-t", "--thetaRange",      
-                            default=[-12., 12.],   
-                            nargs=2, 
-                            type=float,
-                            help ="theta range to be used")
-    clas.add_argument("-Y", "--year",
-                            default = str(datetime.today()).split('-')[0],
-                            help = "year, the measurement was performed")
-    clas.add_argument("-y", "--yRange",          
-                            default=[0, 63],
-                            nargs=2, 
-                            type=int,  
-                            help ="detector y range to be used")
-
-    return clas.parse_args()
-
-#==============================================================================   
-def get_dataPath(clas):
-    if clas.dataPath:
-        dataPath = clas.dataPath + '/'
-        if not os.path.exists(dataPath):
-             sys.exit('# *** the directory "'+dataPath+'" does not exist ***')
-    elif os.path.exists('./raw'):
-        dataPath  = "./raw/"
-    elif os.path.exists('../raw'):
-        dataPath  = "../raw/"
-    else:
-        sys.exit('# *** please provide the path to the .hdf data files (-d <path>, default is "./raw")')
-
-    return dataPath
-
-#==============================================================================   
-def get_directDataPath(clas):
-    #dataPath = clas.dataPath + '/'
-    year = str(datetime.today()).split('-')[0]
-    year_date = str(datetime.today()).split(' ')[0].replace("-", "/", 1)
-    pNr  = str(subprocess.getoutput('/usr/bin/grep "proposal\t" /home/amor/nicosdata/amor/cache/nicos-exp/'+year_date)[-1]).split('\'')[1]
-    dataPath = '/home/amor/nicosdata/amor/data/'+year+'/'+pNr+'/'
-    if not os.path.exists(dataPath):
-         sys.exit('# *** the directory "'+dataPath+'" does not exist ***')
-
-    return dataPath
-
-#==============================================================================
-def main():
-    global verbous, output, dataPath
- 
-    clas = commandLineArgs()
-
-    dataPath = get_dataPath(clas)
-    logging.basicConfig(level=logging.INFO, format='# %(message)s')
-    verbous = True
-    output = 'life_plot.png'
-    process(dataPath, clas.fileIdent, clas)
-
-#==============================================================================
-#==============================================================================
-if __name__ == "__main__":
-    main()     
-
-

neos.py

@@ -1 +0,0 @@
-eos.py

nicos_config.md

@@ -0,0 +1,43 @@
+EOS-Service
+===========
+
+EOS can be used as histogram service to send images to the Nicos instrument control software. 
+For that you need to run it on the amor instrument computer:
+
+```bash
+amor-nicos {-vv}
+```
+
+The instrument config in Nicos needs to configure a Kafka JustBinItImage instance 
+for each histogram that should be used:
+
+```python
+hist_yz = device('nicos_sinq.devices.just_bin_it.JustBinItImage',
+        description = 'Detector pixel histogram over all times',
+        hist_topic = 'AMOR_histograms_YZ',
+        data_topic = 'AMOR_detector',
+        command_topic = 'AMOR_histCommands',
+        brokers = ['linkafka01.psi.ch:9092'],
+        unit = 'evts',
+        hist_type = '2-D SANSLLB',
+        det_width = 446,
+        det_height = 64,
+        ),
+hist_tofz = device('nicos_sinq.devices.just_bin_it.JustBinItImage',
+        description = 'Detector time of flight vs. z-pixel histogram over all y-values',
+        hist_topic = 'AMOR_histograms_TofZ',
+        data_topic = 'AMOR_detector',
+        command_topic = 'AMOR_histCommands',
+        brokers = ['linkafka01.psi.ch:9092'],
+        unit = 'evts',
+        hist_type = '2-D SANSLLB',
+        det_width = 118,
+        det_height = 446,
+        ),
+```
+
+These images have then to be set in the detector configuration as _images_ items:
+
+```                      
+images=['hist_yz', 'hist_tofz'],
+```

requirements.txt

@@ -2,3 +2,7 @@ numpy
 h5py
 orsopy
 numba
+matplotlib
+tabulate
+backports.strenum; python_version<"3.11"
+backports.zoneinfo; python_version<"3.9"

setup.cfg

@@ -3,10 +3,11 @@ universal = 1
 
 [metadata]
 name = amor_eos
-version = attr: libeos.__version__
+version = attr: eos.__version__
 author = Jochen Stahn - Paul Scherrer Institut
 author_email = jochen.stahn@psi.ch
 description = EOS reflectometry reduction for AMOR instrument
+long_description = Reduces data obtained by focusing time of flight neutron reflectivity to full reflectivity curve.
 license = MIT
 classifiers =
     Programming Language :: Python :: 3
@@ -18,14 +19,21 @@ classifiers =
 [options]
 python_requires = >=3.8
 packages =
-    libeos
-scripts =
-    eos.py
+    eos
 install_requires =
     numpy
     h5py
     orsopy
     numba
+    backports.strenum; python_version<"3.11"
+    backports.zoneinfo; python_version<"3.9"
 
 [project.urls]
 Homepage = "https://github.com/jochenstahn/amor"
+
+[options.entry_points]
+console_scripts =
+    eos = eos.__main__:main
+    eosls = eos.ls:main
+    events2histogram = eos.e2h:main
+    amor-nicos = eos.nicos:main

tests/analyze_hdf.py

@@ -0,0 +1,31 @@
+"""
+Small helper to find information about hdf datafiles for debugging
+"""
+
+import h5py
+
+def rec_tree(group, min_size=128):
+    if hasattr(group, 'keys'):
+        output = {}
+        total_size = 0
+        for key in group.keys():
+            subkeys, size = rec_tree(group[key], min_size)
+            total_size += size
+            if size>min_size:
+                if subkeys:
+                    output[key] = subkeys
+                else:
+                    output[key] = size
+        return output, size
+    elif hasattr(group, 'size'):
+        return None, group.size
+    else:
+        return None, 0
+
+if __name__ == "__main__":
+    import sys
+    for fi in sys.argv[1:]:
+        print(fi)
+        print(rec_tree(sys.argv[1]))
+
+    

tests/mock_data.py

@@ -0,0 +1,64 @@
+"""
+Generate a mock dataset in memory for running unit tests.
+"""
+
+import h5py
+import numpy as np
+
+MOCK_METADATA = {
+    'title': 'Testdata',
+    'proposal_id': 'none',
+    'user/name': 'test user',
+    'user/email': 'test@user.de',
+    'sample/name': 'test sample',
+    'sample/model': 'air | Fe 12 | Si',
+    'Amor/source/name': 'SINQ',
+    'start_time': '2025-01-01 00:00:01',
+    }
+MOCK_META_TYPED = {
+    'Amor/chopper/pair_separation': (1000.0, np.float32),
+    'Amor/detector/transformation/distance': (4000.0, np.float64),
+    'Amor/instrument_control_parameters/kappa': (1000.0, np.float64),
+    'Amor/instrument_control_parameters/kappa_offset': (1000.0, np.float64),
+    'Amor/instrument_control_parameters/div': (1.6, np.float64),
+    'Amor/chopper/ch1_trigger_phase': (-9.1, np.float64),
+    'Amor/chopper/ch2_trigger_phase': (6.75, np.float64),
+    'Amor/chopper/ch2_trigger/event_time_zero': ([0.0]*10, np.uint64),
+    'Amor/chopper/ch2_trigger/event_time_offset': ([0.0]*10, np.uint32),
+    'Amor/chopper/rotation_speed': (500.0, np.float64),
+    'Amor/chopper/phase': (0.0, np.float64),
+    'Amor/polarization/configuration/value': (0.0, np.float64),
+    }
+
+def mock_data(mu=1.0, nu=2.0):
+    hdf = h5py.File.in_memory() # requires h5py >=3.13
+    ds = hdf.create_group('entry1')
+    for key, value in MOCK_METADATA.items():
+        ds.create_dataset(key, data=np.array([value.encode('utf-8')]))
+    for key, (value, dtype) in MOCK_META_TYPED.items():
+        if type(value) is list:
+            ds.create_dataset(key, data=np.array(value), dtype=dtype)
+        else:
+            ds.create_dataset(key, data=np.array([value]), dtype=dtype)
+
+    ds.create_dataset('Amor/instrument_control_parameters/mu', np.array([mu]), dtype=np.float64)
+    ds.create_dataset('Amor/instrument_control_parameters/nu', np.array([nu]), dtype=np.float64)
+
+    return hdf
+
+def compare_with_real_data(fname):
+    hdf = h5py.File(fname, 'r')
+    ds = hdf['entry1']
+    for key, value in MOCK_METADATA.items():
+        try:
+            ds[key][0].decode('utf-8')
+        except KeyError:
+            print(f'/entry1/{key} does not exist in file')
+    for key, (value, dtype) in MOCK_META_TYPED.items():
+        try:
+            item = ds[key]
+        except KeyError:
+            print(f'/entry1/{key} does not exist in file')
+        else:
+            if item.dtype != dtype:
+                print(f'/entry1/{key} does not match {dtype}, dataset is {item.dtype}')

tests/test_event_handling.py

@@ -0,0 +1,562 @@
+import os
+import numpy as np
+import logging
+
+from unittest import TestCase
+from datetime import datetime
+from copy import deepcopy
+
+from orsopy.fileio import Person, Experiment, Sample, InstrumentSettings, Value, ValueRange, Polarization
+
+from eos import const
+from eos.header import Header
+from eos.event_data_types import EVENT_BITMASKS, AmorGeometry, AmorTiming, AmorEventStream, \
+    EventDataAction, EventDatasetProtocol, PACKET_TYPE, PC_TYPE, PULSE_TYPE, EVENT_TYPE, append_fields
+from eos.event_handling import ApplyPhaseOffset, ApplyParameterOverwrites, CorrectChopperPhase, CorrectSeriesTime, \
+    AssociatePulseWithMonitor, FilterMonitorThreshold, FilterStrangeTimes, TofTimeCorrection, ApplyMask
+from eos.event_analysis import ExtractWalltime, MergeFrames, AnalyzePixelIDs, CalculateWavelength, CalculateQ, \
+    FilterQzRange, FilterByLog
+from eos.options import MonitorType, IncidentAngle, ExperimentConfig
+
+
+class MockEventData:
+    """
+    Simulated dataset to be used with event handling unit tests
+    """
+    geometry: AmorGeometry
+    timing: AmorTiming
+    data: AmorEventStream
+
+    def __init__(self):
+        self.geometry = AmorGeometry(mu=2.0, nu=1.0, kap=0.5, kad=0.0, div=1.5,
+                                     chopperSeparation=1000.0, detectorDistance=4000., chopperDetectorDistance=18842.)
+        self.timing = AmorTiming(
+                ch1TriggerPhase=-9.1, ch2TriggerPhase=6.75,
+                chopperPhase=0.17, chopperSpeed=500., tau=0.06
+                )
+        self.create_data()
+
+    def create_data(self):
+        # list of events, here with random time of fligh and pixel location
+        events = np.recarray((10000, ), dtype=EVENT_TYPE)
+        events.tof = np.random.uniform(low=0., high=0.12, size=events.shape)
+        events.pixelID = np.random.randint(0, 28671, size=events.shape)
+        events.mask = 0
+
+        # list of data packates containing previous events
+        packets = np.recarray((1000,), dtype=PACKET_TYPE)
+        packets.start_index = np.linspace(0, events.shape[0]-1, packets.shape[0], dtype=np.uint32)
+        packets.time = np.linspace(1700000000000000000, 1700000000000000000+3_600_000_000,
+                                   packets.shape[0], dtype=np.int64)
+
+        # chopper pulses within the measurement time
+        pulses = np.recarray((packets.shape[0],), dtype=PULSE_TYPE)
+        pulses.monitor = 1.0
+        pulses.time = packets.time
+
+        # proton current information with independent timing
+        proton_current = np.recarray((50,), dtype=PC_TYPE)
+        proton_current.current = 1500.0
+        proton_current[np.random.randint(0, proton_current.shape[0]-1, 10)] = 0. # random time with no current
+        proton_current.time = np.linspace(1700000000000000300, 1700000000000000000+3_600_000_000,
+                                   proton_current.shape[0], dtype=np.int64)
+
+        self.data = AmorEventStream(events, packets, pulses, proton_current)
+        self.orig_data = deepcopy(self.data)
+
+
+    def append(self, other):
+        raise NotImplementedError("Just for testing, no append")
+
+    def update_header(self, header:Header):
+        # update a header with the information read from file
+        header.owner = Person(name="test user", affiliation='PSI')
+        header.experiment = Experiment(title='test experiment', instrument='amor',
+                                       start_date=datetime.now(), probe="neutron")
+        header.sample = Sample(name='test sample')
+        header.measurement_instrument_settings = InstrumentSettings(incident_angle=Value(1.5, 'deg'),
+            wavelength = ValueRange(3.0, 12.5, 'angstrom'),
+            polarization = Polarization.unpolarized)
+
+    def update_info_from_logs(self):
+        RELEVANT_ITEMS = ['sample_temperature', 'sample_magnetic_field', 'polarization_config_label']
+        for key, log in self.data.device_logs.items():
+            if key not in RELEVANT_ITEMS:
+                continue
+            if log.value.dtype in [np.int8, np.int16, np.int32, np.int64]:
+                # for integer items (flags) report the most common one
+                value = np.bincount(log.value).argmax()
+                if logging.getLogger().getEffectiveLevel() <= logging.DEBUG \
+                        and np.unique(log.value).shape[0]>1:
+                    logging.debug(f'        filtered values for {key} not unique, '
+                                  f'has {np.unique(log.value).shape[0]} values')
+            else:
+                value = log.value.mean()
+            if key == 'polarization_config_label':
+                self.instrument_settings.polarization = Polarization(const.polarizationConfigs[value])
+            elif key == 'sample_temperature':
+                self.sample.sample_parameters['temperature'].magnitue = value
+            elif key == 'sample_magnetic_field':
+                self.sample.sample_parameters['magnetic_field'].magnitue = value
+
+
+class TestActionClass(TestCase):
+    @classmethod
+    def setUpClass(cls):
+        """
+        Create test classes to be used
+        """
+        class T1(EventDataAction):
+            def perform_action(self, event: EventDatasetProtocol):
+                event.data.events.mask += 1
+        class T2(EventDataAction):
+            def perform_action(self, event: EventDatasetProtocol):
+                event.data.events.mask += 2
+        class T4(EventDataAction):
+            def perform_action(self, event: EventDatasetProtocol):
+                event.data.events.mask += 4
+        cls.T1=T1; cls.T2=T2; cls.T4=T4
+
+        class H1(EventDataAction):
+            def perform_action(self, event: EventDatasetProtocol):
+                ...
+            def update_header(self, header:Header) ->None:
+                header.sample.name = 'h1'
+        class H2(EventDataAction):
+            def perform_action(self, event: EventDatasetProtocol):
+                ...
+            def update_header(self, header: Header) -> None:
+                header.sample.name = 'h2'
+        class HN(EventDataAction):
+            def __init__(self, n):
+                self._n = n
+            def perform_action(self, event: EventDatasetProtocol):
+                ...
+            def update_header(self, header: Header) -> None:
+                header.sample.name = self._n
+        cls.H1=H1; cls.H2=H2; cls.HN = HN
+
+    def setUp(self):
+        self.d = MockEventData()
+        self.header = Header()
+        self.d.update_header(self.header)
+
+    def test_individual(self):
+        t1 = self.T1()
+        t2 = self.T2()
+        t4 = self.T4()
+
+        np.testing.assert_array_equal(self.d.data.events.mask, 0)
+        t1.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 1)
+        t2.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 3)
+        t4.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 7)
+
+    def test_header(self):
+        h1 = self.H1()
+        h2 = self.H2()
+        h3 = self.HN('h3')
+        h4 = self.HN('h4')
+
+        h1.update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h1')
+        h2.update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h2')
+        h3.update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h3')
+        h4.update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h4')
+
+    def test_combination(self):
+        t1 = self.T1()
+        t2 = self.T2()
+        t4 = self.T4()
+        t12 = t1 | t2
+        t24 = t2 | t4
+        t1224 = t1 | t2 | t2 | t4
+        t1224b = t12 | t24
+
+        np.testing.assert_array_equal(self.d.data.events.mask, 0)
+        t12.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 3)
+        t24.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 9)
+
+        t1224.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 18)
+        t1224b.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, 27)
+
+
+    def test_combine_header(self):
+        h1 = self.H1()
+        h2 = self.H2()
+        h3 = self.HN('h3')
+        h4 = self.HN('h4')
+
+        (h1|h2).update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h2')
+        (h2|h1).update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h1')
+        (h3|h4).update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h4')
+        (h4|h3).update_header(self.header)
+        self.assertEqual(self.header.sample.name, 'h3')
+
+    def test_abstract_misssing(self):
+        with self.assertRaises(TypeError):
+            class E(EventDataAction):
+                ...
+            _ = E()
+
+    def test_hash(self):
+        """
+        Check that hashes of different actions are different but
+        instances of same action have same hash
+        """
+        t1 = self.T1()
+        t1b = self.T1()
+        t2 = self.T2()
+        t4 = self.T4()
+        h3 = self.HN('h3')
+        h3b = self.HN('h3')
+        h4 = self.HN('h4')
+
+        self.assertNotEqual(t1.action_hash(), t2.action_hash())
+        self.assertNotEqual(t2.action_hash(), t4.action_hash())
+        self.assertNotEqual(t1.action_hash(), t4.action_hash())
+        self.assertNotEqual(h3.action_hash(), h4.action_hash())
+        self.assertEqual(t1.action_hash(), t1b.action_hash())
+        self.assertEqual(h3.action_hash(), h3b.action_hash())
+
+
+class TestSimpleActions(TestCase):
+    def setUp(self):
+        self.d = MockEventData()
+        self.header = Header()
+        self.d.update_header(self.header)
+
+    def test_chopper_phase(self):
+        cp = CorrectChopperPhase()
+        cp.perform_action(self.d)
+        np.testing.assert_array_equal(
+                self.d.data.events.tof,
+                self.d.orig_data.events.tof+
+                self.d.timing.tau*(self.d.timing.ch1TriggerPhase-self.d.timing.chopperPhase/2)/180
+                )
+
+    def _extract_walltime(self):
+        # Extract wall time for events and orig copy
+        wt = ExtractWalltime()
+        d = self.d.data
+        self.d.data = self.d.orig_data
+        wt.perform_action(self.d)
+        self.d.data = d
+        wt.perform_action(self.d)
+
+    def test_extract_walltime(self):
+        self._extract_walltime()
+        # wallTime should be always a time present in the packet times
+        np.testing.assert_array_equal(np.isin(self.d.data.events.wallTime, self.d.data.packets.time), True)
+        # make sure extraction works on both original and copy
+        np.testing.assert_array_equal(self.d.data.events.wallTime, self.d.orig_data.events.wallTime)
+
+    def test_series_time(self):
+        corr = 100
+        ct = CorrectSeriesTime(corr)
+
+        with self.assertRaises(ValueError):
+            ct.perform_action(self.d)
+
+        self._extract_walltime()
+
+
+        ct.perform_action(self.d)
+        np.testing.assert_array_equal(
+                self.d.data.pulses.time,
+                self.d.orig_data.pulses.time-corr
+                )
+        np.testing.assert_array_equal(
+                self.d.data.events.wallTime,
+                self.d.orig_data.events.wallTime-corr
+                )
+        np.testing.assert_array_equal(
+                self.d.data.proton_current.time,
+                self.d.orig_data.proton_current.time-corr
+                )
+
+    def test_associate_monitor(self):
+        amPC = AssociatePulseWithMonitor(MonitorType.proton_charge)
+        amT = AssociatePulseWithMonitor(MonitorType.time)
+        amN = AssociatePulseWithMonitor(MonitorType.neutron_monitor)
+
+        self.d.data.pulses.monitor = 13
+        amN.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.pulses.monitor, 1)
+
+        self.d.data.pulses.monitor = 13
+        amT.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.pulses.monitor, np.float32(2*self.d.timing.tau))
+
+        self.d.data.pulses.monitor = 13
+        amPC.perform_action(self.d)
+        pcm = self.d.data.proton_current.current *2*self.d.timing.tau*1e-3
+        np.testing.assert_array_equal(np.isin(self.d.data.pulses.monitor, pcm), True)
+
+    def test_filter_monitor_threashold(self):
+        amPC = AssociatePulseWithMonitor(MonitorType.proton_charge)
+        fmt = amPC | FilterMonitorThreshold(1000.)
+        fma = amPC | FilterMonitorThreshold(2000.)
+        fm0 = amPC | FilterMonitorThreshold(-1.0)
+
+        with self.assertRaises(ValueError):
+            fmt.perform_action(self.d)
+
+        self._extract_walltime()
+        fm0.perform_action(self.d)
+        self.assertEqual(self.d.data.events.mask.sum(), 0)
+        fmt.perform_action(self.d)
+        # calculate, which events should have 0 monitor
+        zero_times = self.d.data.pulses.time[self.d.data.pulses.monitor==0]
+        zero_sum = np.isin(self.d.data.events.wallTime, zero_times).sum()
+        self.assertEqual(self.d.data.events.mask.sum(), zero_sum*EVENT_BITMASKS['MonitorThreshold'])
+        # filter all events
+        self.d.data.events.mask = 0
+        fma.perform_action(self.d)
+        self.assertEqual(self.d.data.events.mask.sum(), self.d.data.events.shape[0]*EVENT_BITMASKS['MonitorThreshold'])
+
+    def test_filter_strage_times(self):
+        st = FilterStrangeTimes()
+
+        st.perform_action(self.d)
+        self.assertEqual(self.d.data.events.mask.sum(), 0)
+
+        # half events should be strange times (outside of ToF frame)
+        self.d.data.events.tof += self.d.timing.tau
+        st.perform_action(self.d)
+        self.assertEqual(self.d.data.events.mask.sum(),
+                         (self.d.data.events.tof>2*self.d.timing.tau).sum()*EVENT_BITMASKS['StrangeTimes'])
+
+    def test_apply_phase_offset(self):
+        action = ApplyPhaseOffset(12.5)
+        action.perform_action(self.d)
+        self.assertEqual(self.d.timing.ch1TriggerPhase, 12.5)
+
+    def test_apply_parameter_overwrites(self):
+        action = ApplyParameterOverwrites(ExperimentConfig(muOffset=0.25, mu=3.5, nu=4.5))
+        action.perform_action(self.d)
+        self.assertEqual(self.d.geometry.mu, 3.5)
+        self.assertEqual(self.d.geometry.nu, 4.5)
+
+        action = ApplyParameterOverwrites(ExperimentConfig(muOffset=0.25))
+        action.perform_action(self.d)
+        self.assertEqual(self.d.geometry.mu, 3.75)
+
+        action = ApplyParameterOverwrites(ExperimentConfig(sampleModel='air | Si | Fe'))
+        action.update_header(self.header)
+        self.assertIsNotNone(self.header.sample.model)
+
+    def test_apply_sample_model_file(self):
+        if os.path.isfile('test.yaml'):
+            os.remove('test.yaml')
+        action = ApplyParameterOverwrites(ExperimentConfig(sampleModel='test.yaml'))
+        action.update_header(self.header)
+        self.assertIsNone(self.header.sample.model)
+
+        with open('test.yaml', 'w') as fh:
+            fh.write("""stack: air | Si | Fe""")
+
+        try:
+            action = ApplyParameterOverwrites(ExperimentConfig(sampleModel='test.yaml'))
+            action.update_header(self.header)
+            self.assertEqual(self.header.sample.model.stack, 'air | Si | Fe')
+        finally:
+            os.remove('test.yaml')
+
+    def test_tof_time_correction(self):
+        action = TofTimeCorrection()
+        with self.assertRaises(ValueError):
+            action.perform_action(self.d)
+
+        new_events = append_fields(self.d.data.events, [('delta', np.float64)])
+        new_events.delta = 10.0
+        self.d.data.events = new_events
+        tof_before = self.d.data.events.tof.copy()
+        action.perform_action(self.d)
+        np.testing.assert_allclose(
+            self.d.data.events.tof,
+            tof_before - (10.0 / 180.0) * self.d.timing.tau
+        )
+
+        self.d.create_data()
+        new_events = append_fields(self.d.data.events, [('delta', np.float64)])
+        new_events.delta = 10.0
+        self.d.data.events = new_events
+        tof_before = self.d.data.events.tof.copy()
+        action = TofTimeCorrection(correct_chopper_opening=False)
+        action.perform_action(self.d)
+        np.testing.assert_allclose(
+            self.d.data.events.tof,
+            tof_before - (self.d.geometry.kad / 180.0) * self.d.timing.tau
+        )
+
+    def test_apply_mask(self):
+        self.d.data.events = self.d.data.events[:6].copy()
+        self.d.data.events.mask[:] = [0, 1, 2, 3, 4, 5]
+
+        action = ApplyMask()
+        action.perform_action(self.d)
+        self.assertEqual(self.d.data.events.shape[0], 1)
+        self.assertEqual(self.d.data.events.mask[0], 0)
+
+        self.d.create_data()
+        self.d.data.events = self.d.data.events[:6].copy()
+        self.d.data.events.mask[:] = [0, 1, 2, 3, 4, 5]
+        action = ApplyMask(bitmask_filter=EVENT_BITMASKS['MonitorThreshold'])
+        action.perform_action(self.d)
+        np.testing.assert_array_equal(self.d.data.events.mask, np.array([0, EVENT_BITMASKS['MonitorThreshold']],
+                                                                        dtype=np.int32))
+
+    def test_merge_frames(self):
+        action = MergeFrames(lamdaCut=0.0)
+        action.perform_action(self.d)
+        self.assertEqual(self.d.data.events.tof.shape, self.d.orig_data.events.tof.shape)
+        np.testing.assert_array_compare(lambda x,y: x<=y, self.d.data.events.tof, self.d.orig_data.events.tof)
+        self.assertTrue((-self.d.timing.tau<=self.d.data.events.tof).all())
+        np.testing.assert_array_less(self.d.data.events.tof, self.d.timing.tau)
+
+        action = MergeFrames(lamdaCut=2.0)
+        self.d.data.events.tof = self.d.orig_data.events.tof[:]
+        action.perform_action(self.d)
+        tofCut = 2.0*self.d.geometry.chopperDetectorDistance/const.hdm*1e-13
+        self.assertTrue((tofCut-self.d.timing.tau<=self.d.data.events.tof).all())
+        self.assertTrue((self.d.data.events.tof<=tofCut+self.d.timing.tau).all())
+
+    def test_analyze_pixel_ids(self):
+        action = AnalyzePixelIDs((1000, 1001))
+        action.perform_action(self.d)
+        self.assertIn('detZ', self.d.data.events.dtype.names)
+        self.assertIn('detXdist', self.d.data.events.dtype.names)
+        self.assertIn('delta', self.d.data.events.dtype.names)
+        self.assertEqual(
+            np.bitwise_and(self.d.data.events.mask, EVENT_BITMASKS['yRange']).astype(bool).sum(),
+            self.d.data.events.shape[0]
+        )
+        # TODO: maybe add a test actually checking correct detector-id resolution
+
+    def test_calculate_wavelength(self):
+        action = CalculateWavelength((3.0, 5.0))
+        with self.assertRaises(ValueError):
+            action.perform_action(self.d)
+
+        new_events = append_fields(self.d.data.events, [('detXdist', np.float64)])
+        new_events.detXdist = 0.0
+        self.d.data.events = new_events
+        action.perform_action(self.d)
+        self.assertIn('lamda', self.d.data.events.dtype.names)
+        flt = self.d.data.events.mask!=EVENT_BITMASKS['LamdaRange']
+        # check all wavelength in range not filtered
+        np.testing.assert_array_less(self.d.data.events.lamda[flt], 5.0)
+        np.testing.assert_array_less(3.0, self.d.data.events.lamda[flt])
+        # check all wavelength out of range filtered
+        flt = self.d.data.events.mask==EVENT_BITMASKS['LamdaRange']
+        self.assertTrue(((self.d.data.events.lamda[flt]<3.0)|(self.d.data.events.lamda[flt]>5.0)).all())
+
+    def test_calculate_q(self):
+        action = CalculateQ(IncidentAngle.alphaF)
+        with self.assertRaises(ValueError):
+            action.perform_action(self.d)
+
+        # TODO: add checks for actual resulting values
+
+        new_events = append_fields(self.d.data.events, [('lamda', np.float64), ('delta', np.float64)])
+        new_events.lamda = 5.0
+        new_events.delta = 0.0
+        self.d.data.events = new_events
+        action.perform_action(self.d)
+        self.assertIn('qz', self.d.data.events.dtype.names)
+        self.assertNotIn('qx', self.d.data.events.dtype.names)
+        action.update_header(self.header)
+        self.assertEqual(self.header.measurement_scheme, 'angle- and energy-dispersive')
+
+        self.d.create_data()
+        new_events = append_fields(self.d.data.events, [('lamda', np.float64), ('delta', np.float64)])
+        new_events.lamda = 5.0
+        new_events.delta = 0.0
+        self.d.data.events = new_events
+        action = CalculateQ(IncidentAngle.mu)
+        action.perform_action(self.d)
+        self.assertIn('qz', self.d.data.events.dtype.names)
+        self.assertIn('qx', self.d.data.events.dtype.names)
+        action.update_header(self.header)
+        self.assertEqual(self.header.measurement_scheme, 'energy-dispersive')
+
+        self.d.create_data()
+        new_events = append_fields(self.d.data.events, [('lamda', np.float64), ('delta', np.float64)])
+        new_events.lamda = 5.0
+        new_events.delta = 0.0
+        self.d.data.events = new_events
+        action = CalculateQ(IncidentAngle.nu)
+        action.perform_action(self.d)
+        self.assertIn('qz', self.d.data.events.dtype.names)
+        self.assertNotIn('qx', self.d.data.events.dtype.names)
+        action.update_header(self.header)
+        self.assertEqual(self.header.measurement_scheme, 'energy-dispersive')
+
+    def test_filter_qz_range(self):
+        action = FilterQzRange((0.1, 0.2))
+        with self.assertRaises(ValueError):
+            action.perform_action(self.d)
+
+        self.d.data.events = self.d.data.events[:5].copy()
+        new_events = append_fields(self.d.data.events, [('qz', np.float64)])
+        new_events.qz = np.array([0.05, 0.1, 0.15, 0.2, 0.25])
+        self.d.data.events = new_events
+        action.perform_action(self.d)
+        np.testing.assert_array_equal(
+            self.d.data.events.mask,
+            np.array([1, 0, 0, 0, 1], dtype=np.int32) * EVENT_BITMASKS['qRange']
+        )
+
+    def test_filter_by_log(self):
+        action = FilterByLog("test_log==0") | ApplyMask()
+        class LogWarnError(Exception):
+            ...
+        def warn_raise(*args, **kwargs):
+            raise LogWarnError()
+        _orig_warn = logging.warning
+        try:
+            logging.warning = warn_raise
+            with self.assertRaises(LogWarnError):
+                action.perform_action(self.d)
+        finally:
+            logging.warning = _orig_warn
+
+        self._extract_walltime()
+
+        test_log = np.recarray(shape=(2,), dtype=np.dtype([('value', np.int32),
+                                                           ('time', np.int64)]))
+        test_log.time = [-5, self.d.data.pulses.time[100]+123]
+        test_log.value = [0, 1]
+        self.d.data.device_logs['test_log'] = test_log
+        action.perform_action(self.d)
+        self.assertEqual(self.d.data.pulses.shape[0], 101)
+
+    def test_filter_by_log_switchpulse(self):
+        action = FilterByLog("!test_log==0") | ApplyMask()
+        self._extract_walltime()
+
+        test_log = np.recarray(shape=(2,), dtype=np.dtype([('value', np.int32),
+                                                           ('time', np.int64)]))
+        test_log.time = [-5, self.d.data.pulses.time[100]+123]
+        test_log.value = [0, 1]
+        self.d.data.device_logs['test_log'] = test_log
+        self.d.data.device_logs['check_log'] = test_log.copy()
+        action.perform_action(self.d)
+        self.assertEqual(self.d.data.pulses.shape[0], 100)
+        np.testing.assert_array_equal(
+                self.d.data.device_logs['test_log'],
+                self.d.data.device_logs['check_log'],
+                )

tests/test_full_analysis.py

@@ -1,16 +1,29 @@
 import os
 import cProfile
+import numpy as np
 from unittest import TestCase
-from libeos import options, reduction, logconfig
+from dataclasses import fields, MISSING
+from eos import options, reduction_reflectivity, logconfig
+from orsopy import fileio
 
 logconfig.setup_logging()
-logconfig.update_loglevel(True, False)
+logconfig.update_loglevel(1)
 
-# TODO: add test for new features like proton charge normalization
+# TODO: add unit tests for individual parts of reduction
 
 class FullAmorTest(TestCase):
     @classmethod
     def setUpClass(cls):
+        # generate map for option defaults
+        cls._field_defaults = {}
+        for opt in [options.ExperimentConfig, options.ReflectivityReductionConfig, options.ReflectivityOutputConfig]:
+            defaults = {}
+            for field in fields(opt):
+                if field.default not in [None, MISSING]:
+                    defaults[field.name] = field.default
+                elif field.default_factory not in [None, MISSING]:
+                    defaults[field.name] = field.default_factory()
+            cls._field_defaults[opt.__name__] = defaults
         cls.pr = cProfile.Profile()
 
     @classmethod
@@ -20,92 +33,132 @@ class FullAmorTest(TestCase):
     def setUp(self):
         self.pr.enable()
         self.reader_config = options.ReaderConfig(
-                year=2023,
-                rawPath=(os.path.join('..', "test_data"),),
+                year=2025,
+                rawPath=["test_data"],
                 )
 
     def tearDown(self):
         self.pr.disable()
-        for fi in ['test.Rqz.ort', '614.norm']:
-            try:
-                os.unlink(os.path.join(self.reader_config.rawPath[0], fi))
-            except FileNotFoundError:
-                pass
-
+        for fi in ['test_results/test.Rqz.ort', 'test_results/5952.norm']:
+           try:
+               os.unlink(fi)
+           except FileNotFoundError:
+               pass
 
     def test_time_slicing(self):
         experiment_config = options.ExperimentConfig(
                 chopperPhase=-13.5,
                 chopperPhaseOffset=-5,
-                monitorType=options.Defaults.monitorType,
-                lowCurrentThreshold=options.Defaults.lowCurrentThreshold,
-                yRange=(11., 41.),
-                lambdaRange=(2., 15.),
-                qzRange=(0.005, 0.30),
-                incidentAngle=options.Defaults.incidentAngle,
+                yRange=(18, 48),
+                lambdaRange=(3., 11.5),
                 mu=0,
                 nu=0,
                 muOffset=0.0,
                 sampleModel='air | 10 H2O | D2O'
                 )
-        reduction_config = options.ReductionConfig(
-                normalisationMethod=options.Defaults.normalisationMethod,
+        reduction_config = options.ReflectivityReductionConfig(
                 qResolution=0.01,
-                qzRange=options.Defaults.qzRange,
-                thetaRange=(-12., 12.),
-                thetaRangeR=(-12., 12.),
-                fileIdentifier=["610"],
+                qzRange=(0.01, 0.15),
+                thetaRange=(-0.75, 0.75),
+                fileIdentifier=["6003-6005"],
                 scale=[1],
                 normalisationFileIdentifier=[],
                 timeSlize=[300.0]
                 )
-        output_config = options.OutputConfig(
-                outputFormats=["Rqz.ort"],
+        output_config = options.ReflectivityOutputConfig(
+                outputFormats=[options.OutputFomatOption.Rqz_ort],
                 outputName='test',
-                outputPath=os.path.join('..', 'test_results'),
+                outputPath='test_results',
                 )
-        config=options.EOSConfig(self.reader_config, experiment_config, reduction_config, output_config)
+        config=options.ReflectivityConfig(self.reader_config, experiment_config, reduction_config, output_config)
         # run three times to get similar timing to noslicing runs
-        reducer = reduction.AmorReduction(config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
         reducer.reduce()
-        reducer = reduction.AmorReduction(config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
         reducer.reduce()
-        reducer = reduction.AmorReduction(config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
         reducer.reduce()
 
     def test_noslicing(self):
         experiment_config = options.ExperimentConfig(
                 chopperPhase=-13.5,
                 chopperPhaseOffset=-5,
-                monitorType=options.Defaults.monitorType,
-                lowCurrentThreshold=options.Defaults.lowCurrentThreshold,
-                yRange=(11., 41.),
-                lambdaRange=(2., 15.),
-                qzRange=(0.005, 0.30),
-                incidentAngle=options.Defaults.incidentAngle,
+                yRange=(18, 48),
+                lambdaRange=(3., 11.5),
                 mu=0,
                 nu=0,
-                muOffset=0.0
+                muOffset=0.0,
                 )
-        reduction_config = options.ReductionConfig(
+        reduction_config = options.ReflectivityReductionConfig(
                 qResolution=0.01,
-                qzRange=options.Defaults.qzRange,
-                normalisationMethod=options.Defaults.normalisationMethod,
-                thetaRange=(-12., 12.),
-                thetaRangeR=(-12., 12.),
-                fileIdentifier=["610", "611", "608,612-613", "609"],
+                thetaRange=(-0.75, 0.75),
+                fileIdentifier=["6003", "6004", "6005"],
                 scale=[1],
-                normalisationFileIdentifier=["608"],
-                autoscale=(True, True)
+                normalisationFileIdentifier=["5952"],
+                autoscale=(0.0, 0.05),
                 )
-        output_config = options.OutputConfig(
-                outputFormats=["Rqz.ort"],
+        output_config = options.ReflectivityOutputConfig(
+                outputFormats=[options.OutputFomatOption.Rqz_ort],
                 outputName='test',
-                outputPath=os.path.join('..', 'test_results'),
+                outputPath='test_results',
                 )
-        config=options.EOSConfig(self.reader_config, experiment_config, reduction_config, output_config)
-        reducer = reduction.AmorReduction(config)
+        config=options.ReflectivityConfig(self.reader_config, experiment_config, reduction_config, output_config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
         reducer.reduce()
         # run second time to reuse norm file
-        reducer = reduction.AmorReduction(config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
         reducer.reduce()
+
+    def test_eventfilter(self):
+        self.reader_config.year = 2026
+        experiment_config = options.ExperimentConfig()
+        reduction_config = options.ReflectivityReductionConfig(fileIdentifier=["826"],
+                                                               logfilter=['polarization_config_label==2'])
+        output_config = options.ReflectivityOutputConfig(
+                outputFormats=[options.OutputFomatOption.Rqz_ort],
+                outputName='test',
+                outputPath='test_results',
+                )
+        config=options.ReflectivityConfig(self.reader_config, experiment_config, reduction_config, output_config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
+        reducer.reduce()
+        espin_up = reducer.dataset.data.events.shape[0]
+
+        reduction_config.logfilter = ['polarization_config_label==3']
+        output_config.append = True
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
+        reducer.reduce()
+        espin_down = reducer.dataset.data.events.shape[0]
+        # measurement should have about 2x as many counts in spin_down
+        self.assertAlmostEqual(espin_down/espin_up, 2., 2)
+
+        # perform the same filter but remove pulses during which the switch occured
+        reduction_config.logfilter = ['!polarization_config_label==3']
+        output_config.append = True
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
+        reducer.reduce()
+        espin_down2 = reducer.dataset.data.events.shape[0]
+        # measurement should have about 2x as many counts in spin_down
+        self.assertLess(espin_down2, espin_down)
+
+    def test_polsplitting(self):
+        self.reader_config.year = 2026
+        experiment_config = options.ExperimentConfig()
+        reduction_config = options.ReflectivityReductionConfig(fileIdentifier=["826"])
+        output_config = options.ReflectivityOutputConfig(
+                outputFormats=[options.OutputFomatOption.Rqz_ort],
+                outputName='test',
+                outputPath='test_results',
+                )
+        config=options.ReflectivityConfig(self.reader_config, experiment_config, reduction_config, output_config)
+        reducer = reduction_reflectivity.ReflectivityReduction(config)
+        reducer.reduce()
+
+        results = fileio.load_orso(os.path.join(output_config.outputPath, output_config.outputName+'.Rqz.ort'))
+        self.assertEqual(len(results), 2)
+        self.assertEqual(results[0].info.data_source.measurement.instrument_settings.polarization, 'po')
+        self.assertEqual(results[1].info.data_source.measurement.instrument_settings.polarization, 'mo')
+        espin_up = np.nansum(results[0].data[:,1])
+        espin_down = np.nansum(results[1].data[:,1])
+        # the total intensity should be around equal as events are doubled and monitor counts are doubled
+        self.assertAlmostEqual(espin_down/espin_up, 1., 2)

update.md

@@ -0,0 +1,16 @@
+Make new release
+================
+
+- Update revision in `eos/__init__.py`
+- Commit changes `git commit -a -m "your message here"`
+- Tag version `git tag v3.x.y`
+- Push changes `git push` and `git push --tags`
+- This should trigger the **Release** action on GitHub that builds a new version and uploads it to PyPI.
+
+
+Update on AMOR
+==============
+
+- Login via SSH using the **amor** user.
+- Activate eos virtual environment `source /home/software/virtualenv/eosenv/bin/activate`
+- Update eos packge `pip install --upgrade amor-eos`

windows_build.spec

@@ -1,8 +1,15 @@
 # -*- mode: python ; coding: utf-8 -*-
+from PyInstaller.utils.hooks import collect_all
+
+datas = []
+binaries = []
+hiddenimports = []
+tmp_ret = collect_all('tzdata')
+datas += tmp_ret[0]; binaries += tmp_ret[1]; hiddenimports += tmp_ret[2]
 
 
 a = Analysis(
-    ['eos.py'],
+    ['eos/__main__.py'],
     pathex=[],
     binaries=[],
     datas=[],
@@ -12,23 +19,20 @@ a = Analysis(
     runtime_hooks=[],
     excludes=[],
     noarchive=False,
-    optimize=0,
+    optimize=1,
 )
 pyz = PYZ(a.pure)
 
 exe = EXE(
     pyz,
     a.scripts,
-    a.binaries,
-    a.datas,
     [],
+    exclude_binaries=True,
     name='eos',
     debug=False,
     bootloader_ignore_signals=False,
     strip=False,
     upx=True,
-    upx_exclude=[],
-    runtime_tmpdir=None,
     console=True,
     disable_windowed_traceback=False,
     argv_emulation=False,
@@ -36,3 +40,12 @@ exe = EXE(
     codesign_identity=None,
     entitlements_file=None,
 )
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='eos',
+)