Update version information for commit before merge of version 3

Faster pulsetimes

.github/workflows/release.yml

@@ -19,12 +19,14 @@ on:
           - all
           - windows
           - linux
-          - mac
+          - all_incl_release
 
 jobs:
   build-ubuntu-latest:
     runs-on: ubuntu-latest
-    if: ${{ (github.event_name != 'workflow_dispatch') || (contains(fromJson('["all", "linux"]'), github.event.inputs.build-items)) }}
+    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
@@ -38,31 +40,66 @@ jobs:
     - name: Build PyPI package
       run: |
         python3 -m build
-    - 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
+    - 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
+
+  build-windows:
+    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
+    - 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_folder.spec
+        cd dist\eos
+        Compress-Archive -Path .\* -Destination ..\..\eos.zip
+    - name: Archive distribution
+      uses: actions/upload-artifact@v4
+      with:
+        name: windows-dist
+        path: |
+          eos.zip
+
+  release:
+    if:  ${{ (github.event_name != 'workflow_dispatch') || (contains(fromJson('["all_incl_release"]'), github.event.inputs.build-items)) }}
+    runs-on: ubuntu-latest
+    needs: [build-ubuntu-latest, build-windows]
+    steps:
+    - uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+        fetch-tags: true
     - uses: actions/download-artifact@v4
       with:
         name: linux-dist
+    - uses: actions/download-artifact@v4
+      with:
+        name: windows-dist
+    - name: get latest version tag
+      run: echo "RELEASE_TAG=$(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

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-22.04
     strategy:
       matrix:
-        python-version: [3.8, 3.9, '3.10', '3.11', '3.12']
+        python-version: ['3.8', '3.9', '3.10', '3.11', '3.12']
       fail-fast: false
 
     steps:
@@ -31,6 +31,11 @@ jobs:
           pip install pytest
           pip install -r requirements.txt
 
+      - name: Backport to 3.8
+        if: matrix.python-version == '3.8'
+        run: |
+          pip install backports.zoneinfo
+
       - name: Test with pytest
         run: |
           cd tests

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.py --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

events2histogram.py

@@ -860,7 +860,7 @@ def process(dataPath, ident, clas):
         try: lamdaMax
         except NameError: lamdaMax = lamdaMin + tau * hdm/chopperDetectorDistance * 1e13
     
-        tofOffset = tau * chopperPhase / 180.                       # mismatch of chopper pulse and time-zero
+        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 
@@ -960,7 +960,7 @@ def commandLineArgs():
                             type=float,
                             help ="value of nu")
     clas.add_argument("-P", "--chopperPhase",    
-                            default=7.5,                     
+                            default=-7.5,                     
                             type=float,
                             help ="chopper phase offset")
     clas.add_argument("-p", "--plot",            

libeos/__init__.py

@@ -2,5 +2,6 @@
 Package to handle data redction at AMOR instrument to be used by eos.py script.
 """
 
-__version__ = '2.1.1'
-__date__    = '2024-10-30'
+__version__ = '2.2.0'
+__date__    = '2025-09-16'
+

libeos/command_line.py

@@ -185,6 +185,7 @@ def command_line_options():
         )
     experiment_config = ExperimentConfig(
         sampleModel                  = clas.sampleModel,
+        chopperSpeed                 = clas.chopperSpeed,
         chopperPhase                 = clas.chopperPhase,
         chopperPhaseOffset           = clas.chopperPhaseOffset,
         yRange                       = clas.yRange,

libeos/file_reader.py

@@ -2,7 +2,12 @@ import logging
 import os
 import subprocess
 import sys
-from datetime import datetime
+from datetime import datetime, timezone
+try:
+    import zoneinfo
+except ImportError:
+    # for python versions < 3.9 try to use the backports version
+    from backports import zoneinfo
 from typing import List
 
 import h5py
@@ -20,18 +25,8 @@ try:
 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
+# Time zone used to interpret time strings
+AMOR_LOCAL_TIMEZONE = zoneinfo.ZoneInfo(key='Europe/Zurich')
 
 class AmorData:
     """read meta-data and event streams from .hdf file(s), apply filters and conversions"""
@@ -39,6 +34,8 @@ class AmorData:
     chopperDistance: float
     chopperPhase: float
     chopperSpeed: float
+    chopper1TriggerPhase: float
+    chopper2TriggerPhase: float
     div: float
     data_file_numbers: List[int]
     delta_z: np.ndarray
@@ -101,22 +98,6 @@ class AmorData:
         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'
@@ -180,7 +161,8 @@ class AmorData:
                                                    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,
+                #polarization = fileio.Polarization.unpolarized,
+                polarization = self.polarizationConfig
                 )
             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')
@@ -195,26 +177,29 @@ class AmorData:
         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.correct_for_chopper_phases()
 
-        self.associate_pulse_with_monitor()
+        self.read_chopper_trigger_stream()
 
         self.extract_walltime(norm)
 
+        self.read_proton_current_stream()
+
+        self.associate_pulse_with_monitor()
+
         # following lines: debugging output to trace the time-offset of proton current and neutron pulses
-        if self.config.monitorType == 'p':
+        if self.config.monitorType == 'x':
             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.average_events_per_pulse() # for debugging only. VERY time consuming!!!
 
         self.monitor_threshold()
 
         self.filter_strange_times()
 
-        self.merge_frames()
+        self.merge_time_frames()
 
         self.filter_project_x()
 
@@ -224,40 +209,17 @@ class AmorData:
 
         self.filter_qz_range(norm)
 
-        logging.info(f'      number of events: total = {totalNumber:7d}, filtered = {np.shape(self.lamda_e)[0]:7d}')
+        logging.info(f'      number of events: total = {self.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]
+    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)
 
-        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 correct_for_chopper_phases(self):
+        #print(f'chopperTriggerPhase: {self.ch1TriggerPhase}')
+        self.tof_e += self.tau * (self.ch1TriggerPhase + self.chopperPhase/2)/180
 
     def extract_walltime(self, norm):
         if nb_helpers:
@@ -270,25 +232,75 @@ class AmorData:
         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 read_chopper_trigger_stream(self):
+        self.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(self.chopper1TriggerTime)[0] > 2:
+            self.startTime = self.chopper1TriggerTime[0]
+            self.stopTime = self.chopper1TriggerTime[-1]
+            self.pulseTimeS = self.chopper1TriggerTime
+        else:
+            logging.warn('     no chopper trigger data available, using event steram instead')
+            self.startTime = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][0], dtype=np.int64)
+            self.stopTime = np.array(self.hdf['/entry1/Amor/detector/data/event_time_zero'][-2], dtype=np.int64)
+            self.pulseTimeS = np.arange(self.startTime, self.stopTime, self.tau*1e9) 
+        if self.seriesStartTime is None:
+            self.seriesStartTime = self.startTime
+            logging.debug(f'      series start time (epoch): {self.seriesStartTime/1e9:13.2f} s')
+        self.pulseTimeS -= self.seriesStartTime
+        logging.debug(f'      epoch time from {self.startTime/1e9:13.2f} s to {self.stopTime/1e9:13.2f} s')
+        logging.debug(f'      => counting time {self.stopTime/1e9-self.startTime/1e9:8.2f} s')
+
+    def read_proton_current_stream(self):
+        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)
+
+    def get_current_per_pulse(self, 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):
+            while ti >= currentTimeS[j+1]: 
+                j += 1
+            pulseCurrentS[i] = currents[j]
+            #print(f' {i}  {pulseTimeS[i]}  {pulseCurrentS[i]}') 
+        return pulseCurrentS
+
+    def associate_pulse_with_monitor(self):
+        if self.config.monitorType == 'p': # protonCharge
+            self.currentTime -= np.int64(self.seriesStartTime)
+            self.monitorPerPulse = self.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])*2*self.tau
+        else: # pulses
+            self.monitorPerPulse = np.ones(np.shape(self.pulseTimeS)[0])
+
     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}')
+                logging.info(f'pulse: {i:6.0f}, events: {events:6.0f}, monitor: {self.monitorPerPulse[i]:6.2f}')
 
     def monitor_threshold(self):
-        if self.config.monitorType == 'p': # fix to check for file compatibility
+        #if self.config.monitorType == 'p': # fix to check for file compatibility
+        self.totalNumber = np.shape(self.tof_e[self.tof_e<=self.stopTime])[0]
+        if True:
             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'      low-beam rejected pulses: {np.shape(self.monitorPerPulse)[0]-1-np.shape(goodTimeS)[0]} out of {np.shape(self.monitorPerPulse)[0]-1}')
+            logging.info(f'          with {np.shape(filter_e)[0]-np.shape(self.tof_e)[0]} events')
             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:
+        if self.config.qzRange[1]<0.5 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]
@@ -297,7 +309,8 @@ class AmorData:
 
     def calculate_derived_properties(self):
         self.lamdaMax = const.lamdaCut+1.e13*self.tau*const.hdm/(self.chopperDetectorDistance+124.)
-        if nb_helpers:
+        #if nb_helpers:
+        if False:
             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,
@@ -347,8 +360,9 @@ class AmorData:
             # 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.
+    # TODO: - handle each neutron pulse individually, - associate with correct monitor also for slow neutrons
+    def merge_time_frames(self):
+        total_offset = self.tofCut + self.tau * (self.ch1TriggerPhase + self.chopperPhase/2)/180
         if nb_helpers:
             self.tof_e = nb_helpers.merge_frames(self.tof_e, self.tofCut, self.tau, total_offset)
         else:
@@ -361,26 +375,7 @@ class AmorData:
         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
+            logging.warning(f'        strange times: {np.shape(filter_e)[0]-np.shape(self.tof_e)[0]}')
 
     def read_individual_header(self):
         self.chopperDistance = float(np.take(self.hdf['entry1/Amor/chopper/pair_separation'], 0))
@@ -388,19 +383,39 @@ class AmorData:
         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
 
+        polarizationConfigs = ['undefined', 'unpolarized', 'po', 'mo', 'op', 'pp', 'mp', 'om', 'pm', 'mm']
         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))
+            self.mu   = float(np.take(self.hdf['/entry1/Amor/instrument_control_parameters/mu'], 0))
+            self.nu   = float(np.take(self.hdf['/entry1/Amor/instrument_control_parameters/nu'], 0))
+            self.kap  = float(np.take(self.hdf['/entry1/Amor/instrument_control_parameters/kap'], 0))
+            self.kad  = float(np.take(self.hdf['/entry1/Amor/instrument_control_parameters/kad'], 0))
+            self.div  = float(np.take(self.hdf['/entry1/Amor/instrument_control_parameters/div'], 0))
+            self.ch1TriggerPhase = float(np.take(self.hdf['/entry1/Amor/chopper/ch1_trigger_phase'], 0))
+            self.ch2TriggerPhase = float(np.take(self.hdf['/entry1/Amor/chopper/ch2_trigger_phase'], 0))
+            try: 
+                chopperTriggerTime = float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][2])\
+                                     - float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_zero'][1])
+                self.tau = int(1e-6*chopperTriggerTime/2+0.5)*(1e-3)
+                self.chopperSpeed = 30/self.tau
+                chopperTriggerTimeDiff =  float(self.hdf['entry1/Amor/chopper/ch2_trigger/event_time_offset'][2])
+                chopperTriggerPhase = 180e-9*chopperTriggerTimeDiff/self.tau
+                #TODO: check the next line
+                self.chopperPhase = chopperTriggerPhase + self.ch1TriggerPhase - self.ch2TriggerPhase
+            except(KeyError, IndexError):
+                logging.debug('      chopper speed and phase taken from .hdf file')
+                self.chopperSpeed = float(np.take(self.hdf['/entry1/Amor/chopper/rotation_speed'], 0))
+                self.chopperPhase = float(np.take(self.hdf['/entry1/Amor/chopper/phase'], 0))
+                self.tau = 30/self.chopperSpeed
+            try:
+                polarizationConfigLabel = int(self.hdf['/entry1/Amor/polarization/configuration/value'][0])
+            except(KeyError, IndexError):
+                polarizationConfigLabel = 0
+            self.polarizationConfig = polarizationConfigs[polarizationConfigLabel]
+            logging.debug(f'      polarization configuration: {self.polarizationConfig} (index {polarizationConfigLabel} (index {polarizationConfigLabel}))')
         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/'
+            # TODO: check new cache pathes
             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)
@@ -414,20 +429,35 @@ class AmorData:
             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
+            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-chopper_phase/{year_date}')).split('\t')[-1]
+            self.chopperPhase = float(value)
+            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-ch1_trigger_phase/{year_date}')).split('\t')[-1]
+            self.ch1TriggerPhase = float(value)
+            value = str(subprocess.getoutput(f'/usr/bin/grep "value" {cachePath}nicos-ch2_trigger_phase/{year_date}')).split('\t')[-1]
+            self.ch2TriggerPhase = float(value)
+            
+            self.tau     = 30. / self.chopperSpeed
 
+        logging.debug(f'        tau = {self.tau} s')
         if self.config.muOffset:
+            logging.debug(f'        set muOffset = {self.config.muOffset}')
             self.mu += self.config.muOffset
         if self.config.mu:
+            logging.debug(f'        replaced mu = {self.mu} with {self.config.mu}')
             self.mu = self.config.mu
         if self.config.nu:
+            logging.debug(f'        replaced nu = {self.nu} with {self.config.nu}')
             self.nu = self.config.nu
+        if self.config.chopperPhaseOffset:
+            logging.debug(f'        replaced ch1TriggerPhase = {self.ch1TriggerPhase} with {self.config.chopperPhaseOffset}')
+            self.ch1TriggerPhase = self.config.chopperPhaseOffset
 
-        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 
+        # extract start time as unix time, adding UTC offset of 1h to time string
+        dz = datetime.fromisoformat(self.hdf['/entry1/start_time'][0].decode('utf-8'))
+        self.fileDate=dz.replace(tzinfo=AMOR_LOCAL_TIMEZONE)
+        #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

libeos/options.py

@@ -29,10 +29,10 @@ class Defaults:
     thetaRange                  = [-12., 12.]
     thetaRangeR                 = [-0.75, 0.75]
     yRange                      = [11, 41]
-    qzRange                     = [0.005, 0.30]
+    qzRange                     = [0.005, 0.51]
     chopperSpeed                = 500
-    chopperPhase                = -13.5
-    chopperPhaseOffset          = 7
+    chopperPhase                = 0.0
+    chopperPhaseOffset          = -9.1
     muOffset                    = 0
     mu                          = 0
     nu                          = 0
@@ -52,6 +52,7 @@ class ReaderConfig:
 class ExperimentConfig:
     incidentAngle: str 
     chopperPhase: float
+    chopperSpeed: float
     yRange: Tuple[float, float]
     lambdaRange: Tuple[float, float]
     qzRange: Tuple[float, float]

libeos/reduction.py

@@ -22,7 +22,7 @@ class AmorReduction:
         self.header = Header()
         self.header.reduction.call = config.call_string()
 
-        self.monitorUnit = {'n': 'cnts', 'p': 'mC', 't': 's'}
+        self.monitorUnit = {'n': 'cnts', 'p': 'mC', 't': 's', 'auto': 'pulses'}
 
     def reduce(self):
         if not os.path.exists(f'{self.output_config.outputPath}'):
@@ -178,11 +178,11 @@ class AmorReduction:
         interval = self.reduction_config.timeSlize[0]
         try:
             start = self.reduction_config.timeSlize[1]
-        except:
+        except IndexError:
             start = 0
         try:
             stop = self.reduction_config.timeSlize[2]
-        except:
+        except IndexError:
             stop = wallTime_e[-1]
         # make overwriting log lines possible by removing newline at the end
         #logging.StreamHandler.terminator = "\r"
@@ -195,10 +195,16 @@ class AmorReduction:
             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]}')
+            logging.info(f'      {ti:<4d}  {time:6.0f}  {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)
+            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]
             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]),
@@ -265,7 +271,7 @@ class AmorReduction:
                 scale = 1.
         R_q  /= scale
         dR_q /= scale
-        logging.debug(f'      scaling factor = {scale}')
+        logging.info(f'      scaling factor = {1/scale}')
 
         return R_q, dR_q
 
@@ -345,19 +351,23 @@ class AmorReduction:
             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
+            norm_lz, bins_l, bins_z = np.histogram2d(lamda_e, detZ_e, bins = (self.grid.lamda(), self.grid.z()))
+            norm_lz = np.where(norm_lz>2, norm_lz, np.nan)
+            if self.reduction_config.normalisationMethod == 'd':
+                # direct reference => invert map vertically
+                self.norm_lz = np.flip(norm_lz, 1)
+            else:
+                # correct for reference 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
+                # TODO: introduce variable for `m` and propably for the slope
+                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_lz  = norm_lz / Rsm_lz
 
             if len(lamda_e) > 1e6:
                 with open(n_path, 'wb') as fh:
@@ -372,23 +382,26 @@ class AmorReduction:
         # projection on lambda-z-grid
         lamda_l  = self.grid.lamda()
         alphaF_z  = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
+        # TODO: implement various methods to obtain alpha_i.
         #if self.experiment_config.incidentAngle == 'alphaF':
+        #  # for specular reflectometry with a highly divergent beam
         #  alphaF_z  = fromHDF.nu - fromHDF.mu + fromHDF.delta_z
         #elif self.experiment_config.incidentAngle == 'nu':
+        #  # for specular reflectometry, using kappa nad nu but ignoring mu
         #  alphaF_z  = (fromHDF.nu + fromHDF.delta_z + fromHDF.kap + fromHDF.kad) / 2.
         #else:
+        #  # using kappa, for a collimated incoming beam
         #  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:
+        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))
+        elif 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:
@@ -396,10 +409,6 @@ class AmorReduction:
                                               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))
@@ -424,21 +433,24 @@ class AmorReduction:
 
         if self.reduction_config.normalisationMethod == 'o':
             logging.debug('      assuming an overilluminated sample and correcting for the angle of incidence')
+            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.logical_and(mask_lz, np.where(np.absolute(thetaN_lz)>5e-3, True, False))
             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))
+            logging.error('unknown normalisation method! Use [u]nder, [o]ver or [d]irect illumination')
+            ref_lz    = (int_lz / norm_lz)
         if self.monitor > 1e-6 :
             ref_lz   *= self.normMonitor / self.monitor
         else:
-            logging.warning('   too small monitor value for normalisation -> ignoring monitors')
+            logging.info('       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%)

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()     
-
-

setup.cfg

@@ -7,6 +7,7 @@ version = attr: libeos.__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

windows_build.spec

@@ -12,7 +12,7 @@ a = Analysis(
     runtime_hooks=[],
     excludes=[],
     noarchive=False,
-    optimize=0,
+    optimize=1,
 )
 pyz = PYZ(a.pure)
 

windows_folder.spec

@@ -0,0 +1,51 @@
+# -*- 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'],
+    pathex=[],
+    binaries=[],
+    datas=[],
+    hiddenimports=[],
+    hookspath=[],
+    hooksconfig={},
+    runtime_hooks=[],
+    excludes=[],
+    noarchive=False,
+    optimize=1,
+)
+pyz = PYZ(a.pure)
+
+exe = EXE(
+    pyz,
+    a.scripts,
+    [],
+    exclude_binaries=True,
+    name='eos',
+    debug=False,
+    bootloader_ignore_signals=False,
+    strip=False,
+    upx=True,
+    console=True,
+    disable_windowed_traceback=False,
+    argv_emulation=False,
+    target_arch=None,
+    codesign_identity=None,
+    entitlements_file=None,
+)
+coll = COLLECT(
+    exe,
+    a.binaries,
+    a.datas,
+    strip=False,
+    upx=True,
+    upx_exclude=[],
+    name='eos',
+)