Release changed version with log value filters

eos/__init__.py

@@ -2,5 +2,5 @@
 Package to handle data redction at AMOR instrument to be used by __main__.py script.
 """
 
-__version__ = '3.1.1'
+__version__ = '3.2.0'
 __date__    = '2026-02-26'

eos/event_analysis.py

@@ -124,5 +124,60 @@ class FilterQzRange(EventDataAction):
         if not 'qz' in dataset.data.events.dtype.names:
             raise ValueError("FilterQzRange requires dataset with qz values per events, perform WavelengthAndQ first")
 
-        if self.qzRange[1]<0.5:
-            d.events.mask += EVENT_BITMASKS["qRange"]*((self.qzRange[0]>d.events.qz) | (d.events.qz>self.qzRange[1]))
+        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):
+        self.filter_string = filter_string
+
+    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
+            self.add_log_to_pulses(filter_variable, dataset)
+        fltr_pulses = eval(self.filter_string, {filter_variable: dataset.data.pulses[filter_variable]})
+        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
+
+
+    def add_log_to_pulses(self, key, dataset: EventDatasetProtocol):
+        """
+        Add a log value for each pulse to the pulses array.
+        """
+        # TODO: perform some interpolation for the pulse where a change occured
+        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/event_data_types.py

@@ -1,8 +1,8 @@
 """
 Specify the data type and protocol used for event datasets.
 """
-from typing import List, Optional, Protocol, Tuple
-from dataclasses import dataclass
+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
@@ -34,6 +34,7 @@ EVENT_TYPE = np.dtype([('tof', np.float64), ('pixelID', np.uint32), ('mask', np.
 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 = {
@@ -60,6 +61,7 @@ class AmorEventStream:
     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):
     """

eos/event_handling.py

@@ -71,6 +71,8 @@ class CorrectSeriesTime(EventDataAction):
         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}')

eos/file_reader.py

@@ -5,7 +5,6 @@ from typing import BinaryIO, List, Union
 
 import h5py
 import numpy as np
-import platform
 import logging
 import subprocess
 
@@ -16,7 +15,8 @@ from orsopy.fileio.model_language import SampleModel
 
 from . import const
 from .header import Header
-from .event_data_types import AmorGeometry, AmorTiming, AmorEventStream, PACKET_TYPE, EVENT_TYPE, PULSE_TYPE, PC_TYPE
+from .event_data_types import AmorGeometry, AmorTiming, AmorEventStream, LOG_TYPE, PACKET_TYPE, EVENT_TYPE, PULSE_TYPE, \
+    PC_TYPE
 
 try:
     import zoneinfo
@@ -28,16 +28,39 @@ except ImportError:
 # Time zone used to interpret time strings
 AMOR_LOCAL_TIMEZONE = zoneinfo.ZoneInfo(key='Europe/Zurich')
 
-if  platform.node().startswith('amor'):
-    NICOS_CACHE_DIR = '/home/data/nicosdata/cache/'
-    GREP = '/usr/bin/grep "value"'
-else:
-    NICOS_CACHE_DIR = None
-
 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, 'ignore'),
+            sample_magnetic_field=('entry1/sample/magnetic_field', float, 'ignore'),
+
+            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:
@@ -48,6 +71,8 @@ class AmorHeader:
         else:
             self.hdf = h5py.File(fileName, 'r')
 
+        self._log_keys = []
+
         self.read_header_info()
         self.read_instrument_configuration()
 
@@ -57,52 +82,81 @@ class AmorHeader:
         del(self.hdf)
 
     def _replace_if_missing(self, key, nicos_key, dtype=float, suffix=''):
-        try:
-            return dtype(self.hdf[f'/entry1/Amor/{key}'][0])
-        except(KeyError, IndexError):
-            if NICOS_CACHE_DIR:
-                try:
-                    logging.info(f"     using parameter {nicos_key} from nicos cache")
-                    year_date = self.fileDate.strftime('%Y')
-                    call = f'{GREP} {NICOS_CACHE_DIR}nicos-{nicos_key}/{year_date}{suffix}'
-                    value = str(subprocess.getoutput(call)).split('\t')[-1]
-                    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)
+        from .nicos_interface import lookup_nicos_value
+        year = self.fileDate.strftime('%Y')
+        return lookup_nicos_value(key, nicos_key, dtype, suffix, year)
 
-    def get_hdf_single_entry(self, path):
-        if not np.shape(self.hdf['entry1/title']):
+    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':
+                self._log_keys.append(key)
+                # 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:
+                    return dtype(hdfgrp['value'][start_index])
+                else:
+                    return dtype(hdfgrp['value'][start_index, 0])
+            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
+        start_date = datetime.fromisoformat(start_time)
+        self.fileDate = start_date.replace(tzinfo=AMOR_LOCAL_TIMEZONE)
+        self._start_time_ns = np.uint64(self.fileDate.timestamp()*1e9)
+
         # read general information and first data set
-        title = self.get_hdf_single_entry('entry1/title')
-        proposal_id = self.get_hdf_single_entry('entry1/proposal_id')
-        user_name = self.get_hdf_single_entry('entry1/user/name')
+        title = self.rv('title')
+        proposal_id = self.rv('proposal_id')
+        user_name = self.rv('user_name')
         user_affiliation = 'unknown'
-        user_email = self.get_hdf_single_entry('entry1/user/email')
+        user_email = self.rv('user_email')
         user_orcid = None
-        sampleName = self.get_hdf_single_entry('entry1/sample/name')
+        sampleName = self.rv('sample_name')
         instrumentName = 'Amor'
-        source = self.get_hdf_single_entry('entry1/Amor/source/name')
+        source = self.rv('source_name')
         sourceProbe = 'neutron'
-        model = self.get_hdf_single_entry('entry1/sample/model')
+        model = self.rv('sample_model')
         if 'stack' in model:
             import yaml
             model = yaml.safe_load(model)
         else:
             model = dict(stack=model)
-        start_time = self.get_hdf_single_entry('entry1/start_time')
-        # extract start time as unix time, adding UTC offset of 1h to time string
-        start_date = datetime.fromisoformat(start_time)
-        self.fileDate = start_date.replace(tzinfo=AMOR_LOCAL_TIMEZONE)
 
         self.owner = fileio.Person(
                 name=user_name,
@@ -130,28 +184,28 @@ class AmorHeader:
                 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 \
-            and float(self.hdf['entry1/sample/temperature/average_value'][0])>0:
-            self.sample.sample_parameters['temperature'] = fileio.Value(
-                    float(self.hdf['entry1/sample/temperature/average_value'][0]), unit='K')
+        if self.hdf['entry1/sample'].get('temperature', None) is not None:
+            sample_temperature = self.rv('sample_temperature')
+            self.sample.sample_parameters['temperature'] = fileio.Value(sample_temperature, unit='K')
         if self.hdf['entry1/sample'].get('magnetic_field', None) is not None:
-            self.sample.sample_parameters['magnetic_field'] = fileio.Value(
-                    float(self.hdf['entry1/sample/magnetic_field/average_value'][0]), unit='T')
+            sample_magnetic_field = self.rv('sample_magnetic_field')
+            self.sample.sample_parameters['magnetic_field'] = fileio.Value(sample_magnetic_field, unit='T')
 
     def read_instrument_configuration(self):
-        chopperSeparation = float(np.take(self.hdf['entry1/Amor/chopper/pair_separation'], 0))
-        detectorDistance = float(np.take(self.hdf['entry1/Amor/detector/transformation/distance'], 0))
-        chopperDetectorDistance = detectorDistance-float(np.take(self.hdf['entry1/Amor/chopper/distance'], 0))
+        chopperSeparation = self.rv('chopper_separation')
+        detectorDistance = self.rv('detector_distance')
+        chopperDistance = self.rv('chopper_distance')
+        chopperDetectorDistance = detectorDistance - chopperDistance
 
         polarizationConfigs = ['unpolarized', 'unpolarized', 'po', 'mo', 'op', 'pp', 'mp', 'om', 'pm', 'mm']
 
-        mu = self._replace_if_missing('instrument_control_parameters/mu', 'mu', float)
-        nu = self._replace_if_missing('instrument_control_parameters/nu', 'nu', float)
-        kap = self._replace_if_missing('instrument_control_parameters/kappa', 'kappa', float)
-        kad = self._replace_if_missing('instrument_control_parameters/kappa_offset', 'kappa_offset', float)
-        div = self._replace_if_missing('instrument_control_parameters/div', 'div', float)
-        ch1TriggerPhase = self._replace_if_missing('chopper/ch1_trigger_phase', 'ch1_trigger_phase', float)
-        ch2TriggerPhase = self._replace_if_missing('chopper/ch2_trigger_phase', 'ch2_trigger_phase', float)
+        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])) \
@@ -159,8 +213,8 @@ class AmorHeader:
             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._replace_if_missing('chopper/rotation_speed', 'chopper_phase', float)
-            chopperPhase = self._replace_if_missing('chopper/phase', 'chopper_phase', float)
+            chopperSpeed = self.rv('chopper_speed')
+            chopperPhase = self.rv('chopper_phase')
             tau = 30/chopperSpeed
         else:
             tau = int(1e-6*chopperTriggerTime/2+0.5)*(1e-3)
@@ -172,7 +226,7 @@ class AmorHeader:
                                      chopperSeparation, detectorDistance, chopperDetectorDistance)
         self.timing = AmorTiming(ch1TriggerPhase, ch2TriggerPhase, chopperSpeed, chopperPhase, tau)
 
-        polarizationConfigLabel = self._replace_if_missing('polarization/configuration/average_value', 'polarization_config_label', int, suffix='/*')
+        polarizationConfigLabel = self.rv('polarization_config_label')
         polarizationConfig = fileio.Polarization(polarizationConfigs[polarizationConfigLabel])
         logging.debug(f'      polarization configuration: {polarizationConfig} (index {polarizationConfigLabel})')
 
@@ -258,7 +312,13 @@ class AmorEventData(AmorHeader):
 
         super().__init__(hdf)
         self.hdf = hdf
-        self.read_event_stream()
+        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
@@ -281,15 +341,26 @@ class AmorEventData(AmorHeader):
             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]
-            self.last_index = packets.start_index[end_packet]-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
@@ -308,6 +379,22 @@ class AmorEventData(AmorHeader):
             # 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=LOG_TYPE)
+            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 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)
@@ -316,7 +403,7 @@ class AmorEventData(AmorHeader):
             startTime = chopper1TriggerTime[0]
             pulseTimeS = chopper1TriggerTime
         else:
-            logging.warn('     no chopper trigger data available, using event steram instead')
+            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)
@@ -324,7 +411,7 @@ class AmorEventData(AmorHeader):
         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:
+        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
@@ -332,7 +419,11 @@ class AmorEventData(AmorHeader):
     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'][:]
-        proton_current.current = self.hdf['entry1/Amor/detector/proton_current/value'][:,0]
+        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])]

eos/kafka_serializer.py

@@ -160,7 +160,7 @@ class ESSSerializer:
                     )
             self.producer.flush()
             if isinstance(command, Stop):
-                if command.hist_id == self._active_histogram_yz:
+                if command.hist_id in [self._active_histogram_yz, self._active_histogram_tofz]:
                     self.count_stopped.set()
                 else:
                     return

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/options.py

@@ -471,6 +471,15 @@ class ReflectivityReductionConfig(ArgParsable):
                 },
             )
 
+    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"

eos/reduction_e2h.py

@@ -268,7 +268,7 @@ class E2HReduction:
             return
         try:
             # check that events exist in the new file
-            AmorEventData(new_files[-1], 0, max_events=1000)
+            AmorEventData(new_files[-1], 0, max_events=1_000)
         except Exception:
             logging.debug("Problem when trying to load new dataset", exc_info=True)
             return

eos/reduction_reflectivity.py

@@ -68,7 +68,10 @@ class ReflectivityReduction:
         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)
-        self.dataevent_actions |= ea.FilterQzRange(self.config.reduction.qzRange)
+        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)

tests/test_event_handling.py

@@ -0,0 +1,498 @@
+import os
+import numpy as np
+
+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
+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,
+                                   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,
+                                   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)
+
+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']
+        )

tests/test_full_analysis.py

@@ -61,7 +61,7 @@ class FullAmorTest(TestCase):
         reduction_config = options.ReflectivityReductionConfig(
                 normalisationMethod=self._field_defaults['ReflectivityReductionConfig']['normalisationMethod'],
                 qResolution=0.01,
-                qzRange=self._field_defaults['ReflectivityReductionConfig']['qzRange'],
+                qzRange=(0.01, 0.15),
                 thetaRange=(-0.75, 0.75),
                 fileIdentifier=["6003-6005"],
                 scale=[1],