release with -m functionality and better lambda resolution in e2h

# Conflicts:
#	eos/file_reader.py

eos/__init__.py

@@ -2,5 +2,5 @@
 Package to handle data redction at AMOR instrument to be used by __main__.py script.
 """
 
-__version__ = '3.0.1'
-__date__    = '2025-10-10'
+__version__ = '3.0.6'
+__date__    = '2025-11-05'

eos/event_analysis.py

@@ -25,8 +25,15 @@ class ExtractWalltime(EventDataAction):
         dataset.data.events = new_events
 
 class MergeFrames(EventDataAction):
+    def __init__(self, lamdaCut=None):
+        self.lamdaCut=lamdaCut
+
     def perform_action(self, dataset: EventDatasetProtocol)->None:
-        tofCut = const.lamdaCut*dataset.geometry.chopperDetectorDistance/const.hdm*1e-13
+        if self.lamdaCut is None:
+            lamdaCut = const.lamdaCut
+        else:
+            lamdaCut = self.lamdaCut
+        tofCut = lamdaCut*dataset.geometry.chopperDetectorDistance/const.hdm*1e-13
         total_offset = (tofCut +
                         dataset.timing.tau * (dataset.timing.ch1TriggerPhase + dataset.timing.chopperPhase/2)/180)
         dataset.data.events.tof = merge_frames(dataset.data.events.tof, tofCut, dataset.timing.tau, total_offset)

eos/file_reader.py

@@ -41,6 +41,7 @@ class AmorHeader:
 
     def __init__(self, fileName:Union[str, h5py.File, BinaryIO]):
         if type(fileName) is str:
+            logging.warning(f'    {fileName.split("/")[-1]}')
             self.hdf = h5py.File(fileName, 'r', swmr=True)
         elif type(fileName) is h5py.File:
             self.hdf = fileName
@@ -128,7 +129,7 @@ class AmorHeader:
         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', 'kad', 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)
@@ -222,6 +223,7 @@ class AmorEventData(AmorHeader):
 
     def __init__(self, fileName:Union[str, h5py.File, BinaryIO], first_index:int=0, max_events:int=100_000_000):
         if type(fileName) is str:
+            logging.warning(f'    {fileName.split("/")[-1]}')
             self.file_list = [fileName]
             hdf = h5py.File(fileName, 'r', swmr=True)
         elif type(fileName) is h5py.File:

eos/instrument.py

@@ -65,9 +65,14 @@ class LZGrid:
     def qzRange(self):
         return self._qzRange
 
-    def __init__(self, qResolution, qzRange):
+    def __init__(self, qResolution, qzRange, lambda_overwrite=None):
         self._qResolution = qResolution
         self._qzRange = qzRange
+        if lambda_overwrite is None:
+            self.lamdaMax = const.lamdaMax
+            self.lamdaCut = const.lamdaCut
+        else:
+            self.lamdaCut, self.lamdaMax = lambda_overwrite
 
     @property
     @cache
@@ -92,8 +97,8 @@ class LZGrid:
 
     @cache
     def lamda(self):
-        lamdaMax = 16
-        lamdaMin = const.lamdaCut
+        lamdaMax = self.lamdaMax
+        lamdaMin = self.lamdaCut
         lamda_grid = lamdaMin*(1+self.dldl)**np.arange(int(np.log(lamdaMax/lamdaMin)/np.log(1+self.dldl)+1))
         return lamda_grid
 

eos/kafka_events.py

@@ -0,0 +1,165 @@
+"""
+Collect AMOR detector events send via Kafka.
+"""
+
+import logging
+import numpy as np
+from threading import Thread, Event
+from time import time
+
+from .event_data_types import AmorGeometry, AmorTiming, AmorEventStream, PACKET_TYPE, EVENT_TYPE, PULSE_TYPE, PC_TYPE
+
+from uuid import uuid4
+from streaming_data_types.eventdata_ev44 import EventData
+from streaming_data_types.logdata_f144 import ExtractedLogData
+from streaming_data_types import deserialise_f144, deserialise_ev44
+from confluent_kafka import Consumer
+
+from .header import Header
+
+
+try:
+    from streaming_data_types.utils import get_schema
+except ImportError:
+    from streaming_data_types.utils import _get_schema as get_schema
+
+
+KAFKA_BROKER = 'linkafka01.psi.ch:9092'
+AMOR_EVENTS = 'amor_ev44'
+AMOR_NICOS = 'AMOR_nicosForwarder'
+
+class KafkaFrozenData:
+    """
+    Represents event stream data from Kafka at a given time.
+    Will be returned by KafkaEventData to be use in conjunction
+    with data processing and projections.
+
+    Implements EventDatasetProtocol
+    """
+    geometry: AmorGeometry
+    timing: AmorTiming
+    data: AmorEventStream
+
+    def __init__(self, geometry, timing, data, monitor=1.):
+        self.geometry = geometry
+        self.timing = timing
+        self.data = data
+        self.monitor = monitor
+
+    def append(self, other):
+        raise NotImplementedError("can't append live datastream to other event data")
+
+    def update_header(self, header:Header):
+        # maybe makes sense later, but for now just used for live vizualization
+        ...
+
+class KafkaEventData(Thread):
+    """
+    Read Nicos information and events from Kafka. Creates a background
+    thread that listens to Kafka events and converts them to eos compatible information.
+    """
+    geometry: AmorGeometry
+    timing: AmorTiming
+    events: np.recarray
+
+    def __init__(self):
+        self.stop_event = Event()
+        self.stop_counting = Event()
+        self.new_events = Event()
+        self.last_read = 0
+        self.last_read_time = 0.
+        self.start_time = time()
+        self.consumer = Consumer(
+                {'bootstrap.servers': 'linkafka01.psi.ch:9092',
+                 'group.id': uuid4()})
+        self.consumer.subscribe([AMOR_EVENTS, AMOR_NICOS])
+        self.geometry = AmorGeometry(1.0, 2.0, 0., 0., 1.5, 10.0, 4.0, 10.0)
+        self.timing = AmorTiming(0., 0., 500., 0., 30./500.)
+        # create empty dataset
+        self.events = np.recarray(0, dtype=EVENT_TYPE)
+        super().__init__()
+
+    def run(self):
+        while not self.stop_event.is_set():
+            messages = self.consumer.consume(10, timeout=1)
+            for message in messages:
+                self.process_message(message)
+
+    def process_message(self, message):
+        if message.error():
+            logging.info(f"  received Kafka message with error: {message.error()}")
+            return
+        schema = get_schema(message.value())
+        if message.topic()==AMOR_EVENTS and schema=='ev44':
+            events:EventData = deserialise_ev44(message.value())
+            self.add_events(events)
+            self.new_events.set()
+            logging.debug(f'  new events {events}')
+        elif message.topic()==AMOR_NICOS and schema=='f144':
+            nicos_data:ExtractedLogData = deserialise_f144(message.value())
+            if nicos_data.source_name in self.nicos_mapping.keys():
+                logging.debug(f'  {nicos_data.source_name} = {nicos_data.value}')
+                self.update_instrument(nicos_data)
+
+    def add_events(self, events:EventData):
+        """
+        Add new events to the Dataset. The object keeps raw events
+        and only copies the latest set to the self.data object,
+        this allows to run the event processing to be performed on a "clean"
+        evnet stream each time.
+        """
+        if self.stop_counting.is_set():
+            return
+        prev_size = self.events.shape[0]
+        new_events = events.pixel_id.shape[0]
+        self.events.resize(prev_size+new_events, refcheck=False)
+        self.events.pixelID[prev_size:] = events.pixel_id
+        self.events.mask[prev_size:] = 0
+        self.events.tof[prev_size:] = events.time_of_flight/1.e9
+
+    nicos_mapping = {
+        'mu': ('geometry', 'mu'),
+        'nu':  ('geometry', 'nu'),
+        'kappa':  ('geometry', 'kap'),
+        'kappa_offset':  ('geometry', 'kad'),
+        'ch1_trigger_phase':  ('timing', 'ch1TriggerPhase'),
+        'ch2_trigger_phase':  ('timing', 'ch2TriggerPhase'),
+        'ch2_speed':  ('timing', 'chopperSpeed'),
+        'chopper_phase':  ('timing', 'chopperPhase'),
+        }
+
+    def update_instrument(self, nicos_data:ExtractedLogData):
+        if nicos_data.source_name in self.nicos_mapping:
+            attr, subattr = self.nicos_mapping[nicos_data.source_name]
+            setattr(getattr(self, attr), subattr, nicos_data.value)
+            if nicos_data.source_name=='ch2_speed':
+                self.timing.tau = 30./self.timing.chopperSpeed
+
+    def monitor(self):
+        return time()-self.start_time
+
+    def restart(self):
+        # empty event buffer
+        self.events = np.recarray(0, dtype=EVENT_TYPE)
+        self.stop_counting.clear()
+        self.last_read = 0
+        self.start_time = time()
+        self.new_events.clear()
+
+    def get_events(self, total_counts=False):
+        packets = np.recarray(0, dtype=PACKET_TYPE)
+        pulses = np.recarray(0, dtype=PULSE_TYPE)
+        pc = np.recarray(0, dtype=PC_TYPE)
+        if total_counts:
+            last_read = 0
+        else:
+            last_read = self.last_read
+        if last_read>=self.events.shape[0]:
+            raise EOFError("No new events arrived")
+        data = AmorEventStream(self.events[last_read:].copy(), packets, pulses, pc)
+        self.last_read = self.events.shape[0]
+        self.new_events.clear()
+        t_now = time()
+        monitor = t_now-self.last_read_time
+        self.last_read_time = t_now
+        return KafkaFrozenData(self.geometry, self.timing, data, monitor=monitor)

eos/kafka_serializer.py

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

eos/nicos.py

@@ -0,0 +1,46 @@
+"""
+events2histogram vizualising data from Amor@SINQ, PSI
+
+Author: Jochen Stahn (algorithms, python draft),
+        Artur Glavic (structuring and optimisation of code)
+"""
+import logging
+
+# need to do absolute import here as pyinstaller requires it
+from eos.options import E2HConfig, ReaderConfig, ExperimentConfig, E2HReductionConfig
+from eos.command_line import commandLineArgs
+from eos.logconfig import setup_logging, update_loglevel
+
+
+def main():
+    setup_logging()
+    logging.getLogger('matplotlib').setLevel(logging.WARNING)
+
+    # read command line arguments and generate classes holding configuration parameters
+    clas = commandLineArgs([ReaderConfig, ExperimentConfig, E2HReductionConfig],
+                           'amor-nicos')
+    update_loglevel(clas.verbose)
+    if clas.verbose<2:
+        # only log info level in logfile
+        logger = logging.getLogger()  # logging.getLogger('quicknxs')
+        logger.setLevel(logging.INFO)
+
+    reader_config = ReaderConfig.from_args(clas)
+    experiment_config = ExperimentConfig.from_args(clas)
+    reduction_config = E2HReductionConfig.from_args(clas)
+    config = E2HConfig(reader_config, experiment_config, reduction_config)
+
+    logging.warning('######## amor-nicos - Nicos histogram for Amor ########')
+    from eos.reduction_kafka import KafkaReduction
+
+    # only import heavy module if sufficient command line parameters were provided
+    from eos.reduction_reflectivity import ReflectivityReduction
+    # Create reducer with these arguments
+    reducer = KafkaReduction(config)
+    # Perform actual reduction
+    reducer.reduce()
+
+    logging.info('######## amor-nicos - finished ########')
+
+if __name__ == '__main__':
+    main()

eos/options.py

@@ -91,10 +91,12 @@ class ArgParsable:
             if get_origin(typ) is list:
                 args['nargs'] = '+'
                 typ = get_args(typ)[0]
+                if get_origin(typ) is tuple:
+                    # tuple of items are put together during evaluation
+                    typ = get_args(typ)[0]
             elif get_origin(typ) is tuple:
                 args['nargs'] = len(get_args(typ))
                 typ = get_args(typ)[0]
-
             if issubclass(typ, StrEnum):
                 args['choices'] = [ci.value for ci in typ]
                 if field.default is not MISSING:
@@ -149,7 +151,12 @@ class ArgParsable:
             if get_origin(field.type) is Union and type(None) in get_args(field.type):
                 # optional argument
                 typ = get_args(field.type)[0]
-
+            if get_origin(typ) is list:
+                item_typ = get_args(typ)[0]
+                if get_origin(item_typ) is tuple:
+                    # tuple of items are put together during evaluation
+                    tuple_length = len(get_args(item_typ))
+                    value = [tuple(value[i*tuple_length+j] for j in range(tuple_length)) for i in range(len(value)//tuple_length)]
             if isinstance(typ, type) and issubclass(typ, StrEnum):
                 # convert str to enum
                 try:
@@ -359,6 +366,14 @@ class ReflectivityReductionConfig(ArgParsable):
                 'help': 'theta region of interest w.r.t. beam center',
                 },
             )
+    thetaFilters: List[Tuple[float, float]] = field(
+            default_factory=lambda: [],
+            metadata={
+                'short': 'TF',
+                'group': 'region of interest',
+                'help': 'add one or more theta ranges that will be filtered in reduction',
+                },
+            )
     normalisationMethod: NormalisationMethod = field(
             default=NormalisationMethod.over_illuminated,
             metadata={
@@ -640,6 +655,14 @@ class E2HReductionConfig(ArgParsable):
                 },
             )
 
+    kafka: bool = field(
+            default=False,
+            metadata={
+                'group': 'output',
+                'help': 'send result to kafka for Nicos',
+                },
+            )
+
     plotArgs: E2HPlotArguments = field(
             default=E2HPlotArguments.Default,
             metadata={
@@ -701,6 +724,15 @@ class E2HReductionConfig(ArgParsable):
                 },
             )
 
+    thetaFilters: List[Tuple[float, float]] = field(
+            default_factory=lambda: [],
+            metadata={
+                'short': 'TF',
+                'group': 'region of interest',
+                'help': 'add one or more theta ranges that will be filtered in reduction',
+                },
+            )
+
     fontsize: float = field(
             default=8.,
             metadata={

eos/projection.py

@@ -298,7 +298,7 @@ class LZProjection(ProjectionInterface):
                 plt.colorbar(label='I / cpm')
         plt.xlabel('$\\lambda$ / $\\AA$')
         plt.ylabel('$\\Theta$ / °')
-        plt.xlim(3., 12.)
+        plt.xlim(self.lamda[0,0], self.lamda[-1,0])
         af = self.alphaF[self.data.mask]
         plt.ylim(af.min(), af.max())
         plt.title('Wavelength vs. Reflection Angle')
@@ -544,12 +544,12 @@ class TofZProjection(ProjectionInterface):
             ('err', np.float64),
             ])
 
-    def __init__(self, tau, foldback=False):
+    def __init__(self, tau, foldback=False, combine=1):
         self.z = np.arange(Detector.nBlades*Detector.nWires+1)-0.5
         if foldback:
-            self.tof = np.arange(0, tau, 0.0005)
+            self.tof = np.arange(0, tau, 0.0005*combine)
         else:
-            self.tof = np.arange(0, 2*tau, 0.0005)
+            self.tof = np.arange(0, 2*tau, 0.0005*combine)
         self.data = np.zeros((self.tof.shape[0]-1, self.z.shape[0]-1), dtype=self._dtype).view(np.recarray)
         self.monitor = 0.
 
@@ -760,7 +760,7 @@ class TProjection(ProjectionInterface):
 
         plt.xlabel('Reflection Angle / °')
         plt.ylabel('I / cpm')
-        plt.xlim(self.theta[0], self.theta[-1])
+        plt.xlim(self.theta[-1], self.theta[0])
         plt.title('Theta')
 
     def update_plot(self):

eos/reduction_e2h.py

@@ -37,6 +37,8 @@ class E2HReduction:
 
         self.header = Header()
 
+        self.fig = plt.figure()
+        self.register_colormap()
         self.prepare_actions()
 
     def prepare_actions(self):
@@ -47,9 +49,9 @@ class E2HReduction:
         self.file_list = self.path_resolver.resolve(self.config.reduction.fileIdentifier)
         self.file_index = 0
         self.plot_kwds = {}
-        self.fig = plt.figure()
         plt.rcParams.update({'font.size': self.config.reduction.fontsize})
 
+        self.overwrite = eh.ApplyParameterOverwrites(self.config.experiment) # some actions use instrument parameters, change before that
         if self.config.reduction.update:
             # live update implies plotting
             self.config.reduction.show_plot = True
@@ -57,6 +59,9 @@ class E2HReduction:
         if self.config.reduction.plot==E2HPlotSelection.Raw:
             # Raw implies fast caculations
             self.config.reduction.fast = True
+        if not self.config.experiment.is_default('lambdaRange'):
+            # filtering wavelength requires frame analysis
+            self.config.reduction.fast = False
 
         if not self.config.reduction.fast or self.config.reduction.plot in NEEDS_LAMDA:
             from . import event_analysis as ea
@@ -64,6 +69,7 @@ class E2HReduction:
         # Actions on datasets not used for normalization
         self.event_actions = eh.ApplyPhaseOffset(self.config.experiment.chopperPhaseOffset)
         if not self.config.reduction.fast:
+            self.event_actions |= self.overwrite
             self.event_actions |= eh.CorrectChopperPhase()
             self.event_actions |= ea.ExtractWalltime()
         else:
@@ -83,8 +89,8 @@ class E2HReduction:
                 # perform corrections for tof if not fast mode
                 self.event_actions |= eh.TofTimeCorrection(self.config.experiment.incidentAngle==IncidentAngle.alphaF)
         # select needed actions in depenence of plots
-        if self.config.reduction.plot in NEEDS_LAMDA:
-            self.event_actions |= ea.MergeFrames()
+        if self.config.reduction.plot in NEEDS_LAMDA or not self.config.experiment.is_default('lambdaRange'):
+            self.event_actions |= ea.MergeFrames(lamdaCut=self.config.experiment.lambdaRange[0])
             self.event_actions |= ea.AnalyzePixelIDs(self.config.experiment.yRange)
             self.event_actions |= eh.TofTimeCorrection(self.config.experiment.incidentAngle==IncidentAngle.alphaF)
             self.event_actions |= ea.CalculateWavelength(self.config.experiment.lambdaRange)
@@ -92,7 +98,8 @@ class E2HReduction:
 
         # plot dependant options
         if self.config.reduction.plot in [E2HPlotSelection.All, E2HPlotSelection.LT, E2HPlotSelection.Q]:
-            self.grid = LZGrid(0.01, [0.0, 0.25])
+            self.grid = LZGrid(0.05, [0.0, 0.25], lambda_overwrite=self.config.experiment.lambdaRange)
+            self.grid.dldl = 0.01
 
         if self.config.reduction.plot in [E2HPlotSelection.All, E2HPlotSelection.Raw,
                                           E2HPlotSelection.LT, E2HPlotSelection.YT,
@@ -102,8 +109,6 @@ class E2HReduction:
             if self.config.reduction.plotArgs==E2HPlotArguments.Linear:
                 self.plot_kwds['norm'] = None
 
-        self.register_colormap()
-
     def reduce(self):
         if self.config.reduction.plot in [E2HPlotSelection.All, E2HPlotSelection.LT, E2HPlotSelection.Q]:
             if self.config.reduction.normalizationModel:
@@ -125,6 +130,12 @@ class E2HReduction:
         if self.config.reduction.plotArgs==E2HPlotArguments.Default and not self.config.reduction.update:
             # safe to image file if not auto-updating graph
             plt.savefig(f'e2h_{self.config.reduction.plot}.png', dpi=300)
+        if self.config.reduction.kafka:
+            from .kafka_serializer import ESSSerializer
+            self.serializer = ESSSerializer()
+            self.fig.canvas.mpl_connect('close_event', self.serializer.end_command_thread)
+            self.serializer.start_command_thread()
+            self.serializer.send(self.projection)
         if self.config.reduction.update:
             self.timer = self.fig.canvas.new_timer(1000)
             self.timer.add_callback(self.update)
@@ -132,8 +143,9 @@ class E2HReduction:
         if self.config.reduction.show_plot:
             plt.show()
 
+
     def register_colormap(self):
-        cmap = plt.colormaps['gnuplot'](np.arange(256))
+        cmap = plt.colormaps['turbo'](np.arange(256))
         cmap[:1, :] = np.array([256/256, 255/256, 236/256, 1])
         cmap = ListedColormap(cmap, name='jochen_deluxe', N=cmap.shape[0])
         #cmap.set_bad((1.,1.,0.9))
@@ -141,6 +153,7 @@ class E2HReduction:
 
     def prepare_graphs(self):
         last_file_header = AmorHeader(self.file_list[-1])
+        self.overwrite.perform_action(last_file_header)
         tthh  = last_file_header.geometry.nu - last_file_header.geometry.mu
 
         if not self.config.reduction.is_default('thetaRangeR'):
@@ -155,6 +168,8 @@ class E2HReduction:
                 self.projection.correct_gravity(last_file_header.geometry.detectorDistance)
             self.projection.apply_lamda_mask(self.config.experiment.lambdaRange)
             self.projection.apply_theta_mask(thetaRange)
+            for thi in self.config.reduction.thetaFilters:
+                self.projection.apply_theta_filter((thi[0]+tthh, thi[1]+tthh))
             self.projection.apply_norm_mask(self.norm)
 
         if self.config.reduction.plot==E2HPlotSelection.Q:
@@ -164,6 +179,8 @@ class E2HReduction:
             plz.calculate_q()
             plz.apply_lamda_mask(self.config.experiment.lambdaRange)
             plz.apply_theta_mask(thetaRange)
+            for thi in self.config.reduction.thetaFilters:
+                self.projection.apply_theta_filter((thi[0]+tthh, thi[1]+tthh))
             plz.apply_norm_mask(self.norm)
             self.projection = ReflectivityProjector(plz, self.norm)
 
@@ -192,6 +209,8 @@ class E2HReduction:
             plz.calculate_q()
             plz.apply_lamda_mask(self.config.experiment.lambdaRange)
             plz.apply_theta_mask(thetaRange)
+            for thi in self.config.reduction.thetaFilters:
+                plz.apply_theta_filter((thi[0]+tthh, thi[1]+tthh))
             plz.apply_norm_mask(self.norm)
             pr = ReflectivityProjector(plz, self.norm)
             pyz = YZProjection()
@@ -227,7 +246,7 @@ class E2HReduction:
             self.projection.normalize_over_illuminated(self.norm)
 
     def create_file_output(self):
-        ...
+        raise NotImplementedError("Export to text output not yet implemented")
 
     def create_title(self):
         output = "Events to Histogram - "
@@ -247,14 +266,23 @@ class E2HReduction:
         new_files = self.path_resolver.resolve(f'{latest}')
         if not os.path.exists(new_files[-1]):
             return
+        try:
+            # check that events exist in the new file
+            AmorEventData(new_files[-1], 0, max_events=1000)
+        except Exception:
+            logging.debug("Problem when trying to load new dataset", exc_info=True)
+            return
+
         logging.warning(f"Preceding to next file {latest}")
         self.file_list = new_files
         self.file_index = 0
+        self.prepare_actions()
+        self.prepare_graphs()
         self.read_data()
-        self.projection.clear()
         self.add_data()
         self.fig.clear()
         self.create_graph()
+        plt.draw()
 
     def update(self):
         logging.debug("    check for update")
@@ -284,4 +312,7 @@ class E2HReduction:
 
         self.projection.update_plot()
         plt.suptitle(self.create_title())
-        plt.draw()
+        plt.draw()
+
+        if self.config.reduction.kafka:
+            self.serializer.send(self.projection)

eos/reduction_kafka.py

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

eos/reduction_reflectivity.py

@@ -117,7 +117,7 @@ class ReflectivityReduction:
             plt.show()
 
     def read_file_block(self, i, short_notation):
-        logging.warning('reading input:')
+        logging.warning('input:')
         file_list = self.path_resolver.resolve(short_notation)
 
         self.header.measurement_data_files = []
@@ -159,7 +159,7 @@ class ReflectivityReduction:
 
     def analyze_unsliced(self, i):
         self.monitor = self.dataset.data.pulses.monitor.sum()
-        logging.warning(f'    monitor = {self.monitor:8.2f} {MONITOR_UNITS[self.config.experiment.monitorType]}')
+        logging.info(f'    monitor = {self.monitor:8.2f} {MONITOR_UNITS[self.config.experiment.monitorType]}')
 
         proj:LZProjection = self.project_on_lz()
         try:
@@ -373,8 +373,8 @@ class ReflectivityReduction:
         proj = LZProjection.from_dataset(dataset, self.grid,
                                          has_offspecular=(self.config.experiment.incidentAngle!=IncidentAngle.alphaF))
 
+        t0 = dataset.geometry.nu-dataset.geometry.mu
         if not self.config.reduction.is_default('thetaRangeR'):
-            t0 = dataset.geometry.nu - dataset.geometry.mu
             # adjust range based on detector center
             thetaRange = [ti+t0 for ti in self.config.reduction.thetaRangeR]
             proj.apply_theta_mask(thetaRange)
@@ -384,6 +384,9 @@ class ReflectivityReduction:
             thetaRange = [dataset.geometry.nu - dataset.geometry.mu - dataset.geometry.div/2,
                                               dataset.geometry.nu - dataset.geometry.mu + dataset.geometry.div/2]
             proj.apply_theta_mask(thetaRange)
+        for thi in self.config.reduction.thetaFilters:
+            # apply theta filters relative to angle on detector (issues with parts of the incoming divergence)
+            proj.apply_theta_filter((thi[0]+t0, thi[1]+t0))
 
         proj.apply_lamda_mask(self.config.experiment.lambdaRange)
 

nicos_config.md

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

setup.cfg

@@ -35,3 +35,4 @@ Homepage = "https://github.com/jochenstahn/amor"
 console_scripts =
     eos = eos.__main__:main
     events2histogram = eos.e2h:main
+    amor-nicos = eos.nicos:main

update.md

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