Loader version 2 with much less steps.

scipp · Dec 11, 2023 · 63890d2 · 63890d2
1 parent 1aa688f
commit 63890d2
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diff --git a/docs/examples/workflow.ipynb b/docs/examples/workflow.ipynb
@@ -47,20 +47,22 @@
    "source": [
     "import sciline as sl\n",
     "from ess.nmx.workflow import collect_default_parameters, providers\n",
-    "from ess.nmx.loader import FileTypeMcStas, InputFileName, MaximumProbability, DefaultMaximumProbability\n",
+    "from ess.nmx.loader import InputFileName, MaximumProbability, DefaultMaximumProbability\n",
     "from ess.nmx.data import small_mcstas_sample\n",
-    "from ess.nmx.reduction import TimeBinned, TimeBinStep\n",
+    "from ess.nmx.reduction import TimeBinned, TimeBinStep, get_intervals_mcstas\n",
+    "from ess.nmx.logging import get_logger as get_nmx_logger\n",
     "\n",
     "file_path = small_mcstas_sample()  # Replace it with your data file path\n",
     "\n",
-    "nmx_workflow = sl.Pipeline(list(providers), params={\n",
+    "nmx_workflow = sl.Pipeline(list(providers)+[get_nmx_logger, get_intervals_mcstas],\n",
+    "                           params={\n",
     "                            **collect_default_parameters(),\n",
     "                            MaximumProbability: DefaultMaximumProbability,\n",
     "                            TimeBinStep: TimeBinStep(1),\n",
     "                            InputFileName: InputFileName(file_path),\n",
     "                        })\n",
     "\n",
-    "time_binned = nmx_workflow.compute(TimeBinned[FileTypeMcStas])\n",
+    "time_binned = nmx_workflow.compute(TimeBinned)\n",
     "time_binned"
    ]
   },
@@ -89,7 +91,8 @@
     "## Collect Providers and Parameters\n",
     "\n",
     "There is a helper to collect default parameters and collection of all providers in ``ess.nmx.workflow`` module. <br>\n",
-    "If you need to replace any parameters, you can replace them in the dictionary and use it to build a pipeline. <br>"
+    "If you need to replace any parameters, you can replace them in the dictionary and use it to build a pipeline. <br>\n",
+    "For example, in order to load McStas events, you can set which schema to use by setting ``McStasEventDataSchema`` as a parameter."
    ]
   },
   {
@@ -101,13 +104,16 @@
     "import scipp as sc\n",
     "from ess.nmx.workflow import collect_default_parameters, providers\n",
     "from ess.nmx.loader import InputFileName\n",
+    "from ess.nmx.loader import McStasEventDataSchema, DefaultMcStasEventDataSchema, MaximumProbability, DefaultMaximumProbability\n",
     "\n",
     "# Collect Parameters\n",
     "# ``TimeBinStep`` and ``InputFileName`` are not included in the default parameters.\n",
     "params = {\n",
     "    **collect_default_parameters(),\n",
     "    TimeBinStep: TimeBinStep(1),\n",
-    "    InputFileName: small_mcstas_sample()\n",
+    "    InputFileName: small_mcstas_sample(),\n",
+    "    MaximumProbability: DefaultMaximumProbability,\n",
+    "    McStasEventDataSchema: DefaultMcStasEventDataSchema  \n",
     "}\n",
     "\n",
     "# Parameters to run the workflow:\n",
@@ -118,7 +124,10 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Build Pipeline"
+    "## Build Pipeline\n",
+    "\n",
+    "To read McStas file, you might need to insert more providers. <br>\n",
+    "Otherwise, it will use default parameters."
    ]
   },
   {
@@ -128,17 +137,15 @@
    "outputs": [],
    "source": [
     "import sciline as sl\n",
-    "from ess.nmx.loader import FileTypeMcStas\n",
     "from ess.nmx.reduction import TimeBinned\n",
     "\n",
     "pl = sl.Pipeline(\n",
-    "    list(providers),\n",
+    "    list(providers)+[get_intervals_mcstas],\n",
     "    params=params\n",
     ")\n",
     "\n",
-    "file_type = FileTypeMcStas\n",
-    "mcstas_workflow_graph = pl.get(TimeBinned[FileTypeMcStas])\n",
-    "mcstas_workflow_diagram = mcstas_workflow_graph.visualize(graph_attr={'rankdir': 'LR'})\n",
+    "mcstas_workflow_graph = pl.get(TimeBinned)\n",
+    "mcstas_workflow_diagram = mcstas_workflow_graph.visualize()\n",
     "mcstas_workflow_diagram.render('mcstas_workflow_graph', 'png')\n",
     "mcstas_workflow_diagram"
    ]
@@ -158,7 +165,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "time_binned = mcstas_workflow_graph.compute(TimeBinned[FileTypeMcStas])\n",
+    "time_binned = mcstas_workflow_graph.compute(TimeBinned)\n",
     "time_binned"
    ]
   },
@@ -178,8 +185,8 @@
    "source": [
     "from ess.nmx.reduction import GroupedByPixelID\n",
     "\n",
-    "multiple_results = pl.get((GroupedByPixelID[file_type], TimeBinned[file_type])).compute()\n",
-    "grouped = multiple_results[GroupedByPixelID[file_type]]\n",
+    "multiple_results = pl.get((GroupedByPixelID, TimeBinned)).compute()\n",
+    "grouped = multiple_results[GroupedByPixelID]\n",
     "\n",
     "grouped"
    ]

diff --git a/src/ess/nmx/detector.py b/src/ess/nmx/detector.py
@@ -1,12 +1,14 @@
 # SPDX-License-Identifier: BSD-3-Clause
 # Copyright (c) 2023 Scipp contributors (https://github.com/scipp)
 from dataclasses import dataclass
-from typing import NewType
+from typing import NewType, TypeVar
 
 MaxNumberOfPixelsPerAxis = NewType("MaxNumberOfPixelsPerAxis", int)
 PixelStep = NewType("PixelStep", int)
 NumberOfDetectors = NewType("NumberOfDetectors", int)
 NumberOfAxes = NewType("NumberOfAxes", int)
+Detector = TypeVar("Detector")
+DetectorID = NewType("DetectorID", int)
 
 
 @dataclass