Stats calc tool

docs/changes/2628.feature.rst

@@ -0,0 +1 @@
+Add a generic stats-calculation tool utilizing the PixelStatisticsCalculator.

docs/user-guide/tools.rst

@@ -17,6 +17,7 @@ Data Processing Tools
 * ``ctapipe-quickstart``: create some default analysis configurations and a working directory
 * ``ctapipe-process``: Process event data in any supported format from R0/R1/DL0 to DL1 or DL2 HDF5 files.
 * ``ctapipe-apply-models``: Tool to apply machine learning models in bulk (as opposed to event by event).
+* ``ctapipe-calculate-pixel-statistics``: Tool to aggregate statistics and detect outliers from pixel-wise image data.
 * ``ctapipe-train-disp-reconstructor`` : Train the ML models for the  `ctapipe.reco.DispReconstructor` (monoscopic reconstruction)
 * ``ctapipe-train-energy-regressor``:  Train the ML models for the `ctapipe.reco.EnergyRegressor` (energy estimation)
 * ``ctapipe-train-particle-classifier``: Train the ML models for the  `ctapipe.reco.ParticleClassifier` (gamma-hadron separation)

pyproject.toml

@@ -107,6 +107,7 @@ ctapipe-process = "ctapipe.tools.process:main"
 ctapipe-merge = "ctapipe.tools.merge:main"
 ctapipe-fileinfo = "ctapipe.tools.fileinfo:main"
 ctapipe-quickstart = "ctapipe.tools.quickstart:main"
+ctapipe-calculate-pixel-statistics = "ctapipe.tools.calculate_pixel_stats:main"
 ctapipe-train-energy-regressor = "ctapipe.tools.train_energy_regressor:main"
 ctapipe-train-particle-classifier = "ctapipe.tools.train_particle_classifier:main"
 ctapipe-train-disp-reconstructor = "ctapipe.tools.train_disp_reconstructor:main"

src/ctapipe/resources/calculate_pixel_stats.yaml

@@ -0,0 +1,32 @@
+StatisticsCalculatorTool:
+  allowed_tels: [1,2,3,4]
+  input_column_name: image
+  output_column_name: statistics
+
+PixelStatisticsCalculator:
+  stats_aggregator_type:
+    - ["type", "LST*", "SigmaClippingAggregator"]
+    - ["type", "MST*", "PlainAggregator"]
+
+  chunk_shift: 1000
+  faulty_pixels_fraction: 0.1
+  outlier_detector_list:
+    -   name: MedianOutlierDetector
+        apply_to: median
+        config:
+            median_range_factors: [-15, 15]
+    -   name: RangeOutlierDetector
+        apply_to: median
+        config:
+            validity_range: [-20, 120]
+    -   name: StdOutlierDetector
+        apply_to: std
+        config:
+            std_range_factors: [-15, 15]
+
+SigmaClippingAggregator:
+  chunk_size: 2500
+  max_sigma: 4
+  iterations: 5
+PlainAggregator:
+  chunk_size: 2500

src/ctapipe/tools/calculate_pixel_stats.py

@@ -0,0 +1,194 @@
+"""
+Perform statistics calculation from pixel-wise image data
+"""
+
+import pathlib
+
+import numpy as np
+from astropy.table import vstack
+
+from ctapipe.core import Tool
+from ctapipe.core.tool import ToolConfigurationError
+from ctapipe.core.traits import (
+    Bool,
+    CInt,
+    Path,
+    Set,
+    Unicode,
+    classes_with_traits,
+)
+from ctapipe.instrument import SubarrayDescription
+from ctapipe.io import write_table
+from ctapipe.io.tableloader import TableLoader
+from ctapipe.monitoring.calculator import PixelStatisticsCalculator
+
+
+class StatisticsCalculatorTool(Tool):
+    """
+    Perform statistics calculation for pixel-wise image data
+    """
+
+    name = "StatisticsCalculatorTool"
+    description = "Perform statistics calculation for pixel-wise image data"
+
+    examples = """
+    To calculate statistics of pixel-wise image data files:
+
+    > ctapipe-calculate-pixel-statistics --TableLoader.input_url input.dl1.h5 --output_path /path/monitoring.h5 --overwrite
+
+    """
+
+    allowed_tels = Set(
+        trait=CInt(),
+        default_value=None,
+        allow_none=True,
+        help=(
+            "List of allowed tel_ids, others will be ignored. "
+            "If None, all telescopes in the input stream will be included."
+        ),
+    ).tag(config=True)
+
+    input_column_name = Unicode(
+        default_value="image",
+        allow_none=False,
+        help="Column name of the pixel-wise image data to calculate statistics",
+    ).tag(config=True)
+
+    output_table_name = Unicode(
+        default_value="statistics",
+        allow_none=False,
+        help="Table name of the output statistics",
+    ).tag(config=True)
+
+    output_path = Path(
+        help="Output filename", default_value=pathlib.Path("monitoring.h5")
+    ).tag(config=True)
+
+    overwrite = Bool(help="Overwrite output file if it exists").tag(config=True)
+
+    aliases = {
+        ("i", "input_url"): "TableLoader.input_url",
+        ("o", "output_path"): "StatisticsCalculatorTool.output_path",
+    }
+
+    flags = {
+        "overwrite": (
+            {"StatisticsCalculatorTool": {"overwrite": True}},
+            "Overwrite existing files",
+        ),
+    }
+
+    classes = [
+        TableLoader,
+    ] + classes_with_traits(PixelStatisticsCalculator)
+
+    def setup(self):
+        # Read the input data with the 'TableLoader'
+        self.input_data = TableLoader(
+            parent=self,
+        )
+        # Check that the input and output files are not the same
+        if self.input_data.input_url == self.output_path:
+            raise ToolConfigurationError(
+                "Input and output files are same. Fix your configuration / cli arguments."
+            )
+        # Load the subarray description from the input file
+        subarray = SubarrayDescription.from_hdf(self.input_data.input_url)
+        # Get the telescope ids from the input data or use the allowed_tels configuration
+        self.tel_ids = (
+            subarray.tel_ids if self.allowed_tels is None else self.allowed_tels
+        )
+        # Initialization of the statistics calculator
+        self.stats_calculator = PixelStatisticsCalculator(
+            parent=self, subarray=subarray
+        )
+
+    def start(self):
+        # Iterate over the telescope ids and calculate the statistics
+        for tel_id in self.tel_ids:
+            # Read the whole dl1 images for one particular telescope
+            dl1_table = self.input_data.read_telescope_events_by_id(
+                telescopes=[
+                    tel_id,
+                ],
+                dl1_images=True,
+                dl1_parameters=False,
+                dl1_muons=False,
+                dl2=False,
+                simulated=False,
+                true_images=False,
+                true_parameters=False,
+                instrument=False,
+                pointing=False,
+            )[tel_id]
+            # Check if the chunk size does not exceed the table length of the input data
+            if self.stats_calculator.stats_aggregators[
+                self.stats_calculator.stats_aggregator_type.tel[tel_id]
+            ].chunk_size > len(dl1_table):
+                raise ToolConfigurationError(
+                    f"Change --StatisticsAggregator.chunk_size to decrease the chunk size "
+                    f"of the aggregation to a maximum of '{len(dl1_table)}' (table length of the "
+                    f"input data for telescope 'tel_id={tel_id}')."
+                )
+            # Check if the input column name is in the table
+            if self.input_column_name not in dl1_table.colnames:
+                raise ToolConfigurationError(
+                    f"Column '{self.input_column_name}' not found "
+                    f"in the input data for telescope 'tel_id={tel_id}'."
+                )
+            # Perform the first pass of the statistics calculation
+            aggregated_stats = self.stats_calculator.first_pass(
+                table=dl1_table,
+                tel_id=tel_id,
+                col_name=self.input_column_name,
+            )
+            # Check if 'chunk_shift' is selected
+            if self.stats_calculator.chunk_shift is not None:
+                # Check if there are any faulty chunks to perform a second pass over the data
+                if np.any(~aggregated_stats["is_valid"].data):
+                    # Perform the second pass of the statistics calculation
+                    aggregated_stats_secondpass = self.stats_calculator.second_pass(
+                        table=dl1_table,
+                        valid_chunks=aggregated_stats["is_valid"].data,
+                        tel_id=tel_id,
+                        col_name=self.input_column_name,
+                    )
+                    # Stack the statistic values from the first and second pass
+                    aggregated_stats = vstack(
+                        [aggregated_stats, aggregated_stats_secondpass]
+                    )
+                    # Sort the stacked aggregated statistic values by starting time
+                    aggregated_stats.sort(["time_start"])
+                else:
+                    self.log.info(
+                        "No faulty chunks found for telescope 'tel_id=%d'. Skipping second pass.",
+                        tel_id,
+                    )
+            # Add metadata to the aggregated statistics
+            aggregated_stats.meta["event_type"] = dl1_table["event_type"][0]
+            aggregated_stats.meta["input_column_name"] = self.input_column_name
+            # Write the aggregated statistics and their outlier mask to the output file
+            write_table(
+                aggregated_stats,
+                self.output_path,
+                f"/dl1/monitoring/telescope/{self.output_table_name}/tel_{tel_id:03d}",
+                overwrite=self.overwrite,
+            )
+
+    def finish(self):
+        self.log.info(
+            "DL1 monitoring data was stored in '%s' under '%s'",
+            self.output_path,
+            f"/dl1/monitoring/telescope/{self.output_table_name}",
+        )
+        self.log.info("Tool is shutting down")
+
+
+def main():
+    # Run the tool
+    tool = StatisticsCalculatorTool()
+    tool.run()
+
+
+if __name__ == "main":
+    main()

src/ctapipe/tools/quickstart.py

@@ -12,6 +12,7 @@
 
 CONFIGS_TO_WRITE = [
     "base_config.yaml",
+    "calculate_pixel_stats.yaml",
     "stage1_config.yaml",
     "stage2_config.yaml",
     "ml_preprocessing_config.yaml",

src/ctapipe/tools/tests/test_calculate_pixel_stats.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python3
+"""
+Test ctapipe-calculate-pixel-statistics tool
+"""
+
+import pytest
+from traitlets.config.loader import Config
+
+from ctapipe.core import run_tool
+from ctapipe.core.tool import ToolConfigurationError
+from ctapipe.io import read_table
+from ctapipe.tools.calculate_pixel_stats import StatisticsCalculatorTool
+
+
+def test_calculate_pixel_stats_tool(tmp_path, dl1_image_file):
+    """check statistics calculation from pixel-wise image data files"""
+
+    # Create a configuration suitable for the test
+    tel_id = 3
+    config = Config(
+        {
+            "StatisticsCalculatorTool": {
+                "allowed_tels": [tel_id],
+                "input_column_name": "image",
+                "output_table_name": "statistics",
+            },
+            "PixelStatisticsCalculator": {
+                "stats_aggregator_type": [
+                    ("id", tel_id, "PlainAggregator"),
+                ],
+            },
+            "PlainAggregator": {
+                "chunk_size": 1,
+            },
+        }
+    )
+    # Set the output file path
+    monitoring_file = tmp_path / "monitoring.dl1.h5"
+    # Run the tool with the configuration and the input file
+    run_tool(
+        StatisticsCalculatorTool(config=config),
+        argv=[
+            f"--input_url={dl1_image_file}",
+            f"--output_path={monitoring_file}",
+            "--overwrite",
+        ],
+        cwd=tmp_path,
+        raises=True,
+    )
+    # Check that the output file has been created
+    assert monitoring_file.exists()
+    # Check that the output file is not empty
+    assert (
+        read_table(
+            monitoring_file,
+            path=f"/dl1/monitoring/telescope/statistics/tel_{tel_id:03d}",
+        )["mean"]
+        is not None
+    )
+
+
+def test_tool_config_error(tmp_path, dl1_image_file):
+    """check tool configuration error"""
+
+    # Run the tool with the configuration and the input file
+    config = Config(
+        {
+            "StatisticsCalculatorTool": {
+                "allowed_tels": [3],
+                "input_column_name": "image_charges",
+                "output_table_name": "statistics",
+            }
+        }
+    )
+    # Set the output file path
+    monitoring_file = tmp_path / "monitoring.dl1.h5"
+    # Check if ToolConfigurationError is raised
+    # when the column name of the pixel-wise image data is not correct
+    with pytest.raises(
+        ToolConfigurationError, match="Column 'image_charges' not found"
+    ):
+        run_tool(
+            StatisticsCalculatorTool(config=config),
+            argv=[
+                f"--input_url={dl1_image_file}",
+                f"--output_path={monitoring_file}",
+                "--StatisticsAggregator.chunk_size=1",
+                "--overwrite",
+            ],
+            cwd=tmp_path,
+            raises=True,
+        )
+    # Check if ToolConfigurationError is raised
+    # when the input and output files are the same
+    with pytest.raises(
+        ToolConfigurationError, match="Input and output files are same."
+    ):
+        run_tool(
+            StatisticsCalculatorTool(),
+            argv=[
+                f"--input_url={dl1_image_file}",
+                f"--output_path={dl1_image_file}",
+                "--overwrite",
+            ],
+            cwd=tmp_path,
+            raises=True,
+        )
+    # Check if ToolConfigurationError is raised
+    # when the chunk size is larger than the number of events in the input file
+    with pytest.raises(
+        ToolConfigurationError, match="Change --StatisticsAggregator.chunk_size"
+    ):
+        run_tool(
+            StatisticsCalculatorTool(),
+            argv=[
+                f"--input_url={dl1_image_file}",
+                f"--output_path={monitoring_file}",
+                "--StatisticsCalculatorTool.allowed_tels=3",
+                "--StatisticsAggregator.chunk_size=2500",
+                "--overwrite",
+            ],
+            cwd=tmp_path,
+            raises=True,
+        )