From 1c92cfb08dddf9f33150923aae888fb3b59c1813 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 28 Aug 2025 10:52:39 -0700 Subject: [PATCH 001/111] add fill value attribute requirement for std file --- docs/standard_format.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/docs/standard_format.md b/docs/standard_format.md index 23537b9..4371d19 100644 --- a/docs/standard_format.md +++ b/docs/standard_format.md @@ -200,6 +200,7 @@ The possible units fields are the following: - Location of the probes must be defined as attributes following a spatial description convention. - If geographic coordinates are used, a CRS must be included. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - In the following example, the array dimensions can be: - time -> ($N_t$) - data (temperature, wind_speed, *etc.*) -> ($N_t$) @@ -228,6 +229,7 @@ The possible units fields are the following: - If geographic coordinates are used, a CRS must be included. - Each dataset (wind_speed, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - The coordinate arrays may be 1D, or time-dependent 1D, depending on the grid type (regular, curvilinear, moving). - In the following example, the array dimensions can be: - time -> ($N_t$) @@ -251,6 +253,7 @@ The possible units fields are the following: - If geographic coordinates are used, a CRS must be included. - Each dataset (rate_of_spread, wind_u, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - The coordinate arrays may be 1D, 2D, or time-dependent 2D, depending on the grid type (regular, curvilinear, moving). - In the following example, the array dimensions can be: - time -> ($N_t$) @@ -285,6 +288,7 @@ The possible units fields are the following: - If geographic coordinates are used, a CRS must be included. - Each dataset (temperature, wind_u, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - The coordinate arrays may be 1D, 3D, or time-dependent 3D, depending on the grid type (regular, curvilinear, moving). - In the following example, the array dimensions can be: - time -> ($N_t$) @@ -312,6 +316,7 @@ The possible units fields are the following: - Datasets must be grouped at the lowest common level that minimizes data duplication. Variables sharing the same time coordinate and the same spatial coordinate are placed in the same data group. - All spatial coordinates must follow the Spatial Information Convention, including CRS where applicable. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - Each dataset (temperature, wind_u, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. - The following example proposes a structure for a particle trajectories dataset, an output of a model using an unstructured mesh, and a dataset containing building positions and information about buildings. @@ -343,6 +348,7 @@ The possible units fields are the following: - Polygons that have a specific time stamp must contain an attribute `time` following the time format convention (each polygon dataset has its own time attribute). - Per-polygon attributes (e.g., building type, perimeter source) should be stored as attributes at the lowest common level. Group attributes are considered common to all datasets contained in the group. If information is specific to a polygon, it should be stored as a dataset attribute. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - Each dataset (fire perimeter, buildings, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. - Polygons are stored as (Nvertices, 2) or (Nvertices, 3) arrays following a Spatial Information Convention. @@ -366,6 +372,7 @@ The possible units fields are the following: - Each fuel property (fuel load, fuel height, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. - Each fuel property dataset must contain the attributes `long_name` describing the property, `unit`, and `type` describing the variable type in the numpy array (*e.g.* float64, object, int32). String variables will be using the object type. - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. - The number of fuel categories contained in a fuel model must be specified by the attribute `nb_fuel_cat` of the fuel model group. - In the following example, the array dimensions must share one dimension size defined by the attribute `nb_fuel_cat` of `Anderson13` and `WUDAPT10` groups. The size of the first dimension of all category-dependent datasets must match `nb_fuel_cat`. For example the dataset for a fuel parameter can have the shape ($N$) or ($N$, $N_2$) if $N$ is the number of fuel categories (`nb_fuel_cat`) and $N_2$ a parameter specific dimension (*e.g.*, size classes, depth layers). @@ -390,4 +397,6 @@ The possible units fields are the following: - Spatial and temporal metadata following the relevant conventions in this standard, if applicable. - Naming of datasets should remain descriptive and avoid collisions with reserved names in the standard. - Use of `/miscellaneous` should be temporary whenever possible; data types that become common should be proposed for inclusion in future versions of the standard. -- The structure of `/miscellaneous` is unconstrained, but good practice is to group related datasets together to improve clarity. \ No newline at end of file +- The structure of `/miscellaneous` is unconstrained, but good practice is to group related datasets together to improve clarity. +- Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. +- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. \ No newline at end of file From f933e80c36ff2c2dae3f3f1dbcc4726ebb704452 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 8 Sep 2025 14:40:41 +0200 Subject: [PATCH 002/111] add check-consistency-namespace in makefile - checks that the list of variables in namespace is the same between the python class and the documentation --- .../check_namespace_file_consistency.py | 63 +++++++++++++++++++ Makefile | 6 +- pyproject.toml | 4 +- 3 files changed, 71 insertions(+), 2 deletions(-) create mode 100644 .github/actions/check_namespace_file_consistency.py diff --git a/.github/actions/check_namespace_file_consistency.py b/.github/actions/check_namespace_file_consistency.py new file mode 100644 index 0000000..6aad306 --- /dev/null +++ b/.github/actions/check_namespace_file_consistency.py @@ -0,0 +1,63 @@ +""" +Dev-only namespace consistency checker. + +- Reads a fixed Markdown file to get the list of standard variable names. +- Imports `svn` from `firebench` to get the authoritative list from code. +- Compares both lists and prints differences. +- No argparse, no unit/type checks, no JSON. + +Usage: + in root directory: make check-consistency-namespace +""" + +import re +from pathlib import Path + +# Docs path +NAMESPACE_DOCS = Path("docs/namespace.md") +_MD_NAME_RE = re.compile(r"-\s*`([^`]+)`") + +def get_md_variables(md_path: Path) -> list[str]: + """Return the list of variable names listed in the Markdown spec.""" + text = md_path.read_text(encoding="utf-8") + return _MD_NAME_RE.findall(text) + +def get_svn_variables() -> list[str]: + """Return the list of variable names from `firebench.svn`. + + Supports several shapes: + - Enum class (uses __members__) + - dict of names + - list/tuple/set of names + - module / object with UPPERCASE attributes + """ + from firebench import svn + + # Enum class (most common) + members = getattr(svn, "__members__", None) + if isinstance(members, dict): + return sorted(members.keys()) + + # collect UPPERCASE attribute names on the object/module + names = [n for n in dir(svn) if n.isupper() and not n.startswith("_")] + return sorted(set(names)) + +def check_consistency() -> None: + md_vars = set(get_md_variables(NAMESPACE_DOCS)) + svn_vars = set(get_svn_variables()) + + only_in_docs = sorted(md_vars - svn_vars) + only_in_svn = sorted(svn_vars - md_vars) + common = sorted(md_vars & svn_vars) + + print("== Namespace Consistency Check ==") + print(f"Common: {len(common)}") + print(f"Only in docs (missing from svn): {len(only_in_docs)}") + for name in only_in_docs: + print(f" - {name}") + print(f"Only in svn (missing from docs): {len(only_in_svn)}") + for name in only_in_svn: + print(f" - {name}") + +if __name__ == "__main__": + check_consistency() diff --git a/Makefile b/Makefile index 3310882..9241bde 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ # Declare these targets as phony to avoid conflicts with files of the same name -.PHONY: test test-cov lint update-lint-score code-formatting bandit update-docs-changelog generate-api-doc docs clean +.PHONY: test test-cov lint update-lint-score code-formatting bandit update-docs-changelog check-consistency-namespace generate-api-doc docs clean # Run all tests without coverage report test: @@ -32,6 +32,10 @@ bandit: update-docs-changelog: python .github/actions/update_changelog_in_docs.py +# Check consistency between namespace in package and in docs +check-consistency-namespace: + python .github/actions/check_namespace_file_consistency.py + # Update API documentation generate-api-doc: python .github/actions/generate_api_docs.py diff --git a/pyproject.toml b/pyproject.toml index 9509484..3375076 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "hatchling.build" [project] name = "firebench" -version = "0.7.0" +version = "0.8.0a" authors = [ {name = "Aurélien Costes", email = "aurelien.costes31@gmail.com"}, ] @@ -20,6 +20,8 @@ dependencies = [ "h5py < 4.0", "geopandas < 2.0", "matplotlib > 3.8", + "rasterio < 2.0", + "pyproj < 4.0", ] readme = "README.md" license = {file = "LICENSE"} From 007f6fbb0351eede0a961bef10d8611dbe4bea39 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 9 Sep 2025 09:44:10 +0200 Subject: [PATCH 003/111] add missing variables in namespace docs --- .../check_namespace_file_consistency.py | 8 ++++++-- docs/dependencies.md | 6 ++++-- docs/namespace.md | 20 +++++++++++++++++++ 3 files changed, 30 insertions(+), 4 deletions(-) diff --git a/.github/actions/check_namespace_file_consistency.py b/.github/actions/check_namespace_file_consistency.py index 6aad306..938b8b5 100644 --- a/.github/actions/check_namespace_file_consistency.py +++ b/.github/actions/check_namespace_file_consistency.py @@ -17,11 +17,13 @@ NAMESPACE_DOCS = Path("docs/namespace.md") _MD_NAME_RE = re.compile(r"-\s*`([^`]+)`") + def get_md_variables(md_path: Path) -> list[str]: """Return the list of variable names listed in the Markdown spec.""" text = md_path.read_text(encoding="utf-8") return _MD_NAME_RE.findall(text) + def get_svn_variables() -> list[str]: """Return the list of variable names from `firebench.svn`. @@ -42,13 +44,14 @@ def get_svn_variables() -> list[str]: names = [n for n in dir(svn) if n.isupper() and not n.startswith("_")] return sorted(set(names)) + def check_consistency() -> None: md_vars = set(get_md_variables(NAMESPACE_DOCS)) svn_vars = set(get_svn_variables()) only_in_docs = sorted(md_vars - svn_vars) - only_in_svn = sorted(svn_vars - md_vars) - common = sorted(md_vars & svn_vars) + only_in_svn = sorted(svn_vars - md_vars) + common = sorted(md_vars & svn_vars) print("== Namespace Consistency Check ==") print(f"Common: {len(common)}") @@ -59,5 +62,6 @@ def check_consistency() -> None: for name in only_in_svn: print(f" - {name}") + if __name__ == "__main__": check_consistency() diff --git a/docs/dependencies.md b/docs/dependencies.md index eaaebf4..4f4bcbd 100644 --- a/docs/dependencies.md +++ b/docs/dependencies.md @@ -2,13 +2,15 @@ ## Required +- geopandas < 2.0 - h5py < 4.0 +- matplotlib > 3.8 - numpy < 3.0 - pint < 1.0 +- pyproj < 4.0 +- rasterio < 2.0 - SALib < 2.0 - scipy < 2.0 -- geopandas < 2.0 -- matplotlib > 3.8 ## Optional These dependencies are required for advanced use (testing, offline generation of documentation, etc.) diff --git a/docs/namespace.md b/docs/namespace.md index 8d46257..4ab2599 100644 --- a/docs/namespace.md +++ b/docs/namespace.md @@ -14,9 +14,17 @@ The Standard Variable Namespace `svn` is accessed in workflows using `from fireb - `AIR_TEMPERATURE`: Air temperature [K] - `ALPHA`: Alpha - `BETA`: Beta +- `BUILDING_RATIO_FIRE_RESISTANT`: Ratio of fire resistant building [-] +- `BUILDING_RATIO_STRUCTURE_WOOD_BARE`: Ratio of buildings made with bare wood materials [-] +- `BUILDING_RATIO_STRUCTURE_WOOD_MORTAR`: Ratio of buildings made with mortar [-] +- `BUILDING_LENGTH_SEPARATION`: Buildings seperation length [m] +- `BUILDING_LENGTH_SIDE`: Buildings side length [m] - `CANOPY_DENSITY_BULK`: Canopy bulk density [kg m-3] - `CANOPY_HEIGHT_BOTTOM`: Canopy height bottom [m] - `CANOPY_HEIGHT_TOP`: Canopy height top [m] +- `DIRECTION`: Direction [-] +- `FIRE_ARRIVAL_TIME`: Fire arrival time [s] +- `FIRE_BURN_SEVERITY`: Fire burn severity [-] - `FUEL_CLASS`: Fuel class (int) - `FUEL_CHAPARRAL_FLAG`: Fuel chaparral flag (int) - `FUEL_COVER`: Fuel cover [%] @@ -65,10 +73,22 @@ The Standard Variable Namespace `svn` is accessed in workflows using `from fireb - `FUEL_WIND_HEIGHT`: Fuel wind height [m] - `FUEL_WIND_REDUCTION_FACTOR`: Fuel wind reduction factor [-] - `IGNITION_LENGTH`: Ignition length [m] +- `LATITUDE`: Latitude [deg] - `LENGTH`: Length [m] +- `LONGITUDE`: Longitude [deg] +- `MAGNITUDE`: Magnitude +- `NORMAL_SPREAD_DIR_X`: Fire front normal vector component in x direction [-] +- `NORMAL_SPREAD_DIR_Y`: Fire front normal vector component in y direction [-] - `RATE_OF_SPREAD`: Rate of spread [m s-1] - `RELATIVE_HUMIDITY`: Relative Humidity [m s-1] - `SLOPE_ANGLE`: Slope angle [deg] - `TEMPERATURE`: Temperature [K] - `TIME`: Time [s] +- `WIND_DIRECTION`: Wind direction [deg] - `WIND_SPEED`: Wind [m s-1] +- `WIND_SPEED_U`: Wind speed in x direction [m s-1] +- `WIND_SPEED_V`: Wind speed in y direction [m s-1] +- `WIND_SPEED_W`: Wind speed in z direction [m s-1] +- `X`: x coordinate [m] +- `Y`: y coordinate [m] +- `Z`: z coordinate [m] From ef676825fe723f94f84b7c39f4d1b96c3afcb91e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 9 Sep 2025 09:44:45 +0200 Subject: [PATCH 004/111] add standardization of mtbs data --- src/firebench/__init__.py | 2 +- src/firebench/standardize/__init__.py | 2 + src/firebench/standardize/mtbs.py | 130 ++++++++++++++++++++++++++ src/firebench/standardize/tools.py | 58 ++++++++++++ src/firebench/tools/namespace.py | 1 + 5 files changed, 192 insertions(+), 1 deletion(-) create mode 100644 src/firebench/standardize/__init__.py create mode 100644 src/firebench/standardize/mtbs.py create mode 100644 src/firebench/standardize/tools.py diff --git a/src/firebench/__init__.py b/src/firebench/__init__.py index 5eab604..be86d31 100644 --- a/src/firebench/__init__.py +++ b/src/firebench/__init__.py @@ -1,4 +1,4 @@ -from . import ros_models, tools, wind_interpolation, stats, metrics, sensors +from . import ros_models, tools, wind_interpolation, stats, metrics, sensors, standardize from .tools.logging_config import logger from .tools.namespace import StandardVariableNames as svn from .tools.units import ureg diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py new file mode 100644 index 0000000..2a6ff62 --- /dev/null +++ b/src/firebench/standardize/__init__.py @@ -0,0 +1,2 @@ +from .mtbs import convert_mtbs_geotiff +from .tools import VERSION_STD diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py new file mode 100644 index 0000000..1ae29c7 --- /dev/null +++ b/src/firebench/standardize/mtbs.py @@ -0,0 +1,130 @@ +from datetime import datetime +from pathlib import Path + +import h5py +import numpy as np +import rasterio +from pyproj import CRS, Transformer + +from ..tools import StandardVariableNames as svn +from ..tools import current_datetime_iso8601, datetime_to_iso8601 +from .tools import VERSION_STD, check_std_version +from ..tools.logging_config import logger + + +def convert_mtbs_geotiff( + path: str, + dst: str, + group_name: str | None = None, + authors: str = "", + std_file_description: str = "auto", + overwrite: bool = False, +): + """ + Convert a MTBS GeoTIFF to Firebench HDF5 standard file. + + Parameters + ---------- + path : str + Path to the MTBS GeoTIFF (ending with *_dnbr6.tif). + dst : str + Path to target HDF5 file (created or appended). + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. '2D_raster/'. + authors: str + Add the names of the authors in the standard file in the file attribute `created_by`. + std_file_description: str + Add custom description of the mtbs group. If description is "auto" the description will be "\n- Burn severity from MTBS". + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + + Returns + ------- + str + The actual HDF5 group written (with suffix if collision). + """ # pylint: disable=line-too-long + with rasterio.open(path) as src: + data = src.read(1) + severity_raw = {"data": data, "transform": src.transform, "crs": src.crs, "nodata": src.nodata} + logger.info(f"Loaded {path}: shape={data.shape}, CRS={src.crs}") + + rows, cols = data.shape + + # Build pixel center coordinates (projected) + # col indices (x-direction), row indices (y-direction) + jj = np.arange(cols) + ii = np.arange(rows) + # vectorized center coordinates from affine: + # x = a*col + b*row + c ; y = d*col + e*row + f + # Use broadcasting to form 2-D arrays without Python loops. + x = ( + severity_raw["transform"].a * jj[None, :] + + severity_raw["transform"].b * ii[:, None] + + severity_raw["transform"].c + ) + y = ( + severity_raw["transform"].d * jj[None, :] + + severity_raw["transform"].e * ii[:, None] + + severity_raw["transform"].f + ) + + # Reproject to geographic lat/lon + # Choose your target CRS: + # - WGS84 geographic: + epsg_code = 4269 + tgt_crs = CRS.from_epsg(epsg_code) # lon/lat + # - OR NAD83 geographic: + # tgt_crs = CRS.from_epsg(4269) # lon/lat + + # always_xy=True -> transformer expects/returns (x, y) = (lon, lat) ordering for geographic CRS + transformer = Transformer.from_crs(severity_raw["crs"], tgt_crs, always_xy=True) + lon, lat = transformer.transform(x, y) # lon, lat are 2-D arrays aligned with `data` + + with h5py.File(dst, "a") as h5: + + if check_std_version(h5): + h5.attrs["FireBench_io_version"] = VERSION_STD + + h5.attrs["created_on"] = current_datetime_iso8601(include_seconds=False) + + if "created_by" in h5.attrs.keys(): + if authors is not None: + h5.attrs["created_by"] = h5.attrs["created_by"] + f", {authors}" + else: + h5.attrs["created_by"] = authors + + if std_file_description == "auto": + std_file_description = "\n- Burn severity from MTBS" + if "description" in h5.attrs.keys(): + h5.attrs["description"] = h5.attrs["description"] + "\n- Burn severity from MTBS" + else: + h5.attrs["description"] = "This file contains:\n- Burn severity from MTBS" + + group_name = f"/2D_raster/{group_name}" + if group_name in h5.keys(): + if overwrite: + del h5[group_name] + else: + logger.warning( + "group name %s already exists in file %s. Group not updated. Set `overwrite` to True to update the dataset.", + group_name, + path, + ) + return + + g = h5.create_group(group_name) + g.attrs["data_source"] = f"MTBS {path}" + + # TODO: add some useful metadata from _metadata.xml (fire_ignition_time, pre_fire_image_date, post_fire_image_date) + + # Lat/Lon as 2-D arrays + dlat = g.create_dataset("latitude", data=lat, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + dlat = g.create_dataset("longitude", data=lon, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8) + ddata.attrs["crs"] = f"EPSG:{epsg_code}" + ddata.attrs["units"] = "dimensionless" + ddata.attrs["_FillValue"] = 0 diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py new file mode 100644 index 0000000..4dfb26d --- /dev/null +++ b/src/firebench/standardize/tools.py @@ -0,0 +1,58 @@ +import h5py +from ..tools.logging_config import logger + +VERSION_STD = "0.1" + +VERSION_STD_COMPATIBILITY = { + "0.1": [], +} + + +def check_std_version(file: h5py.File): + """ + Determine whether the standard version of a file should be updated to the latest. + + This function inspects the `FireBench_io_version` attribute stored in the given HDF5 file. + It compares the file's version with the current standard version defined in `VERSION_STD`. + The return value indicates whether the caller should update the file's standard version. + + The logic is as follows: + - If the attribute is missing, the file is treated as new and should be updated. + - If the attribute matches the current standard version, the file is already up to date and may be updated. + - If the attribute is in the compatibility list for the current version, the file is considered valid + but should not be updated; a warning is logged instead. + - If the attribute is not compatible with the current version, a `ValueError` is raised. + + Parameters + ---------- + file : h5py.File + An open HDF5 file object to be checked. The file is expected to potentially have a + `FireBench_io_version` attribute at the root. + + Returns + ------- + bool + True if the file should be updated to the latest standard version. + False if the file has a compatible version and should not be updated. + + Raises + ------ + ValueError + If the file contains a `FireBench_io_version` attribute that is incompatible with the + current standard version. + """ # pylint: disable=line-too-long + if "FireBench_io_version" not in file.attrs: + return True + + file_version = file.attrs["FireBench_io_version"] + + if file_version == VERSION_STD: + return True + + if file_version in VERSION_STD_COMPATIBILITY[VERSION_STD]: + logger.warning( + "FireBench_io_version differs but is compatible: file=%s, current=%s", file_version, VERSION_STD + ) + return False + + raise ValueError(f"Standard version {file_version} not compatible with {VERSION_STD}") diff --git a/src/firebench/tools/namespace.py b/src/firebench/tools/namespace.py index 32803f6..ee2343e 100644 --- a/src/firebench/tools/namespace.py +++ b/src/firebench/tools/namespace.py @@ -24,6 +24,7 @@ class StandardVariableNames(Enum): # pragma: no cover CANOPY_HEIGHT_TOP = "canopy_height_top" DIRECTION = "direction" FIRE_ARRIVAL_TIME = "fire_arrival_time" + FIRE_BURN_SEVERITY = "fire_burn_severity" FUEL_CLASS = "fuel_class" FUEL_CHAPARRAL_FLAG = "fuel_chaparral_flag" FUEL_COVER = "fuel_cover" From 116001eb222a4e0ceb9723beed4963c71406a390 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 19 Sep 2025 18:17:20 +0200 Subject: [PATCH 005/111] add mtbs global analysis --- src/firebench/metrics/__init__.py | 3 +- src/firebench/metrics/mtbs/__init__.py | 1 + src/firebench/metrics/mtbs/mtbs_analysis.py | 203 ++++++++++++++++++++ src/firebench/standardize/mtbs.py | 3 +- src/firebench/tools/utils.py | 2 + 5 files changed, 208 insertions(+), 4 deletions(-) create mode 100644 src/firebench/metrics/mtbs/__init__.py create mode 100644 src/firebench/metrics/mtbs/mtbs_analysis.py diff --git a/src/firebench/metrics/__init__.py b/src/firebench/metrics/__init__.py index ef78b26..3fbec48 100644 --- a/src/firebench/metrics/__init__.py +++ b/src/firebench/metrics/__init__.py @@ -1,2 +1 @@ -from . import perimeter -from . import stats +from . import perimeter, stats, mtbs diff --git a/src/firebench/metrics/mtbs/__init__.py b/src/firebench/metrics/mtbs/__init__.py new file mode 100644 index 0000000..1cc055c --- /dev/null +++ b/src/firebench/metrics/mtbs/__init__.py @@ -0,0 +1 @@ +from .mtbs_analysis import global_accuracy diff --git a/src/firebench/metrics/mtbs/mtbs_analysis.py b/src/firebench/metrics/mtbs/mtbs_analysis.py new file mode 100644 index 0000000..7736461 --- /dev/null +++ b/src/firebench/metrics/mtbs/mtbs_analysis.py @@ -0,0 +1,203 @@ +import h5py +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import BoundaryNorm, ListedColormap, Normalize +from scipy.sparse import coo_matrix +from ...tools.namespace import StandardVariableNames as svn +from ...tools.utils import FIGSIZE_DEFAULT +from sklearn.metrics import confusion_matrix + +COLORS_MTBS = [ + (1, 1, 1, 0), # 0: no data + (0, 0.391, 0, 1), # 1: unburnt to low + (0.498, 1, 0.831, 1), # 2: low + (1, 1, 0, 1), # 3: moderate + (1, 0, 0, 1), # 4: high + (0.498, 1, 0.0039, 1), # 5: increased greenness +] + +LABELS_MTBS = [ + "0: no data", + "1: unburnt to low", + "2: low", + "3: moderate", + "4: high", + "5: increased greenness", +] + + +def global_accuracy( + filepath_eval: str, + mtbs_group_path_eval: str, + filepath_ref: str, + mtbs_group_path_ref: str, + figure_name: str = "mtbs_global_accuracy.png", + fig_dpi: int = 150, + ignore_greenness: bool = True, +): + """ + Run the MTBS global accuracy analysis between two datasets. + + The evaluated dataset and the reference dataset are located (respectively) in + filepath_1/mtbs_group_path_1 (filepath_2/mtbs_group_path_2). + + The mtbs group must contain the following datasets: + - fire_burn_severity + - latitude + - longitude + and the following attributes: + - crs + """ + # Import evaluated dataset + with h5py.File(filepath_eval, "r") as h5: + grp_1 = h5[mtbs_group_path_eval] + lat_1 = grp_1[svn.LATITUDE.value][:, :] + lon_1 = grp_1[svn.LONGITUDE.value][:, :] + mtbs_1 = grp_1[svn.FIRE_BURN_SEVERITY.value][:, :] + crs_1 = grp_1.attrs["crs"] + + # Import reference dataset + with h5py.File(filepath_ref, "r") as h5: + grp_2 = h5[mtbs_group_path_ref] + lat_2 = grp_2[svn.LATITUDE.value][:, :] + lon_2 = grp_2[svn.LONGITUDE.value][:, :] + mtbs_2 = grp_2[svn.FIRE_BURN_SEVERITY.value][:, :] + crs_2 = grp_2.attrs["crs"] + + # TODO: Projection eval -> ref + # TODO: interpolation nearest + + # process greenness + + # compute confusion matrix + # Flatten in case your labels are 2D rasters + y_true = np.asarray(mtbs_2).ravel() + y_pred = np.asarray(mtbs_1).ravel() + + # If you know your class list; otherwise infer: + classes = np.unique(np.concatenate([y_true, y_pred])) + K = classes.size + + # Map labels to 0..K-1 (robust if classes aren’t consecutive) + to_idx = {c: i for i, c in enumerate(classes)} + ti = np.vectorize(to_idx.get)(y_true) + pi = np.vectorize(to_idx.get)(y_pred) + + # bincount trick + cm = np.bincount(ti * K + pi, minlength=K * K).reshape(K, K) + + # calculate score + M = 100 * cm / cm.sum(axis=1, keepdims=True).clip(min=1) + accuracy_per_class_bps = np.zeros(5) + accuracy_per_class_total = np.zeros(5) + + for k in range(K - 1): + accuracy_per_class_bps[k] = cm[k + 1, k + 1] / np.sum(cm[k+1, 1:]) if np.sum(cm[k+1, 1:]) > 0 else 0.0 + accuracy_per_class_total[k] = cm[k + 1, k + 1] / (np.sum(cm[k+1, :]) + cm[0, k+1]) + + print(accuracy_per_class_bps) + print(accuracy_per_class_total) + + # or using add.at + cm = np.zeros((K, K), dtype=int) + np.add.at(cm, (ti, pi), 1) + + fig, axes = plt.subplots(2, 2, figsize=(5, 6), constrained_layout=True) + ax1 = axes[0, 0] + ax2 = axes[0, 1] + ax3 = axes[1, 0] + ax4 = axes[1, 1] + + # Build discrete colormap + cmap = ListedColormap(COLORS_MTBS) + bounds = np.arange(-0.5, 6.5, 1) + norm = BoundaryNorm(bounds, cmap.N) + + # panel 2: Evaluated + im1 = ax1.pcolormesh(lon_1, lat_1, mtbs_1, cmap=cmap, norm=norm, edgecolors="none") + + # panel 2: Reference + im2 = ax2.pcolormesh(lon_2, lat_2, mtbs_2, cmap=cmap, norm=norm, edgecolors="none") + + # panel 3: Difference + im3 = ax3.pcolormesh( + lon_2, lat_2, mtbs_1 - mtbs_2, cmap="RdYlGn_r", norm=Normalize(vmin=-5, vmax=5), edgecolors="none" + ) + + # panel 4: confusion matrix + im4 = ax4.imshow(M, cmap="Blues", origin="upper", interpolation="nearest") + ax4.set_xlabel("Evaluated") + ax4.set_ylabel("Reference") + ax4.set_xticks(np.arange(K), labels=classes) + ax4.set_yticks(np.arange(K), labels=classes) + + # Shared colorbar on top of the first row + cbar = fig.colorbar( + im1, + ax=axes[0, :], # span first row only + orientation="horizontal", + location="top", + fraction=0.08, + pad=0.05, # control size/spacing + ticks=range(6), + ) + cbar.ax.set_xticklabels(LABELS_MTBS) + cbar.ax.tick_params(axis="x", rotation=30) + plt.setp(cbar.ax.get_xticklabels(), ha="left") + cbar.set_label("MTBS classes", fontsize=10) + + cbar = fig.colorbar( + im3, + ax=ax3, + orientation="horizontal", + location="bottom", + ticks=[-5, -2, 0, 2, 5], + label="Difference [-]", + ) + + for i in range(K): + for j in range(K): + val = M[i, j] + ax4.text(j, i, f"{val:3.0f}", ha="center", va="center", color="black", fontsize=7) + + ax1.text(0.02, 0.9, "Evaluated", fontsize=8, transform=ax1.transAxes) + ax2.text(0.02, 0.9, "Reference", fontsize=8, transform=ax2.transAxes) + ax4.text(1.03, 1.02, "BPS", fontsize=8, transform=ax4.transAxes) + ax4.text(1.20, 1.02, "total", fontsize=8, transform=ax4.transAxes) + for k in range(K - 1): + ax4.text( + 1.03, 0.73 - 0.165 * k, f"{accuracy_per_class_bps[k]:.2f}", fontsize=7, transform=ax4.transAxes + ) + ax4.text( + 1.20, + 0.73 - 0.165 * k, + f"{accuracy_per_class_total[k]:.2f}", + fontsize=7, + transform=ax4.transAxes, + ) + ax4.text(1.03, 0.01, "__________", fontsize=7, transform=ax4.transAxes) + ax4.text(1.03, -0.1, f"{np.mean(accuracy_per_class_bps):.2f}", fontsize=7, transform=ax4.transAxes) + ax4.text(1.20, -0.1, f"{np.mean(accuracy_per_class_total):.2f}", fontsize=7, transform=ax4.transAxes) + + cbar = fig.colorbar( + im4, + ax=ax4, + orientation="horizontal", + location="bottom", + ) + cbar.set_label("Fraction") # or "Proportion" if normalized + + fig.set_constrained_layout_pads(w_pad=0.02, h_pad=0.02, hspace=0.0, wspace=0.0) + for ax in axes.ravel(): + ax.tick_params(direction="in", top=True, right=True, which="both") + + for ax in [ax1, ax2, ax3]: + ax.set_xlabel("longitude [deg]") + ax.set_ylabel("latitude [deg]") + + if not figure_name.endswith(".png"): + figure_name += ".png" + + fig.savefig(figure_name, dpi=fig_dpi) + + return fig diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py index 1ae29c7..ac9b726 100644 --- a/src/firebench/standardize/mtbs.py +++ b/src/firebench/standardize/mtbs.py @@ -114,7 +114,7 @@ def convert_mtbs_geotiff( g = h5.create_group(group_name) g.attrs["data_source"] = f"MTBS {path}" - + g.attrs["crs"] = f"EPSG:{epsg_code}" # TODO: add some useful metadata from _metadata.xml (fire_ignition_time, pre_fire_image_date, post_fire_image_date) # Lat/Lon as 2-D arrays @@ -125,6 +125,5 @@ def convert_mtbs_geotiff( dlat.attrs["units"] = "degrees" ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8) - ddata.attrs["crs"] = f"EPSG:{epsg_code}" ddata.attrs["units"] = "dimensionless" ddata.attrs["_FillValue"] = 0 diff --git a/src/firebench/tools/utils.py b/src/firebench/tools/utils.py index 2e13823..86f33ee 100644 --- a/src/firebench/tools/utils.py +++ b/src/firebench/tools/utils.py @@ -4,6 +4,8 @@ from pint import Quantity from .logging_config import logger +FIGSIZE_DEFAULT = (6, 6) + def is_scalar_quantity(x: any): """ From 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z3T^V`4}{rwRT}na>avpKo&P?HR7;c#4Jew)xd>Yu&<9A;1o1tm`FZe044$*9KqzH$&;7Ii@fR>w~JZ*R&{n>1gfK KOi{PE_`d;>vxtrW literal 0 HcmV?d00001 diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md new file mode 100644 index 0000000..48e0bfc --- /dev/null +++ b/docs/benchmarks/California/01_Caldor.md @@ -0,0 +1,74 @@ +# 2021 Caldor Fire + +**Version**: 1.0a +**Status**: In Progress + +### Contributors +- Aurélien Costes, [Wildfire Interdisciplinary Research Center](https://www.wildfirecenter.org/), San Jose State University, [aurelien.costes@sjsu.edu](mailto:aurelien.costes@sjsu.edu), [ORCID](https://orcid.org/0000-0003-4543-5107) + + +## Description + +This collection of benchmarks uses the public ressources about the 2021 Caldor Fire. +It contains observation datasets for: +- Nearby weather stations (nb of associated benchmarks) +- Infrared fire perimeters +- Burn severity +- Building damaged +- (Plume top height) + +## Buildings damage benchmarks + +### Dataset + +The data has been collected using **CAL FIRE Damage Inspection (DINS) Data** (version of 2025/11/05). +The original csv file containing multiple fires has been processed to extract the building damaged from the Caldor Fire only. The dataset contains the position (lat, lon) of buildings in the area of influence from the fire. The state of buildings is one of the following: +- 'No Damage', +- 'Affected (1-9%)', +- 'Minor (10-25%)', +- 'Major (26-50%)', +- 'Destroyed (>50%)', +- 'Inaccessible'. + +The column names from the original csv file have been corrected from "* Damage" to "Damage" and "* Incident Name" to "Incident Name". +The hash of the original source file is: *0190a5a51aafafa20270fe046a7ae17a53697b1fb218ff8096a3d8ebbc9ef983*. + +If the evaluated model does not explicitly represent individual buildings, the model should consider every building within a cell to share the cell value for building damage (deterministic models) or share the median of bulding damage distribution (probabilistic models). + +Figure 1 shows the spatial distribution of building damage for the Caldor Fire. +![blockdiagram](../../_static/benchmarks/FB001/Caldor_bd_map.png) +

+ + Fig. 1 + + : + + Buildings damage map + +

+ +Figure 2 shows the distribution of building damage for the Caldor Fire. The following table shows the number of strucure in each damage category. +Damage category | Counts [-] +---------------------- | ----------------- +No Damage | 3356 +Affected (1-9%) | 56 +Minor (10-25%) | 18 +Major (26-50%) | 7 +Destroyed (>50%) | 1005 +Inaccessible | 2 +Total | 4444 + +![blockdiagram](../../_static/benchmarks/FB001/Caldor_bd_distribution.png) +

+ + Fig. 2 + + : + + Distribution of buildings damage + +

+ +### Confusion matrix + +**Benchmark ID**: FB001-BD01 \ No newline at end of file diff --git a/src/firebench/metrics/mtbs/mtbs_analysis.py b/src/firebench/metrics/mtbs/mtbs_analysis.py index 7736461..a3f2358 100644 --- a/src/firebench/metrics/mtbs/mtbs_analysis.py +++ b/src/firebench/metrics/mtbs/mtbs_analysis.py @@ -2,10 +2,8 @@ import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import BoundaryNorm, ListedColormap, Normalize -from scipy.sparse import coo_matrix from ...tools.namespace import StandardVariableNames as svn from ...tools.utils import FIGSIZE_DEFAULT -from sklearn.metrics import confusion_matrix COLORS_MTBS = [ (1, 1, 1, 0), # 0: no data From 34013123242e4eed2d25d1cfaec53f806dd84cc9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 13 Nov 2025 10:26:57 -0800 Subject: [PATCH 007/111] improve MTBS workflow --- src/firebench/metrics/mtbs/mtbs_analysis.py | 36 ++++++++++----------- src/firebench/standardize/mtbs.py | 14 ++++---- 2 files changed, 24 insertions(+), 26 deletions(-) diff --git a/src/firebench/metrics/mtbs/mtbs_analysis.py b/src/firebench/metrics/mtbs/mtbs_analysis.py index 7736461..1fa915a 100644 --- a/src/firebench/metrics/mtbs/mtbs_analysis.py +++ b/src/firebench/metrics/mtbs/mtbs_analysis.py @@ -1,11 +1,10 @@ import h5py import matplotlib.pyplot as plt import numpy as np +from pyproj import CRS, Transformer from matplotlib.colors import BoundaryNorm, ListedColormap, Normalize -from scipy.sparse import coo_matrix from ...tools.namespace import StandardVariableNames as svn from ...tools.utils import FIGSIZE_DEFAULT -from sklearn.metrics import confusion_matrix COLORS_MTBS = [ (1, 1, 1, 0), # 0: no data @@ -50,29 +49,30 @@ def global_accuracy( """ # Import evaluated dataset with h5py.File(filepath_eval, "r") as h5: - grp_1 = h5[mtbs_group_path_eval] - lat_1 = grp_1[svn.LATITUDE.value][:, :] - lon_1 = grp_1[svn.LONGITUDE.value][:, :] - mtbs_1 = grp_1[svn.FIRE_BURN_SEVERITY.value][:, :] - crs_1 = grp_1.attrs["crs"] + grp = h5[mtbs_group_path_eval] + lat_eval = grp[svn.LATITUDE.value][:, :] + lon_eval = grp[svn.LONGITUDE.value][:, :] + mtbs_eval = grp[svn.FIRE_BURN_SEVERITY.value][:, :] + crs_eval = CRS(grp.attrs["crs"]) # Import reference dataset with h5py.File(filepath_ref, "r") as h5: - grp_2 = h5[mtbs_group_path_ref] - lat_2 = grp_2[svn.LATITUDE.value][:, :] - lon_2 = grp_2[svn.LONGITUDE.value][:, :] - mtbs_2 = grp_2[svn.FIRE_BURN_SEVERITY.value][:, :] - crs_2 = grp_2.attrs["crs"] + grp = h5[mtbs_group_path_ref] + lat_ref = grp[svn.LATITUDE.value][:, :] + lon_ref = grp[svn.LONGITUDE.value][:, :] + mtbs_ref = grp[svn.FIRE_BURN_SEVERITY.value][:, :] + crs_ref = CRS(grp.attrs["crs"]) + + transform_crs_eval_to_ref = Transformer.from_crs(crs_eval, crs_ref, always_xy=True) - # TODO: Projection eval -> ref # TODO: interpolation nearest # process greenness # compute confusion matrix # Flatten in case your labels are 2D rasters - y_true = np.asarray(mtbs_2).ravel() - y_pred = np.asarray(mtbs_1).ravel() + y_true = np.asarray(mtbs_ref).ravel() + y_pred = np.asarray(mtbs_eval).ravel() # If you know your class list; otherwise infer: classes = np.unique(np.concatenate([y_true, y_pred])) @@ -114,14 +114,14 @@ def global_accuracy( norm = BoundaryNorm(bounds, cmap.N) # panel 2: Evaluated - im1 = ax1.pcolormesh(lon_1, lat_1, mtbs_1, cmap=cmap, norm=norm, edgecolors="none") + im1 = ax1.pcolormesh(lon_eval, lat_eval, mtbs_eval, cmap=cmap, norm=norm, edgecolors="none") # panel 2: Reference - im2 = ax2.pcolormesh(lon_2, lat_2, mtbs_2, cmap=cmap, norm=norm, edgecolors="none") + im2 = ax2.pcolormesh(lon_ref, lat_ref, mtbs_ref, cmap=cmap, norm=norm, edgecolors="none") # panel 3: Difference im3 = ax3.pcolormesh( - lon_2, lat_2, mtbs_1 - mtbs_2, cmap="RdYlGn_r", norm=Normalize(vmin=-5, vmax=5), edgecolors="none" + lon_ref, lat_ref, mtbs_eval - mtbs_ref, cmap="RdYlGn_r", norm=Normalize(vmin=-5, vmax=5), edgecolors="none" ) # panel 4: confusion matrix diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py index ac9b726..fa4fb2b 100644 --- a/src/firebench/standardize/mtbs.py +++ b/src/firebench/standardize/mtbs.py @@ -23,6 +23,8 @@ def convert_mtbs_geotiff( """ Convert a MTBS GeoTIFF to Firebench HDF5 standard file. + MTBS data uses NAD83 projection. + Parameters ---------- path : str @@ -56,7 +58,6 @@ def convert_mtbs_geotiff( ii = np.arange(rows) # vectorized center coordinates from affine: # x = a*col + b*row + c ; y = d*col + e*row + f - # Use broadcasting to form 2-D arrays without Python loops. x = ( severity_raw["transform"].a * jj[None, :] + severity_raw["transform"].b * ii[:, None] @@ -69,12 +70,9 @@ def convert_mtbs_geotiff( ) # Reproject to geographic lat/lon - # Choose your target CRS: - # - WGS84 geographic: - epsg_code = 4269 - tgt_crs = CRS.from_epsg(epsg_code) # lon/lat - # - OR NAD83 geographic: - # tgt_crs = CRS.from_epsg(4269) # lon/lat + # NAD83 geographic + CRS_NAME = "EPSG:4269" + tgt_crs = CRS(CRS_NAME) # always_xy=True -> transformer expects/returns (x, y) = (lon, lat) ordering for geographic CRS transformer = Transformer.from_crs(severity_raw["crs"], tgt_crs, always_xy=True) @@ -114,7 +112,7 @@ def convert_mtbs_geotiff( g = h5.create_group(group_name) g.attrs["data_source"] = f"MTBS {path}" - g.attrs["crs"] = f"EPSG:{epsg_code}" + g.attrs["crs"] = CRS_NAME # TODO: add some useful metadata from _metadata.xml (fire_ignition_time, pre_fire_image_date, post_fire_image_date) # Lat/Lon as 2-D arrays From 616e4ec225603181b2a9af1acdba6bec1e8c9549 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 09:20:43 -0800 Subject: [PATCH 008/111] add caldor fire content --- docs/benchmarks/California/01_Caldor.md | 29 +++++++++++++++++++++---- docs/benchmarks/index.md | 18 ++++++++------- 2 files changed, 35 insertions(+), 12 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 48e0bfc..1f4347e 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -1,9 +1,9 @@ # 2021 Caldor Fire **Version**: 1.0a -**Status**: In Progress +**Case ID**: FB001 -### Contributors +## Contributors - Aurélien Costes, [Wildfire Interdisciplinary Research Center](https://www.wildfirecenter.org/), San Jose State University, [aurelien.costes@sjsu.edu](mailto:aurelien.costes@sjsu.edu), [ORCID](https://orcid.org/0000-0003-4543-5107) @@ -16,6 +16,7 @@ It contains observation datasets for: - Burn severity - Building damaged - (Plume top height) +- (Smoke concentration ground) ## Buildings damage benchmarks @@ -69,6 +70,26 @@ Total | 4444

-### Confusion matrix +### Benchmark FB001-BD01 -**Benchmark ID**: FB001-BD01 \ No newline at end of file +**KPI**: Binary structure loss accuracy + +### Benchmark FB001-BD02 + +**KPI**: Binary structure loss precision + +### Benchmark FB001-BD03 + +**KPI**: Binary structure loss recall + +### Benchmark FB001-BD04 + +**KPI**: Binary structure loss false negative rate + +### Benchmark FB001-BD05 + +**KPI**: Binary structure loss false positive rate + +### Benchmark FB001-BD06 + +**KPI**: Binary structure loss F1 score \ No newline at end of file diff --git a/docs/benchmarks/index.md b/docs/benchmarks/index.md index d289158..98597af 100644 --- a/docs/benchmarks/index.md +++ b/docs/benchmarks/index.md @@ -1,21 +1,23 @@ # 6. Benchmarks list This section groups the workflows, tests, and benchmarks proposed by FireBench. -## Fire submodels +## Events ```{toctree} :maxdepth: 1 -fire_submodels/rate_of_spread/Anderson_2015_Validation/index.md -fire_submodels/rate_of_spread/Computational_cost/index.md -fire_submodels/rate_of_spread/Global_sensitivity_env/index.md +California/01_Caldor.md ``` -## 2D Fire Spread models -## 3D Coupled dynamics +## Fire submodels -## Fuel Moisture +```{toctree} +:maxdepth: 1 + +fire_submodels/rate_of_spread/Anderson_2015_Validation/index.md +fire_submodels/rate_of_spread/Computational_cost/index.md +fire_submodels/rate_of_spread/Global_sensitivity_env/index.md -## Risk/Danger \ No newline at end of file +``` \ No newline at end of file From 6c313aef8dfba9107128761883756eefa3a7aa63 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 19:18:36 -0800 Subject: [PATCH 009/111] add is_iso8601 and validate_h5_std functions --- src/firebench/standardize/__init__.py | 2 +- src/firebench/standardize/tools.py | 37 ++++++++++++++++++++++++++- src/firebench/tools/__init__.py | 1 + 3 files changed, 38 insertions(+), 2 deletions(-) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 2a6ff62..c85d74b 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -1,2 +1,2 @@ from .mtbs import convert_mtbs_geotiff -from .tools import VERSION_STD +from .tools import VERSION_STD, validate_h5_std, is_iso8601 diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 4dfb26d..00d534e 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -1,12 +1,23 @@ import h5py from ..tools.logging_config import logger +import re -VERSION_STD = "0.1" +VERSION_STD = "0.2" VERSION_STD_COMPATIBILITY = { "0.1": [], + "0.2": [], } +VALIDATION_SCHEME_1 = ["0.1", "0.2"] + + +ISO8601_REGEX = re.compile( + r"^\d{4}-\d{2}-\d{2}T" # Date + 'T' + r"\d{2}:\d{2}" # HH:MM (always present) + r"(?:\:\d{2}(?:\.\d+)?)?" # optional :SS[.ffffff] + r"(?:Z|[+-]\d{2}:\d{2})?$" # optional timezone +) def check_std_version(file: h5py.File): """ @@ -56,3 +67,27 @@ def check_std_version(file: h5py.File): return False raise ValueError(f"Standard version {file_version} not compatible with {VERSION_STD}") + +def is_iso8601(s: str) -> bool: + return bool(ISO8601_REGEX.match(s)) + +def validate_h5_std(file: h5py.File): + """ + Validate that the mandatory structure in the h5 file is compliant with the standard + """ + if "FireBench_io_version" not in file.attrs: + raise ValueError(f"Attribute `FireBench_io_version` not found.") + + if file.attrs["FireBench_io_version"] in VALIDATION_SCHEME_1: + # check creation date + if "created_on" not in file.attrs: + raise ValueError(f"Attribute `created_on` not found.") + + if not is_iso8601(file.attrs["created_on"]): + raise ValueError(f"Attribute `created_on` not compliant with ISO8601.") + + # check authors + if "created_by" not in file.attrs: + raise ValueError(f"Attribute `created_by` not found.") + + \ No newline at end of file diff --git a/src/firebench/tools/__init__.py b/src/firebench/tools/__init__.py index 6dd3096..6b2bdd2 100644 --- a/src/firebench/tools/__init__.py +++ b/src/firebench/tools/__init__.py @@ -29,6 +29,7 @@ logger, logging, set_logging_level, + create_file_handler, ) from .input_info import ParameterType from .fuel_models_utils import ( From 5f5bde997b634b326be29416fa29ea0abbcf2f24 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 19:52:48 -0800 Subject: [PATCH 010/111] fix logging default level to allow for debug and above --- src/firebench/tools/logging_config.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/src/firebench/tools/logging_config.py b/src/firebench/tools/logging_config.py index f716966..39d01f9 100644 --- a/src/firebench/tools/logging_config.py +++ b/src/firebench/tools/logging_config.py @@ -3,11 +3,10 @@ # Create a custom logger logger = logging.getLogger("firebench") +logger.setLevel(logging.DEBUG) # Create a stream handler by default c_handler = logging.StreamHandler() - -# Set logging level for the stream handler c_handler.setLevel(logging.WARNING) # Create formatter and add it to the stream handler @@ -17,9 +16,6 @@ # Add the stream handler to the logger logger.addHandler(c_handler) -# Set default logging level for the logger -logger.setLevel(logging.WARNING) - # Prevent propagation to the root logger logger.propagate = True From 3819086b26dcbfa9ece613b758eed80e8341cd14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 19:53:39 -0800 Subject: [PATCH 011/111] fix regex for ISO8601 check and add validate_h5_requirement - regex now handles HH:MM:SS or HH:MM in ISO8601 --- src/firebench/standardize/tools.py | 37 ++++++++++++++++++++++-------- 1 file changed, 28 insertions(+), 9 deletions(-) diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 00d534e..99ee6c1 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -1,6 +1,6 @@ import h5py from ..tools.logging_config import logger -import re +import re VERSION_STD = "0.2" @@ -13,12 +13,13 @@ ISO8601_REGEX = re.compile( - r"^\d{4}-\d{2}-\d{2}T" # Date + 'T' - r"\d{2}:\d{2}" # HH:MM (always present) - r"(?:\:\d{2}(?:\.\d+)?)?" # optional :SS[.ffffff] - r"(?:Z|[+-]\d{2}:\d{2})?$" # optional timezone + r"^\d{4}-\d{2}-\d{2}T" # Date + 'T' + r"\d{2}:\d{2}" # HH:MM (always present) + r"(?:\:\d{2}(?:\.\d+)?)?" # optional :SS[.ffffff] + r"(?:Z|[+-]\d{2}:\d{2})?$" # optional timezone ) + def check_std_version(file: h5py.File): """ Determine whether the standard version of a file should be updated to the latest. @@ -68,26 +69,44 @@ def check_std_version(file: h5py.File): raise ValueError(f"Standard version {file_version} not compatible with {VERSION_STD}") + def is_iso8601(s: str) -> bool: return bool(ISO8601_REGEX.match(s)) + def validate_h5_std(file: h5py.File): """ Validate that the mandatory structure in the h5 file is compliant with the standard """ if "FireBench_io_version" not in file.attrs: raise ValueError(f"Attribute `FireBench_io_version` not found.") - + if file.attrs["FireBench_io_version"] in VALIDATION_SCHEME_1: # check creation date if "created_on" not in file.attrs: raise ValueError(f"Attribute `created_on` not found.") - + if not is_iso8601(file.attrs["created_on"]): raise ValueError(f"Attribute `created_on` not compliant with ISO8601.") # check authors if "created_by" not in file.attrs: raise ValueError(f"Attribute `created_by` not found.") - - \ No newline at end of file + + +def validate_h5_requirement(file: h5py.File, required: dict[str, list[str]]): + """ + Check if all datasets and associated attributs are present in the file. + Return False and the name of the first missing item if either the dataset or an attribute is missing + """ + for dset_path, attrs in required.items(): + if dset_path not in file: + return False, dset_path + + dset = file[dset_path] + + for attr_name in attrs: + if attr_name not in dset.attrs: + return False, f"{dset}: {attr_name}" + + return True, None From 0a5bd9bb200c72403bfc130a2be6a3604946f653 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 19:54:02 -0800 Subject: [PATCH 012/111] add validate_h5_requirement to tools --- src/firebench/standardize/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index c85d74b..643fe1d 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -1,2 +1,2 @@ from .mtbs import convert_mtbs_geotiff -from .tools import VERSION_STD, validate_h5_std, is_iso8601 +from .tools import VERSION_STD, validate_h5_std, is_iso8601, validate_h5_requirement From 737c1b3815ac9c9e5c2b1cbd75e92009ffdd3429 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 20:06:34 -0800 Subject: [PATCH 013/111] improve return string of missing items for validate_h5_requirement --- src/firebench/standardize/tools.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 99ee6c1..f17b526 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -101,12 +101,12 @@ def validate_h5_requirement(file: h5py.File, required: dict[str, list[str]]): """ for dset_path, attrs in required.items(): if dset_path not in file: - return False, dset_path + return False, f"dataset `{dset_path}`" dset = file[dset_path] for attr_name in attrs: if attr_name not in dset.attrs: - return False, f"{dset}: {attr_name}" + return False, f"attr `{attr_name}` of dataset `{dset_path}`" return True, None From dd7427b5b320d552074c3b081b7862a627c64ed8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 21:37:02 -0800 Subject: [PATCH 014/111] add binary_confusion_matrix construction --- src/firebench/metrics/__init__.py | 2 +- .../metrics/confusion_matrix/__init__.py | 1 + .../metrics/confusion_matrix/utils.py | 51 +++++++++++++++++++ 3 files changed, 53 insertions(+), 1 deletion(-) create mode 100644 src/firebench/metrics/confusion_matrix/__init__.py create mode 100644 src/firebench/metrics/confusion_matrix/utils.py diff --git a/src/firebench/metrics/__init__.py b/src/firebench/metrics/__init__.py index 3fbec48..355f04e 100644 --- a/src/firebench/metrics/__init__.py +++ b/src/firebench/metrics/__init__.py @@ -1 +1 @@ -from . import perimeter, stats, mtbs +from . import perimeter, stats, mtbs, confusion_matrix diff --git a/src/firebench/metrics/confusion_matrix/__init__.py b/src/firebench/metrics/confusion_matrix/__init__.py new file mode 100644 index 0000000..0c54616 --- /dev/null +++ b/src/firebench/metrics/confusion_matrix/__init__.py @@ -0,0 +1 @@ +from .utils import binary_confusion_matrix diff --git a/src/firebench/metrics/confusion_matrix/utils.py b/src/firebench/metrics/confusion_matrix/utils.py new file mode 100644 index 0000000..ddf0394 --- /dev/null +++ b/src/firebench/metrics/confusion_matrix/utils.py @@ -0,0 +1,51 @@ +import numpy as np + + +def binary_confusion_matrix(y_true, y_pred): + """ + Compute the binary confusion matrix for two boolean-compatible arrays. + + This function compares predicted binary values against ground-truth binary values + and computes the 2x2 confusion matrix in the conventional layout: + + [[TN, FP], + [FN, TP]] + + Both inputs must be broadcastable to the same shape and must contain values that + can be interpreted as booleans (e.g., {0,1}, {False,True}, or arrays castable to bool). + + The logic is as follows: + - True Negative (TN): elements where both `y_true` and `y_pred` are False. + - False Positive (FP): elements where `y_true` is False but `y_pred` is True. + - False Negative (FN): elements where `y_true` is True but `y_pred` is False. + - True Positive (TP): elements where both `y_true` and `y_pred` are True. + + Parameters + ---------- + y_true : array_like + Ground-truth binary values. Must be castable to a boolean NumPy array. + y_pred : array_like + Predicted binary values. Must be castable to a boolean NumPy array. + + Returns + ------- + numpy.ndarray + A 2x2 NumPy array of integers arranged as: + [[TN, FP], + [FN, TP]] + + Examples + -------- + >>> binary_confusion_matrix_matrix([0, 1, 1, 0], [0, 1, 0, 0]) + array([[2, 0], + [1, 1]]) + """ + y_true = np.asarray(y_true).astype(bool) + y_pred = np.asarray(y_pred).astype(bool) + + tp = np.sum(y_true & y_pred) + tn = np.sum(~y_true & ~y_pred) + fp = np.sum(~y_true & y_pred) + fn = np.sum(y_true & ~y_pred) + + return np.array([[tn, fp], [fn, tp]]) From 33d5cde9b492e91fcb8cefbb40d74b401d6f29af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 21:48:21 -0800 Subject: [PATCH 015/111] add binary_accuracy for binary confusion matrix --- .../metrics/confusion_matrix/__init__.py | 3 +- .../confusion_matrix/binary_performance.py | 35 +++++++++++++++++++ .../metrics/confusion_matrix/utils.py | 4 +-- 3 files changed, 39 insertions(+), 3 deletions(-) create mode 100644 src/firebench/metrics/confusion_matrix/binary_performance.py diff --git a/src/firebench/metrics/confusion_matrix/__init__.py b/src/firebench/metrics/confusion_matrix/__init__.py index 0c54616..9b732b5 100644 --- a/src/firebench/metrics/confusion_matrix/__init__.py +++ b/src/firebench/metrics/confusion_matrix/__init__.py @@ -1 +1,2 @@ -from .utils import binary_confusion_matrix +from .utils import binary_cm +from .binary_performance import binary_accuracy \ No newline at end of file diff --git a/src/firebench/metrics/confusion_matrix/binary_performance.py b/src/firebench/metrics/confusion_matrix/binary_performance.py new file mode 100644 index 0000000..e40179c --- /dev/null +++ b/src/firebench/metrics/confusion_matrix/binary_performance.py @@ -0,0 +1,35 @@ +import numpy as np + +def binary_accuracy(bcm: np.ndarray): + """ + Compute the binary classification accuracy from a 2x2 confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + where: + - TN: True Negatives + - FP: False Positives + - FN: False Negatives + - TP: True Positives + + Accuracy is defined as the proportion of correct predictions out of all + predictions: + + accuracy = (TP + TN) / (TP + TN + FP + FN) + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix in the order + [[TN, FP], [FN, TP]]. The array must be broadcastable to this shape. + + Returns + ------- + float + The accuracy value, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + tn, fp, fn, tp = bcm.ravel() + return (tp + tn) / (tp + tn + fp + fn) \ No newline at end of file diff --git a/src/firebench/metrics/confusion_matrix/utils.py b/src/firebench/metrics/confusion_matrix/utils.py index ddf0394..bcbd0fa 100644 --- a/src/firebench/metrics/confusion_matrix/utils.py +++ b/src/firebench/metrics/confusion_matrix/utils.py @@ -1,7 +1,7 @@ import numpy as np -def binary_confusion_matrix(y_true, y_pred): +def binary_cm(y_true, y_pred): """ Compute the binary confusion matrix for two boolean-compatible arrays. @@ -39,7 +39,7 @@ def binary_confusion_matrix(y_true, y_pred): >>> binary_confusion_matrix_matrix([0, 1, 1, 0], [0, 1, 0, 0]) array([[2, 0], [1, 1]]) - """ + """ # pylint: disable=line-too-long y_true = np.asarray(y_true).astype(bool) y_pred = np.asarray(y_pred).astype(bool) From e0ef78645e5855c69bfb425cbf4302bd63e9a088 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 22:00:14 -0800 Subject: [PATCH 016/111] add performance mertrics for binary confusion matrix - binary_f_score, binary_false_positive_rate, binary_negative_predicted_value, binary_precision, binary_recall_rate, binary_specific_rate --- .../metrics/confusion_matrix/__init__.py | 2 +- .../confusion_matrix/binary_performance.py | 174 +++++++++++++++++- 2 files changed, 172 insertions(+), 4 deletions(-) diff --git a/src/firebench/metrics/confusion_matrix/__init__.py b/src/firebench/metrics/confusion_matrix/__init__.py index 9b732b5..cd14bc4 100644 --- a/src/firebench/metrics/confusion_matrix/__init__.py +++ b/src/firebench/metrics/confusion_matrix/__init__.py @@ -1,2 +1,2 @@ from .utils import binary_cm -from .binary_performance import binary_accuracy \ No newline at end of file +from .binary_performance import binary_accuracy, binary_f_score, binary_false_positive_rate, binary_negative_predicted_value, binary_precision, binary_recall_rate, binary_specific_rate \ No newline at end of file diff --git a/src/firebench/metrics/confusion_matrix/binary_performance.py b/src/firebench/metrics/confusion_matrix/binary_performance.py index e40179c..7416023 100644 --- a/src/firebench/metrics/confusion_matrix/binary_performance.py +++ b/src/firebench/metrics/confusion_matrix/binary_performance.py @@ -23,8 +23,6 @@ def binary_accuracy(bcm: np.ndarray): Parameters ---------- bcm : numpy.ndarray - A 2x2 array representing the binary confusion matrix in the order - [[TN, FP], [FN, TP]]. The array must be broadcastable to this shape. Returns ------- @@ -32,4 +30,174 @@ def binary_accuracy(bcm: np.ndarray): The accuracy value, ranging from 0.0 to 1.0. """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() - return (tp + tn) / (tp + tn + fp + fn) \ No newline at end of file + return (tp + tn) / (tp + tn + fp + fn) + +def binary_precision(bcm: np.ndarray): + """ + Compute the precision from a 2x2 binary confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + where: + - TN: True Negatives + - FP: False Positives + - FN: False Negatives + - TP: True Positives + + Precision (also called Positive Predictive Value, PPV) measures the proportion + of positive predictions that are correct: + + precision = TP / (TP + FP) + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix. + + Returns + ------- + float + The precision value, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + tn, fp, fn, tp = bcm.ravel() + return tp / (tp + fp) + +def binary_false_positive_rate(bcm: np.ndarray): + """ + Compute the false positive rate (FPR) from a 2x2 binary confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + False Positive Rate measures how often negative samples are incorrectly + classified as positive: + + FPR = FP / (FP + TN) + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix. + + Returns + ------- + float + The false positive rate, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + tn, fp, fn, tp = bcm.ravel() + return fp / (fp + tn) + +def binary_negative_predicted_value(bcm: np.ndarray): + """ + Compute the negative predictive value (NPV) from a 2x2 binary confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + Negative Predictive Value measures the proportion of predicted negatives + that are actually negative: + + NPV = TN / (TN + FN) + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix. + + Returns + ------- + float + The negative predictive value, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + tn, fp, fn, tp = bcm.ravel() + return tn / (fn + tn) + +def binary_recall_rate(bcm: np.ndarray): + """ + Compute the recall rate (true positive rate, TPR) from a 2x2 binary confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + Recall, also called sensitivity, measures how many actual positives are + correctly identified: + + recall = TP / (TP + FN) + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix. + + Returns + ------- + float + The recall value, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + tn, fp, fn, tp = bcm.ravel() + return tp / (tp + fn) + +def binary_specific_rate(bcm: np.ndarray): + """ + Compute the specificity (true negative rate, TNR) from a 2x2 binary confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + Specificity measures the proportion of actual negatives that are correctly + identified: + + specificity = TN / (TN + FP) + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix. + + Returns + ------- + float + The specificity value, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + tn, fp, fn, tp = bcm.ravel() + return tn / (tn + fp) + +def binary_f_score(bcm: np.ndarray): + """ + Compute the F1-score from a 2x2 binary confusion matrix. + + This function expects a confusion matrix of the form: + + [[TN, FP], + [FN, TP]] + + The F1-score is the harmonic mean of precision and recall: + + F1 = 2 * (precision * recall) / (precision + recall) + + It provides a single metric that balances false positives and false negatives. + + Parameters + ---------- + bcm : numpy.ndarray + A 2x2 array representing the binary confusion matrix. + + Returns + ------- + float + The F1-score, ranging from 0.0 to 1.0. + """ # pylint: disable=line-too-long + pr = binary_precision(bcm) + rc = binary_recall_rate(bcm) + return 2.0 * (pr * rc) / (pr + rc) \ No newline at end of file From fd5b06e62d1065ea86dd452b1a17bab096642bf2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 22:13:02 -0800 Subject: [PATCH 017/111] update caldor case docs --- docs/benchmarks/California/01_Caldor.md | 67 +++++++++++++++++++------ docs/metrics/index.md | 63 +++++------------------ docs/standard_format.md | 7 +-- 3 files changed, 68 insertions(+), 69 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 1f4347e..5a3f3b1 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -1,12 +1,13 @@ # 2021 Caldor Fire -**Version**: 1.0a -**Case ID**: FB001 +**Version**: 1.0a
+**Case ID**: FB001
+**FireBench IO std version**: >= 0.2
+**Date of last update**: 11/19/2025 ## Contributors - Aurélien Costes, [Wildfire Interdisciplinary Research Center](https://www.wildfirecenter.org/), San Jose State University, [aurelien.costes@sjsu.edu](mailto:aurelien.costes@sjsu.edu), [ORCID](https://orcid.org/0000-0003-4543-5107) - ## Description This collection of benchmarks uses the public ressources about the 2021 Caldor Fire. @@ -70,26 +71,62 @@ Total | 4444

-### Benchmark FB001-BD01 +### Processing of dataset + +#### Binary classes of building damaged +In order to performe some calculations, the building damaged classes can be aggrageted to form binary classes. The `Inaccessible` is ignored. The following aggragtion method is used: +- `unburnt` binary class contains `No Damage`, `Affected (1-9%)`, and `Minor (10-25%)`, +- `burnt` binary class contains `Major (26-50%)`, and `Destroyed (>50%)`. + +### Benchmarks + +See Key Performance Indicator (KPI) defintion [here](../../metrics/index.md). + +#### FB001-BD01 + +**KPI**: Binary Structure Loss Accuracy
+This benchmark is performed on the binary classes for damaged buildings. + +#### FB001-BD02 -**KPI**: Binary structure loss accuracy +**KPI**: Binary Structure Loss Precision
+This benchmark is performed on the binary classes for damaged buildings. -### Benchmark FB001-BD02 +#### FB001-BD03 -**KPI**: Binary structure loss precision +**KPI**: Binary Structure Loss Recall
+This benchmark is performed on the binary classes for damaged buildings. -### Benchmark FB001-BD03 +#### FB001-BD04 -**KPI**: Binary structure loss recall +**KPI**: Binary Structure Loss False Negative Rate
+This benchmark is performed on the binary classes for damaged buildings. -### Benchmark FB001-BD04 +#### FB001-BD05 -**KPI**: Binary structure loss false negative rate +**KPI**: Binary Structure Loss False Positive Rate
+This benchmark is performed on the binary classes for damaged buildings. -### Benchmark FB001-BD05 +#### FB001-BD06 -**KPI**: Binary structure loss false positive rate +**KPI**: Binary Structure Loss F1 Score
+This benchmark is performed on the binary classes for damaged buildings. + + +## Requirements + +The following sections list the datasets requirements to run the different benchmarks. When the benchmark script is run, each requirement is validated. If a requirement is met, each corresponding benchmark is run. +Each requirement lists the datasets (as path) required to run the benchmark as well as mandatory attributs for each dataset. The shape of the dataset is given between parenthesis. + +*Example* +``` +- /path/of/dataset (shape): attribut1, attribut2 +``` + +Requirement | Benchmarks +---------------------- | ----------------- +R01 | FB001-BD01, FB001-BD02, FB001-BD03, FB001-BD04, FB001-BD05, FB001-BD06 -### Benchmark FB001-BD06 +### R01 -**KPI**: Binary structure loss F1 score \ No newline at end of file +- `/points/building_damaged/building_damage`: units \ No newline at end of file diff --git a/docs/metrics/index.md b/docs/metrics/index.md index bca86eb..27ef2bb 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -1,64 +1,25 @@ # 5. Metrics information -This section describes the high-level metrics available in `FireBench`, organized by the type and structure of the observational data they support. These metrics are designed to evaluate model performance in realistic settings and are grouped into categories that reflect typical data sources (e.g., weather stations, satellite imagery, fire perimeters). -Some metrics support observation uncertainty, and others are specifically designed for deterministic or ensemble simulations. +This section describes the high-level metrics available in `FireBench`, listed as `Key Performance Indicator` (KPI). Each KPI represents a quantitative evaluation of performance. For implementation details, refer to the [API references](../api/index.rst). A full list of metrics is also available on the [Content page](../content.md). -## Single Point (0D, Time Series) +## Key Performance Indicator -These metrics apply to **0D signals**, *i.e.*, time series at a single spatial location. This is typical for **weather station data** or **virtual probes** in simulations. -**Use these metrics when**: -- You have observations at fixed points in space (e.g., 10-meter wind at a weather station) -- You want to compute per-station RMSE, bias, correlation, etc. +### Binary Structure Loss Accuracy -**List of metrics** -- Bias -- RMSE -- NMSE with range normalization -- NMSE with power normalization +*Category*: Structure Damage
+*Name used in result files*: Binary Structure Loss Accuracy -## Network of Probes +Measure how accurately the model predicts which structures are destroyed or not destroyed by the fire, based on binary (burned / not burned) observations. -Metrics in this category are designed to evaluate a **network of time series** across multiple locations, such as a set of weather stations. +The measure of [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. -**Use these metrics when**: -- You want to evaluate performance across a full observation network -- You need to analyze spatial structure, coherence, or regional error statistics +$$ +Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, +$$ +where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) -## Line or Polygon Observations (1D in Space, Sparse in Time) - -These metrics apply to **1D spatial data** that are available at discrete times, for example GIS polygons representing fire perimeters, or airborne measurements along a path - -**Use these metrics when**: -- You want to compare the shape, location, or evolution of 1D features -- You need to evaluate model accuracy along a known line or within a boundary - -**List of metrics** -- Jaccard index (Intersection over Union) -- Sorensen-Dice index - -## 2D Raster Data (Sparse in Time) - -Metrics in this group apply to **2D spatial data**, such as satellite imagery, available at discrete times. - -**Use these metrics when**: -- You are comparing model outputs to gridded observations -- You want to use spatial scores (e.g., FSS, SAL) or object-based comparison methods - -**List of metrics** -- Jaccard index (Intersection over Union) -- Sorensen-Dice index - -## 3D Sparse or Semi-Sparse Observations - -This category includes **3D datasets** that may be dense in two dimensions and sparse in the third (typically time). Examples include: - -* **Lidar scans** (e.g., vertical cross-sections of wind or aerosol) -* **Radar volumes** or **profiling instruments** - -**Use these metrics when**: -- Your data span two spatial dimensions (e.g., x-z or y-z) over time -- You want to assess how well the model reproduces layered structures or vertical evolution \ No newline at end of file +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. \ No newline at end of file diff --git a/docs/standard_format.md b/docs/standard_format.md index 4371d19..9d95b62 100644 --- a/docs/standard_format.md +++ b/docs/standard_format.md @@ -1,8 +1,8 @@ # 4. Standard FireBench file format -- **Version**: 0.1 +- **Version**: 0.2 - **Status**: Draft -- **Last update**: 2025-08-08 +- **Last update**: 2025-11-19 This document defines the I/O format standard for benchmark datasets used in the `FireBench` benchmarking framework. The standard is based on the [HDF5 file format](https://www.hdfgroup.org/solutions/hdf5/) (`.h5`) and describes the structure, expected groups, metadata, and conventions. @@ -12,7 +12,8 @@ Each .h5 file must adhere to the following structure: ``` / (root) -├── probes/ (point-based time series) +├── points/ (geopolygones) +├── time_series/ (point-based time series) ├── 1D_raster/ (1D gridded spatial data + time) ├── 2D_raster/ (2D gridded spatial data + time) ├── 3D_raster/ (3D gridded spatial data + time) From c39f8e077c7767338d2fe40bd78f88727d78a268 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 19 Nov 2025 22:13:08 -0800 Subject: [PATCH 018/111] minor code formatting --- src/firebench/metrics/mtbs/mtbs_analysis.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/src/firebench/metrics/mtbs/mtbs_analysis.py b/src/firebench/metrics/mtbs/mtbs_analysis.py index 1fa915a..300feb9 100644 --- a/src/firebench/metrics/mtbs/mtbs_analysis.py +++ b/src/firebench/metrics/mtbs/mtbs_analysis.py @@ -92,8 +92,10 @@ def global_accuracy( accuracy_per_class_total = np.zeros(5) for k in range(K - 1): - accuracy_per_class_bps[k] = cm[k + 1, k + 1] / np.sum(cm[k+1, 1:]) if np.sum(cm[k+1, 1:]) > 0 else 0.0 - accuracy_per_class_total[k] = cm[k + 1, k + 1] / (np.sum(cm[k+1, :]) + cm[0, k+1]) + accuracy_per_class_bps[k] = ( + cm[k + 1, k + 1] / np.sum(cm[k + 1, 1:]) if np.sum(cm[k + 1, 1:]) > 0 else 0.0 + ) + accuracy_per_class_total[k] = cm[k + 1, k + 1] / (np.sum(cm[k + 1, :]) + cm[0, k + 1]) print(accuracy_per_class_bps) print(accuracy_per_class_total) @@ -121,7 +123,12 @@ def global_accuracy( # panel 3: Difference im3 = ax3.pcolormesh( - lon_ref, lat_ref, mtbs_eval - mtbs_ref, cmap="RdYlGn_r", norm=Normalize(vmin=-5, vmax=5), edgecolors="none" + lon_ref, + lat_ref, + mtbs_eval - mtbs_ref, + cmap="RdYlGn_r", + norm=Normalize(vmin=-5, vmax=5), + edgecolors="none", ) # panel 4: confusion matrix From ad88121d4172e1ac33643bc6e671465705de413f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 21 Nov 2025 09:08:47 -0800 Subject: [PATCH 019/111] caldor docs update --- docs/api/metrics.rst | 14 ++++++++++ docs/benchmarks/California/01_Caldor.md | 4 +-- docs/metrics/index.md | 35 ++++++++++++++++++++++++- 3 files changed, 50 insertions(+), 3 deletions(-) diff --git a/docs/api/metrics.rst b/docs/api/metrics.rst index 306ed93..9c6f884 100644 --- a/docs/api/metrics.rst +++ b/docs/api/metrics.rst @@ -9,3 +9,17 @@ Perimeters :undoc-members: :show-inheritance: +Confusion Matrix +---------------- + +.. automodule:: firebench.metrics.confusion_matrix.binary_performance + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: firebench.metrics.confusion_matrix.utils + :members: + :undoc-members: + :show-inheritance: + + diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 5a3f3b1..6b413bd 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -116,11 +116,11 @@ This benchmark is performed on the binary classes for damaged buildings. ## Requirements The following sections list the datasets requirements to run the different benchmarks. When the benchmark script is run, each requirement is validated. If a requirement is met, each corresponding benchmark is run. -Each requirement lists the datasets (as path) required to run the benchmark as well as mandatory attributs for each dataset. The shape of the dataset is given between parenthesis. +Each requirement lists the required datasets (as path) as well as mandatory attributs for each dataset. *Example* ``` -- /path/of/dataset (shape): attribut1, attribut2 +- /path/of/dataset: attribut1, attribut2 ``` Requirement | Benchmarks diff --git a/docs/metrics/index.md b/docs/metrics/index.md index 27ef2bb..72f710f 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -22,4 +22,37 @@ Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, $$ where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. \ No newline at end of file +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss Precision + +*Category*: Structure Damage
+*Name used in result files*: Binary Structure Loss Precision + +Measures how accurately the model predicts which structures are destroyed, by evaluating the proportion of predicted-destroyed buildings that were actually destroyed. + +The measure of [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Precision = \frac{TP}{TP + FP}, +$$ +where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset). + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + + +### Binary Structure Loss Recall + +*Category*: Structure Damage
+*Name used in result files*: Binary Structure Loss Recall + +Measures how completely the model captures the buildings that were actually destroyed, indicating the fraction of truly destroyed structures that the model successfully identifies. + +The measure of [recall](https://en.wikipedia.org/wiki/Precision_and_recall) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Recall = \frac{TP}{TP + FN}, +$$ +where $TP$ = True positive (buildings destroyed in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. \ No newline at end of file From 1a30c3b70dd6d1045b7b91ed17d560cae807f449 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 21 Nov 2025 10:55:50 -0800 Subject: [PATCH 020/111] add KPI - KPIs add best/lowest score - add KPIs: Binary Structure Loss Specificity, Binary Structure Loss Negative Predictive Value, Binary Structure Loss F1 Score - chaneg name of binary_specific_rate function to binary_specificity - Updaet Caldor benchmarks with new KPIs --- docs/benchmarks/California/01_Caldor.md | 4 +- docs/metrics/index.md | 71 +++++++++++++++++-- .../metrics/confusion_matrix/__init__.py | 2 +- .../confusion_matrix/binary_performance.py | 2 +- 4 files changed, 69 insertions(+), 10 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 6b413bd..1957cd3 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -99,12 +99,12 @@ This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD04 -**KPI**: Binary Structure Loss False Negative Rate
+**KPI**: Binary Structure Loss Specificity
This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD05 -**KPI**: Binary Structure Loss False Positive Rate
+**KPI**: Binary Structure Loss Negative Predictive Value
This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD06 diff --git a/docs/metrics/index.md b/docs/metrics/index.md index 72f710f..5ac64a1 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -11,7 +11,9 @@ A full list of metrics is also available on the [Content page](../content.md). ### Binary Structure Loss Accuracy *Category*: Structure Damage
-*Name used in result files*: Binary Structure Loss Accuracy +*Name used in result files*: Binary Structure Loss Accuracy
+*Best Score*: 1
+*Lowest Score*: 0 Measure how accurately the model predicts which structures are destroyed or not destroyed by the fire, based on binary (burned / not burned) observations. @@ -22,12 +24,14 @@ Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, $$ where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. ### Binary Structure Loss Precision *Category*: Structure Damage
-*Name used in result files*: Binary Structure Loss Precision +*Name used in result files*: Binary Structure Loss Precision
+*Best Score*: 1
+*Lowest Score*: 0 Measures how accurately the model predicts which structures are destroyed, by evaluating the proportion of predicted-destroyed buildings that were actually destroyed. @@ -38,13 +42,15 @@ Precision = \frac{TP}{TP + FP}, $$ where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset). -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. ### Binary Structure Loss Recall *Category*: Structure Damage
-*Name used in result files*: Binary Structure Loss Recall +*Name used in result files*: Binary Structure Loss Recall
+*Best Score*: 1
+*Lowest Score*: 0 Measures how completely the model captures the buildings that were actually destroyed, indicating the fraction of truly destroyed structures that the model successfully identifies. @@ -55,4 +61,57 @@ Recall = \frac{TP}{TP + FN}, $$ where $TP$ = True positive (buildings destroyed in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. \ No newline at end of file +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss Specificity + +*Category*: Structure Damage
+*Name used in result files*: Binary Structure Loss Specificity
+*Best Score*: 1
+*Lowest Score*: 0 + +Measures how accurately the model identifies buildings that survived, by quantifying the fraction of intact structures correctly predicted as not destroyed. + +The measure of [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Specificity = \frac{TN}{TN + FP} +$$ +where $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets). + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss Negative Predictive Value + +*Category*: Structure Damage
+*Name used in result files*: Binary Structure Loss Negative Predictive Value
+*Best Score*: 1
+*Lowest Score*: 0 + +Measures the reliability of the model’s predictions for surviving structures, indicating the proportion of predicted-intact buildings that were indeed not destroyed. + +The measure of [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Negative Predictive Value = \frac{TN}{TN + FN} +$$ +where $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets). + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss F1 Score + +*Category*: Structure Damage
+*Name used in result files*: Binary Structure Loss F1 Score
+*Best Score*: 1
+*Lowest Score*: 0 + +Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies destroyed buildings while limiting false alarms. + +The measure of [F1 Score](https://en.wikipedia.org/wiki/F-score) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} +$$ + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. \ No newline at end of file diff --git a/src/firebench/metrics/confusion_matrix/__init__.py b/src/firebench/metrics/confusion_matrix/__init__.py index cd14bc4..cecf479 100644 --- a/src/firebench/metrics/confusion_matrix/__init__.py +++ b/src/firebench/metrics/confusion_matrix/__init__.py @@ -1,2 +1,2 @@ from .utils import binary_cm -from .binary_performance import binary_accuracy, binary_f_score, binary_false_positive_rate, binary_negative_predicted_value, binary_precision, binary_recall_rate, binary_specific_rate \ No newline at end of file +from .binary_performance import binary_accuracy, binary_f_score, binary_false_positive_rate, binary_negative_predicted_value, binary_precision, binary_recall_rate, binary_specificity \ No newline at end of file diff --git a/src/firebench/metrics/confusion_matrix/binary_performance.py b/src/firebench/metrics/confusion_matrix/binary_performance.py index 7416023..c83c53c 100644 --- a/src/firebench/metrics/confusion_matrix/binary_performance.py +++ b/src/firebench/metrics/confusion_matrix/binary_performance.py @@ -146,7 +146,7 @@ def binary_recall_rate(bcm: np.ndarray): tn, fp, fn, tp = bcm.ravel() return tp / (tp + fn) -def binary_specific_rate(bcm: np.ndarray): +def binary_specificity(bcm: np.ndarray): """ Compute the specificity (true negative rate, TNR) from a 2x2 binary confusion matrix. From d4ebf9d7cdcd7ba970a314cd9fc224ca0d518c48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 23 Nov 2025 11:24:16 -0800 Subject: [PATCH 021/111] add signing method for dict --- src/firebench/__init__.py | 3 +- .../metrics/confusion_matrix/__init__.py | 10 +++- .../confusion_matrix/binary_performance.py | 23 +++++--- .../metrics/confusion_matrix/utils.py | 2 +- src/firebench/signing/__init__.py | 1 + src/firebench/signing/signing.py | 58 +++++++++++++++++++ 6 files changed, 86 insertions(+), 11 deletions(-) create mode 100644 src/firebench/signing/__init__.py create mode 100644 src/firebench/signing/signing.py diff --git a/src/firebench/__init__.py b/src/firebench/__init__.py index be86d31..12b1d54 100644 --- a/src/firebench/__init__.py +++ b/src/firebench/__init__.py @@ -1,6 +1,7 @@ -from . import ros_models, tools, wind_interpolation, stats, metrics, sensors, standardize +from . import ros_models, tools, wind_interpolation, stats, metrics, sensors, standardize, signing from .tools.logging_config import logger from .tools.namespace import StandardVariableNames as svn from .tools.units import ureg +from .signing.signing import sign_output_dict, verify_output_dict Quantity = ureg.Quantity diff --git a/src/firebench/metrics/confusion_matrix/__init__.py b/src/firebench/metrics/confusion_matrix/__init__.py index cecf479..e0f9c6a 100644 --- a/src/firebench/metrics/confusion_matrix/__init__.py +++ b/src/firebench/metrics/confusion_matrix/__init__.py @@ -1,2 +1,10 @@ from .utils import binary_cm -from .binary_performance import binary_accuracy, binary_f_score, binary_false_positive_rate, binary_negative_predicted_value, binary_precision, binary_recall_rate, binary_specificity \ No newline at end of file +from .binary_performance import ( + binary_accuracy, + binary_f_score, + binary_false_positive_rate, + binary_negative_predicted_value, + binary_precision, + binary_recall_rate, + binary_specificity, +) diff --git a/src/firebench/metrics/confusion_matrix/binary_performance.py b/src/firebench/metrics/confusion_matrix/binary_performance.py index c83c53c..7fce635 100644 --- a/src/firebench/metrics/confusion_matrix/binary_performance.py +++ b/src/firebench/metrics/confusion_matrix/binary_performance.py @@ -1,5 +1,6 @@ import numpy as np + def binary_accuracy(bcm: np.ndarray): """ Compute the binary classification accuracy from a 2x2 confusion matrix. @@ -28,10 +29,11 @@ def binary_accuracy(bcm: np.ndarray): ------- float The accuracy value, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() return (tp + tn) / (tp + tn + fp + fn) + def binary_precision(bcm: np.ndarray): """ Compute the precision from a 2x2 binary confusion matrix. @@ -61,10 +63,11 @@ def binary_precision(bcm: np.ndarray): ------- float The precision value, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() return tp / (tp + fp) + def binary_false_positive_rate(bcm: np.ndarray): """ Compute the false positive rate (FPR) from a 2x2 binary confusion matrix. @@ -88,10 +91,11 @@ def binary_false_positive_rate(bcm: np.ndarray): ------- float The false positive rate, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() return fp / (fp + tn) + def binary_negative_predicted_value(bcm: np.ndarray): """ Compute the negative predictive value (NPV) from a 2x2 binary confusion matrix. @@ -115,10 +119,11 @@ def binary_negative_predicted_value(bcm: np.ndarray): ------- float The negative predictive value, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() return tn / (fn + tn) + def binary_recall_rate(bcm: np.ndarray): """ Compute the recall rate (true positive rate, TPR) from a 2x2 binary confusion matrix. @@ -142,10 +147,11 @@ def binary_recall_rate(bcm: np.ndarray): ------- float The recall value, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() return tp / (tp + fn) + def binary_specificity(bcm: np.ndarray): """ Compute the specificity (true negative rate, TNR) from a 2x2 binary confusion matrix. @@ -169,10 +175,11 @@ def binary_specificity(bcm: np.ndarray): ------- float The specificity value, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long tn, fp, fn, tp = bcm.ravel() return tn / (tn + fp) + def binary_f_score(bcm: np.ndarray): """ Compute the F1-score from a 2x2 binary confusion matrix. @@ -197,7 +204,7 @@ def binary_f_score(bcm: np.ndarray): ------- float The F1-score, ranging from 0.0 to 1.0. - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long pr = binary_precision(bcm) rc = binary_recall_rate(bcm) - return 2.0 * (pr * rc) / (pr + rc) \ No newline at end of file + return 2.0 * (pr * rc) / (pr + rc) diff --git a/src/firebench/metrics/confusion_matrix/utils.py b/src/firebench/metrics/confusion_matrix/utils.py index bcbd0fa..b37acca 100644 --- a/src/firebench/metrics/confusion_matrix/utils.py +++ b/src/firebench/metrics/confusion_matrix/utils.py @@ -39,7 +39,7 @@ def binary_cm(y_true, y_pred): >>> binary_confusion_matrix_matrix([0, 1, 1, 0], [0, 1, 0, 0]) array([[2, 0], [1, 1]]) - """ # pylint: disable=line-too-long + """ # pylint: disable=line-too-long y_true = np.asarray(y_true).astype(bool) y_pred = np.asarray(y_pred).astype(bool) diff --git a/src/firebench/signing/__init__.py b/src/firebench/signing/__init__.py new file mode 100644 index 0000000..f63ee22 --- /dev/null +++ b/src/firebench/signing/__init__.py @@ -0,0 +1 @@ +from .signing import sign_output_dict, verify_output_dict diff --git a/src/firebench/signing/signing.py b/src/firebench/signing/signing.py new file mode 100644 index 0000000..b41d388 --- /dev/null +++ b/src/firebench/signing/signing.py @@ -0,0 +1,58 @@ +import hmac +import hashlib +import json + +from . import _secret_key + + +def canonical_json_dumps(data: dict) -> bytes: + return json.dumps(data, sort_keys=True, separators=(",", ":")).encode("utf-8") + + +def compute_hmac(json_bytes: bytes) -> str: + """ + Compute the HMAC-SHA256 of the given JSON bytes using the secret key + stored in the compiled extension. + """ + key = _secret_key.get_secret_key() + mac = hmac.new(key, json_bytes, hashlib.sha256).digest() + return mac.hex() + + +def sign_output_dict(data: dict) -> dict: + """ + Return a copy of the dictionary with an added "mac" field. + + The MAC is computed on the canonical JSON representation of the input + dictionary *without* the "mac" field. Any change to the content will + invalidate the MAC. + """ + json_bytes = canonical_json_dumps(data) + mac = compute_hmac(json_bytes) + + out = dict(data) + out["mac"] = mac + return out + + +def verify_output_dict(signed_data: dict) -> bool: + """ + Verify that the 'mac' field in the given dictionary matches the HMAC + computed from the remaining content. + + Returns + ------- + bool + True if the MAC is valid, False otherwise. + """ + mac = signed_data.get("mac") + if mac is None: + return False + + data_without_mac = dict(signed_data) + data_without_mac.pop("mac", None) + + json_bytes = canonical_json_dumps(data_without_mac) + expected_mac = compute_hmac(json_bytes) + + return hmac.compare_digest(mac, expected_mac) From a6d68643e4c208b7c04389f1cbf5ba100eaaee36 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 12:51:49 -0800 Subject: [PATCH 022/111] add MTBS map to Caldor benchmarks --- docs/_static/benchmarks/FB001/mtbs_map.jpg | Bin 0 -> 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+ + Fig. 3 + + : + + Map of burn severity from MTBS + +

+ +### Benchmarks + ## Requirements The following sections list the datasets requirements to run the different benchmarks. When the benchmark script is run, each requirement is validated. If a requirement is met, each corresponding benchmark is run. @@ -129,4 +154,8 @@ R01 | FB001-BD01, FB001-BD02, FB001-BD03, FB001-BD04, FB001-B ### R01 -- `/points/building_damaged/building_damage`: units \ No newline at end of file +- `/points/building_damaged/building_damage`: units + +## Notes + +- Each file hash has been performed using `firebench.standardize.calculate_sha256`. \ No newline at end of file From 812ffa83bcf7635b36cf22061fd0157cb26cbb09 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 12:54:20 -0800 Subject: [PATCH 023/111] refactoring: move time utils for std in standardize package - move functions _resolve_tz, datetime_to_iso8601, current_datetime_iso8601 to firebench.standardize.time - move read_quantity_from_fb_dataset to firebench.standardize.tools --- src/firebench/standardize/__init__.py | 13 +++- .../time.py} | 53 +------------ src/firebench/standardize/tools.py | 77 +++++++++++++++++++ src/firebench/tools/__init__.py | 5 -- 4 files changed, 90 insertions(+), 58 deletions(-) rename src/firebench/{tools/standard_file_utils.py => standardize/time.py} (67%) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 643fe1d..51af369 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -1,2 +1,13 @@ from .mtbs import convert_mtbs_geotiff -from .tools import VERSION_STD, validate_h5_std, is_iso8601, validate_h5_requirement +from .tools import ( + VERSION_STD, + validate_h5_std, + is_iso8601, + validate_h5_requirement, + read_quantity_from_fb_dataset, + new_std_file, +) +from .time import ( + current_datetime_iso8601, + datetime_to_iso8601, +) diff --git a/src/firebench/tools/standard_file_utils.py b/src/firebench/standardize/time.py similarity index 67% rename from src/firebench/tools/standard_file_utils.py rename to src/firebench/standardize/time.py index 2317020..97b0d55 100644 --- a/src/firebench/tools/standard_file_utils.py +++ b/src/firebench/standardize/time.py @@ -3,9 +3,7 @@ from datetime import datetime, tzinfo from zoneinfo import ZoneInfo from typing import Optional, Union -import h5py -from .logging_config import logger -from .units import ureg +from ..tools.logging_config import logger _TZLike = Union[str, tzinfo] @@ -114,52 +112,3 @@ def current_datetime_iso8601( # Start from local now, then convert to requested tz for consistency now_local = datetime.now().astimezone() return datetime_to_iso8601(now_local, include_seconds, tz) - - -def read_quantity_from_fb_dataset(dataset_path: str, file_object: h5py.File | h5py.Group | h5py.Dataset): - """ - Read a dataset from an HDF5 file, group, or dataset node and return it as a Pint Quantity - according to the FireBench I/O standard. - - This function expects the dataset to comply with the FireBench standard I/O format - (version >= 0.1), meaning it must define a string `units` attribute specifying the - physical units of the stored values. The full dataset is loaded into memory and - wrapped into a `pint.Quantity` using the global `ureg` registry. - - Parameters - ---------- - dataset_path : str - Path to the target dataset relative to `file_object`. For an `h5py.File`, - this is the absolute or group-relative path (e.g., "/2D_raster/0001/temperature"). - file_object : h5py.File | h5py.Group | h5py.Dataset - HDF5 file, group, or dataset object from which the dataset will be read. - Must support item access via `__getitem__` and store datasets with `.attrs`. - - Returns - ------- - pint.Quantity - The dataset values loaded into memory, associated with the units taken from - the dataset's `units` attribute. - - Raises - ------ - KeyError - If `dataset_path` does not exist in `file_object`. - ValueError - If the dataset has no `units` attribute or it is not a non-empty string. - - Notes - ----- - - The function reads the **entire dataset** into memory; for very large datasets, - consider reading subsets instead. - - Compliant with FireBench I/O standard >= 0.1. - """ # pylint: disable=line-too-long - ds = file_object[dataset_path] - - units = ds.attrs.get("units", None) - if not isinstance(units, str) or not units.strip(): - raise ValueError( - f"Dataset '{dataset_path}' is missing a valid `units` attribute required by FireBench I/O standard." - ) - - return ureg.Quantity(ds[()], ds.attrs["units"]) diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index f17b526..5844719 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -1,6 +1,9 @@ import h5py from ..tools.logging_config import logger +from ..tools.units import ureg +from .time import current_datetime_iso8601 import re +from pathlib import Path VERSION_STD = "0.2" @@ -110,3 +113,77 @@ def validate_h5_requirement(file: h5py.File, required: dict[str, list[str]]): return False, f"attr `{attr_name}` of dataset `{dset_path}`" return True, None + + +def read_quantity_from_fb_dataset(dataset_path: str, file_object: h5py.File | h5py.Group | h5py.Dataset): + """ + Read a dataset from an HDF5 file, group, or dataset node and return it as a Pint Quantity + according to the FireBench I/O standard. + + This function expects the dataset to comply with the FireBench standard I/O format + (version >= 0.1), meaning it must define a string `units` attribute specifying the + physical units of the stored values. The full dataset is loaded into memory and + wrapped into a `pint.Quantity` using the global `ureg` registry. + + Parameters + ---------- + dataset_path : str + Path to the target dataset relative to `file_object`. For an `h5py.File`, + this is the absolute or group-relative path (e.g., "/2D_raster/0001/temperature"). + file_object : h5py.File | h5py.Group | h5py.Dataset + HDF5 file, group, or dataset object from which the dataset will be read. + Must support item access via `__getitem__` and store datasets with `.attrs`. + + Returns + ------- + pint.Quantity + The dataset values loaded into memory, associated with the units taken from + the dataset's `units` attribute. + + Raises + ------ + KeyError + If `dataset_path` does not exist in `file_object`. + ValueError + If the dataset has no `units` attribute or it is not a non-empty string. + + Notes + ----- + - The function reads the **entire dataset** into memory; for very large datasets, + consider reading subsets instead. + - Compliant with FireBench I/O standard >= 0.1. + """ # pylint: disable=line-too-long + ds = file_object[dataset_path] + + units = ds.attrs.get("units", None) + if not isinstance(units, str) or not units.strip(): + raise ValueError( + f"Dataset '{dataset_path}' is missing a valid `units` attribute required by FireBench I/O standard." + ) + + return ureg.Quantity(ds[()], ds.attrs["units"]) + + +def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.File: + """ + Create a new file using FireBench standard. + Return the file object. + Notes + ----- + Do not forget to close the file once edited. This function opens the h5 file but do not close it. + """ + if Path(filepath).exists(): + if overwrite: + logger.info("file %s already exists and is being replaced.", filepath) + else: + logger.error( + "file %s already exists. Use `overwrite=True` to overwrite the existing file.", filepath + ) + raise FileExistsError() + + h5 = h5py.File(filepath, mode="w") + h5.attrs["FireBench_io_version"] = VERSION_STD + h5.attrs["created_on"] = current_datetime_iso8601(include_seconds=False) + h5.attrs["created_by"] = authors + + return h5 diff --git a/src/firebench/tools/__init__.py b/src/firebench/tools/__init__.py index 6b2bdd2..6dc2e5c 100644 --- a/src/firebench/tools/__init__.py +++ b/src/firebench/tools/__init__.py @@ -48,8 +48,3 @@ calculate_sha256, ) from .raster_to_perimeters import array_to_geopolygons -from .standard_file_utils import ( - current_datetime_iso8601, - datetime_to_iso8601, - read_quantity_from_fb_dataset, -) From 1db8f90bb221cbe6d384c38c4b66e49b756aff1d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 16:53:05 -0800 Subject: [PATCH 024/111] refactoring standardize_mtbs_from_geotiff --- src/firebench/standardize/__init__.py | 2 +- src/firebench/standardize/mtbs.py | 119 +++++++++++--------------- 2 files changed, 53 insertions(+), 68 deletions(-) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 51af369..5ed9bfa 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -1,4 +1,4 @@ -from .mtbs import convert_mtbs_geotiff +from .mtbs import standardize_mtbs_from_geotiff from .tools import ( VERSION_STD, validate_h5_std, diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py index fa4fb2b..88592eb 100644 --- a/src/firebench/standardize/mtbs.py +++ b/src/firebench/standardize/mtbs.py @@ -1,4 +1,3 @@ -from datetime import datetime from pathlib import Path import h5py @@ -7,48 +6,47 @@ from pyproj import CRS, Transformer from ..tools import StandardVariableNames as svn -from ..tools import current_datetime_iso8601, datetime_to_iso8601 -from .tools import VERSION_STD, check_std_version +from .tools import check_std_version from ..tools.logging_config import logger -def convert_mtbs_geotiff( - path: str, - dst: str, +def standardize_mtbs_from_geotiff( + geotiff_path: str, + h5file: h5py.File, group_name: str | None = None, - authors: str = "", - std_file_description: str = "auto", + projection: str = None, overwrite: bool = False, + invert_y: bool = False, ): """ Convert a MTBS GeoTIFF to Firebench HDF5 standard file. - MTBS data uses NAD83 projection. + Use source data projection as default. Can be reprojected by specifying the CRS in projection. Parameters ---------- - path : str + geotiff_path : str Path to the MTBS GeoTIFF (ending with *_dnbr6.tif). - dst : str - Path to target HDF5 file (created or appended). + h5file : h5py.File + target HDF5 file group_name : str | None - HDF5 group path. If None, auto-derive from filename, e.g. '2D_raster/'. - authors: str - Add the names of the authors in the standard file in the file attribute `created_by`. - std_file_description: str - Add custom description of the mtbs group. If description is "auto" the description will be "\n- Burn severity from MTBS". + HDF5 group path. If None, auto-derive from filename, e.g. '2D_raster/'. overwrite: bool Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data Returns ------- - str + h5py.File The actual HDF5 group written (with suffix if collision). """ # pylint: disable=line-too-long - with rasterio.open(path) as src: + logger.debug("Standardize MTBS dataset from file %s ", geotiff_path) + check_std_version(h5file) + with rasterio.open(geotiff_path) as src: data = src.read(1) severity_raw = {"data": data, "transform": src.transform, "crs": src.crs, "nodata": src.nodata} - logger.info(f"Loaded {path}: shape={data.shape}, CRS={src.crs}") + logger.info(f"Loaded {geotiff_path}: shape={data.shape}, CRS={src.crs}") rows, cols = data.shape @@ -70,58 +68,45 @@ def convert_mtbs_geotiff( ) # Reproject to geographic lat/lon - # NAD83 geographic - CRS_NAME = "EPSG:4269" - tgt_crs = CRS(CRS_NAME) + if projection is None: + projection = severity_raw["crs"] + tgt_crs = CRS(projection) # always_xy=True -> transformer expects/returns (x, y) = (lon, lat) ordering for geographic CRS transformer = Transformer.from_crs(severity_raw["crs"], tgt_crs, always_xy=True) lon, lat = transformer.transform(x, y) # lon, lat are 2-D arrays aligned with `data` - with h5py.File(dst, "a") as h5: + if invert_y: + lat = lat[::-1, :] + lon = lon[::-1, :] + severity_raw["data"] = severity_raw["data"][::-1, :] - if check_std_version(h5): - h5.attrs["FireBench_io_version"] = VERSION_STD + if group_name is None: + group_name = Path(geotiff_path).stem - h5.attrs["created_on"] = current_datetime_iso8601(include_seconds=False) - - if "created_by" in h5.attrs.keys(): - if authors is not None: - h5.attrs["created_by"] = h5.attrs["created_by"] + f", {authors}" - else: - h5.attrs["created_by"] = authors - - if std_file_description == "auto": - std_file_description = "\n- Burn severity from MTBS" - if "description" in h5.attrs.keys(): - h5.attrs["description"] = h5.attrs["description"] + "\n- Burn severity from MTBS" + group_name = f"/2D_raster/{group_name}" + if group_name in h5file.keys(): + if overwrite: + del h5file[group_name] else: - h5.attrs["description"] = "This file contains:\n- Burn severity from MTBS" - - group_name = f"/2D_raster/{group_name}" - if group_name in h5.keys(): - if overwrite: - del h5[group_name] - else: - logger.warning( - "group name %s already exists in file %s. Group not updated. Set `overwrite` to True to update the dataset.", - group_name, - path, - ) - return - - g = h5.create_group(group_name) - g.attrs["data_source"] = f"MTBS {path}" - g.attrs["crs"] = CRS_NAME - # TODO: add some useful metadata from _metadata.xml (fire_ignition_time, pre_fire_image_date, post_fire_image_date) - - # Lat/Lon as 2-D arrays - dlat = g.create_dataset("latitude", data=lat, dtype=np.float64) - dlat.attrs["units"] = "degrees" - - dlat = g.create_dataset("longitude", data=lon, dtype=np.float64) - dlat.attrs["units"] = "degrees" - - ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8) - ddata.attrs["units"] = "dimensionless" - ddata.attrs["_FillValue"] = 0 + logger.warning( + "group name %s already exists in file %s. Group not updated. Set `overwrite` to True to update the dataset.", + group_name, + geotiff_path, + ) + return + + g = h5file.create_group(group_name) + g.attrs["data_source"] = f"MTBS {geotiff_path}" + g.attrs["crs"] = str(tgt_crs) + + # Lat/Lon as 2-D arrays + dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8) + ddata.attrs["units"] = "dimensionless" + ddata.attrs["_FillValue"] = 0 From 9c4ba1e33707f5a2b80b37baa9e42cd76cdd1009 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 16:53:22 -0800 Subject: [PATCH 025/111] update std file format for polygons --- docs/standard_format.md | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/docs/standard_format.md b/docs/standard_format.md index 9d95b62..63a626c 100644 --- a/docs/standard_format.md +++ b/docs/standard_format.md @@ -343,26 +343,23 @@ The possible units fields are the following: ### polygons -- Contains data stored as polygons with an explicit coordinate reference system (CRS), such as those derived from .kml or shapefiles. -- All spatial coordinates must follow the Spatial Information Convention, including a required `crs` attribute at the group level. Optional attributes or datasets for holes/multipolygons can be added. -- Each polygon is stored as a separate dataset within a group. This dataset contains the polygon geometry (list of vertices) and has its own attributes for time, CRS, and other metadata. Multipolygons (island, holes) can be stored in the same dataset as long as they share the same attributes. -- Polygons that have a specific time stamp must contain an attribute `time` following the time format convention (each polygon dataset has its own time attribute). -- Per-polygon attributes (e.g., building type, perimeter source) should be stored as attributes at the lowest common level. Group attributes are considered common to all datasets contained in the group. If information is specific to a polygon, it should be stored as a dataset attribute. +- As HDF5 is not a file format that is practical to use for vectorized dataset, the polygons are stored using the `KML` file format. +- The HDF5 file contains the necessary metadata to point to the KML file containing the polygons dataset in a group registered in the `/polygons` main group. +- Each group contains a referen to one and only one KML file. +- The mandatory attributs are the following + - `rel_path` (str): relative path to the kml file + - `file_size_bytes` (int): KML file size in bytes + - `sha256` (str): hash of the KML file using `firebench.tools.calculate_sha256` - Users are encouraged to add an attribute `description` to groups and datasets for information/context about the data. -- If no data value or fill value is used, an attribute `_FillValue` must be defined at the dataset level. -- Each dataset (fire perimeter, buildings, *etc.*) must be named using the [Standard Variable Namespace](./namespace.md). If the name of the variable is not present, use a variable name as descriptive as possible and open a pull request to add the variable name to the Standard Variable Namespace. Units must be defined as an attribute `units` compatible with [Pint library](https://pint.readthedocs.io/en/stable/) terminology. -- Polygons are stored as (Nvertices, 2) or (Nvertices, 3) arrays following a Spatial Information Convention. +In the following exmaple, we have a standard file `dataset.h5`, containing one polygons dataset. The group `/polygons/fire_perimeters` has the attribut `rel_path="polygons.kml"`. ``` +dataset.h5 / (root) ├── polygons/ (geopolygones) -│ ├── fire_perimeters (group containing fire perimeter polygons and related metadata) -│ │ ├── perimeter_1 (polygons describing the perimeter at time 1) -│ │ ├── perimeter_2 (polygons describing the perimeter at time 2) -│ │ ├── perimeter_3 (polygons describing the perimeter at time 3) -│ ├── buildings_info_1 (group data from a building dataset) -│ │ ├── position_structure -│ │ ├── roof_type +│ ├── fire_perimeters (group containing kml metadata) + +polygons.kml ``` **Note**: This part of the standard is in an early stage and intentionally allows significant flexibility to accommodate diverse geopolygons data types. The structure and required fields may evolve in future versions based on user feedback and practical experience. From e85fdb2837bb255521652b097d86908b21744a64 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 18:19:07 -0800 Subject: [PATCH 026/111] add mandatory separator for authors lists add merge authors list function --- docs/standard_format.md | 2 +- src/firebench/standardize/__init__.py | 5 +- src/firebench/standardize/files.py | 58 ++++++++++++ src/firebench/standardize/tools.py | 49 +++++----- tests/unit/test_std_tools.py | 123 ++++++++++++++++++++++++++ 5 files changed, 210 insertions(+), 27 deletions(-) create mode 100644 src/firebench/standardize/files.py create mode 100644 tests/unit/test_std_tools.py diff --git a/docs/standard_format.md b/docs/standard_format.md index 63a626c..8836132 100644 --- a/docs/standard_format.md +++ b/docs/standard_format.md @@ -34,7 +34,7 @@ Attributs | Type | Description :--------- | :----: | :----------- `FireBench_io_version` | str | Version of the I/O standard used `created_on` | str | ISO 8601 date-time of file creation -`created_by` | str | Creator identifier (name, email, etc) +`created_by` | str | Creator identifier (name, email, etc). Each contributor must be separated by `;` (parsing authors). Suggested additional attributes: diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 5ed9bfa..e560405 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -5,9 +5,12 @@ is_iso8601, validate_h5_requirement, read_quantity_from_fb_dataset, - new_std_file, + merge_authors, ) from .time import ( current_datetime_iso8601, datetime_to_iso8601, ) +from .files import ( + new_std_file, +) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py new file mode 100644 index 0000000..a2bfc5b --- /dev/null +++ b/src/firebench/standardize/files.py @@ -0,0 +1,58 @@ +import h5py +from ..tools.logging_config import logger +from .time import current_datetime_iso8601 +from pathlib import Path +from .tools import VERSION_STD, validate_h5_std, merge_authors + +def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.File: + """ + Create a new file using FireBench standard. + Return the file object. + Notes + ----- + Do not forget to close the file once edited. This function opens the h5 file but do not close it. + """ + if Path(filepath).exists(): + if overwrite: + logger.info("file %s already exists and is being replaced.", filepath) + else: + logger.error( + "file %s already exists. Use `overwrite=True` to overwrite the existing file.", filepath + ) + raise FileExistsError() + + h5 = h5py.File(filepath, mode="w") + h5.attrs["FireBench_io_version"] = VERSION_STD + h5.attrs["created_on"] = current_datetime_iso8601(include_seconds=False) + h5.attrs["created_by"] = authors + + return h5 + +def merge_two_std_files(filepath_1:str, filepath_2:str, filepath_target:str, conflict_solver = dict, overwrite:bool = False): + """ + Try to merge two std FireBench files + + Check if both files are std, then check for any group/dataset/attribut conflict + + Then merge the list of authors without duplicates. Keep order as much as possible (first authors from file1 then first author from file2 then second from file 1...) + """ + file1 = h5py.File(filepath_1, "r") + validate_h5_std(file1) + file2 = h5py.File(filepath_2, "r") + validate_h5_std(file2) + + # Check for any conflicts + conflicts = [] + + # Find both list of authors + merged_authors = merge_authors(file1.attrs["created_by"], file2.attrs["created_by"]) + + # Create the new file + merged_file = new_std_file(filepath_target, authors=merged_authors, overwrite=overwrite) + + # fill the content of merged_file witht the content of both files + + file1.close() + file2.close() + merged_file.close() + diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 5844719..678fcae 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -1,9 +1,7 @@ import h5py from ..tools.logging_config import logger from ..tools.units import ureg -from .time import current_datetime_iso8601 import re -from pathlib import Path VERSION_STD = "0.2" @@ -164,26 +162,27 @@ def read_quantity_from_fb_dataset(dataset_path: str, file_object: h5py.File | h5 return ureg.Quantity(ds[()], ds.attrs["units"]) -def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.File: - """ - Create a new file using FireBench standard. - Return the file object. - Notes - ----- - Do not forget to close the file once edited. This function opens the h5 file but do not close it. - """ - if Path(filepath).exists(): - if overwrite: - logger.info("file %s already exists and is being replaced.", filepath) - else: - logger.error( - "file %s already exists. Use `overwrite=True` to overwrite the existing file.", filepath - ) - raise FileExistsError() - - h5 = h5py.File(filepath, mode="w") - h5.attrs["FireBench_io_version"] = VERSION_STD - h5.attrs["created_on"] = current_datetime_iso8601(include_seconds=False) - h5.attrs["created_by"] = authors - - return h5 +def merge_authors(authors_1:str, authors_2:str): + list_authors_1 = [a.strip() for a in authors_1.split(";") if a.strip()] + list_authors_2 = [a.strip() for a in authors_2.split(";") if a.strip()] + n1, n2 = len(list_authors_1), len(list_authors_2) + merged_authors: list[str] = [] + seen = set() + + max_len = max(n1, n2) + for i in range(max_len): + if i < n1: + a1 = list_authors_1[i] + if a1 and a1 not in seen: + merged_authors.append(a1) + seen.add(a1) + if i < n2: + a2 = list_authors_2[i] + if a2 and a2 not in seen: + merged_authors.append(a2) + seen.add(a2) + + if not merged_authors: + return "" + + return ";".join(merged_authors) + ";" \ No newline at end of file diff --git a/tests/unit/test_std_tools.py b/tests/unit/test_std_tools.py new file mode 100644 index 0000000..b60aacf --- /dev/null +++ b/tests/unit/test_std_tools.py @@ -0,0 +1,123 @@ +import pytest +from firebench.standardize import merge_authors + +@pytest.mark.parametrize( + "created_by_1, created_by_2, expected", + [ + # 1. Simple case: same length, no overlaps + # file1: alice, bob + # file2: carol, dan + # order: a1, a2, b1, b2 + ( + "alice;bob;", + "carol;dan;", + "alice;carol;bob;dan;", + ), + + # 2. Different length, no overlaps (file1 longer) + # file1: alice, bob, charlie + # file2: dan, erin + # positions: + # i=0: alice, dan + # i=1: bob, erin + # i=2: charlie (only file1) + ( + "alice;bob;charlie;", + "dan;erin;", + "alice;dan;bob;erin;charlie;", + ), + + # 3. Different length, no overlaps (file2 longer) + # file1: alice, bob + # file2: carol, dan, erin + # positions: + # i=0: alice, carol + # i=1: bob, dan + # i=2: erin (only file2) + ( + "alice;bob;", + "carol;dan;erin;", + "alice;carol;bob;dan;erin;", + ), + + # 4. Overlap across lists + # file1: alice, bob + # file2: bob, carol + # positions: + # i=0: alice, bob -> alice, bob + # i=1: bob (already seen), carol -> carol + # merged: alice, bob, carol + ( + "alice;bob;", + "bob;carol;", + "alice;bob;carol;", + ), + + # 5. Duplicate within the same list + overlap + # file1: alice, alice, bob + # file2: carol, alice + # positions: + # i=0: alice, carol -> alice, carol + # i=1: alice (seen), alice (seen) -> no new author + # i=2: bob -> bob + # merged: alice, carol, bob + ( + "alice;alice;bob;", + "carol;alice;", + "alice;carol;bob;", + ), + + # 6. One side empty (no authors in file1) + # file1: "" + # file2: alice, bob + ( + "", + "alice;bob;", + "alice;bob;", + ), + + # 7. One side empty (no authors in file2) + # file1: alice, bob + # file2: "" + ( + "alice;bob;", + "", + "alice;bob;", + ), + + # 8. Both empty + ( + "", + "", + "", + ), + + # 9. Trailing semicolons with possible stray spaces + # Expect that your function strips whitespace around names. + # file1: " alice ", "bob" + # file2: "bob ", " carol" + # merged: alice, bob, carol (no duplicates, trimmed) + ( + " alice ;bob ;", + "bob ; carol ;", + "alice;bob;carol;", + ), + + # 10. Multiple overlaps and reordering + # file1: alice, bob, charlie, dave + # file2: bob, erin, charlie, frank + # positions: + # i=0: alice, bob -> alice, bob + # i=1: bob(seen), erin -> erin + # i=2: charlie, charlie -> charlie + # i=3: dave, frank -> dave, frank + # merged: alice, bob, erin, charlie, dave, frank + ( + "alice;bob;charlie;dave;", + "bob;erin;charlie;frank;", + "alice;bob;erin;charlie;dave;frank;", + ), + ], +) +def test_merge_authors(created_by_1, created_by_2, expected): + assert merge_authors(created_by_1, created_by_2) == expected \ No newline at end of file From 14438c638fd00cabfad1a4ddd308e17714497d70 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 18:46:52 -0800 Subject: [PATCH 027/111] add collect conflicts function for merging two std files --- src/firebench/standardize/__init__.py | 1 + src/firebench/standardize/files.py | 13 ++- src/firebench/standardize/tools.py | 132 +++++++++++++++++++++++++- tests/unit/test_std_tools.py | 12 +-- 4 files changed, 142 insertions(+), 16 deletions(-) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index e560405..baf4ab5 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -13,4 +13,5 @@ ) from .files import ( new_std_file, + merge_two_std_files, ) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py index a2bfc5b..5a0da60 100644 --- a/src/firebench/standardize/files.py +++ b/src/firebench/standardize/files.py @@ -2,7 +2,8 @@ from ..tools.logging_config import logger from .time import current_datetime_iso8601 from pathlib import Path -from .tools import VERSION_STD, validate_h5_std, merge_authors +from .tools import VERSION_STD, validate_h5_std, merge_authors, collect_conflicts + def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.File: """ @@ -28,7 +29,8 @@ def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.F return h5 -def merge_two_std_files(filepath_1:str, filepath_2:str, filepath_target:str, conflict_solver = dict, overwrite:bool = False): + +def merge_two_std_files(filepath_1: str, filepath_2: str, filepath_target: str, overwrite: bool = False): """ Try to merge two std FireBench files @@ -42,7 +44,11 @@ def merge_two_std_files(filepath_1:str, filepath_2:str, filepath_target:str, co validate_h5_std(file2) # Check for any conflicts - conflicts = [] + conflicts = collect_conflicts(file1, file2) + if not conflicts: + logger.error("Try to merge files but conflicts have been found.") + print(conflicts) + raise ValueError() # Find both list of authors merged_authors = merge_authors(file1.attrs["created_by"], file2.attrs["created_by"]) @@ -55,4 +61,3 @@ def merge_two_std_files(filepath_1:str, filepath_2:str, filepath_target:str, co file1.close() file2.close() merged_file.close() - diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 678fcae..f88c534 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -2,6 +2,7 @@ from ..tools.logging_config import logger from ..tools.units import ureg import re +import numpy as np VERSION_STD = "0.2" @@ -20,6 +21,10 @@ r"(?:Z|[+-]\d{2}:\d{2})?$" # optional timezone ) +IGNORE_ATTRIBUTES = { + "/": {"created_on", "created_by", "FireBench_io_version"}, +} + def check_std_version(file: h5py.File): """ @@ -162,7 +167,7 @@ def read_quantity_from_fb_dataset(dataset_path: str, file_object: h5py.File | h5 return ureg.Quantity(ds[()], ds.attrs["units"]) -def merge_authors(authors_1:str, authors_2:str): +def merge_authors(authors_1: str, authors_2: str): list_authors_1 = [a.strip() for a in authors_1.split(";") if a.strip()] list_authors_2 = [a.strip() for a in authors_2.split(";") if a.strip()] n1, n2 = len(list_authors_1), len(list_authors_2) @@ -185,4 +190,127 @@ def merge_authors(authors_1:str, authors_2:str): if not merged_authors: return "" - return ";".join(merged_authors) + ";" \ No newline at end of file + return ";".join(merged_authors) + ";" + + +def collect_conflicts(file1, file2, path: str = "/", conflicts: list | None = None): + """ + Recursively collect conflicts between two HDF5 trees. + + A *conflict* is defined as one of: + - Same path exists in both files but with different node types + (group vs dataset). + - Same dataset path exists in both files but has different + shape or dtype. + - Same attribute key exists in both objects but has different value. + + Parameters + ---------- + file1, file2 : + Open h5py.File or h5py.Group objects that share the same logical layout. + path : str, optional + Current absolute HDF5 path to compare (default is root "/"). + conflicts : list, optional + List that will be extended with conflict dicts. If None, a new + list is created and returned. + + Returns + ------- + list + The list of collected conflict dicts. Each conflict has keys: + - "path" : str, the HDF5 path where the conflict occurs + - "kind" : str, one of {"node_type", "dataset_mismatch", "attr_mismatch"} + - "detail" : str, human-readable description + """ + if conflicts is None: + conflicts = [] + + obj1 = file1[path] + obj2 = file2[path] + + # Helper: compare attributes of two objects at same path + def _compare_attrs(o1, o2, obj_path: str): + ignore_set = IGNORE_ATTRIBUTES.get(obj_path, set()) + + keys1 = set(o1.attrs.keys()) - ignore_set + keys2 = set(o2.attrs.keys()) - ignore_set + common = keys1 & keys2 + + for key in common: + v1 = o1.attrs[key] + v2 = o2.attrs[key] + + # Use np.array_equal to handle scalars, strings, arrays, etc. + if not np.array_equal(v1, v2): + conflicts.append( + { + "path": obj_path, + "kind": "attr_mismatch", + "detail": f"Attribute '{key}' differs: {v1!r} vs {v2!r}", + } + ) + + is_group1 = isinstance(obj1, h5py.Group) + is_group2 = isinstance(obj2, h5py.Group) + is_dset1 = isinstance(obj1, h5py.Dataset) + is_dset2 = isinstance(obj2, h5py.Dataset) + + # 1) Different node types at the same path: group vs dataset + if (is_group1 and is_dset2) or (is_dset1 and is_group2): + conflicts.append( + { + "path": path, + "kind": "node_type", + "detail": f"Different node types at {path}: " + f"{type(obj1).__name__} vs {type(obj2).__name__}", + } + ) + return conflicts + + # 2) Both datasets: check shape/dtype + attributes + if is_dset1 and is_dset2: + if obj1.shape != obj2.shape or obj1.dtype != obj2.dtype: + conflicts.append( + { + "path": path, + "kind": "dataset_mismatch", + "detail": ( + f"Dataset mismatch at {path}: " + f"shape/dtype {obj1.shape}, {obj1.dtype} vs " + f"{obj2.shape}, {obj2.dtype}" + ), + } + ) + + _compare_attrs(obj1, obj2, path) + return conflicts + + # 3) Both groups: compare attributes + recurse on common children + if is_group1 and is_group2: + _compare_attrs(obj1, obj2, path) + + keys1 = set(obj1.keys()) + keys2 = set(obj2.keys()) + common_keys = keys1 & keys2 + + # Only common children can conflict. Extra children are fine. + for name in common_keys: + if path == "/": + child_path = f"/{name}" + else: + child_path = f"{path.rstrip('/')}/{name}" + + collect_conflicts(file1, file2, path=child_path, conflicts=conflicts) + + return conflicts + + # Should not really reach here, but in case of exotic types: + conflicts.append( + { + "path": path, + "kind": "node_type", + "detail": f"Unsupported node type combination at {path}: " + f"{type(obj1).__name__} vs {type(obj2).__name__}", + } + ) + return conflicts diff --git a/tests/unit/test_std_tools.py b/tests/unit/test_std_tools.py index b60aacf..eb659c8 100644 --- a/tests/unit/test_std_tools.py +++ b/tests/unit/test_std_tools.py @@ -1,6 +1,7 @@ import pytest from firebench.standardize import merge_authors + @pytest.mark.parametrize( "created_by_1, created_by_2, expected", [ @@ -13,7 +14,6 @@ "carol;dan;", "alice;carol;bob;dan;", ), - # 2. Different length, no overlaps (file1 longer) # file1: alice, bob, charlie # file2: dan, erin @@ -26,7 +26,6 @@ "dan;erin;", "alice;dan;bob;erin;charlie;", ), - # 3. Different length, no overlaps (file2 longer) # file1: alice, bob # file2: carol, dan, erin @@ -39,7 +38,6 @@ "carol;dan;erin;", "alice;carol;bob;dan;erin;", ), - # 4. Overlap across lists # file1: alice, bob # file2: bob, carol @@ -52,7 +50,6 @@ "bob;carol;", "alice;bob;carol;", ), - # 5. Duplicate within the same list + overlap # file1: alice, alice, bob # file2: carol, alice @@ -66,7 +63,6 @@ "carol;alice;", "alice;carol;bob;", ), - # 6. One side empty (no authors in file1) # file1: "" # file2: alice, bob @@ -75,7 +71,6 @@ "alice;bob;", "alice;bob;", ), - # 7. One side empty (no authors in file2) # file1: alice, bob # file2: "" @@ -84,14 +79,12 @@ "", "alice;bob;", ), - # 8. Both empty ( "", "", "", ), - # 9. Trailing semicolons with possible stray spaces # Expect that your function strips whitespace around names. # file1: " alice ", "bob" @@ -102,7 +95,6 @@ "bob ; carol ;", "alice;bob;carol;", ), - # 10. Multiple overlaps and reordering # file1: alice, bob, charlie, dave # file2: bob, erin, charlie, frank @@ -120,4 +112,4 @@ ], ) def test_merge_authors(created_by_1, created_by_2, expected): - assert merge_authors(created_by_1, created_by_2) == expected \ No newline at end of file + assert merge_authors(created_by_1, created_by_2) == expected From b4d2dc036dcd9ca7508680a897f7f40edfc2f533 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 19:01:03 -0800 Subject: [PATCH 028/111] add merge_trees function and add it to merge_two_std_files --- src/firebench/standardize/files.py | 18 +++- src/firebench/standardize/tools.py | 151 +++++++++++++++++++++++++++++ 2 files changed, 166 insertions(+), 3 deletions(-) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py index 5a0da60..f06a716 100644 --- a/src/firebench/standardize/files.py +++ b/src/firebench/standardize/files.py @@ -2,7 +2,7 @@ from ..tools.logging_config import logger from .time import current_datetime_iso8601 from pathlib import Path -from .tools import VERSION_STD, validate_h5_std, merge_authors, collect_conflicts +from .tools import VERSION_STD, validate_h5_std, merge_authors, collect_conflicts, merge_trees def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.File: @@ -30,7 +30,13 @@ def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.F return h5 -def merge_two_std_files(filepath_1: str, filepath_2: str, filepath_target: str, overwrite: bool = False): +def merge_two_std_files( + filepath_1: str, + filepath_2: str, + filepath_target: str, + merged_description: str = "", + overwrite: bool = False, +): """ Try to merge two std FireBench files @@ -38,6 +44,7 @@ def merge_two_std_files(filepath_1: str, filepath_2: str, filepath_target: str, Then merge the list of authors without duplicates. Keep order as much as possible (first authors from file1 then first author from file2 then second from file 1...) """ + logger.debug("Merge two std files") file1 = h5py.File(filepath_1, "r") validate_h5_std(file1) file2 = h5py.File(filepath_2, "r") @@ -45,7 +52,7 @@ def merge_two_std_files(filepath_1: str, filepath_2: str, filepath_target: str, # Check for any conflicts conflicts = collect_conflicts(file1, file2) - if not conflicts: + if conflicts: logger.error("Try to merge files but conflicts have been found.") print(conflicts) raise ValueError() @@ -56,8 +63,13 @@ def merge_two_std_files(filepath_1: str, filepath_2: str, filepath_target: str, # Create the new file merged_file = new_std_file(filepath_target, authors=merged_authors, overwrite=overwrite) + merge_trees(file1, file2, merged_file) + + merged_file.attrs["description"] = merged_description + # fill the content of merged_file witht the content of both files file1.close() file2.close() merged_file.close() + logger.info("Standard files merge successfull") diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index f88c534..98f997a 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -314,3 +314,154 @@ def _compare_attrs(o1, o2, obj_path: str): } ) return conflicts + + +def merge_trees( + file1: h5py.File, + file2: h5py.File, + merged_file: h5py.File, + conflict_solver: dict | None = None, +) -> None: + """ + Recursively fill `merged_file` with content from `file1` and `file2`. + + Order: + ------ + 1. Copy the full tree from file1 into merged_file. + 2. Merge the full tree from file2 into merged_file: + - If a path does not exist in merged_file, copy it. + - If a dataset already exists: + * If a conflict solver is provided for this path, use it to + combine existing and incoming data. + * Otherwise, keep the existing data (we assume they are compatible + because conflicts were checked earlier). + - Group attributes: + * Add attributes that don't exist yet in merged_file. + * Attributes listed in IGNORE_ATTRIBUTES are skipped. + """ + + # 1) copy everything from file1 (no conflicts, merged_file is new/empty) + _merge_from_source( + src=file1, + dst=merged_file, + path="/", + conflict_solver={}, # nothing to resolve, just copy + overwrite_existing=False, + ) + + # 2) merge from file2, now conflict_solver may apply + _merge_from_source( + src=file2, + dst=merged_file, + path="/", + conflict_solver={}, + overwrite_existing=True, + ) + + +def _merge_from_source( + src: h5py.Group, + dst: h5py.Group, + path: str, + conflict_solver: dict, + overwrite_existing: bool, +) -> None: + """ + Recursive helper. + + Parameters + ---------- + src : h5py.Group or h5py.File + Source root/node. + dst : h5py.Group or h5py.File + Destination root/node. + path : str + Absolute HDF5 path in src (and corresponding path in dst). + conflict_solver : dict + Mapping path -> solver(existing_data, incoming_data) -> resolved_data. + overwrite_existing : bool + If False, never touch existing objects (used for file1). + If True, apply conflict_solver or keep existing by default (used for file2). + """ + src_obj = src[path] + dst_obj = dst[path] # must exist; for root "/" this is the file itself + + # Copy/merge attributes for this object (group or dataset) + _copy_attributes(src_obj, dst_obj, path) + + if isinstance(src_obj, h5py.Dataset): + # Nothing more to recurse into + return + + if not isinstance(src_obj, h5py.Group): + # ignore exotic node types for now (SoftLink, ExternalLink, etc.) + return + + # src_obj is a Group: iterate over its children + for name, child in src_obj.items(): + if path == "/": + child_path = f"/{name}" + else: + child_path = f"{path.rstrip('/')}/{name}" + + if name not in dst_obj: + # Child does not exist yet in dst: copy entire subtree + _copy_entire_object(src_obj, dst_obj, name) + else: + # TODO: allow for conflict solver + raise ValueError("Conflict detected. Merge stopped") + + +def _copy_entire_object(src_parent: h5py.Group, dst_parent: h5py.Group, name: str) -> None: + """ + Copy a group or dataset `name` from src_parent to dst_parent, including + all attributes and children (for groups). + """ + obj = src_parent[name] + + if isinstance(obj, h5py.Dataset): + # Create dataset with same data and attrs + dset = dst_parent.create_dataset(name, data=obj[...], dtype=obj.dtype) + _copy_attributes(obj, dset, _build_child_path(dst_parent.name, name)) + + elif isinstance(obj, h5py.Group): + # Create group and copy attributes + grp = dst_parent.create_group(name) + _copy_attributes(obj, grp, _build_child_path(dst_parent.name, name)) + + # Recurse to copy all children + for child_name in obj.keys(): + _copy_entire_object(obj, grp, child_name) + + else: + # Exotic object types (SoftLink, ExternalLink, etc.): skip or handle if needed + # For now, we skip them to keep the implementation simple. + pass + + +def _copy_attributes( + src_obj: h5py.Dataset | h5py.Group, dst_obj: h5py.Dataset | h5py.Group, path: str +) -> None: + """ + Copy attributes from src_obj to dst_obj, skipping attributes that: + - are in IGNORE_ATTRIBUTES for this path, or + - already exist in dst_obj. + """ + ignore_set = IGNORE_ATTRIBUTES.get(path, set()) + + for key, value in src_obj.attrs.items(): + if key in ignore_set: + continue + if key in dst_obj.attrs: + # Keep existing attr value + continue + dst_obj.attrs[key] = value + + +def _build_child_path(parent_path: str, name: str) -> str: + """ + Utility to build a proper HDF5 path for a child object. + """ + if parent_path == "/": + return f"/{name}" + return f"{parent_path.rstrip('/')}/{name}" From 1a82eb42a24713e7b7a3dc6a1e26a2c2ef72ca48 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 24 Nov 2025 22:12:00 -0800 Subject: [PATCH 029/111] add burn severity benchmarks for Caldor --- docs/benchmarks/California/01_Caldor.md | 94 +++++++++++++++++--- docs/metrics/index.md | 110 +++++++++++++++++++++++- 2 files changed, 191 insertions(+), 13 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index b3192b4..9863ad8 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -32,8 +32,8 @@ The original csv file containing multiple fires has been processed to extract th - 'Destroyed (>50%)', - 'Inaccessible'. -The column names from the original csv file have been corrected from "* Damage" to "Damage" and "* Incident Name" to "Incident Name". -The hash of the original source file is: *0190a5a51aafafa20270fe046a7ae17a53697b1fb218ff8096a3d8ebbc9ef983*. + +The sha256 of the original source file is: *0190a5a51aafafa20270fe046a7ae17a53697b1fb218ff8096a3d8ebbc9ef983*. If the evaluated model does not explicitly represent individual buildings, the model should consider every building within a cell to share the cell value for building damage (deterministic models) or share the median of bulding damage distribution (probabilistic models). @@ -73,7 +73,15 @@ Total | 4444 ### Processing of dataset +*Performed at obs dataset level* + +The data from the original CSV was standardized without any modification. +The column names from the original csv file were corrected from "* Damage" to "Damage" and "* Incident Name" to "Incident Name" to simplify processing. + #### Binary classes of building damaged + +*Performed at benchmark run level* + In order to performe some calculations, the building damaged classes can be aggrageted to form binary classes. The `Inaccessible` is ignored. The following aggragtion method is used: - `unburnt` binary class contains `No Damage`, `Affected (1-9%)`, and `Minor (10-25%)`, - `burnt` binary class contains `Major (26-50%)`, and `Destroyed (>50%)`. @@ -119,11 +127,22 @@ This benchmark is performed on the binary classes for damaged buildings. The data has been collected using [Monitoring Trends in Burning Severity](https://mtbs.gov/) (MTBS). The original zip file contains burn severity, pre/post burn images, and final fire perimeter. -The source of the burn severity used in FireBench is the file `ca3858612053820210815_20210805_20220723_dnbr6.tif`. +The source of the burn severity used in FireBench is the file `ca3858612053820210815_20210805_20220723_dnbr6.tif`. The source of the final fire perimeter is the kmz file `ca3858612053820210815_20210805_20220723.kmz`. + +The burn severity categories, described with the corresponding index used in the dataset, are the following: +- 'no data': 0 +- 'unburnt to low': 1 +- 'low': 2 +- 'moderate': 3 +- 'high': 4 +- 'increased greeness': 5 The hash of the original source files are: - zip file: 171b9604c0654d8612eaabcfcad93d2374762661ab34b4d62718630a13469841 - tif dnbr6: 33db74d3c5798c41ff3a4fc5ee57da9105fdc7a75d7f8af0d053d2f82cfdc0b6 +- final perimeter kmz: 4ed7a0ee585f8118b65a29375a3d5ee8a69e85a95ee155205ba5d781289c6e2b + +Figure 3 shows the MTBS map from the original source. ![blockdiagram](../../_static/benchmarks/FB001/mtbs_map.jpg)

@@ -132,29 +151,80 @@ The hash of the original source files are: : - Map of burn severity from MTBS + Map of burn severity from MTBS. Source: MTBS (`ca3858612053820210815_map.pdf`)

+### Processing of dataset + +*Performed at obs dataset level* + +The burn severity array is extracted from the original file without any modification. The latitude and longitude array are reconstructed using proejction parameters (see `firebench.standardize.mtbs.standardize_mtbs_from_geotiff`). The final perimeter has been procress using QGIS. The original data (kmz file) has been imported and cleaned. Extra perimeters have been removed to only conserve the final fire perimeter. No modification to the polygons have been performed. Then, the multipolygons was exported to kml format and integrated in the dataset HDF5 file. + +#### Binary classes for high severity + +*Performed at benchmark run level* + +To perform the high severity benchmarks using binary confusion matrix, we construct a binary field based of the high severity index. All the points will a buen severity equal to 4 ('high') will be assigned the value 1. The other points are assigned a value 0. This processing is done when the benchmark is performed. + ### Benchmarks +See Key Performance Indicator (KPI) defintion [here](../../metrics/index.md). + +#### FB001-SV01 + +**KPI**: Binary High Severity Accuracy
+This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) + +#### FB001-SV02 + +**KPI**: Binary High Severity Precision
+This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) + +#### FB001-SV03 + +**KPI**: Binary High Severity Recall
+This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) + +#### FB001-SV04 + +**KPI**: Binary High Severity Specificity
+This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) + +#### FB001-SV05 + +**KPI**: Binary High Severity Negative Predictive Value
+This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) + +#### FB001-SV06 + +**KPI**: Binary High Severity F1 Score
+This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) + ## Requirements -The following sections list the datasets requirements to run the different benchmarks. When the benchmark script is run, each requirement is validated. If a requirement is met, each corresponding benchmark is run. -Each requirement lists the required datasets (as path) as well as mandatory attributs for each dataset. +The following sections list the datasets requirements to run the different benchmarks. When the benchmark script is run, each requirement is validated on the HDF5 file passed as input (from the model ouput/data the user wants to evaluate). If a requirement is met, each corresponding benchmark is run. +Each requirement lists the required datasets/groups (as path) as well as mandatory attributs for each dataset/group. +The current version of FireBench does not integrate more complex checks (e.g., array size, dtype). -*Example* -``` -- /path/of/dataset: attribut1, attribut2 -``` Requirement | Benchmarks ---------------------- | ----------------- R01 | FB001-BD01, FB001-BD02, FB001-BD03, FB001-BD04, FB001-BD05, FB001-BD06 +R02 | FB001-SV01, FB001-SV02, FB001-SV03, FB001-SV04, FB001-SV05, FB001-SV06 ### R01 - -- `/points/building_damaged/building_damage`: units +Mandatory group/dataset| Mandatory attirbutes +---------------------- | -------------------- +`/points/building_damaged/building_damage` | units + +### R02 +Mandatory group/dataset| Mandatory attirbutes +---------------------- | -------------------- +`/2D_raster/Caldor_MTBS`| crs +`/2D_raster/Caldor_MTBS/fire_burn_severity`| units, _FillValue +`/2D_raster/Caldor_MTBS/position_lat`| units +`/2D_raster/Caldor_MTBS/position_lon`| units ## Notes diff --git a/docs/metrics/index.md b/docs/metrics/index.md index 5ac64a1..d2c8b28 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -5,8 +5,116 @@ For implementation details, refer to the [API references](../api/index.rst). A full list of metrics is also available on the [Content page](../content.md). -## Key Performance Indicator +## Burn Severity KPIs +### Binary High Severity Accuracy + +*Category*: Burn Severity
+*Name used in result files*: Binary High Severity Accuracy
+*Best Score*: 1
+*Lowest Score*: 0 + +Measure how accurately the model predicts which point are identified as high severity, based on binary (high severity / not high severity) observations. + +The measure of [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, +$$ +where $TP$ = True positive (high severity in both datasets); $FP$ = False positive (high severity only in model dataset); $TN$ = True negative (not high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Precision + +*Category*: Burn Severity
+*Name used in result files*: Binary High Severity Precision
+*Best Score*: 1
+*Lowest Score*: 0 + +Measures how accurately the model predicts which cells are high severity, by evaluating the proportion of predicted high severity points that were actually high severity. + +The measure of [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Precision = \frac{TP}{TP + FP}, +$$ +where $TP$ = True positive (high severity in both datasets); $FP$ = False positive (high severity only in model dataset) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Recall + +*Category*: Burn Severity
+*Name used in result files*: Binary High Severity Recall
+*Best Score*: 1
+*Lowest Score*: 0 + +Measures how completely the model captures the cells with a high severity index, indicating the fraction of truly high severity cells that the model successfully identifies. + +The measure of [recall](https://en.wikipedia.org/wiki/Precision_and_recall) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Recall = \frac{TP}{TP + FN}, +$$ +where $TP$ = True positive (high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Specificity + +*Category*: Burn Severity
+*Name used in result files*: Binary High Severity Specificity
+*Best Score*: 1
+*Lowest Score*: 0 + +Measures how accurately the model identifies cells with another severity index than high, by quantifying the fraction of other indices correctly predicted as other (not high). + +The measure of [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Specificity = \frac{TN}{TN + FP} +$$ +where $FP$ = False positive (high severity only in model dataset); $TN$ = True negative (not high severity in both datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Negative Predictive Value + +*Category*: Burn Severity
+*Name used in result files*: Binary High Severity Negative Predictive Value
+*Best Score*: 1
+*Lowest Score*: 0 + +Measures the reliability of the model’s predictions for cells identified with another severuty index than high, indicating the proportion of points predicted index as other (not high) that were indeed observed as other. + +The measure of [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Negative Predictive Value = \frac{TN}{TN + FN} +$$ +where $TN$ = True negative (not high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity F1 Score + +*Category*: Burn Severity
+*Name used in result files*: Binary High Severity F1 Score
+*Best Score*: 1
+*Lowest Score*: 0 + +Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies high severity cells while limiting false alarms. + +The measure of [F1 Score](https://en.wikipedia.org/wiki/F-score) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} +$$ + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +## Structure Loss KPIs ### Binary Structure Loss Accuracy From 4ced0dc7653787fb53799286e029741de59ff4dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 26 Nov 2025 14:37:09 -0800 Subject: [PATCH 030/111] improve signing process --- src/firebench/__init__.py | 2 +- src/firebench/signing/__init__.py | 2 +- src/firebench/signing/signing.py | 35 ++++++++++----------------- src/firebench/standardize/__init__.py | 2 +- 4 files changed, 16 insertions(+), 25 deletions(-) diff --git a/src/firebench/__init__.py b/src/firebench/__init__.py index 12b1d54..f26b7c6 100644 --- a/src/firebench/__init__.py +++ b/src/firebench/__init__.py @@ -2,6 +2,6 @@ from .tools.logging_config import logger from .tools.namespace import StandardVariableNames as svn from .tools.units import ureg -from .signing.signing import sign_output_dict, verify_output_dict +from .signing.signing import verify_output_dict, write_case_results Quantity = ureg.Quantity diff --git a/src/firebench/signing/__init__.py b/src/firebench/signing/__init__.py index f63ee22..2765e21 100644 --- a/src/firebench/signing/__init__.py +++ b/src/firebench/signing/__init__.py @@ -1 +1 @@ -from .signing import sign_output_dict, verify_output_dict +from .signing import verify_output_dict, write_case_results diff --git a/src/firebench/signing/signing.py b/src/firebench/signing/signing.py index b41d388..68a1714 100644 --- a/src/firebench/signing/signing.py +++ b/src/firebench/signing/signing.py @@ -5,30 +5,17 @@ from . import _secret_key -def canonical_json_dumps(data: dict) -> bytes: +def _canonical_json_dumps(data: dict) -> bytes: return json.dumps(data, sort_keys=True, separators=(",", ":")).encode("utf-8") -def compute_hmac(json_bytes: bytes) -> str: - """ - Compute the HMAC-SHA256 of the given JSON bytes using the secret key - stored in the compiled extension. - """ - key = _secret_key.get_secret_key() - mac = hmac.new(key, json_bytes, hashlib.sha256).digest() - return mac.hex() - +def _compute_hmac(json_bytes: bytes) -> str: + return _secret_key.compute_hmac(json_bytes) -def sign_output_dict(data: dict) -> dict: - """ - Return a copy of the dictionary with an added "mac" field. - The MAC is computed on the canonical JSON representation of the input - dictionary *without* the "mac" field. Any change to the content will - invalidate the MAC. - """ - json_bytes = canonical_json_dumps(data) - mac = compute_hmac(json_bytes) +def _finalize_payload(data: dict) -> dict: + json_bytes = _canonical_json_dumps(data) + mac = _compute_hmac(json_bytes) out = dict(data) out["mac"] = mac @@ -38,7 +25,7 @@ def sign_output_dict(data: dict) -> dict: def verify_output_dict(signed_data: dict) -> bool: """ Verify that the 'mac' field in the given dictionary matches the HMAC - computed from the remaining content. + computed from the remaining content. Returns ------- @@ -52,7 +39,11 @@ def verify_output_dict(signed_data: dict) -> bool: data_without_mac = dict(signed_data) data_without_mac.pop("mac", None) - json_bytes = canonical_json_dumps(data_without_mac) - expected_mac = compute_hmac(json_bytes) + json_bytes = _canonical_json_dumps(data_without_mac) + expected_mac = _compute_hmac(json_bytes) return hmac.compare_digest(mac, expected_mac) + +def write_case_results(path: str, output_dict: dict): + with open(path, "w") as f: + json.dump(_finalize_payload(output_dict), f, indent=4, sort_keys=True) \ No newline at end of file diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index baf4ab5..9b3a494 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -14,4 +14,4 @@ from .files import ( new_std_file, merge_two_std_files, -) +) \ No newline at end of file From 853044bd7ef6f5050873e596e78f96dd817f04aa Mon Sep 17 00:00:00 2001 From: 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z3GoR7zgSf3^=)$%Q+LrFz0ZQw+QhKgvCfA+FnZUCaV5H%F}jDpOYmfTL&*)am*LM) z*h>Km*_K6xyZxW5e9;srwkvi9cD#<@!RVEY2jYb-00?c>nxKwXq-G-(6*Nn-c> zu^jjVBo^D*1fc0#EMZR2#W3=DSc}qLrtT3{dB_)lXdY=Y6<3!HTNdBfY*2Hg*GzE} zrw20Pvp=X(1IM;Y@?RRb91rW)AjG`aSCh5UqhigZ-{F=`b#QzGKe+7pp8g%-%>Mz0 CV+oD` literal 0 HcmV?d00001 diff --git a/docs/metrics/index.md b/docs/metrics/index.md index d2c8b28..be2471b 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -1,13 +1,130 @@ -# 5. Metrics information -This section describes the high-level metrics available in `FireBench`, listed as `Key Performance Indicator` (KPI). Each KPI represents a quantitative evaluation of performance. +# 5. Metrics and Scores information +This section describes the high-level metrics available in `FireBench`, listed as `Key Performance Indicator` (KPI). Each KPI represents one, and only one, quantitative evaluation of performance. +The KPI value can be normalized and multiple KPIs can be aggregated to construct a score. For implementation details, refer to the [API references](../api/index.rst). A full list of metrics is also available on the [Content page](../content.md). -## Burn Severity KPIs +## Scores -### Binary High Severity Accuracy +This section details the processes used to construct a unique score for each benchmark case from the set of KPIs contained in the benchmark case. +A `Score` is defined as a real number, with 4 significant digit, between 0.000 (worst) and 100.0 (best). +Scores are derived from KPI values and allow the comparison of models and benchmark results. +As a KPI value is not necessarily a number that is compliant with the score definition, a `normalization` process is required to convert a KPI value to a score, called `Unit Score`. + +$$ +KPI \overset{Normalization}{\longmapsto} Unit Score +$$ + +The different normalization functions available are described in Section `KPI Normalization`. + +Each KPI is transformed into a `Unit Score`, corresponding to one, and only one, KPI. +To further simplify the interpretation the result of multiple benchmarks, these Unit Scores can be aggregated to form `Group Scores`. They represent the overall performance accross multiple indicators, generally evaluating the model for similar physical processes or on the same data. +The `Total Score` is the aggregation of all group scores into one, and only one, score, representing the overall performance of the model for the studied case. + +Figure 1 shows an example of normalization of each KPI for the case *FB001*. Each KPI is normalized into a Unit Score. Then Unit Scores are aggregated into two Group Scores representing the overall performance for *Building Damaged* benchmarks and *Burn Severity* benchmarks. Finally, both Group Scores are aggregated to form the Total Score. +![blockdiagram](../_static/images/Scoring_diagram.png) +

    + + Fig. 1 + + : + + Diagram of Scores construction from KPIs using two categories of KPI (BD: Building Damaged, SV: Burn Severity). + +

    + +The aggregation can be performed using multiple aggregation schemes. The simplest scheme is to aggregate score using a mean function. This gives the same weight to each KPI in the Total Score. We can also develop more complex aggregation schemes to give more weight to certain benchmarks/KPIs. Therefore, for each benchmarking case (collection of dataset and KPIs), we can define multiple aggregation schemes to evaluate different classes of models. Each aggregation scheme will be noted using a letter. For example `FB001-A`, `FB001-B`. + + +Figure 2 shows exmaple of KPI values and their correspond range between brackets. The KPI FB001-BD01 has a value of 0.34763 and a range of [0, 1] (FB001-BD01 can represent a binary confusion matrix index), whereas the KPI FB001-SV03 has a value of 3.489 and a range of [0, $+\infty$[ (FB001-SV03 can represent an absolute bias). All KPI with a limited range of values are normaized using the linear normalization function (see next Section). The KPI FB001-SV03 is normalized using the linear semi bounded normization function with a parameter $M=5$, which means that if the KPI value is above 5, the score will be 0. +Then, Unit Scores are aggregated using uniform weights (represented by the green numbers above aggregation lines) to form Group Scores. +Finally, a weighted aggregation is performed to calculate the Total Score of the case, giving an double importance to benchmarks related to **Building Damaged*. +The list of aggregation scheme and their weights are explicitely defined in the case documentation. + +![blockdiagram](../_static/images/Scoring_diagram_example.png) +

    + + Fig. 2 + + : + + Example of Scores construction from KPIs using two categories of KPI. + +

    + +Figure 3 displays an `Score Card` that is a table representing the data showed in Figure 2. This type of score card can become the standard of presentation for case results. The first row shows the total score and contains the case that has been run (FB001), the aggregation scheme used (B, defined in the case documentation), the model name, and the total score calculated in Figure 2. +The rest of the table is organized as: +- one group row that describes the name of the group and the associated score. A keyword **Group** is added to emphasis the row. +- All the benchmark scores related to the group are displayed after. The name of the benchmark is added as a reference. Here the case id (FB001) is omitted for clarity as it is already displayed in the first row. + + +![blockdiagram](../_static/images/Score_card_example.png) +

    + + Fig. 3 + + : + + Example of Score card layout + +

    + +```{note} +Note: The example here above are not related to the real case FB001 (Caldor Fire) and all KPI names and +values are given as examples. +``` + +## KPI Normalization + +This section describes several normalization schemes used to convert KPI values into a score in the range ([0, 100]). +Throughout, $x$ denotes the KPI value. + +### Linear Bounded Normalization + +For KPIs with a **bounded acceptable range** $[a, b]$, with $(a < b)$, the normalization function is defined as: +$$ +\mathcal N(x, a, b) = 100 \, \left( \frac{x - a}{b - a} \right) +$$ +Here, +- $a$ corresponds to the **worst** score (0), +- $b$ corresponds to the **best** score (100). + +### Linear Half-Open Normalization + +For KPIs that have a **minimum acceptable value** ($a$) but no finite upper limit, i.e. values in $[a, +\infty)$, we define: +$$ +\mathcal N(x, a, m) = 100 \, \max \left(0, 1 - \frac{x-a}{m-a} \right) +$$ +where $m > a$ is a parameter specifying the value of $x$ at which the score reaches **0**. + +Here, +- $a$ corresponds to the **best** score (100), +- $m$ corresponds to the **worst** score (0). + +### Exponential Half-Open Normalization + +For KPIs with a minimum acceptable value $a$ and domain $[a, +\infty[$, we define an exponentially decaying score: +$$ +\mathcal N(x, a, m) =100 , \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). +$$ +This formulation ensures: +$$ +\mathcal N(a,a,m) = 100, \qquad +\mathcal N(m,a,m) = 50. +$$ +Thus, $m$ is the KPI value at which the score is exactly **50**. +Here, +- $a$ corresponds to the **best** possible score (100), +- $m$ corresponds to the value at which the score has decreased to **50**, +- scores decay smoothly toward 0 as $x \to +\infty$. + +## Key Performan Indicator Defintions + +### Burn Severity KPIs + +#### Binary High Severity Accuracy *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Accuracy
    @@ -25,7 +142,7 @@ where $TP$ = True positive (high severity in both datasets); $FP$ = False positi The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary High Severity Precision +#### Binary High Severity Precision *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Precision
    @@ -43,7 +160,7 @@ where $TP$ = True positive (high severity in both datasets); $FP$ = False positi The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary High Severity Recall +#### Binary High Severity Recall *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Recall
    @@ -61,7 +178,7 @@ where $TP$ = True positive (high severity in both datasets); $FN$ = False negati The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary High Severity Specificity +#### Binary High Severity Specificity *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Specificity
    @@ -79,7 +196,7 @@ where $FP$ = False positive (high severity only in model dataset); $TN$ = True n The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary High Severity Negative Predictive Value +#### Binary High Severity Negative Predictive Value *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Negative Predictive Value
    @@ -97,7 +214,7 @@ where $TN$ = True negative (not high severity in both datasets); $FN$ = False ne The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary High Severity F1 Score +#### Binary High Severity F1 Score *Category*: Burn Severity
    *Name used in result files*: Binary High Severity F1 Score
    @@ -114,9 +231,9 @@ $$ The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -## Structure Loss KPIs +### Structure Loss KPIs -### Binary Structure Loss Accuracy +#### Binary Structure Loss Accuracy *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Accuracy
    @@ -134,7 +251,7 @@ where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary Structure Loss Precision +#### Binary Structure Loss Precision *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Precision
    @@ -153,7 +270,7 @@ where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary Structure Loss Recall +#### Binary Structure Loss Recall *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Recall
    @@ -171,7 +288,7 @@ where $TP$ = True positive (buildings destroyed in both datasets); $FN$ = False The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary Structure Loss Specificity +#### Binary Structure Loss Specificity *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Specificity
    @@ -189,7 +306,7 @@ where $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary Structure Loss Negative Predictive Value +#### Binary Structure Loss Negative Predictive Value *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Negative Predictive Value
    @@ -207,7 +324,7 @@ where $TN$ = True negative (buildings not damaged in both datasets); $FN$ = Fals The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. -### Binary Structure Loss F1 Score +#### Binary Structure Loss F1 Score *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss F1 Score
    From ce0b3b06acc271a0b8701c1d7bc15c23cfe70f54 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 26 Nov 2025 18:56:14 -0800 Subject: [PATCH 032/111] fix typo --- docs/metrics/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/metrics/index.md b/docs/metrics/index.md index be2471b..45c1e25 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -120,7 +120,7 @@ Here, - $m$ corresponds to the value at which the score has decreased to **50**, - scores decay smoothly toward 0 as $x \to +\infty$. -## Key Performan Indicator Defintions +## Key Performance Indicator Defintions ### Burn Severity KPIs From 7935ff2a443f6bb5c17b8aadd807865c2792ea60 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 26 Nov 2025 18:56:58 -0800 Subject: [PATCH 033/111] fix equation spacing --- docs/metrics/index.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/docs/metrics/index.md b/docs/metrics/index.md index 45c1e25..df11ba1 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -84,9 +84,11 @@ Throughout, $x$ denotes the KPI value. ### Linear Bounded Normalization For KPIs with a **bounded acceptable range** $[a, b]$, with $(a < b)$, the normalization function is defined as: + $$ \mathcal N(x, a, b) = 100 \, \left( \frac{x - a}{b - a} \right) $$ + Here, - $a$ corresponds to the **worst** score (0), - $b$ corresponds to the **best** score (100). @@ -94,9 +96,11 @@ Here, ### Linear Half-Open Normalization For KPIs that have a **minimum acceptable value** ($a$) but no finite upper limit, i.e. values in $[a, +\infty)$, we define: + $$ \mathcal N(x, a, m) = 100 \, \max \left(0, 1 - \frac{x-a}{m-a} \right) $$ + where $m > a$ is a parameter specifying the value of $x$ at which the score reaches **0**. Here, @@ -106,14 +110,18 @@ Here, ### Exponential Half-Open Normalization For KPIs with a minimum acceptable value $a$ and domain $[a, +\infty[$, we define an exponentially decaying score: + $$ \mathcal N(x, a, m) =100 , \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). $$ + This formulation ensures: + $$ \mathcal N(a,a,m) = 100, \qquad \mathcal N(m,a,m) = 50. $$ + Thus, $m$ is the KPI value at which the score is exactly **50**. Here, - $a$ corresponds to the **best** possible score (100), From 9f6bf5b05a3379f87722cdea05469740e7ad7cc5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 27 Nov 2025 09:31:22 -0800 Subject: [PATCH 034/111] add kpi normalization functions - kpi_norm_bounded_linear - kpi_norm_half_open_linear - kpi_norm_half_open_exponential --- docs/metrics/index.md | 6 +- src/firebench/metrics/__init__.py | 5 + src/firebench/metrics/kpi_normalization.py | 120 +++++++++++++++++++++ src/firebench/signing/signing.py | 5 +- src/firebench/standardize/__init__.py | 2 +- 5 files changed, 132 insertions(+), 6 deletions(-) create mode 100644 src/firebench/metrics/kpi_normalization.py diff --git a/docs/metrics/index.md b/docs/metrics/index.md index df11ba1..a007683 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -83,7 +83,7 @@ Throughout, $x$ denotes the KPI value. ### Linear Bounded Normalization -For KPIs with a **bounded acceptable range** $[a, b]$, with $(a < b)$, the normalization function is defined as: +For KPIs with a **bounded acceptable range** $[a, b]$, with $a < b$, the normalization function is defined as: $$ \mathcal N(x, a, b) = 100 \, \left( \frac{x - a}{b - a} \right) @@ -95,7 +95,7 @@ Here, ### Linear Half-Open Normalization -For KPIs that have a **minimum acceptable value** ($a$) but no finite upper limit, i.e. values in $[a, +\infty)$, we define: +For KPIs that have a **minimum acceptable value** $a$ but no finite upper limit, i.e. values in $[a, +\infty[$, we define: $$ \mathcal N(x, a, m) = 100 \, \max \left(0, 1 - \frac{x-a}{m-a} \right) @@ -112,7 +112,7 @@ Here, For KPIs with a minimum acceptable value $a$ and domain $[a, +\infty[$, we define an exponentially decaying score: $$ -\mathcal N(x, a, m) =100 , \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). +\mathcal N(x, a, m) =100 \, \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). $$ This formulation ensures: diff --git a/src/firebench/metrics/__init__.py b/src/firebench/metrics/__init__.py index 355f04e..adc219e 100644 --- a/src/firebench/metrics/__init__.py +++ b/src/firebench/metrics/__init__.py @@ -1 +1,6 @@ from . import perimeter, stats, mtbs, confusion_matrix +from .kpi_normalization import ( + kpi_norm_bounded_linear, + kpi_norm_half_open_linear, + kpi_norm_half_open_exponential, +) diff --git a/src/firebench/metrics/kpi_normalization.py b/src/firebench/metrics/kpi_normalization.py new file mode 100644 index 0000000..ec934cc --- /dev/null +++ b/src/firebench/metrics/kpi_normalization.py @@ -0,0 +1,120 @@ +from math import exp, log + + +def kpi_norm_bounded_linear(x, a, b): + """ + Linearly normalize a KPI that has a fully bounded acceptable range [a, b]. + + This function maps: + - x = a -> score = 0 + - x = b -> score = 100 + with a strictly linear transformation. + + Use this normalization when the KPI is known to lie within a finite closed interval. + + Parameters + ---------- + x : float + KPI value to normalize. + a : float + Lower bound of the acceptable range (score = 0). + b : float + Upper bound of the acceptable range (score = 100). Must satisfy a < b. + + Returns + ------- + float + Normalized score in the range with 0 at `a` and 100 at `b`. + + Raises + ------ + ValueError + If `x < a` or if `x > b`. + """ + if x < a: + raise ValueError(f"KPI value {x} smaller than lower limit {a}") + if x > b: + raise ValueError(f"KPI value {x} greater than upper limit {b}") + return 100.0 * (x - a) / (b - a) + + +def kpi_norm_half_open_linear(x, a, m): + """ + Linearly normalize a KPI defined on the half-open interval [a, ∞). + + This function applies a linear decay starting from: + - x = a -> score = 100 + - x = m -> score = 0 + Values above `m` yield a score of 0 through clipping. + + Use this normalization when the KPI has a minimum acceptable value `a` + but no finite upper bound, and when the degradation beyond `a` can be + meaningfully represented by a linear decline over the scale [a, m]. + + Parameters + ---------- + x : float + KPI value to normalize. Must satisfy x >= a. + a : float + Minimum acceptable (or optimal) value at which the score is 100. + m : float + Threshold at which the score reaches 0. Must satisfy m > a. + + Returns + ------- + float + Normalized score in the range [0, 100], with linear decay from + 100 at `a` to 0 at `m`. + + Raises + ------ + ValueError + If `x < a` or if `m <= a`. + """ + if x < a: + raise ValueError(f"KPI value {x} smaller than lower limit {a}") + if m <= a: + raise ValueError(f"Parameter m ({m}) smaller than lower limit a ({a})") + return 100.0 * max(0, 1 - (x - a) / (m - a)) + + +def kpi_norm_half_open_exponential(x, a, m): + """ + Exponentially normalize a KPI defined on the half-open interval [a, ∞). + + This function applies a smooth exponential decay such that: + - x = a -> score = 100 + - x = m -> score = 50 + - x -> ∞ -> score -> 0 + ensuring a monotonic and asymptotic decline. + + Use this normalization when the KPI has a minimum acceptable value `a` + but no finite upper bound, and when deviations beyond `a` should be + penalized progressively rather than linearly. The parameter `m` defines + the characteristic decay scale at which performance is reduced by half. + + Parameters + ---------- + x : float + KPI value to normalize. Must satisfy x >= a. + a : float + Minimum acceptable (or optimal) value at which the score is 100. + m : float + Value at which the score reaches 50. Must satisfy m > a. + + Returns + ------- + float + Normalized score in the range (0, 100], decaying exponentially + from 100 at `a` toward 0 as `x` increases. + + Raises + ------ + ValueError + If `x < a` or if `m <= a`. + """ + if x < a: + raise ValueError(f"KPI value {x} smaller than lower limit {a}") + if m <= a: + raise ValueError(f"Parameter m ({m}) smaller than lower limit a ({a})") + return 100.0 * exp(-log(2) * (x - a) / (m - a)) diff --git a/src/firebench/signing/signing.py b/src/firebench/signing/signing.py index 68a1714..71e43aa 100644 --- a/src/firebench/signing/signing.py +++ b/src/firebench/signing/signing.py @@ -25,7 +25,7 @@ def _finalize_payload(data: dict) -> dict: def verify_output_dict(signed_data: dict) -> bool: """ Verify that the 'mac' field in the given dictionary matches the HMAC - computed from the remaining content. + computed from the remaining content. Returns ------- @@ -44,6 +44,7 @@ def verify_output_dict(signed_data: dict) -> bool: return hmac.compare_digest(mac, expected_mac) + def write_case_results(path: str, output_dict: dict): with open(path, "w") as f: - json.dump(_finalize_payload(output_dict), f, indent=4, sort_keys=True) \ No newline at end of file + json.dump(_finalize_payload(output_dict), f, indent=4, sort_keys=True) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 9b3a494..baf4ab5 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -14,4 +14,4 @@ from .files import ( new_std_file, merge_two_std_files, -) \ No newline at end of file +) From 187be30d4f9cbc3dbb0a70cbb011dc56c9e2e5e2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 27 Nov 2025 11:54:49 -0800 Subject: [PATCH 035/111] add normalization and aggregation in Caldor - add version tracking --- docs/benchmarks/California/01_Caldor.md | 29 +++++++++++++++++++++++-- docs/metrics/index.md | 2 +- src/firebench/__init__.py | 8 +++++++ 3 files changed, 36 insertions(+), 3 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 9863ad8..c4060c7 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -88,36 +88,42 @@ In order to performe some calculations, the building damaged classes can be aggr ### Benchmarks -See Key Performance Indicator (KPI) defintion [here](../../metrics/index.md). +See Key Performance Indicator (KPI) and normalization defintions [here](../../metrics/index.md). #### FB001-BD01 **KPI**: Binary Structure Loss Accuracy
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD02 **KPI**: Binary Structure Loss Precision
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD03 **KPI**: Binary Structure Loss Recall
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD04 **KPI**: Binary Structure Loss Specificity
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD05 **KPI**: Binary Structure Loss Negative Predictive Value
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD06 **KPI**: Binary Structure Loss F1 Score
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for damaged buildings. @@ -169,36 +175,42 @@ To perform the high severity benchmarks using binary confusion matrix, we constr ### Benchmarks -See Key Performance Indicator (KPI) defintion [here](../../metrics/index.md). +See Key Performance Indicator (KPI) and normalization defintions [here](../../metrics/index.md). #### FB001-SV01 **KPI**: Binary High Severity Accuracy
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV02 **KPI**: Binary High Severity Precision
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV03 **KPI**: Binary High Severity Recall
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV04 **KPI**: Binary High Severity Specificity
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV05 **KPI**: Binary High Severity Negative Predictive Value
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV06 **KPI**: Binary High Severity F1 Score
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) ## Requirements @@ -226,6 +238,19 @@ Mandatory group/dataset| Mandatory attirbutes `/2D_raster/Caldor_MTBS/position_lat`| units `/2D_raster/Caldor_MTBS/position_lon`| units +## Aggregation Schemes + +This section provides the weights used to aggregate KPI Unit Scores. More information about aggregation methods [here](../../metrics/index.md). If aggregation scheme `0` is specified, then no aggregation is performed. Therefore, group scores and total score are not computed. + +### Scheme A + +Group | Group Weight | Benchmark ID | Benchmark weight +---------------------- | ------------ | ------------ | ---------------- +Building Damage | 1 | | +| | | BD01 to BD06 | 1 +Burn Severity | 1 | | +| | | SV01 to SV06 | 1 + ## Notes - Each file hash has been performed using `firebench.standardize.calculate_sha256`. \ No newline at end of file diff --git a/docs/metrics/index.md b/docs/metrics/index.md index a007683..d703e80 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -128,7 +128,7 @@ Here, - $m$ corresponds to the value at which the score has decreased to **50**, - scores decay smoothly toward 0 as $x \to +\infty$. -## Key Performance Indicator Defintions +## Key Performance Indicator Definitions ### Burn Severity KPIs diff --git a/src/firebench/__init__.py b/src/firebench/__init__.py index f26b7c6..d2f26c5 100644 --- a/src/firebench/__init__.py +++ b/src/firebench/__init__.py @@ -1,3 +1,4 @@ +from importlib.metadata import version, PackageNotFoundError from . import ros_models, tools, wind_interpolation, stats, metrics, sensors, standardize, signing from .tools.logging_config import logger from .tools.namespace import StandardVariableNames as svn @@ -5,3 +6,10 @@ from .signing.signing import verify_output_dict, write_case_results Quantity = ureg.Quantity + +try: + __version__ = version("firebench") +except PackageNotFoundError: + __version__ = "unknown" + +__all__ = ["__version__"] From 9804e2687ebcf8aac1244dc76e27c2f6b042544b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 28 Nov 2025 21:30:12 -0800 Subject: [PATCH 036/111] add score card pdf export --- docs/_static/images/Metrics_diagram.png | Bin 0 -> 48572 bytes docs/metrics/index.md | 282 +++++++++++++++++++++--- pyproject.toml | 1 + src/firebench/metrics/__init__.py | 1 + src/firebench/metrics/table.py | 177 +++++++++++++++ 5 files changed, 436 insertions(+), 25 deletions(-) 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    + For implementation details, refer to the [API references](../api/index.rst). A full list of metrics is also available on the [Content page](../content.md). @@ -76,6 +90,144 @@ Note: The example here above are not related to the real case FB001 (Caldor Fire values are given as examples. ``` +## Metrics + +### 1D metrics + +**Input:** Two 1D vectors of size $N$: + +- $x_i$: evaluated dataset +- $y_i$: reference dataset + +#### Mean + +**Description:** Average value of a 1D vector $x$.
    +**Range:** Same as range of $x$.
    +**Units:** Same as input units.
    +**Formula:** +$$ +\bar x = \frac{1}{N} \sum_{i=1}^N x_i +$$ + +#### Bias + +**Description:** Difference between the mean of $x$ and the mean of $y$.
    +**Range:** Same as range of input values.
    +**Units:** Same as input units.
    +**Formula:** +$$ +B = \bar x - \bar y +$$ + +#### Root Mean Square Error + +**Description:** Square root of the mean squared difference between (x) and (y), noted RMSE.
    +**Range:** $[0, +\infty[$.
    +**Units:** Same as input units.
    +**Formula:** +$$ +RMSE(x, y) = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - y_i)^2} +$$ + +#### Normalized MSE - power normalization + +**Description:** RMSE normalized by the range of the reference dataset.
    +**Range:** $[0, +\infty)$.
    +**Units:** Dimensionless.
    +**Formula:** +$$ +NMSE_p = \frac{RMSE(x, y)}{\max(y) - \min(y)} +$$ + +#### Normalized MSE – range normalization +**Description:** Squared RMSE normalized by the product of mean values of the datasets.
    +**Range:** $[0, +\infty)$ (undefined if $\bar x = 0$ or $\bar y = 0$).
    +**Units:** Dimensionless.
    +**Formula:** +$$ +NMSE_r = \frac{RMSE(x, y)^2}{\bar x \, \bar y} +$$ + + +### Binary Confusion Matrix + +**Input:** Two 1D binary vectors (0 or 1) of size $N$: + +- $x_i$: evaluated dataset +- $y_i$: reference dataset + +The following metrics are derived from the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from both dataset. The Binary confusion matrix is a 2x2 matrix containing: + +| | Reference = 1 | Reference = 0 | +| ------------ | ------------- | ------------- | +| **Eval = 1** | TP | FP | +| **Eval = 0** | FN | TN | + +Where: +- TP: True Positive +- FP: False Positive +- FN: False Negative +- TN: True Negative + +#### Accuracy + +**Description:** Fraction of correct predictions among all samples (see [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** +$$ +Accuracy = \frac{TP + TN}{TP + TN + FP + FN} +$$ + +#### Precision +**Description:** Fraction of predicted positives that are true positives (see [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** +$$ +Precision = \frac{TP}{TP + FP}, +$$ + +#### Recall +**Description:** Fraction of actual positives correctly identified (see [recall](https://en.wikipedia.org/wiki/Precision_and_recall)). Recall can also be named Sensitivity or True Positive Rate.
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** +$$ +Recall = \frac{TP}{TP + FN}, +$$ + +#### Specificity +**Description:** Fraction of actual negatives correctly identified (see [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity)). Recall can also be named True Negative Rate.
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** +$$ +Specificity = \frac{TN}{TN + FP} +$$ + +#### Negative Predictive Value +**Description:** Fraction of predicted negatives that are true negatives (see [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** +$$ +Negative Predictive Value = \frac{TN}{TN + FN} +$$ + +#### F1 Score +**Description:** Harmonic mean of Precision and Recall (see [F1 Score](https://en.wikipedia.org/wiki/F-score)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** +$$ +F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} +$$ + +### Time related + +#### Time above threshold + ## KPI Normalization This section describes several normalization schemes used to convert KPI values into a score in the range ([0, 100]). @@ -136,8 +288,8 @@ Here, *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Accuracy
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measure how accurately the model predicts which point are identified as high severity, based on binary (high severity / not high severity) observations. @@ -154,8 +306,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Precision
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures how accurately the model predicts which cells are high severity, by evaluating the proportion of predicted high severity points that were actually high severity. @@ -172,8 +324,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Recall
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures how completely the model captures the cells with a high severity index, indicating the fraction of truly high severity cells that the model successfully identifies. @@ -190,8 +342,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Specificity
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures how accurately the model identifies cells with another severity index than high, by quantifying the fraction of other indices correctly predicted as other (not high). @@ -208,8 +360,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Burn Severity
    *Name used in result files*: Binary High Severity Negative Predictive Value
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures the reliability of the model’s predictions for cells identified with another severuty index than high, indicating the proportion of points predicted index as other (not high) that were indeed observed as other. @@ -226,8 +378,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Burn Severity
    *Name used in result files*: Binary High Severity F1 Score
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies high severity cells while limiting false alarms. @@ -245,8 +397,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Accuracy
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measure how accurately the model predicts which structures are destroyed or not destroyed by the fire, based on binary (burned / not burned) observations. @@ -263,8 +415,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Precision
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures how accurately the model predicts which structures are destroyed, by evaluating the proportion of predicted-destroyed buildings that were actually destroyed. @@ -282,8 +434,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Recall
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures how completely the model captures the buildings that were actually destroyed, indicating the fraction of truly destroyed structures that the model successfully identifies. @@ -300,8 +452,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Specificity
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures how accurately the model identifies buildings that survived, by quantifying the fraction of intact structures correctly predicted as not destroyed. @@ -318,8 +470,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss Negative Predictive Value
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Measures the reliability of the model’s predictions for surviving structures, indicating the proportion of predicted-intact buildings that were indeed not destroyed. @@ -336,8 +488,8 @@ The implementation of this KPI is done using the `firebench.metrics.confusion_ma *Category*: Structure Damage
    *Name used in result files*: Binary Structure Loss F1 Score
    -*Best Score*: 1
    -*Lowest Score*: 0 +*Best*: 1
    +*Worst*: 0 Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies destroyed buildings while limiting false alarms. @@ -347,4 +499,84 @@ $$ F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} $$ -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. \ No newline at end of file +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Weather stations KPIs + +#### 24h forecast Wind Speed Bias Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Speed Bias Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + +Provides the average bias over multiple weather stations over a period 24h for wind speed. + +The average bias is calculated as: + +$$ +\overline{Bias} = \frac{1}{N_s} \sum_{k=1}^{N_s} \frac{1}{N_t} \sum_{i=1}^{N_t} (m_{ki} - o_{ki}) +$$ +where $N_s$ is the number of weather stations, $N_t$ is the number of time step over the 24h period, $m_{ki}$ is the model data for station $k$ at time step $i$, and $o_{ki}$ is the observation data for station $k$ at time step $i$. + +The implementation of this KPI is done using `firebench.metrics.stats.bias`. + +#### 24h forecast Wind Speed RMSE Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Speed RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + +Provides the average RMSE over multiple weather stations over a period 24h for wind speed. + +The average RMSE is calculated as: + +$$ +\overline{RMSE} = \frac{1}{N_s} \sum_{k=1}^{N_s} \sqrt{\frac{1}{N_t} \sum_{i=1}^{N_t} (m_{ki} - o_{ki})^2} +$$ +where $N_s$ is the number of weather stations, $N_t$ is the number of time step over the 24h period, $m_{ki}$ is the model data for station $k$ at time step $i$, and $o_{ki}$ is the observation data for station $k$ at time step $i$. + +The implementation of this KPI is done using `firebench.metrics.stats.rmse`. + +#### 24h forecast Wind Direction RMSE Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Direction RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + + +Wind direction difference is calculated as +$$ +d_{wd} = ((x - y + 180) \mod 360 ) - 180 +$$ + +#### 24h forecast Wind Gust RMSE Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Gust RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + +#### 24h forecast Sustained Wind Speed Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Gust RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time +- $U_0$: Value of thresehold + +Calculate the amount of time when the wind speed is above a defined threshold. Compare this time to observation and average it over multiple weather stations. + + diff --git a/pyproject.toml b/pyproject.toml index 3375076..21a7fac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -22,6 +22,7 @@ dependencies = [ "matplotlib > 3.8", "rasterio < 2.0", "pyproj < 4.0", + "reportlab < 5.0", ] readme = "README.md" license = {file = "LICENSE"} diff --git a/src/firebench/metrics/__init__.py b/src/firebench/metrics/__init__.py index adc219e..acea084 100644 --- a/src/firebench/metrics/__init__.py +++ b/src/firebench/metrics/__init__.py @@ -4,3 +4,4 @@ kpi_norm_half_open_linear, kpi_norm_half_open_exponential, ) +from .table import save_as_table diff --git a/src/firebench/metrics/table.py b/src/firebench/metrics/table.py new file mode 100644 index 0000000..30abcf8 --- /dev/null +++ b/src/firebench/metrics/table.py @@ -0,0 +1,177 @@ +from reportlab.lib import colors +from reportlab.lib.pagesizes import A4 +from reportlab.lib.units import mm +from reportlab.platypus import SimpleDocTemplate, Table, TableStyle +from pathlib import Path +from ..tools import logger + + +def _score_to_color(score): + """ + Map a score from 0 to 100 to a color from red -> yellow -> green. + Output: hex string "#RRGGBB". + """ + + # Clamp score + s = max(0, min(100, score)) / 100.0 + + # Red → Yellow for s ∈ [0,0.5] + # Yellow → Green for s ∈ [0.5,1] + if s < 0.5: + # red (255,0,0) to yellow (255,255,0) + r = 255 + g = int(255 * (s / 0.5)) + b = 0 + else: + # yellow (255,255,0) to green (0,255,0) + r = int(255 * ((1 - s) / 0.5)) + g = 255 + b = 0 + + return "#{:02x}{:02x}{:02x}".format(r, g, b) + + +def save_as_table(filename: Path, data: dict): + logger.info("Save data dict as score card report pdf") + if filename.suffix.lower() != ".pdf": + filename = filename.with_suffix(".pdf") + + COLOR_ROWS = [ + "#f7d5cd", + "#fbebe8", + ] + + # Get the number of row + nb_rows = 3 # header and footer + nb_bench = len(data["benchmarks"].keys()) + score_card = data.get("score_card") + if score_card is None: + # No aggregation, no total score + nb_rows += nb_bench + else: + # get number of rows from schemes + for group_name, group_content in data["score_card"]["Scheme"].items(): + nb_rows += len(group_content["benchmarks"]) + 1 + + # ------------------------------------------------------------------ + # 1) Create PDF + # ------------------------------------------------------------------ + doc = SimpleDocTemplate( + str(filename.resolve()), + pagesize=(165 * mm, nb_rows * 8 * mm), + leftMargin=0 * mm, + rightMargin=0 * mm, + topMargin=0 * mm, + bottomMargin=0 * mm, + ) + + # ------------------------------------------------------------------ + # 2) Table data (3×3) + # ------------------------------------------------------------------ + text_table = [] + + # header + scheme_name = "0" + valid_scheme = False + if "score_card" in data: + scheme_name = data["score_card"]["aggregation_scheme_name"] + valid_scheme = True + text_table.append( + [ + f"Total Score {data['case_id']} agg. {scheme_name}: {data['evaluated_model_name']}", + "", + f"{data['score_card']['Score Total']:.2f}", + ] + ) + text_table.append(["Benchmark ID/Group Name", "Weight", "Score"]) + + # rows + group_rows = [] + if valid_scheme: + for group_name, group_content in data["score_card"]["Scheme"].items(): + # add group row + group_score = data["score_card"][f"Score {group_name}"] + group_rows.append(len(text_table)) + text_table.append([f"Group: {group_name}", f"{group_content["weight"]}", f"{group_score:.2f}"]) + # add benchamrk rows + for bench_id, bench_weight in group_content["benchmarks"].items(): + bench_score = data["benchmarks"][bench_id]["Score"] + kpi_name = [i for i in data["benchmarks"][bench_id].keys() if i != "Score"][0] + text_table.append([f"{kpi_name}", f"{bench_weight}", f"{bench_score:.2f}"]) + else: + # Only print benchmarks + for bench_id in data["benchmarks"].keys(): + text_table.append([f"{bench_id}", "None", f"{data['benchmarks'][bench_id]['Score']:.2f}"]) + + # footer + text_table.append( + [f"FireBench version: {data['firebench_version']} case version: {data['case_version']}", "", ""] + ) + + col_widths = [100 * mm, 30 * mm, 30 * mm] + + # ------------------------------------------------------------------ + # 3) Table style with both merges + # ------------------------------------------------------------------ + table_style = [ + # === MERGE FIRST 2 COLUMNS OF FIRST ROW === + ("SPAN", (0, 0), (1, 0)), + # === MERGE ALL 3 COLUMNS OF LAST ROW === + ("SPAN", (0, nb_rows - 1), (2, nb_rows - 1)), + # Borders + ("BOX", (0, 0), (-1, -1), 0.75, colors.black), + ("INNERGRID", (0, 0), (-1, -1), 0.25, colors.grey), + # Background colors for clarity + ("BACKGROUND", (0, 0), (-1, -1), colors.HexColor("#c04f15")), # merged header row + ("BACKGROUND", (0, 1), (-1, 1), colors.HexColor("#e97132")), # merged header row + ( + "BACKGROUND", + (0, -1), + (-1, -1), + colors.HexColor("#d9d9d9"), + ), # merged footer row + # Alignment + ("ALIGN", (0, 0), (-1, -1), "CENTER"), + ("ALIGN", (0, 0), (0, 0), "LEFT"), + ("ALIGN", (0, -1), (0, -1), "LEFT"), + ("VALIGN", (0, 0), (-1, -1), "MIDDLE"), + # Fonts + ("FONTNAME", (0, 0), (-1, -1), "Helvetica"), + ("FONTSIZE", (0, 0), (-1, -1), 9), + ] + + for i in range(nb_rows - 2): + table_style.append( + ( + "BACKGROUND", + (0, i + 2), + (-1, i + 2), + colors.HexColor(COLOR_ROWS[i % len(COLOR_ROWS)]), + ), # merged header row + ) + table_style.append(("ALIGN", (0, i + 1), (0, i + 1), "LEFT")) + + if valid_scheme: + table_style.append( + ( + "BACKGROUND", + (2, 0), + (2, 0), + colors.HexColor(_score_to_color(data["score_card"]["Score Total"])), + ), # merged header row + ) + for i_row in group_rows: + table_style.append( + ("BACKGROUND", (0, i_row), (-1, i_row), colors.HexColor("#f2aa84")), + ) + else: + text_table[0][2] = "INVALID" + table_style.append( + ("BACKGROUND", (2, 0), (2, 0), colors.HexColor("#ff0000")), # merged header row + ) + + table = Table(text_table, colWidths=col_widths) + style = TableStyle(table_style) + table.setStyle(style) + + doc.build([table]) From 55c84a3401a8c870b77f9ded1f70b6ea77fbeefe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sat, 29 Nov 2025 15:53:20 -0800 Subject: [PATCH 037/111] update scorr card pdf with KPI values - update colors for total score - bold for total score - change color for last row --- src/firebench/metrics/table.py | 76 +++++++++++++++++----------------- 1 file changed, 37 insertions(+), 39 deletions(-) diff --git a/src/firebench/metrics/table.py b/src/firebench/metrics/table.py index 30abcf8..5fb9931 100644 --- a/src/firebench/metrics/table.py +++ b/src/firebench/metrics/table.py @@ -11,24 +11,13 @@ def _score_to_color(score): Map a score from 0 to 100 to a color from red -> yellow -> green. Output: hex string "#RRGGBB". """ - - # Clamp score - s = max(0, min(100, score)) / 100.0 - - # Red → Yellow for s ∈ [0,0.5] - # Yellow → Green for s ∈ [0.5,1] - if s < 0.5: - # red (255,0,0) to yellow (255,255,0) - r = 255 - g = int(255 * (s / 0.5)) - b = 0 - else: - # yellow (255,255,0) to green (0,255,0) - r = int(255 * ((1 - s) / 0.5)) - g = 255 - b = 0 - - return "#{:02x}{:02x}{:02x}".format(r, g, b) + if score < 33.33: + return "#D6452A" + + if score < 66.66: + return "#E8C441" + + return "#6BAF5F" def save_as_table(filename: Path, data: dict): @@ -65,9 +54,6 @@ def save_as_table(filename: Path, data: dict): bottomMargin=0 * mm, ) - # ------------------------------------------------------------------ - # 2) Table data (3×3) - # ------------------------------------------------------------------ text_table = [] # header @@ -80,10 +66,11 @@ def save_as_table(filename: Path, data: dict): [ f"Total Score {data['case_id']} agg. {scheme_name}: {data['evaluated_model_name']}", "", + "", f"{data['score_card']['Score Total']:.2f}", ] ) - text_table.append(["Benchmark ID/Group Name", "Weight", "Score"]) + text_table.append(["Benchmark ID/Group Name", "KPI value", "Weight", "Score"]) # rows group_rows = [] @@ -92,32 +79,42 @@ def save_as_table(filename: Path, data: dict): # add group row group_score = data["score_card"][f"Score {group_name}"] group_rows.append(len(text_table)) - text_table.append([f"Group: {group_name}", f"{group_content["weight"]}", f"{group_score:.2f}"]) + text_table.append([f"Group: {group_name}", "", f"{group_content["weight"]}", f"{group_score:.2f}"]) # add benchamrk rows for bench_id, bench_weight in group_content["benchmarks"].items(): - bench_score = data["benchmarks"][bench_id]["Score"] - kpi_name = [i for i in data["benchmarks"][bench_id].keys() if i != "Score"][0] - text_table.append([f"{kpi_name}", f"{bench_weight}", f"{bench_score:.2f}"]) + for key, item in data["benchmarks"][bench_id].items(): + if key == "Score": + bench_score = item + kpi_name = [i for i in data["benchmarks"][bench_id].keys() if i != "Score"][0] + else: + # KPI + kpi_score = item + text_table.append([f"{kpi_name}", f"{kpi_score:.2e}", f"{bench_weight}", f"{bench_score:.2f}"]) else: # Only print benchmarks for bench_id in data["benchmarks"].keys(): - text_table.append([f"{bench_id}", "None", f"{data['benchmarks'][bench_id]['Score']:.2f}"]) + for key, item in data["benchmarks"][bench_id].items(): + if key == "Score": + bench_score = item + else: + kpi_score = item + text_table.append([f"{bench_id}", f"{kpi_score:.2e}", "None", f"{bench_score:.2f}"]) # footer text_table.append( - [f"FireBench version: {data['firebench_version']} case version: {data['case_version']}", "", ""] + [f"FireBench version: {data['firebench_version']} case version: {data['case_version']}", "", "", ""] ) - col_widths = [100 * mm, 30 * mm, 30 * mm] + col_widths = [100 * mm, 20 * mm, 20 * mm, 20 * mm] # ------------------------------------------------------------------ # 3) Table style with both merges # ------------------------------------------------------------------ table_style = [ - # === MERGE FIRST 2 COLUMNS OF FIRST ROW === - ("SPAN", (0, 0), (1, 0)), - # === MERGE ALL 3 COLUMNS OF LAST ROW === - ("SPAN", (0, nb_rows - 1), (2, nb_rows - 1)), + # === MERGE FIRST 3 COLUMNS OF FIRST ROW === + ("SPAN", (0, 0), (2, 0)), + # === MERGE ALL 4 COLUMNS OF LAST ROW === + ("SPAN", (0, nb_rows - 1), (3, nb_rows - 1)), # Borders ("BOX", (0, 0), (-1, -1), 0.75, colors.black), ("INNERGRID", (0, 0), (-1, -1), 0.25, colors.grey), @@ -128,19 +125,20 @@ def save_as_table(filename: Path, data: dict): "BACKGROUND", (0, -1), (-1, -1), - colors.HexColor("#d9d9d9"), + colors.HexColor("#A79F9A"), ), # merged footer row # Alignment ("ALIGN", (0, 0), (-1, -1), "CENTER"), - ("ALIGN", (0, 0), (0, 0), "LEFT"), + ("ALIGN", (0, 0), (0, -1), "LEFT"), ("ALIGN", (0, -1), (0, -1), "LEFT"), ("VALIGN", (0, 0), (-1, -1), "MIDDLE"), # Fonts ("FONTNAME", (0, 0), (-1, -1), "Helvetica"), ("FONTSIZE", (0, 0), (-1, -1), 9), + ('FONT', (3,0), (3,0), 'Helvetica-Bold', 9), ] - for i in range(nb_rows - 2): + for i in range(nb_rows - 3): table_style.append( ( "BACKGROUND", @@ -155,8 +153,8 @@ def save_as_table(filename: Path, data: dict): table_style.append( ( "BACKGROUND", - (2, 0), - (2, 0), + (3, 0), + (3, 0), colors.HexColor(_score_to_color(data["score_card"]["Score Total"])), ), # merged header row ) @@ -167,7 +165,7 @@ def save_as_table(filename: Path, data: dict): else: text_table[0][2] = "INVALID" table_style.append( - ("BACKGROUND", (2, 0), (2, 0), colors.HexColor("#ff0000")), # merged header row + ("BACKGROUND", (3, 0), (3, 0), colors.HexColor("#ff0000")), # merged header row ) table = Table(text_table, colWidths=col_widths) From f050537db321d08f198b51be745a229746463ec1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 2 Dec 2025 09:53:00 -0800 Subject: [PATCH 038/111] handle import exception for signing functions --- src/firebench/signing/signing.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/firebench/signing/signing.py b/src/firebench/signing/signing.py index 71e43aa..308cdad 100644 --- a/src/firebench/signing/signing.py +++ b/src/firebench/signing/signing.py @@ -2,8 +2,10 @@ import hashlib import json -from . import _secret_key - +try: + from . import _secret_key +except ImportError: + _secret_key = None def _canonical_json_dumps(data: dict) -> bytes: return json.dumps(data, sort_keys=True, separators=(",", ":")).encode("utf-8") From f6eb3997e3ae2e600ac506b7d75bb354d57579ed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 2 Dec 2025 09:53:20 -0800 Subject: [PATCH 039/111] organize dependencies in docs and pyproject --- docs/dependencies.md | 1 + pyproject.toml | 18 +++++++++--------- 2 files changed, 10 insertions(+), 9 deletions(-) diff --git a/docs/dependencies.md b/docs/dependencies.md index 4f4bcbd..dc5cc74 100644 --- a/docs/dependencies.md +++ b/docs/dependencies.md @@ -9,6 +9,7 @@ - pint < 1.0 - pyproj < 4.0 - rasterio < 2.0 +- reportlab < 5.0 - SALib < 2.0 - scipy < 2.0 diff --git a/pyproject.toml b/pyproject.toml index 21a7fac..5ac7e93 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,16 +13,16 @@ maintainers = [ ] description = "FireBench is a Python library designed for the systematic benchmarking and inter-comparison of fire models." dependencies = [ - "numpy < 3.0", - "pint < 1.0", - "scipy < 2.0", - "SALib < 2.0", - "h5py < 4.0", "geopandas < 2.0", + "h5py < 4.0", "matplotlib > 3.8", - "rasterio < 2.0", + "numpy < 3.0", + "pint < 1.0", "pyproj < 4.0", + "rasterio < 2.0", "reportlab < 5.0", + "SALib < 2.0", + "scipy < 2.0", ] readme = "README.md" license = {file = "LICENSE"} @@ -44,12 +44,12 @@ classifiers = [ ] [project.optional-dependencies] dev = [ + "bandit", + "black", + "pylint", "pytest", "pytest-cov", "pytest-mock", - "bandit", - "pylint", - "black", "shapely", ] [tool.hatch.envs.default] From 2faab14163fbf173c9d00f558a1797c910e29306 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 3 Dec 2025 08:57:34 -0800 Subject: [PATCH 040/111] fix spaces before formulas --- docs/metrics/index.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/docs/metrics/index.md b/docs/metrics/index.md index e3b4dac..b768f0b 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -105,6 +105,7 @@ values are given as examples. **Range:** Same as range of $x$.
    **Units:** Same as input units.
    **Formula:** + $$ \bar x = \frac{1}{N} \sum_{i=1}^N x_i $$ @@ -115,6 +116,7 @@ $$ **Range:** Same as range of input values.
    **Units:** Same as input units.
    **Formula:** + $$ B = \bar x - \bar y $$ @@ -125,6 +127,7 @@ $$ **Range:** $[0, +\infty[$.
    **Units:** Same as input units.
    **Formula:** + $$ RMSE(x, y) = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - y_i)^2} $$ @@ -135,6 +138,7 @@ $$ **Range:** $[0, +\infty)$.
    **Units:** Dimensionless.
    **Formula:** + $$ NMSE_p = \frac{RMSE(x, y)}{\max(y) - \min(y)} $$ @@ -144,6 +148,7 @@ $$ **Range:** $[0, +\infty)$ (undefined if $\bar x = 0$ or $\bar y = 0$).
    **Units:** Dimensionless.
    **Formula:** + $$ NMSE_r = \frac{RMSE(x, y)^2}{\bar x \, \bar y} $$ @@ -175,6 +180,7 @@ Where: **Range:** $[0, 1]$
    **Units:** Dimensionless.
    **Formula:** + $$ Accuracy = \frac{TP + TN}{TP + TN + FP + FN} $$ @@ -184,6 +190,7 @@ $$ **Range:** $[0, 1]$
    **Units:** Dimensionless.
    **Formula:** + $$ Precision = \frac{TP}{TP + FP}, $$ @@ -193,6 +200,7 @@ $$ **Range:** $[0, 1]$
    **Units:** Dimensionless.
    **Formula:** + $$ Recall = \frac{TP}{TP + FN}, $$ @@ -202,6 +210,7 @@ $$ **Range:** $[0, 1]$
    **Units:** Dimensionless.
    **Formula:** + $$ Specificity = \frac{TN}{TN + FP} $$ @@ -211,6 +220,7 @@ $$ **Range:** $[0, 1]$
    **Units:** Dimensionless.
    **Formula:** + $$ Negative Predictive Value = \frac{TN}{TN + FN} $$ @@ -220,6 +230,7 @@ $$ **Range:** $[0, 1]$
    **Units:** Dimensionless.
    **Formula:** + $$ F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} $$ From d005b382cf04714af07d9a43f9b719f800a4fa6f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 3 Dec 2025 08:57:49 -0800 Subject: [PATCH 041/111] fix title numbering --- .github/actions/update_changelog_in_docs.py | 2 +- docs/dependencies.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/actions/update_changelog_in_docs.py b/.github/actions/update_changelog_in_docs.py index fb6450d..ece8c42 100644 --- a/.github/actions/update_changelog_in_docs.py +++ b/.github/actions/update_changelog_in_docs.py @@ -17,7 +17,7 @@ def update_changelog_in_docs(): changelog_content = root_file.read() # Front matter for the docs/changelog.md file - front_matter = """# 11. """ + front_matter = """# 12. """ # Combine front matter and changelog content full_changelog_content = front_matter + changelog_content[2:] diff --git a/docs/dependencies.md b/docs/dependencies.md index 4f4bcbd..c4e2f79 100644 --- a/docs/dependencies.md +++ b/docs/dependencies.md @@ -1,4 +1,4 @@ -# 12. Dependencies +# 13. Dependencies ## Required From 1a7e8b9389d50c3dbfcd5fc534efafe8e97a2735 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 3 Dec 2025 16:20:54 -0800 Subject: [PATCH 042/111] code formatting --- src/firebench/metrics/table.py | 21 +++++++++++++++------ src/firebench/signing/signing.py | 1 + 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/src/firebench/metrics/table.py b/src/firebench/metrics/table.py index 5fb9931..dffbfaa 100644 --- a/src/firebench/metrics/table.py +++ b/src/firebench/metrics/table.py @@ -13,10 +13,10 @@ def _score_to_color(score): """ if score < 33.33: return "#D6452A" - + if score < 66.66: return "#E8C441" - + return "#6BAF5F" @@ -79,7 +79,9 @@ def save_as_table(filename: Path, data: dict): # add group row group_score = data["score_card"][f"Score {group_name}"] group_rows.append(len(text_table)) - text_table.append([f"Group: {group_name}", "", f"{group_content["weight"]}", f"{group_score:.2f}"]) + text_table.append( + [f"Group: {group_name}", "", f"{group_content['weight']}", f"{group_score:.2f}"] + ) # add benchamrk rows for bench_id, bench_weight in group_content["benchmarks"].items(): for key, item in data["benchmarks"][bench_id].items(): @@ -89,7 +91,9 @@ def save_as_table(filename: Path, data: dict): else: # KPI kpi_score = item - text_table.append([f"{kpi_name}", f"{kpi_score:.2e}", f"{bench_weight}", f"{bench_score:.2f}"]) + text_table.append( + [f"{kpi_name}", f"{kpi_score:.2e}", f"{bench_weight}", f"{bench_score:.2f}"] + ) else: # Only print benchmarks for bench_id in data["benchmarks"].keys(): @@ -102,7 +106,12 @@ def save_as_table(filename: Path, data: dict): # footer text_table.append( - [f"FireBench version: {data['firebench_version']} case version: {data['case_version']}", "", "", ""] + [ + f"FireBench version: {data['firebench_version']} case version: {data['case_version']}", + "", + "", + "", + ] ) col_widths = [100 * mm, 20 * mm, 20 * mm, 20 * mm] @@ -135,7 +144,7 @@ def save_as_table(filename: Path, data: dict): # Fonts ("FONTNAME", (0, 0), (-1, -1), "Helvetica"), ("FONTSIZE", (0, 0), (-1, -1), 9), - ('FONT', (3,0), (3,0), 'Helvetica-Bold', 9), + ("FONT", (3, 0), (3, 0), "Helvetica-Bold", 9), ] for i in range(nb_rows - 3): diff --git a/src/firebench/signing/signing.py b/src/firebench/signing/signing.py index 308cdad..ec16535 100644 --- a/src/firebench/signing/signing.py +++ b/src/firebench/signing/signing.py @@ -7,6 +7,7 @@ except ImportError: _secret_key = None + def _canonical_json_dumps(data: dict) -> bytes: return json.dumps(data, sort_keys=True, separators=(",", ":")).encode("utf-8") From 11a9dbf2a688fccc3a2d87b5a1718d3c818ff305 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 4 Dec 2025 08:27:30 -0800 Subject: [PATCH 043/111] add scheme B and S for Caldor --- docs/benchmarks/California/01_Caldor.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index c4060c7..faf7143 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -251,6 +251,20 @@ Building Damage | 1 | | Burn Severity | 1 | | | | | SV01 to SV06 | 1 +### Scheme B + +Group | Group Weight | Benchmark ID | Benchmark weight +---------------------- | ------------ | ------------ | ---------------- +Building Damage | 1 | | +| | | BD01 to BD06 | 1 + +### Scheme S + +Group | Group Weight | Benchmark ID | Benchmark weight +---------------------- | ------------ | ------------ | ---------------- +Burn Severity | 1 | | +| | | SV01 to SV06 | 1 + ## Notes - Each file hash has been performed using `firebench.standardize.calculate_sha256`. \ No newline at end of file From 05ec1c36668ccadee7039691a7831ff32f78ff73 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 8 Dec 2025 11:31:23 -0800 Subject: [PATCH 044/111] add benchmark in KPI links diagram --- 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z=T@xzAB%+R_Qo`(a{7AO^`1cF<^DES Date: Mon, 8 Dec 2025 11:31:45 -0800 Subject: [PATCH 045/111] add merge_std_files --- src/firebench/standardize/__init__.py | 1 + src/firebench/standardize/files.py | 62 +++++++++++++++++++++++++-- src/firebench/standardize/tools.py | 2 +- 3 files changed, 61 insertions(+), 4 deletions(-) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index baf4ab5..22a104f 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -14,4 +14,5 @@ from .files import ( new_std_file, merge_two_std_files, + merge_std_files, ) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py index f06a716..77e91d2 100644 --- a/src/firebench/standardize/files.py +++ b/src/firebench/standardize/files.py @@ -3,6 +3,8 @@ from .time import current_datetime_iso8601 from pathlib import Path from .tools import VERSION_STD, validate_h5_std, merge_authors, collect_conflicts, merge_trees +from pathlib import Path +import shutil def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.File: @@ -13,7 +15,9 @@ def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.F ----- Do not forget to close the file once edited. This function opens the h5 file but do not close it. """ - if Path(filepath).exists(): + new_file_path = Path(filepath) + new_file_path.parent.mkdir(parents=True, exist_ok=True) + if new_file_path.exists(): if overwrite: logger.info("file %s already exists and is being replaced.", filepath) else: @@ -53,9 +57,9 @@ def merge_two_std_files( # Check for any conflicts conflicts = collect_conflicts(file1, file2) if conflicts: - logger.error("Try to merge files but conflicts have been found.") + logger.critical("Try to merge files but conflicts have been found.") print(conflicts) - raise ValueError() + raise ValueError("Try to merge files but conflicts have been found.") # Find both list of authors merged_authors = merge_authors(file1.attrs["created_by"], file2.attrs["created_by"]) @@ -73,3 +77,55 @@ def merge_two_std_files( file2.close() merged_file.close() logger.info("Standard files merge successfull") + + +def merge_std_files( + filespath: list[str], filepath_target: str, merged_description: str = "", overwrite: bool = False +): + if not filespath: + raise ValueError("filespath must contain at least one file") + + input_paths = [Path(p) for p in filespath] + target_path = Path(filepath_target) + + for p in input_paths: + if not p.is_file(): + raise FileNotFoundError(f"Input file not found: {p}") + + # Single file: just copy it to target (no merge needed) + if len(input_paths) == 1: + if target_path.exists() and not overwrite: + raise FileExistsError(f"Target file already exists: {target_path}") + # Ensure parent directory exists + target_path.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(input_paths[0], target_path) + return + + tmp_path = target_path.with_suffix(target_path.suffix + ".tmp") + current_path = input_paths[0] + + try: + for i, next_path in enumerate(input_paths[1:], start=1): + is_last = i == len(input_paths) - 1 + + out_path = target_path if is_last else tmp_path + + # Always allow overwrite on intermediate/target files inside this routine + merge_two_std_files( + filepath_1=str(current_path), + filepath_2=str(next_path), + filepath_target=str(out_path), + merged_description=merged_description, + overwrite=True, + ) + + current_path = out_path + + finally: + # Clean up temporary file if it exists + if tmp_path.exists(): + try: + tmp_path.unlink() + except OSError: + # If removal fails, we silently ignore; nothing critical. + pass diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 98f997a..cb2041e 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -22,7 +22,7 @@ ) IGNORE_ATTRIBUTES = { - "/": {"created_on", "created_by", "FireBench_io_version"}, + "/": {"created_on", "created_by", "FireBench_io_version", "description"}, } From 8bfbfa7dc468a8701a2bda15061010f7befdd249 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Mon, 8 Dec 2025 16:34:34 -0800 Subject: [PATCH 046/111] refactor score and metric docs into mutliple pages --- docs/benchmarks/California/01_Caldor.md | 12 + docs/metrics/index.md | 592 +----------------------- docs/metrics/kpis.md | 295 ++++++++++++ docs/metrics/metrics.md | 146 ++++++ docs/metrics/normalization.md | 51 ++ docs/metrics/score.md | 70 +++ 6 files changed, 593 insertions(+), 573 deletions(-) create mode 100644 docs/metrics/kpis.md create mode 100644 docs/metrics/metrics.md create mode 100644 docs/metrics/normalization.md create mode 100644 docs/metrics/score.md diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index faf7143..f97f698 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -94,36 +94,42 @@ See Key Performance Indicator (KPI) and normalization defintions [here](../../me **KPI**: Binary Structure Loss Accuracy
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary Structure Loss Accuracy
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD02 **KPI**: Binary Structure Loss Precision
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary Structure Loss Precision
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD03 **KPI**: Binary Structure Loss Recall
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary Structure Loss Recall
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD04 **KPI**: Binary Structure Loss Specificity
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary Structure Loss Specificity
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD05 **KPI**: Binary Structure Loss Negative Predictive Value
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary Structure Loss Negative Predictive Value
    This benchmark is performed on the binary classes for damaged buildings. #### FB001-BD06 **KPI**: Binary Structure Loss F1 Score
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary Structure Loss F1 Score
    This benchmark is performed on the binary classes for damaged buildings. @@ -181,36 +187,42 @@ See Key Performance Indicator (KPI) and normalization defintions [here](../../me **KPI**: Binary High Severity Accuracy
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary High Severity Accuracy
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV02 **KPI**: Binary High Severity Precision
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary High Severity Precision
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV03 **KPI**: Binary High Severity Recall
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary High Severity Recall
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV04 **KPI**: Binary High Severity Specificity
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary High Severity Specificity
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV05 **KPI**: Binary High Severity Negative Predictive Value
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary High Severity Negative Predictive Value
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) #### FB001-SV06 **KPI**: Binary High Severity F1 Score
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: Binary High Severity F1 Score
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) ## Requirements diff --git a/docs/metrics/index.md b/docs/metrics/index.md index b768f0b..e0a2c67 100644 --- a/docs/metrics/index.md +++ b/docs/metrics/index.md @@ -3,6 +3,25 @@ This section describes the high-level metrics available in `FireBench`, listed a KPIs are based on metrics that correspond to the generalization of quantitative comparison of multiple datasets. The KPI value can be normalized and multiple KPIs can be aggregated to construct a score. +We introduce the following definition: +- Metric is a quantifiable measure used to evaluate the performance. +- Key Performance Indicator (KPI) is derived from one or several metrics and describes one quantitative evaluation associated to specific variables (1 number out). +- Score is a number between 0 and 100, with 100 being best performance, allowing for comparison and aggregation. +- Normalization is the process to convert a KPI value (not necessarily bounded) to a Score (bounded between 0 and 100). +- Aggregation is the weighted combination of multiple Scores to form one Score reflecting global performance. Aggregation is done at group of KPIs level (Group Score) and global level (Total Score) +- Benchmark is the group KPI + Normalization. + +**More information about the components in the following pages** +```{toctree} +:maxdepth: 1 + +score.md +metrics.md +kpis.md +normalization.md +``` + + Figure 1 shows the relationship between the different quantitative components. Each KPI is form by using one or several metrics. The output from the KPI can be normalized using a normalization function to form a score. ![blockdiagram](../_static/images/Metrics_diagram.png) @@ -17,577 +36,4 @@ Figure 1 shows the relationship between the different quantitative components. E

    For implementation details, refer to the [API references](../api/index.rst). - A full list of metrics is also available on the [Content page](../content.md). - -## Scores - -This section details the processes used to construct a unique score for each benchmark case from the set of KPIs contained in the benchmark case. -A `Score` is defined as a real number, with 4 significant digit, between 0.000 (worst) and 100.0 (best). -Scores are derived from KPI values and allow the comparison of models and benchmark results. -As a KPI value is not necessarily a number that is compliant with the score definition, a `normalization` process is required to convert a KPI value to a score, called `Unit Score`. - -$$ -KPI \overset{Normalization}{\longmapsto} Unit Score -$$ - -The different normalization functions available are described in Section `KPI Normalization`. - -Each KPI is transformed into a `Unit Score`, corresponding to one, and only one, KPI. -To further simplify the interpretation the result of multiple benchmarks, these Unit Scores can be aggregated to form `Group Scores`. They represent the overall performance accross multiple indicators, generally evaluating the model for similar physical processes or on the same data. -The `Total Score` is the aggregation of all group scores into one, and only one, score, representing the overall performance of the model for the studied case. - -Figure 1 shows an example of normalization of each KPI for the case *FB001*. Each KPI is normalized into a Unit Score. Then Unit Scores are aggregated into two Group Scores representing the overall performance for *Building Damaged* benchmarks and *Burn Severity* benchmarks. Finally, both Group Scores are aggregated to form the Total Score. -![blockdiagram](../_static/images/Scoring_diagram.png) -

    - - Fig. 1 - - : - - Diagram of Scores construction from KPIs using two categories of KPI (BD: Building Damaged, SV: Burn Severity). - -

    - -The aggregation can be performed using multiple aggregation schemes. The simplest scheme is to aggregate score using a mean function. This gives the same weight to each KPI in the Total Score. We can also develop more complex aggregation schemes to give more weight to certain benchmarks/KPIs. Therefore, for each benchmarking case (collection of dataset and KPIs), we can define multiple aggregation schemes to evaluate different classes of models. Each aggregation scheme will be noted using a letter. For example `FB001-A`, `FB001-B`. - - -Figure 2 shows exmaple of KPI values and their correspond range between brackets. The KPI FB001-BD01 has a value of 0.34763 and a range of [0, 1] (FB001-BD01 can represent a binary confusion matrix index), whereas the KPI FB001-SV03 has a value of 3.489 and a range of [0, $+\infty$[ (FB001-SV03 can represent an absolute bias). All KPI with a limited range of values are normaized using the linear normalization function (see next Section). The KPI FB001-SV03 is normalized using the linear semi bounded normization function with a parameter $M=5$, which means that if the KPI value is above 5, the score will be 0. -Then, Unit Scores are aggregated using uniform weights (represented by the green numbers above aggregation lines) to form Group Scores. -Finally, a weighted aggregation is performed to calculate the Total Score of the case, giving an double importance to benchmarks related to **Building Damaged*. -The list of aggregation scheme and their weights are explicitely defined in the case documentation. - -![blockdiagram](../_static/images/Scoring_diagram_example.png) -

    - - Fig. 2 - - : - - Example of Scores construction from KPIs using two categories of KPI. - -

    - -Figure 3 displays an `Score Card` that is a table representing the data showed in Figure 2. This type of score card can become the standard of presentation for case results. The first row shows the total score and contains the case that has been run (FB001), the aggregation scheme used (B, defined in the case documentation), the model name, and the total score calculated in Figure 2. -The rest of the table is organized as: -- one group row that describes the name of the group and the associated score. A keyword **Group** is added to emphasis the row. -- All the benchmark scores related to the group are displayed after. The name of the benchmark is added as a reference. Here the case id (FB001) is omitted for clarity as it is already displayed in the first row. - - -![blockdiagram](../_static/images/Score_card_example.png) -

    - - Fig. 3 - - : - - Example of Score card layout - -

    - -```{note} -Note: The example here above are not related to the real case FB001 (Caldor Fire) and all KPI names and -values are given as examples. -``` - -## Metrics - -### 1D metrics - -**Input:** Two 1D vectors of size $N$: - -- $x_i$: evaluated dataset -- $y_i$: reference dataset - -#### Mean - -**Description:** Average value of a 1D vector $x$.
    -**Range:** Same as range of $x$.
    -**Units:** Same as input units.
    -**Formula:** - -$$ -\bar x = \frac{1}{N} \sum_{i=1}^N x_i -$$ - -#### Bias - -**Description:** Difference between the mean of $x$ and the mean of $y$.
    -**Range:** Same as range of input values.
    -**Units:** Same as input units.
    -**Formula:** - -$$ -B = \bar x - \bar y -$$ - -#### Root Mean Square Error - -**Description:** Square root of the mean squared difference between (x) and (y), noted RMSE.
    -**Range:** $[0, +\infty[$.
    -**Units:** Same as input units.
    -**Formula:** - -$$ -RMSE(x, y) = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - y_i)^2} -$$ - -#### Normalized MSE - power normalization - -**Description:** RMSE normalized by the range of the reference dataset.
    -**Range:** $[0, +\infty)$.
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -NMSE_p = \frac{RMSE(x, y)}{\max(y) - \min(y)} -$$ - -#### Normalized MSE – range normalization -**Description:** Squared RMSE normalized by the product of mean values of the datasets.
    -**Range:** $[0, +\infty)$ (undefined if $\bar x = 0$ or $\bar y = 0$).
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -NMSE_r = \frac{RMSE(x, y)^2}{\bar x \, \bar y} -$$ - - -### Binary Confusion Matrix - -**Input:** Two 1D binary vectors (0 or 1) of size $N$: - -- $x_i$: evaluated dataset -- $y_i$: reference dataset - -The following metrics are derived from the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from both dataset. The Binary confusion matrix is a 2x2 matrix containing: - -| | Reference = 1 | Reference = 0 | -| ------------ | ------------- | ------------- | -| **Eval = 1** | TP | FP | -| **Eval = 0** | FN | TN | - -Where: -- TP: True Positive -- FP: False Positive -- FN: False Negative -- TN: True Negative - -#### Accuracy - -**Description:** Fraction of correct predictions among all samples (see [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification)).
    -**Range:** $[0, 1]$
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -Accuracy = \frac{TP + TN}{TP + TN + FP + FN} -$$ - -#### Precision -**Description:** Fraction of predicted positives that are true positives (see [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition)).
    -**Range:** $[0, 1]$
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -Precision = \frac{TP}{TP + FP}, -$$ - -#### Recall -**Description:** Fraction of actual positives correctly identified (see [recall](https://en.wikipedia.org/wiki/Precision_and_recall)). Recall can also be named Sensitivity or True Positive Rate.
    -**Range:** $[0, 1]$
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -Recall = \frac{TP}{TP + FN}, -$$ - -#### Specificity -**Description:** Fraction of actual negatives correctly identified (see [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity)). Recall can also be named True Negative Rate.
    -**Range:** $[0, 1]$
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -Specificity = \frac{TN}{TN + FP} -$$ - -#### Negative Predictive Value -**Description:** Fraction of predicted negatives that are true negatives (see [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values)).
    -**Range:** $[0, 1]$
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -Negative Predictive Value = \frac{TN}{TN + FN} -$$ - -#### F1 Score -**Description:** Harmonic mean of Precision and Recall (see [F1 Score](https://en.wikipedia.org/wiki/F-score)).
    -**Range:** $[0, 1]$
    -**Units:** Dimensionless.
    -**Formula:** - -$$ -F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} -$$ - -### Time related - -#### Time above threshold - -## KPI Normalization - -This section describes several normalization schemes used to convert KPI values into a score in the range ([0, 100]). -Throughout, $x$ denotes the KPI value. - -### Linear Bounded Normalization - -For KPIs with a **bounded acceptable range** $[a, b]$, with $a < b$, the normalization function is defined as: - -$$ -\mathcal N(x, a, b) = 100 \, \left( \frac{x - a}{b - a} \right) -$$ - -Here, -- $a$ corresponds to the **worst** score (0), -- $b$ corresponds to the **best** score (100). - -### Linear Half-Open Normalization - -For KPIs that have a **minimum acceptable value** $a$ but no finite upper limit, i.e. values in $[a, +\infty[$, we define: - -$$ -\mathcal N(x, a, m) = 100 \, \max \left(0, 1 - \frac{x-a}{m-a} \right) -$$ - -where $m > a$ is a parameter specifying the value of $x$ at which the score reaches **0**. - -Here, -- $a$ corresponds to the **best** score (100), -- $m$ corresponds to the **worst** score (0). - -### Exponential Half-Open Normalization - -For KPIs with a minimum acceptable value $a$ and domain $[a, +\infty[$, we define an exponentially decaying score: - -$$ -\mathcal N(x, a, m) =100 \, \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). -$$ - -This formulation ensures: - -$$ -\mathcal N(a,a,m) = 100, \qquad -\mathcal N(m,a,m) = 50. -$$ - -Thus, $m$ is the KPI value at which the score is exactly **50**. -Here, -- $a$ corresponds to the **best** possible score (100), -- $m$ corresponds to the value at which the score has decreased to **50**, -- scores decay smoothly toward 0 as $x \to +\infty$. - -## Key Performance Indicator Definitions - -### Burn Severity KPIs - -#### Binary High Severity Accuracy - -*Category*: Burn Severity
    -*Name used in result files*: Binary High Severity Accuracy
    -*Best*: 1
    -*Worst*: 0 - -Measure how accurately the model predicts which point are identified as high severity, based on binary (high severity / not high severity) observations. - -The measure of [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, -$$ -where $TP$ = True positive (high severity in both datasets); $FP$ = False positive (high severity only in model dataset); $TN$ = True negative (not high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary High Severity Precision - -*Category*: Burn Severity
    -*Name used in result files*: Binary High Severity Precision
    -*Best*: 1
    -*Worst*: 0 - -Measures how accurately the model predicts which cells are high severity, by evaluating the proportion of predicted high severity points that were actually high severity. - -The measure of [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Precision = \frac{TP}{TP + FP}, -$$ -where $TP$ = True positive (high severity in both datasets); $FP$ = False positive (high severity only in model dataset) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary High Severity Recall - -*Category*: Burn Severity
    -*Name used in result files*: Binary High Severity Recall
    -*Best*: 1
    -*Worst*: 0 - -Measures how completely the model captures the cells with a high severity index, indicating the fraction of truly high severity cells that the model successfully identifies. - -The measure of [recall](https://en.wikipedia.org/wiki/Precision_and_recall) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Recall = \frac{TP}{TP + FN}, -$$ -where $TP$ = True positive (high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary High Severity Specificity - -*Category*: Burn Severity
    -*Name used in result files*: Binary High Severity Specificity
    -*Best*: 1
    -*Worst*: 0 - -Measures how accurately the model identifies cells with another severity index than high, by quantifying the fraction of other indices correctly predicted as other (not high). - -The measure of [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Specificity = \frac{TN}{TN + FP} -$$ -where $FP$ = False positive (high severity only in model dataset); $TN$ = True negative (not high severity in both datasets) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary High Severity Negative Predictive Value - -*Category*: Burn Severity
    -*Name used in result files*: Binary High Severity Negative Predictive Value
    -*Best*: 1
    -*Worst*: 0 - -Measures the reliability of the model’s predictions for cells identified with another severuty index than high, indicating the proportion of points predicted index as other (not high) that were indeed observed as other. - -The measure of [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Negative Predictive Value = \frac{TN}{TN + FN} -$$ -where $TN$ = True negative (not high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary High Severity F1 Score - -*Category*: Burn Severity
    -*Name used in result files*: Binary High Severity F1 Score
    -*Best*: 1
    -*Worst*: 0 - -Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies high severity cells while limiting false alarms. - -The measure of [F1 Score](https://en.wikipedia.org/wiki/F-score) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} -$$ - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -### Structure Loss KPIs - -#### Binary Structure Loss Accuracy - -*Category*: Structure Damage
    -*Name used in result files*: Binary Structure Loss Accuracy
    -*Best*: 1
    -*Worst*: 0 - -Measure how accurately the model predicts which structures are destroyed or not destroyed by the fire, based on binary (burned / not burned) observations. - -The measure of [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, -$$ -where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary Structure Loss Precision - -*Category*: Structure Damage
    -*Name used in result files*: Binary Structure Loss Precision
    -*Best*: 1
    -*Worst*: 0 - -Measures how accurately the model predicts which structures are destroyed, by evaluating the proportion of predicted-destroyed buildings that were actually destroyed. - -The measure of [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Precision = \frac{TP}{TP + FP}, -$$ -where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset). - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - - -#### Binary Structure Loss Recall - -*Category*: Structure Damage
    -*Name used in result files*: Binary Structure Loss Recall
    -*Best*: 1
    -*Worst*: 0 - -Measures how completely the model captures the buildings that were actually destroyed, indicating the fraction of truly destroyed structures that the model successfully identifies. - -The measure of [recall](https://en.wikipedia.org/wiki/Precision_and_recall) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Recall = \frac{TP}{TP + FN}, -$$ -where $TP$ = True positive (buildings destroyed in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary Structure Loss Specificity - -*Category*: Structure Damage
    -*Name used in result files*: Binary Structure Loss Specificity
    -*Best*: 1
    -*Worst*: 0 - -Measures how accurately the model identifies buildings that survived, by quantifying the fraction of intact structures correctly predicted as not destroyed. - -The measure of [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Specificity = \frac{TN}{TN + FP} -$$ -where $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets). - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary Structure Loss Negative Predictive Value - -*Category*: Structure Damage
    -*Name used in result files*: Binary Structure Loss Negative Predictive Value
    -*Best*: 1
    -*Worst*: 0 - -Measures the reliability of the model’s predictions for surviving structures, indicating the proportion of predicted-intact buildings that were indeed not destroyed. - -The measure of [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -Negative Predictive Value = \frac{TN}{TN + FN} -$$ -where $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets). - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -#### Binary Structure Loss F1 Score - -*Category*: Structure Damage
    -*Name used in result files*: Binary Structure Loss F1 Score
    -*Best*: 1
    -*Worst*: 0 - -Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies destroyed buildings while limiting false alarms. - -The measure of [F1 Score](https://en.wikipedia.org/wiki/F-score) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. - -$$ -F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} -$$ - -The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. - -### Weather stations KPIs - -#### 24h forecast Wind Speed Bias Average - -*Category*: Weather Stations
    -*Name used in result files*: 24h forecast Wind Speed Bias Average
    -*Best*: 0
    -*Worst*: increasing $|bias|$
    -*Parameters*: -- $t_s$: 24h period starting time - -Provides the average bias over multiple weather stations over a period 24h for wind speed. - -The average bias is calculated as: - -$$ -\overline{Bias} = \frac{1}{N_s} \sum_{k=1}^{N_s} \frac{1}{N_t} \sum_{i=1}^{N_t} (m_{ki} - o_{ki}) -$$ -where $N_s$ is the number of weather stations, $N_t$ is the number of time step over the 24h period, $m_{ki}$ is the model data for station $k$ at time step $i$, and $o_{ki}$ is the observation data for station $k$ at time step $i$. - -The implementation of this KPI is done using `firebench.metrics.stats.bias`. - -#### 24h forecast Wind Speed RMSE Average - -*Category*: Weather Stations
    -*Name used in result files*: 24h forecast Wind Speed RMSE Average
    -*Best*: 0
    -*Worst*: increasing $|bias|$
    -*Parameters*: -- $t_s$: 24h period starting time - -Provides the average RMSE over multiple weather stations over a period 24h for wind speed. - -The average RMSE is calculated as: - -$$ -\overline{RMSE} = \frac{1}{N_s} \sum_{k=1}^{N_s} \sqrt{\frac{1}{N_t} \sum_{i=1}^{N_t} (m_{ki} - o_{ki})^2} -$$ -where $N_s$ is the number of weather stations, $N_t$ is the number of time step over the 24h period, $m_{ki}$ is the model data for station $k$ at time step $i$, and $o_{ki}$ is the observation data for station $k$ at time step $i$. - -The implementation of this KPI is done using `firebench.metrics.stats.rmse`. - -#### 24h forecast Wind Direction RMSE Average - -*Category*: Weather Stations
    -*Name used in result files*: 24h forecast Wind Direction RMSE Average
    -*Best*: 0
    -*Worst*: increasing $|bias|$
    -*Parameters*: -- $t_s$: 24h period starting time - - -Wind direction difference is calculated as -$$ -d_{wd} = ((x - y + 180) \mod 360 ) - 180 -$$ - -#### 24h forecast Wind Gust RMSE Average - -*Category*: Weather Stations
    -*Name used in result files*: 24h forecast Wind Gust RMSE Average
    -*Best*: 0
    -*Worst*: increasing $|bias|$
    -*Parameters*: -- $t_s$: 24h period starting time - -#### 24h forecast Sustained Wind Speed Average - -*Category*: Weather Stations
    -*Name used in result files*: 24h forecast Wind Gust RMSE Average
    -*Best*: 0
    -*Worst*: increasing $|bias|$
    -*Parameters*: -- $t_s$: 24h period starting time -- $U_0$: Value of thresehold - -Calculate the amount of time when the wind speed is above a defined threshold. Compare this time to observation and average it over multiple weather stations. - - diff --git a/docs/metrics/kpis.md b/docs/metrics/kpis.md new file mode 100644 index 0000000..4918374 --- /dev/null +++ b/docs/metrics/kpis.md @@ -0,0 +1,295 @@ +# Key Performance Indicator Definitions + +This section describes the Key Performance Indicators (KPI) used within FireBench benchmarks. +KPIs are grouped by context. Output bounds are given as `worst` and `best` values. + +KPIs can be described as: +- Template: generic definition of a KPI. It can be related to generic input variable(s) and have parameters. +- Intance: the final form of a KPI that can be used in a benchmark. It has explicitly defined input variable(s) and no parameters. + +## Burn Severity + +### Binary High Severity Accuracy + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measure how accurately the model predicts which point are identified as high severity, based on binary (high severity / not high severity) observations. + +The measure of [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, +$$ +where $TP$ = True positive (high severity in both datasets); $FP$ = False positive (high severity only in model dataset); $TN$ = True negative (not high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Precision + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures how accurately the model predicts which cells are high severity, by evaluating the proportion of predicted high severity points that were actually high severity. + +The measure of [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Precision = \frac{TP}{TP + FP}, +$$ +where $TP$ = True positive (high severity in both datasets); $FP$ = False positive (high severity only in model dataset) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Recall + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures how completely the model captures the cells with a high severity index, indicating the fraction of truly high severity cells that the model successfully identifies. + +The measure of [recall](https://en.wikipedia.org/wiki/Precision_and_recall) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Recall = \frac{TP}{TP + FN}, +$$ +where $TP$ = True positive (high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Specificity + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures how accurately the model identifies cells with another severity index than high, by quantifying the fraction of other indices correctly predicted as other (not high). + +The measure of [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Specificity = \frac{TN}{TN + FP} +$$ +where $FP$ = False positive (high severity only in model dataset); $TN$ = True negative (not high severity in both datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity Negative Predictive Value + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures the reliability of the model’s predictions for cells identified with another severuty index than high, indicating the proportion of points predicted index as other (not high) that were indeed observed as other. + +The measure of [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Negative Predictive Value = \frac{TN}{TN + FN} +$$ +where $TN$ = True negative (not high severity in both datasets); $FN$ = False negative (high severity only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary High Severity F1 Score + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies high severity cells while limiting false alarms. + +The measure of [F1 Score](https://en.wikipedia.org/wiki/F-score) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} +$$ + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +## Structure Loss + +### Binary Structure Loss Accuracy + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measure how accurately the model predicts which structures are destroyed or not destroyed by the fire, based on binary (burned / not burned) observations. + +The measure of [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Accuracy = \frac{TP + TN}{TP + TN + FP + FN}, +$$ +where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_accurary` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss Precision + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures how accurately the model predicts which structures are destroyed, by evaluating the proportion of predicted-destroyed buildings that were actually destroyed. + +The measure of [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Precision = \frac{TP}{TP + FP}, +$$ +where $TP$ = True positive (buildings destroyed in both datasets); $FP$ = False positive (buildings destroyed only in model dataset). + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_precision` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + + +### Binary Structure Loss Recall + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures how completely the model captures the buildings that were actually destroyed, indicating the fraction of truly destroyed structures that the model successfully identifies. + +The measure of [recall](https://en.wikipedia.org/wiki/Precision_and_recall) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Recall = \frac{TP}{TP + FN}, +$$ +where $TP$ = True positive (buildings destroyed in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets) + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_recall_rate` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss Specificity + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures how accurately the model identifies buildings that survived, by quantifying the fraction of intact structures correctly predicted as not destroyed. + +The measure of [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Specificity = \frac{TN}{TN + FP} +$$ +where $FP$ = False positive (buildings destroyed only in model dataset); $TN$ = True negative (buildings not damaged in both datasets). + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_specificity` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss Negative Predictive Value + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Measures the reliability of the model’s predictions for surviving structures, indicating the proportion of predicted-intact buildings that were indeed not destroyed. + +The measure of [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +Negative Predictive Value = \frac{TN}{TN + FN} +$$ +where $TN$ = True negative (buildings not damaged in both datasets); $FN$ = False negative (buildings destroyed only in observational datasets). + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_negative_predicted_value` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +### Binary Structure Loss F1 Score + +*Type*: Instance
    +*Best*: 1
    +*Worst*: 0 + +Provides a balanced measure of model performance by combining precision and recall, capturing how well the model identifies destroyed buildings while limiting false alarms. + +The measure of [F1 Score](https://en.wikipedia.org/wiki/F-score) is based on the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from an observational dataset and a model output dataset. + +$$ +F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} +$$ + +The implementation of this KPI is done using the `firebench.metrics.confusion_matrix.binary_cm` function and `firebench.metrics.confusion_matrix.binary_f_score` functions (see API documentation for implementation). If some data processing (e.g., for category aggregation) is required, this process is described at the case level. + +## Weather stations + +### Wind Speed Bias Average + +*Type*: Template
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_1$: period starting time +- $t_2$: period ending time + +Provides the average bias over multiple weather stations over a period 24h for wind speed. + +The average bias is calculated as: + +$$ +\overline{Bias} = \frac{1}{N_s} \sum_{k=1}^{N_s} \frac{1}{N_t} \sum_{i=1}^{N_t} (m_{ki} - o_{ki}) +$$ +where $N_s$ is the number of weather stations, $N_t$ is the number of time step between $t_1$ and $t_2$, $m_{ki}$ is the model data for station $k$ at time step $i$, and $o_{ki}$ is the observation data for station $k$ at time step $i$. + +The implementation of this KPI is done using `firebench.metrics.stats.bias`. + +#### 24h forecast Wind Speed RMSE Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Speed RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + +Provides the average RMSE over multiple weather stations over a period 24h for wind speed. + +The average RMSE is calculated as: + +$$ +\overline{RMSE} = \frac{1}{N_s} \sum_{k=1}^{N_s} \sqrt{\frac{1}{N_t} \sum_{i=1}^{N_t} (m_{ki} - o_{ki})^2} +$$ +where $N_s$ is the number of weather stations, $N_t$ is the number of time step over the 24h period, $m_{ki}$ is the model data for station $k$ at time step $i$, and $o_{ki}$ is the observation data for station $k$ at time step $i$. + +The implementation of this KPI is done using `firebench.metrics.stats.rmse`. + +#### 24h forecast Wind Direction RMSE Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Direction RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + + +Wind direction difference is calculated as +$$ +d_{wd} = ((x - y + 180) \mod 360 ) - 180 +$$ + +#### 24h forecast Wind Gust RMSE Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Gust RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time + +#### 24h forecast Sustained Wind Speed Average + +*Category*: Weather Stations
    +*Name used in result files*: 24h forecast Wind Gust RMSE Average
    +*Best*: 0
    +*Worst*: increasing $|bias|$
    +*Parameters*: +- $t_s$: 24h period starting time +- $U_0$: Value of thresehold + +Calculate the amount of time when the wind speed is above a defined threshold. Compare this time to observation and average it over multiple weather stations. + + diff --git a/docs/metrics/metrics.md b/docs/metrics/metrics.md new file mode 100644 index 0000000..b1a9c7c --- /dev/null +++ b/docs/metrics/metrics.md @@ -0,0 +1,146 @@ +# Metrics +This section describes all the metrics used within FireBench benchmarks. + +## 1D metrics + +**Input:** Two 1D vectors of size $N$: + +- $x_i$: evaluated dataset +- $y_i$: reference dataset + +### Mean + +**Description:** Average value of a 1D vector $x$.
    +**Range:** Same as range of $x$.
    +**Units:** Same as input units.
    +**Formula:** + +$$ +\bar x = \frac{1}{N} \sum_{i=1}^N x_i +$$ + +### Bias + +**Description:** Difference between the mean of $x$ and the mean of $y$.
    +**Range:** Same as range of input values.
    +**Units:** Same as input units.
    +**Formula:** + +$$ +B = \bar x - \bar y +$$ + +### Root Mean Square Error + +**Description:** Square root of the mean squared difference between (x) and (y), noted RMSE.
    +**Range:** $[0, +\infty[$.
    +**Units:** Same as input units.
    +**Formula:** + +$$ +RMSE(x, y) = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - y_i)^2} +$$ + +### Normalized MSE - power normalization + +**Description:** RMSE normalized by the range of the reference dataset.
    +**Range:** $[0, +\infty)$.
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +NMSE_p = \frac{RMSE(x, y)}{\max(y) - \min(y)} +$$ + +### Normalized MSE – range normalization +**Description:** Squared RMSE normalized by the product of mean values of the datasets.
    +**Range:** $[0, +\infty)$ (undefined if $\bar x = 0$ or $\bar y = 0$).
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +NMSE_r = \frac{RMSE(x, y)^2}{\bar x \, \bar y} +$$ + + +## Binary Confusion Matrix + +**Input:** Two 1D binary vectors (0 or 1) of size $N$: + +- $x_i$: evaluated dataset +- $y_i$: reference dataset + +The following metrics are derived from the [Binary confusion matrix](https://en.wikipedia.org/wiki/Confusion_matrix#Table_of_confusion) generated from both dataset. The Binary confusion matrix is a 2x2 matrix containing: + +| | Reference = 1 | Reference = 0 | +| ------------ | ------------- | ------------- | +| **Eval = 1** | TP | FP | +| **Eval = 0** | FN | TN | + +Where: +- TP: True Positive +- FP: False Positive +- FN: False Negative +- TN: True Negative + +### Accuracy + +**Description:** Fraction of correct predictions among all samples (see [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision#In_classification)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +Accuracy = \frac{TP + TN}{TP + TN + FP + FN} +$$ + +### Precision +**Description:** Fraction of predicted positives that are true positives (see [precision](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values#Definition)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +Precision = \frac{TP}{TP + FP}, +$$ + +### Recall +**Description:** Fraction of actual positives correctly identified (see [recall](https://en.wikipedia.org/wiki/Precision_and_recall)). Recall can also be named Sensitivity or True Positive Rate.
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +Recall = \frac{TP}{TP + FN}, +$$ + +### Specificity +**Description:** Fraction of actual negatives correctly identified (see [specificity](https://en.wikipedia.org/wiki/Sensitivity_and_specificity)). Recall can also be named True Negative Rate.
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +Specificity = \frac{TN}{TN + FP} +$$ + +### Negative Predictive Value +**Description:** Fraction of predicted negatives that are true negatives (see [Negative Predictive Value](https://en.wikipedia.org/wiki/Positive_and_negative_predictive_values)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +Negative Predictive Value = \frac{TN}{TN + FN} +$$ + +### F1 Score +**Description:** Harmonic mean of Precision and Recall (see [F1 Score](https://en.wikipedia.org/wiki/F-score)).
    +**Range:** $[0, 1]$
    +**Units:** Dimensionless.
    +**Formula:** + +$$ +F1 Score = \frac{2 \times Precision \times Recall}{Precision + Recall} +$$ + diff --git a/docs/metrics/normalization.md b/docs/metrics/normalization.md new file mode 100644 index 0000000..e456b9b --- /dev/null +++ b/docs/metrics/normalization.md @@ -0,0 +1,51 @@ +# KPI Normalization + +This section describes several normalization schemes used to convert KPI values into a score in the range $[0, 100]$. +Throughout, $x$ denotes the KPI value. + +## Linear Bounded Normalization + +For KPIs with a **bounded acceptable range** $[a, b]$, with $a < b$, the normalization function is defined as: + +$$ +\mathcal N(x, a, b) = 100 \, \left( \frac{x - a}{b - a} \right) +$$ + +Here, +- $a$ corresponds to the **worst** score (0), +- $b$ corresponds to the **best** score (100). + +## Linear Half-Open Normalization + +For KPIs that have a **minimum acceptable value** $a$ but no finite upper limit, i.e. values in $[a, +\infty[$, we define: + +$$ +\mathcal N(x, a, m) = 100 \, \max \left(0, 1 - \frac{x-a}{m-a} \right) +$$ + +where $m > a$ is a parameter specifying the value of $x$ at which the score reaches **0**. + +Here, +- $a$ corresponds to the **best** score (100), +- $m$ corresponds to the **worst** score (0). + +## Exponential Half-Open Normalization + +For KPIs with a minimum acceptable value $a$ and domain $[a, +\infty[$, we define an exponentially decaying score: + +$$ +\mathcal N(x, a, m) =100 \, \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). +$$ + +This formulation ensures: + +$$ +\mathcal N(a,a,m) = 100, \qquad +\mathcal N(m,a,m) = 50. +$$ + +Thus, $m$ is the KPI value at which the score is exactly **50**. +Here, +- $a$ corresponds to the **best** possible score (100), +- $m$ corresponds to the value at which the score has decreased to **50**, +- scores decay smoothly toward 0 as $x \to +\infty$. \ No newline at end of file diff --git a/docs/metrics/score.md b/docs/metrics/score.md new file mode 100644 index 0000000..517a779 --- /dev/null +++ b/docs/metrics/score.md @@ -0,0 +1,70 @@ +# Scores + +This section details the processes used to construct a unique score for each benchmark case from the set of KPIs contained in the benchmark case. +A `Score` is defined as a real number, with 4 significant digit, between 0.000 (worst) and 100.0 (best). +Scores are derived from KPI values and allow the comparison of models and benchmark results. +As a KPI value is not necessarily a number that is compliant with the score definition, a `normalization` process is required to convert a KPI value to a score, called `Unit Score`. + +$$ +KPI \overset{Normalization}{\longmapsto} Unit Score +$$ + +The different normalization functions available are described in Section `KPI Normalization`. + +Each KPI is transformed into a `Unit Score`, corresponding to one, and only one, KPI. +To further simplify the interpretation the result of multiple benchmarks, these Unit Scores can be aggregated to form `Group Scores`. They represent the overall performance accross multiple indicators, generally evaluating the model for similar physical processes or on the same data. +The `Total Score` is the aggregation of all group scores into one, and only one, score, representing the overall performance of the model for the studied case. + +Figure 1 shows an example of normalization of each KPI for the case *FB001*. Each KPI is normalized into a Unit Score. Then Unit Scores are aggregated into two Group Scores representing the overall performance for *Building Damaged* benchmarks and *Burn Severity* benchmarks. Finally, both Group Scores are aggregated to form the Total Score. +![blockdiagram](../_static/images/Scoring_diagram.png) +

    + + Fig. 1 + + : + + Diagram of Scores construction from KPIs using two categories of KPI (BD: Building Damaged, SV: Burn Severity). + +

    + +The aggregation can be performed using multiple aggregation schemes. The simplest scheme is to aggregate score using a mean function. This gives the same weight to each KPI in the Total Score. We can also develop more complex aggregation schemes to give more weight to certain benchmarks/KPIs. Therefore, for each benchmarking case (collection of dataset and KPIs), we can define multiple aggregation schemes to evaluate different classes of models. Each aggregation scheme will be noted using a letter. For example `FB001-A`, `FB001-B`. + + +Figure 2 shows exmaple of KPI values and their correspond range between brackets. The KPI FB001-BD01 has a value of 0.34763 and a range of [0, 1] (FB001-BD01 can represent a binary confusion matrix index), whereas the KPI FB001-SV03 has a value of 3.489 and a range of [0, $+\infty$[ (FB001-SV03 can represent an absolute bias). All KPI with a limited range of values are normaized using the linear normalization function (see next Section). The KPI FB001-SV03 is normalized using the linear semi bounded normization function with a parameter $M=5$, which means that if the KPI value is above 5, the score will be 0. +Then, Unit Scores are aggregated using uniform weights (represented by the green numbers above aggregation lines) to form Group Scores. +Finally, a weighted aggregation is performed to calculate the Total Score of the case, giving an double importance to benchmarks related to **Building Damaged*. +The list of aggregation scheme and their weights are explicitely defined in the case documentation. + +![blockdiagram](../_static/images/Scoring_diagram_example.png) +

    + + Fig. 2 + + : + + Example of Scores construction from KPIs using two categories of KPI. + +

    + +Figure 3 displays an `Score Card` that is a table representing the data showed in Figure 2. This type of score card can become the standard of presentation for case results. The first row shows the total score and contains the case that has been run (FB001), the aggregation scheme used (B, defined in the case documentation), the model name, and the total score calculated in Figure 2. +The rest of the table is organized as: +- one group row that describes the name of the group and the associated score. A keyword **Group** is added to emphasis the row. +- All the benchmark scores related to the group are displayed after. The name of the benchmark is added as a reference. Here the case id (FB001) is omitted for clarity as it is already displayed in the first row. + + +![blockdiagram](../_static/images/Score_card_example.png) +

    + + Fig. 3 + + : + + Example of Score card layout + +

    + +```{note} +Note: The example here above are not related to the real case FB001 (Caldor Fire) and all KPI names and +values are given as examples. +``` + From 8872f237f04182d594101854dcd7a416d7558859 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 9 Dec 2025 17:26:25 -0800 Subject: [PATCH 047/111] add sanitize_iso8601 to make iso date compliant with XML std --- src/firebench/standardize/__init__.py | 1 + src/firebench/standardize/time.py | 11 +++++++++++ 2 files changed, 12 insertions(+) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 22a104f..94dd8b5 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -10,6 +10,7 @@ from .time import ( current_datetime_iso8601, datetime_to_iso8601, + sanitize_iso8601, ) from .files import ( new_std_file, diff --git a/src/firebench/standardize/time.py b/src/firebench/standardize/time.py index 97b0d55..9c2b39f 100644 --- a/src/firebench/standardize/time.py +++ b/src/firebench/standardize/time.py @@ -112,3 +112,14 @@ def current_datetime_iso8601( # Start from local now, then convert to requested tz for consistency now_local = datetime.now().astimezone() return datetime_to_iso8601(now_local, include_seconds, tz) + +def sanitize_iso8601(dt_str: str) -> str: + """ + Sanitize an ISO8601 datetime string so it becomes XML/KML-safe. + + Rules: + - Replace "-" and ":" with "_" + - Leave all digits and letters unchanged + - Keeps "T" and timezone offset structure preserved + """ + return dt_str.replace("-", "_").replace(":", "_") From 60094eef5fe930d6f6ae3f94f7c09e99042ea2c7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 9 Dec 2025 18:35:52 -0800 Subject: [PATCH 048/111] change _merge_from_source to allow for merge of group at root level --- src/firebench/standardize/files.py | 53 ++++++++++++++++++++---------- src/firebench/standardize/time.py | 3 +- src/firebench/standardize/tools.py | 20 +++++++++-- 3 files changed, 55 insertions(+), 21 deletions(-) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py index 77e91d2..8d9e9f8 100644 --- a/src/firebench/standardize/files.py +++ b/src/firebench/standardize/files.py @@ -2,7 +2,7 @@ from ..tools.logging_config import logger from .time import current_datetime_iso8601 from pathlib import Path -from .tools import VERSION_STD, validate_h5_std, merge_authors, collect_conflicts, merge_trees +from .tools import VERSION_STD, validate_h5_std, merge_authors, collect_conflicts, merge_trees, logger from pathlib import Path import shutil @@ -48,7 +48,7 @@ def merge_two_std_files( Then merge the list of authors without duplicates. Keep order as much as possible (first authors from file1 then first author from file2 then second from file 1...) """ - logger.debug("Merge two std files") + logger.debug("merge_two_std_files: merge %s with %s into %s", filepath_1, filepath_2, filepath_target) file1 = h5py.File(filepath_1, "r") validate_h5_std(file1) file2 = h5py.File(filepath_2, "r") @@ -80,7 +80,10 @@ def merge_two_std_files( def merge_std_files( - filespath: list[str], filepath_target: str, merged_description: str = "", overwrite: bool = False + filespath: list[str], + filepath_target: str, + merged_description: str = "", + overwrite: bool = False, ): if not filespath: raise ValueError("filespath must contain at least one file") @@ -94,38 +97,52 @@ def merge_std_files( # Single file: just copy it to target (no merge needed) if len(input_paths) == 1: - if target_path.exists() and not overwrite: - raise FileExistsError(f"Target file already exists: {target_path}") - # Ensure parent directory exists target_path.parent.mkdir(parents=True, exist_ok=True) shutil.copy2(input_paths[0], target_path) return - tmp_path = target_path.with_suffix(target_path.suffix + ".tmp") - current_path = input_paths[0] + # Prepare two alternating temporary files + suffix = target_path.suffix + tmp1 = target_path.with_suffix(suffix + ".tmp1") + tmp2 = target_path.with_suffix(suffix + ".tmp2") + + # Ensure parent directory exists for target and temp files + target_path.parent.mkdir(parents=True, exist_ok=True) + + current_path: Path = input_paths[0] try: for i, next_path in enumerate(input_paths[1:], start=1): is_last = i == len(input_paths) - 1 - out_path = target_path if is_last else tmp_path + if is_last: + out_path = target_path + else: + # Alternate between tmp1 and tmp2, ensuring out_path != current_path + if current_path == tmp1: + out_path = tmp2 + elif current_path == tmp2: + out_path = tmp1 + else: + # First time we merge, we can pick any temp file + out_path = tmp1 - # Always allow overwrite on intermediate/target files inside this routine merge_two_std_files( filepath_1=str(current_path), filepath_2=str(next_path), filepath_target=str(out_path), merged_description=merged_description, - overwrite=True, + overwrite=True, # safe: we control these temp/target files here ) current_path = out_path finally: - # Clean up temporary file if it exists - if tmp_path.exists(): - try: - tmp_path.unlink() - except OSError: - # If removal fails, we silently ignore; nothing critical. - pass + # Clean up temporary files if they exist + for tmp in (tmp1, tmp2): + if tmp.exists() and tmp != target_path: + try: + tmp.unlink() + except OSError: + # If removal fails, we silently ignore; nothing critical. + pass diff --git a/src/firebench/standardize/time.py b/src/firebench/standardize/time.py index 9c2b39f..ddd92f2 100644 --- a/src/firebench/standardize/time.py +++ b/src/firebench/standardize/time.py @@ -113,10 +113,11 @@ def current_datetime_iso8601( now_local = datetime.now().astimezone() return datetime_to_iso8601(now_local, include_seconds, tz) + def sanitize_iso8601(dt_str: str) -> str: """ Sanitize an ISO8601 datetime string so it becomes XML/KML-safe. - + Rules: - Replace "-" and ":" with "_" - Leave all digits and letters unchanged diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index cb2041e..d58c818 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -408,8 +408,24 @@ def _merge_from_source( # Child does not exist yet in dst: copy entire subtree _copy_entire_object(src_obj, dst_obj, name) else: - # TODO: allow for conflict solver - raise ValueError("Conflict detected. Merge stopped") + # Child already exists in dst: possible conflict + src_child = src_obj[name] + dst_child = dst_obj[name] + + if path == "/" and isinstance(src_child, h5py.Group) and isinstance(dst_child, h5py.Group): + # Recurse into / and merge there + _merge_from_source( + src=src, + dst=dst, + path=child_path, + conflict_solver=conflict_solver, + overwrite_existing=overwrite_existing, + ) + else: + # Deeper levels (path != "/") or non-group conflicts: + # keep strict behavior and raise. + logger.error("_merge_from_source: conflict at path %s for %s", path, name) + raise ValueError("Conflict detected. Merge stopped") def _copy_entire_object(src_parent: h5py.Group, dst_parent: h5py.Group, name: str) -> None: From 0d4183b9980a6966e5297578b3a5e8b56aed50e0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Tue, 9 Dec 2025 20:12:27 -0800 Subject: [PATCH 049/111] add processing of landfire tif --- src/firebench/standardize/__init__.py | 1 + src/firebench/standardize/landfire.py | 118 ++++++++++++++++++++++++++ src/firebench/tools/namespace.py | 1 + 3 files changed, 120 insertions(+) create mode 100644 src/firebench/standardize/landfire.py diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index 94dd8b5..aa30c82 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -17,3 +17,4 @@ merge_two_std_files, merge_std_files, ) +from .landfire import standardize_landfire_from_geotiff diff --git a/src/firebench/standardize/landfire.py b/src/firebench/standardize/landfire.py new file mode 100644 index 0000000..680ae6b --- /dev/null +++ b/src/firebench/standardize/landfire.py @@ -0,0 +1,118 @@ +from pathlib import Path + +import h5py +import numpy as np +import rasterio +from pyproj import CRS, Transformer + +from ..tools import StandardVariableNames as svn +from .tools import check_std_version +from ..tools.logging_config import logger + + +def standardize_landfire_from_geotiff( + geotiff_path: str, + h5file: h5py.File, + variable_name: str, + variable_units: str, + group_name: str | None = None, + projection: str = None, + overwrite: bool = False, + invert_y: bool = False, + fill_value: float = None, +): + """ + Convert a MTBS GeoTIFF to Firebench HDF5 standard file. + + Use source data projection as default. Can be reprojected by specifying the CRS in projection. + + Parameters + ---------- + geotiff_path : str + Path to the MTBS GeoTIFF (ending with *_dnbr6.tif). + h5file : h5py.File + target HDF5 file + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. '2D_raster/'. + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data + + Returns + ------- + h5py.File + The actual HDF5 group written (with suffix if collision). + """ # pylint: disable=line-too-long + logger.debug("Standardize LANDFIRE dataset from file %s ", geotiff_path) + check_std_version(h5file) + with rasterio.open(geotiff_path) as src: + data = src.read(1) + landfire_data = {"data": data, "transform": src.transform, "crs": src.crs, "nodata": src.nodata} + logger.info(f"Loaded {geotiff_path}: shape={data.shape}, CRS={src.crs}") + + rows, cols = data.shape + + # Build pixel center coordinates (projected) + # col indices (x-direction), row indices (y-direction) + jj = np.arange(cols) + ii = np.arange(rows) + # vectorized center coordinates from affine: + # x = a*col + b*row + c ; y = d*col + e*row + f + x = ( + landfire_data["transform"].a * jj[None, :] + + landfire_data["transform"].b * ii[:, None] + + landfire_data["transform"].c + ) + y = ( + landfire_data["transform"].d * jj[None, :] + + landfire_data["transform"].e * ii[:, None] + + landfire_data["transform"].f + ) + + # Reproject to geographic lat/lon + if projection is None: + projection = landfire_data["crs"] + tgt_crs = CRS(projection) + + # always_xy=True -> transformer expects/returns (x, y) = (lon, lat) ordering for geographic CRS + transformer = Transformer.from_crs(landfire_data["crs"], tgt_crs, always_xy=True) + lon, lat = transformer.transform(x, y) # lon, lat are 2-D arrays aligned with `data` + + if invert_y: + lat = lat[::-1, :] + lon = lon[::-1, :] + landfire_data["data"] = landfire_data["data"][::-1, :] + + if group_name is None: + group_name = Path(geotiff_path).stem + + group_name = f"/2D_raster/{group_name}" + if group_name in h5file.keys(): + if overwrite: + del h5file[group_name] + else: + logger.warning( + "group name %s already exists in file %s. Group not updated. Set `overwrite` to True to update the dataset.", + group_name, + geotiff_path, + ) + return + + g = h5file.create_group(group_name) + g.attrs["data_source"] = f"LANDFIRE {geotiff_path}" + g.attrs["crs"] = str(tgt_crs) + + # Lat/Lon as 2-D arrays + dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + ddata = g.create_dataset(variable_name, data=landfire_data["data"], dtype=np.uint8) + ddata.attrs["units"] = variable_units + if fill_value is None: + ddata.attrs["_FillValue"] = landfire_data["nodata"] + else: + ddata.attrs["_FillValue"] = fill_value diff --git a/src/firebench/tools/namespace.py b/src/firebench/tools/namespace.py index ee2343e..9a6e8c0 100644 --- a/src/firebench/tools/namespace.py +++ b/src/firebench/tools/namespace.py @@ -20,6 +20,7 @@ class StandardVariableNames(Enum): # pragma: no cover BUILDING_LENGTH_SEPARATION = "building_length_separation" BUILDING_LENGTH_SIDE = "building_length_side" CANOPY_DENSITY_BULK = "canopy_density_bulk" + CANOPY_HEIGHT = "canopy_height" CANOPY_HEIGHT_BOTTOM = "canopy_height_bottom" CANOPY_HEIGHT_TOP = "canopy_height_top" DIRECTION = "direction" From 645804a1fd3a888999403a7f72daa95cbb74564f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 10 Dec 2025 14:19:28 -0800 Subject: [PATCH 050/111] add import_tif_with_rect_box function --- src/firebench/standardize/__init__.py | 1 + src/firebench/standardize/tools.py | 121 ++++++++++++++++++++++++++ src/firebench/tools/namespace.py | 3 + 3 files changed, 125 insertions(+) diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index aa30c82..a55c34a 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -6,6 +6,7 @@ validate_h5_requirement, read_quantity_from_fb_dataset, merge_authors, + import_tif_with_rect_box, ) from .time import ( current_datetime_iso8601, diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index d58c818..00dffc2 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -3,6 +3,11 @@ from ..tools.units import ureg import re import numpy as np +from pathlib import Path +import rasterio +from pyproj import CRS, Transformer +from rasterio.windows import from_bounds +from rasterio.warp import transform_bounds VERSION_STD = "0.2" @@ -359,6 +364,122 @@ def merge_trees( ) +def import_tif_with_rect_box( + geotiff_path: Path, + lower_left_corner: tuple[float, float], + upper_right_corner: tuple[float, float], + projection: str = None, + invert_y: bool = False, +): + """ + Import a subset of a GeoTIFF using a geographic rectangular bounding box. + + The function loads only the portion of the input GeoTIFF that intersects a + user-defined rectangle in geographic coordinates (lat/lon). The bounding box + is reprojected into the source CRS, a raster window is extracted, and pixel-center + coordinates are computed. The result is returned in the target CRS (default: CRS of + the source dataset unless a different projection is specified). + + Parameters + ---------- + geotiff_path : Path + Path to the GeoTIFF file. + lower_left_corner : tuple[float, float] + Geographic coordinates of the lower-left corner of the selection box, + formatted as (latitude, longitude). + upper_right_corner : tuple[float, float] + Geographic coordinates of the upper-right corner of the selection box, + formatted as (latitude, longitude). + projection : str, optional + Target CRS for returned latitude/longitude arrays. If None, use the CRS + of the source GeoTIFF. Can be any pyproj-compatible CRS string (e.g. "EPSG:4326"). + invert_y : bool, optional + If True, flip the data and latitude/longitude arrays along the vertical + axis (useful for image-style orientation). + + Returns + ------- + lat : np.ndarray + 2-D array of latitude coordinates for each pixel center in the selected region, + expressed in the target CRS. + lon : np.ndarray + 2-D array of longitude coordinates for each pixel center in the selected region, + expressed in the target CRS. + data_arr : np.ndarray + 2-D array containing the raster data extracted within the bounding box. + + Notes + ----- + - Only the requested rectangular region is read from disk, preventing the allocation + of extremely large full-domain coordinate grids. + - The function logs a summary of the loaded subset including its shape, CRS, and window. + - The selection box is provided in geographic coordinates and internally reprojected + to the raster's native projection. + """ # pylint: disable=line-too-long + with rasterio.open(geotiff_path.resolve()) as src: + if projection is None: + projection = src.crs + tgt_crs = CRS(projection) + + lat_min, lon_min = lower_left_corner + lat_max, lon_max = upper_right_corner + + lon_min, lon_max = sorted((lon_min, lon_max)) + lat_min, lat_max = sorted((lat_min, lat_max)) + + bbox_src = transform_bounds( + tgt_crs, + src.crs, + lon_min, + lat_min, + lon_max, + lat_max, + densify_pts=21, + ) + x_min_src, y_min_src, x_max_src, y_max_src = bbox_src + + window = from_bounds(x_min_src, y_min_src, x_max_src, y_max_src, src.transform) + + data = src.read(1, window=window) + transform_window = src.window_transform(window) + + data_dict = { + "data": data, + "transform": transform_window, + "crs": src.crs, + "nodata": src.nodata, + } + + logger.info( + "Loaded subset from %s: shape=%s, CRS=%s, window=%s", + geotiff_path, + data.shape, + src.crs, + window, + ) + + rows, cols = data_dict["data"].shape + + jj = np.arange(cols) + ii = np.arange(rows) + + # center coordinates in source CRS (projected) + T = data_dict["transform"] + x = T.a * jj[None, :] + T.b * ii[:, None] + T.c + y = T.d * jj[None, :] + T.e * ii[:, None] + T.f + + tgt_crs = CRS(projection) + transformer = Transformer.from_crs(data_dict["crs"], tgt_crs, always_xy=True) + lon, lat = transformer.transform(x, y) + + if invert_y: + lat = lat[::-1, :] + lon = lon[::-1, :] + data_dict["data"] = data_dict["data"][::-1, :] + + return np.array(lat), np.array(lon), np.array(data_dict["data"]) + + def _merge_from_source( src: h5py.Group, dst: h5py.Group, diff --git a/src/firebench/tools/namespace.py b/src/firebench/tools/namespace.py index 9a6e8c0..29986a4 100644 --- a/src/firebench/tools/namespace.py +++ b/src/firebench/tools/namespace.py @@ -81,6 +81,9 @@ class StandardVariableNames(Enum): # pragma: no cover NORMAL_SPREAD_DIR_X = "normal_spread_dir_x" NORMAL_SPREAD_DIR_Y = "normal_spread_dir_y" RATE_OF_SPREAD = "rate_of_spread" + RAVG_CANOPY_COVER_LOSS = "ravg_canopy_cover_loss" + RAVG_COMPOSITE_BURN_INDEX_SEVERITY = "ravg_composite_burn_index_severity" + RAVG_LIVE_BASAL_AREA_LOSS = "ravg_live_basal_area_loss" RELATIVE_HUMIDITY = "relative_humidity" SLOPE_ANGLE = "slope_angle" TEMPERATURE = "temperature" From b66e2d9dbd77aa9f8180eb8f37800147a6fbe271 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Wed, 10 Dec 2025 19:53:46 -0800 Subject: [PATCH 051/111] add ravg std function - update mtbs std function to be compliant with new standard - import_tif_with_rect_box returns crs - add std_file_info data to centralize naming of high level structure --- src/firebench/standardize/__init__.py | 15 ++ src/firebench/standardize/files.py | 1 + src/firebench/standardize/mtbs.py | 5 +- src/firebench/standardize/ravg.py | 220 +++++++++++++++++++++ src/firebench/standardize/std_file_info.py | 10 + src/firebench/standardize/tools.py | 2 +- 6 files changed, 250 insertions(+), 3 deletions(-) create mode 100644 src/firebench/standardize/ravg.py create mode 100644 src/firebench/standardize/std_file_info.py diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index a55c34a..af194b3 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -19,3 +19,18 @@ merge_std_files, ) from .landfire import standardize_landfire_from_geotiff +from .ravg import ( + standardize_ravg_cc_from_geotiff, + standardize_ravg_ba_from_geotiff, + standardize_ravg_cbi_from_geotiff, +) +from .std_file_info import ( + POINTS, + TIME_SERIES, + SPATIAL_1D, + SPATIAL_2D, + SPATIAL_3D, + GEOPOLYGONS, + FUEL_MODELS, + MISCELLANEOUS, +) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py index 8d9e9f8..809b077 100644 --- a/src/firebench/standardize/files.py +++ b/src/firebench/standardize/files.py @@ -11,6 +11,7 @@ def new_std_file(filepath: str, authors: str, overwrite: bool = False) -> h5py.F """ Create a new file using FireBench standard. Return the file object. + Notes ----- Do not forget to close the file once edited. This function opens the h5 file but do not close it. diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py index 88592eb..c150400 100644 --- a/src/firebench/standardize/mtbs.py +++ b/src/firebench/standardize/mtbs.py @@ -8,6 +8,7 @@ from ..tools import StandardVariableNames as svn from .tools import check_std_version from ..tools.logging_config import logger +from .std_file_info import SPATIAL_2D def standardize_mtbs_from_geotiff( @@ -30,7 +31,7 @@ def standardize_mtbs_from_geotiff( h5file : h5py.File target HDF5 file group_name : str | None - HDF5 group path. If None, auto-derive from filename, e.g. '2D_raster/'. + HDF5 group path. If None, auto-derive from filename, e.g. 'SPATIAL_2D/'. overwrite: bool Overwrite the group in the HDF5 file. Default: False invert_y: bool @@ -84,7 +85,7 @@ def standardize_mtbs_from_geotiff( if group_name is None: group_name = Path(geotiff_path).stem - group_name = f"/2D_raster/{group_name}" + group_name = f"/{SPATIAL_2D}/{group_name}" if group_name in h5file.keys(): if overwrite: del h5file[group_name] diff --git a/src/firebench/standardize/ravg.py b/src/firebench/standardize/ravg.py new file mode 100644 index 0000000..6191661 --- /dev/null +++ b/src/firebench/standardize/ravg.py @@ -0,0 +1,220 @@ +import h5py +import numpy as np +from .tools import check_std_version, import_tif_with_rect_box +from ..tools.logging_config import logger +from ..tools import StandardVariableNames as svn +from pathlib import Path +from .std_file_info import SPATIAL_2D + + +def standardize_ravg_cc_from_geotiff( + geotiff_path: Path, + h5file: h5py.File, + lower_left_corner: tuple[float, float], + upper_right_corner: tuple[float, float], + group_name: str | None = None, + projection: str = None, + overwrite: bool = False, + invert_y: bool = False, +): + """ + Convert a RAVG GeoTIFF to FireBench HDF5 Standard for Canopy Cover Loss + Use CONUS tif file and define bounding box for data import. + + Use source data projection as default. Can be reprojected by specifying the CRS in projection. + + Parameters + ---------- + geotiff_path : Path + Path to the RAVG Canopy Cover Loss GeoTIFF (ending with *_cc5.tif). + h5file : h5py.File + target HDF5 file + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. 'spatial_2d/'. + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data + + Returns + ------- + h5py.File + The actual HDF5 group written (with suffix if collision). + """ + _standardize_ravg_from_geotiff( + geotiff_path, + h5file, + lower_left_corner, + upper_right_corner, + svn.RAVG_CANOPY_COVER_LOSS.value, + group_name, + projection, + overwrite, + invert_y, + ) + + +def standardize_ravg_cbi_from_geotiff( + geotiff_path: Path, + h5file: h5py.File, + lower_left_corner: tuple[float, float], + upper_right_corner: tuple[float, float], + group_name: str | None = None, + projection: str = None, + overwrite: bool = False, + invert_y: bool = False, +): + """ + Convert a RAVG GeoTIFF to FireBench HDF5 Standard for Composite Burn Index Severity + Use CONUS tif file and define bounding box for data import. + + Use source data projection as default. Can be reprojected by specifying the CRS in projection. + + Parameters + ---------- + geotiff_path : Path + Path to the RAVG Composite Burn Index Severity GeoTIFF (ending with *_cbi4.tif). + h5file : h5py.File + target HDF5 file + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. 'spatial_2d/'. + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data + + Returns + ------- + h5py.File + The actual HDF5 group written (with suffix if collision). + """ + _standardize_ravg_from_geotiff( + geotiff_path, + h5file, + lower_left_corner, + upper_right_corner, + svn.RAVG_COMPOSITE_BURN_INDEX_SEVERITY.value, + group_name, + projection, + overwrite, + invert_y, + ) + + +def standardize_ravg_ba_from_geotiff( + geotiff_path: Path, + h5file: h5py.File, + lower_left_corner: tuple[float, float], + upper_right_corner: tuple[float, float], + group_name: str | None = None, + projection: str = None, + overwrite: bool = False, + invert_y: bool = False, +): + """ + Convert a RAVG GeoTIFF to FireBench HDF5 Standard for Live Basal Area loss + Use CONUS tif file and define bounding box for data import. + + Use source data projection as default. Can be reprojected by specifying the CRS in projection. + + Parameters + ---------- + geotiff_path : Path + Path to the RAVG Live Basal Area loss GeoTIFF (ending with *_ba7.tif). + h5file : h5py.File + target HDF5 file + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. 'spatial_2d/'. + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data + + Returns + ------- + h5py.File + The actual HDF5 group written (with suffix if collision). + """ + _standardize_ravg_from_geotiff( + geotiff_path, + h5file, + lower_left_corner, + upper_right_corner, + svn.RAVG_LIVE_BASAL_AREA_LOSS.value, + group_name, + projection, + overwrite, + invert_y, + ) + + +def _standardize_ravg_from_geotiff( + geotiff_path: Path, + h5file: h5py.File, + lower_left_corner: tuple[float, float], + upper_right_corner: tuple[float, float], + ravg_variable: str, + group_name: str | None = None, + projection: str = None, + overwrite: bool = False, + invert_y: bool = False, +): + """ + Convert a RAVG GeoTIFF to FireBench HDF5 Standard. + Use CONUS tif file and define bounding box for data import. + + Use source data projection as default. Can be reprojected by specifying the CRS in projection. + + Parameters + ---------- + geotiff_path : Path + Path to the RAVG Composite Burn Index Severity GeoTIFF (ending with *_cc5.tif). + h5file : h5py.File + target HDF5 file + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. 'spatial_2d/'. + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data + + Returns + ------- + h5py.File + The actual HDF5 group written (with suffix if collision). + """ + logger.debug("Standardize RAVG %s dataset from file %s ", ravg_variable, geotiff_path) + check_std_version(h5file) + + lat, lon, ravg_data, crs = import_tif_with_rect_box( + geotiff_path, lower_left_corner, upper_right_corner, projection, invert_y + ) + + if group_name is None: + group_name = Path(geotiff_path).stem + + group_name = f"/{SPATIAL_2D}/{group_name}" + if group_name in h5file.keys(): + if overwrite: + del h5file[group_name] + else: + logger.warning( + "group name %s already exists in file %s. Group not updated. Set `overwrite` to True to update the dataset.", + group_name, + geotiff_path, + ) + return + + g = h5file.create_group(group_name) + g.attrs["data_source"] = f"RAVG {geotiff_path}" + g.attrs["crs"] = str(crs) + + dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64) + dlat.attrs["units"] = "degrees" + + ddata = g.create_dataset(ravg_variable, data=ravg_data, dtype=np.uint8) + ddata.attrs["units"] = "dimensionless" + ddata.attrs["_FillValue"] = 0 diff --git a/src/firebench/standardize/std_file_info.py b/src/firebench/standardize/std_file_info.py new file mode 100644 index 0000000..9b0b479 --- /dev/null +++ b/src/firebench/standardize/std_file_info.py @@ -0,0 +1,10 @@ +# centralize high level structure for HDF5 standard file +POINTS = "points" +TIME_SERIES = "time_series" +SPATIAL_1D = "spatial_1d" +SPATIAL_2D = "spatial_2d" +SPATIAL_3D = "spatial_3d" +UNSTRUCTURED = "unstructured" +GEOPOLYGONS = "polygons" +FUEL_MODELS = "fuel_models" +MISCELLANEOUS = "miscellaneous" diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 00dffc2..420a0b4 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -477,7 +477,7 @@ def import_tif_with_rect_box( lon = lon[::-1, :] data_dict["data"] = data_dict["data"][::-1, :] - return np.array(lat), np.array(lon), np.array(data_dict["data"]) + return np.array(lat), np.array(lon), np.array(data_dict["data"]), tgt_crs def _merge_from_source( From 5383718d764c3f34f7780524103d59a6929ddfd6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 11 Dec 2025 19:50:11 -0800 Subject: [PATCH 052/111] refactoring of tif import data - add compression using zstd to standardization functions - add hdf5plugin dependency --- pyproject.toml | 1 + src/firebench/standardize/landfire.py | 56 ++++------------- src/firebench/standardize/mtbs.py | 8 ++- src/firebench/standardize/ravg.py | 12 ++-- src/firebench/standardize/tools.py | 89 +++++++++++++++++---------- 5 files changed, 82 insertions(+), 84 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5ac7e93..a6f431e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,6 +15,7 @@ description = "FireBench is a Python library designed for the systematic benchma dependencies = [ "geopandas < 2.0", "h5py < 4.0", + "hdf5plugin >= 6.0", "matplotlib > 3.8", "numpy < 3.0", "pint < 1.0", diff --git a/src/firebench/standardize/landfire.py b/src/firebench/standardize/landfire.py index 680ae6b..be51e78 100644 --- a/src/firebench/standardize/landfire.py +++ b/src/firebench/standardize/landfire.py @@ -1,14 +1,15 @@ from pathlib import Path import h5py +import hdf5plugin import numpy as np import rasterio from pyproj import CRS, Transformer from ..tools import StandardVariableNames as svn -from .tools import check_std_version +from .tools import check_std_version, import_tif from ..tools.logging_config import logger - +from .std_file_info import SPATIAL_2D def standardize_landfire_from_geotiff( geotiff_path: str, @@ -20,6 +21,7 @@ def standardize_landfire_from_geotiff( overwrite: bool = False, invert_y: bool = False, fill_value: float = None, + compression_lvl:int=3, ): """ Convert a MTBS GeoTIFF to Firebench HDF5 standard file. @@ -46,48 +48,12 @@ def standardize_landfire_from_geotiff( """ # pylint: disable=line-too-long logger.debug("Standardize LANDFIRE dataset from file %s ", geotiff_path) check_std_version(h5file) - with rasterio.open(geotiff_path) as src: - data = src.read(1) - landfire_data = {"data": data, "transform": src.transform, "crs": src.crs, "nodata": src.nodata} - logger.info(f"Loaded {geotiff_path}: shape={data.shape}, CRS={src.crs}") - - rows, cols = data.shape - - # Build pixel center coordinates (projected) - # col indices (x-direction), row indices (y-direction) - jj = np.arange(cols) - ii = np.arange(rows) - # vectorized center coordinates from affine: - # x = a*col + b*row + c ; y = d*col + e*row + f - x = ( - landfire_data["transform"].a * jj[None, :] - + landfire_data["transform"].b * ii[:, None] - + landfire_data["transform"].c - ) - y = ( - landfire_data["transform"].d * jj[None, :] - + landfire_data["transform"].e * ii[:, None] - + landfire_data["transform"].f - ) - - # Reproject to geographic lat/lon - if projection is None: - projection = landfire_data["crs"] - tgt_crs = CRS(projection) - - # always_xy=True -> transformer expects/returns (x, y) = (lon, lat) ordering for geographic CRS - transformer = Transformer.from_crs(landfire_data["crs"], tgt_crs, always_xy=True) - lon, lat = transformer.transform(x, y) # lon, lat are 2-D arrays aligned with `data` - - if invert_y: - lat = lat[::-1, :] - lon = lon[::-1, :] - landfire_data["data"] = landfire_data["data"][::-1, :] + lat, lon, landfire_data, crs, nodata = import_tif(geotiff_path, projection, invert_y) if group_name is None: group_name = Path(geotiff_path).stem - group_name = f"/2D_raster/{group_name}" + group_name = f"/{SPATIAL_2D}/{group_name}" if group_name in h5file.keys(): if overwrite: del h5file[group_name] @@ -101,18 +67,18 @@ def standardize_landfire_from_geotiff( g = h5file.create_group(group_name) g.attrs["data_source"] = f"LANDFIRE {geotiff_path}" - g.attrs["crs"] = str(tgt_crs) + g.attrs["crs"] = str(crs) # Lat/Lon as 2-D arrays - dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64) + dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) dlat.attrs["units"] = "degrees" - dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64) + dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) dlat.attrs["units"] = "degrees" - ddata = g.create_dataset(variable_name, data=landfire_data["data"], dtype=np.uint8) + ddata = g.create_dataset(variable_name, data=landfire_data, dtype=np.uint16, **hdf5plugin.Zstd(clevel=compression_lvl)) ddata.attrs["units"] = variable_units if fill_value is None: - ddata.attrs["_FillValue"] = landfire_data["nodata"] + ddata.attrs["_FillValue"] = nodata else: ddata.attrs["_FillValue"] = fill_value diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py index c150400..8bf45ee 100644 --- a/src/firebench/standardize/mtbs.py +++ b/src/firebench/standardize/mtbs.py @@ -1,6 +1,7 @@ from pathlib import Path import h5py +import hdf5plugin import numpy as np import rasterio from pyproj import CRS, Transformer @@ -18,6 +19,7 @@ def standardize_mtbs_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, + compression_lvl:int=3, ): """ Convert a MTBS GeoTIFF to Firebench HDF5 standard file. @@ -102,12 +104,12 @@ def standardize_mtbs_from_geotiff( g.attrs["crs"] = str(tgt_crs) # Lat/Lon as 2-D arrays - dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64) + dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) dlat.attrs["units"] = "degrees" - dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64) + dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) dlat.attrs["units"] = "degrees" - ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8) + ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8, **hdf5plugin.Zstd(clevel=compression_lvl)) ddata.attrs["units"] = "dimensionless" ddata.attrs["_FillValue"] = 0 diff --git a/src/firebench/standardize/ravg.py b/src/firebench/standardize/ravg.py index 6191661..de3ba22 100644 --- a/src/firebench/standardize/ravg.py +++ b/src/firebench/standardize/ravg.py @@ -1,4 +1,5 @@ import h5py +import hdf5plugin import numpy as np from .tools import check_std_version, import_tif_with_rect_box from ..tools.logging_config import logger @@ -158,6 +159,7 @@ def _standardize_ravg_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, + compression_lvl:int=3, ): """ Convert a RAVG GeoTIFF to FireBench HDF5 Standard. @@ -186,7 +188,7 @@ def _standardize_ravg_from_geotiff( logger.debug("Standardize RAVG %s dataset from file %s ", ravg_variable, geotiff_path) check_std_version(h5file) - lat, lon, ravg_data, crs = import_tif_with_rect_box( + lat, lon, ravg_data, crs, nodata = import_tif_with_rect_box( geotiff_path, lower_left_corner, upper_right_corner, projection, invert_y ) @@ -209,12 +211,12 @@ def _standardize_ravg_from_geotiff( g.attrs["data_source"] = f"RAVG {geotiff_path}" g.attrs["crs"] = str(crs) - dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64) + dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) dlat.attrs["units"] = "degrees" - dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64) + dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) dlat.attrs["units"] = "degrees" - ddata = g.create_dataset(ravg_variable, data=ravg_data, dtype=np.uint8) + ddata = g.create_dataset(ravg_variable, data=ravg_data, dtype=np.uint8, **hdf5plugin.Zstd(clevel=compression_lvl)) ddata.attrs["units"] = "dimensionless" - ddata.attrs["_FillValue"] = 0 + ddata.attrs["_FillValue"] = nodata diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 420a0b4..6be3e6b 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -477,7 +477,62 @@ def import_tif_with_rect_box( lon = lon[::-1, :] data_dict["data"] = data_dict["data"][::-1, :] - return np.array(lat), np.array(lon), np.array(data_dict["data"]), tgt_crs + return np.array(lat), np.array(lon), np.array(data_dict["data"]), tgt_crs, data_dict["nodata"] + +def import_tif( + geotiff_path: str, + projection: str = None, + invert_y: bool = False, +): + """ + Convert a MTBS GeoTIFF to Firebench HDF5 standard file. + + Use source data projection as default. Can be reprojected by specifying the CRS in projection. + + Parameters + ---------- + geotiff_path : str + Path to the MTBS GeoTIFF (ending with *_dnbr6.tif). + h5file : h5py.File + target HDF5 file + group_name : str | None + HDF5 group path. If None, auto-derive from filename, e.g. '2D_raster/'. + overwrite: bool + Overwrite the group in the HDF5 file. Default: False + invert_y: bool + Invert y axis in data + + Returns + ------- + h5py.File + The actual HDF5 group written (with suffix if collision). + """ # pylint: disable=line-too-long + with rasterio.open(geotiff_path) as src: + data = src.read(1) + data_dict = {"data": data, "transform": src.transform, "crs": src.crs, "nodata": src.nodata} + logger.info(f"Loaded {geotiff_path}: shape={data.shape}, CRS={src.crs}") + + rows, cols = data.shape + jj = np.arange(cols) + ii = np.arange(rows) + T = data_dict["transform"] + x = T.a * jj[None, :] + T.b * ii[:, None] + T.c + y = T.d * jj[None, :] + T.e * ii[:, None] + T.f + + if projection is None: + projection = data_dict["crs"] + tgt_crs = CRS(projection) + + # always_xy=True -> transformer expects/returns (x, y) = (lon, lat) ordering for geographic CRS + transformer = Transformer.from_crs(data_dict["crs"], tgt_crs, always_xy=True) + lon, lat = transformer.transform(x, y) + + if invert_y: + lat = lat[::-1, :] + lon = lon[::-1, :] + data_dict["data"] = data_dict["data"][::-1, :] + + return np.array(lat), np.array(lon), np.array(data_dict["data"]), tgt_crs, data_dict["nodata"] def _merge_from_source( @@ -554,26 +609,7 @@ def _copy_entire_object(src_parent: h5py.Group, dst_parent: h5py.Group, name: st Copy a group or dataset `name` from src_parent to dst_parent, including all attributes and children (for groups). """ - obj = src_parent[name] - - if isinstance(obj, h5py.Dataset): - # Create dataset with same data and attrs - dset = dst_parent.create_dataset(name, data=obj[...], dtype=obj.dtype) - _copy_attributes(obj, dset, _build_child_path(dst_parent.name, name)) - - elif isinstance(obj, h5py.Group): - # Create group and copy attributes - grp = dst_parent.create_group(name) - _copy_attributes(obj, grp, _build_child_path(dst_parent.name, name)) - - # Recurse to copy all children - for child_name in obj.keys(): - _copy_entire_object(obj, grp, child_name) - - else: - # Exotic object types (SoftLink, ExternalLink, etc.): skip or handle if needed - # For now, we skip them to keep the implementation simple. - pass + src_parent.copy(name, dst_parent, name=name) def _copy_attributes( @@ -592,13 +628,4 @@ def _copy_attributes( if key in dst_obj.attrs: # Keep existing attr value continue - dst_obj.attrs[key] = value - - -def _build_child_path(parent_path: str, name: str) -> str: - """ - Utility to build a proper HDF5 path for a child object. - """ - if parent_path == "/": - return f"/{name}" - return f"{parent_path.rstrip('/')}/{name}" + dst_obj.attrs[key] = value \ No newline at end of file From 26fd4b0f67b1ffa751162ceb9a3f72d91d171d33 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 11 Dec 2025 19:50:24 -0800 Subject: [PATCH 053/111] change standard file format --- docs/standard_format.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/standard_format.md b/docs/standard_format.md index 8836132..687c926 100644 --- a/docs/standard_format.md +++ b/docs/standard_format.md @@ -12,11 +12,11 @@ Each .h5 file must adhere to the following structure: ``` / (root) -├── points/ (geopolygones) +├── points/ (0D datasets) ├── time_series/ (point-based time series) -├── 1D_raster/ (1D gridded spatial data + time) -├── 2D_raster/ (2D gridded spatial data + time) -├── 3D_raster/ (3D gridded spatial data + time) +├── spatial_1d/ (1D gridded spatial data + time) +├── spatial_2d/ (2D gridded spatial data + time) +├── spatial_3d/ (3D gridded spatial data + time) ├── unstructured/ (unstructured spatial data + time) ├── polygons/ (geopolygones) ├── fuel_models/ (fuel model classification or parameters) From db48917c38aa8b30722c0040f5c44a82c1f1b602 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 11 Dec 2025 20:49:36 -0800 Subject: [PATCH 054/111] add synoptic data standardization - add SOLAR_RADIATION and WIND_GUST to namespace - code formatting --- src/firebench/standardize/__init__.py | 1 + src/firebench/standardize/landfire.py | 15 ++- src/firebench/standardize/mtbs.py | 17 +++- src/firebench/standardize/ravg.py | 14 ++- src/firebench/standardize/synoptic.py | 131 ++++++++++++++++++++++++++ src/firebench/standardize/tools.py | 3 +- src/firebench/tools/namespace.py | 2 + 7 files changed, 170 insertions(+), 13 deletions(-) create mode 100644 src/firebench/standardize/synoptic.py diff --git a/src/firebench/standardize/__init__.py b/src/firebench/standardize/__init__.py index af194b3..aa4985c 100644 --- a/src/firebench/standardize/__init__.py +++ b/src/firebench/standardize/__init__.py @@ -34,3 +34,4 @@ FUEL_MODELS, MISCELLANEOUS, ) +from .synoptic import standardize_synoptic_raws_from_json diff --git a/src/firebench/standardize/landfire.py b/src/firebench/standardize/landfire.py index be51e78..675c321 100644 --- a/src/firebench/standardize/landfire.py +++ b/src/firebench/standardize/landfire.py @@ -11,6 +11,7 @@ from ..tools.logging_config import logger from .std_file_info import SPATIAL_2D + def standardize_landfire_from_geotiff( geotiff_path: str, h5file: h5py.File, @@ -21,7 +22,7 @@ def standardize_landfire_from_geotiff( overwrite: bool = False, invert_y: bool = False, fill_value: float = None, - compression_lvl:int=3, + compression_lvl: int = 3, ): """ Convert a MTBS GeoTIFF to Firebench HDF5 standard file. @@ -70,13 +71,19 @@ def standardize_landfire_from_geotiff( g.attrs["crs"] = str(crs) # Lat/Lon as 2-D arrays - dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) + dlat = g.create_dataset( + "position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl) + ) dlat.attrs["units"] = "degrees" - dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) + dlat = g.create_dataset( + "position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl) + ) dlat.attrs["units"] = "degrees" - ddata = g.create_dataset(variable_name, data=landfire_data, dtype=np.uint16, **hdf5plugin.Zstd(clevel=compression_lvl)) + ddata = g.create_dataset( + variable_name, data=landfire_data, dtype=np.uint16, **hdf5plugin.Zstd(clevel=compression_lvl) + ) ddata.attrs["units"] = variable_units if fill_value is None: ddata.attrs["_FillValue"] = nodata diff --git a/src/firebench/standardize/mtbs.py b/src/firebench/standardize/mtbs.py index 8bf45ee..eb6b15c 100644 --- a/src/firebench/standardize/mtbs.py +++ b/src/firebench/standardize/mtbs.py @@ -19,7 +19,7 @@ def standardize_mtbs_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, - compression_lvl:int=3, + compression_lvl: int = 3, ): """ Convert a MTBS GeoTIFF to Firebench HDF5 standard file. @@ -104,12 +104,21 @@ def standardize_mtbs_from_geotiff( g.attrs["crs"] = str(tgt_crs) # Lat/Lon as 2-D arrays - dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) + dlat = g.create_dataset( + "position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl) + ) dlat.attrs["units"] = "degrees" - dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) + dlat = g.create_dataset( + "position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl) + ) dlat.attrs["units"] = "degrees" - ddata = g.create_dataset(svn.FIRE_BURN_SEVERITY.value, data=severity_raw["data"], dtype=np.uint8, **hdf5plugin.Zstd(clevel=compression_lvl)) + ddata = g.create_dataset( + svn.FIRE_BURN_SEVERITY.value, + data=severity_raw["data"], + dtype=np.uint8, + **hdf5plugin.Zstd(clevel=compression_lvl), + ) ddata.attrs["units"] = "dimensionless" ddata.attrs["_FillValue"] = 0 diff --git a/src/firebench/standardize/ravg.py b/src/firebench/standardize/ravg.py index de3ba22..cffc5a3 100644 --- a/src/firebench/standardize/ravg.py +++ b/src/firebench/standardize/ravg.py @@ -159,7 +159,7 @@ def _standardize_ravg_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, - compression_lvl:int=3, + compression_lvl: int = 3, ): """ Convert a RAVG GeoTIFF to FireBench HDF5 Standard. @@ -211,12 +211,18 @@ def _standardize_ravg_from_geotiff( g.attrs["data_source"] = f"RAVG {geotiff_path}" g.attrs["crs"] = str(crs) - dlat = g.create_dataset("position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) + dlat = g.create_dataset( + "position_lat", data=lat, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl) + ) dlat.attrs["units"] = "degrees" - dlat = g.create_dataset("position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl)) + dlat = g.create_dataset( + "position_lon", data=lon, dtype=np.float64, **hdf5plugin.Zstd(clevel=compression_lvl) + ) dlat.attrs["units"] = "degrees" - ddata = g.create_dataset(ravg_variable, data=ravg_data, dtype=np.uint8, **hdf5plugin.Zstd(clevel=compression_lvl)) + ddata = g.create_dataset( + ravg_variable, data=ravg_data, dtype=np.uint8, **hdf5plugin.Zstd(clevel=compression_lvl) + ) ddata.attrs["units"] = "dimensionless" ddata.attrs["_FillValue"] = nodata diff --git a/src/firebench/standardize/synoptic.py b/src/firebench/standardize/synoptic.py new file mode 100644 index 0000000..34f8f35 --- /dev/null +++ b/src/firebench/standardize/synoptic.py @@ -0,0 +1,131 @@ +from pathlib import Path +import numpy as np +import hdf5plugin +import h5py +import json +from datetime import datetime +from ..tools import StandardVariableNames as svn +from ..tools import logger, calculate_sha256 +from .std_file_info import TIME_SERIES +from .time import datetime_to_iso8601 + +VARIABLE_CONVERSION = { + "air_temp_set_1": { + "std_name": svn.AIR_TEMPERATURE.value, + "units": "degC", + "dtype": np.float64, + }, + "relative_humidity_set_1": { + "std_name": svn.RELATIVE_HUMIDITY.value, + "units": "percent", + "dtype": np.float64, + }, + "wind_direction_set_1": { + "std_name": svn.WIND_DIRECTION.value, + "units": "degree", + "dtype": np.float64, + }, + "wind_speed_set_1": { + "std_name": svn.WIND_SPEED.value, + "units": "m/s", + "dtype": np.float64, + }, + "wind_gust_set_1": { + "std_name": svn.WIND_GUST.value, + "units": "m/s", + "dtype": np.float64, + }, + "solar_radiation_set_1": { + "std_name": svn.SOLAR_RADIATION.value, + "units": "W/m^2", + "dtype": np.float64, + }, + "fuel_moisture_set_1": { + "std_name": svn.FUEL_MOISTURE_CONTENT_10H.value, + "units": "percent", + "dtype": np.float64, + }, +} + + +def standardize_synoptic_raws_from_json( + json_path: Path, + h5file: h5py.File, + overwrite: bool = False, + variable_conversion: dict = VARIABLE_CONVERSION, + compression_lvl: int = 3, +): + sha_source_file = calculate_sha256(json_path.resolve()) + with open(json_path.resolve(), "r") as f: + data = json.load(f) + + if TIME_SERIES in h5file["/"]: + probes = h5file[f"/{TIME_SERIES}"] + else: + probes = h5file.create_group(TIME_SERIES) + + for station_dict in data["STATION"]: + logger.info("Processing station %s", station_dict["STID"]) + + group_name = f"station_{station_dict["STID"]}" + if group_name in h5file.keys(): + if overwrite: + del h5file[group_name] + else: + logger.warning( + "station group name %s already exists in file %s. Group not updated. Set `overwrite` to True to update the dataset.", + group_name, + json_path, + ) + return + + new_station = probes.create_group(group_name) + new_station.attrs["name"] = station_dict["NAME"] + new_station.attrs["ID"] = station_dict["ID"] + new_station.attrs["mnet_id"] = station_dict["MNET_ID"] + new_station.attrs["state"] = station_dict["STATE"] + new_station.attrs["timezone"] = station_dict["TIMEZONE"] + new_station.attrs["position_lat"] = station_dict["LATITUDE"] + new_station.attrs["position_lon"] = station_dict["LONGITUDE"] + new_station.attrs["position_alt"] = station_dict["ELEVATION"] + new_station.attrs["position_lat_units"] = "degree" + new_station.attrs["position_lon_units"] = "degree" + new_station.attrs["position_alt_units"] = station_dict["UNITS"]["elevation"] + new_station.attrs["license"] = "/DATA_LICENSES/Synoptic.txt" + new_station.attrs["data_use_restrictions"] = "No commercial use allowed" + new_station.attrs["public_access_level"] = "Restricted" + new_station.attrs["redistribution_allowed"] = False + new_station.attrs["source_file_sha256"] = sha_source_file + if "ELEV_DEM" in station_dict: + if station_dict["ELEV_DEM"] is not None: + new_station.attrs["elevation_dem"] = station_dict["ELEV_DEM"] + new_station.attrs["elevation_dem_units"] = station_dict["UNITS"]["elevation"] + + for var in station_dict["OBSERVATIONS"]: + if var == "date_time": + time_iso = [] + for t in station_dict["OBSERVATIONS"]["date_time"]: + dt = datetime.strptime(t, "%Y%m%d%H%M%S") + time_iso.append(datetime_to_iso8601(dt, True, tz=station_dict["TIMEZONE"])) + + new_var = new_station.create_dataset( + svn.TIME.value, data=time_iso, **hdf5plugin.Zstd(clevel=compression_lvl) + ) + else: + if var in variable_conversion: + logger.info("> processing %s", var) + var_data = np.array( + station_dict["OBSERVATIONS"][var], dtype=variable_conversion[var]["dtype"] + ) + new_var = new_station.create_dataset( + variable_conversion[var]["std_name"], + data=var_data, + **hdf5plugin.Zstd(clevel=compression_lvl), + ) + new_var.attrs["units"] = variable_conversion[var]["units"] + else: + logger.warning( + "standardize_synoptic_raws_from_json: variable %s from station %s not processed. Add the variable to `variable_conversion` to process it.", + var, + station_dict["STID"], + ) diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 6be3e6b..54918ef 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -479,6 +479,7 @@ def import_tif_with_rect_box( return np.array(lat), np.array(lon), np.array(data_dict["data"]), tgt_crs, data_dict["nodata"] + def import_tif( geotiff_path: str, projection: str = None, @@ -628,4 +629,4 @@ def _copy_attributes( if key in dst_obj.attrs: # Keep existing attr value continue - dst_obj.attrs[key] = value \ No newline at end of file + dst_obj.attrs[key] = value diff --git a/src/firebench/tools/namespace.py b/src/firebench/tools/namespace.py index 29986a4..924dc86 100644 --- a/src/firebench/tools/namespace.py +++ b/src/firebench/tools/namespace.py @@ -85,10 +85,12 @@ class StandardVariableNames(Enum): # pragma: no cover RAVG_COMPOSITE_BURN_INDEX_SEVERITY = "ravg_composite_burn_index_severity" RAVG_LIVE_BASAL_AREA_LOSS = "ravg_live_basal_area_loss" RELATIVE_HUMIDITY = "relative_humidity" + SOLAR_RADIATION = "solar_radiation" SLOPE_ANGLE = "slope_angle" TEMPERATURE = "temperature" TIME = "time" WIND_DIRECTION = "wind_direction" + WIND_GUST = "wind_gust" WIND_SPEED = "wind_speed" WIND_SPEED_U = "wind_speed_u" WIND_SPEED_V = "wind_speed_v" From 6b186c60fa58ef9979fef8880e95f76ded44f7b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 12 Dec 2025 16:46:39 -0800 Subject: [PATCH 055/111] add compression for ravg --- src/firebench/standardize/ravg.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/firebench/standardize/ravg.py b/src/firebench/standardize/ravg.py index cffc5a3..8389799 100644 --- a/src/firebench/standardize/ravg.py +++ b/src/firebench/standardize/ravg.py @@ -17,6 +17,7 @@ def standardize_ravg_cc_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, + compression_lvl: int = 3, ): """ Convert a RAVG GeoTIFF to FireBench HDF5 Standard for Canopy Cover Loss @@ -52,6 +53,7 @@ def standardize_ravg_cc_from_geotiff( projection, overwrite, invert_y, + compression_lvl, ) @@ -64,6 +66,7 @@ def standardize_ravg_cbi_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, + compression_lvl: int = 3, ): """ Convert a RAVG GeoTIFF to FireBench HDF5 Standard for Composite Burn Index Severity @@ -99,6 +102,7 @@ def standardize_ravg_cbi_from_geotiff( projection, overwrite, invert_y, + compression_lvl, ) @@ -111,6 +115,7 @@ def standardize_ravg_ba_from_geotiff( projection: str = None, overwrite: bool = False, invert_y: bool = False, + compression_lvl: int = 3, ): """ Convert a RAVG GeoTIFF to FireBench HDF5 Standard for Live Basal Area loss @@ -146,6 +151,7 @@ def standardize_ravg_ba_from_geotiff( projection, overwrite, invert_y, + compression_lvl, ) @@ -225,4 +231,5 @@ def _standardize_ravg_from_geotiff( ravg_variable, data=ravg_data, dtype=np.uint8, **hdf5plugin.Zstd(clevel=compression_lvl) ) ddata.attrs["units"] = "dimensionless" - ddata.attrs["_FillValue"] = nodata + if nodata is not None: + ddata.attrs["_FillValue"] = nodata From 048c3bbe91dc9e75724bb8745d79a11383247b5a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 12 Dec 2025 16:46:58 -0800 Subject: [PATCH 056/111] refactor std file merge to enable compression --- src/firebench/standardize/files.py | 19 +++- src/firebench/standardize/tools.py | 138 +++++++++++++++++++---------- 2 files changed, 106 insertions(+), 51 deletions(-) diff --git a/src/firebench/standardize/files.py b/src/firebench/standardize/files.py index 809b077..920f776 100644 --- a/src/firebench/standardize/files.py +++ b/src/firebench/standardize/files.py @@ -41,6 +41,7 @@ def merge_two_std_files( filepath_target: str, merged_description: str = "", overwrite: bool = False, + compression_lvl: int = 3, ): """ Try to merge two std FireBench files @@ -49,7 +50,13 @@ def merge_two_std_files( Then merge the list of authors without duplicates. Keep order as much as possible (first authors from file1 then first author from file2 then second from file 1...) """ - logger.debug("merge_two_std_files: merge %s with %s into %s", filepath_1, filepath_2, filepath_target) + logger.info( + "merge_two_std_files: merge %s with %s into %s with compression level %s", + filepath_1, + filepath_2, + filepath_target, + compression_lvl, + ) file1 = h5py.File(filepath_1, "r") validate_h5_std(file1) file2 = h5py.File(filepath_2, "r") @@ -68,7 +75,7 @@ def merge_two_std_files( # Create the new file merged_file = new_std_file(filepath_target, authors=merged_authors, overwrite=overwrite) - merge_trees(file1, file2, merged_file) + merge_trees(file1, file2, merged_file, compression_lvl=compression_lvl) merged_file.attrs["description"] = merged_description @@ -85,6 +92,7 @@ def merge_std_files( filepath_target: str, merged_description: str = "", overwrite: bool = False, + compression_lvl: int = 3, ): if not filespath: raise ValueError("filespath must contain at least one file") @@ -118,6 +126,8 @@ def merge_std_files( if is_last: out_path = target_path + compression_lvl_used = compression_lvl + overwrite_used = overwrite else: # Alternate between tmp1 and tmp2, ensuring out_path != current_path if current_path == tmp1: @@ -127,13 +137,16 @@ def merge_std_files( else: # First time we merge, we can pick any temp file out_path = tmp1 + compression_lvl_used = 1 # minimal compression for tmp files + overwrite_used = True merge_two_std_files( filepath_1=str(current_path), filepath_2=str(next_path), filepath_target=str(out_path), merged_description=merged_description, - overwrite=True, # safe: we control these temp/target files here + overwrite=overwrite_used, + compression_lvl=compression_lvl_used, ) current_path = out_path diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 54918ef..95c3aaa 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -1,6 +1,5 @@ import h5py -from ..tools.logging_config import logger -from ..tools.units import ureg +import hdf5plugin import re import numpy as np from pathlib import Path @@ -8,6 +7,8 @@ from pyproj import CRS, Transformer from rasterio.windows import from_bounds from rasterio.warp import transform_bounds +from ..tools.logging_config import logger +from ..tools.units import ureg VERSION_STD = "0.2" @@ -325,7 +326,7 @@ def merge_trees( file1: h5py.File, file2: h5py.File, merged_file: h5py.File, - conflict_solver: dict | None = None, + compression_lvl: int = 3, ) -> None: """ Recursively fill `merged_file` with content from `file1` and `file2`. @@ -350,8 +351,7 @@ def merge_trees( src=file1, dst=merged_file, path="/", - conflict_solver={}, # nothing to resolve, just copy - overwrite_existing=False, + compression_lvl=compression_lvl, ) # 2) merge from file2, now conflict_solver may apply @@ -359,8 +359,7 @@ def merge_trees( src=file2, dst=merged_file, path="/", - conflict_solver={}, - overwrite_existing=True, + compression_lvl=compression_lvl, ) @@ -536,83 +535,126 @@ def import_tif( return np.array(lat), np.array(lon), np.array(data_dict["data"]), tgt_crs, data_dict["nodata"] +def copy_entire_object_zstd( + src_parent: h5py.Group, + dst_parent: h5py.Group, + name: str, + compression_lvl: int, +) -> None: + """ + Recursively copy `name` from src_parent to dst_parent. + + Policy: + - Groups: + * If missing in dst -> create + * If already exists -> copy only new attrs via _copy_attributes + * Recurse into children + - Datasets: + * If missing -> create with Zstd(clevel=compression_lvl), load ONE dataset at a time + * If exists -> raise + - Links/other: skipped + """ + src_obj = src_parent.get(name, getlink=False) + if src_obj is None: + return + + if isinstance(src_obj, h5py.Dataset): + if name in dst_parent: + # Keep strict behavior by default + raise ValueError( + f"Conflict detected: dataset already exists at {dst_parent.name.rstrip('/')}/{name}" + ) + + dset = dst_parent.create_dataset( + name, + data=src_obj[...], + dtype=src_obj.dtype, + **hdf5plugin.Zstd(clevel=compression_lvl), + ) + _copy_attributes(src_obj, dset, f"{dst_parent.name.rstrip('/')}/{name}") + return + + if isinstance(src_obj, h5py.Group): + if name in dst_parent: + dst_obj = dst_parent[name] + if not isinstance(dst_obj, h5py.Group): + raise ValueError( + f"Conflict detected: expected group at {dst_parent.name.rstrip('/')}/{name}" + ) + grp = dst_obj + else: + grp = dst_parent.create_group(name) + + _copy_attributes(src_obj, grp, f"{dst_parent.name.rstrip('/')}/{name}") + + # recurse + for child_name in src_obj.keys(): + copy_entire_object_zstd( + src_parent=src_obj, + dst_parent=grp, + name=child_name, + compression_lvl=compression_lvl, + ) + return + + # SoftLink/ExternalLink/etc: skip + return + + def _merge_from_source( src: h5py.Group, dst: h5py.Group, path: str, - conflict_solver: dict, - overwrite_existing: bool, + compression_lvl: int, ) -> None: """ Recursive helper. - Parameters - ---------- - src : h5py.Group or h5py.File - Source root/node. - dst : h5py.Group or h5py.File - Destination root/node. - path : str - Absolute HDF5 path in src (and corresponding path in dst). - conflict_solver : dict - Mapping path -> solver(existing_data, incoming_data) -> resolved_data. - overwrite_existing : bool - If False, never touch existing objects (used for file1). - If True, apply conflict_solver or keep existing by default (used for file2). + - Merges attributes at `path` via _copy_attributes (only new attrs). + - For children: + * if missing in dst: copy entire subtree with Zstd + * if exists: + - if both are groups: recurse (at any depth) + - otherwise: strict conflict (raise) """ src_obj = src[path] - dst_obj = dst[path] # must exist; for root "/" this is the file itself + dst_obj = dst[path] # must exist for current node # Copy/merge attributes for this object (group or dataset) _copy_attributes(src_obj, dst_obj, path) if isinstance(src_obj, h5py.Dataset): - # Nothing more to recurse into return if not isinstance(src_obj, h5py.Group): - # ignore exotic node types for now (SoftLink, ExternalLink, etc.) return - # src_obj is a Group: iterate over its children - for name, child in src_obj.items(): - if path == "/": - child_path = f"/{name}" - else: - child_path = f"{path.rstrip('/')}/{name}" + for name in src_obj.keys(): + child_path = f"/{name}" if path == "/" else f"{path.rstrip('/')}/{name}" if name not in dst_obj: - # Child does not exist yet in dst: copy entire subtree - _copy_entire_object(src_obj, dst_obj, name) + copy_entire_object_zstd( + src_parent=src_obj, + dst_parent=dst_obj, + name=name, + compression_lvl=compression_lvl, + ) else: - # Child already exists in dst: possible conflict src_child = src_obj[name] dst_child = dst_obj[name] - if path == "/" and isinstance(src_child, h5py.Group) and isinstance(dst_child, h5py.Group): - # Recurse into / and merge there + if isinstance(src_child, h5py.Group) and isinstance(dst_child, h5py.Group): _merge_from_source( src=src, dst=dst, path=child_path, - conflict_solver=conflict_solver, - overwrite_existing=overwrite_existing, + compression_lvl=compression_lvl, ) else: - # Deeper levels (path != "/") or non-group conflicts: - # keep strict behavior and raise. logger.error("_merge_from_source: conflict at path %s for %s", path, name) raise ValueError("Conflict detected. Merge stopped") -def _copy_entire_object(src_parent: h5py.Group, dst_parent: h5py.Group, name: str) -> None: - """ - Copy a group or dataset `name` from src_parent to dst_parent, including - all attributes and children (for groups). - """ - src_parent.copy(name, dst_parent, name=name) - - def _copy_attributes( src_obj: h5py.Dataset | h5py.Group, dst_obj: h5py.Dataset | h5py.Group, path: str ) -> None: From 911f7ba457ae5fbc43708febe25051d014ddb7f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Fri, 12 Dec 2025 17:20:34 -0800 Subject: [PATCH 057/111] change logging lvl for synoptic variable processing --- src/firebench/standardize/synoptic.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/firebench/standardize/synoptic.py b/src/firebench/standardize/synoptic.py index 34f8f35..3181aa4 100644 --- a/src/firebench/standardize/synoptic.py +++ b/src/firebench/standardize/synoptic.py @@ -113,7 +113,7 @@ def standardize_synoptic_raws_from_json( ) else: if var in variable_conversion: - logger.info("> processing %s", var) + logger.debug("> processing %s", var) var_data = np.array( station_dict["OBSERVATIONS"][var], dtype=variable_conversion[var]["dtype"] ) From 21a47759c2cf3a8a9379f27bf04b48fd642644f2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sat, 13 Dec 2025 20:19:10 -0800 Subject: [PATCH 058/111] add Acknowledgment section to Caldor benchmark - add all the data section in table of content --- docs/benchmarks/California/01_Caldor.md | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index f97f698..1f43f72 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -1,9 +1,9 @@ # 2021 Caldor Fire -**Version**: 1.0a
    +**Version**: 2026.0a
    **Case ID**: FB001
    **FireBench IO std version**: >= 0.2
    -**Date of last update**: 11/19/2025 +**Date of last update**: 12/19/2025 ## Contributors - Aurélien Costes, [Wildfire Interdisciplinary Research Center](https://www.wildfirecenter.org/), San Jose State University, [aurelien.costes@sjsu.edu](mailto:aurelien.costes@sjsu.edu), [ORCID](https://orcid.org/0000-0003-4543-5107) @@ -12,12 +12,13 @@ This collection of benchmarks uses the public ressources about the 2021 Caldor Fire. It contains observation datasets for: -- Nearby weather stations (nb of associated benchmarks) -- Infrared fire perimeters -- Burn severity -- Building damaged -- (Plume top height) -- (Smoke concentration ground) +- Building damaged (CALFIRE) +- Burn severity (MTBS) +- Burn severity (RAVG) +- Canopy cover loss (RAVG) +- Infrared fire perimeters (NIROPS) +- Live basal area change (RAVG) +- Weather stations (Synoptic) ## Buildings damage benchmarks @@ -279,4 +280,8 @@ Burn Severity | 1 | | ## Notes -- Each file hash has been performed using `firebench.standardize.calculate_sha256`. \ No newline at end of file +- Each file hash has been performed using `firebench.standardize.calculate_sha256`. + +## Acknowledgment + +We gratefully acknowledge Synoptic for granting permission to redistribute selected weather-station data as part of the FireBench benchmarking framework. \ No newline at end of file From 69d86867828fe7f68e791708503e8d973c639255 Mon Sep 17 00:00:00 2001 From: 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for: - Live basal area change (RAVG) - Weather stations (Synoptic) -## Buildings damage benchmarks +## Buildings damage ### Dataset @@ -134,7 +134,7 @@ This benchmark is performed on the binary classes for damaged buildings. This benchmark is performed on the binary classes for damaged buildings. -## Burn severity benchmarks +## Burn severity from MTBS ### Dataset @@ -226,6 +226,55 @@ This benchmark is performed on the binary classes for high severity points (Bina **Name in Score Card**: Binary High Severity F1 Score
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) +## Burn Severity from RAVG +### Dataset + +## Canopy cover loss +### Dataset + +## Infrared fire perimeters +### Dataset + +The infrared fire perimeters have been gathered from [NIROPS](https://ftp.wildfire.gov/public/incident_specific_data/calif_n/2021_FEDERAL_Incidents/CA-ENF-024030_Caldor/IR/NIROPS/) dataset. +Every orginal file has been manually processed to extract only the perimeter. The time stamp of the perimeter has been defined from the imaging report (e.g. [Report for 2021/08/17](https://ftp.wildfire.gov/public/incident_specific_data/calif_n/2021_FEDERAL_Incidents/CA-ENF-024030_Caldor/IR/NIROPS/20210818/20210818_Caldor_IR_Topo_11x17.pdf)) using the `Imagery Date` and `Imagery Time`. The burn area obtained using the KML file and python tools has been verified against the `Interpreted Acreage` when specified in the reports. Each fire perimeter (see Fig. 6) is stored as a group within the HDF5 data file with an attributes containing the path of the KML file that contains the fire perimeter dataset. +The perimeters have been processed from August 17th (first IR perimeter available) to September 10th, when the burn area is 99% if the final burn area, as shown in Figure 7 (source: [CALFIRE](https://www.fire.ca.gov/incidents/2021/8/14/caldor-fire/)). +The final dataset contains 21 perimeters. + +The following study periods (see Fig. 7) are defined: +- `W1`: from August 17th 20h20 PDT to September 10th 23h34 PDT, for a total duration of 24 days, 3h, and 14 min. +- `W2`: from August 19th 20h45 PDT to August 21st 21h15 PDT, for a total duration of 2 days, 0h and 30 min. +- `W3`: from August 26th 02h30 PDT to August 28th 20h30 PDT, for a total duration of 2 days, 18h and 0 min. +- `W4`: from August 28th 20h30 PDT to September 3rd 00h40 PDT, for a total duration of 5 days, 4h and 10 min. + +Figure 6 shows teach processed fire perimeter as a colored solid contour. The color of the contour displays the time stamp of the perimeter. + +![blockdiagram](../../_static/benchmarks/FB001/Caldor_perimeters.png) +

    + + Fig. 6 + + : + + Infrared fire perimeters from August 17th to September 10th. + +

    + +Figure 6 shows teach processed fire perimeter as a colored solid contour. The color of the contour displays the time stamp of the perimeter. The study periods are shown as colored rectangle at the bottom of the plot. + +![blockdiagram](../../_static/benchmarks/FB001/Caldor_burnt_area.png) +

    + + Fig. 7 + + : + + Burn area derived from IR perimeters from August 17th to September 10th. Red dashed line shows the final burn area from CALFIRE. The orange dashed line shows the final burn area from MTBS final perimeter. + +

    + +### Benchmarks + + ## Requirements The following sections list the datasets requirements to run the different benchmarks. When the benchmark script is run, each requirement is validated on the HDF5 file passed as input (from the model ouput/data the user wants to evaluate). If a requirement is met, each corresponding benchmark is run. @@ -281,6 +330,7 @@ Burn Severity | 1 | | ## Notes - Each file hash has been performed using `firebench.standardize.calculate_sha256`. +- Collection of forecasts or reanalysis are authorized for benchmarks period (e.g. for fire perimeters) but have to be detailed in the model report attached to the report sent back to FireBench team for collection and validation of results. ## Acknowledgment From 137ea92c4a88c6fc813cba221de27ca1184fa74a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sat, 13 Dec 2025 21:42:11 -0800 Subject: [PATCH 060/111] update changlog --- docs/changelog.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/changelog.md b/docs/changelog.md index 2cae06e..d40f323 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,4 +1,4 @@ -# 11. Changelog +# 12. Changelog All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), From 1634652ad81a639e62df4df2f660c5b02045a51b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sat, 13 Dec 2025 22:06:06 -0800 Subject: [PATCH 061/111] add kpi normalizations - kpi_norm_symmetric_open_linear - kpi_norm_symmetric_open_exponential --- docs/metrics/normalization.md | 39 ++++++++++- src/firebench/metrics/kpi_normalization.py | 76 +++++++++++++++++++++- 2 files changed, 110 insertions(+), 5 deletions(-) diff --git a/docs/metrics/normalization.md b/docs/metrics/normalization.md index e456b9b..356b309 100644 --- a/docs/metrics/normalization.md +++ b/docs/metrics/normalization.md @@ -34,7 +34,7 @@ Here, For KPIs with a minimum acceptable value $a$ and domain $[a, +\infty[$, we define an exponentially decaying score: $$ -\mathcal N(x, a, m) =100 \, \exp \left( - \frac{\ln 2(x-a)}{m-a} \right). +\mathcal N(x, a, m) =100 \, \exp \left( - \frac{\ln 2 \, (x-a)}{m-a} \right). $$ This formulation ensures: @@ -48,4 +48,39 @@ Thus, $m$ is the KPI value at which the score is exactly **50**. Here, - $a$ corresponds to the **best** possible score (100), - $m$ corresponds to the value at which the score has decreased to **50**, -- scores decay smoothly toward 0 as $x \to +\infty$. \ No newline at end of file +- scores decay smoothly toward 0 as $x \to +\infty$. + +## Symmetric Linear Open Normalization + +For KPIs that have a symmetry around 0 but no finite limit, i.e. values in $[-\infty, +\infty[$, we define: + +$$ +\mathcal N(x, m) = 100 \, \max \left(0, 1 - \frac{|x|}{m} \right) +$$ + +where $m > a$ is a parameter specifying the value of $x$ at which the score reaches **0**. + +Here, +- $a$ corresponds to the **best** score (100), +- $m$ corresponds to the **worst** score (0). + +## Symmetric Exponential Open Normalization + +For KPIs that have a symmetry around 0 but no finite limit, i.e. values in $[-\infty, +\infty[$, we define: + +$$ +\mathcal N(x, m) =100 \, \exp \left( - \frac{\ln 2 \, |x|}{m} \right). +$$ + +This formulation ensures: + +$$ +\mathcal N(0,m) = 100, \qquad +\mathcal N(\pm m,m) = 50. +$$ + +Thus, $m$ is the KPI value at which the score is exactly **50**. +Here, +- $0$ corresponds to the **best** possible score (100), +- $m$ corresponds to the value at which the score has decreased to **50**, +- scores decay smoothly toward 0 as $x \to \pm\infty$. \ No newline at end of file diff --git a/src/firebench/metrics/kpi_normalization.py b/src/firebench/metrics/kpi_normalization.py index ec934cc..dd49647 100644 --- a/src/firebench/metrics/kpi_normalization.py +++ b/src/firebench/metrics/kpi_normalization.py @@ -40,7 +40,7 @@ def kpi_norm_bounded_linear(x, a, b): def kpi_norm_half_open_linear(x, a, m): """ - Linearly normalize a KPI defined on the half-open interval [a, ∞). + Linearly normalize a KPI defined on the half-open interval [a, \infty). This function applies a linear decay starting from: - x = a -> score = 100 @@ -80,12 +80,12 @@ def kpi_norm_half_open_linear(x, a, m): def kpi_norm_half_open_exponential(x, a, m): """ - Exponentially normalize a KPI defined on the half-open interval [a, ∞). + Exponentially normalize a KPI defined on the half-open interval [a, \infty). This function applies a smooth exponential decay such that: - x = a -> score = 100 - x = m -> score = 50 - - x -> ∞ -> score -> 0 + - x -> \infty -> score -> 0 ensuring a monotonic and asymptotic decline. Use this normalization when the KPI has a minimum acceptable value `a` @@ -118,3 +118,73 @@ def kpi_norm_half_open_exponential(x, a, m): if m <= a: raise ValueError(f"Parameter m ({m}) smaller than lower limit a ({a})") return 100.0 * exp(-log(2) * (x - a) / (m - a)) + + +def kpi_norm_symmetric_open_linear(x, m): + """ + Linearly normalize a KPI defined on the open interval (-\infty, \infty). + + This function applies a smooth exponential decay such that: + - x = 0 -> score = 100 + - |x| >= m -> score = 0 + + Use this normalization when deviations should be penalized linearly. + + Parameters + ---------- + x : float + KPI value to normalize. + m : float + Value at which the score reaches 0. Must satisfy m > 0. + + Returns + ------- + float + Normalized score in the range (0, 100], dlinear decay from + 100 at `a` to 0 at `m`. + + Raises + ------ + ValueError + If `m <= 0`. + """ + if m <= 0: + raise ValueError(f"Parameter m ({m}) smaller than lower limit 0") + return 100.0 * max(0, 1 - abs(x) / m) + + +def kpi_norm_symmetric_open_exponential(x, m): + """ + Exponentially normalize a KPI defined on the open interval (-\infty, \infty). + + This function applies a smooth exponential decay such that: + - x = 0 -> score = 100 + - x = +/- m -> score = 50 + - x -> \infty -> score -> 0 + ensuring a monotonic and asymptotic decline. + + Use this normalization when deviations should be + penalized progressively rather than linearly. The parameter `m` defines + the characteristic decay scale at which performance is reduced by half. + + Parameters + ---------- + x : float + KPI value to normalize. + m : float + Value at which the score reaches 50. Must satisfy m > 0. + + Returns + ------- + float + Normalized score in the range (0, 100], decaying exponentially + from 100 at `0` toward 0 as `x` increases. + + Raises + ------ + ValueError + If `m <= 0`. + """ + if m <= 0: + raise ValueError(f"Parameter m ({m}) smaller than lower limit 0") + return 100.0 * exp(-log(2) * abs(x) / m) From 85bec9fa3b271accdeacbae02f0929ef67402b5b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 10:11:02 -0800 Subject: [PATCH 062/111] add benchmarks for fire perimeter to Caldor doc --- docs/benchmarks/California/01_Caldor.md | 193 +++++++++++++++++++++--- 1 file changed, 175 insertions(+), 18 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 26faa25..5b08d02 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -15,7 +15,10 @@ It contains observation datasets for: - Building damaged (CALFIRE) - Burn severity (MTBS) - Burn severity (RAVG) +- Canopy bottom height (LANDFIRE) +- Canopy bulk density (LANDFIRE) - Canopy cover loss (RAVG) +- Canopy height (LANDFIRE) - Infrared fire perimeters (NIROPS) - Live basal area change (RAVG) - Weather stations (Synoptic) @@ -50,7 +53,7 @@ Figure 1 shows the spatial distribution of building damage for the Caldor Fire.

    -Figure 2 shows the distribution of building damage for the Caldor Fire. The following table shows the number of strucure in each damage category. +Figure 2 shows the distribution of building damage for the Caldor Fire. The following Table shows the number of strucure in each damage category. Damage category | Counts [-] ---------------------- | ----------------- No Damage | 3356 @@ -91,43 +94,49 @@ In order to performe some calculations, the building damaged classes can be aggr See Key Performance Indicator (KPI) and normalization defintions [here](../../metrics/index.md). -#### FB001-BD01 +#### Binary Structure Loss Accuracy +**IDs**: FB001-BD01
    **KPI**: Binary Structure Loss Accuracy
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Accuracy
    This benchmark is performed on the binary classes for damaged buildings. -#### FB001-BD02 +#### Binary Structure Loss Precision +**IDs**: FB001-BD02
    **KPI**: Binary Structure Loss Precision
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Precision
    This benchmark is performed on the binary classes for damaged buildings. -#### FB001-BD03 +#### Binary Structure Loss Recall +**IDs**: FB001-BD03
    **KPI**: Binary Structure Loss Recall
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Recall
    This benchmark is performed on the binary classes for damaged buildings. -#### FB001-BD04 +#### Binary Structure Loss Specificity +**IDs**: FB001-BD04
    **KPI**: Binary Structure Loss Specificity
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Specificity
    This benchmark is performed on the binary classes for damaged buildings. -#### FB001-BD05 +#### Binary Structure Loss Negative Predictive Value +**IDs**: FB001-BD05
    **KPI**: Binary Structure Loss Negative Predictive Value
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Negative Predictive Value
    This benchmark is performed on the binary classes for damaged buildings. -#### FB001-BD06 +#### Binary Structure Loss F1 Score +**IDs**: FB001-BD06
    **KPI**: Binary Structure Loss F1 Score
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss F1 Score
    @@ -184,43 +193,49 @@ To perform the high severity benchmarks using binary confusion matrix, we constr See Key Performance Indicator (KPI) and normalization defintions [here](../../metrics/index.md). -#### FB001-SV01 +#### Binary High Severity Accuracy +**IDs**: FB001-SV01
    **KPI**: Binary High Severity Accuracy
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Accuracy
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) -#### FB001-SV02 +#### Binary High Severity Precision +**IDs**: FB001-SV02
    **KPI**: Binary High Severity Precision
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Precision
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) -#### FB001-SV03 +#### Binary High Severity Recall +**IDs**: FB001-SV03
    **KPI**: Binary High Severity Recall
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Recall
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) -#### FB001-SV04 +#### Binary High Severity Specificity +**IDs**: FB001-SV04
    **KPI**: Binary High Severity Specificity
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Specificity
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) -#### FB001-SV05 +#### Binary High Severity Negative Predictive Value +**IDs**: FB001-SV05
    **KPI**: Binary High Severity Negative Predictive Value
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Negative Predictive Value
    This benchmark is performed on the binary classes for high severity points (Binary High severity processed variable) -#### FB001-SV06 +#### Binary High Severity F1 Score +**IDs**: FB001-SV06
    **KPI**: Binary High Severity F1 Score
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity F1 Score
    @@ -240,11 +255,14 @@ Every orginal file has been manually processed to extract only the perimeter. Th The perimeters have been processed from August 17th (first IR perimeter available) to September 10th, when the burn area is 99% if the final burn area, as shown in Figure 7 (source: [CALFIRE](https://www.fire.ca.gov/incidents/2021/8/14/caldor-fire/)). The final dataset contains 21 perimeters. -The following study periods (see Fig. 7) are defined: -- `W1`: from August 17th 20h20 PDT to September 10th 23h34 PDT, for a total duration of 24 days, 3h, and 14 min. -- `W2`: from August 19th 20h45 PDT to August 21st 21h15 PDT, for a total duration of 2 days, 0h and 30 min. -- `W3`: from August 26th 02h30 PDT to August 28th 20h30 PDT, for a total duration of 2 days, 18h and 0 min. -- `W4`: from August 28th 20h30 PDT to September 3rd 00h40 PDT, for a total duration of 5 days, 4h and 10 min. +The following study periods (see Fig. 7) are defined in the following Table: + +Name | Start time | End time | Duration | Burn area [acre] +-----|-------------------|--------------------|---------------|----------------- +W1 | Aug 17 20h20 PDT | Sep 10 23h34 PDT | 24d 3h 14min | 166,256 +W2 | Aug 19 20h45 PDT | Aug 21 21h15 PDT | 2d 0h 30min | 24,941 +W3 | Aug 26 02h30 PDT | Aug 28 20h30 PDT | 2d 18h 0min | 19,992 +W4 | Aug 28 20h30 PDT | Sep 3 00h40 PDT | 5d 4h 10min | 56,272 Figure 6 shows teach processed fire perimeter as a colored solid contour. The color of the contour displays the time stamp of the perimeter. @@ -274,6 +292,145 @@ Figure 6 shows teach processed fire perimeter as a colored solid contour. The co ### Benchmarks +See Key Performance Indicator (KPI) and normalization defintions [here](../../metrics/index.md). + +#### Average Jaccard Index over study period + +**IDs**: See Table
    +**KPI**: Average Jaccard Index
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card +-----|--------------|------------------- +FP01 | W1 | Average Jaccard Index W1 +FP02 | W2 | Average Jaccard Index W2 +FP03 | W3 | Average Jaccard Index W3 +FP04 | W4 | Average Jaccard Index W4 + +#### Minimum Jaccard Index over study period + +**IDs**: See Table
    +**KPI**: Minimum Jaccard Index
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card +-----|--------------|------------------- +FP05 | W1 | Minimum Jaccard Index W1 +FP06 | W2 | Minimum Jaccard Index W2 +FP07 | W3 | Minimum Jaccard Index W3 +FP08 | W4 | Minimum Jaccard Index W4 + +#### Maximum Jaccard Index over study period + +**IDs**: See Table
    +**KPI**: Maximum Jaccard Index
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card +-----|--------------|------------------- +FP09 | W1 | Minimum Jaccard Index W1 +FP10 | W2 | Minimum Jaccard Index W2 +FP11 | W3 | Minimum Jaccard Index W3 +FP12 | W4 | Minimum Jaccard Index W4 + +#### Average Dice-Sorensen Index over study period + +**IDs**: See Table
    +**KPI**: Average Dice-Sorensen Index
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card +-----|--------------|------------------- +FP13 | W1 | Average Dice-Sorensen Index W1 +FP14 | W2 | Average Dice-Sorensen Index W2 +FP15 | W3 | Average Dice-Sorensen Index W3 +FP16 | W4 | Average Dice-Sorensen Index W4 + +#### Minimum Dice-Sorensen Index over study period + +**IDs**: See Table
    +**KPI**: Minimum Dice-Sorensen Index
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card +-----|--------------|------------------- +FP17 | W1 | Minimum Dice-Sorensen Index W1 +FP18 | W2 | Minimum Dice-Sorensen Index W2 +FP19 | W3 | Minimum Dice-Sorensen Index W3 +FP20 | W4 | Minimum Dice-Sorensen Index W4 + +#### Maximum Dice-Sorensen Index over study period + +**IDs**: See Table
    +**KPI**: Maximum Dice-Sorensen Index
    +**Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card +-----|--------------|------------------- +FP21 | W1 | Minimum Dice-Sorensen Index W1 +FP22 | W2 | Minimum Dice-Sorensen Index W2 +FP23 | W3 | Minimum Dice-Sorensen Index W3 +FP24 | W4 | Minimum Dice-Sorensen Index W4 + +#### Burn Area Bias + +**IDs**: See Table
    +**KPI**: Burn Area Bias
    +**Normalization**: Symmetric Exponential Open Normalization ($m$ value in Table)
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. +A bias of $m$ acre, representing $B_{50}$% of burn area during the study period, will lead to a score of 50.00. The value of $m$ represents the benchmark difficulty (smaller $m$ means higher difficulty) and must be chosen by the community. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card | $m$ | $B_{50}$ +-----|--------------|--------------------|--------|--------- +FP25 | W1 | Burn Area Bias W1 | 80,000 | 48% +FP26 | W2 | Burn Area Bias W2 | 5,000 | 20% +FP27 | W3 | Burn Area Bias W3 | 5,000 | 25% +FP28 | W4 | Burn Area Bias W4 | 17,000 | 30% + +#### Burn Area RMSE + +**IDs**: See Table
    +**KPI**: Burn Area RMSE
    +**Normalization**: Symmetric Exponential Open Normalization ($m$ value in Table)
    +**Name in Score Card**: See Table
    +The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. +A bias of $m$ acre, representing $B_{50}$% of burn area during the study period, will lead to a score of 50.00. The value of $m$ represents the benchmark difficulty (smaller $m$ means higher difficulty) and must be chosen by the community. + +The following Table gives the correspondance between the bencmark id and the study period: + +ID | Study period | Name in Score Card | $m$ | $B_{50}$ +-----|--------------|--------------------|--------|--------- +FP29 | W1 | Burn Area RMSE W1 | 80,000 | 48% +FP30 | W2 | Burn Area RMSE W2 | 5,000 | 20% +FP31 | W3 | Burn Area RMSE W3 | 5,000 | 25% +FP32 | W4 | Burn Area RMSE W4 | 17,000 | 30% ## Requirements From 87f8c333841e7e9410946cdc8f6b6df92f281e37 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 10:39:35 -0800 Subject: [PATCH 063/111] add FP requirements for Caldor doc --- docs/benchmarks/California/01_Caldor.md | 103 +++++++++++++++++++----- 1 file changed, 81 insertions(+), 22 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 5b08d02..3238b32 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -96,7 +96,7 @@ See Key Performance Indicator (KPI) and normalization defintions [here](../../me #### Binary Structure Loss Accuracy -**IDs**: FB001-BD01
    +**Short IDs**: BD01
    **KPI**: Binary Structure Loss Accuracy
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Accuracy
    @@ -104,7 +104,7 @@ This benchmark is performed on the binary classes for damaged buildings. #### Binary Structure Loss Precision -**IDs**: FB001-BD02
    +**Short IDs**: BD02
    **KPI**: Binary Structure Loss Precision
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Precision
    @@ -112,7 +112,7 @@ This benchmark is performed on the binary classes for damaged buildings. #### Binary Structure Loss Recall -**IDs**: FB001-BD03
    +**Short IDs**: BD03
    **KPI**: Binary Structure Loss Recall
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Recall
    @@ -120,7 +120,7 @@ This benchmark is performed on the binary classes for damaged buildings. #### Binary Structure Loss Specificity -**IDs**: FB001-BD04
    +**Short IDs**: BD04
    **KPI**: Binary Structure Loss Specificity
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Specificity
    @@ -128,7 +128,7 @@ This benchmark is performed on the binary classes for damaged buildings. #### Binary Structure Loss Negative Predictive Value -**IDs**: FB001-BD05
    +**Short IDs**: BD05
    **KPI**: Binary Structure Loss Negative Predictive Value
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss Negative Predictive Value
    @@ -136,7 +136,7 @@ This benchmark is performed on the binary classes for damaged buildings. #### Binary Structure Loss F1 Score -**IDs**: FB001-BD06
    +**Short IDs**: BD06
    **KPI**: Binary Structure Loss F1 Score
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary Structure Loss F1 Score
    @@ -195,7 +195,7 @@ See Key Performance Indicator (KPI) and normalization defintions [here](../../me #### Binary High Severity Accuracy -**IDs**: FB001-SV01
    +**Short IDs**: SV01
    **KPI**: Binary High Severity Accuracy
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Accuracy
    @@ -203,7 +203,7 @@ This benchmark is performed on the binary classes for high severity points (Bina #### Binary High Severity Precision -**IDs**: FB001-SV02
    +**Short IDs**: SV02
    **KPI**: Binary High Severity Precision
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Precision
    @@ -211,7 +211,7 @@ This benchmark is performed on the binary classes for high severity points (Bina #### Binary High Severity Recall -**IDs**: FB001-SV03
    +**Short IDs**: SV03
    **KPI**: Binary High Severity Recall
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Recall
    @@ -219,7 +219,7 @@ This benchmark is performed on the binary classes for high severity points (Bina #### Binary High Severity Specificity -**IDs**: FB001-SV04
    +**Short IDs**: SV04
    **KPI**: Binary High Severity Specificity
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Specificity
    @@ -227,7 +227,7 @@ This benchmark is performed on the binary classes for high severity points (Bina #### Binary High Severity Negative Predictive Value -**IDs**: FB001-SV05
    +**Short IDs**: SV05
    **KPI**: Binary High Severity Negative Predictive Value
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity Negative Predictive Value
    @@ -235,7 +235,7 @@ This benchmark is performed on the binary classes for high severity points (Bina #### Binary High Severity F1 Score -**IDs**: FB001-SV06
    +**Short IDs**: SV06
    **KPI**: Binary High Severity F1 Score
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: Binary High Severity F1 Score
    @@ -296,7 +296,7 @@ See Key Performance Indicator (KPI) and normalization defintions [here](../../me #### Average Jaccard Index over study period -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Average Jaccard Index
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    @@ -313,7 +313,7 @@ FP04 | W4 | Average Jaccard Index W4 #### Minimum Jaccard Index over study period -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Minimum Jaccard Index
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    @@ -330,7 +330,7 @@ FP08 | W4 | Minimum Jaccard Index W4 #### Maximum Jaccard Index over study period -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Maximum Jaccard Index
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    @@ -347,7 +347,7 @@ FP12 | W4 | Minimum Jaccard Index W4 #### Average Dice-Sorensen Index over study period -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Average Dice-Sorensen Index
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    @@ -364,7 +364,7 @@ FP16 | W4 | Average Dice-Sorensen Index W4 #### Minimum Dice-Sorensen Index over study period -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Minimum Dice-Sorensen Index
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    @@ -381,7 +381,7 @@ FP20 | W4 | Minimum Dice-Sorensen Index W4 #### Maximum Dice-Sorensen Index over study period -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Maximum Dice-Sorensen Index
    **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    @@ -398,7 +398,7 @@ FP24 | W4 | Minimum Dice-Sorensen Index W4 #### Burn Area Bias -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Burn Area Bias
    **Normalization**: Symmetric Exponential Open Normalization ($m$ value in Table)
    **Name in Score Card**: See Table
    @@ -416,7 +416,7 @@ FP28 | W4 | Burn Area Bias W4 | 17,000 | 30% #### Burn Area RMSE -**IDs**: See Table
    +**Short IDs**: See Table
    **KPI**: Burn Area RMSE
    **Normalization**: Symmetric Exponential Open Normalization ($m$ value in Table)
    **Name in Score Card**: See Table
    @@ -441,8 +441,12 @@ The current version of FireBench does not integrate more complex checks (e.g., a Requirement | Benchmarks ---------------------- | ----------------- -R01 | FB001-BD01, FB001-BD02, FB001-BD03, FB001-BD04, FB001-BD05, FB001-BD06 -R02 | FB001-SV01, FB001-SV02, FB001-SV03, FB001-SV04, FB001-SV05, FB001-SV06 +R01 | BD01, BD02, BD03, BD04, BD05, BD06 +R02 | SV01, SV02, SV03, SV04, SV05, SV06 +R03 | FP01, FP05, FP09, FP13, FP17, FP21, FP25, FP29 +R04 | FP02, FP06, FP10, FP14, FP18, FP22, FP26, FP30 +R05 | FP03, FP07, FP11, FP15, FP19, FP23, FP27, FP31 +R06 | FP04, FP08, FP12, FP16, FP20, FP24, FP28, FP32 ### R01 Mandatory group/dataset| Mandatory attirbutes @@ -457,6 +461,60 @@ Mandatory group/dataset| Mandatory attirbutes `/2D_raster/Caldor_MTBS/position_lat`| units `/2D_raster/Caldor_MTBS/position_lon`| units +### R03 +Mandatory group/dataset| Mandatory attirbutes +---------------------- | -------------------- +`/polygons/Caldor_2021-08-18T20:30-07:00`| rel_path, time +`/polygons/Caldor_2021-08-19T20:45-07:00`| rel_path, time +`/polygons/Caldor_2021-08-20T20:20-07:00`| rel_path, time +`/polygons/Caldor_2021-08-21T21:15-07:00`| rel_path, time +`/polygons/Caldor_2021-08-24T22:07-07:00`| rel_path, time +`/polygons/Caldor_2021-08-26T03:30-06:00`| rel_path, time +`/polygons/Caldor_2021-08-26T22:15-06:00`| rel_path, time +`/polygons/Caldor_2021-08-27T00:22-06:00`| rel_path, time +`/polygons/Caldor_2021-08-28T21:30-06:00`| rel_path, time +`/polygons/Caldor_2021-08-29T22:32-07:00`| rel_path, time +`/polygons/Caldor_2021-08-30T21:09-07:00`| rel_path, time +`/polygons/Caldor_2021-08-31T21:08-07:00`| rel_path, time +`/polygons/Caldor_2021-09-01T21:12-07:00`| rel_path, time +`/polygons/Caldor_2021-09-03T00:40-07:00`| rel_path, time +`/polygons/Caldor_2021-09-04T23:29-07:00`| rel_path, time +`/polygons/Caldor_2021-09-05T23:41-07:00`| rel_path, time +`/polygons/Caldor_2021-09-06T23:09-07:00`| rel_path, time +`/polygons/Caldor_2021-09-07T22:40-07:00`| rel_path, time +`/polygons/Caldor_2021-09-08T22:33-07:00`| rel_path, time +`/polygons/Caldor_2021-09-10T23:34-07:00`| rel_path, time + +Files (KML) at path defined in `rel_path` attributes must exist. + +### R04 +Mandatory group/dataset| Mandatory attirbutes +---------------------- | -------------------- +`/polygons/Caldor_2021-08-20T20:20-07:00`| rel_path, time +`/polygons/Caldor_2021-08-21T21:15-07:00`| rel_path, time + +Files (KML) at path defined in `rel_path` attributes must exist. + +### R05 +Mandatory group/dataset| Mandatory attirbutes +---------------------- | -------------------- +`/polygons/Caldor_2021-08-26T22:15-06:00`| rel_path, time +`/polygons/Caldor_2021-08-27T00:22-06:00`| rel_path, time +`/polygons/Caldor_2021-08-28T21:30-06:00`| rel_path, time + +Files (KML) at path defined in `rel_path` attributes must exist. + +### R06 +Mandatory group/dataset| Mandatory attirbutes +---------------------- | -------------------- +`/polygons/Caldor_2021-08-29T22:32-07:00`| rel_path, time +`/polygons/Caldor_2021-08-30T21:09-07:00`| rel_path, time +`/polygons/Caldor_2021-08-31T21:08-07:00`| rel_path, time +`/polygons/Caldor_2021-09-01T21:12-07:00`| rel_path, time +`/polygons/Caldor_2021-09-03T00:40-07:00`| rel_path, time + +Files (KML) at path defined in `rel_path` attributes must exist. + ## Aggregation Schemes This section provides the weights used to aggregate KPI Unit Scores. More information about aggregation methods [here](../../metrics/index.md). If aggregation scheme `0` is specified, then no aggregation is performed. Therefore, group scores and total score are not computed. @@ -486,6 +544,7 @@ Burn Severity | 1 | | ## Notes +- **Benchmark identifiers** consist of a *case ID* and a *short ID*, for example `FB001-BD01`. Throughout the documentation, the *short ID* alone (e.g. `BD01`) is used when the benchmark case is unambiguous, in order to improve readability. The *full identifier* (`FB001-BD01`) is used whenever the case context must be explicit, such as when comparing benchmarks across different cases. - Each file hash has been performed using `firebench.standardize.calculate_sha256`. - Collection of forecasts or reanalysis are authorized for benchmarks period (e.g. for fire perimeters) but have to be detailed in the model report attached to the report sent back to FireBench team for collection and validation of results. From 929429c6d6d54dd4ebd23946917edd09f954b2dd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 15:45:36 -0800 Subject: [PATCH 064/111] refactor scheme description section - add schemes for fire perimeters --- docs/benchmarks/California/01_Caldor.md | 104 ++++++++++++++++++++---- 1 file changed, 87 insertions(+), 17 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 3238b32..701165b 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -517,30 +517,100 @@ Files (KML) at path defined in `rel_path` attributes must exist. ## Aggregation Schemes -This section provides the weights used to aggregate KPI Unit Scores. More information about aggregation methods [here](../../metrics/index.md). If aggregation scheme `0` is specified, then no aggregation is performed. Therefore, group scores and total score are not computed. +This section describes the weights used to aggregate KPI unit scores. More information about aggregation methods [here](../../metrics/score.md). If aggregation scheme `0` is specified, then no aggregation is performed. Therefore, group scores and total score are not computed. + +### Group definition + +All benchmarks have a default weight of 1 in each group. If custom weights are applied, refer to custom weight Table. + +Weight precedence: +- Default benchmark weight: 1 +- Group benchmark overrides: apply to all schemes unless overridden +- Scheme benchmark overrides: apply only within that scheme and override everything else + +Group | Benchmark ID +---------------------- | ------------ +Building Damage | BD01 to BD06 +Burn Severity | SV01 to SV06 +Fire Perimeter W1 | FP01, FP05, FP09, FP13, FP17, FP21, FP25, FP29 +Fire Perimeter W2 | FP02, FP06, FP10, FP14, FP18, FP22, FP26, FP30 +Fire Perimeter W3 | FP03, FP07, FP11, FP15, FP19, FP23, FP27, FP31 +Fire Perimeter W4 | FP04, FP08, FP12, FP16, FP20, FP24, FP28, FP32 + +#### Group benchmark override +The custom weight defined in the following Table overrides the default weight from the Group Definition Table. + +Group | Benchmark ID | Weight +---------------------- | ------------ | ------ +Fire Perimeter W1 | FP25, FP29 | 2 +Fire Perimeter W2 | FP26, FP30 | 2 +Fire Perimeter W3 | FP27, FP31 | 2 +Fire Perimeter W4 | FP28, FP32 | 2 ### Scheme A -Group | Group Weight | Benchmark ID | Benchmark weight ----------------------- | ------------ | ------------ | ---------------- -Building Damage | 1 | | -| | | BD01 to BD06 | 1 -Burn Severity | 1 | | -| | | SV01 to SV06 | 1 +Scheme A contains all the groups with default weights. It can be used to evaluate complete model performance with balanced weighting. + +Group | Group Weight +---------------------- | ------------ +Building Damage | 1 +Burn Severity | 1 +Fire Perimeter W1 | 1 +Fire Perimeter W2 | 1 +Fire Perimeter W3 | 1 +Fire Perimeter W4 | 1 + +## Scheme B + +Scheme B contains only the building damage group. It is used to evaluate the model only on building damage benchmarks. -### Scheme B +Group | Group Weight +---------------------- | ------------ +Building Damage | 1 -Group | Group Weight | Benchmark ID | Benchmark weight ----------------------- | ------------ | ------------ | ---------------- -Building Damage | 1 | | -| | | BD01 to BD06 | 1 +### Scheme FP + +Scheme FP contains only the fire perimeter groups. It is used to evaluate the model only on fire perimeter benchmarks for all of the study periods. + +Group | Group Weight +---------------------- | ------------ +Fire Perimeter W1 | 1 +Fire Perimeter W2 | 1 +Fire Perimeter W3 | 1 +Fire Perimeter W4 | 1 + +### Scheme FPW1-W1 + +Scheme FPW1-CW1 applies an alternative benchmark weighting for fire perimeter groups. It uses the number of days for each study period as the weight. + +Group | Group Weight +---------------------- | ------------ +Fire Perimeter W1 | 24.1 +Fire Perimeter W2 | 2.0 +Fire Perimeter W3 | 2.75 +Fire Perimeter W4 | 5.2 + +#### Scheme benchmark override +This scheme also modifies the weights applied to the benchmarks. It gives more weight to the average Jaccard and Dice-Sorensen index compared the min/max indices. It also adds more weight to the burn area benchmarks. + +Group | Benchmark ID | Weight +---------------------- | ------------ | ------ +Fire Perimeter W1 | FP01, FP13 | 2 +| | FP25, FP29 | 4 +Fire Perimeter W2 | FP02, FP14 | 2 +| | FP26, FP30 | 4 +Fire Perimeter W3 | FP03, FP15 | 2 +| | FP27, FP31 | 4 +Fire Perimeter W4 | FP04, FP16 | 2 +| | FP28, FP32 | 4 ### Scheme S -Group | Group Weight | Benchmark ID | Benchmark weight ----------------------- | ------------ | ------------ | ---------------- -Burn Severity | 1 | | -| | | SV01 to SV06 | 1 +Scheme S contains only the burn severity group. It is used to evaluate the model only on building severity from MTBS benchmarks. + +Group | Group Weight +---------------------- | ------------ +Burn Severity | 1 ## Notes @@ -550,4 +620,4 @@ Burn Severity | 1 | | ## Acknowledgment -We gratefully acknowledge Synoptic for granting permission to redistribute selected weather-station data as part of the FireBench benchmarking framework. \ No newline at end of file +We gratefully acknowledge [Synoptic](https://synopticdata.com) for granting permission to redistribute selected weather-station data as part of the FireBench benchmarking framework. \ No newline at end of file From 96a395d5f6b09d5eb133409e56973ef593e52602 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 16:12:20 -0800 Subject: [PATCH 065/111] add KML file check for req validation --- src/firebench/standardize/tools.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/firebench/standardize/tools.py b/src/firebench/standardize/tools.py index 95c3aaa..6bb3822 100644 --- a/src/firebench/standardize/tools.py +++ b/src/firebench/standardize/tools.py @@ -120,6 +120,11 @@ def validate_h5_requirement(file: h5py.File, required: dict[str, list[str]]): for attr_name in attrs: if attr_name not in dset.attrs: return False, f"attr `{attr_name}` of dataset `{dset_path}`" + + # check that KML files exist + if "rel_path" in attrs: + if not Path(dset.attrs["rel_path"]).exists(): + return False, f"file `{dset.attrs["rel_path"]}` not found from `{dset_path}`" return True, None From 200eebe5b091fbd8c1fe070c07afc424b5975772 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 16:19:59 -0800 Subject: [PATCH 066/111] fix score card pdf with 0 agg --- src/firebench/metrics/table.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/src/firebench/metrics/table.py b/src/firebench/metrics/table.py index dffbfaa..a231460 100644 --- a/src/firebench/metrics/table.py +++ b/src/firebench/metrics/table.py @@ -70,6 +70,17 @@ def save_as_table(filename: Path, data: dict): f"{data['score_card']['Score Total']:.2f}", ] ) + else: + # Aggregation scheme is 0 or invalid + scheme_name = "No agg" + text_table.append( + [ + f"Total Score {data['case_id']} agg. {scheme_name}: {data['evaluated_model_name']}", + "", + "", + f"Invalid", + ] + ) text_table.append(["Benchmark ID/Group Name", "KPI value", "Weight", "Score"]) # rows From d1e4a61a6fda896acab95f088b7d9c974a698b85 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 18:22:52 -0800 Subject: [PATCH 067/111] add safeguard kpi normalization --- src/firebench/metrics/__init__.py | 2 ++ src/firebench/metrics/kpi_normalization.py | 16 ++++++++-------- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/src/firebench/metrics/__init__.py b/src/firebench/metrics/__init__.py index acea084..89fbab0 100644 --- a/src/firebench/metrics/__init__.py +++ b/src/firebench/metrics/__init__.py @@ -3,5 +3,7 @@ kpi_norm_bounded_linear, kpi_norm_half_open_linear, kpi_norm_half_open_exponential, + kpi_norm_symmetric_open_linear, + kpi_norm_symmetric_open_exponential, ) from .table import save_as_table diff --git a/src/firebench/metrics/kpi_normalization.py b/src/firebench/metrics/kpi_normalization.py index dd49647..e68ada3 100644 --- a/src/firebench/metrics/kpi_normalization.py +++ b/src/firebench/metrics/kpi_normalization.py @@ -1,7 +1,7 @@ -from math import exp, log +from math import exp, log, isclose -def kpi_norm_bounded_linear(x, a, b): +def kpi_norm_bounded_linear(x, a, b, rtol=1e-12, atol=1e-15): """ Linearly normalize a KPI that has a fully bounded acceptable range [a, b]. @@ -31,14 +31,14 @@ def kpi_norm_bounded_linear(x, a, b): ValueError If `x < a` or if `x > b`. """ - if x < a: + if x < a and not isclose(x, a, rel_tol=rtol, abs_tol=atol): raise ValueError(f"KPI value {x} smaller than lower limit {a}") - if x > b: + if x > b and not isclose(x, b, rel_tol=rtol, abs_tol=atol): raise ValueError(f"KPI value {x} greater than upper limit {b}") return 100.0 * (x - a) / (b - a) -def kpi_norm_half_open_linear(x, a, m): +def kpi_norm_half_open_linear(x, a, m, rtol=1e-12, atol=1e-15): """ Linearly normalize a KPI defined on the half-open interval [a, \infty). @@ -71,14 +71,14 @@ def kpi_norm_half_open_linear(x, a, m): ValueError If `x < a` or if `m <= a`. """ - if x < a: + if x < a and not isclose(x, a, rel_tol=rtol, abs_tol=atol): raise ValueError(f"KPI value {x} smaller than lower limit {a}") if m <= a: raise ValueError(f"Parameter m ({m}) smaller than lower limit a ({a})") return 100.0 * max(0, 1 - (x - a) / (m - a)) -def kpi_norm_half_open_exponential(x, a, m): +def kpi_norm_half_open_exponential(x, a, m, rtol=1e-12, atol=1e-15): """ Exponentially normalize a KPI defined on the half-open interval [a, \infty). @@ -113,7 +113,7 @@ def kpi_norm_half_open_exponential(x, a, m): ValueError If `x < a` or if `m <= a`. """ - if x < a: + if x < a and not isclose(x, a, rel_tol=rtol, abs_tol=atol): raise ValueError(f"KPI value {x} smaller than lower limit {a}") if m <= a: raise ValueError(f"Parameter m ({m}) smaller than lower limit a ({a})") From 53de760eaa604ffdeadd4afea9ce27917e6d57cc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 18:23:04 -0800 Subject: [PATCH 068/111] add safe guard area indices --- src/firebench/metrics/perimeter/shape_index.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/firebench/metrics/perimeter/shape_index.py b/src/firebench/metrics/perimeter/shape_index.py index 4a03a3d..33ec6fe 100644 --- a/src/firebench/metrics/perimeter/shape_index.py +++ b/src/firebench/metrics/perimeter/shape_index.py @@ -66,7 +66,7 @@ def jaccard_polygon(polygon1: GeoDataFrame, polygon2: GeoDataFrame, projection: union = gpd.overlay(polygon1, polygon2, how="union", keep_geom_type=False) # Compute and return IoU - return intersection.area.sum() / union.area.sum() + return max(0, min(1, intersection.area.sum() / union.area.sum())) def jaccard_binary(mask1: np.ndarray, mask2: np.ndarray) -> float: @@ -106,7 +106,7 @@ def jaccard_binary(mask1: np.ndarray, mask2: np.ndarray) -> float: if union == 0: return 1.0 if intersection == 0 else 0.0 # Edge case: both masks empty - return intersection / union + return max(0, min(1, intersection / union)) def sorensen_dice_polygon( @@ -165,7 +165,7 @@ def sorensen_dice_polygon( intersection = gpd.overlay(polygon1, polygon2, how="intersection", keep_geom_type=False) # Compute and return sorensen - return 2 * intersection.area.sum() / (polygon1.area.sum() + polygon2.area.sum()) + return max(0, min( 1, 2 * intersection.area.sum() / (polygon1.area.sum() + polygon2.area.sum()))) def sorensen_dice_binary(mask1: np.ndarray, mask2: np.ndarray) -> float: @@ -207,4 +207,4 @@ def sorensen_dice_binary(mask1: np.ndarray, mask2: np.ndarray) -> float: if denom == 0: return 1.0 if intersection == 0 else 0.0 # Both masks empty or inconsistent - return 2 * intersection / denom + return max(0, min (1, 2 * intersection / denom)) From ee6e18860495fe7d91abbff037b828ff4552b6f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 21:11:24 -0800 Subject: [PATCH 069/111] convert to float for json dump compliance --- src/firebench/metrics/stats/dataset_1D.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/firebench/metrics/stats/dataset_1D.py b/src/firebench/metrics/stats/dataset_1D.py index 1d9e3b3..f19e096 100644 --- a/src/firebench/metrics/stats/dataset_1D.py +++ b/src/firebench/metrics/stats/dataset_1D.py @@ -31,7 +31,7 @@ def rmse(x1: np.ndarray, x2: np.ndarray) -> float: if x1.shape != x2.shape: raise ValueError(f"Input shapes must match, got {x1.shape} and {x2.shape}.") - return np.sqrt(np.nanmean((x1 - x2) ** 2)) + return float(np.sqrt(np.nanmean((x1 - x2) ** 2))) def nmse_range(x1: np.ndarray, x2: np.ndarray) -> float: @@ -72,7 +72,7 @@ def nmse_range(x1: np.ndarray, x2: np.ndarray) -> float: "Cannot normalize RMSE: denominator is zero (no range in reference). Use nmse_power instead." ) - return rmse(x1, x2) / denom + return float(rmse(x1, x2) / denom) def nmse_power(x1: np.ndarray, x2: np.ndarray) -> float: @@ -113,7 +113,7 @@ def nmse_power(x1: np.ndarray, x2: np.ndarray) -> float: if denom == 0: raise ValueError("Cannot normalize MSE: denominator is zero. Use nmse_range instead.") - return np.nanmean((x1 - x2) ** 2) / denom + return float(np.nanmean((x1 - x2) ** 2) / denom) def bias(x1: np.ndarray, x2: np.ndarray) -> float: @@ -144,4 +144,4 @@ def bias(x1: np.ndarray, x2: np.ndarray) -> float: if x1.shape != x2.shape: raise ValueError(f"Input shapes must match, got {x1.shape} and {x2.shape}.") - return np.nanmean(x1) - np.nanmean(x2) + return float(np.nanmean(x1) - np.nanmean(x2)) From 05534251c439f5fc02d8ca780ae57ecd17a5442a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 21:12:01 -0800 Subject: [PATCH 070/111] add projection info for Caldor - change bias to final perimeter bias --- docs/benchmarks/California/01_Caldor.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index 701165b..e55066d 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -301,6 +301,7 @@ See Key Performance Indicator (KPI) and normalization defintions [here](../../me **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. +The area preserving project used is EPSG:5070. The following Table gives the correspondance between the bencmark id and the study period: @@ -318,6 +319,7 @@ FP04 | W4 | Average Jaccard Index W4 **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. +The area preserving project used is EPSG:5070. The following Table gives the correspondance between the bencmark id and the study period: @@ -335,6 +337,7 @@ FP08 | W4 | Minimum Jaccard Index W4 **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. +The area preserving project used is EPSG:5070. The following Table gives the correspondance between the bencmark id and the study period: @@ -352,6 +355,7 @@ FP12 | W4 | Minimum Jaccard Index W4 **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. +The area preserving project used is EPSG:5070. The following Table gives the correspondance between the bencmark id and the study period: @@ -369,6 +373,7 @@ FP16 | W4 | Average Dice-Sorensen Index W4 **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. +The area preserving project used is EPSG:5070. The following Table gives the correspondance between the bencmark id and the study period: @@ -386,6 +391,7 @@ FP20 | W4 | Minimum Dice-Sorensen Index W4 **Normalization**: Linear Bounded Normalization with $a=0$, $b=1$
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. The first perimeter is not used to compute any metric. +The area preserving project used is EPSG:5070. The following Table gives the correspondance between the bencmark id and the study period: @@ -396,13 +402,14 @@ FP22 | W2 | Minimum Dice-Sorensen Index W2 FP23 | W3 | Minimum Dice-Sorensen Index W3 FP24 | W4 | Minimum Dice-Sorensen Index W4 -#### Burn Area Bias +#### Final Burn Area Bias **Short IDs**: See Table
    **KPI**: Burn Area Bias
    **Normalization**: Symmetric Exponential Open Normalization ($m$ value in Table)
    **Name in Score Card**: See Table
    The first perimeter, at the start of the period, can be used as initial condition for the fire perimeter. +The bias is calculated on the last perimeter of the stgudy period as the difference between the model and the observed burn area. A bias of $m$ acre, representing $B_{50}$% of burn area during the study period, will lead to a score of 50.00. The value of $m$ represents the benchmark difficulty (smaller $m$ means higher difficulty) and must be chosen by the community. The following Table gives the correspondance between the bencmark id and the study period: @@ -579,9 +586,9 @@ Fire Perimeter W2 | 1 Fire Perimeter W3 | 1 Fire Perimeter W4 | 1 -### Scheme FPW1-W1 +### Scheme FP-CW1 -Scheme FPW1-CW1 applies an alternative benchmark weighting for fire perimeter groups. It uses the number of days for each study period as the weight. +Scheme FP-CW1 applies an alternative benchmark weighting for fire perimeter groups. It uses the number of days for each study period as the weight. Group | Group Weight ---------------------- | ------------ From 301f3f86b45685633202a0a97ff32b3ef7068354 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 14 Dec 2025 21:16:17 -0800 Subject: [PATCH 071/111] fix headers caldor docs --- docs/benchmarks/California/01_Caldor.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/benchmarks/California/01_Caldor.md b/docs/benchmarks/California/01_Caldor.md index e55066d..9e2dc7a 100644 --- a/docs/benchmarks/California/01_Caldor.md +++ b/docs/benchmarks/California/01_Caldor.md @@ -544,7 +544,8 @@ Fire Perimeter W2 | FP02, FP06, FP10, FP14, FP18, FP22, FP26, FP30 Fire Perimeter W3 | FP03, FP07, FP11, FP15, FP19, FP23, FP27, FP31 Fire Perimeter W4 | FP04, FP08, FP12, FP16, FP20, FP24, FP28, FP32 -#### Group benchmark override +**Group benchmark override** + The custom weight defined in the following Table overrides the default weight from the Group Definition Table. Group | Benchmark ID | Weight @@ -567,7 +568,7 @@ Fire Perimeter W2 | 1 Fire Perimeter W3 | 1 Fire Perimeter W4 | 1 -## Scheme B +### Scheme B Scheme B contains only the building damage group. It is used to evaluate the model only on building damage benchmarks. @@ -597,7 +598,8 @@ Fire Perimeter W2 | 2.0 Fire Perimeter W3 | 2.75 Fire Perimeter W4 | 5.2 -#### Scheme benchmark override +**Scheme benchmark override** + This scheme also modifies the weights applied to the benchmarks. It gives more weight to the average Jaccard and Dice-Sorensen index compared the min/max indices. It also adds more weight to the burn area benchmarks. Group | Benchmark ID | Weight From 4c14f3e037c2b7ba026a52736cc95c67ed4a591e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Thu, 18 Dec 2025 23:16:41 -0800 Subject: [PATCH 072/111] add fill value for ravg processing --- src/firebench/standardize/ravg.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/firebench/standardize/ravg.py b/src/firebench/standardize/ravg.py index 8389799..7bc9cb8 100644 --- a/src/firebench/standardize/ravg.py +++ b/src/firebench/standardize/ravg.py @@ -54,6 +54,7 @@ def standardize_ravg_cc_from_geotiff( overwrite, invert_y, compression_lvl, + fill_value=0, ) @@ -103,6 +104,7 @@ def standardize_ravg_cbi_from_geotiff( overwrite, invert_y, compression_lvl, + fill_value=0, ) @@ -152,6 +154,7 @@ def standardize_ravg_ba_from_geotiff( overwrite, invert_y, compression_lvl, + fill_value=0, ) @@ -166,6 +169,7 @@ def _standardize_ravg_from_geotiff( overwrite: bool = False, invert_y: bool = False, compression_lvl: int = 3, + fill_value = None, ): """ Convert a RAVG GeoTIFF to FireBench HDF5 Standard. @@ -233,3 +237,5 @@ def _standardize_ravg_from_geotiff( ddata.attrs["units"] = "dimensionless" if nodata is not None: ddata.attrs["_FillValue"] = nodata + if fill_value is not None: + ddata.attrs["_FillValue"] = fill_value From 32cd1029d562b6082fa0c09c44678ce6eac0582b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 21 Dec 2025 14:01:20 -0800 Subject: [PATCH 073/111] add context dict tools for benchmarks --- src/firebench/metrics/__init__.py | 4 ++++ src/firebench/metrics/tools.py | 39 +++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+) create mode 100644 src/firebench/metrics/tools.py diff --git a/src/firebench/metrics/__init__.py b/src/firebench/metrics/__init__.py index 89fbab0..6750da6 100644 --- a/src/firebench/metrics/__init__.py +++ b/src/firebench/metrics/__init__.py @@ -7,3 +7,7 @@ kpi_norm_symmetric_open_exponential, ) from .table import save_as_table +from .tools import ( + CTXKey, + ctx_get_or_compute +) \ No newline at end of file diff --git a/src/firebench/metrics/tools.py b/src/firebench/metrics/tools.py new file mode 100644 index 0000000..96b5d24 --- /dev/null +++ b/src/firebench/metrics/tools.py @@ -0,0 +1,39 @@ +from typing import Tuple, Any, Callable +import numpy as np +from ..tools.logging_config import logger + +CTXKey = Tuple[str, str, str] +ComputeFn = Callable[..., Any] + + +class CtxKeyError(KeyError): + """Raised when a context key is not declared in CTX_SPEC.""" + + +class CtxValueError(RuntimeError): + """Raised when a cached value is missing/invalid in a way that suggests misuse.""" + + +def ctx_get_or_compute( + ctx_spec: dict[CTXKey, str], + ctx: dict[CTXKey, Any], + key: CTXKey, + compute: ComputeFn, + *args: Any, + **kwargs: Any, +) -> Any: + if key not in ctx_spec.keys(): + raise CtxKeyError( + f"Context key {key!r} is not declared in CTX_SPEC. " + "Declare it in CTX_SPEC to make cache usage explicit." + ) + + if key in ctx: + logger.debug("CTXKey %s found in CTX dict", key) + return ctx[key] + + logger.debug("Add field %s to CTX dict", key) + value = compute(*args, **kwargs) + ctx[key] = value + + return value \ No newline at end of file From b7cdf7cb3506c768e3f6e9178179e760240d31a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 21 Dec 2025 22:10:21 -0800 Subject: [PATCH 074/111] add sensor height to synoptic processing - add default height for synoptic variables - add __get_sensor_height and __add_variable_to_group --- src/firebench/standardize/synoptic.py | 115 ++++++++++++++++++++++---- 1 file changed, 99 insertions(+), 16 deletions(-) diff --git a/src/firebench/standardize/synoptic.py b/src/firebench/standardize/synoptic.py index 3181aa4..5a0adb2 100644 --- a/src/firebench/standardize/synoptic.py +++ b/src/firebench/standardize/synoptic.py @@ -14,36 +14,43 @@ "std_name": svn.AIR_TEMPERATURE.value, "units": "degC", "dtype": np.float64, + "default_sensor_height": 2, }, "relative_humidity_set_1": { "std_name": svn.RELATIVE_HUMIDITY.value, "units": "percent", "dtype": np.float64, + "default_sensor_height": 2, }, "wind_direction_set_1": { "std_name": svn.WIND_DIRECTION.value, "units": "degree", "dtype": np.float64, + "default_sensor_height": None, }, "wind_speed_set_1": { "std_name": svn.WIND_SPEED.value, "units": "m/s", "dtype": np.float64, + "default_sensor_height": None, }, "wind_gust_set_1": { "std_name": svn.WIND_GUST.value, "units": "m/s", "dtype": np.float64, + "default_sensor_height": None, }, "solar_radiation_set_1": { "std_name": svn.SOLAR_RADIATION.value, "units": "W/m^2", "dtype": np.float64, + "default_sensor_height": None, }, "fuel_moisture_set_1": { "std_name": svn.FUEL_MOISTURE_CONTENT_10H.value, "units": "percent", "dtype": np.float64, + "default_sensor_height": 0.3, }, } @@ -51,6 +58,7 @@ def standardize_synoptic_raws_from_json( json_path: Path, h5file: h5py.File, + skip_stations: list[str] = [], overwrite: bool = False, variable_conversion: dict = VARIABLE_CONVERSION, compression_lvl: int = 3, @@ -64,7 +72,16 @@ def standardize_synoptic_raws_from_json( else: probes = h5file.create_group(TIME_SERIES) + nb_fully_processed = 0 + nb_partially_processed = 0 + nb_skipped = 0 + for station_dict in data["STATION"]: + if station_dict["STID"] in skip_stations: + nb_skipped += 1 + logger.info("Skipping station %s", station_dict["STID"]) + continue + logger.info("Processing station %s", station_dict["STID"]) group_name = f"station_{station_dict["STID"]}" @@ -81,13 +98,13 @@ def standardize_synoptic_raws_from_json( new_station = probes.create_group(group_name) new_station.attrs["name"] = station_dict["NAME"] - new_station.attrs["ID"] = station_dict["ID"] - new_station.attrs["mnet_id"] = station_dict["MNET_ID"] + new_station.attrs["ID"] = int(station_dict["ID"]) + new_station.attrs["mnet_id"] = int(station_dict["MNET_ID"]) new_station.attrs["state"] = station_dict["STATE"] new_station.attrs["timezone"] = station_dict["TIMEZONE"] - new_station.attrs["position_lat"] = station_dict["LATITUDE"] - new_station.attrs["position_lon"] = station_dict["LONGITUDE"] - new_station.attrs["position_alt"] = station_dict["ELEVATION"] + new_station.attrs["position_lat"] = float(station_dict["LATITUDE"]) + new_station.attrs["position_lon"] = float(station_dict["LONGITUDE"]) + new_station.attrs["position_alt"] = float(station_dict["ELEVATION"]) new_station.attrs["position_lat_units"] = "degree" new_station.attrs["position_lon_units"] = "degree" new_station.attrs["position_alt_units"] = station_dict["UNITS"]["elevation"] @@ -98,9 +115,13 @@ def standardize_synoptic_raws_from_json( new_station.attrs["source_file_sha256"] = sha_source_file if "ELEV_DEM" in station_dict: if station_dict["ELEV_DEM"] is not None: - new_station.attrs["elevation_dem"] = station_dict["ELEV_DEM"] + new_station.attrs["elevation_dem"] = float(station_dict["ELEV_DEM"]) new_station.attrs["elevation_dem_units"] = station_dict["UNITS"]["elevation"] + if "PROVIDERS" in station_dict: + if "name" in station_dict["PROVIDERS"]: + new_station.attrs["providers"] = station_dict["PROVIDERS"]["name"] + fully_processed = True for var in station_dict["OBSERVATIONS"]: if var == "date_time": time_iso = [] @@ -108,24 +129,86 @@ def standardize_synoptic_raws_from_json( dt = datetime.strptime(t, "%Y%m%d%H%M%S") time_iso.append(datetime_to_iso8601(dt, True, tz=station_dict["TIMEZONE"])) - new_var = new_station.create_dataset( + new_station.create_dataset( svn.TIME.value, data=time_iso, **hdf5plugin.Zstd(clevel=compression_lvl) ) else: if var in variable_conversion: logger.debug("> processing %s", var) - var_data = np.array( - station_dict["OBSERVATIONS"][var], dtype=variable_conversion[var]["dtype"] - ) - new_var = new_station.create_dataset( - variable_conversion[var]["std_name"], - data=var_data, - **hdf5plugin.Zstd(clevel=compression_lvl), - ) - new_var.attrs["units"] = variable_conversion[var]["units"] + + sensor_height = __get_sensor_height(station_dict["SENSOR_VARIABLES"], var) + + if sensor_height is not None: + # Sensor heigth from metadata + __add_variable_to_group( + new_station, + station_dict["OBSERVATIONS"][var], + variable_conversion[var], + sensor_height, + compression_lvl, + ) + else: + # Sensor height from default, skip if None + fully_processed = False + if variable_conversion[var]["default_sensor_height"] is None: + logger.warning( + "standardize_synoptic_raws_from_json: variable %s from station %s not processed due to lack of sensor height information. Default value is `None` for this variable.", + var, + station_dict["STID"], + ) + else: + logger.info( + "standardize_synoptic_raws_from_json: variable %s from station %s uses firebench default height of %f m", + var, + station_dict["STID"], + variable_conversion[var]["default_sensor_height"], + ) + __add_variable_to_group( + new_station, + station_dict["OBSERVATIONS"][var], + variable_conversion[var], + variable_conversion[var]["default_sensor_height"], + compression_lvl, + ) + else: logger.warning( "standardize_synoptic_raws_from_json: variable %s from station %s not processed. Add the variable to `variable_conversion` to process it.", var, station_dict["STID"], ) + + if fully_processed: + new_station.attrs["sensor_height_source"] = "from_data" + nb_fully_processed += 1 + else: + new_station.attrs["sensor_height_source"] = "firebench_default" + nb_partially_processed += 1 + + logger.info( + "Stats stations: %d fully processed, %d partially processed, %d skipped", + nb_fully_processed, + nb_partially_processed, + nb_skipped, + ) + + +def __get_sensor_height(sensor_variables: dict, variable: str): + for sensor_var in sensor_variables.values(): + if variable in sensor_var: + return sensor_var[variable].get("position") + return None + + +def __add_variable_to_group( + group: h5py.Group, variable, info_dict: dict, sensor_height: float, compression_lvl: int +): + var_data = np.array(variable, dtype=info_dict["dtype"]) + new_var = group.create_dataset( + info_dict["std_name"], + data=var_data, + **hdf5plugin.Zstd(clevel=compression_lvl), + ) + new_var.attrs["units"] = info_dict["units"] + new_var.attrs["sensor_height"] = sensor_height + new_var.attrs["sensor_height_units"] = "m" From a0b417ad68fe01f9134bc47a4c41778081835abc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Aur=C3=A9lien=20Costes?= Date: Sun, 21 Dec 2025 22:10:54 -0800 Subject: [PATCH 075/111] add RAVG CC docs --- .../benchmarks/FB001/RAVG_BA_final.png | Bin 0 -> 137175 bytes .../benchmarks/FB001/RAVG_CBI_final.png | Bin 0 -> 96643 bytes .../benchmarks/FB001/RAVG_CC_final.png | Bin 0 -> 110611 bytes .../benchmarks/FB001/RAVG_CC_masked.png | Bin 0 -> 72954 bytes docs/benchmarks/California/01_Caldor.md | 139 +++++++++++++++++- 5 files changed, 132 insertions(+), 7 deletions(-) create mode 100644 docs/_static/benchmarks/FB001/RAVG_BA_final.png create mode 100644 docs/_static/benchmarks/FB001/RAVG_CBI_final.png create mode 100644 docs/_static/benchmarks/FB001/RAVG_CC_final.png create mode 100644 docs/_static/benchmarks/FB001/RAVG_CC_masked.png diff --git 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b/docs/benchmarks/California/01_Caldor.md @@ -113,6 +113,31 @@ This benchmark is performed on the binary classes for damaged buildings. This benchmark is performed on the binary classes for damaged buildings. +## Burn severity benchmarks + +### Dataset + +The data has been collected using [Monitoring Trends in Burning Severity](https://mtbs.gov/) (MTBS). +The original zip file contains burn severity, pre/post burn images, and final fire perimeter. +The source of the burn severity used in FireBench is the file `ca3858612053820210815_20210805_20220723_dnbr6.tif`. + +The hash of the original source files are: +- zip file: 171b9604c0654d8612eaabcfcad93d2374762661ab34b4d62718630a13469841 +- tif dnbr6: 33db74d3c5798c41ff3a4fc5ee57da9105fdc7a75d7f8af0d053d2f82cfdc0b6 + +![blockdiagram](../../_static/benchmarks/FB001/mtbs_map.jpg) +