diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle index 4680e45c..af7ecf5b 100644 Binary files a/docs/.doctrees/environment.pickle and b/docs/.doctrees/environment.pickle differ diff --git a/docs/.doctrees/pyhazards_datasets.doctree b/docs/.doctrees/pyhazards_datasets.doctree index 022494c4..d971ceed 100644 Binary files a/docs/.doctrees/pyhazards_datasets.doctree and b/docs/.doctrees/pyhazards_datasets.doctree differ diff --git a/docs/_sources/pyhazards_datasets.rst.txt b/docs/_sources/pyhazards_datasets.rst.txt index 70388ad2..2bd18413 100644 --- a/docs/_sources/pyhazards_datasets.rst.txt +++ b/docs/_sources/pyhazards_datasets.rst.txt @@ -4,7 +4,12 @@ Datasets Summary ------- -PyHazards provides a unified dataset interface for hazard prediction across tabular, temporal, and raster data. Each dataset returns a DataBundle containing splits, feature specs, label specs, and metadata. +PyHazards maintains a curated catalog of commonly used hazard datasets and provides +dataset-specific utilities for **download / preprocessing / inspection / visualization**. + +Each dataset page describes: (1) what the dataset is, (2) how to obtain it, and (3) how to +quickly validate local data files via an inspection entrypoint (when available). + Datasets -------------------- @@ -38,53 +43,103 @@ Datasets * - :doc:`goesr ` - High-frequency geostationary multispectral imagery from the `NOAA GOES-R series `_, supporting continuous monitoring (e.g., smoke/thermal context) and early detection workflows when paired with fire and meteorology datasets. + Dataset inspection ------------------ -PyHazards provides a built-in inspection utility that allows users to -quickly explore dataset structure and contents through a unified API. +PyHazards provides dataset inspection entrypoints to quickly validate local files and produce +basic summaries/plots. + +Currently implemented: -The example below demonstrates how to inspect a daily MERRA-2 file using -the PyHazards dataset interface. +- **MERRA-2 (merra2)**: one-shot pipeline to **download raw MERRA-2 → merge SFC+PRES → inspect → save plots/tables**. .. code-block:: bash + # One command: download (if needed) -> merge -> inspect -> save outputs python -m pyhazards.datasets.inspection 20260101 -Core classes ------------- +Notes (MERRA-2) +~~~~~~~~~~~~~~~ + +- Download requires Earthdata credentials via environment variables:: + + export EARTHDATA_USERNAME="YOUR_USERNAME" + export EARTHDATA_PASSWORD="YOUR_PASSWORD" + +- Date formats accepted: ``YYYYMMDD`` (e.g., ``20260101``) or ISO ``YYYY-MM-DD``. +- Optional flags commonly used: + - ``--outdir outputs`` (default: ``outputs`` under repo root) + - ``--skip-download`` / ``--skip-merge`` for re-running on existing files + - ``--force-download`` to re-fetch raw files + - ``--var T2M`` to choose the plotted surface variable (default: ``T2M``) -- ``Dataset``: base class to implement ``_load()`` and return a ``DataBundle``. -- ``DataBundle``: holds named ``DataSplit`` objects, plus ``feature_spec`` and ``label_spec``. -- ``FeatureSpec`` / ``LabelSpec``: describe inputs/targets to simplify model construction. -- ``register_dataset`` / ``load_dataset``: lightweight registry for discovering datasets by name. Example skeleton ---------------- +A "nice" skeleton should make it explicit **what data you load** and how it flows into +**inspection/visualization**. + +Below is the recommended pattern: set ``data`` to a dataset name (e.g., ``"merra2"`` or ``"mtbs"``) +and run the dataset's inspection entrypoint accordingly. + .. code-block:: python - import torch - from pyhazards.datasets import ( - DataBundle, DataSplit, Dataset, FeatureSpec, LabelSpec, register_dataset - ) - - class MyHazardDataset(Dataset): - name = "my_hazard" - - def _load(self): - x = torch.randn(1000, 16) - y = torch.randint(0, 2, (1000,)) - splits = { - "train": DataSplit(x[:800], y[:800]), - "val": DataSplit(x[800:900], y[800:900]), - "test": DataSplit(x[900:], y[900:]), - } - return DataBundle( - splits=splits, - feature_spec=FeatureSpec(input_dim=16, description="example features"), - label_spec=LabelSpec(num_targets=2, task_type="classification"), - ) - - register_dataset(MyHazardDataset.name, MyHazardDataset) + import subprocess + + # 1) Choose what dataset you want to load/inspect + data = "merra2" # e.g., "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr" (use accordingly) + + # 2) Choose the dataset key (identifier) + # - For MERRA-2, the key is a daily date: "YYYYMMDD" (e.g., "20260101") + # - For other datasets (e.g., MTBS), the key could be an event/scene id (to be defined per dataset) + key = "20260101" + + # 3) Run the inspection pipeline (download/preprocess if needed -> inspect -> visualize -> save outputs) + if data == "merra2": + cmd = [ + "python", "-m", "pyhazards.datasets.inspection", + key, + "--var", "T2M", # change variable to plot (e.g., QV2M) + "--outdir", "outputs", # output folder under repo root by default + ] + else: + # Convention for other datasets: + # provide a dataset-specific inspection entrypoint: + # python -m pyhazards.datasets..inspection ... + cmd = ["python", "-m", f"pyhazards.datasets.{data}.inspection", key, "--outdir", "outputs"] + + subprocess.run(cmd, check=True) + + # 4) After running, check outputs/ for saved artifacts (tables + plots). + # Example (MERRA-2): CSV tables for variable inventory + a PDF plot for the selected surface variable. + + +Inspection entrypoints (convention for all datasets) +---------------------------------------------------- + +Each dataset should expose a minimal inspection entrypoint that supports the same user experience: + +- **Input**: a dataset identifier (``key``) such as a date/event id. +- **Work**: download/prepare (if needed) → open files → summarize → visualize. +- **Output**: saved artifacts under ``outputs/`` (tables + figures). + +Recommended CLI shape (dataset-specific): + +.. code-block:: bash + + # Example convention (to be implemented per dataset): + python -m pyhazards.datasets..inspection --outdir outputs + + +Developer note +-------------- + +If you plan to add inspection for a new dataset, mirror the MERRA-2 inspection pattern: + +1) parse CLI args (key + outdir + skip/force flags), +2) materialize required local files (download/preprocess), +3) open files and print structure/statistics, +4) generate at least one saved visualization to ``outputs/``. diff --git a/docs/pyhazards_datasets.html b/docs/pyhazards_datasets.html index 07e1be46..363a111b 100644 --- a/docs/pyhazards_datasets.html +++ b/docs/pyhazards_datasets.html @@ -263,7 +263,10 @@

Datasets

Summary

-

PyHazards provides a unified dataset interface for hazard prediction across tabular, temporal, and raster data. Each dataset returns a DataBundle containing splits, feature specs, label specs, and metadata.

+

PyHazards maintains a curated catalog of commonly used hazard datasets and provides +dataset-specific utilities for download / preprocessing / inspection / visualization.

+

Each dataset page describes: (1) what the dataset is, (2) how to obtain it, and (3) how to +quickly validate local data files via an inspection entrypoint (when available).

Datasets

@@ -304,51 +307,95 @@

Datasets

Dataset inspection

-

PyHazards provides a built-in inspection utility that allows users to -quickly explore dataset structure and contents through a unified API.

-

The example below demonstrates how to inspect a daily MERRA-2 file using -the PyHazards dataset interface.

-
python -m pyhazards.datasets.inspection 20260101
+
PyHazards provides dataset inspection entrypoints to quickly validate local files and produce
+basic summaries/plots.

+
Currently implemented:

+
    
+
  • MERRA-2 (merra2): one-shot pipeline to download raw MERRA-2 → merge SFC+PRES → inspect → save plots/tables.
    • 
+

+
# One command: download (if needed) -> merge -> inspect -> save outputs
+python -m pyhazards.datasets.inspection 20260101
 

 

-
-
-

Core classes

-
    -

  • Dataset: base class to implement _load() and return a DataBundle.

    • -

  • DataBundle: holds named DataSplit objects, plus feature_spec and label_spec.

    • -

  • FeatureSpec / LabelSpec: describe inputs/targets to simplify model construction.

    • -

  • register_dataset / load_dataset: lightweight registry for discovering datasets by name.

    • +
    +

    Notes (MERRA-2)

    +
      +

    • Download requires Earthdata credentials via environment variables:

      +
      export EARTHDATA_USERNAME="YOUR_USERNAME"
+export EARTHDATA_PASSWORD="YOUR_PASSWORD"
+
      +
      +
      • +

    • Date formats accepted: YYYYMMDD (e.g., 20260101) or ISO YYYY-MM-DD.

      • +

    • Optional flags commonly used: +- --outdir outputs (default: outputs under repo root) +- --skip-download / --skip-merge for re-running on existing files +- --force-download to re-fetch raw files +- --var T2M to choose the plotted surface variable (default: T2M)

    +

Example skeleton

-
import torch
-from pyhazards.datasets import (
-    DataBundle, DataSplit, Dataset, FeatureSpec, LabelSpec, register_dataset
-)
+
A “nice” skeleton should make it explicit what data you load and how it flows into
+inspection/visualization.

+
Below is the recommended pattern: set data to a dataset name (e.g., "merra2" or "mtbs")
+and run the dataset’s inspection entrypoint accordingly.

+
import subprocess
+
+# 1) Choose what dataset you want to load/inspect
+data = "merra2"   # e.g., "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr" (use accordingly)
 
-class MyHazardDataset(Dataset):
-    name = "my_hazard"
+# 2) Choose the dataset key (identifier)
+#    - For MERRA-2, the key is a daily date: "YYYYMMDD" (e.g., "20260101")
+#    - For other datasets (e.g., MTBS), the key could be an event/scene id (to be defined per dataset)
+key = "20260101"
 
-    def _load(self):
-        x = torch.randn(1000, 16)
-        y = torch.randint(0, 2, (1000,))
-        splits = {
-            "train": DataSplit(x[:800], y[:800]),
-            "val": DataSplit(x[800:900], y[800:900]),
-            "test": DataSplit(x[900:], y[900:]),
-        }
-        return DataBundle(
-            splits=splits,
-            feature_spec=FeatureSpec(input_dim=16, description="example features"),
-            label_spec=LabelSpec(num_targets=2, task_type="classification"),
-        )
+# 3) Run the inspection pipeline (download/preprocess if needed -> inspect -> visualize -> save outputs)
+if data == "merra2":
+    cmd = [
+        "python", "-m", "pyhazards.datasets.inspection",
+        key,
+        "--var", "T2M",           # change variable to plot (e.g., QV2M)
+        "--outdir", "outputs",    # output folder under repo root by default
+    ]
+else:
+    # Convention for other datasets:
+    # provide a dataset-specific inspection entrypoint:
+    #   python -m pyhazards.datasets.<dataset>.inspection <key> ...
+    cmd = ["python", "-m", f"pyhazards.datasets.{data}.inspection", key, "--outdir", "outputs"]
 
-register_dataset(MyHazardDataset.name, MyHazardDataset)
+subprocess.run(cmd, check=True)
+
+# 4) After running, check outputs/ for saved artifacts (tables + plots).
+#    Example (MERRA-2): CSV tables for variable inventory + a PDF plot for the selected surface variable.
+
+

Inspection entrypoints (convention for all datasets)

+

Each dataset should expose a minimal inspection entrypoint that supports the same user experience:

+
    +

  • Input: a dataset identifier (key) such as a date/event id.

    • +

  • Work: download/prepare (if needed) → open files → summarize → visualize.

    • +

  • Output: saved artifacts under outputs/ (tables + figures).

    • +
+

Recommended CLI shape (dataset-specific):

+
# Example convention (to be implemented per dataset):
+python -m pyhazards.datasets.<dataset>.inspection <key> --outdir outputs
+
+
+
+
+

Developer note

+

If you plan to add inspection for a new dataset, mirror the MERRA-2 inspection pattern:

+
    +

  1. parse CLI args (key + outdir + skip/force flags),

    2. +

  2. materialize required local files (download/preprocess),

    3. +

  3. open files and print structure/statistics,

    4. +

  4. generate at least one saved visualization to outputs/.

    5. +
+

@@ -409,9 +456,13 @@

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"princip": 26, "public": 25, "pyhazard": [0, 1, 2, 3, 4, 5, 6, 18], "quick": [8, 9, 10, 11, 12, 13, 14, 15, 24], "r": 10, "refer": [8, 9, 10, 11, 12, 13, 14, 15, 25], "regist": 22, "registri": [2, 5], "requir": 18, "skeleton": 19, "spatio": [17, 22], "start": 24, "stat": [8, 9, 10, 11, 12, 13, 14, 15], "step": 24, "submodul": [2, 3, 5, 6, 23], "subpackag": 1, "summari": [19, 20, 21, 22, 23], "support": 24, "tabl": 17, "tabular": 24, "team": 26, "tempor": [17, 22], "toi": [17, 24], "train": 16, "trainer": 3, "transform": [2, 16], "typic": [8, 9, 10, 11, 12, 13, 14, 15, 20], "us": [8, 9, 10, 11, 12, 13, 14, 15], "usag": [20, 21, 24], "util": [6, 23], "variabl": [8, 9, 10, 11, 12, 13, 14, 15], "wfig": 15, "wildfir": [17, 22]}}) \ No newline at end of file diff --git a/docs/source/pyhazards_datasets.rst b/docs/source/pyhazards_datasets.rst index 70388ad2..2bd18413 100644 --- a/docs/source/pyhazards_datasets.rst +++ b/docs/source/pyhazards_datasets.rst @@ -4,7 +4,12 @@ Datasets Summary ------- -PyHazards provides a unified dataset interface for hazard prediction across tabular, temporal, and raster data. Each dataset returns a DataBundle containing splits, feature specs, label specs, and metadata. +PyHazards maintains a curated catalog of commonly used hazard datasets and provides +dataset-specific utilities for **download / preprocessing / inspection / visualization**. + +Each dataset page describes: (1) what the dataset is, (2) how to obtain it, and (3) how to +quickly validate local data files via an inspection entrypoint (when available). + Datasets -------------------- @@ -38,53 +43,103 @@ Datasets * - :doc:`goesr ` - High-frequency geostationary multispectral imagery from the `NOAA GOES-R series `_, supporting continuous monitoring (e.g., smoke/thermal context) and early detection workflows when paired with fire and meteorology datasets. + Dataset inspection ------------------ -PyHazards provides a built-in inspection utility that allows users to -quickly explore dataset structure and contents through a unified API. +PyHazards provides dataset inspection entrypoints to quickly validate local files and produce +basic summaries/plots. + +Currently implemented: -The example below demonstrates how to inspect a daily MERRA-2 file using -the PyHazards dataset interface. +- **MERRA-2 (merra2)**: one-shot pipeline to **download raw MERRA-2 → merge SFC+PRES → inspect → save plots/tables**. .. code-block:: bash + # One command: download (if needed) -> merge -> inspect -> save outputs python -m pyhazards.datasets.inspection 20260101 -Core classes ------------- +Notes (MERRA-2) +~~~~~~~~~~~~~~~ + +- Download requires Earthdata credentials via environment variables:: + + export EARTHDATA_USERNAME="YOUR_USERNAME" + export EARTHDATA_PASSWORD="YOUR_PASSWORD" + +- Date formats accepted: ``YYYYMMDD`` (e.g., ``20260101``) or ISO ``YYYY-MM-DD``. +- Optional flags commonly used: + - ``--outdir outputs`` (default: ``outputs`` under repo root) + - ``--skip-download`` / ``--skip-merge`` for re-running on existing files + - ``--force-download`` to re-fetch raw files + - ``--var T2M`` to choose the plotted surface variable (default: ``T2M``) -- ``Dataset``: base class to implement ``_load()`` and return a ``DataBundle``. -- ``DataBundle``: holds named ``DataSplit`` objects, plus ``feature_spec`` and ``label_spec``. -- ``FeatureSpec`` / ``LabelSpec``: describe inputs/targets to simplify model construction. -- ``register_dataset`` / ``load_dataset``: lightweight registry for discovering datasets by name. Example skeleton ---------------- +A "nice" skeleton should make it explicit **what data you load** and how it flows into +**inspection/visualization**. + +Below is the recommended pattern: set ``data`` to a dataset name (e.g., ``"merra2"`` or ``"mtbs"``) +and run the dataset's inspection entrypoint accordingly. + .. code-block:: python - import torch - from pyhazards.datasets import ( - DataBundle, DataSplit, Dataset, FeatureSpec, LabelSpec, register_dataset - ) - - class MyHazardDataset(Dataset): - name = "my_hazard" - - def _load(self): - x = torch.randn(1000, 16) - y = torch.randint(0, 2, (1000,)) - splits = { - "train": DataSplit(x[:800], y[:800]), - "val": DataSplit(x[800:900], y[800:900]), - "test": DataSplit(x[900:], y[900:]), - } - return DataBundle( - splits=splits, - feature_spec=FeatureSpec(input_dim=16, description="example features"), - label_spec=LabelSpec(num_targets=2, task_type="classification"), - ) - - register_dataset(MyHazardDataset.name, MyHazardDataset) + import subprocess + + # 1) Choose what dataset you want to load/inspect + data = "merra2" # e.g., "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr" (use accordingly) + + # 2) Choose the dataset key (identifier) + # - For MERRA-2, the key is a daily date: "YYYYMMDD" (e.g., "20260101") + # - For other datasets (e.g., MTBS), the key could be an event/scene id (to be defined per dataset) + key = "20260101" + + # 3) Run the inspection pipeline (download/preprocess if needed -> inspect -> visualize -> save outputs) + if data == "merra2": + cmd = [ + "python", "-m", "pyhazards.datasets.inspection", + key, + "--var", "T2M", # change variable to plot (e.g., QV2M) + "--outdir", "outputs", # output folder under repo root by default + ] + else: + # Convention for other datasets: + # provide a dataset-specific inspection entrypoint: + # python -m pyhazards.datasets..inspection ... + cmd = ["python", "-m", f"pyhazards.datasets.{data}.inspection", key, "--outdir", "outputs"] + + subprocess.run(cmd, check=True) + + # 4) After running, check outputs/ for saved artifacts (tables + plots). + # Example (MERRA-2): CSV tables for variable inventory + a PDF plot for the selected surface variable. + + +Inspection entrypoints (convention for all datasets) +---------------------------------------------------- + +Each dataset should expose a minimal inspection entrypoint that supports the same user experience: + +- **Input**: a dataset identifier (``key``) such as a date/event id. +- **Work**: download/prepare (if needed) → open files → summarize → visualize. +- **Output**: saved artifacts under ``outputs/`` (tables + figures). + +Recommended CLI shape (dataset-specific): + +.. code-block:: bash + + # Example convention (to be implemented per dataset): + python -m pyhazards.datasets..inspection --outdir outputs + + +Developer note +-------------- + +If you plan to add inspection for a new dataset, mirror the MERRA-2 inspection pattern: + +1) parse CLI args (key + outdir + skip/force flags), +2) materialize required local files (download/preprocess), +3) open files and print structure/statistics, +4) generate at least one saved visualization to ``outputs/``.