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z%Y!=vx`FKIVSGEX;p~^u0XlmMg82F1f%*-X8Qdg8eLhkA2x5K`y<-z-8=Hf3_}19d z5W-)>4QqP6OwZiQ8sbA%9*5TPHaJnq{BS%Z^h(@o8|n4n%Ajkz_GN9PvBPbwhxUxc&J zjTfUNj>_7HhF)<)M$kAzhT19P=FI=3W*Wb)$C kE6F~Y|7-PR8fy({^~vNjGF^nCKD*%s8`AL@e*AL(f6jukV*mgE diff --git a/docs/_sources/pyhazards_datasets.rst.txt b/docs/_sources/pyhazards_datasets.rst.txt index 70388ad2..13664858 100644 --- a/docs/_sources/pyhazards_datasets.rst.txt +++ b/docs/_sources/pyhazards_datasets.rst.txt @@ -4,7 +4,9 @@ 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 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. + Datasets -------------------- @@ -38,19 +40,29 @@ 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 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. +The example below demonstrates how to inspect a daily MERRA-2 file using the PyHazards dataset interface. .. code-block:: bash + # One command: download raw MERRA-2 (if needed) -> merge SFC+PRES -> inspect -> save plots/tables python -m pyhazards.datasets.inspection 20260101 +Notes: + +- MERRA-2 download requires Earthdata credentials via environment variables:: + + export EARTHDATA_USERNAME="YOUR_USERNAME" + export EARTHDATA_PASSWORD="YOUR_PASSWORD" + +- Outputs are written to ``outputs/`` under the repo root by default (can be changed via ``--outdir``). +- Merged NetCDF files are written under ``Prithvi-WxC/data/merra-2`` by default (repo-root inferred automatically). + Core classes ------------ @@ -60,31 +72,61 @@ Core classes - ``FeatureSpec`` / ``LabelSpec``: describe inputs/targets to simplify model construction. - ``register_dataset`` / ``load_dataset``: lightweight registry for discovering datasets by name. + Example skeleton ---------------- +This section provides a minimal, user-facing skeleton that makes the data flow explicit: +**load/download** → **merge** → **inspect** → **visualize**. + +Recommended: one-shot inspection CLI (MERRA-2) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +.. code-block:: bash + + # Full pipeline (recommended) + python -m pyhazards.datasets.inspection 20260101 + + # Change the surface variable to summarize/plot (default: T2M) + python -m pyhazards.datasets.inspection 20260101 --var QV2M + + # If raw/merged files already exist, skip steps accordingly + python -m pyhazards.datasets.inspection 20260101 --skip-download --skip-merge + + # Force re-download even if files already exist + python -m pyhazards.datasets.inspection 20260101 --force-download + + # Change output directory (relative to repo root, unless absolute path) + python -m pyhazards.datasets.inspection 20260101 --outdir outputs_merra2_20260101 + + +Implementing a custom dataset (developer example) +------------------------------------------------- + +If you want to add a new dataset to PyHazards, implement ``Dataset._load()`` and register it. + .. 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 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) diff --git a/docs/pyhazards_datasets.html b/docs/pyhazards_datasets.html index 07e1be46..95d24718 100644 --- a/docs/pyhazards_datasets.html +++ b/docs/pyhazards_datasets.html @@ -263,7 +263,8 @@

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 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.

Datasets

@@ -304,13 +305,23 @@

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 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.

+
# One command: download raw MERRA-2 (if needed) -> merge SFC+PRES -> inspect -> save plots/tables
+python -m pyhazards.datasets.inspection 20260101
 

 

+
Notes:

+
    
+
  • MERRA-2 download requires Earthdata credentials via environment variables:
    
+
    export EARTHDATA_USERNAME="YOUR_USERNAME"
+export EARTHDATA_PASSWORD="YOUR_PASSWORD"
+
    
+
    
+
    • 
+
  • Outputs are written to outputs/ under the repo root by default (can be changed via --outdir).
    • 
+
  • Merged NetCDF files are written under Prithvi-WxC/data/merra-2 by default (repo-root inferred automatically).
    • 
+

Core classes

@@ -323,6 +334,31 @@

Core classes

Example skeleton

+

This section provides a minimal, user-facing skeleton that makes the data flow explicit: +load/download → merge → inspect → visualize.

+ +

+
+

Implementing a custom dataset (developer example)

+

If you want to add a new dataset to PyHazards, implement Dataset._load() and register it.

import torch
 from pyhazards.datasets import (
     DataBundle, DataSplit, Dataset, FeatureSpec, LabelSpec, register_dataset
@@ -411,7 +447,11 @@ 
Example skeletonDatasets
 
  • Dataset inspection
    • 
 
  • Core classes
    • 
-
  • Example skeleton
    • 
+
  • Example skeleton
+
    • 
+
  • Implementing a custom dataset (developer example)
    • 
 
 
 
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 7243a251..ce200b84 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/source/pyhazards_datasets.rst b/docs/source/pyhazards_datasets.rst
index 70388ad2..13664858 100644
--- a/docs/source/pyhazards_datasets.rst
+++ b/docs/source/pyhazards_datasets.rst
@@ -4,7 +4,9 @@ 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 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.
+
 
 Datasets
 --------------------
@@ -38,19 +40,29 @@ 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 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.
+The example below demonstrates how to inspect a daily MERRA-2 file using the PyHazards dataset interface.
 
 .. code-block:: bash
 
+   # One command: download raw MERRA-2 (if needed) -> merge SFC+PRES -> inspect -> save plots/tables
    python -m pyhazards.datasets.inspection 20260101
 
+Notes:
+
+- MERRA-2 download requires Earthdata credentials via environment variables::
+
+     export EARTHDATA_USERNAME="YOUR_USERNAME"
+     export EARTHDATA_PASSWORD="YOUR_PASSWORD"
+
+- Outputs are written to ``outputs/`` under the repo root by default (can be changed via ``--outdir``).
+- Merged NetCDF files are written under ``Prithvi-WxC/data/merra-2`` by default (repo-root inferred automatically).
+
 
 Core classes
 ------------
@@ -60,31 +72,61 @@ Core classes
 - ``FeatureSpec`` / ``LabelSpec``: describe inputs/targets to simplify model construction.
 - ``register_dataset`` / ``load_dataset``: lightweight registry for discovering datasets by name.
 
+
 Example skeleton
 ----------------
 
+This section provides a minimal, user-facing skeleton that makes the data flow explicit:
+**load/download** → **merge** → **inspect** → **visualize**.
+
+Recommended: one-shot inspection CLI (MERRA-2)
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: bash
+
+   # Full pipeline (recommended)
+   python -m pyhazards.datasets.inspection 20260101
+
+   # Change the surface variable to summarize/plot (default: T2M)
+   python -m pyhazards.datasets.inspection 20260101 --var QV2M
+
+   # If raw/merged files already exist, skip steps accordingly
+   python -m pyhazards.datasets.inspection 20260101 --skip-download --skip-merge
+
+   # Force re-download even if files already exist
+   python -m pyhazards.datasets.inspection 20260101 --force-download
+
+   # Change output directory (relative to repo root, unless absolute path)
+   python -m pyhazards.datasets.inspection 20260101 --outdir outputs_merra2_20260101
+
+
+Implementing a custom dataset (developer example)
+-------------------------------------------------
+
+If you want to add a new dataset to PyHazards, implement ``Dataset._load()`` and register it.
+
 .. 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 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)

From 5c059e0365db0cb5c6117cd9c15d26461d440e40 Mon Sep 17 00:00:00 2001
From: Runyang Xu 
Date: Thu, 5 Feb 2026 05:19:21 -0500
Subject: [PATCH 2/4] change dataset part

---
 docs/.doctrees/environment.pickle         | Bin 294873 -> 294436 bytes
 docs/.doctrees/pyhazards_datasets.doctree | Bin 31739 -> 32644 bytes
 docs/_sources/pyhazards_datasets.rst.txt  |  45 +++++++++++--------
 docs/pyhazards_datasets.html              |  50 +++++++++++++---------
 docs/searchindex.js                       |   2 +-
 docs/source/pyhazards_datasets.rst        |  45 +++++++++++--------
 6 files changed, 86 insertions(+), 56 deletions(-)

diff --git a/docs/.doctrees/environment.pickle b/docs/.doctrees/environment.pickle
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Y7*NL(Vm*aPrYDataset inspectionoutputs/ under the repo root by default (can be changed via --outdir).
    
-
  • Merged NetCDF files are written under Prithvi-WxC/data/merra-2 by default (repo-root inferred automatically).
    • 
+
  • By default, outputs are written to outputs/ (can be changed via --outdir).
    • 
+
  • The same inspection pattern is expected to be supported by other datasets (e.g., mtbs, era5),
+with dataset-specific I/O handled inside each dataset implementation.
    •
    @@ -334,28 +335,38 @@

    Core classes

    Example skeleton

    -

    This section provides a minimal, user-facing skeleton that makes the data flow explicit: -load/download → merge → inspect → visualize.

    - -

    Implementing a custom dataset (developer example)

    If you want to add a new dataset to PyHazards, implement Dataset._load() and register it.

    @@ -447,10 +458,7 @@

    Implementing a custom dataset (developer example)Datasets
  • Dataset inspection
  • Core classes
    • -
  • Example skeleton -
    • +
  • Example skeleton
  • Implementing a custom dataset (developer example)
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22, "registri": [2, 5], "requir": 18, "shot": [], "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 13664858..20d32ab8 100644 --- a/docs/source/pyhazards_datasets.rst +++ b/docs/source/pyhazards_datasets.rst @@ -60,8 +60,9 @@ Notes: export EARTHDATA_USERNAME="YOUR_USERNAME" export EARTHDATA_PASSWORD="YOUR_PASSWORD" -- Outputs are written to ``outputs/`` under the repo root by default (can be changed via ``--outdir``). -- Merged NetCDF files are written under ``Prithvi-WxC/data/merra-2`` by default (repo-root inferred automatically). +- By default, outputs are written to ``outputs/`` (can be changed via ``--outdir``). +- The same inspection pattern is expected to be supported by other datasets (e.g., ``mtbs``, ``era5``), + with dataset-specific I/O handled inside each dataset implementation. Core classes @@ -76,28 +77,38 @@ Core classes Example skeleton ---------------- -This section provides a minimal, user-facing skeleton that makes the data flow explicit: -**load/download** → **merge** → **inspect** → **visualize**. +This is a minimal end-to-end skeleton showing **load data → inspect → visualization**. +The key point is to make the data flow explicit (i.e., you should see a line like +``data = load_dataset("merra2")`` or ``data = load_dataset("mtbs")``). -Recommended: one-shot inspection CLI (MERRA-2) -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. code-block:: python -.. code-block:: bash + # 0) Pick a dataset to load (use accordingly) + # Options include: "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr", ... + from pyhazards.datasets import load_dataset - # Full pipeline (recommended) - python -m pyhazards.datasets.inspection 20260101 + dataset_name = "merra2" # or: "mtbs", "era5", ... + data = load_dataset(dataset_name) # dataset-specific config/paths can be passed via kwargs + + # 1) Load / materialize a DataBundle (dataset decides how to interpret the "key" such as date/id) + # Example key: a daily date for MERRA-2, or an event/scene id for other datasets. + bundle = data.load(key="20260101") # replace "key" with the appropriate identifier - # Change the surface variable to summarize/plot (default: T2M) - python -m pyhazards.datasets.inspection 20260101 --var QV2M + # 2) Inspect the returned structure + print("splits:", list(bundle.splits.keys())) + print("feature_spec:", bundle.feature_spec) + print("label_spec:", bundle.label_spec) - # If raw/merged files already exist, skip steps accordingly - python -m pyhazards.datasets.inspection 20260101 --skip-download --skip-merge + split = bundle.splits.get("train", next(iter(bundle.splits.values()))) + x, y = split.x, split.y + print("x:", getattr(x, "shape", type(x)), "y:", getattr(y, "shape", type(y))) - # Force re-download even if files already exist - python -m pyhazards.datasets.inspection 20260101 --force-download + # 3) Visualization (examples; use the appropriate visualization for your data type) + # Raster: show a 2D slice (H x W) or a channel (C) at a given timestep (T) + # Time series: plot a variable curve over time; Tabular: histogram/boxplot + from pyhazards.viz import show_raster # replace with your actual viz utility - # Change output directory (relative to repo root, unless absolute path) - python -m pyhazards.datasets.inspection 20260101 --outdir outputs_merra2_20260101 + show_raster(x[0], title=f"{dataset_name}: first sample (train)") # use accordingly Implementing a custom dataset (developer example) From cb210f4e06956c3e745359d03ce5442e209b4f10 Mon Sep 17 00:00:00 2001 From: Runyang Xu Date: Thu, 5 Feb 2026 05:53:40 -0500 Subject: [PATCH 3/4] change dataset part --- docs/.doctrees/environment.pickle | Bin 294436 -> 294912 bytes docs/.doctrees/pyhazards_datasets.doctree | Bin 32644 -> 34676 bytes 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zqk|&?Wsgggv76oYcc`4lw6Ft59E4#2`Vb`~U}ZfPR=d>_k_uc3PHoKE^^lplaBre8 zti3hX<5oE;H`){HAG*@<@E%KlU$U>@35*qp|1$0t0*}KoKw^9yYyyUW6*71-D1RB0 zUONK9IJe&YVCN!48eZ`Y;RxPvp@C>%q*o1z8;)dYXsE344&et*wdqB merge SFC+PRES -> inspect -> save plots/tables + # One command: download (if needed) -> merge -> inspect -> save outputs python -m pyhazards.datasets.inspection 20260101 -Notes: -- MERRA-2 download requires Earthdata credentials via environment variables:: +Notes (MERRA-2) +~~~~~~~~~~~~~~~ + +- Download requires Earthdata credentials via environment variables:: export EARTHDATA_USERNAME="YOUR_USERNAME" export EARTHDATA_PASSWORD="YOUR_PASSWORD" -- By default, outputs are written to ``outputs/`` (can be changed via ``--outdir``). -- The same inspection pattern is expected to be supported by other datasets (e.g., ``mtbs``, ``era5``), - with dataset-specific I/O handled inside each dataset implementation. - - -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. +- 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 ---------------- -This is a minimal end-to-end skeleton showing **load data → inspect → visualization**. -The key point is to make the data flow explicit (i.e., you should see a line like -``data = load_dataset("merra2")`` or ``data = load_dataset("mtbs")``). +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 - # 0) Pick a dataset to load (use accordingly) - # Options include: "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr", ... - from pyhazards.datasets import load_dataset + import subprocess - dataset_name = "merra2" # or: "mtbs", "era5", ... - data = load_dataset(dataset_name) # dataset-specific config/paths can be passed via kwargs + # 1) Choose what dataset you want to load/inspect + data = "merra2" # e.g., "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr" (use accordingly) - # 1) Load / materialize a DataBundle (dataset decides how to interpret the "key" such as date/id) - # Example key: a daily date for MERRA-2, or an event/scene id for other datasets. - bundle = data.load(key="20260101") # replace "key" with the appropriate identifier + # 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" - # 2) Inspect the returned structure - print("splits:", list(bundle.splits.keys())) - print("feature_spec:", bundle.feature_spec) - print("label_spec:", bundle.label_spec) + # 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"] - split = bundle.splits.get("train", next(iter(bundle.splits.values()))) - x, y = split.x, split.y - print("x:", getattr(x, "shape", type(x)), "y:", getattr(y, "shape", type(y))) + subprocess.run(cmd, check=True) - # 3) Visualization (examples; use the appropriate visualization for your data type) - # Raster: show a 2D slice (H x W) or a channel (C) at a given timestep (T) - # Time series: plot a variable curve over time; Tabular: histogram/boxplot - from pyhazards.viz import show_raster # replace with your actual viz utility + # 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. - show_raster(x[0], title=f"{dataset_name}: first sample (train)") # use accordingly +Inspection entrypoints (convention for all datasets) +---------------------------------------------------- -Implementing a custom dataset (developer example) -------------------------------------------------- +Each dataset should expose a minimal inspection entrypoint that supports the same user experience: -If you want to add a new dataset to PyHazards, implement ``Dataset._load()`` and register it. +- **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). -.. code-block:: python +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: - 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) +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 219b6455..363a111b 100644 --- a/docs/pyhazards_datasets.html +++ b/docs/pyhazards_datasets.html @@ -263,8 +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

    @@ -305,97 +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.

    -
    # One command: download raw MERRA-2 (if needed) -> merge SFC+PRES -> inspect -> save plots/tables
+
    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
 
    
 
    
-
    Notes:
    
+
    
+
    Notes (MERRA-2)
    
 
      
-
    • MERRA-2 download requires Earthdata credentials via environment variables:
      
+
    • Download requires Earthdata credentials via environment variables:
      
 
      export EARTHDATA_USERNAME="YOUR_USERNAME"
 export EARTHDATA_PASSWORD="YOUR_PASSWORD"
 
      
 
      
 
      • 
-
    • By default, outputs are written to outputs/ (can be changed via --outdir).
      • 
-
    • The same inspection pattern is expected to be supported by other datasets (e.g., mtbs, era5),
-with dataset-specific I/O handled inside each dataset implementation.
      • 
+
    • 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)
      • 
 
    
 
    
-
    
-
    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.
      • 
-
    
 
    
 
    
 
    Example skeleton
    
-
    This is a minimal end-to-end skeleton showing load data → inspect → visualization.
-The key point is to make the data flow explicit (i.e., you should see a line like
-data = load_dataset("merra2") or data = load_dataset("mtbs")).
    
-
    # 0) Pick a dataset to load (use accordingly)
-#    Options include: "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr", ...
-from pyhazards.datasets import load_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
 
-dataset_name = "merra2"  # or: "mtbs", "era5", ...
-data = load_dataset(dataset_name)   # dataset-specific config/paths can be passed via kwargs
+# 1) Choose what dataset you want to load/inspect
+data = "merra2"   # e.g., "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr" (use accordingly)
 
-# 1) Load / materialize a DataBundle (dataset decides how to interpret the "key" such as date/id)
-#    Example key: a daily date for MERRA-2, or an event/scene id for other datasets.
-bundle = data.load(key="20260101")  # replace "key" with the appropriate identifier
+# 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"
 
-# 2) Inspect the returned structure
-print("splits:", list(bundle.splits.keys()))
-print("feature_spec:", bundle.feature_spec)
-print("label_spec:", bundle.label_spec)
+# 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"]
 
-split = bundle.splits.get("train", next(iter(bundle.splits.values())))
-x, y = split.x, split.y
-print("x:", getattr(x, "shape", type(x)), "y:", getattr(y, "shape", type(y)))
+subprocess.run(cmd, check=True)
 
-# 3) Visualization (examples; use the appropriate visualization for your data type)
-#    Raster: show a 2D slice (H x W) or a channel (C) at a given timestep (T)
-#    Time series: plot a variable curve over time; Tabular: histogram/boxplot
-from pyhazards.viz import show_raster  # replace with your actual viz utility
-
-show_raster(x[0], title=f"{dataset_name}: first sample (train)")  # use accordingly
+# 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.
 
    
 
    
 
    
-
    
-
    Implementing a custom dataset (developer example)
    
-
    If you want to add a new dataset to PyHazards, implement Dataset._load() and register it.
    
-
    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)
+
    
+
    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. 
+
    
+
    
 
    
 
         
@@ -456,10 +456,13 @@ 
    Implementing a custom dataset (developer example)Datasets
 
 
diff --git a/docs/searchindex.js b/docs/searchindex.js
index e7e68b16..efb99ad6 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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\ No newline at end of file
diff --git a/docs/source/pyhazards_datasets.rst b/docs/source/pyhazards_datasets.rst
index 20d32ab8..2bd18413 100644
--- a/docs/source/pyhazards_datasets.rst
+++ b/docs/source/pyhazards_datasets.rst
@@ -4,8 +4,11 @@ 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
@@ -44,100 +47,99 @@ 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 raw MERRA-2 (if needed) -> merge SFC+PRES -> inspect -> save plots/tables
+   # One command: download (if needed) -> merge -> inspect -> save outputs
    python -m pyhazards.datasets.inspection 20260101
 
-Notes:
 
-- MERRA-2 download requires Earthdata credentials via environment variables::
+Notes (MERRA-2)
+~~~~~~~~~~~~~~~
+
+- Download requires Earthdata credentials via environment variables::
 
      export EARTHDATA_USERNAME="YOUR_USERNAME"
      export EARTHDATA_PASSWORD="YOUR_PASSWORD"
 
-- By default, outputs are written to ``outputs/`` (can be changed via ``--outdir``).
-- The same inspection pattern is expected to be supported by other datasets (e.g., ``mtbs``, ``era5``),
-  with dataset-specific I/O handled inside each dataset implementation.
-
-
-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.
+- 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
 ----------------
 
-This is a minimal end-to-end skeleton showing **load data → inspect → visualization**.
-The key point is to make the data flow explicit (i.e., you should see a line like
-``data = load_dataset("merra2")`` or ``data = load_dataset("mtbs")``).
+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
 
-   # 0) Pick a dataset to load (use accordingly)
-   #    Options include: "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr", ...
-   from pyhazards.datasets import load_dataset
+   import subprocess
 
-   dataset_name = "merra2"  # or: "mtbs", "era5", ...
-   data = load_dataset(dataset_name)   # dataset-specific config/paths can be passed via kwargs
+   # 1) Choose what dataset you want to load/inspect
+   data = "merra2"   # e.g., "merra2", "mtbs", "era5", "firms", "landfire", "wfigs", "goesr" (use accordingly)
 
-   # 1) Load / materialize a DataBundle (dataset decides how to interpret the "key" such as date/id)
-   #    Example key: a daily date for MERRA-2, or an event/scene id for other datasets.
-   bundle = data.load(key="20260101")  # replace "key" with the appropriate identifier
+   # 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"
 
-   # 2) Inspect the returned structure
-   print("splits:", list(bundle.splits.keys()))
-   print("feature_spec:", bundle.feature_spec)
-   print("label_spec:", bundle.label_spec)
+   # 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"]
 
-   split = bundle.splits.get("train", next(iter(bundle.splits.values())))
-   x, y = split.x, split.y
-   print("x:", getattr(x, "shape", type(x)), "y:", getattr(y, "shape", type(y)))
+   subprocess.run(cmd, check=True)
 
-   # 3) Visualization (examples; use the appropriate visualization for your data type)
-   #    Raster: show a 2D slice (H x W) or a channel (C) at a given timestep (T)
-   #    Time series: plot a variable curve over time; Tabular: histogram/boxplot
-   from pyhazards.viz import show_raster  # replace with your actual viz utility
+   # 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.
 
-   show_raster(x[0], title=f"{dataset_name}: first sample (train)")  # use accordingly
 
+Inspection entrypoints (convention for all datasets)
+----------------------------------------------------
 
-Implementing a custom dataset (developer example)
--------------------------------------------------
+Each dataset should expose a minimal inspection entrypoint that supports the same user experience:
 
-If you want to add a new dataset to PyHazards, implement ``Dataset._load()`` and register it.
+- **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).
 
-.. code-block:: python
+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:
 
-   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)
+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/``.

From 77f1c4adae2fc92814f1e76b195f84a9dd7d6635 Mon Sep 17 00:00:00 2001
From: Runyang Xu 
Date: Thu, 5 Feb 2026 06:32:18 -0500
Subject: [PATCH 4/4] change dataset part

---
 docs/searchindex.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/searchindex.js b/docs/searchindex.js
index efb99ad6..e056e83f 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1 +1 @@
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