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use rst for migration guide
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docs/v2/index.rst

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@@ -56,7 +56,6 @@ For optional features, if you are on Linux, make sure wgrib2 is installed and in
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whats-new
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migration-guide
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migration-guide-alt
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.. toctree::
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:maxdepth: 1

docs/v2/migration-guide.md

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@@ -52,6 +52,8 @@ Create a Herbie Object
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.. grid-item-card:: v1
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Specify model with ``model=`` argument
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.. code-block:: python
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from herbie import Herbie
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.. grid-item-card:: v2
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Import and use a Herbie model class
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.. code-block:: python
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from herbie.v2 import HRRR
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step=12
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)
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Or via the namespace:
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Or use model class via the Herbie namespace:
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.. code-block:: python
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.. code-block:: python
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from herbie.v2 import Herbie
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from herbie.v2 import Herbie
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H = Herbie.HRRR("2025-01-01", step=12)
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H = Herbie.HRRR("2025-01-01", step=12)
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----
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H = HRRR("2025-01-01", step=6)
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You can also use a ``timedelta``:
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You can also use a ``timedelta``:
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.. code-block:: python
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.. code-block:: python
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from datetime import timedelta
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from datetime import timedelta
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H = HRRR("2025-01-01", step=timedelta(hours=6))
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H = HRRR("2025-01-01", step=timedelta(hours=6))
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pl.col("variable") == "TMP"
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)
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Benefits:
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Benefits:
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- Faster performance
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- Flexible filtering
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- Access to source metadata
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- Faster performance
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- Flexible filtering
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- Access to source metadata
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H.download("TMP:2 m")
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The basic workflow is unchanged, but downloads now:
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The basic workflow is unchanged, but...
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- Run subsets in **parallel threads**
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- Display **Rich progress bars**
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- Preserve the **remote file path structure**
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- Downloads each subset group in **parallel threads**
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- Displays **Rich progress bars**
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- Preserves the **remote file path structure**
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ds = H.xarray("TMP:2 m")
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If multiple hypercubes are present, v2 returns an **xarray DataTree** rather
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than flattening them.
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If multiple hypercubes are present, v2 returns an **xarray DataTree** rather than flattening them.
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FastHerbie Changes
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------------------
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``FastHerbie`` has been redesigned.
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.. grid:: 2
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.. grid-item-card:: v2
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``FastHerbie`` has been completely redesigned.
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Now enables **building custom GRIB files from multiple model runs**.
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Example use cases:

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