From ffa795b908fe44da8ca3e787e8c40637d49e7a95 Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Thu, 15 Jan 2026 10:47:06 -0700
Subject: [PATCH 1/6] add mode kwarg to samplers.

---
 sup3r/models/abstract.py                      | 17 ++++++---
 sup3r/models/base.py                          | 38 ++++++++++++-------
 sup3r/models/utilities.py                     |  7 ++--
 sup3r/preprocessing/base.py                   |  6 ++-
 sup3r/preprocessing/batch_handlers/factory.py |  4 ++
 sup3r/preprocessing/batch_queues/abstract.py  |  4 --
 sup3r/preprocessing/samplers/base.py          | 34 +++++++++++++++--
 sup3r/preprocessing/samplers/cc.py            |  7 ++++
 sup3r/preprocessing/samplers/dual.py          | 11 +++++-
 tests/batch_handlers/test_bh_general.py       |  2 +
 tests/training/test_train_gan.py              |  1 +
 11 files changed, 100 insertions(+), 31 deletions(-)

diff --git a/sup3r/models/abstract.py b/sup3r/models/abstract.py
index d3e93a8431..5b0b61d522 100644
--- a/sup3r/models/abstract.py
+++ b/sup3r/models/abstract.py
@@ -1230,11 +1230,18 @@ def get_single_grad(
         with tf.device(device_name), tf.GradientTape(
             watch_accessed_variables=False
         ) as tape:
-            tape.watch(training_weights)
-            loss, loss_details, _, _ = self._get_hr_exo_and_loss(
-                low_res, hi_res_true, **calc_loss_kwargs
-            )
-            grad = tape.gradient(loss, training_weights)
+            try:
+                tape.watch(training_weights)
+                loss, loss_details, _, _ = self._get_hr_exo_and_loss(
+                    low_res, hi_res_true, **calc_loss_kwargs
+                )
+                grad = tape.gradient(loss, training_weights)
+            except Exception as e:
+                msg = 'Error computing gradients on device {}: {}'.format(
+                    device_name, e
+                )
+                logger.error(msg)
+                raise RuntimeError(msg) from e
         return grad, loss_details
 
     @abstractmethod
diff --git a/sup3r/models/base.py b/sup3r/models/base.py
index 6ce24edc8e..9e9112fcc1 100644
--- a/sup3r/models/base.py
+++ b/sup3r/models/base.py
@@ -638,8 +638,8 @@ def train(
         adaptive_update_bounds=(0.9, 0.99),
         adaptive_update_fraction=0.0,
         multi_gpu=False,
-        tensorboard_log=False,
-        tensorboard_profile=False,
+        log_tb=False,
+        export_tb=False,
     ):
         """Train the GAN model on real low res data and real high res data
 
@@ -703,12 +703,12 @@ def train(
             rate that the model and optimizer were initialized with.
             If true and multiple gpus are found, ``default_device`` device
             should be set to /gpu:0
-        tensorboard_log : bool
+        log_tb : bool
             Whether to write log file for use with tensorboard. Log data can
             be viewed with ``tensorboard --logdir <logdir>`` where ``<logdir>``
             is the parent directory of ``out_dir``, and pointing the browser to
             the printed address.
-        tensorboard_profile : bool
+        export_tb : bool
             Whether to export profiling information to tensorboard. This can
             then be viewed in the tensorboard dashboard under the profile tab
 
@@ -720,10 +720,8 @@ def train(
         (3) Would like an automatic way to exit the batch handler thread
         instead of manually calling .stop() here.
         """
-        if tensorboard_log:
+        if log_tb:
             self._init_tensorboard_writer(out_dir)
-        if tensorboard_profile:
-            self._write_tb_profile = True
 
         self.set_norm_stats(batch_handler.means, batch_handler.stds)
         params = self.check_batch_handler_attrs(batch_handler)
@@ -759,6 +757,7 @@ def train(
                 train_disc,
                 disc_loss_bounds,
                 multi_gpu=multi_gpu,
+                export_tb=export_tb,
             )
             loss_details.update(
                 self.calc_val_loss(batch_handler, weight_gen_advers)
@@ -1071,7 +1070,7 @@ def _post_batch(self, ib, b_loss_details, n_batches, previous_means):
         disc_loss = self._train_record['train_loss_disc'].values.mean()
         gen_loss = self._train_record['train_loss_gen'].values.mean()
 
-        logger.debug(
+        logger.info(
             'Batch {} out of {} has (gen / disc) loss of: ({:.2e} / {:.2e}). '
             'Running mean (gen / disc): ({:.2e} / {:.2e}). Trained '
             '(gen / disc): ({} / {})'.format(
@@ -1102,6 +1101,7 @@ def _train_epoch(
         train_disc,
         disc_loss_bounds,
         multi_gpu=False,
+        export_tb=False,
     ):
         """Train the GAN for one epoch.
 
@@ -1129,6 +1129,9 @@ def _train_epoch(
             rate that the model and optimizer were initialized with.
             If true and multiple gpus are found, ``default_device`` device
             should be set to /gpu:0
+        export_tb : bool
+            Whether to export profiling information to tensorboard. This can
+            then be viewed in the tensorboard dashboard under the profile tab
 
         Returns
         -------
@@ -1151,9 +1154,10 @@ def _train_epoch(
         only_gen = train_gen and not train_disc
         only_disc = train_disc and not train_gen
 
-        if self._write_tb_profile:
+        if export_tb:
             tf.summary.trace_on(graph=True, profiler=True)
 
+        prev_time = time.time()
         for ib, batch in enumerate(batch_handler):
             start = time.time()
 
@@ -1163,7 +1167,7 @@ def _train_epoch(
             disc_too_bad = (loss_disc > disc_th_high) and train_disc
             gen_too_good = disc_too_bad
 
-            b_loss_details = self.timer(self._train_batch, log=True)(
+            b_loss_details = self._train_batch(
                 batch,
                 train_gen,
                 only_gen,
@@ -1175,17 +1179,25 @@ def _train_epoch(
                 multi_gpu,
             )
 
-            loss_means = self.timer(self._post_batch, log=True)(
+            loss_means = self._post_batch(
                 ib, b_loss_details, len(batch_handler), loss_means
             )
 
+            total_step_time = time.time() - prev_time
+            batch_step_time = time.time() - start
+            batch_load_time = total_step_time - batch_step_time
+
             logger.info(
                 f'Finished batch step {ib + 1} / {len(batch_handler)} in '
-                f'{time.time() - start:.4f} seconds'
+                f'{total_step_time:.4f} seconds. Batch load time: '
+                f'{batch_load_time:.4f} seconds. Batch train time: '
+                f'{batch_step_time:.4f} seconds.'
             )
 
+            prev_time = time.time()
+
         self.total_batches += len(batch_handler)
         loss_details = self._train_record.mean().to_dict()
         loss_details['total_batches'] = int(self.total_batches)
-        self.profile_to_tensorboard('training_epoch')
+        self.profile_to_tensorboard('training_epoch', export_tb=export_tb)
         return loss_details
diff --git a/sup3r/models/utilities.py b/sup3r/models/utilities.py
index 07d169c397..f73a320466 100644
--- a/sup3r/models/utilities.py
+++ b/sup3r/models/utilities.py
@@ -80,7 +80,6 @@ class TensorboardMixIn:
     def __init__(self):
         self._tb_writer = None
         self._tb_log_dir = None
-        self._write_tb_profile = False
         self._total_batches = None
         self._history = None
         self.timer = Timer()
@@ -116,15 +115,17 @@ def dict_to_tensorboard(self, entry):
                 else:
                     tf.summary.scalar(name, value, self.total_batches)
 
-    def profile_to_tensorboard(self, name):
+    def profile_to_tensorboard(self, name, export_tb=True):
         """Write profile data to tensorboard log file.
 
         Parameters
         ----------
         name : str
             Tag name to use for profile info
+        export_tb : bool
+            Flag to enable/disable tensorboard profiling
         """
-        if self._tb_writer is not None and self._write_tb_profile:
+        if self._tb_writer is not None and export_tb:
             with self._tb_writer.as_default():
                 tf.summary.trace_export(
                     name=name,
diff --git a/sup3r/preprocessing/base.py b/sup3r/preprocessing/base.py
index a1e142c85b..6e55652d7f 100644
--- a/sup3r/preprocessing/base.py
+++ b/sup3r/preprocessing/base.py
@@ -24,6 +24,7 @@
     composite_info,
     is_type_of,
 )
+from sup3r.utilities.utilities import Timer
 
 logger = logging.getLogger(__name__)
 
@@ -44,7 +45,7 @@ class Sup3rMeta(ABCMeta, type):
     kwargs as ``*args`` / ``**kwargs`` or those built through factory
     composition, for example."""
 
-    def __new__(mcs, name, bases, namespace, **kwargs):  # noqa: N804
+    def __new__(mcs, name, bases, namespace, **kwargs):
         """Define __name__ and __signature__"""
         sig, doc = _get_class_info(namespace)
         name = namespace.get('__name__', name)
@@ -318,7 +319,7 @@ class Container(metaclass=Sup3rMeta):
     ``Sup3rX`` objects (:class:`.Sup3rDataset`), or a tuple of such objects.
     """
 
-    __slots__ = ['_data']
+    __slots__ = ['_data', 'timer']
 
     def __init__(
         self,
@@ -353,6 +354,7 @@ def __init__(
             :class:`~.samplers.DualSampler`, and a 1-tuple otherwise.
         """
         self.data = data
+        self.timer = Timer()
 
     @property
     def data(self):
diff --git a/sup3r/preprocessing/batch_handlers/factory.py b/sup3r/preprocessing/batch_handlers/factory.py
index 1613da1dcb..c22e19992d 100644
--- a/sup3r/preprocessing/batch_handlers/factory.py
+++ b/sup3r/preprocessing/batch_handlers/factory.py
@@ -211,6 +211,7 @@ def __init__(
                 sample_shape=sample_shape,
                 feature_sets=feature_sets,
                 batch_size=batch_size,
+                mode=mode,
                 sampler_kwargs=sampler_kwargs,
             )
 
@@ -260,6 +261,7 @@ def init_samplers(
             sample_shape,
             feature_sets,
             batch_size,
+            mode,
             sampler_kwargs,
         ):
             """Initialize samplers from given data containers."""
@@ -269,6 +271,7 @@ def init_samplers(
                     sample_shape=sample_shape,
                     feature_sets=feature_sets,
                     batch_size=batch_size,
+                    mode=mode,
                     **sampler_kwargs,
                 )
                 for container in train_containers
@@ -282,6 +285,7 @@ def init_samplers(
                         sample_shape=sample_shape,
                         feature_sets=feature_sets,
                         batch_size=batch_size,
+                        mode=mode,
                         **sampler_kwargs,
                     )
                     for container in val_containers
diff --git a/sup3r/preprocessing/batch_queues/abstract.py b/sup3r/preprocessing/batch_queues/abstract.py
index e6695c8ead..4c5bef05e8 100644
--- a/sup3r/preprocessing/batch_queues/abstract.py
+++ b/sup3r/preprocessing/batch_queues/abstract.py
@@ -153,10 +153,6 @@ def preflight(self):
         )
         assert sampler_bs == self.batch_size, msg
 
-        if self.mode == 'eager':
-            logger.info('Received mode = "eager".')
-            _ = [c.compute() for c in self.containers]
-
     @property
     def queue_thread(self):
         """Get new queue thread."""
diff --git a/sup3r/preprocessing/samplers/base.py b/sup3r/preprocessing/samplers/base.py
index 693eceabfa..bbeb4e0a66 100644
--- a/sup3r/preprocessing/samplers/base.py
+++ b/sup3r/preprocessing/samplers/base.py
@@ -14,7 +14,7 @@
     uniform_box_sampler,
     uniform_time_sampler,
 )
-from sup3r.preprocessing.utilities import compute_if_dask, lowered
+from sup3r.preprocessing.utilities import lowered
 
 logger = logging.getLogger(__name__)
 
@@ -29,6 +29,7 @@ def __init__(
         sample_shape: Optional[tuple] = None,
         batch_size: int = 16,
         feature_sets: Optional[dict] = None,
+        mode: str = 'lazy'
     ):
         """
         Parameters
@@ -65,12 +66,18 @@ def __init__(
                 in the high-resolution observation but not expected to be
                 output from the generative model. An example is high-res
                 topography that is to be injected mid-network.
+        mode : str
+            Mode for sampling data. Options are 'lazy' or 'eager'. 'eager' mode
+            pre-loads all data into memory as numpy arrays for faster access.
+            'lazy' mode samples directly from the underlying data object, which
+            could be backed by dask arrays or on-disk netCDF files.
         """
         super().__init__(data=data)
         feature_sets = feature_sets or {}
         self.features = feature_sets.get('features', self.data.features)
         self._lr_only_features = feature_sets.get('lr_only_features', [])
         self._hr_exo_features = feature_sets.get('hr_exo_features', [])
+        self.mode = mode
         self.sample_shape = sample_shape or (10, 10, 1)
         self.batch_size = batch_size
         self.lr_features = self.features
@@ -134,6 +141,10 @@ def preflight(self):
             logger.warning(msg)
             warn(msg)
 
+        if self.mode == 'eager':
+            logger.info('Received mode = "eager".')
+            _ = self.compute()
+
     @property
     def sample_shape(self) -> tuple:
         """Shape of the data sample to select when ``__next__()`` is called."""
@@ -225,10 +236,27 @@ def _stack_samples(self, samples):
             return (lr, hr)
         return np.stack(samples, axis=0)
 
+    def _compute_samples(self, samples):
+        """Cast samples to numpy arrays. This only does something when samples
+        are dask arrays.
+
+        Parameters
+        ----------
+        samples : tuple[np.ndarray | da.core.Array, ...] |
+                  np.ndarray | da.core.Array
+            Samples retrieved from the underlying data. Could be a tuple
+            in the case of dual datasets.
+        """
+        if self.mode == 'lazy':
+            return samples
+        if isinstance(samples, tuple):
+            return tuple(np.asarray(s) for s in samples)
+        return np.asarray(samples)
+
     def _fast_batch(self):
         """Get batch of samples with adjacent time slices."""
         out = self.data.sample(self.get_sample_index(n_obs=self.batch_size))
-        out = compute_if_dask(out)
+        out = self._compute_samples(out)
         if isinstance(out, tuple):
             return tuple(self._reshape_samples(o) for o in out)
         return self._reshape_samples(out)
@@ -239,7 +267,7 @@ def _slow_batch(self):
             self.data.sample(self.get_sample_index(n_obs=1))
             for _ in range(self.batch_size)
         ]
-        out = compute_if_dask(out)
+        out = self._compute_samples(out)
         return self._stack_samples(out)
 
     def _fast_batch_possible(self):
diff --git a/sup3r/preprocessing/samplers/cc.py b/sup3r/preprocessing/samplers/cc.py
index 5a3fe883de..7ff98965a2 100644
--- a/sup3r/preprocessing/samplers/cc.py
+++ b/sup3r/preprocessing/samplers/cc.py
@@ -36,6 +36,7 @@ def __init__(
         s_enhance: int = 1,
         t_enhance: int = 24,
         feature_sets: Optional[dict] = None,
+        mode: str = 'lazy',
     ):
         """
         Parameters
@@ -64,6 +65,11 @@ def __init__(
                 in the high-resolution observation but not expected to be
                 output from the generative model. An example is high-res
                 topography that is to be injected mid-network.
+        mode : str
+            Mode for sampling data. Options are 'lazy' or 'eager'. 'eager' mode
+            pre-loads all data into memory as numpy arrays for faster access.
+            'lazy' mode samples directly from the underlying data object, which
+            could be backed by dask arrays or on-disk netCDF files.
 
         See Also
         --------
@@ -91,6 +97,7 @@ def __init__(
             t_enhance=t_enhance,
             s_enhance=s_enhance,
             feature_sets=feature_sets,
+            mode=mode,
         )
 
     def check_for_consistent_shapes(self):
diff --git a/sup3r/preprocessing/samplers/dual.py b/sup3r/preprocessing/samplers/dual.py
index b124315ab2..8eb8a446c0 100644
--- a/sup3r/preprocessing/samplers/dual.py
+++ b/sup3r/preprocessing/samplers/dual.py
@@ -29,6 +29,7 @@ def __init__(
         s_enhance: int = 1,
         t_enhance: int = 1,
         feature_sets: Optional[dict] = None,
+        mode: str = 'lazy',
     ):
         """
         Parameters
@@ -57,6 +58,11 @@ def __init__(
                 in the high-resolution observation but not expected to be
                 output from the generative model. An example is high-res
                 topography that is to be injected mid-network.
+        mode : str
+            Mode for sampling data. Options are 'lazy' or 'eager'. 'eager' mode
+            pre-loads all data into memory as numpy arrays for faster access.
+            'lazy' mode samples directly from the underlying data object, which
+            could be backed by dask arrays or on-disk netCDF files.
         """
         msg = (
             f'{self.__class__.__name__} requires a Sup3rDataset object '
@@ -71,7 +77,10 @@ def __init__(
         assert check, msg
 
         super().__init__(
-            data=data, sample_shape=sample_shape, batch_size=batch_size
+            data=data,
+            sample_shape=sample_shape,
+            batch_size=batch_size,
+            mode=mode,
         )
         self.lr_data, self.hr_data = self.data.low_res, self.data.high_res
         self.lr_sample_shape = (
diff --git a/tests/batch_handlers/test_bh_general.py b/tests/batch_handlers/test_bh_general.py
index 9c7270a616..b3e29ac17b 100644
--- a/tests/batch_handlers/test_bh_general.py
+++ b/tests/batch_handlers/test_bh_general.py
@@ -145,7 +145,9 @@ def test_eager_vs_lazy():
     )
 
     assert eager_batcher.loaded
+    assert eager_batcher.containers[0].mode == 'eager'
     assert not lazy_batcher.loaded
+    assert lazy_batcher.containers[0].mode == 'lazy'
 
     assert np.array_equal(
         eager_batcher.data[0].as_array(),
diff --git a/tests/training/test_train_gan.py b/tests/training/test_train_gan.py
index ed60783dc1..1fa3dcef4f 100644
--- a/tests/training/test_train_gan.py
+++ b/tests/training/test_train_gan.py
@@ -70,6 +70,7 @@ def test_train_disc(
             'n_batches': 1,
             'means': None,
             'stds': None,
+            'mode': 'eager'
         }
         batch_handler = BatchHandler(**bh_kwargs)
 

From a5f40a52beede0ae89ce25804f352ca4c1b2306c Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Tue, 10 Feb 2026 09:16:20 -0700
Subject: [PATCH 2/6] test fixes for DC sampler

---
 sup3r/preprocessing/samplers/dc.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/sup3r/preprocessing/samplers/dc.py b/sup3r/preprocessing/samplers/dc.py
index 2ebba06c71..a48c80df57 100644
--- a/sup3r/preprocessing/samplers/dc.py
+++ b/sup3r/preprocessing/samplers/dc.py
@@ -30,6 +30,7 @@ def __init__(
         sample_shape: Optional[tuple] = None,
         batch_size: int = 16,
         feature_sets: Optional[dict] = None,
+        mode: str = 'lazy',
         spatial_weights: Optional[
             Union[np.ndarray, da.core.Array, list]
         ] = None,
@@ -57,6 +58,9 @@ def __init__(
             Optional dictionary describing how the full set of features is
             split between `lr_only_features` and `hr_exo_features`. See
             :class:`~sup3r.preprocessing.Sampler`
+        mode : str
+            Loading mode for sampling.
+            See :class:`~sup3r.preprocessing.Sampler`
         spatial_weights : Union[np.ndarray, da.core.Array] | List | None
             Set of weights used to initialize the spatial sampling. e.g. If we
             want to start off sampling across 2 spatial bins evenly this should
@@ -76,6 +80,7 @@ def __init__(
             sample_shape=sample_shape,
             batch_size=batch_size,
             feature_sets=feature_sets,
+            mode=mode,
         )
 
     def update_weights(self, spatial_weights, temporal_weights):

From 52af139f8e67018c94625220718a4fdbb9f9bcb2 Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Wed, 11 Feb 2026 15:16:53 -0700
Subject: [PATCH 3/6] _run_exo_layer to classmethod

---
 sup3r/models/abstract.py | 27 +++++++++++----------------
 1 file changed, 11 insertions(+), 16 deletions(-)

diff --git a/sup3r/models/abstract.py b/sup3r/models/abstract.py
index 5b0b61d522..6646d4d1f1 100644
--- a/sup3r/models/abstract.py
+++ b/sup3r/models/abstract.py
@@ -1104,7 +1104,8 @@ def generate(
 
         return self._combine_fwp_output(hi_res, exogenous_data)
 
-    def _run_exo_layer(self, layer, input_array, hi_res_exo):
+    @classmethod
+    def _run_exo_layer(cls, layer, input_array, hi_res_exo):
         """Private run_exo_layer method used in ``_tf_generate``. Runs a layer
         that combines exogenous data with the hi_res data. These layers can
         include single or multiple exogenous features."""
@@ -1227,21 +1228,15 @@ def get_single_grad(
         loss_details : dict
             Namespace of the breakdown of loss components
         """
-        with tf.device(device_name), tf.GradientTape(
-            watch_accessed_variables=False
-        ) as tape:
-            try:
-                tape.watch(training_weights)
-                loss, loss_details, _, _ = self._get_hr_exo_and_loss(
-                    low_res, hi_res_true, **calc_loss_kwargs
-                )
-                grad = tape.gradient(loss, training_weights)
-            except Exception as e:
-                msg = 'Error computing gradients on device {}: {}'.format(
-                    device_name, e
-                )
-                logger.error(msg)
-                raise RuntimeError(msg) from e
+        with (
+            tf.device(device_name),
+            tf.GradientTape(watch_accessed_variables=False) as tape,
+        ):
+            tape.watch(training_weights)
+            loss, loss_details, _, _ = self._get_hr_exo_and_loss(
+                low_res, hi_res_true, **calc_loss_kwargs
+            )
+            grad = tape.gradient(loss, training_weights)
         return grad, loss_details
 
     @abstractmethod

From a2f21d89408ed3c8306e4e0ba33737480c7e877c Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Wed, 11 Feb 2026 16:25:44 -0700
Subject: [PATCH 4/6] fix: need to compute samples if not already loaded

---
 pixi.lock                            | 340 +++++++++++++--------------
 pyproject.toml                       |   6 +-
 sup3r/preprocessing/samplers/base.py |   8 +-
 3 files changed, 169 insertions(+), 185 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index d5c67b7383..64d991df64 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -299,16 +299,15 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py311haee01d2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda
-      - pypi: https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl
+      - pypi: https://files.pythonhosted.org/packages/18/a6/907a406bb7d359e6a63f99c313846d9eec4f7e6f7437809e03aa00fa3074/absl_py-2.4.0-py3-none-any.whl
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 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/flatbuffers-23.5.26-h13dd4ca_1.conda
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   md5: 789ac6fe52a8b2ce8dade900eb30f482
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   timestamp: 1701110534084
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   name: google-auth
-  version: 2.45.0
-  sha256: 82344e86dc00410ef5382d99be677c6043d72e502b625aa4f4afa0bdacca0f36
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   requires_dist:
-  - cachetools>=2.0.0,<7.0
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   - cryptography>=38.0.3 ; extra == 'cryptography'
-  - cryptography<39.0.0 ; python_full_version < '3.8' and extra == 'cryptography'
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-  - cryptography ; extra == 'enterprise-cert'
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-  - cryptography>=38.0.3 ; extra == 'pyopenssl'
-  - cryptography<39.0.0 ; python_full_version < '3.8' and extra == 'pyopenssl'
   - pyjwt>=2.0 ; extra == 'pyjwt'
-  - cryptography>=38.0.3 ; extra == 'pyjwt'
-  - cryptography<39.0.0 ; python_full_version < '3.8' and extra == 'pyjwt'
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   - requests>=2.20.0,<3.0.0 ; extra == 'requests'
   - grpcio ; extra == 'testing'
   - flask ; extra == 'testing'
   - freezegun ; extra == 'testing'
-  - mock ; extra == 'testing'
   - oauth2client ; extra == 'testing'
   - pyjwt>=2.0 ; extra == 'testing'
-  - cryptography>=38.0.3 ; extra == 'testing'
-  - cryptography<39.0.0 ; python_full_version < '3.8' and extra == 'testing'
   - pytest ; extra == 'testing'
   - pytest-cov ; extra == 'testing'
   - pytest-localserver ; extra == 'testing'
   - pyopenssl>=20.0.0 ; extra == 'testing'
-  - cryptography>=38.0.3 ; extra == 'testing'
-  - cryptography<39.0.0 ; python_full_version < '3.8' and extra == 'testing'
   - pyu2f>=0.1.5 ; extra == 'testing'
   - responses ; extra == 'testing'
   - urllib3 ; extra == 'testing'
@@ -6428,7 +6410,7 @@ packages:
   - aiohttp<3.10.0 ; extra == 'testing'
   - urllib3 ; extra == 'urllib3'
   - packaging ; extra == 'urllib3'
-  requires_python: '>=3.7'
+  requires_python: '>=3.8'
 - conda: https://conda.anaconda.org/conda-forge/noarch/google-auth-2.35.0-pyhff2d567_0.conda
   sha256: 62533066d372fd2f5bb9f38e8a44465f404a116210703ec75b88d34c28cc4caa
   md5: 7a6b4c81d9062a9e92b9ef0548aebc06
@@ -6448,15 +6430,15 @@ packages:
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   size: 115445
   timestamp: 1726832994561
-- pypi: https://files.pythonhosted.org/packages/ac/84/40ee070be95771acd2f4418981edb834979424565c3eec3cd88b6aa09d24/google_auth_oauthlib-1.2.2-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/84/21/fb96db432d187b07756e62971c4d89bdef70259e4cfa76ee32bcc0ac97d1/google_auth_oauthlib-1.2.4-py3-none-any.whl
   name: google-auth-oauthlib
-  version: 1.2.2
-  sha256: fd619506f4b3908b5df17b65f39ca8d66ea56986e5472eb5978fd8f3786f00a2
+  version: 1.2.4
+  sha256: 0e922eea5f2baacaf8867febb782e46e7b153236c21592ed76ab3ddb77ffd772
   requires_dist:
-  - google-auth>=2.15.0
+  - google-auth>=2.15.0,!=2.43.0,!=2.44.0,!=2.45.0,<3.0.0
   - requests-oauthlib>=0.7.0
   - click>=6.0.0 ; extra == 'tool'
-  requires_python: '>=3.6'
+  requires_python: '>=3.7'
 - conda: https://conda.anaconda.org/conda-forge/noarch/google-auth-oauthlib-1.2.1-pyhd8ed1ab_0.conda
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   md5: b252850143cd2080d87060f891d3b288
@@ -6512,13 +6494,13 @@ packages:
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   timestamp: 1755102333712
-- pypi: https://files.pythonhosted.org/packages/68/86/093c46e9546073cefa789bd76d44c5cb2abc824ca62af0c18be590ff13ba/grpcio-1.76.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/e5/61/8ac32517c1e856677282c34f2e7812d6c328fa02b8f4067ab80e77fdc9c9/grpcio-1.78.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: grpcio
-  version: 1.76.0
-  sha256: 8843114c0cfce61b40ad48df65abcfc00d4dba82eae8718fab5352390848c5da
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   requires_dist:
   - typing-extensions~=4.12
-  - grpcio-tools>=1.76.0 ; extra == 'protobuf'
+  - grpcio-tools>=1.78.0 ; extra == 'protobuf'
   requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/grpcio-1.59.3-py311hf5d242d_0.conda
   sha256: d881b0a9a45e09e2c3dacf68bfb403088d165a5cac7de8e775de5d205bf09718
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   name: identify
-  version: 2.6.15
-  sha256: 1181ef7608e00704db228516541eb83a88a9f94433a8c80bb9b5bd54b1d81757
+  version: 2.6.16
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   requires_dist:
   - ukkonen ; extra == 'license'
-  requires_python: '>=3.9'
+  requires_python: '>=3.10'
 - conda: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_0.conda
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+- pypi: https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl
   name: nodeenv
-  version: 1.9.1
-  sha256: ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9
+  version: 1.10.0
+  sha256: 5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827
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@@ -10618,8 +10600,8 @@ packages:
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 - pypi: ./
   name: nrel-sup3r
-  version: 0.2.6.dev22+g02131652b.d20251215
-  sha256: ac62d810d414883ffdbf0f4a6d11700c889894d8b55063502ac9d9dbde169eda
+  version: 0.2.6.dev35+g52af139f8.d20260211
+  sha256: 4f898038470171cf92fc4c23c5e2ab56d46d79b3825936fdd4670b0bd98982f7
   requires_dist:
   - nrel-rex>=0.2.91
   - nrel-phygnn>=0.0.32
@@ -11278,10 +11260,10 @@ packages:
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   size: 23815
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-- pypi: https://files.pythonhosted.org/packages/5d/c4/b2d28e9d2edf4f1713eb3c29307f1a63f3d67cf09bdda29715a36a68921a/pre_commit-4.5.0-py2.py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/5d/19/fd3ef348460c80af7bb4669ea7926651d1f95c23ff2df18b9d24bab4f3fa/pre_commit-4.5.1-py2.py3-none-any.whl
   name: pre-commit
-  version: 4.5.0
-  sha256: 25e2ce09595174d9c97860a95609f9f852c0614ba602de3561e267547f2335e1
+  version: 4.5.1
+  sha256: 3b3afd891e97337708c1674210f8eba659b52a38ea5f822ff142d10786221f77
   requires_dist:
   - cfgv>=2.0.0
   - identify>=1.0.0
@@ -11550,10 +11532,10 @@ packages:
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   size: 5881100
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-- pypi: https://files.pythonhosted.org/packages/c8/f1/d6a797abb14f6283c0ddff96bbdd46937f64122b8c925cab503dd37f8214/pyasn1-0.6.1-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/44/b5/a96872e5184f354da9c84ae119971a0a4c221fe9b27a4d94bd43f2596727/pyasn1-0.6.2-py3-none-any.whl
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-  version: 0.6.1
-  sha256: 0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629
+  version: 0.6.2
+  sha256: 1eb26d860996a18e9b6ed05e7aae0e9fc21619fcee6af91cca9bad4fbea224bf
   requires_python: '>=3.8'
 - conda: https://conda.anaconda.org/conda-forge/noarch/pyasn1-0.6.1-pyhd8ed1ab_1.conda
   sha256: 7f8d61f80e548ed29e452bb51742f0370614f210156cd8355b89803c3f3999d5
@@ -11646,24 +11628,24 @@ packages:
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+- pypi: https://files.pythonhosted.org/packages/6f/01/c26ce75ba460d5cd503da9e13b21a33804d38c2165dec7b716d06b13010c/pyjwt-2.11.0-py3-none-any.whl
   name: pyjwt
-  version: 2.10.1
-  sha256: dcdd193e30abefd5debf142f9adfcdd2b58004e644f25406ffaebd50bd98dacb
+  version: 2.11.0
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   requires_dist:
   - cryptography>=3.4.0 ; extra == 'crypto'
-  - coverage[toml]==5.0.4 ; extra == 'dev'
+  - coverage[toml]==7.10.7 ; extra == 'dev'
   - cryptography>=3.4.0 ; extra == 'dev'
   - pre-commit ; extra == 'dev'
-  - pytest>=6.0.0,<7.0.0 ; extra == 'dev'
+  - pytest>=8.4.2,<9.0.0 ; extra == 'dev'
   - sphinx ; extra == 'dev'
   - sphinx-rtd-theme ; extra == 'dev'
   - zope-interface ; extra == 'dev'
   - sphinx ; extra == 'docs'
   - sphinx-rtd-theme ; extra == 'docs'
   - zope-interface ; extra == 'docs'
-  - coverage[toml]==5.0.4 ; extra == 'tests'
-  - pytest>=6.0.0,<7.0.0 ; extra == 'tests'
+  - coverage[toml]==7.10.7 ; extra == 'tests'
+  - pytest>=8.4.2,<9.0.0 ; extra == 'tests'
   requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/noarch/pyjwt-2.9.0-pyhd8ed1ab_1.conda
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@@ -13317,10 +13299,10 @@ packages:
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-- pypi: https://files.pythonhosted.org/packages/f9/d5/141f53d7c1eb2a80e6d3e9a390228c3222c27705cbe7f048d3623053f3ca/termcolor-3.2.0-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/33/d1/8bb87d21e9aeb323cc03034f5eaf2c8f69841e40e4853c2627edf8111ed3/termcolor-3.3.0-py3-none-any.whl
   name: termcolor
-  version: 3.2.0
-  sha256: a10343879eba4da819353c55cb8049b0933890c2ebf9ad5d3ecd2bb32ea96ea6
+  version: 3.3.0
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   requires_dist:
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@@ -13424,11 +13406,11 @@ packages:
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-- pypi: https://files.pythonhosted.org/packages/bd/75/8539d011f6be8e29f339c42e633aae3cb73bffa95dd0f9adec09b9c58e85/tomlkit-0.13.3-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/b5/11/87d6d29fb5d237229d67973a6c9e06e048f01cf4994dee194ab0ea841814/tomlkit-0.14.0-py3-none-any.whl
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-  requires_python: '>=3.8'
+  version: 0.14.0
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+  requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/noarch/toolz-1.0.0-pyhd8ed1ab_0.conda
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@@ -13648,13 +13630,14 @@ packages:
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-- pypi: https://files.pythonhosted.org/packages/79/0c/c05523fa3181fdf0c9c52a6ba91a23fbf3246cc095f26f6516f9c60e6771/virtualenv-20.35.4-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/6a/2a/dc2228b2888f51192c7dc766106cd475f1b768c10caaf9727659726f7391/virtualenv-20.36.1-py3-none-any.whl
   name: virtualenv
-  version: 20.35.4
-  sha256: c21c9cede36c9753eeade68ba7d523529f228a403463376cf821eaae2b650f1b
+  version: 20.36.1
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   requires_dist:
   - distlib>=0.3.7,<1
-  - filelock>=3.12.2,<4
+  - filelock>=3.16.1,<4 ; python_full_version < '3.10'
+  - filelock>=3.20.1,<4 ; python_full_version >= '3.10'
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   - platformdirs>=3.9.1,<5
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@@ -13744,10 +13727,10 @@ packages:
   - pkg:pypi/websocket-client?source=hash-mapping
   size: 47066
   timestamp: 1713923494501
-- pypi: https://files.pythonhosted.org/packages/2f/f9/9e082990c2585c744734f85bec79b5dae5df9c974ffee58fe421652c8e91/werkzeug-3.1.4-py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/ad/e4/8d97cca767bcc1be76d16fb76951608305561c6e056811587f36cb1316a8/werkzeug-3.1.5-py3-none-any.whl
   name: werkzeug
-  version: 3.1.4
-  sha256: 2ad50fb9ed09cc3af22c54698351027ace879a0b60a3b5edf5730b2f7d876905
+  version: 3.1.5
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   requires_dist:
   - markupsafe>=2.1.1
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@@ -13764,14 +13747,15 @@ packages:
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   size: 242269
   timestamp: 1724330851536
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+- pypi: https://files.pythonhosted.org/packages/87/22/b76d483683216dde3d67cba61fb2444be8d5be289bf628c13fc0fd90e5f9/wheel-0.46.3-py3-none-any.whl
   name: wheel
-  version: 0.45.1
-  sha256: 708e7481cc80179af0e556bbf0cc00b8444c7321e2700b8d8580231d13017248
+  version: 0.46.3
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   requires_dist:
+  - packaging>=24.0
   - pytest>=6.0.0 ; extra == 'test'
-  - setuptools>=65 ; extra == 'test'
-  requires_python: '>=3.8'
+  - setuptools>=77 ; extra == 'test'
+  requires_python: '>=3.9'
 - conda: https://conda.anaconda.org/conda-forge/noarch/widgetsnbextension-4.0.13-pyhd8ed1ab_0.conda
   sha256: d155adc10f8c96f76d4468dbe37b33b4334dadf5cd4a95841aa009ca9bced5fa
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diff --git a/pyproject.toml b/pyproject.toml
index f0cc65b399..e62322625a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -257,11 +257,11 @@ max-complexity = 12
 ]
 
 [tool.ruff.lint.pylint]
-max-args = 5 # (PLR0913) Maximum number of arguments for function / method
-max-bool-expr = 5 # ( PLR0916) Boolean in a single if statement
+max-args=12 # (PLR0913) Maximum number of arguments for function / method
+max-bool-expr=5 # ( PLR0916) Boolean in a single if statement
 max-branches=12 # (PLR0912) branches allowed for a function or method body
 max-locals=15 # (PLR0912) local variables allowed for a function or method body
-max-nested-blocks = 5 # (PLR1702) nested blocks within a function or method body
+max-nested-blocks=5 # (PLR1702) nested blocks within a function or method body
 max-public-methods=20 # (R0904) public methods allowed for a class
 max-returns=6 # (PLR0911) return statements for a function or method body
 max-statements=50 # (PLR0915) statements allowed for a function or method body
diff --git a/sup3r/preprocessing/samplers/base.py b/sup3r/preprocessing/samplers/base.py
index bbeb4e0a66..7faae0da05 100644
--- a/sup3r/preprocessing/samplers/base.py
+++ b/sup3r/preprocessing/samplers/base.py
@@ -14,7 +14,7 @@
     uniform_box_sampler,
     uniform_time_sampler,
 )
-from sup3r.preprocessing.utilities import lowered
+from sup3r.preprocessing.utilities import compute_if_dask, lowered
 
 logger = logging.getLogger(__name__)
 
@@ -140,7 +140,6 @@ def preflight(self):
         if self.data.shape[2] < self.sample_shape[2] * self.batch_size:
             logger.warning(msg)
             warn(msg)
-
         if self.mode == 'eager':
             logger.info('Received mode = "eager".')
             _ = self.compute()
@@ -208,7 +207,8 @@ def _reshape_samples(self, samples):
         # (batch_size, lats, lons, times, feats)
         return np.transpose(out, axes=(2, 0, 1, 3, 4))
 
-    def _stack_samples(self, samples):
+    @classmethod
+    def _stack_samples(cls, samples):
         """Used to build batch arrays in the case of independent time samples
         (e.g. slow batching)
 
@@ -247,7 +247,7 @@ def _compute_samples(self, samples):
             Samples retrieved from the underlying data. Could be a tuple
             in the case of dual datasets.
         """
-        if self.mode == 'lazy':
+        if self.mode == 'eager':
             return samples
         if isinstance(samples, tuple):
             return tuple(np.asarray(s) for s in samples)

From f352a71de3da52b07ddcb507e28390601a10ae2b Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Wed, 11 Feb 2026 16:34:13 -0700
Subject: [PATCH 5/6] adjust linting rules and refactor methods to classmethods

---
 pixi.lock                            | 4 ++--
 pyproject.toml                       | 2 +-
 sup3r/preprocessing/base.py          | 6 ++++--
 sup3r/preprocessing/samplers/base.py | 4 ++--
 sup3r/preprocessing/samplers/cc.py   | 2 +-
 sup3r/preprocessing/utilities.py     | 4 ++--
 6 files changed, 12 insertions(+), 10 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 64d991df64..14176bfd3f 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -10600,8 +10600,8 @@ packages:
   requires_python: '>=3.9'
 - pypi: ./
   name: nrel-sup3r
-  version: 0.2.6.dev35+g52af139f8.d20260211
-  sha256: 4f898038470171cf92fc4c23c5e2ab56d46d79b3825936fdd4670b0bd98982f7
+  version: 0.2.6.dev36+ga2f21d894.d20260211
+  sha256: 590067793ce17953cef54eb3b422f74ba7a9124e27267767343b85bc0f23320b
   requires_dist:
   - nrel-rex>=0.2.91
   - nrel-phygnn>=0.0.32
diff --git a/pyproject.toml b/pyproject.toml
index e62322625a..08d01ec027 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -257,7 +257,7 @@ max-complexity = 12
 ]
 
 [tool.ruff.lint.pylint]
-max-args=12 # (PLR0913) Maximum number of arguments for function / method
+max-args=15 # (PLR0913) Maximum number of arguments for function / method
 max-bool-expr=5 # ( PLR0916) Boolean in a single if statement
 max-branches=12 # (PLR0912) branches allowed for a function or method body
 max-locals=15 # (PLR0912) local variables allowed for a function or method body
diff --git a/sup3r/preprocessing/base.py b/sup3r/preprocessing/base.py
index 6e55652d7f..803e83ffbe 100644
--- a/sup3r/preprocessing/base.py
+++ b/sup3r/preprocessing/base.py
@@ -202,11 +202,13 @@ def __getattr__(self, attr):
             out = out[0]
         return out
 
-    def _getattr(self, dset, attr):
+    @classmethod
+    def _getattr(cls, dset, attr):
         """Get attribute from single data member."""
         return getattr(dset.sx, attr, getattr(dset, attr))
 
-    def _getitem(self, dset, item):
+    @classmethod
+    def _getitem(cls, dset, item):
         """Get item from single data member."""
         return dset.sx[item] if hasattr(dset, 'sx') else dset[item]
 
diff --git a/sup3r/preprocessing/samplers/base.py b/sup3r/preprocessing/samplers/base.py
index 7faae0da05..36ae109e48 100644
--- a/sup3r/preprocessing/samplers/base.py
+++ b/sup3r/preprocessing/samplers/base.py
@@ -14,7 +14,7 @@
     uniform_box_sampler,
     uniform_time_sampler,
 )
-from sup3r.preprocessing.utilities import compute_if_dask, lowered
+from sup3r.preprocessing.utilities import lowered
 
 logger = logging.getLogger(__name__)
 
@@ -29,7 +29,7 @@ def __init__(
         sample_shape: Optional[tuple] = None,
         batch_size: int = 16,
         feature_sets: Optional[dict] = None,
-        mode: str = 'lazy'
+        mode: str = 'lazy',
     ):
         """
         Parameters
diff --git a/sup3r/preprocessing/samplers/cc.py b/sup3r/preprocessing/samplers/cc.py
index 7ff98965a2..85af6d88c1 100644
--- a/sup3r/preprocessing/samplers/cc.py
+++ b/sup3r/preprocessing/samplers/cc.py
@@ -149,7 +149,7 @@ def reduce_high_res_sub_daily(self, high_res, csr_ind=0):
 
         *Needs review from @grantbuster
         """
-        if self.t_enhance not in (24, 1):
+        if self.t_enhance not in {24, 1}:
             high_res = self.get_middle_days(high_res, self.hr_sample_shape)
             high_res = nsrdb_reduce_daily_data(
                 high_res, self.hr_sample_shape[-1], csr_ind=csr_ind
diff --git a/sup3r/preprocessing/utilities.py b/sup3r/preprocessing/utilities.py
index 981f2b9f9d..f68d590003 100644
--- a/sup3r/preprocessing/utilities.py
+++ b/sup3r/preprocessing/utilities.py
@@ -346,9 +346,9 @@ def get_source_type(file_paths):
 
     _, source_type = os.path.splitext(file_paths[0])
 
-    if source_type in ('.h5', '.hdf'):
+    if source_type in {'.h5', '.hdf'}:
         return 'h5'
-    if source_type in ('.nc',):
+    if source_type in {'.nc'}:
         return 'nc'
     msg = (
         f'Can only handle HDF or NETCDF files. Received unknown extension '

From 7b284e90bd10b0ddc4b2787d6ba7a47b79317bef Mon Sep 17 00:00:00 2001
From: bnb32 <brandon.benton@nrel.gov>
Date: Wed, 11 Feb 2026 18:14:52 -0700
Subject: [PATCH 6/6] refactor: replace sample conversion logic with
 compute_if_dask utility

---
 sup3r/preprocessing/samplers/base.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/sup3r/preprocessing/samplers/base.py b/sup3r/preprocessing/samplers/base.py
index 36ae109e48..b6be139d71 100644
--- a/sup3r/preprocessing/samplers/base.py
+++ b/sup3r/preprocessing/samplers/base.py
@@ -14,7 +14,7 @@
     uniform_box_sampler,
     uniform_time_sampler,
 )
-from sup3r.preprocessing.utilities import lowered
+from sup3r.preprocessing.utilities import compute_if_dask, lowered
 
 logger = logging.getLogger(__name__)
 
@@ -249,9 +249,7 @@ def _compute_samples(self, samples):
         """
         if self.mode == 'eager':
             return samples
-        if isinstance(samples, tuple):
-            return tuple(np.asarray(s) for s in samples)
-        return np.asarray(samples)
+        return compute_if_dask(samples)
 
     def _fast_batch(self):
         """Get batch of samples with adjacent time slices."""