Bnb/nc4 dep removal

.github/workflows/publish_to_pypi.yml

@@ -13,6 +13,10 @@ jobs:
  id-token: write
  steps:
  - uses: actions/checkout@v4
+ with:
+ ref: ${{ github.event.pull_request.head.ref }}
+ fetch-depth: 0
+ fetch-tags: true
  - name: Set up Python
  uses: actions/setup-python@v4
  with:

pyproject.toml

@@ -32,9 +32,10 @@ dependencies = [
  "NREL-farms>=1.0.4",
  "dask>=2022.0",
  "google-auth-oauthlib==0.5.3",
+ "h5netcdf>=1.1.0",
+ "cftime>=1.6.2",
  "matplotlib>=3.1",
  "numpy>=1.7.0",
- "netCDF4==1.5.8",
  "pandas>=2.0",
  "pillow>=10.0",
  "pytest>=5.2",

sup3r/bias/bias_calc.py

@@ -8,19 +8,17 @@
 import os
 from abc import abstractmethod
 from concurrent.futures import ProcessPoolExecutor, as_completed
-from glob import glob
-from pathlib import Path
 
 import h5py
 import numpy as np
 import pandas as pd
 import rex
-from rex.utilities.fun_utils import get_fun_call_str
 from rex.utilities.bc_utils import (
+ sample_q_invlog,
  sample_q_linear,
  sample_q_log,
- sample_q_invlog,
 )
+from rex.utilities.fun_utils import get_fun_call_str
 from scipy import stats
 from scipy.ndimage import gaussian_filter
 from scipy.spatial import KDTree
@@ -29,39 +27,11 @@
 from sup3r.preprocessing.data_handling.base import DataHandler
 from sup3r.utilities import VERSION_RECORD, ModuleName
 from sup3r.utilities.cli import BaseCLI
-from sup3r.utilities.utilities import nn_fill_array
+from sup3r.utilities.utilities import expand_paths, nn_fill_array
 
 logger = logging.getLogger(__name__)
 
 
-def _expand_paths(fps):
- """Expand path(s)
-
- Parameter
- ---------
- fps : str or pathlib.Path or any Sequence of those
- One or multiple paths to file
-
- Returns
- -------
- list[str]
- A list of expanded unique and sorted paths as str
-
- Examples
- --------
- >>> _expand_paths("myfile.h5")
-
- >>> _expand_paths(["myfile.h5", "*.hdf"])
- """
- if isinstance(fps, (str, Path)):
- fps = (fps, )
-
- out = []
- for f in fps:
- out.extend(glob(f))
- return sorted(set(out))
-
-
 class DataRetrievalBase:
  """Base class to handle data retrieval for the biased data and the
  baseline data
@@ -163,8 +133,8 @@ class to be retrieved from the rex/sup3r library. If a
  self._distance_upper_bound = distance_upper_bound
  self.match_zero_rate = match_zero_rate
 
- self.base_fps = _expand_paths(self.base_fps)
- self.bias_fps = _expand_paths(self.bias_fps)
+ self.base_fps = expand_paths(self.base_fps)
+ self.bias_fps = expand_paths(self.bias_fps)
 
  base_sup3r_handler = getattr(sup3r.preprocessing.data_handling,
  base_handler, None)
@@ -1224,6 +1194,7 @@ class QuantileDeltaMappingCorrection(DataRetrievalBase):
  :func:`~sup3r.bias.bias_transforms.local_qdm_bc` to actually correct
  a dataset.
  """
+
  def __init__(self,
  base_fps,
  bias_fps,
@@ -1308,7 +1279,7 @@ def __init__(self,
 
  self.bias_fut_fps = bias_fut_fps
 
- self.bias_fut_fps = _expand_paths(self.bias_fut_fps)
+ self.bias_fut_fps = expand_paths(self.bias_fut_fps)
 
  self.bias_fut_dh = self.bias_handler(self.bias_fut_fps,
  [self.bias_feature],

sup3r/bias/bias_transforms.py

@@ -203,7 +203,7 @@ def local_linear_bc(input,
  lat_lon,
  feature_name,
  bias_fp,
- lr_padded_slice,
+ lr_padded_slice=None,
  out_range=None,
  smoothing=0,
  ):
@@ -292,8 +292,8 @@ def monthly_local_linear_bc(input,
  lat_lon,
  feature_name,
  bias_fp,
- lr_padded_slice,
  time_index,
+ lr_padded_slice=None,
  temporal_avg=True,
  out_range=None,
  smoothing=0,
@@ -318,18 +318,18 @@ def monthly_local_linear_bc(input,
  datasets "{feature_name}_scalar" and "{feature_name}_adder" that are
  the full low-resolution shape of the forward pass input that will be
  sliced using lr_padded_slice for the current chunk.
+ time_index : pd.DatetimeIndex
+ DatetimeIndex object associated with the input data temporal axis
+ (assumed 3rd axis e.g. axis=2). Note that if this method is called as
+ part of a sup3r resolution forward pass, the time_index will be
+ included automatically for the current chunk.
  lr_padded_slice : tuple | None
  Tuple of length four that slices (spatial_1, spatial_2, temporal,
  features) where each tuple entry is a slice object for that axes.
  Note that if this method is called as part of a sup3r forward pass, the
  lr_padded_slice will be included automatically in the kwargs for the
  active chunk. If this is None, no slicing will be done and the full
  bias correction source shape will be used.
- time_index : pd.DatetimeIndex
- DatetimeIndex object associated with the input data temporal axis
- (assumed 3rd axis e.g. axis=2). Note that if this method is called as
- part of a sup3r resolution forward pass, the time_index will be
- included automatically for the current chunk.
  temporal_avg : bool
  Take the average scalars and adders for the chunk's time index, this
  will smooth the transition of scalars/adders from month to month if
@@ -403,6 +403,7 @@ def local_qdm_bc(data: np.array,
  base_dset: str,
  feature_name: str,
  bias_fp,
+ lr_padded_slice=None,
  threshold=0.1,
  relative=True,
  no_trend=False):
@@ -433,6 +434,13 @@ def local_qdm_bc(data: np.array,
  "bias_fut_{feature_name}_params", and "base_{base_dset}_params" that
  are the parameters to define the statistical distributions to be used
  to correct the given `data`.
+ lr_padded_slice : tuple | None
+ Tuple of length four that slices (spatial_1, spatial_2, temporal,
+ features) where each tuple entry is a slice object for that axes.
+ Note that if this method is called as part of a sup3r forward pass, the
+ lr_padded_slice will be included automatically in the kwargs for the
+ active chunk. If this is None, no slicing will be done and the full
+ bias correction source shape will be used.
  no_trend: bool, default=False
  An option to ignore the trend component of the correction, thus
  resulting in an ordinary Quantile Mapping, i.e. corrects the bias by
@@ -485,6 +493,11 @@ def local_qdm_bc(data: np.array,
  feature_name,
  bias_fp,
  threshold)
+ if lr_padded_slice is not None:
+ spatial_slice = (lr_padded_slice[0], lr_padded_slice[1])
+ base = base[spatial_slice]
+ bias = bias[spatial_slice]
+ bias_fut = bias[spatial_slice]
 
  if no_trend:
  mf = None

sup3r/models/base.py

@@ -688,7 +688,8 @@ def train_epoch(self,
 
  if only_gen or (train_gen and not gen_too_good):
  trained_gen = True
- b_loss_details = self.run_gradient_descent(
+ b_loss_details = self.timer(
+ self.run_gradient_descent,
  batch.low_res,
  batch.high_res,
  self.generator_weights,
@@ -700,7 +701,8 @@ def train_epoch(self,
 
  if only_disc or (train_disc and not disc_too_good):
  trained_disc = True
- b_loss_details = self.run_gradient_descent(
+ b_loss_details = self.timer(
+ self.run_gradient_descent,
  batch.low_res,
  batch.high_res,
  self.discriminator_weights,

sup3r/preprocessing/data_handling/mixin.py

@@ -2,7 +2,6 @@
 @author: bbenton
 """
 
-import glob
 import logging
 import os
 import pickle
@@ -18,6 +17,7 @@
 
 from sup3r.utilities.utilities import (
  estimate_max_workers,
+ expand_paths,
  get_source_type,
  ignore_case_path_fetch,
  uniform_box_sampler,
@@ -644,22 +644,15 @@ def file_paths(self, file_paths):
  ----------
  file_paths : str | list
  A list of files to extract raster data from. Each file must have
- the same number of timesteps. Can also pass a string with a
- unix-style file path which will be passed through glob.glob
+ the same number of timesteps. Can also pass a string or list of
+ strings with a unix-style file path which will be passed through
+ glob.glob
  """
- self._file_paths = file_paths
- if isinstance(self._file_paths, str):
- if '*' in file_paths:
- self._file_paths = glob.glob(self._file_paths)
- else:
- self._file_paths = [self._file_paths]
-
+ self._file_paths = expand_paths(file_paths)
  msg = ('No valid files provided to DataHandler. '
  f'Received file_paths={file_paths}. Aborting.')
  assert file_paths is not None and len(self._file_paths) > 0, msg
 
- self._file_paths = sorted(self._file_paths)
-
  @property
  def ti_workers(self):
  """Get max number of workers for computing time index"""

sup3r/utilities/__init__.py

@@ -1,20 +1,19 @@
 """Sup3r utilities"""
 import sys
-import pandas as pd
-import numpy as np
-import tensorflow as tf
-import sklearn
-import dask
-import xarray
-import netCDF4
 from enum import Enum
 
+import dask
+import h5netcdf
+import numpy as np
+import pandas as pd
 import phygnn
 import rex
+import sklearn
+import tensorflow as tf
+import xarray
 
 from sup3r import __version__
 
-
 VERSION_RECORD = {'sup3r': __version__,
  'tensorflow': tf.__version__,
  'sklearn': sklearn.__version__,
@@ -24,7 +23,7 @@
  'nrel-rex': rex.__version__,
  'python': sys.version,
  'xarray': xarray.__version__,
- 'netCDF4': netCDF4.__version__,
+ 'h5netcdf': h5netcdf.__version__,
  'dask': dask.__version__,
  }
 
@@ -56,6 +55,7 @@ def __format__(self, format_spec):
  @classmethod
  def all_names(cls):
  """All module names.
+
  Returns
  -------
  set