Merge branch 'feature/split-datalib-to-separate-repo' into develop-3.X

doc/source/conf.py

@@ -85,7 +85,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = 'en'
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

podpac/core/algorithm/stats.py

@@ -80,7 +80,7 @@ def chunk_size(self):
 
         chunk_size = podpac.settings["CHUNK_SIZE"]
         if chunk_size == "auto":
-            return 1024**2  # TODO
+            return 1024 ** 2  # TODO
         else:
             return chunk_size
 
@@ -611,7 +611,7 @@ def reduce_chunked(self, xs, output):
             Nx = np.isfinite(x).sum(dim=self._dims)
             M1x = x.mean(dim=self._dims)
             Ex = x - M1x
-            Ex2 = Ex**2
+            Ex2 = Ex ** 2
             Ex3 = Ex2 * Ex
             M2x = (Ex2).sum(dim=self._dims)
             M3x = (Ex3).sum(dim=self._dims)
@@ -632,13 +632,13 @@ def reduce_chunked(self, xs, output):
             n = Nb + Nx
             NNx = Nb * Nx
 
-            M3.data[b] += M3x + d**3 * NNx * (Nb - Nx) / n**2 + 3 * d * (Nb * M2x - Nx * M2b) / n
-            M2.data[b] += M2x + d**2 * NNx / n
+            M3.data[b] += M3x + d ** 3 * NNx * (Nb - Nx) / n ** 2 + 3 * d * (Nb * M2x - Nx * M2b) / n
+            M2.data[b] += M2x + d ** 2 * NNx / n
             M1.data[b] += d * Nx / n
             N.data[b] = n
 
         # calculate skew
-        skew = np.sqrt(N) * M3 / np.sqrt(M2**3)
+        skew = np.sqrt(N) * M3 / np.sqrt(M2 ** 3)
         return skew
 
 
@@ -697,9 +697,9 @@ def reduce_chunked(self, xs, output):
             Nx = np.isfinite(x).sum(dim=self._dims)
             M1x = x.mean(dim=self._dims)
             Ex = x - M1x
-            Ex2 = Ex**2
+            Ex2 = Ex ** 2
             Ex3 = Ex2 * Ex
-            Ex4 = Ex2**2
+            Ex4 = Ex2 ** 2
             M2x = (Ex2).sum(dim=self._dims)
             M3x = (Ex3).sum(dim=self._dims)
             M4x = (Ex4).sum(dim=self._dims)
@@ -724,18 +724,18 @@ def reduce_chunked(self, xs, output):
 
             M4.data[b] += (
                 M4x
-                + d**4 * NNx * (Nb**2 - NNx + Nx**2) / n**3
-                + 6 * d**2 * (Nb**2 * M2x + Nx**2 * M2b) / n**2
+                + d ** 4 * NNx * (Nb ** 2 - NNx + Nx ** 2) / n ** 3
+                + 6 * d ** 2 * (Nb ** 2 * M2x + Nx ** 2 * M2b) / n ** 2
                 + 4 * d * (Nb * M3x - Nx * M3b) / n
             )
 
-            M3.data[b] += M3x + d**3 * NNx * (Nb - Nx) / n**2 + 3 * d * (Nb * M2x - Nx * M2b) / n
-            M2.data[b] += M2x + d**2 * NNx / n
+            M3.data[b] += M3x + d ** 3 * NNx * (Nb - Nx) / n ** 2 + 3 * d * (Nb * M2x - Nx * M2b) / n
+            M2.data[b] += M2x + d ** 2 * NNx / n
             M1.data[b] += d * Nx / n
             N.data[b] = n
 
         # calculate kurtosis
-        kurtosis = N * M4 / M2**2 - 3
+        kurtosis = N * M4 / M2 ** 2 - 3
         return kurtosis
 
 

podpac/core/coordinates/polar_coordinates.py

@@ -58,7 +58,7 @@ def __init__(self, center, radius, theta=None, theta_size=None, dims=None):
     def _validate_dims(self, d):
         val = d["value"]
         for dim in val:
-            if dim not in ["lat", "lon"]: # Hardcoding example. What is actually trying to be accomplished?
+            if dim not in ["lat", "lon"]:  # Hardcoding example. What is actually trying to be accomplished?
                 raise ValueError("PolarCoordinates dims must be 'lat' or 'lon', not '%s'" % dim)
         if val[0] == val[1]:
             raise ValueError("Duplicate dimension '%s'" % val[0])

podpac/core/data/csv_source.py

@@ -44,6 +44,7 @@ class CSVRaw(FileKeysMixin, LoadFileMixin, BaseFileSource):
     --------
     CSV : Interpolated CSV file datasource for general use.
     """
+
     # No support here for custom Dimension names? selection in dataset_source.py
     header = tl.Any(default_value=0).tag(attr=True)
     lat_key = tl.Union([tl.Unicode(), tl.Int()], default_value="lat").tag(attr=True)

podpac/core/data/dataset_source.py

@@ -45,6 +45,7 @@ class DatasetRaw(FileKeysMixin, LoadFileMixin, BaseFileSource):
     --------
     Dataset : Interpolated xarray dataset source for general use.
     """
+
     # selection lets you use other dims
     # dataset = tl.Instance(xr.Dataset).tag(readonly=True)
     selection = tl.Dict(allow_none=True, default_value=None).tag(attr=True)

podpac/core/data/file_source.py

@@ -163,6 +163,7 @@ class FileKeysMixin(tl.HasTraits):
     cf_calendar : str
         calendar, when decoding CF datetimes
     """
+
     # Other dims?
     data_key = tl.Union([tl.Unicode(), tl.List(trait=tl.Unicode())]).tag(attr=True)
     lat_key = tl.Unicode(default_value="lat").tag(attr=True)

podpac/core/data/zarr_source.py

@@ -52,6 +52,7 @@ class ZarrRaw(S3Mixin, FileKeysMixin, BaseFileSource):
     --------
     Zarr : Interpolated Zarr Datasource for general use.
     """
+
     # Doesnt support other dims
     file_mode = tl.Unicode(default_value="r").tag(readonly=True)
     coordinate_index_type = "slice"

podpac/core/interpolation/nearest_neighbor_interpolator.py

@@ -310,7 +310,7 @@ def _get_uniform_index(self, dim, source, request, bounds=None):
             # Find all the 0.5 and 1.5's that were rounded to even numbers, and make sure they all round down
             I = (index % 0.5) == 0
             rindex[I] = np.ceil(index[I])
-        else: # "unbiased", that's the default np.around behavior, so do nothing
+        else:  # "unbiased", that's the default np.around behavior, so do nothing
             pass
 
         stop_ind = int(source.size)

podpac/core/interpolation/none_interpolator.py

@@ -21,6 +21,7 @@
 from podpac.core.coordinates.utils import get_timedelta
 from podpac.core.interpolation.selector import Selector, _higher_precision_time_coords1d, _higher_precision_time_stack
 
+
 @common_doc(COMMON_INTERPOLATOR_DOCS)
 class NoneInterpolator(Interpolator):
     """None Interpolation"""

podpac/core/interpolation/test/test_interpolation.py

@@ -44,6 +44,7 @@ class InterpArray(InterpolationMixin, ArrayRaw):
         np.testing.assert_array_equal(iaso.data, aso.data)
         np.testing.assert_array_equal(abso.data, data)
 
+
 from podpac.core.coordinates.utils import VALID_DIMENSION_NAMES
 
 

podpac/core/test/test_units.py

@@ -83,7 +83,7 @@ def test_pow(self):
             dims=["lat", "lon", "alt"],
             attrs={"units": ureg.meter},
         )
-        assert (a**2).attrs["units"] == ureg.meter**2
+        assert (a ** 2).attrs["units"] == ureg.meter ** 2
 
     def test_set_to_value_using_UnitsDataArray_as_mask_does_nothing_if_mask_has_dim_not_in_array(self):
         a = UnitsDataArray(
@@ -273,7 +273,7 @@ def test_units_allpass(self):
         assert a6[0, 0].data[()] == False
 
         a7 = a1 * a2
-        assert a7[0, 0].to(ureg.m**2).data[()] == (1 * ureg.meter * ureg.inch).to(ureg.meter**2).magnitude
+        assert a7[0, 0].to(ureg.m ** 2).data[()] == (1 * ureg.meter * ureg.inch).to(ureg.meter ** 2).magnitude
 
         a8 = a2 / a1
         assert a8[0, 0].to_base_units().data[()] == (1 * ureg.inch / ureg.meter).to_base_units().magnitude
@@ -328,7 +328,7 @@ def test_ufuncs(self):
         np.mean(a1)
         np.min(a1)
         np.max(a1)
-        a1**2
+        a1 ** 2
 
         # These don't have units!
         np.dot(a2.T, a1)

podpac/core/utils.py

@@ -37,6 +37,7 @@
 from . import settings
 from podpac.core.coordinates.utils import VALID_DIMENSION_NAMES
 
+
 def common_doc(doc_dict):
     """Decorator: replaces commond fields in a function docstring
 
@@ -144,6 +145,7 @@ def validate(self, obj, value):
             super(OrderedDictTrait, self).validate(obj, value)
             return value
 
+
 else:
     OrderedDictTrait = tl.Dict
 

podpac/datalib/__init__.py

@@ -1,24 +1,16 @@
 """
 Datalib Public API
 
-This module gets imported in the root __init__.py
+This module import the podpacdatalib package
 and exposed its contents to podpac.datalib
 """
 
-import sys
+try:
+    from podpacdatalib import *
+except ModuleNotFoundError:
+    import logging
 
-from podpac.datalib.cosmos_stations import COSMOSStations
-from podpac.datalib.drought_monitor import DroughtCategory, DroughtMonitorCategory
-from podpac.datalib.egi import EGI
-from podpac.datalib.gfs import GFS, GFSLatest
-from podpac.datalib.modis_pds import MODIS
-from podpac.datalib.satutils import Landsat8, Sentinel2
-from podpac.datalib.smap_egi import SMAP
-from podpac.datalib.terraintiles import TerrainTiles
-from podpac.datalib.weathercitizen import WeatherCitizen
-from podpac.datalib.soilscape import SoilSCAPE20min
-from podpac.datalib import soilgrids
-
-# intake requires python >= 3.6
-if sys.version >= "3.6":
-    from podpac.datalib.intake_catalog import IntakeCatalog
+    _logger = logging.getLogger(__name__)
+    _logger.warning(
+        "The podpacdatalib module is not installed but user tried to import podpac.datalib which depends on it."
+    )