[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

docs/conf.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# -*- coding: utf-8 -*-
 #
 # pocean-core documentation build configuration file, created by
 # sphinx-quickstart on Fri Feb 10 16:09:19 2017.

pocean/__init__.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 
 # Package level logger
 import logging

pocean/cf.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 import itertools
 import os
 import re
@@ -46,7 +45,7 @@ def load(cls, path):
         try:
             dsg = cls(path)
             for klass in subs:
-                logger.debug('Trying {}...'.format(klass.__name__))
+                logger.debug(f'Trying {klass.__name__}...')
                 if hasattr(klass, 'is_mine'):
                     if klass.is_mine(dsg):
                         return klass(path)
@@ -65,7 +64,7 @@ def load(cls, path):
         )
 
     def axes(self, name):
-        return getattr(self, '{}_axes'.format(name.lower()))()
+        return getattr(self, f'{name.lower()}_axes')()
 
     def t_axes(self):
 
@@ -197,7 +196,7 @@ def cf_safe_name(name):
     if isinstance(name, str):
         if re.match('^[0-9_]', name):
             # Add a letter to the front
-            name = "v_{}".format(name)
+            name = f"v_{name}"
         return re.sub(r'[^_a-zA-Z0-9]', "_", name)
 
-    raise ValueError('Could not convert "{}" to a safe name'.format(name))
+    raise ValueError(f'Could not convert "{name}" to a safe name')

pocean/dataset.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 import warnings
 from collections import OrderedDict
 
@@ -48,7 +47,7 @@ def close(self):
         if not self.isopen():
             return
 
-        super(EnhancedDataset, self).close()
+        super().close()
 
     def vatts(self, vname):
         d = {}
@@ -169,7 +168,7 @@ def apply_json(self, meta, create_vars=True, create_dims=True, create_data=True)
                     continue
 
                 if 'shape' not in vvalue and 'type' not in vvalue:
-                    L.debug("Skipping {} creation, no shape or no type defined".format(vname))
+                    L.debug(f"Skipping {vname} creation, no shape or no type defined")
                     continue
                 shape = vvalue.get('shape', [])  # Dimension names
                 vardtype = string_to_dtype(vvalue.get('type'))
@@ -257,19 +256,19 @@ def update_attributes(self, attributes):
             try:
                 self.setncattr(k, v)
             except BaseException:
-                L.warning('Could not set global attribute {}: {}'.format(k, v))
+                L.warning(f'Could not set global attribute {k}: {v}')
 
         for k, v in attributes.items():
             if k in self.variables:
                 for n, z in v.items():
 
                     # Don't re-assign fill value attributes
                     if n in ['_FillValue', 'missing_value']:
-                        L.warning('Refusing to set {} on {}'.format(n, k))
+                        L.warning(f'Refusing to set {n} on {k}')
                         continue
 
                     try:
                         self.variables[k].setncattr(n, z)
                     except BaseException:
-                        L.warning('Could not set attribute {} on {}'.format(n, k))
+                        L.warning(f'Could not set attribute {n} on {k}')
         self.sync()

pocean/dsg/__init__.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 
 # Profile
 from .profile.im import IncompleteMultidimensionalProfile

pocean/dsg/profile/__init__.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import namedtuple
 
 from shapely.geometry import LineString, Point

pocean/dsg/profile/im.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import OrderedDict
 from copy import copy
 
@@ -92,7 +91,7 @@ def from_dataframe(cls, df, output, **kwargs):
         if unique_dims is True:
             # Rename the dimension to avoid a dimension and coordinate having the same name
             # which is not support in xarray
-            changed_axes = { k: '{}_dim'.format(v) for k, v in axes._asdict().items() }
+            changed_axes = { k: f'{v}_dim' for k, v in axes._asdict().items() }
             daxes = get_default_axes(changed_axes)
 
         # Downcast anything from int64 to int32
@@ -235,10 +234,10 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
                 # Carry through size 1 variables
                 if vdata.size == 1:
                     if vdata[0] is np.ma.masked:
-                        L.warning("Skipping variable {} that is completely masked".format(dnam))
+                        L.warning(f"Skipping variable {dnam} that is completely masked")
                         continue
                 else:
-                    L.warning("Skipping variable {} since it didn't match any dimension sizes".format(dnam))
+                    L.warning(f"Skipping variable {dnam} since it didn't match any dimension sizes")
                     continue
 
             # Mark rows with data so we don't remove them with clear_rows
@@ -264,7 +263,7 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
         return df
 
     def nc_attributes(self, axes, daxes):
-        atts = super(IncompleteMultidimensionalProfile, self).nc_attributes()
+        atts = super().nc_attributes()
         return dict_update(atts, {
             'global' : {
                 'featureType': 'profile',

pocean/dsg/profile/om.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import OrderedDict
 from copy import copy
 
@@ -173,10 +172,10 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
                 # Carry through size 1 variables
                 if vdata.size == 1:
                     if vdata[0] is np.ma.masked:
-                        L.warning("Skipping variable {} that is completely masked".format(dnam))
+                        L.warning(f"Skipping variable {dnam} that is completely masked")
                         continue
                 else:
-                    L.warning("Skipping variable {} since it didn't match any dimension sizes".format(dnam))
+                    L.warning(f"Skipping variable {dnam} since it didn't match any dimension sizes")
                     continue
 
             # Mark rows with data so we don't remove them with clear_rows

pocean/dsg/timeseries/cr.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from pocean import logger  # noqa
 from pocean.cf import CFDataset
 

pocean/dsg/timeseries/im.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from pocean import logger  # noqa
 from pocean.cf import CFDataset
 

pocean/dsg/timeseries/ir.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from pocean import logger  # noqa
 from pocean.cf import CFDataset
 

pocean/dsg/timeseries/om.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import OrderedDict
 from copy import copy
 
@@ -85,7 +84,7 @@ def from_dataframe(cls, df, output, **kwargs):
         if unique_dims is True:
             # Rename the dimension to avoid a dimension and coordinate having the same name
             # which is not support in xarray
-            changed_axes = { k: '{}_dim'.format(v) for k, v in axes._asdict().items() }
+            changed_axes = { k: f'{v}_dim' for k, v in axes._asdict().items() }
             daxes = get_default_axes(changed_axes)
 
         # Downcast anything from int64 to int32
@@ -169,7 +168,7 @@ def ts(i):
                     try:
                         v[ts(i)] = vvalues
                     except BaseException:
-                        L.debug('{} was not written. Likely a metadata variable'.format(v.name))
+                        L.debug(f'{v.name} was not written. Likely a metadata variable')
 
             # Set global attributes
             nc.update_attributes(attributes)
@@ -234,11 +233,11 @@ def to_dataframe(self, clean_cols=False, clean_rows=False, **kwargs):
             # Carry through size 1 variables
             if vdata.size == 1:
                 if vdata[0] is np.ma.masked:
-                    L.warning("Skipping variable {} that is completely masked".format(dnam))
+                    L.warning(f"Skipping variable {dnam} that is completely masked")
                     continue
             else:
                 if dvar[:].flatten().size != t.size:
-                    L.warning("Variable {} is not the correct size, skipping.".format(dnam))
+                    L.warning(f"Variable {dnam} is not the correct size, skipping.")
                     continue
 
             # Mark rows with data so we don't remove them with clear_rows
@@ -264,7 +263,7 @@ def to_dataframe(self, clean_cols=False, clean_rows=False, **kwargs):
         return df
 
     def nc_attributes(self, axes, daxes):
-        atts = super(OrthogonalMultidimensionalTimeseries, self).nc_attributes()
+        atts = super().nc_attributes()
         return dict_update(atts, {
             'global' : {
                 'featureType': 'timeseries',

pocean/dsg/timeseriesProfile/im.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from pocean.cf import CFDataset
 
 

pocean/dsg/timeseriesProfile/om.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import OrderedDict
 from copy import copy
 
@@ -76,7 +75,7 @@ def from_dataframe(cls, df, output, **kwargs):
         if unique_dims is True:
             # Rename the dimension to avoid a dimension and coordinate having the same name
             # which is not supported in xarray
-            changed_axes = { k: '{}_dim'.format(v) for k, v in axes._asdict().items() }
+            changed_axes = { k: f'{v}_dim' for k, v in axes._asdict().items() }
             daxes = get_default_axes(changed_axes)
 
         # Downcast anything from int64 to int32
@@ -167,7 +166,7 @@ def from_dataframe(cls, df, output, **kwargs):
                 try:
                     v[:] = vvalues.reshape(v.shape)
                 except BaseException:
-                    L.exception('Failed to add {}'.format(c))
+                    L.exception(f'Failed to add {c}')
                     continue
 
             full_columns = [ f for f in data_columns if f not in detach_z_columnms ]
@@ -253,7 +252,7 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
             # Carry through size 1 variables
             if vdata.size == 1:
                 if vdata[0] is np.ma.masked:
-                    L.warning("Skipping variable {} that is completely masked".format(dnam))
+                    L.warning(f"Skipping variable {dnam} that is completely masked")
                     continue
 
             # Carry through profile only variables
@@ -264,12 +263,12 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
                 vdata[:, 1:] = np.ma.masked
                 vdata = vdata.flatten()
                 if vdata.size != t.size:
-                    L.warning("Variable {} is not the correct size, skipping.".format(dnam))
+                    L.warning(f"Variable {dnam} is not the correct size, skipping.")
                     continue
 
             else:
                 if vdata.size != t.size:
-                    L.warning("Variable {} is not the correct size, skipping.".format(dnam))
+                    L.warning(f"Variable {dnam} is not the correct size, skipping.")
                     continue
 
             # Mark rows with data so we don't remove them with clear_rows
@@ -295,7 +294,7 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
         return df
 
     def nc_attributes(self, axes, daxes):
-        atts = super(OrthogonalMultidimensionalTimeseriesProfile, self).nc_attributes()
+        atts = super().nc_attributes()
         return dict_update(atts, {
             'global' : {
                 'featureType': 'timeSeriesProfile',

pocean/dsg/timeseriesProfile/r.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import OrderedDict
 from copy import copy
 
@@ -75,7 +74,7 @@ def from_dataframe(cls, df, output, **kwargs):
         if unique_dims is True:
             # Rename the dimension to avoid a dimension and coordinate having the same name
             # which is not supported in xarray
-            changed_axes = { k: '{}_dim'.format(v) for k, v in axes._asdict().items() }
+            changed_axes = { k: f'{v}_dim' for k, v in axes._asdict().items() }
             daxes = get_default_axes(changed_axes)
 
         # Downcast anything from int64 to int32
@@ -155,7 +154,7 @@ def from_dataframe(cls, df, output, **kwargs):
                     else:
                         v[:] = vvalues.reshape(v.shape)
                 except BaseException:
-                    L.exception('Failed to add {}'.format(c))
+                    L.exception(f'Failed to add {c}')
                     continue
 
             # Metadata variables
@@ -282,10 +281,10 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
                 # Carry through size 1 variables
                 if vdata.size == 1:
                     if vdata[0] is np.ma.masked:
-                        L.warning("Skipping variable {} that is completely masked".format(dnam))
+                        L.warning(f"Skipping variable {dnam} that is completely masked")
                         continue
                 else:
-                    L.warning("Skipping variable {} since it didn't match any dimension sizes".format(dnam))
+                    L.warning(f"Skipping variable {dnam} since it didn't match any dimension sizes")
                     continue
 
             # Mark rows with data so we don't remove them with clear_rows
@@ -311,7 +310,7 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
         return df
 
     def nc_attributes(self, axes, daxes):
-        atts = super(RaggedTimeseriesProfile, self).nc_attributes()
+        atts = super().nc_attributes()
         return dict_update(atts, {
             'global' : {
                 'featureType': 'timeSeriesProfile',

pocean/dsg/trajectory/__init__.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import namedtuple
 
 from shapely.geometry import LineString, Point

pocean/dsg/trajectory/cr.py

@@ -1,5 +1,4 @@
 #!python
-# coding=utf-8
 from collections import OrderedDict
 from copy import copy
 
@@ -69,7 +68,7 @@ def from_dataframe(cls, df, output, **kwargs):
         if unique_dims is True:
             # Rename the dimension to avoid a dimension and coordinate having the same name
             # which is not support in xarray
-            changed_axes = { k: '{}_dim'.format(v) for k, v in axes._asdict().items() }
+            changed_axes = { k: f'{v}_dim' for k, v in axes._asdict().items() }
             daxes = get_default_axes(changed_axes)
 
         # Downcast anything from int64 to int32
@@ -123,7 +122,7 @@ def from_dataframe(cls, df, output, **kwargs):
                     try:
                         v[i] = vvalues
                     except BaseException:
-                        L.exception('Failed to add {}'.format(c))
+                        L.exception(f'Failed to add {c}')
                         continue
 
             # Add all of the columns based on the sample dimension. Take all columns and remove the
@@ -149,7 +148,7 @@ def from_dataframe(cls, df, output, **kwargs):
                     else:
                         v[:] = vvalues.reshape(v.shape)
                 except BaseException:
-                    L.exception('Failed to add {}'.format(c))
+                    L.exception(f'Failed to add {c}')
                     continue
 
             # Metadata variables
@@ -218,10 +217,10 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
                 # Carry through size 1 variables
                 if vdata.size == 1:
                     if vdata[0] is np.ma.masked:
-                        L.warning("Skipping variable {} that is completely masked".format(dnam))
+                        L.warning(f"Skipping variable {dnam} that is completely masked")
                         continue
                 else:
-                    L.warning("Skipping variable {} since it didn't match any dimension sizes".format(dnam))
+                    L.warning(f"Skipping variable {dnam} since it didn't match any dimension sizes")
                     continue
 
             # Mark rows with data so we don't remove them with clear_rows
@@ -247,7 +246,7 @@ def to_dataframe(self, clean_cols=True, clean_rows=True, **kwargs):
         return df
 
     def nc_attributes(self, axes, daxes):
-        atts = super(ContiguousRaggedTrajectory, self).nc_attributes()
+        atts = super().nc_attributes()
         return dict_update(atts, {
             'global' : {
                 'featureType': 'trajectory',