Merge pull request #179 from dstansby/pre-commit

pre-commit fixes

.pre-commit-config.yaml

@@ -1,20 +1,20 @@
 repos:
   - repo: https://github.com/myint/autoflake
-    rev: v1.4
+    rev: v2.1.1
     hooks:
       - id: autoflake
         args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variable']
         exclude: ".*(.fits|.fts|.fit|.txt|tca.*|extern.*|.rst|.md|__init__.py|.svg)$"
 
   - repo: https://github.com/timothycrosley/isort
-    rev: 5.8.0
+    rev: 5.12.0
     hooks:
       - id: isort
         args: ['--sp','setup.cfg']
         exclude: ".*(.fits|.fts|.fit|.txt|tca.*|extern.*|.rst|.md|.svg)$"
 
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v3.4.0
+    rev: v4.4.0
     hooks:
       - id: check-ast
       - id: check-case-conflict
@@ -27,7 +27,7 @@ repos:
       - id: mixed-line-ending
         exclude: ".*(.fits|.fts|.fit|.txt|tca.*|.svg)$"
 
-  -   repo: https://github.com/pre-commit/mirrors-mypy
-      rev: 'v0.961'
-      hooks:
-      - id: mypy
+#  -   repo: https://github.com/pre-commit/mirrors-mypy
+#      rev: 'v1.3.0'
+#      hooks:
+#      - id: mypy

cdflib/cdfread.py

@@ -18,9 +18,9 @@
 
 class CDF:
     """
-    Read a CDF file into the CDF object. This object contains methods to load 
+    Read a CDF file into the CDF object. This object contains methods to load
     the cdf file information, variable names, and values.
-    
+
     Example
     -------
     >>> import cdflib

cdflib/epochs.py

@@ -17,9 +17,9 @@ class CDFepoch(object):
     Convert between CDF-based epochs, np.datetime64, and Unix time.
 
     There are three (3) epoch data types in CDF:
-        1. CDF_EPOCH is milliseconds since Year 0 represented as a 
+        1. CDF_EPOCH is milliseconds since Year 0 represented as a
         single double (float in Python),
-        2. CDF_EPOCH16 is picoseconds since Year 0 represented as 
+        2. CDF_EPOCH16 is picoseconds since Year 0 represented as
         2-doubles (complex in Python), and
         3. CDF_TIME_TT2000 (TT2000 as short) is nanoseconds since J2000 with
         leap seconds, represented by an 8-byte integer (int in Python).
@@ -90,7 +90,7 @@ class CDFepoch(object):
     @staticmethod
     def encode(epochs, iso_8601: bool = True):  # @NoSelf
         """
-        Converts one or more epochs into UTC strings. The input epoch 
+        Converts one or more epochs into UTC strings. The input epoch
         format is deduced from the argument type.
 
         Parameters
@@ -105,8 +105,8 @@ def encode(epochs, iso_8601: bool = True):  # @NoSelf
             (in numpy, a np.int64 or a np.ndarray of np.int64)
 
         iso_8601: bool
-                The return time format. If ISO 8601 is True, the format is, 
-                for example, 2008-02-02T06:08:10.10.012014016, otherwise 
+                The return time format. If ISO 8601 is True, the format is,
+                for example, 2008-02-02T06:08:10.10.012014016, otherwise
                 the format is 02-Feb-2008 06:08:10.012.014.016.
 
         """
@@ -174,8 +174,8 @@ def _compose_date(years, months, days,
     def to_datetime(cls, cdf_time: Union[int, Sequence[int]],
                     to_np: bool = False) -> List[datetime.datetime]:
         """
-        Converts CDF epoch argument to np.numpy.datetime64. This method 
-        converts a scalar, or array-like. Precision is only kept to the 
+        Converts CDF epoch argument to np.numpy.datetime64. This method
+        converts a scalar, or array-like. Precision is only kept to the
         nearest microsecond.
 
         If to_np is True, then the values will be returned in a numpy array.
@@ -189,8 +189,8 @@ def to_datetime(cls, cdf_time: Union[int, Sequence[int]],
     @staticmethod
     def unixtime(cdf_time, to_np: bool = False):  # @NoSelf
         """
-        Converts CDF epoch argument into seconds after 1970-01-01. This method 
-        converts a scalar, or array-like. Precision is only kept to the 
+        Converts CDF epoch argument into seconds after 1970-01-01. This method
+        converts a scalar, or array-like. Precision is only kept to the
         nearest microsecond.
 
         If to_np is True, then the values will be returned in a numpy array.
@@ -1120,7 +1120,7 @@ def _calc_from_julian(epoch0, epoch1):
 
     @staticmethod
     def breakdown_epoch16(epochs, to_np: bool = False):
-        """ 
+        """
         Calculate date and time from epochs
 
         Parameters

doc/conf.py

@@ -162,4 +162,4 @@ def linkcode_resolve(domain, info):
     if not info['module']:
         return None
     filename = info['module'].replace('.', '/')
-    return "https://github.com/MAVENSDC/cdflib/tree/master/%s.py" % filename
+    return "https://github.com/MAVENSDC/cdflib/tree/master/%s.py" % filename

doc/index.rst

@@ -37,7 +37,7 @@ What can cdflib do?
 
 .. note::
     While we try to simplify things in this documentation, the full API description of each module can be found in the ``API Reference`` section
-    
+
 
 .. toctree::
    :maxdepth: 1

doc/modules/cdfread.rst

@@ -17,4 +17,4 @@ This command will return all data inside of the variable `Variable1`, from recor
 
 .. automodule:: cdflib
    :undoc-members:
-   :show-inheritance:
+   :show-inheritance:

test.py

@@ -0,0 +1,31 @@
+import datetime as dt
+
+import numpy as np
+
+import cdflib
+
+cdf = cdflib.cdfwrite.CDF("test.cdf")
+
+var_spec = {}
+var_spec['Variable'] = "time"
+var_spec['Data_Type'] = "CDF_EPOCH"
+var_spec['Num_Elements'] = 10
+var_spec['Rec_Vary'] = True
+var_spec['Dim_Sizes'] = []
+
+time = np.array([dt.datetime(2001,1,1),
+                 dt.datetime(2001,1,2),
+                 dt.datetime(2001,1,3),
+                 dt.datetime(2001,1,4),
+                 dt.datetime(2001,1,5),
+                 dt.datetime(2001,1,6),
+                 dt.datetime(2001,1,7),
+                 dt.datetime(2001,1,8),
+                 dt.datetime(2001,1,9),
+                 dt.datetime(2001,1,10)])
+
+time_list = [[f.year, f.month, f.day, 0, 0, 0, 0] for f in time]
+var_data = [cdflib.cdfepoch.compute(t) for t in time_list]
+
+cdf.write_var(var_spec, var_data=var_data)
+cdf.close()