Merge pull request #96 from TUW-GEO/dev

merge for v1.2

rt1/__init__.py

@@ -2,5 +2,5 @@
 Import module for RT1 module
 """
 
-__version__ = '1.1.9'
+__version__ = '1.2'
 __author__ = 'Raphael Quast'

rt1/general_functions.py

@@ -276,3 +276,55 @@ def groupby_unsorted(a, key=lambda x: x, sort=False, get=lambda x: x):
         return OrderedDict(sorted(d.items()))
     else:
         return d
+
+
+def interpolate_to_index(data, index, data_index=None, **interp1d_kwargs):
+    '''
+    A wrapper around scipy.interp1d to interpolate a dataset to a given index
+
+    Parameters
+    ----------
+    data : list, array-like, pandas.Series or pandas.DataFrame
+        The input-data as list, array, pandas.Series or pandas.DataFrame
+        If the data is provided as pandas Series or DataFrame, the index
+        must support a method .to_julian_date() to convert the timestamps
+        into numerical values.
+    index : array-like
+        the index to which the dataset should be interpolated.
+        It must support a method .to_julian_date()
+    data_index : TYPE, optional
+        DESCRIPTION. The default is None.
+    **interp1d_kwargs :
+        additional keyword-arguments passed to scipy.interpolate.interp1d
+        the default is (fill_value=None, bounds_error=False)
+
+    Returns
+    -------
+    TYPE
+        DESCRIPTION.
+
+    '''
+    from pandas import Series, DataFrame
+    from scipy.interpolate import interp1d
+
+    kwargs = dict(fill_value=None, bounds_error=False)
+    kwargs.update(interp1d_kwargs)
+
+    if isinstance(data, Series):
+    # perform a linear interpolation to the timestamps of the auxiliary data
+        f = interp1d(data.index.to_julian_date(), data.values, **kwargs)
+        x = f(index.to_julian_date())
+        return Series(x, index)
+    elif isinstance(data, DataFrame):
+        f = interp1d(data.index.to_julian_date(),  data.values, axis=0,
+                     **kwargs)
+        x = f(index.to_julian_date())
+        return DataFrame(x, index, columns=data.columns)
+
+    elif isinstance(data, (list, np.ndarray)):
+        assert data_index is not None, ('you must provide "data_index"' +
+                                        'if data is provided as list or array')
+
+        f = interp1d(data_index.to_julian_date(), data.values, **kwargs)
+        x = f(index.to_julian_date())
+        return Series(x, index)

rt1/processing_config.py

@@ -192,7 +192,7 @@ def preprocess(self, **kwargs):
         return
 
 
-    def reader(self, **reader_arg):
+    def reader(self, reader_arg):
         '''
         a function that is called for each site to obtain the dataset
         '''

rt1/rtparse.py

@@ -225,13 +225,17 @@ def _parse_dict(self, section, int_keys=[], float_keys=[], bool_keys=[],
                 #assert inp[key].startswith('['), f'{key}  must start with "[" '
                 #assert inp[key].endswith(']'), f'{key} must end with "]" '
                 #val = inp[key][1:-1].replace(' ', '').split(',')
-                if inp[key] is None:
+                if inp[key] == 'None':
                     val = []
                 else:
                     val = inp[key].replace(' ', '').split(',')
                     val = [i for i in val if len(i) > 0]
             else:
-                val = inp[key]
+                # parse None as "real" None
+                if inp[key] == 'None':
+                    val = None
+                else:
+                    val = inp[key]
 
             parsed_dict[key] = val
         return parsed_dict
@@ -432,6 +436,10 @@ def get_process_specs(self):
 
         process_specs = dict()
         for key, val in inp.items():
+            # parse None as a 'real' None and not as the string 'None'
+            if val == 'None':
+                val = None
+
             if key.startswith('datetime__'):
                 date = dict(zip(['s', 'fmt'],
                         [i.strip() for i in val.split('fmt=')]))