Merge pull request #350 from MarcoGorelli/optional-common-time

Add use_common_time argument to MultipleSeries.to_pandas

pyleoclim/core/multipleseries.py

@@ -2098,7 +2098,7 @@ def stripes(self, ref_period=None, figsize=None, savefig_settings=None,
             mpl.rcParams.update(current_style)
             return ax
 
-    def to_pandas(self, *args, **kwargs):
+    def to_pandas(self, *args, use_common_time=False, **kwargs):
         """
         Align Series and place in DataFrame.
 
@@ -2110,10 +2110,17 @@ def to_pandas(self, *args, **kwargs):
         ----------
         *args, **kwargs
             Arguments and keyword arguments to pass to ``common_time``.
+        use_common_time, bool
+            Pass True if you want to use ``common_time`` to align the Series
+            to have common times. Else, times for which some Series doesn't
+            have values will be filled with NaN (default).
          
         Returns
         -------
         pandas.DataFrame
         """
-        ms_aligned = self.common_time(*args, **kwargs)
-        return pd.DataFrame({ser.metadata['label']: ser.to_pandas() for ser in ms_aligned.series_list})
+        if use_common_time:
+            ms = self.common_time(*args, **kwargs)
+        else:
+            ms = self
+        return pd.DataFrame({ser.metadata['label']: ser.to_pandas() for ser in ms.series_list})

pyleoclim/tests/test_core_MultipleSeries.py

@@ -516,23 +516,37 @@ def test_remove_overload(self):
         assert ms.series_list[0].equals(ts1) == (True, True)
 
 class TestToPandas:
-    def test_to_pandas(self): 
+    def test_to_pandas_with_common_time(self): 
         ts1 = pyleo.Series(time=np.array([1, 2, 4]), value=np.array([7, 4, 9]), time_unit='years CE', label='foo')
         ts2 = pyleo.Series(time=np.array([1, 3, 4]), value=np.array([7, 8, 1]), time_unit='years CE', label='bar')
         ms = pyleo.MultipleSeries([ts1, ts2])
-        result = ms.to_pandas()
+        result = ms.to_pandas(use_common_time=True)
         expected_index = pd.DatetimeIndex(
             np.array(['0000-12-31 05:48:45', '0002-07-02 02:31:54','0003-12-31 23:15:03'], dtype='datetime64[s]'),
             name='datetime',
         )
         expected = pd.DataFrame({'foo': [7, 5.25,9.00], 'bar': [7, 7.75,1.00]}, index=expected_index)
         pd.testing.assert_frame_equal(result, expected)
-    
-    def test_to_pandas_args_kwargs(self):
+
+    def test_to_pandas_defau(self): 
         ts1 = pyleo.Series(time=np.array([1, 2, 4]), value=np.array([7, 4, 9]), time_unit='years CE', label='foo')
         ts2 = pyleo.Series(time=np.array([1, 3, 4]), value=np.array([7, 8, 1]), time_unit='years CE', label='bar')
         ms = pyleo.MultipleSeries([ts1, ts2])
-        result = ms.to_pandas('bin', start=2)
+        result = ms.to_pandas()
+        expected_index = pd.DatetimeIndex(
+            np.array(['0000-12-31 05:48:45', '0001-12-31 11:37:31', '0002-12-31 17:26:17', '0003-12-31 23:15:03'],
+                     dtype='datetime64[s]'),
+            name='datetime',
+            freq='31556926S',
+        )
+        expected = pd.DataFrame({'foo': [7, 4, np.nan, 9], 'bar': [7, np.nan, 8, 1]}, index=expected_index)
+        pd.testing.assert_frame_equal(result, expected)
+
+    def test_to_pandas_args_kwargs(self):
+        ts1 = pyleo.Series(time=np.array([1, 2, 4]), value=np.array([7, 4, 9]), time_unit='years CE', label='foo',verbose=False)
+        ts2 = pyleo.Series(time=np.array([1, 3, 4]), value=np.array([7, 8, 1]), time_unit='years CE', label='bar',verbose=False)
+        ms = pyleo.MultipleSeries([ts1, ts2])
+        result = ms.to_pandas('bin', use_common_time=True, start=2)
         expected_index = pd.DatetimeIndex(
             np.array(['0002-12-31 17:26:17'], dtype='datetime64[s]'),
             name='datetime',