ensure noisy gap filling is correctly applied

- propagated fillval from the downloaded CDF to fill_gaps
- made sure fill_gaps' output data wasn't overwriting the input

advect_imf.py

@@ -48,7 +48,10 @@ def load_acedata(tstart, tend, noise=True, proxy=None):
         for dataset, local_name, cdaweb_name in [(mag_data, 'b', 'BGSM'),
                                                  (swepam_data, 'u', 'V_GSM'),
                                                  (swepam_data, '', 'SC_pos_GSM')]:
-            t, values = dataset['Epoch'], fill_gaps(dataset[cdaweb_name][:, i], noise=noise)
+            t = dataset['Epoch']
+            values = fill_gaps(dataset[cdaweb_name][:, i],
+                               fillval=dataset[cdaweb_name].attrs['FILLVAL'],
+                               noise=noise)
 
             # Grab the appropriate component from
             # VALIDMIN and VALIDMAX attributes
@@ -99,7 +102,10 @@ def load_dscovr(tstart, tend, noise=True, proxy=None):
                 (mag_data, 'b', 'B1GSE', 'Epoch1'),
                 (plasma_data, 'u', 'V_GSE', 'Epoch'),
                 (orbit_data, '', 'GSE_POS', 'Epoch')]:
-            t, values = dataset[cdaweb_time_var], fill_gaps(dataset[cdaweb_name][:, i], noise=noise)
+            t = dataset[cdaweb_time_var]
+            values = fill_gaps(dataset[cdaweb_name][:, i],
+                               fillval=dataset[cdaweb_name].attrs['FILLVAL'],
+                               noise=noise)
 
             for attr in ('VALIDMIN', 'VALIDMAX'):
 

missing.py

@@ -60,11 +60,12 @@ def fill_gaps(data, fillval=9999999, sigma=5, winsor=0.05, noise=False, constrai
         # draw fluctuations from CDF and apply to linearly filled gaps
         for gap in gaps:
             for i in range(gap[1]-gap[0]+1):
-                data[gap[0]+i] += dx[p.searchsorted(random.random())]
+                series[gap[0]+i] += dx[p.searchsorted(random.random())]
 
         # cap variable if it should be strictly positive (e.g. number density)
         # use lowest measured value as floor
         if constrain and series.min() > 0.0:
-            data[data < series.min()] = series.min()
+            series[series < series.min()] = series.min()
+        return series
 
     return data