change approach to pandas future warning in CODS

rdtools/soiling.py

@@ -2017,6 +2017,7 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
 
  # Ensure numeric index
  zs_series = zs_series.copy() # Make copy, so as not to change input
+ zs_series = zs_series.astype(float)
  original_index = zs_series.index.copy()
  if (original_index.dtype not in [int, 'int64']):
  zs_series.index = range(len(zs_series))
@@ -2048,22 +2049,20 @@ def _Kalman_filter_for_SR(self, zs_series, process_noise=1e-4, zs_std=.05,
  soiling_events = soiling_events[soiling_events].index.tolist()
 
  # Initialize various parameters
- with pd.option_context('future.no_silent_downcasting', True):
- if ffill:
- rolling_median_13 = \
- zs_series.ffill().rolling(13, center=True).median().ffill().bfill()
- rolling_median_7 = \
- zs_series.ffill().rolling(7, center=True).median().ffill().bfill()
- else:
- rolling_median_13 = \
- zs_series.bfill().rolling(13, center=True).median().ffill().bfill()
- rolling_median_7 = \
- zs_series.bfill().rolling(7, center=True).median().ffill().bfill()
+ if ffill:
+ rolling_median_13 = \
+ zs_series.ffill().rolling(13, center=True).median().ffill().bfill()
+ rolling_median_7 = \
+ zs_series.ffill().rolling(7, center=True).median().ffill().bfill()
+ else:
+ rolling_median_13 = \
+ zs_series.bfill().rolling(13, center=True).median().ffill().bfill()
+ rolling_median_7 = \
+ zs_series.bfill().rolling(7, center=True).median().ffill().bfill()
  # A rough estimate of the measurement noise
  measurement_noise = (rolling_median_13 - zs_series).var()
  # An initial guess of the slope
- with pd.option_context('future.no_silent_downcasting', True):
- initial_slope = np.array(theilslopes(zs_series.bfill().iloc[:14]))
+ initial_slope = np.array(theilslopes(zs_series.bfill().iloc[:14]))
  dt = 1 # All time stemps are one day
 
  # Initialize Kalman filter
@@ -2491,12 +2490,11 @@ def _collapse_cleaning_events(inferred_ce_in, metric, f=4):
 def _rolling_median_ce_detection(x, y, ffill=True, rolling_window=9, tuner=1.5):
  ''' Finds cleaning events in a time series of performance index (y) '''
  y = pd.Series(index=x, data=y)
+ y = y.astype(float)
  if ffill: # forward fill NaNs in y before running mean
- with pd.option_context('future.no_silent_downcasting', True):
- rm = y.ffill().rolling(rolling_window, center=True).median()
+ rm = y.ffill().rolling(rolling_window, center=True).median()
  else: # ... or backfill instead
- with pd.option_context('future.no_silent_downcasting', True):
- rm = y.bfill().rolling(rolling_window, center=True).median()
+ rm = y.bfill().rolling(rolling_window, center=True).median()
  Q3 = rm.diff().abs().quantile(.75)
  Q1 = rm.diff().abs().quantile(.25)
  limit = Q3 + tuner * (Q3 - Q1)
@@ -2507,11 +2505,11 @@ def _rolling_median_ce_detection(x, y, ffill=True, rolling_window=9, tuner=1.5):
 def _soiling_event_detection(x, y, ffill=True, tuner=5):
  ''' Finds cleaning events in a time series of performance index (y) '''
  y = pd.Series(index=x, data=y)
- with pd.option_context('future.no_silent_downcasting', True):
-  if ffill: # forward fill NaNs in y before running mean
-  rm = y.ffill().rolling(9, center=True).median()
-  else: # ... or backfill instead
-  rm = y.bfill().rolling(9, center=True).median()
+ y = y.astype(float)
+ if ffill: # forward fill NaNs in y before running mean
+ rm = y.ffill().rolling(9, center=True).median()
+ else: # ... or backfill instead
+ rm = y.bfill().rolling(9, center=True).median()
  Q3 = rm.diff().abs().quantile(.99)
  Q1 = rm.diff().abs().quantile(.01)
  limit = Q1 - tuner * (Q3 - Q1)