Perform dayofyear-based calculations according to UTC, not local time

docs/sphinx/source/whatsnew/v0.11.0.rst

@@ -23,6 +23,8 @@ Enhancements
 
 Bug fixes
 ~~~~~~~~~
+* Fixes clearsky.lookup_linke_turbidity and irradiance.get_extra_radiation to be timezone-aware
+  (:issue:`2054`, :pull:`2055`)
 
 
 Testing
@@ -42,3 +44,4 @@ Contributors
 * Cliff Hansen (:ghuser:`cwhanse`)
 * Mark Mikofski (:ghuser:`mikofski`)
 * Siddharth Kaul (:ghuser:`k10blogger`)
+* Yunho Kee (:ghuser:`yhkee0404`)

pvlib/clearsky.py

@@ -169,6 +169,14 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
     Returns
     -------
     turbidity : Series
+
+    Notes
+    -----
+    Linke turbidity is obtained from a file of historical monthly averages.
+    The returned value for each time is either the monthly value or an
+    interpolated value to smooth the transition between months.
+    Interpolation is done on the day of year, and for this purpose time is
+    first converted to UTC.
     """
 
     # The .h5 file 'LinkeTurbidities.h5' contains a single 2160 x 4320 x 12
@@ -198,13 +206,16 @@ def lookup_linke_turbidity(time, latitude, longitude, filepath=None,
     with h5py.File(filepath, 'r') as lt_h5_file:
         lts = lt_h5_file['LinkeTurbidity'][latitude_index, longitude_index]
 
+    time_utc = tools._pandas_to_utc(time)
+
     if interp_turbidity:
-        linke_turbidity = _interpolate_turbidity(lts, time)
+        linke_turbidity = _interpolate_turbidity(lts, time_utc)
     else:
-        months = time.month - 1
-        linke_turbidity = pd.Series(lts[months], index=time)
+        months = time_utc.month - 1
+        linke_turbidity = pd.Series(lts[months], index=time_utc)
 
     linke_turbidity /= 20.
+    linke_turbidity.index = time
 
     return linke_turbidity
 

pvlib/irradiance.py

@@ -126,7 +126,7 @@ def _handle_extra_radiation_types(datetime_or_doy, epoch_year):
     # a better way to do it.
     if isinstance(datetime_or_doy, pd.DatetimeIndex):
         to_doy = tools._pandas_to_doy  # won't be evaluated unless necessary
-        def to_datetimeindex(x): return x                       # noqa: E306
+        to_datetimeindex = tools._pandas_to_utc
         to_output = partial(pd.Series, index=datetime_or_doy)
     elif isinstance(datetime_or_doy, pd.Timestamp):
         to_doy = tools._pandas_to_doy

pvlib/solarposition.py

@@ -24,7 +24,7 @@
 import warnings
 import datetime
 
-from pvlib import atmosphere
+from pvlib import atmosphere, tools
 from pvlib.tools import datetime_to_djd, djd_to_datetime
 
 
@@ -200,11 +200,7 @@ def spa_c(time, latitude, longitude, pressure=101325, altitude=0,
         raise ImportError('Could not import built-in SPA calculator. ' +
                           'You may need to recompile the SPA code.')
 
-    # if localized, convert to UTC. otherwise, assume UTC.
-    try:
-        time_utc = time.tz_convert('UTC')
-    except TypeError:
-        time_utc = time
+    time_utc = tools._pandas_to_utc(time)
 
     spa_out = []
 
@@ -645,11 +641,7 @@ def pyephem(time, latitude, longitude, altitude=0, pressure=101325,
     except ImportError:
         raise ImportError('PyEphem must be installed')
 
-    # if localized, convert to UTC. otherwise, assume UTC.
-    try:
-        time_utc = time.tz_convert('UTC')
-    except TypeError:
-        time_utc = time
+    time_utc = tools._pandas_to_utc(time)
 
     sun_coords = pd.DataFrame(index=time)
 
@@ -766,11 +758,7 @@ def ephemeris(time, latitude, longitude, pressure=101325, temperature=12):
     # the SPA algorithm needs time to be expressed in terms of
     # decimal UTC hours of the day of the year.
 
-    # if localized, convert to UTC. otherwise, assume UTC.
-    try:
-        time_utc = time.tz_convert('UTC')
-    except TypeError:
-        time_utc = time
+    time_utc = tools._pandas_to_utc(time)
 
     # strip out the day of the year and calculate the decimal hour
     DayOfYear = time_utc.dayofyear

pvlib/tests/test_clearsky.py

@@ -191,7 +191,7 @@ def test_ineichen_altitude():
 
 def test_lookup_linke_turbidity():
     times = pd.date_range(start='2014-06-24', end='2014-06-25',
-                          freq='12h', tz='America/Phoenix')
+                          freq='12h', tz='utc').tz_convert('America/Phoenix')
     # expect same value on 2014-06-24 0000 and 1200, and
     # diff value on 2014-06-25
     expected = pd.Series(
@@ -203,7 +203,7 @@ def test_lookup_linke_turbidity():
 
 def test_lookup_linke_turbidity_leapyear():
     times = pd.date_range(start='2016-06-24', end='2016-06-25',
-                          freq='12h', tz='America/Phoenix')
+                          freq='12h', tz='utc').tz_convert('America/Phoenix')
     # expect same value on 2016-06-24 0000 and 1200, and
     # diff value on 2016-06-25
     expected = pd.Series(
@@ -214,8 +214,8 @@ def test_lookup_linke_turbidity_leapyear():
 
 
 def test_lookup_linke_turbidity_nointerp():
-    times = pd.date_range(start='2014-06-24', end='2014-06-25',
-                          freq='12h', tz='America/Phoenix')
+    times = pd.date_range(start='2014-06-01', end='2014-06-02',
+                          freq='12h', tz='utc').tz_convert('America/Phoenix')
     # expect same value for all days
     expected = pd.Series(np.array([3., 3., 3.]), index=times)
     out = clearsky.lookup_linke_turbidity(times, 32.125, -110.875,
@@ -225,7 +225,7 @@ def test_lookup_linke_turbidity_nointerp():
 
 def test_lookup_linke_turbidity_months():
     times = pd.date_range(start='2014-04-01', end='2014-07-01',
-                          freq='1M', tz='America/Phoenix')
+                          freq='1M', tz='utc').tz_convert('America/Phoenix')
     expected = pd.Series(
         np.array([2.89918032787, 2.97540983607, 3.19672131148]), index=times
     )
@@ -235,7 +235,7 @@ def test_lookup_linke_turbidity_months():
 
 def test_lookup_linke_turbidity_months_leapyear():
     times = pd.date_range(start='2016-04-01', end='2016-07-01',
-                          freq='1M', tz='America/Phoenix')
+                          freq='1M', tz='utc').tz_convert('America/Phoenix')
     expected = pd.Series(
         np.array([2.89918032787, 2.97540983607, 3.19672131148]), index=times
     )

pvlib/tests/test_irradiance.py

@@ -28,7 +28,7 @@
 def times():
     # must include night values
     return pd.date_range(start='20140624', freq='6h', periods=4,
-                         tz='US/Arizona')
+                         tz='utc').tz_convert('US/Arizona')
 
 
 @pytest.fixture
@@ -112,7 +112,7 @@ def test_get_extra_radiation_nrel_numba(times):
         # and reset to no-numba state
         irradiance.get_extra_radiation(times, method='nrel')
     assert_allclose(result,
-                    [1322.332316, 1322.296282, 1322.261205, 1322.227091])
+                    [1322.375560, 1322.338415, 1322.302221, 1322.266984])
 
 
 def test_get_extra_radiation_invalid():
@@ -612,7 +612,7 @@ def test_disc_value(pressure, expected):
     # see GH 449 for pressure=None vs. 101325.
     columns = ['dni', 'kt', 'airmass']
     times = pd.DatetimeIndex(['2014-06-24T1200', '2014-06-24T1800'],
-                             tz='America/Phoenix')
+                             tz='utc').tz_convert('America/Phoenix')
     ghi = pd.Series([1038.62, 254.53], index=times)
     zenith = pd.Series([10.567, 72.469], index=times)
     out = irradiance.disc(ghi, zenith, times, pressure=pressure)

pvlib/tools.py

@@ -207,7 +207,28 @@ def _pandas_to_doy(pd_object):
     -------
     dayofyear
     """
-    return pd_object.dayofyear
+    return _pandas_to_utc(pd_object).dayofyear
+
+
+def _pandas_to_utc(pd_object):
+    """
+    Converts a pandas datetime-like object to UTC, if localized.
+    Otherwise, assume UTC.
+
+    Parameters
+    ----------
+    pd_object : DatetimeIndex or Timestamp
+
+    Returns
+    -------
+    pandas object localized to or assumed to be UTC.
+    """
+    # 
+    try:
+        pd_object_utc = pd_object.tz_convert('UTC')
+    except TypeError:
+        pd_object_utc = pd_object
+    return pd_object_utc
 
 
 def _doy_to_datetimeindex(doy, epoch_year=2014):
@@ -230,11 +251,16 @@ def _doy_to_datetimeindex(doy, epoch_year=2014):
 
 
 def _datetimelike_scalar_to_doy(time):
-    return pd.DatetimeIndex([pd.Timestamp(time)]).dayofyear
+    return _datetimelike_scalar_to_datetimeindex(time).dayofyear
 
 
 def _datetimelike_scalar_to_datetimeindex(time):
-    return pd.DatetimeIndex([pd.Timestamp(time)])
+
+    timestamp = pd.Timestamp(time)
+
+    timestamp_utc = _pandas_to_utc(timestamp)
+
+    return pd.DatetimeIndex([timestamp_utc])
 
 
 def _scalar_out(arg):