Handle overflow condition in pvlib.clearsky.haurwitz (#364)

* Improve convergence of lambertw for very large arguments

* Revert "Improve convergence of lambertw for very large arguments"

This reverts commit d83d77d.

* Handle overflow warning; revise test case

Handles overflow when apparent zenith is slight greater than 90. Revised
test case to operate on zenith rather than time, removes dependence on
solarposition.

* Fix test case

Resolve errors in test definition

* more fixes

Remove use of np.where in haurwitz, fix declaration of pandas objects

* Fix pandas objects

Changed np arrays from 2d to 1d for use as dataframe indices

* more fixes

Resolving type conflict inside haurwitz function for casting output as a
dataframe

* Final edits

Fix line length, formatting and documentation in clearsky.haurwitz.
Create whatsnew\v0.5.1.rst. Delete commented old code from
test\test_clearsky.py

Author: cwhanse

docs/sphinx/source/whatsnew.rst

@@ -6,6 +6,7 @@ What's New
 
 These are new features and improvements of note in each release.
 
+.. include:: whatsnew/v0.5.1.rst
 .. include:: whatsnew/v0.5.0.rst
 .. include:: whatsnew/v0.4.5.txt
 .. include:: whatsnew/v0.4.4.txt

docs/sphinx/source/whatsnew/v0.5.1.rst

@@ -0,0 +1,28 @@
+.. _whatsnew_0500:
+
+v0.5.0 (August 11, 2017)
+------------------------
+
+API Changes
+~~~~~~~~~~~
+* 
+
+Bug fixes
+~~~~~~~~~
+* Remove condition causing Overflow warning from clearsky.haurwitz
+
+Enhancements
+~~~~~~~~~~~~
+* 
+
+Documentation
+~~~~~~~~~~~~~
+* 
+
+Testing
+~~~~~~~
+* Changed test for clearsky.haurwitz to operate on zenith angles
+
+Contributors
+~~~~~~~~~~~~
+* Cliff Hansen

pvlib/clearsky.py

@@ -294,9 +294,9 @@ def haurwitz(apparent_zenith):
 
     Implements the Haurwitz clear sky model for global horizontal
     irradiance (GHI) as presented in [1, 2]. A report on clear
-    sky models found the Haurwitz model to have the best performance of
-    models which require only zenith angle [3]. Extreme care should
-    be taken in the interpretation of this result!
+    sky models found the Haurwitz model to have the best performance 
+    in terms of average monthly error among models which require only 
+    zenith angle [3].
 
     Parameters
     ----------
@@ -306,7 +306,7 @@ def haurwitz(apparent_zenith):
 
     Returns
     -------
-    pd.Series
+    pd.DataFrame
     The modeled global horizonal irradiance in W/m^2 provided
     by the Haurwitz clear-sky model.
 
@@ -326,13 +326,14 @@ def haurwitz(apparent_zenith):
      Laboratories, SAND2012-2389, 2012.
     '''
 
-    cos_zenith = tools.cosd(apparent_zenith)
+    cos_zenith = tools.cosd(apparent_zenith.values)
+    clearsky_ghi = np.zeros_like(apparent_zenith.values)
+    clearsky_ghi[cos_zenith>0] = 1098.0 * cos_zenith[cos_zenith>0] * \
+                                    np.exp(-0.059/cos_zenith[cos_zenith>0])
 
-    clearsky_ghi = 1098.0 * cos_zenith * np.exp(-0.059/cos_zenith)
-
-    clearsky_ghi[clearsky_ghi < 0] = 0
-
-    df_out = pd.DataFrame({'ghi': clearsky_ghi})
+    df_out = pd.DataFrame(index=apparent_zenith.index,
+                          data=clearsky_ghi,
+                          columns=['ghi'])
 
     return df_out
 

pvlib/test/test_clearsky.py

@@ -234,25 +234,24 @@ def test_lookup_linke_turbidity_nointerp_months():
 
 
 def test_haurwitz():
-    tus = Location(32.2, -111, 'US/Arizona', 700)
-    times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h')
-    times_localized = times.tz_localize(tus.tz)
-    ephem_data = solarposition.get_solarposition(times_localized, tus.latitude,
-                                                 tus.longitude)
-    expected = pd.DataFrame(np.array([[0.],
-                                      [0.],
-                                      [82.85934048],
-                                      [699.74514735],
-                                      [1016.50198354],
-                                      [838.32103769],
-                                      [271.90853863],
-                                      [0.],
-                                      [0.]]),
-                             columns=['ghi'], index=times_localized)
-    out = clearsky.haurwitz(ephem_data['zenith'])
+    apparent_solar_elevation = np.array([-20, -0.05, -0.001, 5, 10, 30, 50, 90])
+    apparent_solar_zenith = 90 - apparent_solar_elevation
+    data_in = pd.DataFrame(data=apparent_solar_zenith,
+                           index=apparent_solar_zenith,
+                           columns=['apparent_zenith'])
+    expected = pd.DataFrame(np.array([0.,
+                                      0.,
+                                      0.,
+                                      48.6298687941956,
+                                      135.741748091813,
+                                      487.894132885425,
+                                      778.766689344363,
+                                      1035.09203253450]),
+                             columns=['ghi'],
+                             index=apparent_solar_zenith)
+    out = clearsky.haurwitz(data_in['apparent_zenith'])
     assert_frame_equal(expected, out)
 
-
 def test_simplified_solis_series_elevation():
     tus = Location(32.2, -111, 'US/Arizona', 700)
     times = pd.date_range(start='2014-06-24', end='2014-06-25', freq='3h')