rename losses.py to soiling.py

docs/examples/plot_greensboro_kimber_soiling.py

@@ -7,7 +7,7 @@
 
 # %%
 # This example shows basic usage of pvlib's Kimber Soiling model [1]_ with
-# :py:meth:`pvlib.losses.soiling_kimber`.
+# :py:func:`pvlib.soiling.kimber`.
 #
 # References
 # ----------
@@ -33,7 +33,7 @@
 import pathlib
 from matplotlib import pyplot as plt
 from pvlib.iotools import read_tmy3
-from pvlib.losses import soiling_kimber
+from pvlib.soiling import kimber
 import pvlib
 
 # get full path to the data directory
@@ -45,7 +45,7 @@
 greensboro_rain = greensboro.Lprecipdepth
 # calculate soiling with no wash dates and cleaning threshold of 25-mm of rain
 THRESHOLD = 25.0
-soiling_no_wash = soiling_kimber(greensboro_rain, cleaning_threshold=THRESHOLD)
+soiling_no_wash = kimber(greensboro_rain, cleaning_threshold=THRESHOLD)
 soiling_no_wash.name = 'soiling'
 # daily rain totals
 daily_rain = greensboro_rain.iloc[:-1].resample('D').sum()

docs/sphinx/source/api.rst

@@ -310,10 +310,8 @@ Functions for fitting diode models
 
 Other
 -----
-
 .. autosummary::
    :toctree: generated/
-
    pvsystem.retrieve_sam
    pvsystem.systemdef
    pvsystem.scale_voltage_current_power
@@ -332,8 +330,8 @@ Effects on PV System Output
 .. autosummary::
    :toctree: generated/
 
-   losses.soiling_hsu
-   losses.soiling_kimber
+   soiling.hsu
+   soiling.kimber
 
 
 

docs/sphinx/source/whatsnew/v0.7.2.rst

@@ -10,6 +10,11 @@ API Changes
 * :py:func:`pvlib.iotools.read_tmy3` when coerced to a single year now returns
   indices that are monotonically increasing. Therefore, the last index will be
   January 1, 00:00 of the *next* year. (:pull:`910`)
+* Renamed ``pvlib.losses`` to :py:mod:`pvlib.soiling`. Additional loss
+  models will go into code modules named for the loss or effect type.
+  (:issue:`935`, :issue:`891`)
+* Renamed ``pvlib.losses.soiling_hsu`` to :py:func:`pvlib.soiling.hsu`
+  (:issue:`935`)
 
 Enhancements
 ~~~~~~~~~~~~
@@ -19,7 +24,8 @@ Enhancements
 * Add :py:func:`~pvlib.pvsystem.PVSystem.faiman` and added
   ``temperature_model='faiman'`` option to :py:class:`~pvlib.modelchain.ModelChain`
   (:pull:`897`) (:issue:`836`).
-* Add Kimber soiling model :py:func:`pvlib.losses.soiling_kimber`. (:pull:`860`)
+* Add Kimber soiling model :py:func:`pvlib.soiling.kimber`. (:pull:`860`,
+   :issue`935`)
 * Add :func:`~pvlib.iotools.read_pvgis_tmy` for files downloaded using the
   PVGIS tool. (:issue:`880`)
 * Add new module :py:mod:`pvlib.snow` to contain models related to snow coverage and effects on a PV system. (:pull:`764`)

pvlib/__init__.py

@@ -14,5 +14,5 @@
 from pvlib import modelchain  # noqa: F401
 from pvlib import singlediode  # noqa: F401
 from pvlib import bifacial  # noqa: F401
-from pvlib import losses  # noqa: F401
+from pvlib import soiling  # noqa: F401
 from pvlib import snow  # noqa: F401

pvlib/losses.py → pvlib/soiling.py

@@ -1,7 +1,6 @@
 # -*- coding: utf-8 -*-
 """
-This module contains functions for losses of various types: soiling, mismatch,
-snow cover, etc.
+This module contains functions for soiling models
 """
 
 import datetime
@@ -10,8 +9,8 @@
 from pvlib.tools import cosd
 
 
-def soiling_hsu(rainfall, cleaning_threshold, tilt, pm2_5, pm10,
-                depo_veloc=None, rain_accum_period=pd.Timedelta('1h')):
+def hsu(rainfall, cleaning_threshold, tilt, pm2_5, pm10,
+        depo_veloc=None, rain_accum_period=pd.Timedelta('1h')):
     """
     Calculates soiling ratio given particulate and rain data using the model
     from Humboldt State University (HSU).
@@ -66,7 +65,7 @@ def soiling_hsu(rainfall, cleaning_threshold, tilt, pm2_5, pm10,
     try:
         from scipy.special import erf
     except ImportError:
-        raise ImportError("The soiling_hsu function requires scipy.")
+        raise ImportError("The pvlib.soiling.hsu function requires scipy.")
 
     # never use mutable input arguments
     if depo_veloc is None:
@@ -96,9 +95,9 @@ def soiling_hsu(rainfall, cleaning_threshold, tilt, pm2_5, pm10,
     return soiling_ratio
 
 
-def soiling_kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
-                   grace_period=14, max_soiling=0.3, manual_wash_dates=None,
-                   initial_soiling=0, rain_accum_period=24):
+def kimber(rainfall, cleaning_threshold=6, soiling_loss_rate=0.0015,
+           grace_period=14, max_soiling=0.3, manual_wash_dates=None,
+           initial_soiling=0, rain_accum_period=24):
     """
     Calculates fraction of energy lost due to soiling given rainfall data and
     daily loss rate using the Kimber model.

pvlib/tests/test_losses.py → pvlib/tests/test_soiling.py

@@ -5,7 +5,7 @@
 import numpy as np
 import pandas as pd
 from pandas.util.testing import assert_series_equal
-from pvlib.losses import soiling_hsu, soiling_kimber
+from pvlib.soiling import hsu, kimber
 from pvlib.iotools import read_tmy3
 from conftest import (
     requires_scipy, needs_pandas_0_22, DATA_DIR)
@@ -75,7 +75,7 @@ def rainfall_input():
 
 @requires_scipy
 @needs_pandas_0_22
-def test_soiling_hsu_no_cleaning(rainfall_input, expected_output):
+def test_hsu_no_cleaning(rainfall_input, expected_output):
     """Test Soiling HSU function"""
 
     rainfall = rainfall_input
@@ -85,15 +85,15 @@ def test_soiling_hsu_no_cleaning(rainfall_input, expected_output):
     tilt = 0.
     expected_no_cleaning = expected_output
 
-    result = soiling_hsu(rainfall=rainfall, cleaning_threshold=10., tilt=tilt,
-                         pm2_5=pm2_5, pm10=pm10, depo_veloc=depo_veloc,
-                         rain_accum_period=pd.Timedelta('1h'))
+    result = hsu(rainfall=rainfall, cleaning_threshold=10., tilt=tilt,
+                 pm2_5=pm2_5, pm10=pm10, depo_veloc=depo_veloc,
+                 rain_accum_period=pd.Timedelta('1h'))
     assert_series_equal(result, expected_no_cleaning)
 
 
 @requires_scipy
 @needs_pandas_0_22
-def test_soiling_hsu(rainfall_input, expected_output_2):
+def test_hsu(rainfall_input, expected_output_2):
     """Test Soiling HSU function with cleanings"""
 
     rainfall = rainfall_input
@@ -104,21 +104,21 @@ def test_soiling_hsu(rainfall_input, expected_output_2):
     expected = expected_output_2
 
     # three cleaning events at 4:00-6:00, 8:00-11:00, and 17:00-20:00
-    result = soiling_hsu(rainfall=rainfall, cleaning_threshold=0.5, tilt=tilt,
-                         pm2_5=pm2_5, pm10=pm10, depo_veloc=depo_veloc,
-                         rain_accum_period=pd.Timedelta('3h'))
+    result = hsu(rainfall=rainfall, cleaning_threshold=0.5, tilt=tilt,
+                 pm2_5=pm2_5, pm10=pm10, depo_veloc=depo_veloc,
+                 rain_accum_period=pd.Timedelta('3h'))
 
     assert_series_equal(result, expected)
 
 
 @requires_scipy
 @needs_pandas_0_22
-def test_soiling_hsu_defaults(rainfall_input, expected_output_1):
+def test_hsu_defaults(rainfall_input, expected_output_1):
     """
     Test Soiling HSU function with default deposition velocity and default rain
     accumulation period.
     """
-    result = soiling_hsu(
+    result = hsu(
         rainfall=rainfall_input, cleaning_threshold=0.5, tilt=0.0, pm2_5=1.0,
         pm10=2.0)
     assert np.allclose(result.values, expected_output_1)
@@ -132,50 +132,45 @@ def greensboro_rain():
 
 
 @pytest.fixture
-def expected_kimber_soiling_nowash():
+def expected_kimber_nowash():
     return pd.read_csv(
         DATA_DIR / 'greensboro_kimber_soil_nowash.dat',
         parse_dates=True, index_col='timestamp')
 
 
 @needs_pandas_0_22
-def test_kimber_soiling_nowash(greensboro_rain,
-                               expected_kimber_soiling_nowash):
+def test_kimber_nowash(greensboro_rain, expected_kimber_nowash):
     """Test Kimber soiling model with no manual washes"""
     # Greensboro typical expected annual rainfall is 8345mm
     assert greensboro_rain.sum() == 8345
     # calculate soiling with no wash dates
-    soiling_nowash = soiling_kimber(greensboro_rain)
+    nowash = kimber(greensboro_rain)
     # test no washes
-    assert np.allclose(
-        soiling_nowash.values,
-        expected_kimber_soiling_nowash['soiling'].values)
+    assert np.allclose(nowash.values, expected_kimber_nowash['soiling'].values)
 
 
 @pytest.fixture
-def expected_kimber_soiling_manwash():
+def expected_kimber_manwash():
     return pd.read_csv(
         DATA_DIR / 'greensboro_kimber_soil_manwash.dat',
         parse_dates=True, index_col='timestamp')
 
 
 @needs_pandas_0_22
-def test_kimber_soiling_manwash(greensboro_rain,
-                                expected_kimber_soiling_manwash):
+def test_kimber_manwash(greensboro_rain, expected_kimber_manwash):
     """Test Kimber soiling model with a manual wash"""
     # a manual wash date
     manwash = [datetime.date(1990, 2, 15), ]
     # calculate soiling with manual wash
-    soiling_manwash = soiling_kimber(
-        greensboro_rain, manual_wash_dates=manwash)
+    manwash = kimber(greensboro_rain, manual_wash_dates=manwash)
     # test manual wash
     assert np.allclose(
-        soiling_manwash.values,
-        expected_kimber_soiling_manwash['soiling'].values)
+        manwash.values,
+        expected_kimber_manwash['soiling'].values)
 
 
 @pytest.fixture
-def expected_kimber_soiling_norain():
+def expected_kimber_norain():
     # expected soiling reaches maximum
     soiling_loss_rate = 0.0015
     max_loss_rate = 0.3
@@ -186,19 +181,18 @@ def expected_kimber_soiling_norain():
 
 
 @needs_pandas_0_22
-def test_kimber_soiling_norain(greensboro_rain,
-                               expected_kimber_soiling_norain):
+def test_kimber_norain(greensboro_rain, expected_kimber_norain):
     """Test Kimber soiling model with no rain"""
     # a year with no rain
     norain = pd.Series(0, index=greensboro_rain.index)
     # calculate soiling with no rain
-    soiling_norain = soiling_kimber(norain)
+    norain = kimber(norain)
     # test no rain, soiling reaches maximum
-    assert np.allclose(soiling_norain.values, expected_kimber_soiling_norain)
+    assert np.allclose(norain.values, expected_kimber_norain)
 
 
 @pytest.fixture
-def expected_kimber_soiling_initial_soil():
+def expected_kimber_initial_soil():
     # expected soiling reaches maximum
     soiling_loss_rate = 0.0015
     max_loss_rate = 0.3
@@ -209,13 +203,11 @@ def expected_kimber_soiling_initial_soil():
 
 
 @needs_pandas_0_22
-def test_kimber_soiling_initial_soil(greensboro_rain,
-                                     expected_kimber_soiling_initial_soil):
+def test_kimber_initial_soil(greensboro_rain, expected_kimber_initial_soil):
     """Test Kimber soiling model with initial soiling"""
     # a year with no rain
     norain = pd.Series(0, index=greensboro_rain.index)
     # calculate soiling with no rain
-    soiling_norain = soiling_kimber(norain, initial_soiling=0.1)
+    norain = kimber(norain, initial_soiling=0.1)
     # test no rain, soiling reaches maximum
-    assert np.allclose(
-        soiling_norain.values, expected_kimber_soiling_initial_soil)
+    assert np.allclose(norain.values, expected_kimber_initial_soil)