Matt updats to SRR and CODS

docs/notebook_requirements.txt

@@ -10,18 +10,18 @@ decorator==4.3.0
 defusedxml==0.7.1
 entrypoints==0.2.3
 html5lib==1.0.1
-ipykernel==4.8.2
-ipython==8.10.0
+ipykernel==6.29.4
+ipython==8.23.0
 ipython-genutils==0.2.0
 ipywidgets==7.3.0
 jedi==0.16.0
-Jinja2==3.0.0
+Jinja2==3.1.3
 jsonschema==2.6.0
 jupyter==1.0.0
-jupyter-client==6.1.7
-jupyter-console==6.4.0
-jupyter-core==4.11.2
-jupyterlab-pygments==0.2.2
+jupyter-client==8.6.1
+jupyter-console==6.6.3
+jupyter-core==5.7.2
+jupyterlab-pygments==0.3.0
 lxml==4.9.1
 MarkupSafe==2.0.0
 mistune==2.0.3
@@ -30,17 +30,17 @@ nbconvert==7.0.0
 nbformat==5.1.0
 nest-asyncio==1.5.5
 notebook==6.4.12
-numexpr==2.8.0
-pandocfilters==1.4.2
+numexpr==2.10.0
+pandocfilters==1.5.1
 parso==0.5.2
 pexpect==4.6.0
 pickleshare==0.7.5
 prometheus-client==0.3.0
-prompt-toolkit==3.0.30
+prompt-toolkit==3.0.43
 ptyprocess==0.6.0
 pycparser==2.20
 Pygments==2.15.0
-pyzmq==22.2.1
+pyzmq==26.0.2
 qtconsole==4.3.1
 Send2Trash==1.8.0
 simplegeneric==0.8.1
@@ -49,7 +49,7 @@ terminado==0.8.3
 testpath==0.3.1
 tinycss2==1.1.1
 tornado==6.3.3
-traitlets==5.0.0
+traitlets==5.14.3
 wcwidth==0.1.7
 webencodings==0.5.1
-widgetsnbextension==3.3.0
+widgetsnbextension==3.3.0

docs/sphinx/source/api.rst

@@ -9,14 +9,14 @@ Submodules
 ==========
 
 RdTools is organized into submodules focused on different parts of the data
-analysis workflow.  
+analysis workflow.
 
 .. autosummary::
    :toctree: generated/
 
    analysis_chains
    degradation
-   soiling   
+   soiling
    availability
    filtering
    normalization
@@ -42,8 +42,8 @@ Object-oriented end-to-end analysis
    analysis_chains.TrendAnalysis.plot_soiling_monte_carlo
    analysis_chains.TrendAnalysis.plot_pv_vs_irradiance
    analysis_chains.TrendAnalysis.plot_degradation_timeseries
-   
-   
+
+
 Degradation
 ===========
 
@@ -86,7 +86,7 @@ System Availability
 
 .. autosummary::
    :toctree: generated/
-   
+
    AvailabilityAnalysis
    AvailabilityAnalysis.run
    AvailabilityAnalysis.plot
@@ -100,12 +100,14 @@ Filtering
 
 .. autosummary::
    :toctree: generated/
-    
+
    clip_filter
    quantile_clip_filter
    logic_clip_filter
    xgboost_clip_filter
+   clearsky_filter
    csi_filter
+   pvlib_clearsky_filter
    poa_filter
    tcell_filter
    normalized_filter

docs/sphinx/source/changelog.rst

@@ -1,5 +1,6 @@
 RdTools Change Log
 ==================
+.. include:: changelog/pending.rst
 .. include:: changelog/v2.2.0-beta.2.rst
 .. include:: changelog/v2.2.0-beta.1.rst
 .. include:: changelog/v2.1.8.rst

docs/sphinx/source/changelog/pending.rst

@@ -0,0 +1,27 @@
+*******
+pending
+*******
+
+Breaking changes
+------------
+These changes have the potential to change answers in existing scripts
+when compared with older versions of RdTools
+
+* Use the pvlib method for clear sky detection by default in :py:func:`~rdtools.analysis_chains.TrendAnalysis` (:pull:`412`)
+
+Enhancements
+------------
+* Added a new wrapper function for clearsky filters (:pull:`412`)
+
+Bug fixes
+---------
+* tbd
+
+Requirements
+------------
+* Specified versions in ``requirements.txt`` and ``docs/notebook_requirements.txt`` have been updated (:pull:`412`)
+
+Contributors
+------------
+* Martin Springer (:ghuser:`martin-springer`)
+* Michael Deceglie (:ghuser:`mdeceglie`)

rdtools/__init__.py

@@ -9,7 +9,9 @@
 from rdtools.degradation import degradation_year_on_year
 from rdtools.aggregation import aggregation_insol
 from rdtools.clearsky_temperature import get_clearsky_tamb
+from rdtools.filtering import clearsky_filter
 from rdtools.filtering import csi_filter
+from rdtools.filtering import pvlib_clearsky_filter
 from rdtools.filtering import poa_filter
 from rdtools.filtering import tcell_filter
 from rdtools.filtering import clip_filter

rdtools/aggregation.py

@@ -22,7 +22,7 @@ def aggregation_insol(energy_normalized, insolation, frequency='D'):
     aggregated : pandas.Series
         Insolation weighted average, aggregated at frequency
     '''
-    aggregated = (insolation * energy_normalized).resample(frequency).sum() / \
-        insolation.resample(frequency).sum()
+    aggregated = (insolation * energy_normalized).resample(frequency, origin='start_day').sum() / \
+        insolation.resample(frequency, origin='start_day').sum()
 
     return aggregated