Comply with pv-terms

docs/degradation_and_soiling_example.ipynb

@@ -56,7 +56,10 @@
     "                           'figure.figsize': [4.5, 3],\n",
     "                           'lines.markeredgewidth': 0,\n",
     "                           'lines.markersize': 2\n",
-    "                           })"
+    "                           })\n",
+    "# Register time series plotting in pandas > 1.0\n",
+    "from pandas.plotting import register_matplotlib_converters\n",
+    "register_matplotlib_converters()"
    ]
   },
   {
@@ -111,7 +114,7 @@
     "    pass  # Python 3 strings are already unicode literals\n",
     "df = df.rename(columns = {\n",
     "    u'12 BP Solar - Active Power (kW)':'power',\n",
-    "    u'12 BP Solar - Wind Speed (m/s)': 'wind',\n",
+    "    u'12 BP Solar - Wind Speed (m/s)': 'wind_speed',\n",
     "    u'12 BP Solar - Weather Temperature Celsius (\\xb0C)': 'Tamb',\n",
     "    u'12 BP Solar - Global Horizontal Radiation (W/m\\xb2)': 'ghi',\n",
     "    u'12 BP Solar - Diffuse Horizontal Radiation (W/m\\xb2)': 'dhi'\n",
@@ -121,10 +124,10 @@
     "meta = {\"latitude\": -23.762028,\n",
     "        \"longitude\": 133.874886,\n",
     "        \"timezone\": 'Australia/North',\n",
-    "        \"tempco\": -0.005,\n",
+    "        \"gamma_pdc\": -0.005,\n",
     "        \"azimuth\": 0,\n",
     "        \"tilt\": 20,\n",
-    "        \"pdc\": 5100.0,\n",
+    "        \"power_dc_rated\": 5100.0,\n",
     "        \"temp_model_params\": pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']}\n",
     "\n",
     "df.index = pd.to_datetime(df.Timestamp)\n",
@@ -162,7 +165,7 @@
     "df['poa'] = beam + sky\n",
     "\n",
     "# Calculate cell temperature\n",
-    "df['Tcell'] = pvlib.temperature.sapm_cell(df.poa, df.Tamb, df.wind, **meta['temp_model_params'])\n",
+    "df['Tcell'] = pvlib.temperature.sapm_cell(df.poa, df.Tamb, df.wind_speed, **meta['temp_model_params'])\n",
     "\n",
     "# plot the AC power time series\n",
     "fig, ax = plt.subplots(figsize=(4,3))\n",
@@ -200,11 +203,11 @@
    "source": [
     "# Specify the keywords for the pvwatts model\n",
     "pvwatts_kws = {\"poa_global\" : df.poa,\n",
-    "              \"P_ref\" : meta['pdc'],\n",
-    "              \"T_cell\" : df.Tcell,\n",
-    "              \"G_ref\" : 1000,\n",
-    "              \"T_ref\": 25,\n",
-    "              \"gamma_pdc\" : meta['tempco']}\n",
+    "              \"power_dc_rated\" : meta['power_dc_rated'],\n",
+    "              \"temperature_cell\" : df.Tcell,\n",
+    "              \"poa_global_ref\" : 1000,\n",
+    "              \"temperature_cell_ref\": 25,\n",
+    "              \"gamma_pdc\" : meta['gamma_pdc']}\n",
     "\n",
     "# Calculate the normaliztion, the function also returns the relevant insolation for\n",
     "# each point in the normalized PV energy timeseries\n",
@@ -680,11 +683,11 @@
    "outputs": [],
    "source": [
     "clearsky_pvwatts_kws = {\"poa_global\" : df.clearsky_poa,\n",
-    "              \"P_ref\" : meta['pdc'],\n",
-    "              \"T_cell\" :df.clearsky_Tcell,\n",
-    "              \"G_ref\" : 1000,\n",
-    "              \"T_ref\": 25,\n",
-    "              \"gamma_pdc\" : meta['tempco']}\n",
+    "              \"power_dc_rated\" : meta['power_dc_rated'],\n",
+    "              \"temperature_cell\" :df.clearsky_Tcell,\n",
+    "              \"poa_global_ref\" : 1000,\n",
+    "              \"temperature_cell_ref\": 25,\n",
+    "              \"gamma_pdc\" : meta['gamma_pdc']}\n",
     "\n",
     "clearsky_normalized, clearsky_insolation = rdtools.normalize_with_pvwatts(df.energy, clearsky_pvwatts_kws)\n",
     "\n",
@@ -823,7 +826,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.7"
   }
  },
  "nbformat": 4,

docs/degradation_and_soiling_example_pvdaq_4.ipynb

@@ -51,7 +51,10 @@
     "                           'figure.figsize': [4.5, 3],\n",
     "                           'lines.markeredgewidth': 0,\n",
     "                           'lines.markersize': 2\n",
-    "                           })"
+    "                           })\n",
+    "# Register time series plotting in pandas > 1.0\n",
+    "from pandas.plotting import register_matplotlib_converters\n",
+    "register_matplotlib_converters()"
    ]
   },
   {
@@ -111,8 +114,8 @@
     "    df.to_pickle(cache_file)\n",
     "\n",
     "df = df.rename(columns = {\n",
-    "    'ac_power':'power',\n",
-    "    'wind_speed': 'wind',\n",
+    "    'ac_power':'power_ac',\n",
+    "    'wind_speed': 'wind_speed',\n",
     "    'ambient_temp': 'Tamb',\n",
     "    'poa_irradiance': 'poa',\n",
     "})\n",
@@ -121,10 +124,10 @@
     "meta = {\"latitude\": 39.7406,\n",
     "        \"longitude\": -105.1774,\n",
     "        \"timezone\": 'Etc/GMT+7',\n",
-    "        \"tempco\": -0.005,\n",
+    "        \"gamma_pdc\": -0.005,\n",
     "        \"azimuth\": 180,\n",
     "        \"tilt\": 40,\n",
-    "        \"pdc\": 1000.0,\n",
+    "        \"power_dc_rated\": 1000.0,\n",
     "        \"temp_model_params\":\n",
     "        pvlib.temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_polymer']}\n",
     "\n",
@@ -146,11 +149,11 @@
     "\n",
     "# Calculate cell temperature\n",
     "df['Tcell'] = pvlib.temperature.sapm_cell(df.poa, df.Tamb,\n",
-    "                                          df.wind, **meta['temp_model_params'])\n",
+    "                                          df.wind_speed, **meta['temp_model_params'])\n",
     "\n",
     "# plot the AC power time series\n",
     "fig, ax = plt.subplots(figsize=(4,3))\n",
-    "ax.plot(df.index, df.power, 'o', alpha=0.01)\n",
+    "ax.plot(df.index, df.power_ac, 'o', alpha=0.01)\n",
     "ax.set_ylim(0,1500)\n",
     "fig.autofmt_xdate()\n",
     "ax.set_ylabel('AC Power (W)');"
@@ -183,12 +186,12 @@
    ],
    "source": [
     "# Calculate the expected power with a simple PVWatts DC model\n",
-    "modeled_power = pvlib.pvsystem.pvwatts_dc(df['poa'], df['Tcell'], meta['pdc'],\n",
-    "                                          meta['tempco'], 25.0 )\n",
+    "modeled_power = pvlib.pvsystem.pvwatts_dc(df['poa'], df['Tcell'], meta['power_dc_rated'],\n",
+    "                                          meta['gamma_pdc'], 25.0 )\n",
     "\n",
     "# Calculate the normalization, the function also returns the relevant insolation for\n",
     "# each point in the normalized PV energy timeseries\n",
-    "normalized, insolation = rdtools.normalize_with_expected_power(df['power'],\n",
+    "normalized, insolation = rdtools.normalize_with_expected_power(df['power_ac'],\n",
     "                                                               modeled_power,\n",
     "                                                               df['poa'])\n",
     "\n",
@@ -238,7 +241,7 @@
     "tcell_mask = rdtools.tcell_filter(df['Tcell'])\n",
     "# Note: This clipping mask may be disabled when you are sure the system is not \n",
     "# experiencing clipping due to high DC/AC ratio\n",
-    "clip_mask = rdtools.clip_filter(df['power'])\n",
+    "clip_mask = rdtools.clip_filter(df['power_ac'])\n",
     "\n",
     "# filter the time series and keep only the columns needed for the\n",
     "# remaining steps\n",
@@ -695,12 +698,12 @@
     "# Calculate the expected power with a simple PVWatts DC model\n",
     "clearsky_modeled_power = pvlib.pvsystem.pvwatts_dc(df['clearsky_poa'],\n",
     "                                                   df['clearsky_Tcell'],\n",
-    "                                                   meta['pdc'], meta['tempco'], 25.0 )\n",
+    "                                                   meta['power_dc_rated'], meta['gamma_pdc'], 25.0 )\n",
     "\n",
     "# Calculate the normalization, the function also returns the relevant insolation for\n",
     "# each point in the normalized PV energy timeseries\n",
     "clearsky_normalized, clearsky_insolation = rdtools.normalize_with_expected_power(\n",
-    "    df['power'],\n",
+    "    df['power_ac'],\n",
     "    clearsky_modeled_power,\n",
     "    df['clearsky_poa']\n",
     ")\n",
@@ -848,7 +851,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.7"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,

docs/sphinx/source/changelog/v2.0.0.rst

@@ -19,6 +19,21 @@ API Changes
   has changed relative to prior versions using ``precip_clean_only=True`` (:pull:`176`).
 * Remove ``random_seed`` parameter from soiling module. Functionality can be obtained by running
   ``numpy.random.seed()`` outside of RdTools functions. (:pull:`176`)
+* Many kwargs have changed name (but not input order) to bring nomenclature into 
+  closer alignment with the `DuraMAT pv-terms project <https://duramat.github.io/pv-terms>`_: (:pull:`185`)
+
+  * :py:func:`~rdtools.aggregation.aggregation_insol` first kwarg is now ``energy_normalized``.
+  * :py:func:`~rdtools.degradation.degradation_year_on_year`, 
+    :py:func:`~rdtools.degradation.degradation_ols` and 
+    :py:func:`~rdtools.degradation.degradation_classical_decomposition`
+    first kwarg is now ``energy_normalized``.
+  * :py:func:`~rdtools.filtering.normalized_filter` input kwargs are now ``energy_normalized``, ``energy_normalized_low`` and ``energy_normalized_high``.
+  * :py:func:`~rdtools.filtering.poa_filter` input kwargs are now ``poa_global``, ``poa_global_low`` and ``poa_global_high``.
+  * :py:func:`~rdtools.filtering.tcell_filter` input kwargs are now ``temperature_cell``, ``temperature_cell_low`` and ``temperature_cell_high``.
+  * :py:func:`~rdtools.filtering.clip_filter` input kwargs are now ``power_ac`` and ``quantile``.
+  * :py:func:`~rdtools.filtering.csi_filter` first two kwargs are now ``poa_global_measured``, ``poa_global_clearsky``.
+  * :py:func:`~rdtools.normalization.normalize_with_pvwatts` pvwatts_kws dictionary keys have been renamed.
+  * :py:func:`~rdtools.normalization.pvwatts_dc_power` input kwargs are now ``poa_global``, ``power_dc_rated``, ``temperature_cell``, ``poa_global_ref``, ``temperature_cell_ref``, ``gamma_pdc``.
 
 Enhancements
 ------------
@@ -67,6 +82,12 @@ Example Updates
   variable names (:pull:`139`).
 * Add a new example notebook that analyzes data from a PV system located at
   NREL's South Table Mountain campus (PVDAQ system #4) (:pull:`171`).
+* Explicitly register pandas datetime converters which were `deprecated <https://github.com/pandas-dev/pandas/issues/18301>`_.
+
 
 Contributors
 ------------
+* Mike Deceglie (:ghuser:`mdeceglie`)
+* Kevin Anderson (:ghuser:`kanderso-nrel`)
+* Chris Deline (:ghuser:`cdeline`)
+

docs/sphinx/source/index.rst

@@ -7,7 +7,7 @@
    :width: 600
 
 .. pipe character renders as a blank line, used as spacer after the logo
-|
+  |
 
 RdTools Overview
 ================
@@ -146,30 +146,31 @@ The most frequently used functions are:
    normalization.normalize_with_pvwatts(energy, pvwatts_kws)
      '''
      Inputs: Pandas time series of raw energy, PVwatts dict for system analysis 
-       (poa_global, P_ref, T_cell, G_ref, T_ref, gamma_pdc)
+       (poa_global, power_dc_rated, temperature_cell, poa_global_ref, temperature_cell_ref, gamma_pdc)
      Outputs: Pandas time series of normalized energy and POA insolation
      '''
 
 .. code:: python
 
-   filtering.poa_filter(poa); filtering.tcell_filter(Tcell); filtering.clip_filter(power); 
-   filtering.csi_filter(insolation, clearsky_insolation); filtering.normalized_filter(normalized_power)
+   filtering.poa_filter(poa_global); filtering.tcell_filter(temperature_cell); 
+   filtering.clip_filter(power_ac); filtering.normalized_filter(energy_normalized);
+   filtering.csi_filter(poa_global_measured, poa_global_clearsky); 
      '''
      Inputs: Pandas time series of raw data to be filtered.
      Output: Boolean mask where `True` indicates acceptable data
      '''
 
 .. code:: python
 
-   aggregation.aggregation_insol(normalized, insolation, frequency='D')
+   aggregation.aggregation_insol(energy_normalized, insolation, frequency='D')
      '''
      Inputs: Normalized energy and insolation
      Output: Aggregated data, weighted by the insolation.
      '''
 
 .. code:: python
 
-   degradation.degradation_year_on_year(aggregated)
+   degradation.degradation_year_on_year(energy_normalized)
      '''
      Inputs: Aggregated, normalized, filtered time series data
      Outputs: Tuple: `yoy_rd`: Degradation rate 
@@ -178,7 +179,7 @@ The most frequently used functions are:
 
 .. code:: python
 
-   soiling.soiling_srr(aggregated, aggregated_insolation)
+   soiling.soiling_srr(energy_normalized_daily, insolation_daily)
      '''
      Inputs: Daily aggregated, normalized, filtered time series data for normalized performance and insolation
      Outputs: Tuple: `sr`: Insolation-weighted soiling ratio 

rdtools/aggregation.py

@@ -1,16 +1,16 @@
 '''Functions for calculating weighted aggregates of PV system data.'''
 
 
-def aggregation_insol(normalized_energy, insolation, frequency='D'):
+def aggregation_insol(energy_normalized, insolation, frequency='D'):
     '''
     Insolation weighted aggregation
 
     Parameters
     ----------
-    normalized_energy : pd.Series
+    energy_normalized : pd.Series
         Normalized energy time series
     insolation : pd.Series
-        Time series of insolation associated with each `normalized_energy`
+        Time series of insolation associated with each `energy_normalized`
         point
     frequency : Pandas offset string, default 'D'
         Target frequency at which to aggregate
@@ -20,7 +20,7 @@ def aggregation_insol(normalized_energy, insolation, frequency='D'):
     aggregated : pd.Series
         Insolation weighted average, aggregated at frequency
     '''
-    aggregated = (insolation * normalized_energy).resample(frequency).sum() / \
+    aggregated = (insolation * energy_normalized).resample(frequency).sum() / \
         insolation.resample(frequency).sum()
 
     return aggregated