Use SAM to calculate power output

prereise/gather/demanddata/EIA/demo/AssembleBAfromExcel_demo.ipynb

@@ -20,10 +20,8 @@
     "from dateutil.parser import parse\n",
     "\n",
     "import os\n",
-    "import sys\n",
-    "sys.path.append(\"..\")\n",
     "\n",
-    "import get_eia_data"
+    "from prereise.gather.demanddata.eia import get_eia_data"
    ]
   },
   {
@@ -183,7 +181,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.7rc2"
   }
  },
  "nbformat": 4,

prereise/gather/demanddata/EIA/get_eia_data.py

@@ -14,14 +14,14 @@ def from_download(tok, start_date, end_date, offset_days, series_list):
     """Downloads and assemble dataset of demand data per balancing authority \ 
         for desired date range.
 
-    :param string tok: token obtained by registering with EIA.
+    :param str tok: token obtained by registering with EIA.
     :param timestamp start: start date.
     :param timestamp end: end data.
     :param list series_list: list of demand series names provided by EIA, \ 
         e.g., ['EBA.AVA-ALL.D.H', 'EBA.AZPS-ALL.D.H'].
     :param int offset_days: number of business days for data to stabilize.
-    :return: data frame indexed with hourly UTC time and BA series \ 
-        name for column names.
+    :return: (*pandas*) -- data frame indexed with hourly UTC time and BA \ 
+        series name for column names.
     """
 
     timespan = pd.date_range(start_date,
@@ -44,12 +44,12 @@ def from_excel(directory, series_list, start_date, end_date):
     """Assembles EIA balancing authority (BA) data from pre-downloaded Excel \ 
         spreadsheets. The spreadsheets contain data from July 2015 to present.
 
-    :param string directory: location of Excel files.
+    :param str directory: location of Excel files.
     :param list series_list: list of BA initials, e.g., ['PSE',BPAT','CISO'].
     :param timestamp start_date: desired start of dataset.
     :param timestamp end_date: desired end of dataset.
-    :return: data frame indexed with hourly UTC time and BA series \ 
-        name for column names.
+    :return: (*pandas*) -- data frame indexed with hourly UTC time and BA \ 
+        series name for column names.
     """
     timespan = pd.date_range(start_date, end_date, freq='H')
     df_all = pd.DataFrame(index=timespan)
@@ -70,14 +70,15 @@ def from_excel(directory, series_list, start_date, end_date):
 
 
 class EIAgov(object):
+    """Copied from `this link <https://quantcorner.wordpress.com/\
+        2014/11/18/downloading-eias-data-with-python/>`_.
+
+    :param str token: EIA token.
+    :param list series: id code(s) of the series to be downloaded.
+
+    """
+
     def __init__(self, token, series):
-        """Initialises the EIAgov class. Copied from \ 
-        `https://quantcorner.wordpress.com/2014/11/18/downloading-eias-data-with-python/` 
-
-        :param string token: EIA token
-        :param list series: id code(s) of the series to be downloaded
-        """
-
         self.token = token
         self.series = series
 
@@ -107,7 +108,9 @@ def raw(self, ser):
             print('Reason: ', e.reason)
 
     def get_data(self):
-        """Convert json files into pandas DataFrame
+        """Converts json files into data frame.
+
+        :return: (*pandas*) -- data frame.
         """
 
         date_ = self.raw(self.series[0])

prereise/gather/demanddata/EIA/test/__init__.py

@@ -1 +1 @@
-__all__= ["test_EIAdownload", "test_from_excel"]
+__all__= ["test_eia_download", "test_from_excel"]

prereise/gather/demanddata/EIA/test/test_eia_download.py

@@ -8,21 +8,18 @@
 import pandas as pd
 import pytest
 
-from .. import get_eia_data
+from prereise.gather.demanddata.eia import get_eia_data
 
 
 @pytest.mark.skip(reason="Need API key")
 def test_eia_download():
-    '''
-    Check pandas DataFrame assembled from data download \ 
-    by API call from EIA. Test checks that the correct \ 
-    number of files are downloaded and correct number of \ 
-    columns are created.
-
-    Token string can be obtained by registering in 
-    https://www.eia.gov/opendata/
-
-    '''
+    """Check data frame assembled from data download by API call from \ 
+        EIA. Test checks that the correct number of files are downloaded and \ 
+        correct number of columns are created.
+
+        Token string can be obtained by registering \ 
+        `here <https://www.eia.gov/opendata/>`_.
+    """
     print(
         'A API key is required for the API download. The key '
         'can be obtained by a user by registering at '

prereise/gather/demanddata/EIA/test/test_from_excel.py

@@ -8,16 +8,14 @@
 import pandas as pd
 import pytest
 
-from .. import get_eia_data
+from prereise.gather.demanddata.eia import get_eia_data
 
 
 def test_from_excel():
-    '''
-    Test pandas DataFrame assembled from Excel spreadsheets \ 
-    manually downloaded from EIA.  Test checks that correct \ 
-    number of columns are created.
-
-    '''
+    """Tests data frame assembled from Excel spreadsheets manually \ 
+        downloaded from EIA. Test checks that correct number of columns are \ 
+        created.
+    """
 
     dir1 = os.path.join(os.path.dirname(__file__), 'data')
 

prereise/gather/solardata/ga_wind/demo/ga_wind_demo.ipynb

@@ -21,30 +21,16 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "from westernintnet.westernintnet import WesternIntNet\n",
-    "import sys\n",
-    "\n",
-    "sys.path.append(\"../\")\n",
-    "\n",
-    "import ga_wind\n",
-    "from matplotlib import pyplot\n",
-    "from helpers import to_reise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1. Solar Plants in Network"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\program files\\python\\python36\\lib\\site-packages\\matplotlib\\__init__.py:886: MatplotlibDeprecationWarning: \n",
+      "examples.directory is deprecated; in the future, examples will be found relative to the 'datapath' directory.\n",
+      "  \"found relative to the 'datapath' directory.\".format(key))\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -56,16 +42,30 @@
       "Loading branches\n",
       "Loading resources\n",
       "Loading net_generation\n",
-      "Load solar data\n",
-      "Load wind data\n",
-      "Load hydro data\n",
-      "Load demand data\n",
       "Done loading\n"
      ]
     }
    ],
    "source": [
-    "grid = WesternIntNet()\n",
+    "from westernintnet.westernintnet import win_data as grid\n",
+    "from prereise.gather.solardata.ga_wind import ga_wind, helpers\n",
+    "\n",
+    "from matplotlib import pyplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Solar Plants in Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "solar_plant = grid.genbus.groupby('type').get_group('solar')"
    ]
   },
@@ -653,7 +653,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data_reise = to_reise(data)"
+    "data_reise = helpers.to_reise(data)"
    ]
   },
   {

prereise/gather/solardata/ga_wind/ga_wind.py

@@ -6,7 +6,7 @@
 import pandas as pd
 from tqdm import tqdm
 
-from .helpers import ll2ij
+from prereise.gather.solardata.ga_wind import helpers
 
 
 def retrieve_data(solar_plant, hs_api_key,
@@ -15,11 +15,12 @@ def retrieve_data(solar_plant, hs_api_key,
     """Retrieve irradiance data from Gridded Atmospheric Wind Integration \ 
         National Dataset.
 
-    :param solar_plant: pandas DataFrame with the following structure: \ 
-        ['plantID'(index), 'lat', 'lon', 'GenMWMax'].
-    :param year: year.
-    :return: pandas DataFrame with the following structure: ['Pout', \ 
-        'plantID', 'ts', 'tsID']. The power output is in MW.
+    :param pandas solar_plant: data frame with *'lat'*, *'lon'* and \ 
+        *'GenMWMax'* and *'plantID'* as indices.
+    :param str hs_api_key: API key.
+    :param str year: year.
+    :return: (*pandas*) -- data frame with *'Pout'*, *'plantID'*, *'ts'* and \ 
+        *'tsID'* as columns. The power output is in MWh.
     """
 
     # Information on solar plants
@@ -52,7 +53,7 @@ def retrieve_data(solar_plant, hs_api_key,
     lat_origin, lon_origin = f['coordinates'][0][0]
     ij = {}
     for key in coord.keys():
-        ij[key] = ll2ij(lon_origin, lat_origin, key[0], key[1])
+        ij[key] = helpers.ll2ij(lon_origin, lat_origin, key[0], key[1])
 
     # Extract time serie
     dt = f['datetime']

prereise/gather/solardata/ga_wind/helpers.py

@@ -6,9 +6,9 @@
 def ll2ij(lon_origin, lat_origin, lon, lat):
     """Find nearest x/y indices for a given lat/lon.
 
-    :param lat: Latitude of coordinate of interest.
-    :param lon: Longitude of coordinate of interest.
-    :return: coordinate of the closest pixel in the database.
+    :param float lat: Latitude of coordinate of interest.
+    :param float lon: Longitude of coordinate of interest.
+    :return: (*tuple*) -- coordinate of the closest pixel in the database.
     """
 
     proj_string = """+proj=lcc +lat_1=30 +lat_2=60
@@ -32,9 +32,9 @@ def ll2ij(lon_origin, lat_origin, lon, lat):
 def to_reise(data):
     """Format data for REISE.
 
-    :param data: pandas DataFrame as returned by \ 
-        :py:func:`prereise.gather.solardata.ga_wind.ga_wind.retrieve_data`.
-    :return: pandas DataFrame formated for REISE.
+    :param pandas data: pandas data frame as returned by \ 
+        :func:`prereise.gather.solardata.ga_wind.ga_wind.retrieve_data`.
+    :return: (*pandas*) data frame formated for REISE.
     """
     ts = data['ts'].unique()
     plantID = data[data.tsID == 1].plantID.values

prereise/gather/solardata/nsrdb/__init__.py

@@ -1 +1 @@
-__all__ = ["helpers", "nsrdb"]
+__all__ = ["helpers", "naive", "sam"]