Source code for

"""Functions to read NREL PVDAQ data.

# Code originally written by Bennet Meyers (@bmeyers), Stanford, SLAC in
# Adapated by Will Holmgren (@wholmgren), University of Arizona

import json
from io import StringIO

import requests
import pandas as pd

# consider adding an auth=(username, password) kwarg (default None) to
# support private data queries

[docs]def get_pvdaq_metadata(system_id, api_key): """Query PV system metadata from NREL's PVDAQ data service. Parameters ---------- system_id: int The system ID corresponding to the site that data should be queried from. api_key: string Your NREL API key ( Returns ------- list of dict """ params = {'system_id': system_id, 'api_key': api_key} sites_url = '' r = requests.get(sites_url, params=params) r.raise_for_status() outputs = json.loads(r.content)['outputs'] return outputs
[docs]def get_pvdaq_data(system_id, year, api_key='DEMO_KEY'): """Query PV system data from NREL's PVDAQ data service: This function uses the annual raw data file API, which is the most efficient way of accessing multi-year, sub-hourly time series data. Parameters ---------- system_id: int The system ID corresponding to the site that data should be queried from. year: int or list of ints Either the year to request or the list of years to request. Multiple years will be concatenated into a single DataFrame. api_key: string Your NREL API key ( Returns ------- pandas.DataFrame A DataFrame containing the time series data from the PVDAQ service over the years requested. Times are typically in local time. Notes ----- The PVDAQ metadata contains a key "available_years" that is a useful value for the *year* argument. """ try: year = int(year) except TypeError: year = [int(yr) for yr in year] else: year = [year] # Each year must queries separately, so iterate over the years and # generate a list of dataframes. # Consider putting this loop in its own private function with # try / except / try again pattern for network issues and NREL API # throttling df_list = [] for yr in year: params = { 'api_key': api_key, 'system_id': system_id, 'year': yr } base_url = '' response = requests.get(base_url, params=params) response.raise_for_status() df = pd.read_csv(StringIO(response.text)) df_list.append(df) # concatenate the list of yearly DataFrames df = pd.concat(df_list, axis=0, sort=True) df['Date-Time'] = pd.to_datetime(df['Date-Time']) df.set_index('Date-Time', inplace=True) return df