def make_data_matrix(self,
use_col=None,
start_day_ix=None,
end_day_ix=None,
differentiate=False):
if not self.__time_axis_standardized:
df = standardize_time_axis(self.data_frame)
self.data_frame = df
self.__time_axis_standardized = True
else:
df = self.data_frame
if use_col is None:
use_col = df.columns[0]
if differentiate:
pass
self.raw_data_matrix, day_index = make_2d(df,
key=use_col,
return_day_axis=True)
self.raw_data_matrix = self.raw_data_matrix[:, start_day_ix:end_day_ix]
self.num_days = self.raw_data_matrix.shape[1]
if self.raw_data_matrix.shape[0] <= 1400:
self.data_sampling = int(24 * 60 / self.raw_data_matrix.shape[0])
else:
self.data_sampling = 24 * 60 / self.raw_data_matrix.shape[0]
self.use_column = use_col
self.day_index = day_index[start_day_ix:end_day_ix]
self.start_doy = self.day_index.dayofyear[0]
return
def get_pvdaq_data(sysid=2,
api_key='DEMO_KEY',
year=2011,
delim=',',
standardize=True):
"""
This fuction queries one or more years of raw PV system data from NREL's PVDAQ data service:
https://maps.nrel.gov/pvdaq/
"""
# Force year to be a list of integers
ti = time()
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.
df_list = []
it = 0
for yr in year:
progress(it, len(year), 'querying year {}'.format(year[it]))
req_params = {'api_key': api_key, 'system_id': sysid, 'year': yr}
base_url = 'https://developer.nrel.gov/api/pvdaq/v3/data_file?'
param_list = [
str(item[0]) + '=' + str(item[1]) for item in req_params.items()
]
req_url = base_url + '&'.join(param_list)
response = requests.get(req_url)
if int(response.status_code) != 200:
print('\n error: ', response.status_code)
return
df = pd.read_csv(StringIO(response.text), delimiter=delim)
df_list.append(df)
it += 1
tf = time()
progress(it, len(year),
'queries complete in {:.1f} seconds '.format(tf - ti))
# concatenate the list of yearly data frames
df = pd.concat(df_list, axis=0, sort=True)
if standardize:
df = standardize_time_axis(df,
datetimekey='Date-Time',
timeindex=False)
return df