def test_point_metadata(self):
'''Validate pandas return of sites ordered by distance to the nearest point to
a WKT using metadata
'''
wkt = "POINT(-103.128662109375 40.24179856487036)"
result = get_3tiersites_from_wkt(wkt)
self.assertEqual(53252, result.index[0])
def get_nrel_site(state):
"""Retrieve ID of wind farms in given state.
:param list state: list of state abbreviation.
:return: (*pandas.DataFrame*) -- data frame with *'site_id'*, *'lat'*,
*'lon'*, *'capacity'* and *'capacity_factor'* as columns.
"""
site = None
for s in state:
coord = np.column_stack((states[s]['lons'], states[s]['lats']))
# Prepare coordinates for call
# Convert into string format
out_tot = []
for i in coord:
out = str(i[0]) + ' ' + str(i[1]) + ','
out_tot.append(out)
out = str(coord[0][0]) + ' ' + str(coord[0][1])
out_tot.append(out)
str1 = ''.join(out_tot)
str_final = 'POLYGON((' + str1 + '))'
print('Retrieving nrel sites for ' + s)
site_tmp = get_3tiersites_from_wkt(str_final)
print('Got ' + str(site_tmp.shape[0]) + ' sites in ' + s)
site_tmp = site_tmp.reset_index()
if site is not None:
site = site.append(site_tmp[['site_id', 'lat', 'lon', 'capacity',
'capacity_factor']])
else:
site = site_tmp[['site_id', 'lat', 'lon', 'capacity',
'capacity_factor']].copy()
return site
def handler(event, context):
'''Expose API thru Lambda. Restrict returns to MAX_RETURN_SIZE. An estimate
on return size is made and if it exceeds the limit data will not be pulled in.
Required event parameters:
type - "site", "forecast", "metrology"
Must have only one of the following event parameters:
wkt - Well Known Text string of area to return site data
sites - list of site ids
Optional event parameters, some required for certain data types:
wkt - Well Known Text string of area to return site data
sites - list of site ids
date_from - Unix timestamp of start date
date_to - Unix timestamp of end date
Returns:
{"success": true, "data": data} for success
{"success": false, "message": reason} for failure
'''
required_params = set(["type"])
missing_params = required_params - set(event.keys())
if len(missing_params) > 0:
return {
"success": False,
"message": "Missing event parameters: %s" % list(missing_params)
}
dtype = event["type"]
valid_dtypes = ["site", "forecast", "metrology"]
if dtype not in valid_dtypes:
return {
"success": False,
"message": "Invalid data type, must be one of %s" % valid_dtypes
}
if "wkt" in event.keys() and "sites" not in event.keys():
site_list = get_3tiersites_from_wkt(event["wkt"])
elif "sites" in event.keys() and "wkt" not in event.keys():
site_list = site.loc(event["sites"]).copy()
else:
return {
"success": False,
"message":
"Must define either wkt containing sites or a list of sites"
}
if dtype == "site":
ret_data = site_list
retsize = df_size(ret_data)
if retsize > MAX_RETURN_SIZE:
return {
"success":
False,
"data":
"Return data too large at %.2f MB, limit is %s MB" %
(retsize, MAX_RETURN_SIZE)
}
return {"success": True, "data": ret_data.to_json()}
def test_rectangle(self):
'''Validate pandas return of multiple sites within a rectangle using metadata
'''
#https://mapsbeta.nrel.gov/api/developer_proxy?wkt=POLYGON((-120.82763671875+34.452218472826566%2C-119.19616699218749+34.452218472826566%2C-119.19616699218749+33.920571528675104%2C-120.82763671875+33.920571528675104%2C-120.82763671875+34.452218472826566))&attributes=power%2Ctemperature&names=2012&site_url=wind-toolkit%2Fwind%2Fwtk_download.json&full_name=Your+Name&email=your.email%40here&affiliation=NREL&mailing_list=false&reason=Development+testing&leap_day=true&utc=true
wkt = "POLYGON((-120.82763671875 34.452218472826566,-119.19616699218749 34.452218472826566,-119.19616699218749 33.920571528675104,-120.82763671875 33.920571528675104,-120.82763671875 34.452218472826566))"
sites = get_3tiersites_from_wkt(wkt)
from_zip = [
29375, 29733, 29872, 30019, 30190, 30539, 30712, 30713, 30873,
30874, 31032, 31033, 31034, 31189, 31190, 31191, 31192, 31320,
31321, 31322, 31323, 31324, 31563, 32060, 32314, 32834, 33203,
34828
]
#expected = set([str(x) for x in from_zip])
self.assertEqual(set(from_zip), set(sites.index))
def get_wind_data_by_wkt(wkt,
names,
attributes=None,
interval=5,
leap_day=True,
utc=False,
dataset="met"):
'''Duplicate functionality of URL data grabber at
https://mapsbeta.nrel.gov/api/developer_proxy
The data in both the condensed hdf and original nc files are in UTC
Required Args:
wkt - (String) Well Known Text describing a point of area for use in GIS
mapping to wind sites
names - (List of Strings) List of year names
Optional Args:
attributes - (String or List of Strings) List of attributes to retrieve
from the database. Limited to MET_ATTRS.
interval - Limited to H5_AVAILABLE_DATA_INTERVALS
leap_day - (boolean) Include leap day data or remove it. Defaults to
True, include leap day data
utc - (boolean) Keep as UTC or convert to local time. Defaults to local
dataset - (String) Dataset to retrieve, met or forecast. Defaults to met
Returns:
dict of site_id to Pandas dataframe containing requested data
'''
if dataset == "met":
data_dir = WIND_MET_NC_DIR
elif dataset == "forecast":
data_dir = WIND_FCST_DIR
else:
raise Exception("Invalid data to retrieve: %s" % type)
ret_dict = {}
for site_id in get_3tiersites_from_wkt(wkt).index.values:
site_tz = timezones.iloc[site_id]['zoneName']
_logger.info("Site %s is in %s", site_id, site_tz)
ret_df = pandas.DataFrame()
for year in names:
if utc == False:
# Min datetime in UTC
min_dt = pandas.Timestamp('%s-01-01' % year,
tz=site_tz).tz_convert('utc')
# Max datetime in UTC
max_dt = pandas.Timestamp('%s-12-31 23:59:59' % year,
tz=site_tz).tz_convert('utc')
else:
min_dt = pandas.Timestamp('%s-01-01' % year, tz='utc')
max_dt = pandas.Timestamp('%s-12-31 23:59:59' % year, tz='utc')
#ret_df = ret_df.append(data_function(site_id, min_dt, max_dt, attributes, leap_day, utc))
ret_df = ret_df.append(
get_nc_data(site_id, min_dt, max_dt, attributes, leap_day, utc,
data_dir))
ret_dict[site_id] = ret_df
# Break on POINT as it will return all sites in order of distance to the
# wkt point
if 'POINT' in wkt:
break
return ret_dict