def test_out_of_bounds(self):
london_lat, london_long = 51.5072, 0.1275
with self.assertRaises(NameError):
df = daymet_timeseries(lon=london_long,
lat=london_lat,
start_year=2012,
end_year=2012)
def run(self):
ts = daymetpy.daymet_timeseries(
lat=self.lat, lon=self.lon, start_year=1980, end_year=2019
)
int_cols = ["year", "yday"]
ts[int_cols] = ts[int_cols].astype(int)
ts.write_csv(self.output().path, index_label="ts", float_format="%.1f")
def test_ornl_df(self):
ornl_lat, ornl_long = 35.9313167, -84.3104124
df = daymet_timeseries(lon=ornl_long, lat=ornl_lat, start_year=2012, end_year=2012)
self.assertTrue(df.year.count() == 365)
self.assertTrue("tmax" in df.columns)
self.assertTrue("tmin" in df.columns)
self.assertTrue("prcp" in df.columns)
def test_ornl_df(self):
ornl_lat, ornl_long = 35.9313167, -84.3104124
df = daymet_timeseries(lon=ornl_long,
lat=ornl_lat,
start_year=2012,
end_year=2012)
self.assertTrue(df.year.count() == 365)
self.assertTrue("tmax" in df.columns)
self.assertTrue("tmin" in df.columns)
self.assertTrue("prcp" in df.columns)
def test_out_of_bounds(self):
london_lat, london_long = 51.5072, 0.1275
with self.assertRaises(NameError):
df = daymet_timeseries(lon=london_long, lat=london_lat, start_year=2012, end_year=2012)
coord_arr = np.array(coords, dtype=np.dtype("float,float"))
# need to test if numpy masked arrays or xarray masking works better. using xarr for now
# np.ma.masked_where(L_dn==-9999, L_dn)
emis = emis.where(L_dn != -9999)
emis = emis * .0001
L_dn = L_dn.where(L_dn != -9999)
L_dn = L_dn * .001
Tr_K = Tr_K.where(Tr_K != -9999)
Tr_K = Tr_K * .1
aoi_met = daymetpy.daymet_timeseries(
lon=center[0], lat=center[1], start_year=2002,
end_year=2002) # used to estimate mock params for aoi
import pvlib
import datetime
from timezonefinder import TimezoneFinder
time = pd.to_datetime(datetime.datetime(2002, 8, 3, 10, 4))
time = pd.DatetimeIndex([time])
tf = TimezoneFinder(in_memory=True)
timezone = tf.certain_timezone_at(lng=center[0], lat=center[1])
time = time.tz_localize(timezone)
solar_df = pvlib.solarposition.get_solarposition(
time, center[1],
center[0]) # can be made more accurate with temp and pressure from daymet
# Functions in Daymetpy allow for painless use of this service # DaymetPy package developed by Koen Hufkens # https://bitbucket.org/khufkens/daymetpy # In[1]: import sys sys.path.append(r"J:\Projects\NCCSC\phenocam\Tools\DaymetPy") import daymetpy # In[2]: ornl_lat, ornl_long = 35.9313167, -84.3104124 df = daymetpy.daymet_timeseries(lon=ornl_long, lat=ornl_lat, start_year=2012, end_year=2013) # ### Which gives us a nice data frame with weather data for the Oak Ridge National Lab # In[3]: df.head() # ## Which we can visualize using matplotlib and seaborn # In[4]: import pandas as pd