def test_get_data_as_dataframes():
for test_pixel_kwargs in test_pixels:
pixel_data = daymet.get_daymet_singlepixel(**test_pixel_kwargs)
assert pixel_data.shape == (len(test_pixel_kwargs['years'])*365, len(test_pixel_kwargs['variables'])+2)
assert pixel_data.iloc[0].year == test_pixel_kwargs['years'][0]
assert pixel_data.iloc[-1].year == test_pixel_kwargs['years'][-1]
for var in test_pixel_kwargs['variables']:
assert var in pixel_data.keys()
def test_get_data_as_dicts():
for test_pixel_kwargs in test_pixels:
test_pixel_kwargs['as_dataframe'] = False
pixel_data = daymet.get_daymet_singlepixel(**test_pixel_kwargs)
assert len(pixel_data.keys()) == len(test_pixel_kwargs['variables'])
assert "{}-01-01".format(test_pixel_kwargs['years'][0]) in pixel_data[test_pixel_kwargs['variables'][0]].keys()
assert "{}-12-30".format(test_pixel_kwargs['years'][-1]) in pixel_data[test_pixel_kwargs['variables'][0]].keys()
for var in test_pixel_kwargs['variables']:
assert var in pixel_data.keys()
def test_get_data_as_dataframes():
for test_pixel_kwargs in test_pixels:
pixel_data = daymet.get_daymet_singlepixel(**test_pixel_kwargs)
assert pixel_data.shape == (len(test_pixel_kwargs['years']) * 365,
len(test_pixel_kwargs['variables']) + 2)
assert pixel_data.iloc[0].year == test_pixel_kwargs['years'][0]
assert pixel_data.iloc[-1].year == test_pixel_kwargs['years'][-1]
for var in test_pixel_kwargs['variables']:
assert var in pixel_data.keys()
def test_get_data_as_dicts():
for test_pixel_kwargs in test_pixels:
test_pixel_kwargs['as_dataframe'] = False
pixel_data = daymet.get_daymet_singlepixel(**test_pixel_kwargs)
assert len(pixel_data.keys()) == len(test_pixel_kwargs['variables'])
assert "{}-01-01".format(test_pixel_kwargs['years'][0]) in pixel_data[
test_pixel_kwargs['variables'][0]].keys()
assert "{}-12-30".format(test_pixel_kwargs['years'][-1]) in pixel_data[
test_pixel_kwargs['variables'][0]].keys()
for var in test_pixel_kwargs['variables']:
assert var in pixel_data.keys()
#
# Daymet is a a daily gridded weather dataset distributed through the ORNL DAAC https://daymet.ornl.gov/
#
# As part of their tools for obtaining data they have created a webservice that allows one to download a timeseries of temperature and precipitation for any location in the US.
#
# In[1]:
from ulmo.nasa import daymet
# In[2]:
ornl_lat, ornl_long = 35.9313167, -84.3104124
df = daymet.get_daymet_singlepixel(longitude=ornl_long, latitude=ornl_lat,
years=[2012,2013])
# ### Which gives us a dataframe with daily 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
import seaborn as sns
#
# Daymet is a a daily gridded weather dataset distributed through the ORNL DAAC https://daymet.ornl.gov/
#
# As part of their tools for obtaining data they have created a webservice that allows one to download a timeseries of temperature and precipitation for any location in the US.
#
# In[1]:
from ulmo.nasa import daymet
# In[2]:
ornl_lat, ornl_long = 35.9313167, -84.3104124
df = daymet.get_daymet_singlepixel(longitude=ornl_long,
latitude=ornl_lat,
years=[2012, 2013])
# ### Which gives us a dataframe with daily 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
import seaborn as sns
import matplotlib.pyplot as plt