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