def test_ndbc_realtime_spec():
"""Test that we are properly parsing realtime spec observations."""
df = NDBC.realtime_observations('41002', data_type='spec')
assert_almost_equal(df['significant_wave_height'][3], 1.2, 1)
assert_almost_equal(df['swell_height'][3], 0.7, 1)
assert_almost_equal(df['swell_period'][3], 6.7, 1)
assert_almost_equal(df['wind_wave_height'][3], 1.0, 1)
assert_almost_equal(df['wind_wave_period'][3], 3.7, 1)
assert (df['swell_direction'][3] == 'SSE')
assert (df['wind_wave_direction'][3] == 'SSE')
assert (df['steepness'][3] == 'STEEP')
assert_almost_equal(df['average_wave_period'][3], 4.5, 1)
assert_almost_equal(df['dominant_wave_direction'][3], 150, 1)
assert_equal(df['time'][3], datetime(2018, 8, 1, 11, 40, 0))
assert (df.units['significant_wave_height'] == 'meters')
assert (df.units['swell_height'] == 'meters')
assert (df.units['swell_period'] == 'seconds')
assert (df.units['wind_wave_height'] == 'meters')
assert (df.units['wind_wave_period'] == 'seconds')
assert (df.units['swell_direction'] is None)
assert (df.units['wind_wave_direction'] is None)
assert (df.units['steepness'] is None)
assert (df.units['average_wave_period'] == 'seconds')
assert (df.units['dominant_wave_direction'] == 'degrees')
assert (df.units['time'] is None)
def test_ndbc_realtime_ocean():
"""Test that we are properly parsing realtime ocean observations."""
df = NDBC.realtime_observations('45183', data_type='ocean')
assert_almost_equal(df['measurement_depth'][3], 1.0, 1)
assert_equal(df['ocean_temperature'][3], np.nan)
assert_equal(df['conductivity'][3], np.nan)
assert_equal(df['salinity'][3], np.nan)
assert_almost_equal(df['oxygen_concentration'][3], 106.4, 1)
assert_almost_equal(df['oxygen_concentration_ppm'][3], 9.52, 1)
assert_equal(df['chlorophyll_concentration'][3], np.nan)
assert_almost_equal(df['turbidity'][3], 0, 1)
assert_almost_equal(df['pH'][3], 8.38, 1)
assert_equal(df['Eh'][3], np.nan)
assert_equal(df['time'][3], datetime(2018, 8, 1, 13, 30, 0))
assert (df.units['measurement_depth'] == 'meters')
assert (df.units['ocean_temperature'] == 'degC')
assert (df.units['conductivity'] == 'milliSiemens/centimeter')
assert (df.units['salinity'] == 'psu')
assert (df.units['oxygen_concentration'] == 'percent')
assert (df.units['oxygen_concentration_ppm'] == 'ppm')
assert (df.units['chlorophyll_concentration'] == 'micrograms/liter')
assert (df.units['turbidity'] == 'ftu')
assert (df.units['pH'] == 'dimensionless')
assert (df.units['Eh'] == 'millivolts')
assert (df.units['time'] is None)
def test_ndbc_realtime_drift():
"""Test that we are properly parsing realtime drift observations."""
df = NDBC.realtime_observations('22101', data_type='drift')
assert_almost_equal(df['latitude'][3], 37.24, 2)
assert_almost_equal(df['longitude'][3], 126.02, 2)
assert_almost_equal(df['wind_direction'][3], 280.0, 1)
assert_almost_equal(df['wind_speed'][3], 3.0, 1)
assert_equal(df['wind_gust'][3], np.nan)
assert_almost_equal(df['pressure'][3], 1003.4, 1)
assert_equal(df['3hr_pressure_tendency'][3], np.nan)
assert_almost_equal(df['air_temperature'][3], 25.3, 1)
assert_almost_equal(df['water_temperature'][3], 22.8, 1)
assert_equal(df['time'][3], datetime(2018, 8, 1, 8, 0))
assert(df.units['latitude'] == 'degrees')
assert(df.units['longitude'] == 'degrees')
assert(df.units['wind_direction'] == 'degrees')
assert(df.units['wind_speed'] == 'meters/second')
assert(df.units['wind_gust'] == 'meters/second')
assert(df.units['pressure'] == 'hPa')
assert(df.units['air_temperature'] == 'degC')
assert(df.units['water_temperature'] == 'degC')
assert(df.units['3hr_pressure_tendency'] == 'hPa')
assert(df.units['time'] is None)
def test_ndbc_realtime_txt():
"""Test that we are properly parsing realtime txt (met) observations."""
df = NDBC.realtime_observations('41002')
assert_almost_equal(df['wind_direction'][3], 150.0, 1)
assert_almost_equal(df['wind_speed'][3], 7.0, 1)
assert_almost_equal(df['wind_gust'][3], 8.0, 1)
assert_almost_equal(df['wave_height'][3], 1.2, 1)
assert_equal(df['dominant_wave_period'][3], np.nan)
assert_almost_equal(df['average_wave_period'][3], 4.5, 1)
assert_almost_equal(df['dominant_wave_direction'][3], 209.0, 1)
assert_almost_equal(df['pressure'][3], 1022.9, 1)
assert_equal(df['air_temperature'][3], np.nan)
assert_almost_equal(df['water_temperature'][3], 28.0, 1)
assert_equal(df['dewpoint'][3], np.nan)
assert_equal(df['visibility'][3], np.nan)
assert_equal(df['3hr_pressure_tendency'][3], np.nan)
assert_equal(df['water_level_above_mean'][3], np.nan)
assert_equal(df['time'][3], datetime(2018, 8, 1, 14, 40))
assert (df.units['wind_direction'] == 'degrees')
assert (df.units['wind_speed'] == 'meters/second')
assert (df.units['wind_gust'] == 'meters/second')
assert (df.units['wave_height'] == 'meters')
assert (df.units['dominant_wave_period'] == 'seconds')
assert (df.units['average_wave_period'] == 'seconds')
assert (df.units['dominant_wave_direction'] == 'degrees')
assert (df.units['pressure'] == 'hPa')
assert (df.units['air_temperature'] == 'degC')
assert (df.units['water_temperature'] == 'degC')
assert (df.units['dewpoint'] == 'degC')
assert (df.units['visibility'] == 'nautical_mile')
assert (df.units['3hr_pressure_tendency'] == 'hPa')
assert (df.units['water_level_above_mean'] == 'feet')
assert (df.units['time'] is None)
def test_ndbc_realtime_txt():
"""Test that we are properly parsing realtime txt (met) observations."""
df = NDBC.realtime_observations('41002')
assert_almost_equal(df['wind_direction'][3], 150.0, 1)
assert_almost_equal(df['wind_speed'][3], 7.0, 1)
assert_almost_equal(df['wind_gust'][3], 8.0, 1)
assert_almost_equal(df['wave_height'][3], 1.2, 1)
assert_equal(df['dominant_wave_period'][3], np.nan)
assert_almost_equal(df['average_wave_period'][3], 4.5, 1)
assert_almost_equal(df['dominant_wave_direction'][3], 209.0, 1)
assert_almost_equal(df['pressure'][3], 1022.9, 1)
assert_equal(df['air_temperature'][3], np.nan)
assert_almost_equal(df['water_temperature'][3], 28.0, 1)
assert_equal(df['dewpoint'][3], np.nan)
assert_equal(df['visibility'][3], np.nan)
assert_equal(df['3hr_pressure_tendency'][3], np.nan)
assert_equal(df['water_level_above_mean'][3], np.nan)
assert_equal(df['time'][3], datetime(2018, 8, 1, 14, 40))
assert(df.units['wind_direction'] == 'degrees')
assert(df.units['wind_speed'] == 'meters/second')
assert(df.units['wind_gust'] == 'meters/second')
assert(df.units['wave_height'] == 'meters')
assert(df.units['dominant_wave_period'] == 'seconds')
assert(df.units['average_wave_period'] == 'seconds')
assert(df.units['dominant_wave_direction'] == 'degrees')
assert(df.units['pressure'] == 'hPa')
assert(df.units['air_temperature'] == 'degC')
assert(df.units['water_temperature'] == 'degC')
assert(df.units['dewpoint'] == 'degC')
assert(df.units['visibility'] == 'nautical_mile')
assert(df.units['3hr_pressure_tendency'] == 'hPa')
assert(df.units['water_level_above_mean'] == 'feet')
assert(df.units['time'] is None)
def test_ndbc_realtime_drift():
"""Test that we are properly parsing realtime drift observations."""
df = NDBC.realtime_observations('22101', data_type='drift')
assert_almost_equal(df['latitude'][3], 37.24, 2)
assert_almost_equal(df['longitude'][3], 126.02, 2)
assert_almost_equal(df['wind_direction'][3], 280.0, 1)
assert_almost_equal(df['wind_speed'][3], 3.0, 1)
assert_equal(df['wind_gust'][3], np.nan)
assert_almost_equal(df['pressure'][3], 1003.4, 1)
assert_equal(df['3hr_pressure_tendency'][3], np.nan)
assert_almost_equal(df['air_temperature'][3], 25.3, 1)
assert_almost_equal(df['water_temperature'][3], 22.8, 1)
assert_equal(df['time'][3], datetime(2018, 8, 1, 8, 0))
assert (df.units['latitude'] == 'degrees')
assert (df.units['longitude'] == 'degrees')
assert (df.units['wind_direction'] == 'degrees')
assert (df.units['wind_speed'] == 'meters/second')
assert (df.units['wind_gust'] == 'meters/second')
assert (df.units['pressure'] == 'hPa')
assert (df.units['air_temperature'] == 'degC')
assert (df.units['water_temperature'] == 'degC')
assert (df.units['3hr_pressure_tendency'] == 'hPa')
assert (df.units['time'] is None)
def test_ndbc_realtime_ocean():
"""Test that we are properly parsing realtime ocean observations."""
df = NDBC.realtime_observations('45183', data_type='ocean')
assert_almost_equal(df['measurement_depth'][3], 1.0, 1)
assert_equal(df['ocean_temperature'][3], np.nan)
assert_equal(df['conductivity'][3], np.nan)
assert_equal(df['salinity'][3], np.nan)
assert_almost_equal(df['oxygen_concentration'][3], 106.4, 1)
assert_almost_equal(df['oxygen_concentration_ppm'][3], 9.52, 1)
assert_equal(df['chlorophyll_concentration'][3], np.nan)
assert_almost_equal(df['turbidity'][3], 0, 1)
assert_almost_equal(df['pH'][3], 8.38, 1)
assert_equal(df['Eh'][3], np.nan)
assert_equal(df['time'][3], datetime(2018, 8, 1, 13, 30, 0))
assert (df.units['measurement_depth'] == 'meters')
assert (df.units['ocean_temperature'] == 'degC')
assert (df.units['conductivity'] == 'milliSiemens/centimeter')
assert (df.units['salinity'] == 'psu')
assert (df.units['oxygen_concentration'] == 'percent')
assert (df.units['oxygen_concentration_ppm'] == 'ppm')
assert (df.units['chlorophyll_concentration'] == 'micrograms/liter')
assert (df.units['turbidity'] == 'ftu')
assert (df.units['pH'] == 'dimensionless')
assert (df.units['Eh'] == 'millivolts')
assert (df.units['time'] is None)
def test_ndbc_realtime_spec():
"""Test that we are properly parsing realtime spec observations."""
df = NDBC.realtime_observations('41002', data_type='spec')
assert_almost_equal(df['significant_wave_height'][3], 1.2, 1)
assert_almost_equal(df['swell_height'][3], 0.7, 1)
assert_almost_equal(df['swell_period'][3], 6.7, 1)
assert_almost_equal(df['wind_wave_height'][3], 1.0, 1)
assert_almost_equal(df['wind_wave_period'][3], 3.7, 1)
assert(df['swell_direction'][3] == 'SSE')
assert(df['wind_wave_direction'][3] == 'SSE')
assert(df['steepness'][3] == 'STEEP')
assert_almost_equal(df['average_wave_period'][3], 4.5, 1)
assert_almost_equal(df['dominant_wave_direction'][3], 150, 1)
assert_equal(df['time'][3], datetime(2018, 8, 1, 11, 40, 0))
assert(df.units['significant_wave_height'] == 'meters')
assert(df.units['swell_height'] == 'meters')
assert(df.units['swell_period'] == 'seconds')
assert(df.units['wind_wave_height'] == 'meters')
assert(df.units['wind_wave_period'] == 'seconds')
assert(df.units['swell_direction'] is None)
assert(df.units['wind_wave_direction'] is None)
assert(df.units['steepness'] is None)
assert(df.units['average_wave_period'] == 'seconds')
assert(df.units['dominant_wave_direction'] == 'degrees')
assert(df.units['time'] is None)
def test_ndbc_realtime_supl():
"""Test that we are properly parsing realtime supl observations."""
df = NDBC.realtime_observations('PVGF1', data_type='supl')
assert_almost_equal(df['hourly_low_pressure'][3], 1019.0, 1)
assert df['hourly_low_pressure_time'][3] == datetime(2018,
7,
31,
14,
36,
tzinfo=utc)
assert_almost_equal(df['hourly_high_wind'][3], 6, 1)
assert_equal(df['hourly_high_wind_direction'][3], np.nan)
assert df['hourly_high_wind_time'][3] == datetime(2018,
7,
31,
14,
36,
tzinfo=utc)
assert df['time'][3] == datetime(2018, 7, 31, 14, 42, tzinfo=utc)
assert (df.units['hourly_low_pressure'] == 'hPa')
assert (df.units['hourly_low_pressure_time'] is None)
assert (df.units['hourly_high_wind'] == 'meters/second')
assert (df.units['hourly_high_wind_direction'] == 'degrees')
assert (df.units['hourly_high_wind_time'] is None)
assert (df.units['time'] is None)
def test_ndbc_realtime_rain():
"""Test that we are properly parsing realtime rain observations."""
df = NDBC.realtime_observations('BDVF1', data_type='rain')
assert_almost_equal(df['hourly_accumulation'][3], 0.0, 1)
assert_equal(df['time'][3], datetime(2018, 8, 1, 11))
assert (df.units['hourly_accumulation'] == 'millimeters')
assert (df.units['time'] is None)
def test_ndbc_realtime_rain():
"""Test that we are properly parsing realtime rain observations."""
df = NDBC.realtime_observations('BDVF1', data_type='rain')
assert_almost_equal(df['hourly_accumulation'][3], 0.0, 1)
assert_equal(df['time'][3], datetime(2018, 8, 1, 11))
assert (df.units['hourly_accumulation'] == 'millimeters')
assert (df.units['time'] is None)
def test_ndbc_realtime_dart():
"""Test that we are properly parsing realtime dart observations."""
df = NDBC.realtime_observations('41421', data_type='dart')
assert_almost_equal(df['measurement_type'][3], 15, 1)
assert_almost_equal(df['height'][3], 5806.845, 3)
assert_equal(df['time'][3], datetime(2018, 8, 1, 11, 15))
assert (df.units['measurement_type'] == 'minutes')
assert (df.units['height'] == 'meters')
assert (df.units['time'] is None)
def test_ndbc_realtime_dart():
"""Test that we are properly parsing realtime dart observations."""
df = NDBC.realtime_observations('41421', data_type='dart')
assert_almost_equal(df['measurement_type'][3], 15, 1)
assert_almost_equal(df['height'][3], 5806.845, 3)
assert_equal(df['time'][3], datetime(2018, 8, 1, 11, 15))
assert (df.units['measurement_type'] == 'minutes')
assert (df.units['height'] == 'meters')
assert (df.units['time'] is None)
def test_ndbc_buoy_data_types():
"""Test determination of available buoy data."""
resp = NDBC.buoy_data_types('41002')
truth = {'txt': 'standard meteorological data',
'spec': 'spectral wave summaries',
'data_spec': 'raw spectral wave data',
'swdir': 'spectral wave data (alpha1)',
'swdir2': 'spectral wave data (alpha2)',
'swr1': 'spectral wave data (r1)',
'swr2': 'spectral wave data (r2)',
'supl': 'supplemental measurements data'}
assert(resp == truth)
def test_ndbc_realtime_srad():
"""Test that we are properly parsing realtime srad observations."""
df = NDBC.realtime_observations('45027', data_type='srad')
assert_almost_equal(df['shortwave_radiation_licor'][3], 51.6, 1)
assert_equal(df['shortwave_radiation_eppley'][3], np.nan)
assert_equal(df['longwave_radiation'][3], np.nan)
assert_equal(df['time'][3], datetime(2018, 8, 1, 14, 30))
assert (df.units['shortwave_radiation_licor'] == 'watts/meter^2')
assert (df.units['shortwave_radiation_eppley'] == 'watts/meter^2')
assert (df.units['longwave_radiation'] == 'watts/meter^2')
assert (df.units['time'] is None)
def test_ndbc_realtime_srad():
"""Test that we are properly parsing realtime srad observations."""
df = NDBC.realtime_observations('45027', data_type='srad')
assert_almost_equal(df['shortwave_radiation_licor'][3], 51.6, 1)
assert_equal(df['shortwave_radiation_eppley'][3], np.nan)
assert_equal(df['longwave_radiation'][3], np.nan)
assert_equal(df['time'][3], datetime(2018, 8, 1, 14, 30))
assert (df.units['shortwave_radiation_licor'] == 'watts/meter^2')
assert (df.units['shortwave_radiation_eppley'] == 'watts/meter^2')
assert (df.units['longwave_radiation'] == 'watts/meter^2')
assert (df.units['time'] is None)
def test_ndbc_buoy_data_types():
"""Test determination of available buoy data."""
resp = NDBC.buoy_data_types('41002')
truth = {
'txt': 'standard meteorological data',
'spec': 'spectral wave summaries',
'data_spec': 'raw spectral wave data',
'swdir': 'spectral wave data (alpha1)',
'swdir2': 'spectral wave data (alpha2)',
'swr1': 'spectral wave data (r1)',
'swr2': 'spectral wave data (r2)',
'supl': 'supplemental measurements data'
}
assert (resp == truth)
def test_ndbc_realtime_cwind():
"""Test that we are properly parsing realtime cwind observations."""
df = NDBC.realtime_observations('41008', data_type='cwind')
assert_almost_equal(df['wind_direction'][3], 156, 1)
assert_almost_equal(df['wind_speed'][3], 6.7, 1)
assert_equal(df['gust_direction'][3], np.nan)
assert_almost_equal(df['wind_gust'][0], 9.0, 1)
assert_equal(df['gust_time'][0], datetime(2018, 8, 1, 14, 49, 0))
assert_equal(df['time'][0], datetime(2018, 8, 1, 14, 50, 0))
assert (df.units['wind_direction'] == 'degrees')
assert (df.units['wind_speed'] == 'meters/second')
assert (df.units['gust_direction'] == 'degrees')
assert (df.units['wind_gust'] == 'meters/second')
assert (df.units['gust_time'] is None)
assert (df.units['time'] is None)
def test_ndbc_realtime_supl():
"""Test that we are properly parsing realtime supl observations."""
df = NDBC.realtime_observations('PVGF1', data_type='supl')
assert_almost_equal(df['hourly_low_pressure'][3], 1019.0, 1)
assert_equal(df['hourly_low_pressure_time'][3], datetime(2018, 7, 31, 14, 36))
assert_almost_equal(df['hourly_high_wind'][3], 6, 1)
assert_equal(df['hourly_high_wind_direction'][3], np.nan)
assert_equal(df['hourly_high_wind_time'][3], datetime(2018, 7, 31, 14, 36))
assert_equal(df['time'][3], datetime(2018, 7, 31, 14, 42))
assert (df.units['hourly_low_pressure'] == 'hPa')
assert (df.units['hourly_low_pressure_time'] is None)
assert (df.units['hourly_high_wind'] == 'meters/second')
assert (df.units['hourly_high_wind_direction'] == 'degrees')
assert (df.units['hourly_high_wind_time'] is None)
assert (df.units['time'] is None)
def test_ndbc_realtime_cwind():
"""Test that we are properly parsing realtime cwind observations."""
df = NDBC.realtime_observations('41008', data_type='cwind')
assert_almost_equal(df['wind_direction'][3], 156, 1)
assert_almost_equal(df['wind_speed'][3], 6.7, 1)
assert_equal(df['gust_direction'][3], np.nan)
assert_almost_equal(df['wind_gust'][0], 9.0, 1)
assert_equal(df['gust_time'][0], datetime(2018, 8, 1, 14, 49, 0))
assert_equal(df['time'][0], datetime(2018, 8, 1, 14, 50, 0))
assert(df.units['wind_direction'] == 'degrees')
assert(df.units['wind_speed'] == 'meters/second')
assert(df.units['gust_direction'] == 'degrees')
assert(df.units['wind_gust'] == 'meters/second')
assert(df.units['gust_time'] is None)
assert(df.units['time'] is None)
def test_ndbc_latest():
"""Test that we are properly parsing latest NDBC observations."""
df = NDBC.latest_observations()
assert (df['station'][10] == '41004')
assert_almost_equal(df['latitude'][10], 32.501, 3)
assert_almost_equal(df['longitude'][10], -79.0989, 3)
assert_almost_equal(df['wind_direction'][10], 200.0, 1)
assert_almost_equal(df['wind_speed'][10], 5.0, 1)
assert_almost_equal(df['wind_gust'][10], 7.0, 1)
assert_equal(df['wave_height'][10], np.nan)
assert_equal(df['dominant_wave_period'][10], np.nan)
assert_equal(df['average_wave_period'][10], np.nan)
assert_equal(df['dominant_wave_direction'][10], np.nan)
assert_almost_equal(df['pressure'][10], 1016.9, 1)
assert_almost_equal(df['air_temperature'][10], 28.1, 1)
assert_almost_equal(df['water_temperature'][10], 28.8, 1)
assert_almost_equal(df['dewpoint'][10], 25.9, 1)
assert_equal(df['visibility'][10], np.nan)
assert_equal(df['3hr_pressure_tendency'][10], np.nan)
assert_equal(df['water_level_above_mean'][10], np.nan)
assert_equal(df['time'][10], datetime(2018, 7, 30, 21, 10))
assert (df.units['station'] is None)
assert (df.units['latitude'] == 'degrees')
assert (df.units['longitude'] == 'degrees')
assert (df.units['wind_direction'] == 'degrees')
assert (df.units['wind_speed'] == 'meters/second')
assert (df.units['wind_gust'] == 'meters/second')
assert (df.units['wave_height'] == 'meters')
assert (df.units['dominant_wave_period'] == 'seconds')
assert (df.units['average_wave_period'] == 'seconds')
assert (df.units['dominant_wave_direction'] == 'degrees')
assert (df.units['pressure'] == 'hPa')
assert (df.units['air_temperature'] == 'degC')
assert (df.units['water_temperature'] == 'degC')
assert (df.units['dewpoint'] == 'degC')
assert (df.units['visibility'] == 'nautical_mile')
assert (df.units['3hr_pressure_tendency'] == 'hPa')
assert (df.units['water_level_above_mean'] == 'feet')
assert (df.units['time'] is None)
def test_ndbc_latest():
"""Test that we are properly parsing latest NDBC observations."""
df = NDBC.latest_observations()
assert(df['station'][10] == '41004')
assert_almost_equal(df['latitude'][10], 32.501, 3)
assert_almost_equal(df['longitude'][10], -79.0989, 3)
assert_almost_equal(df['wind_direction'][10], 200.0, 1)
assert_almost_equal(df['wind_speed'][10], 5.0, 1)
assert_almost_equal(df['wind_gust'][10], 7.0, 1)
assert_equal(df['wave_height'][10], np.nan)
assert_equal(df['dominant_wave_period'][10], np.nan)
assert_equal(df['average_wave_period'][10], np.nan)
assert_equal(df['dominant_wave_direction'][10], np.nan)
assert_almost_equal(df['pressure'][10], 1016.9, 1)
assert_almost_equal(df['air_temperature'][10], 28.1, 1)
assert_almost_equal(df['water_temperature'][10], 28.8, 1)
assert_almost_equal(df['dewpoint'][10], 25.9, 1)
assert_equal(df['visibility'][10], np.nan)
assert_equal(df['3hr_pressure_tendency'][10], np.nan)
assert_equal(df['water_level_above_mean'][10], np.nan)
assert_equal(df['time'][10], datetime(2018, 7, 30, 21, 10))
assert(df.units['station'] is None)
assert(df.units['latitude'] == 'degrees')
assert(df.units['longitude'] == 'degrees')
assert(df.units['wind_direction'] == 'degrees')
assert(df.units['wind_speed'] == 'meters/second')
assert(df.units['wind_gust'] == 'meters/second')
assert(df.units['wave_height'] == 'meters')
assert(df.units['dominant_wave_period'] == 'seconds')
assert(df.units['average_wave_period'] == 'seconds')
assert(df.units['dominant_wave_direction'] == 'degrees')
assert(df.units['pressure'] == 'hPa')
assert(df.units['air_temperature'] == 'degC')
assert(df.units['water_temperature'] == 'degC')
assert(df.units['dewpoint'] == 'degC')
assert(df.units['visibility'] == 'nautical_mile')
assert(df.units['3hr_pressure_tendency'] == 'hPa')
assert(df.units['water_level_above_mean'] == 'feet')
assert(df.units['time'] is None)
"""
NDBC Buoy Meteorological Data Request
=====================================
The NDBC keeps a 45-day recent rolling file for each buoy. This examples shows how to access
the basic meteorological data from a buoy and make a simple plot.
"""
import matplotlib.pyplot as plt
from siphon.simplewebservice.ndbc import NDBC
####################################################
# Get a pandas data frame of all of the observations, meteorological data is the default
# observation set to query.
df = NDBC.realtime_observations('46006')
df.head()
####################################################
# Let's make a simple time series plot to checkout what the data look like.
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))
ax2b = ax2.twinx()
# Pressure
ax1.plot(df['time'], df['pressure'], color='black')
ax1.set_ylabel('Pressure [hPa]')
# Wind speed, gust, direction
ax2.plot(df['time'], df['wind_speed'], color='tab:orange')
ax2.plot(df['time'], df['wind_gust'], color='tab:olive', linestyle='--')
ax2b.plot(df['time'], df['wind_direction'], color='tab:blue', linestyle='-')
"""
NDBC Buoy Meteorological Data Request
=====================================
The NDBC keeps a 45-day recent rolling file for each buoy. This examples shows how to access
the basic meteorological data from a buoy and make a simple plot.
"""
import matplotlib.pyplot as plt
from siphon.simplewebservice.ndbc import NDBC
####################################################
# Get a pandas data frame of all of the observations, meteorological data is the default
# observation set to query.
df = NDBC.realtime_observations('41002')
df.head()
####################################################
# Let's make a simple time series plot to checkout what the data look like.
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(12, 10))
ax2b = ax2.twinx()
# Pressure
ax1.plot(df['time'], df['pressure'], color='black')
ax1.set_ylabel('Pressure [hPa]')
# Wind speed, gust, direction
ax2.plot(df['time'], df['wind_speed'], color='tab:orange')
ax2.plot(df['time'], df['wind_gust'], color='tab:olive', linestyle='--')
ax2b.plot(df['time'], df['wind_direction'], color='tab:blue', linestyle='-')
NDBC Latest Data Request
========================
This example shows how to use siphon's `simplewebswervice` support query the most recent
observations from all of the NDBC buoys at once.
"""
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
from siphon.simplewebservice.ndbc import NDBC
####################################################
# Get a pandas data frame of all of the observations
df = NDBC.latest_observations()
df.head()
####################################################
# In this case I'm going to drop buoys that do not have water temperature measurements.
df.dropna(subset=['water_temperature'], inplace=True)
####################################################
# Let's make a simple plot of the buoy positions and color by water temperature
proj = ccrs.LambertConformal(central_latitude=45.,
central_longitude=-100.,
standard_parallels=[30, 60])
fig = plt.figure(figsize=(17., 11.))
ax = plt.axes(projection=proj)
ax.coastlines('50m', edgecolor='black')
from siphon.simplewebservice.ndbc import NDBC
import numpy as np
import math
from oceans.plotting import stick_plot
from oceans.ocfis import spdir2uv
from matplotlib.ticker import AutoMinorLocator
# hurricane = 'Henri'
#ndbc = ['44017', '44097'] # henri
# time = ['2021-08-19', '2021-08-24']
hurricane = 'Ida'
ndbc = ['42040', '42084'] #ida
time = ['2021-08-27', '2021-08-31']
df = NDBC.realtime_observations(ndbc[0])
df2 = NDBC.realtime_observations(ndbc[1])
df['datetime'] = pd.to_datetime(df['time'].dt.strftime('%Y-%m-%d %H:%M:%S'))
df.sort_values(['datetime'], inplace=True)
df.set_index('datetime', inplace=True)
df2['datetime'] = pd.to_datetime(df2['time'].dt.strftime('%Y-%m-%d %H:%M:%S'))
df2.sort_values(['datetime'], inplace=True)
df2.set_index('datetime', inplace=True)
df = df.tz_localize(None)
df2 = df2.tz_localize(None)
df = df.loc[time[0]:time[1]]
df2 = df2.loc[time[0]:time[1]]
from metpy.units import units
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Make a plot of buoy data.')
parser.add_argument('--cmap', default='Oranges', help='mpl color map')
parser.add_argument('--var', default='water_temperature', help='variable to plot')
parser.add_argument('--savefig', action='store_true', help='save a figure instead of displaying')
parser.add_argument('--imgformat', default='png', help='saved image foramt')
parser.add_argument('--min', default=None, type=int, help='Minimum color bar bound.')
parser.add_argument('--max', default=None, type=int, help='Maximum color bar bound.')
parser.add_argument('--msize', default=5, type=int, help='Marker size')
args = parser.parse_args()
print('Downloading data...')
df = NDBC.latest_observations()
print(f'Complete. {len(df)} stations')
print(df.columns)
# Drop any rows with NaN for the data we want
df.dropna(subset=[args.var], inplace=True)
print(f'{len(df)} stations with variable {args.var}\nPlotting...')
# Make an LCC map projection
proj = ccrs.LambertConformal()
# Plot the map
fig = plt.figure(figsize=(12, 7))
ax = plt.axes(projection=proj)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
ax.add_feature(cfeature.OCEAN.with_scale('50m'))
ax.add_feature(cfeature.LAND.with_scale('50m'))
ax.add_feature(cfeature.BORDERS.with_scale('50m'), linestyle=':')
# Copyright (c) 2018 Siphon Contributors.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""
NDBC Buoy Data Request (of any type)
====================================
The NDBC keeps a 40-day recent rolling file for each buoy. This examples shows how to access
the other types of data available for a buoy.
"""
from siphon.simplewebservice.ndbc import NDBC
####################################################
# Request the types of data available from a given buoy.
data_aval = NDBC.buoy_data_types('42002')
print(data_aval)
####################################################
# Get a pandas data frame of all of the observations, meteorological data is the default
# observation set to query.
df = NDBC.realtime_observations('42002', data_type='supl')
df.head()
def test_ndbc_realtime_keyerror():
"""Ensure an error is raised for invalid parsed data type requests."""
with pytest.raises(KeyError):
NDBC.realtime_observations('41002', data_type='dartt')
def test_ndbc_realtime_keyerror():
"""Ensure an error is raised for invalid parsed data type requests."""
with pytest.raises(KeyError):
NDBC.realtime_observations('41002', data_type='dartt')
