/
ndbc.py
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/
ndbc.py
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# Copyright (c) 2013-2015 Siphon Contributors.
# Distributed under the terms of the BSD 3-Clause License.
# SPDX-License-Identifier: BSD-3-Clause
"""Read data from the National Data Buoy Center."""
from io import StringIO
import warnings
import numpy as np
import pandas as pd
import requests
from ..http_util import HTTPEndPoint
warnings.filterwarnings('ignore', "Pandas doesn\'t allow columns to be created", UserWarning)
class NDBC(HTTPEndPoint):
"""Download and parse data from the National Data Buoy Center."""
def __init__(self):
"""Set up endpoint."""
super(NDBC, self).__init__('https://www.ndbc.noaa.gov/')
@classmethod
def realtime_observations(cls, buoy, data_type='txt'):
"""Retrieve the realtime buoy data from NDBC.
Parameters
----------
buoy : str
Name of buoy
data_type : str
Type of data requested, must be one of
'txt' standard meteorological data
'drift' meteorological data from drifting buoys and limited moored buoy data
mainly from international partners
'cwind' continuous winds data (10 minute average)
'spec' spectral wave summaries
'ocean' oceanographic data
'srad' solar radiation data
'dart' water column height
'supl' supplemental measurements data
'rain' hourly rain data
Returns
-------
Raw data string
"""
endpoint = cls()
parsers = {'txt': endpoint._parse_met,
'drift': endpoint._parse_drift,
'cwind': endpoint._parse_cwind,
'spec': endpoint._parse_spec,
'ocean': endpoint._parse_ocean,
'srad': endpoint._parse_srad,
'dart': endpoint._parse_dart,
'supl': endpoint._parse_supl,
'rain': endpoint._parse_rain}
if data_type not in parsers:
raise KeyError('Data type must be txt, drift, cwind, spec, ocean, srad, dart,'
'supl, or rain for parsed realtime data.')
raw_data = endpoint.raw_buoy_data(buoy, data_type=data_type)
return parsers[data_type](raw_data)
@staticmethod
def _parse_met(content):
"""Parse standard meteorological data from NDBC buoys.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'wind_direction', 'wind_speed', 'wind_gust',
'wave_height', 'dominant_wave_period', 'average_wave_period',
'dominant_wave_direction', 'pressure',
'air_temperature', 'water_temperature', 'dewpoint',
'visibility', '3hr_pressure_tendency', 'water_level_above_mean']
col_units = {'wind_direction': 'degrees',
'wind_speed': 'meters/second',
'wind_gust': 'meters/second',
'wave_height': 'meters',
'dominant_wave_period': 'seconds',
'average_wave_period': 'seconds',
'dominant_wave_direction': 'degrees',
'pressure': 'hPa',
'air_temperature': 'degC',
'water_temperature': 'degC',
'dewpoint': 'degC',
'visibility': 'nautical_mile',
'3hr_pressure_tendency': 'hPa',
'water_level_above_mean': 'feet',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _parse_drift(content):
"""Parse meteorological data from drifting buoys and limited moored buoy data.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour_minute',
'latitude', 'longitude',
'wind_direction', 'wind_speed', 'wind_gust',
'pressure', '3hr_pressure_tendency',
'air_temperature', 'water_temperature']
col_units = {'latitude': 'degrees',
'longitude': 'degrees',
'wind_direction': 'degrees',
'wind_speed': 'meters/second',
'wind_gust': 'meters/second',
'pressure': 'hPa',
'air_temperature': 'degC',
'water_temperature': 'degC',
'3hr_pressure_tendency': 'hPa',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['hour'] = np.floor(df['hour_minute'] / 100)
df['minute'] = df['hour_minute'] - df['hour'] * 100
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour_minute', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _parse_cwind(content):
"""Parse continuous wind data (10 minute average).
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'wind_direction', 'wind_speed', 'gust_direction',
'wind_gust', 'gust_time']
col_units = {'wind_direction': 'degrees',
'wind_speed': 'meters/second',
'gust_direction': 'degrees',
'wind_gust': 'meters/second',
'gust_time': None,
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['gust_direction'] = df['gust_direction'].replace(999, np.nan)
df['wind_gust'] = df['wind_gust'].replace(99.0, np.nan)
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df['hours'] = np.floor(df['gust_time'] / 100)
df['minutes'] = df['gust_time'] - df['hours'] * 100
df['hours'] = df['hours'].replace(99, np.nan)
df['minutes'] = df['minutes'].replace(99, np.nan)
df['gust_time'] = pd.to_datetime(df[['year', 'month', 'day', 'hours', 'minutes']],
utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute',
'hours', 'minutes'])
df.units = col_units
return df
@staticmethod
def _parse_spec(content):
"""Parse spectral wave summaries.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'significant_wave_height', 'swell_height',
'swell_period', 'wind_wave_height', 'wind_wave_period',
'swell_direction', 'wind_wave_direction', 'steepness',
'average_wave_period', 'dominant_wave_direction']
col_units = {'significant_wave_height': 'meters',
'swell_height': 'meters',
'swell_period': 'seconds',
'wind_wave_height': 'meters',
'wind_wave_period': 'seconds',
'swell_direction': None,
'wind_wave_direction': None,
'steepness': None,
'average_wave_period': 'seconds',
'dominant_wave_direction': 'degrees',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _parse_ocean(content):
"""Parse oceanographic data.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'measurement_depth', 'ocean_temperature',
'conductivity', 'salinity', 'oxygen_concentration',
'oxygen_concentration_ppm', 'chlorophyll_concentration',
'turbidity', 'pH', 'Eh']
col_units = {'measurement_depth': 'meters',
'ocean_temperature': 'degC',
'conductivity': 'milliSiemens/centimeter',
'salinity': 'psu',
'oxygen_concentration': 'percent',
'oxygen_concentration_ppm': 'ppm',
'chlorophyll_concentration': 'micrograms/liter',
'turbidity': 'ftu',
'pH': 'dimensionless',
'Eh': 'millivolts',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _parse_srad(content):
"""Parse solar radiation data.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'shortwave_radiation_licor', 'shortwave_radiation_eppley',
'longwave_radiation']
col_units = {'shortwave_radiation_licor': 'watts/meter^2',
'shortwave_radiation_eppley': 'watts/meter^2',
'longwave_radiation': 'watts/meter^2',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _parse_dart(content):
"""Parse water column height data.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute', 'second',
'measurement_type', 'height']
col_units = {'measurement_type': 'minutes',
'height': 'meters',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
# Replace measurement type integer with minute value
# 1 = 15-minute measurement
# 2 = 1-minute measurement
# 3 = 15-second measurement
df['measurement_type'] = df['measurement_type'].replace(1, 15)
df['measurement_type'] = df['measurement_type'].replace(2, 1)
df['measurement_type'] = df['measurement_type'].replace(3, 0.25)
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour',
'minute', 'second']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute', 'second'])
df.units = col_units
return df
@staticmethod
def _parse_rain(content):
"""Parse hourly rain data.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'hourly_accumulation']
col_units = {'hourly_accumulation': 'millimeters',
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _parse_supl(content):
"""Parse supplemental measurements data.
Parameters
----------
content : str
Data to parse
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
col_names = ['year', 'month', 'day', 'hour', 'minute',
'hourly_low_pressure', 'hourly_low_pressure_time',
'hourly_high_wind', 'hourly_high_wind_direction',
'hourly_high_wind_time']
col_units = {'hourly_low_pressure': 'hPa',
'hourly_low_pressure_time': None,
'hourly_high_wind': 'meters/second',
'hourly_high_wind_direction': 'degrees',
'hourly_high_wind_time': None,
'time': None}
df = pd.read_csv(StringIO(content), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df['hours'] = np.floor(df['hourly_low_pressure_time'] / 100)
df['minutes'] = df['hourly_low_pressure_time'] - df['hours'] * 100
df['hours'] = df['hours'].replace(99, np.nan)
df['minutes'] = df['minutes'].replace(99, np.nan)
df['hourly_low_pressure_time'] = pd.to_datetime(df[['year', 'month', 'day', 'hours',
'minutes']], utc=True)
df['hours'] = np.floor(df['hourly_high_wind_time'] / 100)
df['minutes'] = df['hourly_high_wind_time'] - df['hours'] * 100
df['hours'] = df['hours'].replace(99, np.nan)
df['minutes'] = df['minutes'].replace(99, np.nan)
df['hourly_high_wind_time'] = pd.to_datetime(df[['year', 'month', 'day',
'hours', 'minutes']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute', 'hours', 'minutes'])
df.units = col_units
return df
@classmethod
def latest_observations(cls):
"""Retrieve the latest observations for all buoys from NDBC.
Returns
-------
:class:`pandas.DataFrame` containing the data
"""
endpoint = cls()
col_names = ['station', 'latitude', 'longitude',
'year', 'month', 'day', 'hour', 'minute',
'wind_direction', 'wind_speed', 'wind_gust',
'wave_height', 'dominant_wave_period', 'average_wave_period',
'dominant_wave_direction', 'pressure', '3hr_pressure_tendency',
'air_temperature', 'water_temperature', 'dewpoint',
'visibility', 'water_level_above_mean']
col_units = {'station': None,
'latitude': 'degrees',
'longitude': 'degrees',
'wind_direction': 'degrees',
'wind_speed': 'meters/second',
'wind_gust': 'meters/second',
'wave_height': 'meters',
'dominant_wave_period': 'seconds',
'average_wave_period': 'seconds',
'dominant_wave_direction': 'degrees',
'pressure': 'hPa',
'air_temperature': 'degC',
'water_temperature': 'degC',
'dewpoint': 'degC',
'visibility': 'nautical_mile',
'3hr_pressure_tendency': 'hPa',
'water_level_above_mean': 'feet',
'time': None}
resp = endpoint.get_path('data/latest_obs/latest_obs.txt')
df = pd.read_csv(StringIO(resp.text), comment='#', na_values='MM',
names=col_names, sep=r'\s+')
df['time'] = pd.to_datetime(df[['year', 'month', 'day', 'hour', 'minute']], utc=True)
df = df.drop(columns=['year', 'month', 'day', 'hour', 'minute'])
df.units = col_units
return df
@staticmethod
def _check_if_url_valid(url):
"""Check if a url is valid (returns 200) or not.
Parameters
----------
url : str
URL to check
Returns
-------
bool if url is valid
"""
r = requests.head(url)
if r.status_code == 200:
return True
else:
return False
@classmethod
def buoy_data_types(cls, buoy):
"""Determine which types of data are available for a given buoy.
Parameters
----------
buoy : str
Buoy name
Returns
-------
dict of valid file extensions and their descriptions
"""
endpoint = cls()
file_types = {'txt': 'standard meteorological data',
'drift': 'meteorological data from drifting buoys and limited moored'
'buoy data mainly from international partners',
'cwind': 'continuous wind data (10 minute average)',
'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)',
'adcp': 'acoustic doppler current profiler',
'ocean': 'oceanographic data',
'tide': 'tide data',
'srad': 'solar radiation data',
'dart': 'water column height',
'supl': 'supplemental measurements data',
'rain': 'hourly rain data'}
available_data = {}
buoy_url = 'https://www.ndbc.noaa.gov/data/realtime2/' + buoy + '.'
for key in file_types:
if endpoint._check_if_url_valid(buoy_url + key):
available_data[key] = file_types[key]
return available_data
@classmethod
def raw_buoy_data(cls, buoy, data_type='txt'):
"""Retrieve the raw buoy data contents from NDBC.
Parameters
----------
buoy : str
Name of buoy
data_type : str
Type of data requested, must be one of
'txt' standard meteorological data
'drift' meteorological data from drifting buoys and limited moored buoy data
mainly from international partners
'cwind' continuous winds data (10 minute average)
'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)
'adcp' acoustic doppler current profiler
'ocean' oceanographic data
'tide' tide data
'srad' solar radiation data
'dart' water column height
'supl' supplemental measurements data
'rain' hourly rain data
Returns
-------
Raw data string
"""
endpoint = cls()
resp = endpoint.get_path('data/realtime2/{}.{}'.format(buoy, data_type))
return resp.text