Collect ocean measurement data based on location and timeframe
Features
- Closest active station is selected for each measurement
- Reaches into secondary historical archives when required
- Returns pandas dataframe compatible dictionaries
- Available measurements: Tide, wind, water temp, air temp, air pressure, conductivity and swell information
- Datasources: NOAA NDBC, NOAA Tides and Currents
pip install seastate
from seastate import SeaState
from datetime import datetime
# make SeaState object for specific location
san_diego = SeaState(32,-117)
# retrieve measurements for today
san_diego_today = san_diego.from_date_range(datetime.today())
san_diego_today['tide'] -> [{t: time, v: value, s: stdev}]
san_diego_today['wind']-> [{t: time, v: value, d: direction, g: gust}]
san_diego_today['water_temp']-> [{t: time, v: value}]
san_diego_today['air_temp']-> [{t: time, v: value}]
san_diego_today['air_press']-> [{t: time, v: value}]
san_diego_today['wave']-> [{t: time, v: Wave Height, dpd: Dominant Period, mwd: dpd Direction}]
san_diego_today['conductivity']-> [{t: time, v: value}]
Measurement details for NDBC are here and for Tides and Currents here
Useful metadata can be accessed through the SeaState object
san_diego = SeaState(32,-117)
# Each measurement has an entry point
san_diego.tide
-> (api.mediator Object)
# The valid keys are:
valid_measurements = {
'tide',
'wind',
'water_temp',
'air_temp',
'air_press',
'wave',
'conductivity',
}
# the original lat/lon are available
san_diego.lat
-> 32
san_diego.tide._target_lon
-> 32
# the distance of each measurement station to target gps
san_diego.tide.distance
-> xyz kilometers
# and other useful exposed api keys
san_diego.tide.station.name
san_diego.tide.station.id
san_diego.tide.station.lat
san_diego.tide.station.lon
| Measurement | T&C | NDBC |
|---|---|---|
| tide | y | y |
| wind | y | y |
| water_temp | y | y |
| air_temp | y | y |
| air_press | y | y |
| wave | y | |
| conductivity | y |
from seastate import SeaState
from datetime import datetime, timedelta
start = datetime.today()-timedelta(days=30)
end = datetime.today()
san_diego_past_30 = san_diego.measurements_from_date_range(start,end)
san_diego_past_30_hourly = san_diego.hourly(start,end) # this returns a single reading per hour
# experimental feature
# no guarantee between APIs that readings will align or exist in all cases
