Note: this notebook requires python3.
This notebook demonstrates a simple Planet OS API use case using the HYCOM Global Ocean Forecast dataset.
API documentation is available at http://docs.planetos.com. If you have questions or comments, join the Planet OS Slack community to chat with our development team.
For general information on usage of IPython/Jupyter and Matplotlib, please refer to their corresponding documentation. https://ipython.org/ and http://matplotlib.org/
In [1]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import dateutil.parser
import datetime
from urllib.request import urlopen, Request
import simplejson as json
In [2]:
def extract_reference_time(API_data_loc):
"""Find reference time that corresponds to most complete forecast. Should be the earliest value."""
reftimes = set()
for i in API_data_loc['entries']:
reftimes.update([i['axes']['reftime']])
reftimes=list(reftimes)
if len(reftimes)>1:
reftime = reftimes[0] if dateutil.parser.parse(reftimes[0])<dateutil.parser.parse(reftimes[1]) else reftimes[1]
else:
reftime = reftimes[0]
return reftime
Let's choose a location near Oahu, Hawaii...
In [3]:
location = 'Hawaii Oahu'
if location == 'Est':
longitude = 24.+45./60
latitude = 59+25/60.
elif location == 'Au':
longitude = 149. + 7./60
latitude = -35.-18./60
elif location == "Hawaii Oahu":
latitude = 21.205
longitude = -158.35
elif location == 'Somewhere':
longitude == -20.
latitude == 10.
Important! You'll need to replace apikey below with your actual Planet OS API key, which you'll find on the Planet OS account settings page.
In [4]:
apikey = open('APIKEY').readlines()[0].strip() #'<YOUR API KEY HERE>'
API_url = "http://api.planetos.com/v1/datasets/hycom_glbu0.08_91.2_global_0.08d/point?lon={0}&lat={1}&count=10000&verbose=false&apikey={2}".format(longitude,latitude,apikey)
request = Request(API_url)
response = urlopen(request)
API_data = json.loads(response.read())
Show the available variables and their contexts...
In [5]:
varlist = []
print("{0:<50} {1}".format("Variable","Context"))
print()
for k,v in set([(j,i['context']) for i in API_data['entries'] for j in i['data'].keys()]):
print("{0:<50} {1}".format(k,v))
varlist.append(k)
In [6]:
reftime = extract_reference_time(API_data)
Now let's extract data for all variables and create a different plot for each...
In [7]:
vardict = {}
for i in varlist:
vardict['time_'+i]=[]
vardict['data_'+i]=[]
for i in API_data['entries']:
#print(i['context'])
reftime = extract_reference_time(API_data)
for j in i['data']:
if reftime == i['axes']['reftime']:
if j != 'surf_el':
if i['axes']['z'] < 1.:
vardict['data_'+j].append(i['data'][j])
vardict['time_'+j].append(dateutil.parser.parse(i['axes']['time']))
else:
vardict['data_'+j].append(i['data'][j])
vardict['time_'+j].append(dateutil.parser.parse(i['axes']['time']))
In [8]:
for i in varlist:
fig = plt.figure(figsize=(15,3))
plt.title(i)
ax = fig.add_subplot(111)
plt.plot(vardict['time_'+i],vardict['data_'+i],color='r')
ax.set_ylabel(i)
In [9]:
print(API_data['entries'][0]['data'])
print(API_data['entries'][0]['axes'])
print(API_data['entries'][0]['context'])