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API Time Selectors - Using Start, End, and Reftime with Forecast Data

Forecast datasets typically have two time dimensions, reftime and time. The reftime of a forecast data product represents the run time of the forecast model, while time represents the actual time (typically in the future) that the values are valid. For each reftime, values are unique for each unique time value.

Typically the most recent forecast is most desirable to users, and the Planet OS API intentionally returns these values by default. However, there may be cases when one wants to investigate a historical forecast, or how values at a specific time differ between successive forecasts.

The RTOFS data product is a daily updated forecast that provides a 3-day, hourly forecast. This notebook highlights ways to use the start, end, reftime_start, and reftime_end API parameters and explain the expected response.





In [1]:



    


%matplotlib inline

import pandas as pd
import simplejson as json
from urllib.parse import urlencode
from urllib.request import urlopen, Request
import datetime
import numpy as np

A Planet OS API key is required to run this notebook. Keys are displayed in the account settings page on the Planet OS Datahub. If you do not have a Planet OS account, you can sign up for free.





In [2]:



    


apikey = open('APIKEY').readlines()[0].strip() #'<YOUR API KEY HERE>'

Request the Most Recent Model Run

By default, our API returns the most recent model run. The example below returns all variables for the most recent forecast run (e.g. reftime). At the time of running, this was 2016-11-12T00:00:00. Given the hourly resolution and 3-day extent, we expect 72 unique values to be returned.





In [3]:



    


# Set the Planet OS API query parameters
count = 100
id = 'noaa_rtofs_surface_1h_diag'
lat=39.37858604638528
lon=-72.57739563685647
time_order = 'desc'

query_dict = {'apikey': apikey,
              'count': count,
              'lon': lon,
              'lat': lat,
              'time_order': time_order,
              }
query = urlencode(query_dict)

api_url = "http://api.planetos.com/v1/datasets/%s/point?%s" % (id, query)

request = Request(api_url)
response = urlopen(request)
response_json = json.loads(response.read())

data = response_json['entries']

# let's flatten the response and create a Pandas dataframe
df = pd.io.json.json_normalize(data)

# then index by time using the axes.time column
pd.to_datetime(df["axes.time"])
df.set_index('axes.time', inplace=False)

print(df.count())
df.head()



    


















    






axes.latitude                            73
axes.longitude                           73
axes.reftime                             73
axes.time                                73
classifiers.reference_time               73
context                                  73
data.ice_coverage                        73
data.ice_thickness                       73
data.mixed_layer_thickness               73
data.ssh                                 73
data.surface_boundary_layer_thickness    73
data.u_barotropic_velocity               73
data.v_barotropic_velocity               73
dtype: int64










    
Out[3]:











  
    

      
      axes.latitude
      axes.longitude
      axes.reftime
      axes.time
      classifiers.reference_time
      context
      data.ice_coverage
      data.ice_thickness
      data.mixed_layer_thickness
      data.ssh
      data.surface_boundary_layer_thickness
      data.u_barotropic_velocity
      data.v_barotropic_velocity
    

  
  
    

      0
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-18T00:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      2.393272
      -0.586881
      1.003857
      0.052923
      -0.051910
    

    

      1
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T23:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.461914
      -0.582981
      1.005212
      0.048648
      -0.042568
    

    

      2
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T22:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.939150
      -0.577908
      1.008560
      0.047611
      -0.033235
    

    

      3
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T21:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.904634
      -0.573326
      1.012362
      0.049494
      -0.023227
    

    

      4
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T20:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.972110
      -0.571742
      1.015378
      0.052853
      -0.012663

Request a Single Model Run

By default, the system will return the most recent model run (e.g. reftime). However, a specific model run can be acquired by requesting an identical reftime_start and reftime_end value. In the example below, we request values from only the 2016-11-12T00:00:00 model run.





In [4]:



    


df



    


















    
Out[4]:











  
    

      
      axes.latitude
      axes.longitude
      axes.reftime
      axes.time
      classifiers.reference_time
      context
      data.ice_coverage
      data.ice_thickness
      data.mixed_layer_thickness
      data.ssh
      data.surface_boundary_layer_thickness
      data.u_barotropic_velocity
      data.v_barotropic_velocity
    

  
  
    

      0
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-18T00:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      2.393272
      -0.586881
      1.003857
      0.052923
      -0.051910
    

    

      1
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T23:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.461914
      -0.582981
      1.005212
      0.048648
      -0.042568
    

    

      2
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T22:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.939150
      -0.577908
      1.008560
      0.047611
      -0.033235
    

    

      3
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T21:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.904634
      -0.573326
      1.012362
      0.049494
      -0.023227
    

    

      4
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T20:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.972110
      -0.571742
      1.015378
      0.052853
      -0.012663
    

    

      5
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T19:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.099548
      -0.573966
      1.016540
      0.056213
      -0.002916
    

    

      6
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T18:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.365773
      -0.577964
      1.015528
      0.059150
      0.003989
    

    

      7
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T17:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      2.068107
      -0.581208
      1.012883
      0.062220
      0.006222
    

    

      8
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T16:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      4.017891
      -0.582298
      1.009745
      0.066123
      0.003401
    

    

      9
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T15:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      5.489046
      -0.580531
      1.007133
      0.071520
      -0.003411
    

    

      10
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T14:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.375851
      -0.576848
      1.005445
      0.077729
      -0.012819
    

    

      11
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T13:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.100214
      -0.572208
      1.779485
      0.083688
      -0.023142
    

    

      12
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T12:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.878569
      -0.566653
      4.922035
      0.087533
      -0.033732
    

    

      13
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T11:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      8.117210
      -0.562986
      7.326427
      0.088285
      -0.044048
    

    

      14
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T10:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      8.067168
      -0.563954
      8.341889
      0.085997
      -0.053084
    

    

      15
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T09:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.911420
      -0.567991
      8.159277
      0.081106
      -0.059898
    

    

      16
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T08:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.622293
      -0.573117
      7.675351
      0.075394
      -0.062901
    

    

      17
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T07:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.980652
      -0.578922
      6.264008
      0.069813
      -0.061442
    

    

      18
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T06:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      5.889813
      -0.583953
      4.226895
      0.064880
      -0.055497
    

    

      19
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T05:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      4.677490
      -0.587404
      2.576871
      0.061353
      -0.045680
    

    

      20
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T04:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      3.624086
      -0.587819
      1.619146
      0.058696
      -0.035362
    

    

      21
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T03:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      3.030861
      -0.583119
      2.229293
      0.057034
      -0.027551
    

    

      22
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T02:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      2.669391
      -0.575806
      2.036594
      0.057097
      -0.024253
    

    

      23
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T01:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.870689
      -0.566830
      1.006377
      0.059213
      -0.025850
    

    

      24
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-17T00:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.931675
      -0.559775
      1.007717
      0.063999
      -0.030282
    

    

      25
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T23:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.785911
      -0.556264
      1.009856
      0.070086
      -0.035647
    

    

      26
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T22:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.789303
      -0.556530
      1.012161
      0.076357
      -0.040276
    

    

      27
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T21:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.808828
      -0.561607
      1.013881
      0.081628
      -0.044166
    

    

      28
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T20:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.840858
      -0.570146
      1.014458
      0.084696
      -0.048383
    

    

      29
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T19:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      0.891038
      -0.578862
      1.013723
      0.086110
      -0.052792
    

    

      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    

    

      43
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T05:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      2.560007
      -0.559192
      1.010422
      0.101492
      -0.075716
    

    

      44
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T04:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.883909
      -0.559145
      1.008574
      0.109294
      -0.084072
    

    

      45
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T03:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.600063
      -0.556311
      1.007095
      0.115196
      -0.096286
    

    

      46
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T02:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.387641
      -0.553329
      1.006572
      0.117911
      -0.109475
    

    

      47
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T01:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.241458
      -0.551900
      1.007071
      0.116294
      -0.120298
    

    

      48
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-16T00:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.296358
      -0.551413
      1.008421
      0.110358
      -0.126584
    

    

      49
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T23:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.471651
      -0.552021
      1.010378
      0.101472
      -0.127311
    

    

      50
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T22:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      1.829469
      -0.553539
      1.012423
      0.090876
      -0.123713
    

    

      51
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T21:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      2.514715
      -0.556953
      1.013952
      0.081366
      -0.116807
    

    

      52
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T20:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      3.332845
      -0.561778
      1.014560
      0.074210
      -0.108862
    

    

      53
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T19:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      3.905654
      -0.563903
      1.013991
      0.070646
      -0.100568
    

    

      54
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T18:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      4.684608
      -0.565803
      1.012341
      0.071928
      -0.092220
    

    

      55
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T17:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.014578
      -0.566297
      1.010074
      0.076021
      -0.085030
    

    

      56
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T16:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.824544
      -0.565571
      1.007725
      0.081834
      -0.078454
    

    

      57
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T15:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.435776
      -0.563867
      1.005924
      0.086961
      -0.073020
    

    

      58
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T14:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      8.045163
      -0.561568
      1.005155
      0.089870
      -0.068959
    

    

      59
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T13:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      8.491595
      -0.557745
      1.424048
      0.089890
      -0.066688
    

    

      60
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T12:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      9.150079
      -0.553083
      1.949899
      0.086221
      -0.065420
    

    

      61
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T11:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      9.390242
      -0.549542
      1.010224
      0.081564
      -0.062221
    

    

      62
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T10:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      9.082260
      -0.550943
      1.012923
      0.077397
      -0.055966
    

    

      63
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T09:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      8.622544
      -0.555049
      1.014957
      0.075107
      -0.047349
    

    

      64
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T08:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.833182
      -0.559612
      1.015818
      0.073993
      -0.039062
    

    

      65
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T07:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      7.119994
      -0.564287
      1.015359
      0.074347
      -0.033303
    

    

      66
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T06:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.824449
      -0.568943
      1.013925
      0.075650
      -0.031629
    

    

      67
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T05:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.660968
      -0.571769
      1.011828
      0.077772
      -0.034339
    

    

      68
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T04:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.740618
      -0.569308
      2.776619
      0.080424
      -0.039260
    

    

      69
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T03:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.696674
      -0.566965
      4.110346
      0.082897
      -0.044958
    

    

      70
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T02:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.547153
      -0.568515
      6.062504
      0.083444
      -0.049476
    

    

      71
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T01:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.359508
      -0.571273
      6.360367
      0.079072
      -0.052278
    

    

      72
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T00:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.393537
      -0.574618
      6.448367
      0.068051
      -0.051244
    

  



73 rows × 13 columns



















In [5]:



    


# Request only data from a specific model run
reftime_end = np.max(df['axes.reftime'])
reftime_start = np.max(df['axes.reftime'])

query_dict = {'apikey': apikey,
              'count': count,
              'lat': lat,
              'lon': lon,
              'reftime_end': reftime_end,
              'reftime_start': reftime_start,
              'time_order': time_order,
              }
query = urlencode(query_dict)

api_url = "http://api.planetos.com/v1/datasets/%s/point?%s" % (id, query)

request = Request(api_url)
response = urlopen(request)
response_json = json.loads(response.read())

data = response_json['entries']

# let's flatten the response and create a Pandas dataframe
df = pd.io.json.json_normalize(data)

# then index by time using the axes.time column
pd.to_datetime(df["axes.time"])
df.set_index('axes.time', inplace=False)

print(df.count())



    


















    






axes.latitude                            73
axes.longitude                           73
axes.reftime                             73
axes.time                                73
classifiers.reference_time               73
context                                  73
data.ice_coverage                        73
data.ice_thickness                       73
data.mixed_layer_thickness               73
data.ssh                                 73
data.surface_boundary_layer_thickness    73
data.u_barotropic_velocity               73
data.v_barotropic_velocity               73
dtype: int64

All available model run values for a specific time

This example looks for data at a specific future datetime (e.g. 2016-11-14T00:00:00) and returns values from all available model runs (e.g. reftimes). From the result at the time of running, we can see that two forecasts provide values for Nov 14th at 00:00, the 2016-11-11T00:00:00 and 2016-11-12T00:00:00 model runs.





In [ ]:



    






    











In [6]:



    


# Request only data from past model run (e.g. 2016-11-11T00:00:00)

end = np.min(df['axes.time'])
start = datetime.datetime.strftime(datetime.datetime.strptime(df['axes.reftime'][0],'%Y-%m-%dT%H:%M:%S') - datetime.timedelta(days=1),'%Y-%m-%dT%H:%M:%S')

query_dict = {'apikey': apikey,
              'count': count,
              'end': end,
              'lat': lat,
              'lon': lon,
              'reftime_recent': 'false',
              'start': start,
              'time_order': time_order,
              }
query = urlencode(query_dict)

api_url = "http://api.planetos.com/v1/datasets/%s/point?%s" % (id, query)

request = Request(api_url)
response = urlopen(request)
response_json = json.loads(response.read())

data = response_json['entries']

# let's flatten the response and create a Pandas dataframe
df = pd.io.json.json_normalize(data)
print (df)
# then index by time using the axes.time column
pd.to_datetime(df["axes.time"])
df.set_index('axes.time', inplace=True)

print(df.count())
df.head()



    


















    






   axes.latitude  axes.longitude         axes.reftime            axes.time  \
0      39.390355      -72.616137  2018-06-15T00:00:00  2018-06-15T00:00:00   

  classifiers.reference_time                    context  data.ice_coverage  \
0   2018-06-15T00:00:00.000Z  reference_time_MT_lat_lon                0.0   

   data.ice_thickness  data.mixed_layer_thickness  data.ssh  \
0                 0.0                    6.393537 -0.574618   

   data.surface_boundary_layer_thickness  data.u_barotropic_velocity  \
0                               6.448367                    0.068051   

   data.v_barotropic_velocity  
0                   -0.051244  
axes.latitude                            1
axes.longitude                           1
axes.reftime                             1
classifiers.reference_time               1
context                                  1
data.ice_coverage                        1
data.ice_thickness                       1
data.mixed_layer_thickness               1
data.ssh                                 1
data.surface_boundary_layer_thickness    1
data.u_barotropic_velocity               1
data.v_barotropic_velocity               1
dtype: int64










    
Out[6]:











  
    

      
      axes.latitude
      axes.longitude
      axes.reftime
      classifiers.reference_time
      context
      data.ice_coverage
      data.ice_thickness
      data.mixed_layer_thickness
      data.ssh
      data.surface_boundary_layer_thickness
      data.u_barotropic_velocity
      data.v_barotropic_velocity
    

    

      axes.time
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

      2018-06-15T00:00:00
      39.390355
      -72.616137
      2018-06-15T00:00:00
      2018-06-15T00:00:00.000Z
      reference_time_MT_lat_lon
      0.0
      0.0
      6.393537
      -0.574618
      6.448367
      0.068051
      -0.051244
    

  




















In [ ]:

Content source: planet-os/notebooks

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