Python Jupyter notebook API example¶

In [3]:

# This is the only package actually required to make a request:
import requests

# The remaining packages are useful in displaying or using the response data.
import json
import getpass
from collections import namedtuple
import base64
import rasterio
from rasterio import MemoryFile
from rasterio.session import AWSSession
from matplotlib import pyplot as plt
import folium
import branca
import geopandas as gpd
import numpy as np
from matplotlib import cm
from matplotlib import pyplot as plt
import pandas as pd
from IPython.core.display import display, HTML
from PIL import Image
from io import BytesIO
import base64
import re, os
from pathlib import Path
%matplotlib inline

# This is the only package actually required to make a request: import requests # The remaining packages are useful in displaying or using the response data. import json import getpass from collections import namedtuple import base64 import rasterio from rasterio import MemoryFile from rasterio.session import AWSSession from matplotlib import pyplot as plt import folium import branca import geopandas as gpd import numpy as np from matplotlib import cm from matplotlib import pyplot as plt import pandas as pd from IPython.core.display import display, HTML from PIL import Image from io import BytesIO import base64 import re, os from pathlib import Path %matplotlib inline

Set up¶

In [4]:

url = "https://api.looc-b.farm"
token = getpass.getpass()

url = "https://api.looc-b.farm" token = getpass.getpass()

Create a feature¶

In [5]:

feature = {"type":"FeatureCollection", "features": [
{"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[144.27287356885532,-37.64955948737578],[144.27146513389454,-37.64844433430535],[144.2700566989338,-37.64459185850357],[144.26826414534747,-37.64484531699941],[144.26788002672177,-37.64514946605291],[144.26967258030817,-37.65042118509808],[144.26775198717982,-37.651181498314806],[144.2668557103867,-37.65067462370187],[144.266471591761,-37.650674623701896],[144.2671758092414,-37.651992490499424],[144.26295050435914,-37.65133356002399],[144.26141402985658,-37.65706099099198],[144.25750882382906,-37.65787191896435],[144.25520411207518,-37.6565034728732],[144.25385969688529,-37.657365090019546],[144.2596214762702,-37.663953596430915],[144.2595574564992,-37.66425766722557],[144.2522592026118,-37.66339613007215],[144.2507867478801,-37.6612169032267],[144.24777781864574,-37.659240339833346],[144.24732968024918,-37.6590882943156],[144.24726566047826,-37.65944306670585],[144.2466894825398,-37.65954442993453],[144.2464974232269,-37.66141962470176],[144.24726566047826,-37.66238254414847],[144.24726566047832,-37.66283865952678],[144.24521702780805,-37.66238254414845],[144.2438726126184,-37.66689289526888],[144.2345257260607,-37.66577800251491],[144.23497386445732,-37.66973072852797],[144.22882796644674,-37.66907195547822],[144.2251788395031,-37.69324002946069],[144.21704832859342,-37.69227751042105],[144.21653617042585,-37.69703932472337],[144.22411490907842,-37.69808709676632],[144.22257764281488,-37.7098442397424],[144.2320289549939,-37.71309552886318],[144.23081257934598,-37.72038804995129],[144.22063343576616,-37.71937524272531],[144.2181366646994,-37.73451526676643],[144.21864882286692,-37.73593290316538],[144.22281010797818,-37.740691912547184],[144.22293814752007,-37.741350049338216],[144.2275475710279,-37.74200818027777],[144.22799570942456,-37.74038815513155],[144.23971132750702,-37.74155255178905],[144.23798279369163,-37.75238562522827],[144.24246417765752,-37.75284118702674],[144.2413758415515,-37.76007918147898],[144.25033860948344,-37.761192656234165],[144.25072272810922,-37.759421211245304],[144.26026167397958,-37.76043347071921],[144.26096589146,-37.75511895394572],[144.25072272810917,-37.75390415364863],[144.2514269455895,-37.750360872188736],[144.24957037223217,-37.7494497152455],[144.24969841177406,-37.746260577593794],[144.25238724215367,-37.745197501169464],[144.2522592026118,-37.744741892313165],[144.2429763358251,-37.74357754582716],[144.24329643467988,-37.74145130063287],[144.24464084986965,-37.74140067500286],[144.2446408498697,-37.74226130600406],[144.2561003888683,-37.74408378568038],[144.25661254703593,-37.743121926998725],[144.25834108085135,-37.74286880421552],[144.2596854960411,-37.7412487979051],[144.26058177283437,-37.74089441679842],[144.26167010894034,-37.74089441679847],[144.26301452413006,-37.74003376990457],[144.26621551267732,-37.74064128639778],[144.26877630351495,-37.740489407741904],[144.27076091641416,-37.739426248425495],[144.2705048373305,-37.738312446098014],[144.27248945022964,-37.73750239751598],[144.27517828060925,-37.73694548397431],[144.27581847831866,-37.73714799847421],[144.2804279018265,-37.73400896147415],[144.28241251472573,-37.73436337554227],[144.2819643763292,-37.736591082266465],[144.2827326135805,-37.73679359773579],[144.28433310785402,-37.735274718212956],[144.28548546373102,-37.73492030850734],[144.2868938986917,-37.733705176637045],[144.28849439296528,-37.73304697188071],[144.2916953815124,-37.732692551511406],[144.29214351990896,-37.73264191989162],[144.29291175716025,-37.726869688291366],[144.298865595858,-37.724641689088024],[144.29905765517083,-37.72312256029457],[144.29035096632256,-37.722160429276975],[144.29111920357394,-37.71658995145543],[144.29156734197053,-37.713753910937285],[144.28650978006607,-37.713196818793854],[144.28702193823352,-37.71031000151101],[144.28836635342336,-37.70798020731534],[144.2900308674679,-37.70813215264767],[144.29111920357388,-37.70261127227757],[144.2906710651773,-37.701902138575115],[144.29111920357388,-37.70038254351181],[144.29611274570746,-37.70088907866076],[144.29752118066816,-37.70250996787827],[144.2985454970033,-37.70286453267003],[144.29867353654515,-37.70139561034853],[144.29937775402547,-37.70093973198531],[144.2991216749418,-37.70028123606662],[144.2984174574614,-37.70043319718243],[144.2974571608972,-37.700635811518886],[144.29700902250065,-37.69987600490165],[144.29617676547835,-37.69957208007418],[144.29611274570743,-37.698862917297966],[144.29732912135535,-37.69840702335954],[144.29720108181345,-37.697444571394165],[144.29464029097574,-37.69790047125106],[144.2938080339535,-37.69729260415218],[144.29310381647315,-37.69663407583754],[144.29188744082523,-37.69592488495728],[144.29073508494827,-37.696228824736586],[144.29015890700978,-37.695519629980026],[144.28939066975846,-37.69364529689742],[144.2881742941106,-37.69374661341057],[144.28817429411052,-37.69278410094669],[144.2872780173174,-37.69232816962929],[144.28759811617212,-37.6909603588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"fill": "#ff0000", "fill-opacity": 1.0, "stroke": "#ff0000", "stroke-opacity": 1.0}}
]}

gdf = gpd.GeoDataFrame.from_features(feature)
gdf.plot()

feature = {"type":"FeatureCollection", "features": [ {"type":"Feature","geometry":{"type":"Polygon","coordinates":[[[144.27287356885532,-37.64955948737578],[144.27146513389454,-37.64844433430535],[144.2700566989338,-37.64459185850357],[144.26826414534747,-37.64484531699941],[144.26788002672177,-37.64514946605291],[144.26967258030817,-37.65042118509808],[144.26775198717982,-37.651181498314806],[144.2668557103867,-37.65067462370187],[144.266471591761,-37.650674623701896],[144.2671758092414,-37.651992490499424],[144.26295050435914,-37.65133356002399],[144.26141402985658,-37.65706099099198],[144.25750882382906,-37.65787191896435],[144.25520411207518,-37.6565034728732],[144.25385969688529,-37.657365090019546],[144.2596214762702,-37.663953596430915],[144.2595574564992,-37.66425766722557],[144.2522592026118,-37.66339613007215],[144.2507867478801,-37.6612169032267],[144.24777781864574,-37.659240339833346],[144.24732968024918,-37.6590882943156],[144.24726566047826,-37.65944306670585],[144.2466894825398,-37.65954442993453],[144.2464974232269,-37.66141962470176],[144.24726566047826,-37.66238254414847],[144.24726566047832,-37.66283865952678],[144.24521702780805,-37.66238254414845],[144.2438726126184,-37.66689289526888],[144.2345257260607,-37.66577800251491],[144.23497386445732,-37.66973072852797],[144.22882796644674,-37.66907195547822],[144.2251788395031,-37.69324002946069],[144.21704832859342,-37.69227751042105],[144.21653617042585,-37.69703932472337],[144.22411490907842,-37.69808709676632],[144.22257764281488,-37.7098442397424],[144.2320289549939,-37.71309552886318],[144.23081257934598,-37.72038804995129],[144.22063343576616,-37.71937524272531],[144.2181366646994,-37.73451526676643],[144.21864882286692,-37.73593290316538],[144.22281010797818,-37.740691912547184],[144.22293814752007,-37.741350049338216],[144.2275475710279,-37.74200818027777],[144.22799570942456,-37.74038815513155],[144.23971132750702,-37.74155255178905],[144.23798279369163,-37.75238562522827],[144.24246417765752,-37.75284118702674],[144.2413758415515,-37.76007918147898],[144.25033860948344,-37.761192656234165],[144.25072272810922,-37.759421211245304],[144.26026167397958,-37.76043347071921],[144.26096589146,-37.75511895394572],[144.25072272810917,-37.75390415364863],[144.2514269455895,-37.750360872188736],[144.24957037223217,-37.7494497152455],[144.24969841177406,-37.746260577593794],[144.25238724215367,-37.745197501169464],[144.2522592026118,-37.744741892313165],[144.2429763358251,-37.74357754582716],[144.24329643467988,-37.74145130063287],[144.24464084986965,-37.74140067500286],[144.2446408498697,-37.74226130600406],[144.2561003888683,-37.74408378568038],[144.25661254703593,-37.743121926998725],[144.25834108085135,-37.74286880421552],[144.2596854960411,-37.7412487979051],[144.26058177283437,-37.74089441679842],[144.26167010894034,-37.74089441679847],[144.26301452413006,-37.74003376990457],[144.26621551267732,-37.74064128639778],[144.26877630351495,-37.740489407741904],[144.27076091641416,-37.739426248425495],[144.2705048373305,-37.738312446098014],[144.27248945022964,-37.73750239751598],[144.27517828060925,-37.73694548397431],[144.27581847831866,-37.73714799847421],[144.2804279018265,-37.73400896147415],[144.28241251472573,-37.73436337554227],[144.2819643763292,-37.736591082266465],[144.2827326135805,-37.73679359773579],[144.28433310785402,-37.735274718212956],[144.28548546373102,-37.73492030850734],[144.2868938986917,-37.733705176637045],[144.28849439296528,-37.73304697188071],[144.2916953815124,-37.732692551511406],[144.29214351990896,-37.73264191989162],[144.29291175716025,-37.726869688291366],[144.298865595858,-37.724641689088024],[144.29905765517083,-37.72312256029457],[144.29035096632256,-37.722160429276975],[144.29111920357394,-37.71658995145543],[144.29156734197053,-37.713753910937285],[144.28650978006607,-37.713196818793854],[144.28702193823352,-37.71031000151101],[144.28836635342336,-37.70798020731534],[144.2900308674679,-37.70813215264767],[144.29111920357388,-37.70261127227757],[144.2906710651773,-37.701902138575115],[144.29111920357388,-37.70038254351181],[144.29611274570746,-37.70088907866076],[144.29752118066816,-37.70250996787827],[144.2985454970033,-37.70286453267003],[144.29867353654515,-37.70139561034853],[144.29937775402547,-37.70093973198531],[144.2991216749418,-37.70028123606662],[144.2984174574614,-37.70043319718243],[144.2974571608972,-37.700635811518886],[144.29700902250065,-37.69987600490165],[144.29617676547835,-37.69957208007418],[144.29611274570743,-37.698862917297966],[144.29732912135535,-37.69840702335954],[144.29720108181345,-37.697444571394165],[144.29464029097574,-37.69790047125106],[144.2938080339535,-37.69729260415218],[144.29310381647315,-37.69663407583754],[144.29188744082523,-37.69592488495728],[144.29073508494827,-37.696228824736586],[144.29015890700978,-37.695519629980026],[144.28939066975846,-37.69364529689742],[144.2881742941106,-37.69374661341057],[144.28817429411052,-37.69278410094669],[144.2872780173174,-37.69232816962929],[144.28759811617212,-37.6909603588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"fill": "#ff0000", "fill-opacity": 1.0, "stroke": "#ff0000", "stroke-opacity": 1.0}} ]} gdf = gpd.GeoDataFrame.from_features(feature) gdf.plot()

Out[5]:

<AxesSubplot:>

Monitoring endpoint¶

In [20]:

%%time

endpoint = 'monitoring'
params = {"polygon": json.dumps(feature),            # GeoJSON 
          "mode": endpoint,                          # Which mode: monitoring or planning
          "start_year": 2012,                        # Start of the assessment. The earliest possible is 2012.
          "end_year": 2020,                          # End of the assessment. The latest possible is 2020.
          "action_year": 2015,                       # In which year an action was implemented (optional).
          "dataset": ["habitat-condition"],          # Which metric to asses.
          "download_geotiff": False                  # Whether or not to save derived maps in GeoTIFF format
         }


header = {
    'Authorization': 'Bearer {}'.format(token)      # Format the token for authorization
}                                                   

response = requests.post(f'{url}/{endpoint}',       # Make the request
                         json = params, 
                         headers=header)
print(response.status_code)                          # All going well, the returned HTTP code should be 200

%%time endpoint = 'monitoring' params = {"polygon": json.dumps(feature), # GeoJSON "mode": endpoint, # Which mode: monitoring or planning "start_year": 2012, # Start of the assessment. The earliest possible is 2012. "end_year": 2020, # End of the assessment. The latest possible is 2020. "action_year": 2015, # In which year an action was implemented (optional). "dataset": ["habitat-condition"], # Which metric to asses. "download_geotiff": False # Whether or not to save derived maps in GeoTIFF format } header = { 'Authorization': 'Bearer {}'.format(token) # Format the token for authorization } response = requests.post(f'{url}/{endpoint}', # Make the request json = params, headers=header) print(response.status_code) # All going well, the returned HTTP code should be 200

200
CPU times: user 25 ms, sys: 9.95 ms, total: 34.9 ms
Wall time: 4.39 s

In [7]:

data = response.json()
data.keys()

data = response.json() data.keys()

Out[7]:

dict_keys(['assessment_area', 'datasets'])

Response data¶

Walk through the various items returned in the reponse json:

In [8]:

data['assessment_area'] # area of polygon in ha

data['assessment_area'] # area of polygon in ha

Out[8]:

5741.671426094147

In [9]:

len(data['datasets']) # data for a single dataset (i.e. habitat-condition)

len(data['datasets']) # data for a single dataset (i.e. habitat-condition)

Out[9]:

1

In [10]:

data['datasets'][0].keys() # the following data is returned

data['datasets'][0].keys() # the following data is returned

Out[10]:

dict_keys(['name', 'summary', 'annual_averages', 'figure', 'geotiff', 'units', 'attribution', 'mode'])

In [11]:

data['datasets'][0]['name']

data['datasets'][0]['name']

Out[11]:

'habitat-condition'

In [12]:

data['datasets'][0]['summary']

data['datasets'][0]['summary']

Out[12]:

{'mean_percent_changes': {'values': {'starting_estimate': 36,
   'latest_estimate': 42,
   'expected_change': 14},
  'units': 'percent'},
 'potential_change_habitat_area': {'values': {'potential_change_ha': 819},
  'units': 'ha'}}

In [13]:

data['datasets'][0]['annual_averages'] # summary data

data['datasets'][0]['annual_averages'] # summary data

Out[13]:

{'mean': {'2012': 0.3685349814381611,
  '2013': 0.33665712882679005,
  '2014': 0.31464855668989017,
  '2015': 0.28385356409555534,
  '2016': 0.2881668873003967,
  '2017': 0.3043787981844996,
  '2018': 0.32366209348301467,
  '2019': 0.3982214881890765,
  '2020': 0.4264946014925753},
 'fifth_percentile': {'2012': 0.12931382060050964,
  '2013': 0.13048578500747682,
  '2014': 0.12763504981994628,
  '2015': 0.1259802758693695,
  '2016': 0.12419400066137314,
  '2017': 0.1254882127046585,
  '2018': 0.13024066984653473,
  '2019': 0.15146154761314393,
  '2020': 0.15404867827892305},
 'ninety_fifth_percentile': {'2012': 0.853738832473754,
  '2013': 0.8010057568550105,
  '2014': 0.7763315320014949,
  '2015': 0.7773002028465268,
  '2016': 0.8122637391090384,
  '2017': 0.8584174990653989,
  '2018': 0.9129437088966369,
  '2019': 0.9810299515724175,
  '2020': 1.0}}

In [14]:

pd.DataFrame(data['datasets'][0]['annual_averages']) # as table

pd.DataFrame(data['datasets'][0]['annual_averages']) # as table

Out[14]:

	mean	fifth_percentile	ninety_fifth_percentile
2012	0.368535	0.129314	0.853739
2013	0.336657	0.130486	0.801006
2014	0.314649	0.127635	0.776332
2015	0.283854	0.125980	0.777300
2016	0.288167	0.124194	0.812264
2017	0.304379	0.125488	0.858417
2018	0.323662	0.130241	0.912944
2019	0.398221	0.151462	0.981030
2020	0.426495	0.154049	1.000000

In [15]:

display(HTML(data['datasets'][0]['figure']))

display(HTML(data['datasets'][0]['figure']))

In [16]:

images_base64 = data['datasets'][0]['geotiff']
print(images_base64.keys())
l, b, r, t = list(images_base64['bounds'].values())

images_base64 = data['datasets'][0]['geotiff'] print(images_base64.keys()) l, b, r, t = list(images_base64['bounds'].values())

dict_keys(['bounds', 'images', 'url'])

In [18]:

# Plot images on map
l, b, r, t = gdf.total_bounds
x, y = list(*gdf.loc[0, 'geometry'].centroid.coords)

m = folium.Map([y, x], zoom_start=13)#, width = 800, height = 400)

esri_img_url = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png'
imagery = folium.TileLayer(esri_img_url, 
                           overlay = True, control = True, 
                           show = False, attr='CSIRO')
imagery.layer_name = "Esri World Imagery"
imagery.add_to(m)

cmap = cm.get_cmap("RdYlGn").copy()
cmap = plt.get_cmap('RdYlGn')

# add legend
colormap = branca.colormap.linear.RdYlGn_11.scale(0, 100)
colormap = colormap.to_step(index=np.arange(0, 110, 10))
colormap.caption = 'Habitat condition (%)'
colormap.add_to(m)

l, b, r, t = list(images_base64['bounds'].values())
ll = (b, l)
ur = (t, r)

for t, img in images_base64['images'].items():
    
    # decode
    image = Image.open(BytesIO(base64.b64decode(img)))
    img = np.array(image)
    
    condition = folium.raster_layers.ImageOverlay(
        image=img,
        bounds=[ll, ur]
    )
    condition.layer_name = f'Condition: {t}'
    condition.add_to(m)

control = folium.LayerControl().add_to(m)

m

# Plot images on map l, b, r, t = gdf.total_bounds x, y = list(*gdf.loc[0, 'geometry'].centroid.coords) m = folium.Map([y, x], zoom_start=13)#, width = 800, height = 400) esri_img_url = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}.png' imagery = folium.TileLayer(esri_img_url, overlay = True, control = True, show = False, attr='CSIRO') imagery.layer_name = "Esri World Imagery" imagery.add_to(m) cmap = cm.get_cmap("RdYlGn").copy() cmap = plt.get_cmap('RdYlGn') # add legend colormap = branca.colormap.linear.RdYlGn_11.scale(0, 100) colormap = colormap.to_step(index=np.arange(0, 110, 10)) colormap.caption = 'Habitat condition (%)' colormap.add_to(m) l, b, r, t = list(images_base64['bounds'].values()) ll = (b, l) ur = (t, r) for t, img in images_base64['images'].items(): # decode image = Image.open(BytesIO(base64.b64decode(img))) img = np.array(image) condition = folium.raster_layers.ImageOverlay( image=img, bounds=[ll, ur] ) condition.layer_name = f'Condition: {t}' condition.add_to(m) control = folium.LayerControl().add_to(m) m

Out[18]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Map data¶

Getting the GeoTiff data

The simplest route to accessing the data is to access the provided url, which will download the associated GeoTIFF file:

In [175]:

# The simplest route to accessing the data is to access the provided url, which will download the associated GeoTIFF file:
data['datasets'][0]['geotiff']['url']

# The simplest route to accessing the data is to access the provided url, which will download the associated GeoTIFF file: data['datasets'][0]['geotiff']['url']

Out[175]:

'https://biodiversity-data.s3.amazonaws.com/user-data/a34bbd5f-42bb-4e50-8973-bd0fde67edf4.tif?AWSAccessKeyId=ASIARZGLS5YROFUYDBW4&Signature=0cd4Xj4mMzPGs66i0zW9AesLDRI%3D&x-amz-security-token=IQoJb3JpZ2luX2VjEDAaDmFwLXNvdXRoZWFzdC0yIkYwRAIgVa%2Fc4DykK2JCmMOzQHmTx%2Bdp3oa5xTAla%2FTaYVwL%2FDICIEULdnRMPTGH6mD9X4wT4J1eAhobIjnEKmvCGbP8e4uDKogDCGkQABoMMTIyODMzNTMwNDAyIgz2HILi5dvdzJ4JVTsq5QL3XSQCMPK3Vo%2F%2F5IczZFqDfyYwy%2F%2B0MjpB7AlMK2qy%2FGph1J6liWxBdK5IXk2RpqYP2lJCTET8PKB6Vm9sEjYJwxfvk122b8Jw1Tw9QzyhdOt%2Bei%2BYXQFA3RVgHiJ5zKpuc22gP7rxZBr%2Bxim2mbB9lPxFm0YHcW84793EkyKTy31rZqJFcyqL9Qqk%2FUhb53Cr2ePy0D60DzByeeOZqOxLCa%2FW8mZh3jJxq22Vxz47Krokd05jrE7QHLJMqeJlUkg4nPL0lZwB6UCBU5wWteCHD5yVRnFRTDlnPPJk01%2BS9xJF9knwZ4izyI4%2BunNQssuyddcrpfTH5h1pEgxK%2FGR2wZLUtP5cvqh3mG2C%2BWTtHSnYkYQAv3oSEZhppoOrUoT9KilJpZzHDecEnADvMwG%2FuS4rrPDg8xKcDSDRpj5MTGgXmNMZRi%2FnDLblTZeHOqXH1Do0GnQwOaU%2F32SQmHcpm4atGPAwwLHsjwY6pwFguQgE6UqSUlw230aE7dU1p4uLxCSQa1Mlcn5O0yCXseYxwPLyFhWL71D91CqqTT%2FPXH7vXTOtQdoknnV5jgrSKvimMamSEGqEfA1R8KQekJpsjViIFIV0pxeGv4OuJkkfaX15HgPPWB47Uufg8xXYKrIf48GNlrvb6zGUxn9gyj2S5dyUCuLtK3yBUVfuyIxxDLgnwopuen%2BFo55f13JpAcDVbor29g%3D%3D&Expires=1643953858'

Reading the GeoTiff dataset

To avoid downloading the data to file, and instead simply consume the data, it's possible to read the data directly. This takes a little (a lot of ?!) munging of the presigned URL returned via the API call:

In [214]:

url, credentials = data['datasets'][0]['geotiff']['url'].split('?')
credentials = credentials.split('&')

# Extract the relevant credentials
aws_access_key_id = re.sub('AWSAccessKeyId=', '', credentials[0])
aws_secret_access_key = re.sub('Signature=', '', credentials[1])
aws_session_token = re.sub('x-amz-security-token=', '', credentials[2])  

# Create a filepath
p = Path(url)
_, bucket, folder, file = p.parts
gdal_path = f's3://{bucket.split(".s3")[0]}/{folder}/{file}'

# Create a rasterio AWS session
session = AWSSession(aws_access_key_id = aws_access_key_id, 
                     aws_secret_access_key = aws_secret_access_key,
                     aws_session_token = aws_session_token)

# Now we can access the data
with rasterio.Env(session):
    with rasterio.open(gdal_path) as src:
        print(src.profile)

url, credentials = data['datasets'][0]['geotiff']['url'].split('?') credentials = credentials.split('&') # Extract the relevant credentials aws_access_key_id = re.sub('AWSAccessKeyId=', '', credentials[0]) aws_secret_access_key = re.sub('Signature=', '', credentials[1]) aws_session_token = re.sub('x-amz-security-token=', '', credentials[2]) # Create a filepath p = Path(url) _, bucket, folder, file = p.parts gdal_path = f's3://{bucket.split(".s3")[0]}/{folder}/{file}' # Create a rasterio AWS session session = AWSSession(aws_access_key_id = aws_access_key_id, aws_secret_access_key = aws_secret_access_key, aws_session_token = aws_session_token) # Now we can access the data with rasterio.Env(session): with rasterio.open(gdal_path) as src: print(src.profile)

{'driver': 'GTiff', 'dtype': 'float32', 'nodata': -9999.0, 'width': 187, 'height': 143, 'count': 9, 'crs': CRS.from_epsg(4326), 'transform': Affine(0.0009767256956049466, 0.0, 147.62603759765625,
       0.0, -0.0009676267117016805, -42.58443313755392), 'tiled': False, 'interleave': 'pixel'}

In [208]:

# And read the data
with rasterio.Env(session):
    with rasterio.open(gdal_path) as src:
        arr = src.read(masked = True)
        l, b, r, t = src.bounds
        band_names = src.descriptions

# And read the data with rasterio.Env(session): with rasterio.open(gdal_path) as src: arr = src.read(masked = True) l, b, r, t = src.bounds band_names = src.descriptions

In [213]:

# And display the time series
fig, axes = plt.subplots(nrows = 1, ncols = arr.shape[0], figsize = (36, 24))
for idx, ax in enumerate(axes):
    ax.imshow(arr[idx, :, :], extent = [l, r, b, t], cmap = 'RdYlGn')
    ax.set_title(band_names[idx])

# And display the time series fig, axes = plt.subplots(nrows = 1, ncols = arr.shape[0], figsize = (36, 24)) for idx, ax in enumerate(axes): ax.imshow(arr[idx, :, :], extent = [l, r, b, t], cmap = 'RdYlGn') ax.set_title(band_names[idx])

Planning¶

In [21]:

%%time
endpoint = 'planning'
params = {"polygon": json.dumps(feature), 
          "mode": 'planning',
#           "start_year": 2020, 
#           "end_year": 2020,
#           "action_year": 2015,
          "action_type": "environmental_planting", 
          "dataset": ["habitat-condition"]
         }
auth = {'Authorization': 'Bearer {}'.format(token)}
response = requests.post(f'{url}/{endpoint}?', 
                         json = params, 
                         headers=auth)
print(response.status_code)

%%time endpoint = 'planning' params = {"polygon": json.dumps(feature), "mode": 'planning', # "start_year": 2020, # "end_year": 2020, # "action_year": 2015, "action_type": "environmental_planting", "dataset": ["habitat-condition"] } auth = {'Authorization': 'Bearer {}'.format(token)} response = requests.post(f'{url}/{endpoint}?', json = params, headers=auth) print(response.status_code)

200
CPU times: user 28 ms, sys: 7.11 ms, total: 35.1 ms
Wall time: 4.61 s

Response data¶

In [22]:

data = response.json()
data.keys()

data = response.json() data.keys()

Out[22]:

dict_keys(['assessment_area', 'datasets'])

In [23]:

display(HTML(data['datasets'][0]['figure']))

display(HTML(data['datasets'][0]['figure']))

In [24]:

# the response is the same as the monitoring example

# the response is the same as the monitoring example

In [ ]: