diff --git a/toar_location_services/settings.py b/toar_location_services/settings.py
index 7e2caab3fcedfc699529ebc30b2766192e67df13..7afb2ab71381361af789acce6e880834d0c02c24 100644
--- a/toar_location_services/settings.py
+++ b/toar_location_services/settings.py
@@ -43,7 +43,8 @@ INSTALLED_APPS = [
'major-roads',
'population-density',
'topography-tandem-x',
- 'stable_night_lights'
+ 'stable_night_lights',
+ 'wheat_production'
]
MIDDLEWARE = [
@@ -145,4 +146,5 @@ USE_LOCAL_OVERPASS_DATA = False
USE_DUMMY_POPULATION_DATA = True
USE_DUMMY_TOPOGRAPHY_TANDEM_DATA = False
USE_DUMMY_STABLE_NIGHT_LIGHTS_DATA = False
-
+USE_DUMMY_WHEAT_DATA = False
+PLOT_DATA = False
diff --git a/toar_location_services/urls.py b/toar_location_services/urls.py
index 8fb7bc0f1e2da53c5d0e4538e45b54cd895afc98..0fd8d49bdd7897755f20e3c8f07e7d75fc98499b 100644
--- a/toar_location_services/urls.py
+++ b/toar_location_services/urls.py
@@ -8,5 +8,6 @@ urlpatterns = [
url(r'major-roads/', include('major-roads.urls'), name='major-roads'),
url(r'population-density/', include('population-density.urls'), name='population-density-density'),
url(r'topography-tandem-x/', include('topography-tandem-x.urls'), name='topography-tandem-x'),
- url(r'stable_night_lights/', include('stable_night_lights.urls'), name='stable_night_lights')
+ url(r'stable_night_lights/', include('stable_night_lights.urls'), name='stable_night_lights'),
+ url(r'wheat_production/', include('wheat_production.urls'), name='wheat_production')
]
diff --git a/toar_location_services/views.py b/toar_location_services/views.py
index 075480761b458fa2bc8100b0d70ad7568431734b..a90aa29405f319a04c1be88af373a297009aded9 100644
--- a/toar_location_services/views.py
+++ b/toar_location_services/views.py
@@ -3,7 +3,6 @@ from rest_framework.views import APIView
from rest_framework.response import Response
-
class LocationServicesRootView(APIView):
"""Local services REST services"""
@@ -16,6 +15,7 @@ class LocationServicesRootView(APIView):
('population-density', request.build_absolute_uri()+'population-density/'),
('topography-tandem-x', request.build_absolute_uri()+'topography-tandem-x/'),
('stable_night_lights', request.build_absolute_uri()+'stable_night_lights/'),
+ ('wheat_production', request.build_absolute_uri()+'wheat_production/'),
]),
]
# add admin view if staff user
diff --git a/wheat_production/serializers.py b/wheat_production/serializers.py
new file mode 100644
index 0000000000000000000000000000000000000000..09234953d78f6f3c97f24dfb69d6360878675513
--- /dev/null
+++ b/wheat_production/serializers.py
@@ -0,0 +1,75 @@
+"""serializer for wheat production data
+"""
+
+from collections import OrderedDict
+import datetime as dt
+from rest_framework.serializers import BaseSerializer
+
+
+# helper functions
+
+def get_provenance(obj):
+ """construct provenance information on population density dataset"""
+ prov = OrderedDict([
+ ('dataset_name', '?'),
+ ('dataset_description', """Wheat production ?"""),
+ ('data_source', """?"""),
+ ('datacenter_url', '?'),
+ ('download_date', '?'),
+ ('timestamp', dt.datetime.now().isoformat())
+ ])
+ return prov
+
+
+# serializer classes
+class AggSerializer(BaseSerializer):
+ """ see http://www.django-rest-framework.org/api-guide/serializers/#baseserializer """
+
+ def to_representation(self, obj):
+ """takes dictionary-like obj and returns geojson compliant structure"""
+ agg_function = obj['agg_function']
+ val = obj[agg_function]
+
+ # build GeoJSON response with 'provenance' extension
+ # ToDo (probably in views): change content-type to application/vnd.geo+json
+ # ToDo: enable support for different output formats
+ # format properties depending on 'by_direction' (vector or not)
+ try:
+ vlength = len(val)
+ except TypeError:
+ vlength = 1
+ if vlength > 1:
+ properties = OrderedDict([
+ ('agg_function', agg_function),
+ ('many', True),
+ (agg_function, OrderedDict([
+ (d, v) for d, v in zip(obj['direction'], val)
+ ])),
+ ('units', 'km-2'),
+ ('radius', obj['radius']),
+ ])
+ if obj['direction'] is None:
+ properties.pop('direction')
+ else:
+ properties = OrderedDict([
+ ('agg_function', agg_function),
+ ('many', False),
+ (agg_function, val),
+ ('units', 'km-2'),
+ ('radius', obj['radius']),
+ ('direction', obj['direction']),
+ ])
+ if obj['direction'] is None:
+ properties.pop('direction')
+
+ response = OrderedDict([
+ ('type', 'Feature'),
+ ('geometry', OrderedDict([
+ ('type', 'Point'),
+ ('coordinates', [obj['lon'], obj['lat']]),
+ ])),
+ ('properties', properties),
+ ('provenance', get_provenance(obj)),
+ ])
+
+ return response
diff --git a/wheat_production/tests.py b/wheat_production/tests.py
index 7ce503c2dd97ba78597f6ff6e4393132753573f6..1e0e74f059a55bd8bbf617f1ccb2fd0bcc3050ee 100644
--- a/wheat_production/tests.py
+++ b/wheat_production/tests.py
@@ -1,3 +1,10 @@
from django.test import TestCase
# Create your tests here.
+
+from wheat_file_extraction import read_proxydata
+
+
+FILENAME = "wheat.txt"
+
+read_proxydata(FILENAME, dummy=False)
diff --git a/wheat_production/urls.py b/wheat_production/urls.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b0dbc25b9c680646c3b023897bc7c349228820d
--- /dev/null
+++ b/wheat_production/urls.py
@@ -0,0 +1,6 @@
+from django.conf.urls import url
+from .views import WheatView
+
+urlpatterns = [
+ url(r'^$', WheatView.as_view()),
+ ]
diff --git a/wheat_production/views.py b/wheat_production/views.py
index 91ea44a218fbd2f408430959283f0419c921093e..7134e695b5d2bd3b7b802ecf9141b4f79cf12147 100644
--- a/wheat_production/views.py
+++ b/wheat_production/views.py
@@ -1,3 +1,94 @@
-from django.shortcuts import render
+import datetime as dt
+import numpy as np
+from collections import OrderedDict
+from rest_framework.views import APIView
+from rest_framework.response import Response
+
+from toar_location_services.settings import DEBUG
+from .wheat_file_extraction import read_proxydata
+from .serializers import AggSerializer
+from utils.views_commons import get_query_params, get_agg_function
+from utils.geoutils import Directions
+from utils.extraction_tools import extract_value, extract_value_stats
+
+FILENAME = "wheat.txt"
+
+lonvec, latvec, data, datainfo = read_proxydata(FILENAME)
+
+if DEBUG:
+ print("File %s successfully loaded" % FILENAME, datainfo)
+
+
+class WheatView(APIView):
+
+ def _extract(self, lat, lon, radius, agg, direction, by_direction):
+ """perform actual extraction of desired quantity"""
+ print('**by_direction:', by_direction, '** radius, agg = ', radius, agg)
+ if agg is not None and radius is not None and radius > 0.:
+ agg_function = get_agg_function(agg)
+ min_angle = None
+ max_angle = None
+ if by_direction:
+ result = np.zeros((16,))
+ direction = Directions.LABELS
+ for i, d in enumerate(Directions.LABELS):
+ min_angle, max_angle = Directions.edges(d)
+ # ToDo: once we serve the data via rasdaman the calls with directions should use
+ # a polygon query for efficiency reasons.
+ result[i] = extract_value_stats(lonvec, latvec, data, lon, lat, default_value=-999.,
+ out_of_bounds_value=0., min_valid=0., max_valid=2.e6,
+ radius=radius, min_angle=min_angle, max_angle=max_angle,
+ agg=agg_function)
+ else:
+ if direction is not None:
+ min_angle, max_angle = Directions.edges(direction)
+ # ToDo: once we serve the data via rasdaman the calls with directions should use
+ # a polygon query for efficiency reasons.
+ result = extract_value_stats(lonvec, latvec, data, lon, lat, default_value=-999.,
+ out_of_bounds_value=0., min_valid=0., max_valid=2.e6,
+ radius=radius, min_angle=min_angle, max_angle=max_angle,
+ agg=agg_function)
+ else:
+ agg = 'value'
+ result = extract_value(lonvec, latvec, data, lon, lat, default_value=-999.,
+ out_of_bounds_value=0., min_valid=0., max_valid=2.e6)
+ # return data, also return agg and direction as they may have been overwritten
+ return result, agg, direction
+
+ def get(self, request, format=None):
+ """process GET requests for wheat_production app
+
+ returns a Geo-JSON response with information about the wheat production at or
+ around a point location.
+
+ required arguments:
+ lat: latitude in degrees_north
+ lng: longitude in degrees_east (can be either -180 to 180 or 0 to 360)
+
+ optional arguments:
+ radius: search radius in m. See settings.py for default and max allowed values.
+ Without 'agg', the radius defaults to None and the wheat production at the
+ point location is returned.
+ agg: method of aggregation for data around point location. See settings.py for
+ default method. Only evaluated if radius > 0. Allowed methods are mean,
+ min, max, median, and NN-percentile (see views_commons.py)
+ direction: return data aggregation in one direction (wind sector) only.
+ Direction must be given as wind sector (e.g. 'N', 'NNE', 'NE', etc.).
+ by_direction: if True, data are returned as vector with one value aggregated
+ over each of 16 wind directions.
+ """
+ lat, lon, radius, agg, direction, by_direction = get_query_params(request.query_params,
+ ['lat', 'lon', 'radius', 'agg', 'direction', 'by_direction'])
+ result, agg, direction = self._extract(lat, lon, radius, agg, direction, by_direction)
+
+ rawdata = OrderedDict([
+ ("lat", lat),
+ ("lon", lon),
+ ("radius", radius),
+ ("direction", direction),
+ ("agg_function", agg),
+ (agg, result),
+ ])
+ response = AggSerializer(rawdata).data
+ return Response(response)
-# Create your views here.
diff --git a/wheat_production/wheat_file_extraction.py b/wheat_production/wheat_file_extraction.py
new file mode 100644
index 0000000000000000000000000000000000000000..c855e51796a4c7b9383cb42a319ea87ee4132cd3
--- /dev/null
+++ b/wheat_production/wheat_file_extraction.py
@@ -0,0 +1,90 @@
+#!/usr/bin/python
+"""
+Import wheat production from .txt-file
+
+Author: Lukas Leufen, FZ Juelich (24th May 2019)
+"""
+
+import numpy as np
+import os
+from toar_location_services.settings import DATA_DIR, DEBUG, USE_DUMMY_WHEAT_DATA, PLOT_DATA
+import matplotlib.pyplot as plt
+
+
+def read_proxydata(filename, dummy=DEBUG and USE_DUMMY_WHEAT_DATA):
+ """Read the ascii file and return the data array together with
+ some dataset properties for use in the extraction routines.
+
+ filename: name of data file (wheat.txt)
+ dummy: if true a small set of dummy data are returned to speed up development of other services
+ """
+
+ if dummy:
+ return create_dummy_data()
+
+ if DEBUG:
+ print("DATA_DIR = ", DATA_DIR, "...")
+ print("Opening ", os.path.join(DATA_DIR, filename), "...")
+
+ with open(os.path.join(DATA_DIR, filename), "r") as dataset:
+ tok, cols = dataset.readline().split()
+ tok, rows = dataset.readline().split()
+ cols = int(cols)
+ rows = int(rows)
+ tok, lon0 = dataset.readline().split()
+ tok, lat0 = dataset.readline().split()
+ tok, dlon = dataset.readline().split()
+ lon0 = float(lon0)
+ lat0 = float(lat0)
+ dlon = float(dlon)
+ dlat = dlon
+ tok, missval = dataset.readline().split()
+
+ # construct data array and lonvec, latvec
+ data = np.zeros((rows, cols), dtype='f4')
+ lonvec = np.linspace(lon0, lon0 + cols * dlon, cols)
+ latvec = np.linspace(lat0, lat0 + rows * dlat, rows)
+
+ # data are flipped, therefore reverse latitudes
+ # trick from http://stackoverflow.com/questions/6771428/most-efficient-way-to-reverse-a-numpy-array
+ latvec = np.fliplr(np.atleast_2d((latvec)))[0]
+
+ # read actual data
+ for i, line in enumerate(dataset):
+ row = np.array([float(x) for x in line.split()], dtype='f4')
+ data[i, :] = row
+
+ # correct missing values
+ data[data == float(missval)] = np.nan
+
+ logdata = data.copy()
+ logdata[logdata <= 1.e-4] = 1.e-4
+
+ if DEBUG and PLOT_DATA:
+ plt.contourf(lonvec, latvec, np.log10(logdata))
+ plt.savefig('../plots/global_wheat_production.png')
+ plt.close()
+
+ # set metadata
+ boundingbox = [lon0, lat0, lonvec.max(), latvec.max()]
+
+ datainfo = {'size': (rows, cols), 'resolution': (np.abs(dlon), np.abs(dlat)),
+ 'boundingbox': boundingbox}
+ return lonvec, latvec, data, datainfo
+
+
+def create_dummy_data():
+ """generate some small dummy data set for testing of other services
+ This avoids loading of a large file
+ Eventually this should become obsolete when we have rasdaman running..."""
+ lonvec = np.array([4., 5., 6.])
+ latvec = np.array([52., 53., 54.])
+ data = np.array([[1., 10., 11.], [0., 2.5, 3.], [6., 7., 8.]])
+ datainfo = {'size': (3, 3), 'resolution': (1., 1.),
+ 'boundingbox': [4., 52., 6., 54.]}
+ msg = "#DEBUG: Using dummy data for wheat production"
+ if DEBUG:
+ print(msg)
+ else:
+ raise UserWarning(msg)
+ return lonvec, latvec, data, datainfo