Merge branch 'release/1.15.0'

deployment/ansible/group_vars/all

@@ -21,7 +21,7 @@ postgresql_database: mmw
 postgresql_version: "9.4"
 postgresql_package_version: "9.4.*.pgdg14.04+1"
 postgresql_support_repository_channel: "main"
-postgresql_support_libpq_version: "9.5.*.pgdg14.04+1"
+postgresql_support_libpq_version: "9.6.*.pgdg14.04+1"
 postgresql_support_psycopg2_version: "2.6"
 postgis_version: "2.1"
 postgis_package_version: "2.1.*.pgdg14.04+1"
@@ -32,7 +32,7 @@ nodejs_npm_version: 2.1.17
 
 apache_version: "2.4.7-*"
 
-java_version: "7u101-*"
+java_version: "7u111-*"
 
 graphite_carbon_version: "0.9.13-pre1"
 graphite_whisper_version: "0.9.13-pre1"

deployment/ansible/roles/model-my-watershed.spark-jobserver/templates/upstart-spark-jobserver.conf.j2

@@ -24,7 +24,7 @@ exec /usr/bin/docker run \
   --volume {{ geop_home }}:{{ geop_home }} \
   --volume {{ sjs_data_dir }}:{{ sjs_data_dir }} \
   --log-driver syslog \
-  {{ sjs_container_image }} --driver-memory {{ sjs_driver_memory }}
+  {{ sjs_container_image }} --driver-memory {{ sjs_driver_memory }} --driver-java-options "-Xss5m -Dlog4j.configuration=file:log4j.properties"
 
 post-stop script
   /usr/bin/docker kill spark-jobserver || true

scripts/aws/setupdb.sh

@@ -115,7 +115,8 @@ fi
 
 if [ "$load_mapshed" = "true" ] ; then
     # Fetch map shed specific vector features
-    FILES=("ms_weather.sql.gz" "ms_weather_station.sql.gz" "ms_pointsource.sql.gz" "ms_county_animals.sql.gz")
+    FILES=("ms_weather.sql.gz" "ms_weather_station.sql.gz" "ms_pointsource.sql.gz"
+           "ms_pointsource_drb.sql.gz" "ms_county_animals.sql.gz")
 
     download_and_load $FILES
 fi

src/mmw/apps/core/templates/base.html

@@ -38,9 +38,6 @@
     </div>
 
     <div id="sidebar">
-        <button class="btn btn-primary tab-content-toggle" type="button" title="Show/Hide results sidebar">
-            <i class="fa fa-angle-right"></i>
-        </button>
         <div id="sidebar-content"></div>
     </div>
 

src/mmw/apps/core/templates/patterns.html

@@ -3184,8 +3184,8 @@
                 </g>
             </g>
         </pattern>
-        <!-- Detention Basin aka Vegitation Infill Basin aka Infiltration Trench-->
-        <pattern id="fill-infiltration_trench" overflow="visible" patternUnits="userSpaceOnUse" width="72" height="72">
+        <!-- Detention Basin aka Vegitation Infill Basin aka Infiltration Basin-->
+        <pattern id="fill-infiltration_basin" overflow="visible" patternUnits="userSpaceOnUse" width="72" height="72">
             <polygon points="0.305,-71.606 71.911,-71.606 71.911,0 0.305,0 " transform="matrix(1.0055023,0,0,1.0055023,-0.3066782,71.999998)" style="fill:#b1c4a1" />
             <path d="M 70.301,19.757998 C 70.241,19.757998 70.171,19.726998 70.121,19.677998 70.081,19.636998 70.051,19.576998 70.051,19.507998 70.051,19.437998 70.081,19.366998 70.131,19.326998 70.211,19.236998 70.381,19.236998 70.482,19.326998 70.532,19.376998 70.551,19.436998 70.551,19.507998 70.551,19.566998 70.531,19.636998 70.482,19.677998 70.432,19.726998 70.372,19.757998 70.301,19.757998 z" style="fill:#231f20" />
             <path d="M 11.051,18.507998 C 10.982,18.507998 10.921,18.476998 10.871,18.427998 10.831,18.386998 10.801,18.316998 10.801,18.257998 10.801,18.187998 10.831,18.126998 10.871,18.076998 10.972,17.986998 11.131,17.986998 11.222,18.076998 11.282,18.126998 11.301,18.196998 11.301,18.257998 11.301,18.316998 11.272,18.386998 11.232,18.427998 11.182,18.476998 11.112,18.507998 11.051,18.507998 z" style="fill:#231f20" />

src/mmw/apps/home/views.py

@@ -135,6 +135,7 @@ def get_client_settings(request):
             'map_controls': settings.MAP_CONTROLS,
             'vizer_urls': settings.VIZER_URLS,
             'model_packages': get_model_packages(),
+            'mapshed_max_area': settings.GWLFE_CONFIG['MaxAoIArea']
         }),
         'google_maps_api_key': settings.GOOGLE_MAPS_API_KEY,
     }

src/mmw/apps/modeling/calcs.py

@@ -0,0 +1,94 @@
+# -*- coding: utf-8 -*-
+from __future__ import print_function
+from __future__ import unicode_literals
+from __future__ import absolute_import
+
+from django.contrib.gis.geos import GEOSGeometry
+
+from django.conf import settings
+from django.db import connection
+
+from apps.modeling.mapshed.calcs import (animal_energy_units,
+                                         get_point_source_table)
+
+DRB = settings.DRB_PERIMETER
+
+ANIMAL_DISPLAY_NAMES = {
+    'sheep': 'Sheep',
+    'horses': 'Horses',
+    'turkeys': 'Turkeys',
+    'layers': 'Chickens, Layers',
+    'beef_cows': 'Cows, Beef',
+    'hogs': 'Pigs/Hogs/Swine',
+    'dairy_cows': 'Cows, Dairy',
+    'broilers': 'Chickens, Broilers',
+}
+
+
+def animal_population(geojson):
+    """
+    Given a GeoJSON shape, call MapShed's `animal_energy_units` method
+    to calculate the area-weighted county animal population. Returns a
+    dictionary to append to the outgoing JSON for analysis results.
+    """
+    geom = GEOSGeometry(geojson, srid=4326)
+    aeu_for_geom = animal_energy_units(geom)[2]
+    aeu_return_values = []
+
+    for animal, aeu_value in aeu_for_geom.iteritems():
+        aeu_return_values.append({
+            'type': ANIMAL_DISPLAY_NAMES[animal],
+            'aeu': int(aeu_value),
+        })
+
+    return {
+        'displayName': 'Animals',
+        'name': 'animals',
+        'categories': aeu_return_values
+    }
+
+
+def point_source_pollution(geojson):
+    """
+    Given a GeoJSON shape, retrieve point source pollution data
+    from the `ms_pointsource` or `ms_pointsource_drb table to display
+    in the Analyze tab.
+
+    Returns a dictionary to append to the outgoing JSON for analysis
+    results.
+    """
+    geom = GEOSGeometry(geojson, srid=4326)
+    drb = geom.within(DRB)
+    table_name = get_point_source_table(drb)
+    sql = '''
+          SELECT city, state, npdes_id, mgd, kgn_yr, kgp_yr, latitude,
+                 longitude, {facilityname}
+          FROM {table_name}
+          WHERE ST_Intersects(geom, ST_SetSRID(ST_GeomFromText(%s), 4326))
+          '''.format(facilityname='facilityname' if drb else 'null',
+                     table_name=table_name)
+
+    with connection.cursor() as cursor:
+        cursor.execute(sql, [geom.wkt])
+
+        if cursor.rowcount != 0:
+            columns = [col[0] for col in cursor.description]
+            point_source_results = [
+                dict(zip(columns,
+                         [row[0], row[1], row[2],
+                          float(row[3]) if row[3] else None,
+                          float(row[4]) if row[4] else None,
+                          float(row[5]) if row[5] else None,
+                          float(row[6]) if row[6] else None,
+                          float(row[7]) if row[7] else None,
+                          row[8]]))
+                for row in cursor.fetchall()
+            ]
+        else:
+            point_source_results = []
+
+    return {
+        'displayName': 'Point Source',
+        'name': 'pointsource',
+        'categories': point_source_results
+    }