def get_db_session():
dataset = get_dataset_name()
if dataset == None:
return get_session()
else:
return get_session(database_config=("database_%s" % dataset))
def process_database(commit=False):
session = get_session()
# This speeds up the t2_survey_site spatial query by a factor of about 6
session.execute("SET SESSION TRANSACTION ISOLATION LEVEL READ UNCOMMITTED")
if not (is_empty(session, "t2_processed_survey")
and is_empty(session, "t2_survey_site")):
log.error(
"Existing outputs found - please drop and recreate these tables first: t2_processed_sighting, t2_processed_survey, t2_survey_site"
)
log.info("""Run:
SET FOREIGN_KEY_CHECKS = 0;
TRUNCATE t2_processed_sighting;
TRUNCATE t2_processed_survey;
TRUNCATE t2_survey_site;
SET FOREIGN_KEY_CHECKS = 1;
""")
return
process_sites(session)
process_grid(session)
if commit:
log.info("Committing changes")
session.commit()
else:
log.info("Rolling back changes (dry-run only)")
session.rollback()
def main():
logging.basicConfig(
stream=sys.stdout,
level=logging.INFO,
format='%(asctime)-15s %(name)s %(levelname)-8s %(message)s')
parser = argparse.ArgumentParser(
description='Import grid cells from shapefile')
parser.add_argument('filename',
type=str,
help='Shapefile containing grid cells')
args = parser.parse_args()
session = get_session()
with open_shapefile(args.filename, 'EPSG:4326') as (shp, reproject):
for feature in tqdm(shp):
props = feature['properties']
geometry = reproject(shape(feature['geometry']))
session.execute(
"""INSERT INTO grid_cell (id, geometry)
VALUES (:search_type_id, ST_GeomFromWKB(_BINARY :geometry_wkb))""", {
'search_type_id': props['GridID'],
'geometry_wkb': shapely.wkb.dumps(geometry)
})
session.commit()
def export_to_sqlite(source_id, path):
session = get_session()
dest_db = init_sqlite(path)
# tables = [table for (table,) in session.execute("show tables").fetchall()]
tables = [
("t1_survey", "source_id = :source_id"),
("t1_site", "source_id = :source_id"),
("t1_sighting",
"survey_id IN (SELECT id FROM t1_survey WHERE source_id = :source_id)"
),
("taxon",
"id IN (SELECT taxon_id FROM t1_sighting, t1_survey WHERE survey_id = t1_survey.id AND source_id = :source_id)"
), ("aggregated_by_month", "FALSE"), ("aggregated_by_year", "FALSE"),
("taxon_group", None), ("taxon_status", None), ("search_type", None),
("source", "id = :source_id"), ("t2_site", "FALSE"),
("data_source", "source_id = :source_id"), ("region", "FALSE"),
("unit", None), ("taxon_source_alpha_hull", "FALSE"),
("intensive_management", None), ("experimental_design_type", None),
("response_variable_type", None), ("monitoring_program", None)
]
for table, where_clause in tables:
# print(table)
copy_table_schema(session, table, dest_db)
copy_table_data(session, table, dest_db, where_clause,
{'source_id': source_id})
return path
def process_database(species = None, commit = False):
session = get_session()
if species == None:
taxa = [taxon_id for (taxon_id,) in session.execute("SELECT DISTINCT taxon_id FROM processing_method WHERE data_type = 2").fetchall()]
else:
taxa = [taxon_id for (taxon_id,) in session.execute(
"SELECT DISTINCT taxon_id FROM processing_method, taxon WHERE taxon.id = taxon_id AND data_type = 2 AND spno IN %s" % sql_list_placeholder('species', species),
sql_list_argument('species', species)
).fetchall()]
create_region_lookup_table(session)
log.info("Step 1/2: Monthly aggregation")
fn = functools.partial(aggregate_by_month, commit = commit)
for result, error in tqdm(run_parallel(fn, taxa), total = len(taxa)):
pass
log.info("Step 2/2: Yearly aggregation")
fn = functools.partial(aggregate_by_year, commit = commit)
for result, error in tqdm(run_parallel(fn, taxa), total = len(taxa)):
pass
cleanup_region_lookup_table(session)
def process_database(species=None, commit=False):
session = get_session()
if species == None:
taxa = [
taxon_id for (taxon_id, ) in session.execute(
"SELECT DISTINCT taxon_id FROM t1_sighting").fetchall()
]
else:
taxa = [
taxon_id for (taxon_id, ) in session.execute(
"SELECT DISTINCT taxon_id FROM t1_sighting, taxon WHERE taxon.id = taxon_id AND spno IN (%s)"
% sql_list_placeholder('species', species),
sql_list_argument('species', species)).fetchall()
]
create_region_lookup_table(session)
# Process in parallel
tasks = [(taxon_id, commit) for taxon_id in taxa]
log.info("Step 1/2: Monthly aggregation")
for result, error in tqdm(run_parallel(aggregate_monthly, tasks),
total=len(tasks)):
if error:
print error
log.info("Step 1/2: Yearly aggregation")
for result, error in tqdm(run_parallel(aggregate_yearly, tasks),
total=len(tasks)):
if error:
print error
cleanup_region_lookup_table(session)
def main():
logging.basicConfig(
stream=sys.stdout,
level=logging.INFO,
format='%(asctime)-15s %(name)s %(levelname)-8s %(message)s')
parser = argparse.ArgumentParser(
description='Import species range polygons into TSX database')
parser.add_argument('dir',
type=str,
help='Directory containing species range shapefiles')
args = parser.parse_args()
session = get_session()
filenames = [f for f in os.listdir(args.dir) if f.endswith('.shp')]
for filename in tqdm(filenames):
spno = int(filename[0:-4])
try:
# https://pyproj4.github.io/pyproj/stable/crs_compatibility.html#fiona
with fiona.Env(OSR_WKT_FORMAT="WKT2_2018"), fiona.open(
os.path.join(args.dir,
filename), encoding='Windows-1252') as shp:
process_shp(session, spno, shp)
except KeyboardInterrupt:
log.info("Aborting - no changes saved")
return
session.commit()
def process_database(species=None, commit=False):
session = get_session()
# Just get taxa that have range polygons
if species == None:
taxa = session.execute(
"SELECT DISTINCT taxon_id FROM taxon_range").fetchall()
else:
taxa = session.execute(
"SELECT DISTINCT taxon_id FROM taxon_range, taxon WHERE taxon_id = taxon.id AND spno IN (%s)"
% sql_list_placeholder('species', species),
sql_list_argument('species', species)).fetchall()
# Unwrap tuple
taxa = [taxon_id for (taxon_id, ) in taxa]
# Delete old results
if commit and len(taxa) > 0:
# session.execute("DELETE FROM t2_ultrataxon_sighting WHERE taxon_id IN (:taxa)", { 'taxa': taxa })
session.execute(
"DELETE FROM t2_ultrataxon_sighting WHERE taxon_id IN (%s)" %
sql_list_placeholder('taxa', taxa),
sql_list_argument('taxa', taxa))
session.commit()
# Process in parallel
tasks = [(taxon_id, commit) for taxon_id in taxa]
# This is important because we are about to spawn child processes, and this stops them attempting to share the
# same database connection pool
session.close()
for result, error in tqdm(run_parallel(process_taxon, tasks),
total=len(tasks)):
if error:
print(error)
def get_subibra():
session = get_session()
rows = session.execute("""SELECT id, name, state FROM region""").fetchall()
region_info = []
for id, name, state in rows:
region_info.append({'id': int(id), 'name': name, 'state': state})
session.close()
return jsonify(region_info)
def get_status():
session = get_session()
rows = session.execute(
"""SELECT id, description FROM taxon_status""").fetchall()
res_info = []
for id, description in rows:
res_info.append({'id': int(id), 'description': description})
session.close()
return jsonify(res_info)
def get_responsevariable():
session = get_session()
rows = session.execute(
"""SELECT id, description FROM response_variable_type""").fetchall()
res_info = []
for id, description in rows:
res_info.append({'id': int(id), 'description': description})
session.close()
return jsonify(res_info)
def get_searchtype():
session = get_session()
rows = session.execute(
"""SELECT id, description FROM search_type""").fetchall()
searchtype_info = []
for searchtype_id, name in rows:
searchtype_info.append({'id': int(searchtype_id), 'name': name})
session.close()
return jsonify(searchtype_info)
def process_database(species=None, commit=False):
"""
Calculates spatial representativeness using alpha hulls
Generates alpha hulls from each source x taxon combination
Intersects alpha hulls with range layers, and then calculates percentage of range covered
"""
session = get_session()
if commit:
if species == None:
session.execute("DELETE FROM taxon_source_alpha_hull")
else:
session.execute(
"""DELETE FROM taxon_source_alpha_hull
WHERE taxon_id IN (SELECT id FROM taxon WHERE spno IN (%s))"""
% sql_list_placeholder('species', species),
sql_list_argument('species', species))
session.commit()
# Load coastal shapefile
coastal_shape_filename = tsx.config.config.get("processing.alpha_hull",
"coastal_shp")
with fiona.Env(OSR_WKT_FORMAT="WKT2_2018"), fiona.open(
coastal_shape_filename, 'r') as coastal_shape:
# Convert from fiona dictionary to shapely geometry and reproject
shp_to_working_transformer = pyproj.Transformer.from_proj(
pyproj.CRS.from_wkt(coastal_shape.crs_wkt),
working_proj,
always_xy=True)
coastal_shape = reproject(shape(coastal_shape[0]['geometry']),
shp_to_working_transformer)
# Simplify coastal boundary - makes things run ~20X faster
log.info("Simplifying coastal boundary")
coastal_shape = coastal_shape.buffer(10000).simplify(10000)
log.info("Generating alpha shapes")
for data_type in 1, 2:
log.info("Processing type %s data" % data_type)
taxa = get_taxa(session, data_type, species)
tasks = [(taxon_id, coastal_shape, data_type, commit)
for taxon_id in taxa]
# This is important because we are about to spawn child processes, and this stops them attempting to share the
# same database connection pool
session.close() # TODO: not sure if this is needed now
# Process all the species in parallel
for result, error in tqdm(run_parallel(process, tasks),
total=len(tasks)):
if error:
print(error)
def get_species():
session = get_session()
rows = session.execute(
"""SELECT spno, common_name FROM taxon ORDER BY common_name"""
).fetchall()
species_info = []
for spno, common_name in rows:
species_info.append({'spno': int(spno), 'common_name': common_name})
session.close()
return jsonify(species_info)
def main():
logging.basicConfig(
stream=sys.stdout,
level=logging.INFO,
format='%(asctime)-15s %(name)s %(levelname)-8s %(message)s')
parser = argparse.ArgumentParser(
description='Import regions from shapefile')
parser.add_argument('filename',
type=str,
help='Shapefile containing regions')
args = parser.parse_args()
session = get_session()
session.execute("DELETE FROM t1_survey_region")
with fiona.open(args.filename, encoding='Windows-1252') as shp:
for index, feature in enumerate(tqdm(shp)):
props = feature['properties']
geometry = shape(
transform_geom(shp.crs, 'EPSG:4326', feature['geometry']))
geometry = geometry.buffer(0)
session.execute(
"""INSERT INTO region (id, name, geometry, state, positional_accuracy_in_m)
VALUES (:id, :name, ST_GeomFromWKB(_BINARY :geometry_wkb), :state, :positional_accuracy_in_m)""",
{
'id': index,
'name': props['RegName'],
'geometry_wkb': shapely.wkb.dumps(
to_multipolygon(geometry)),
'state': props['StateName'],
'positional_accuracy_in_m': int(props['Accuracy'])
})
for geometry in subdivide_geometry(geometry):
session.execute(
"""INSERT INTO region_subdiv (id, name, geometry)
VALUES (:id, :name, ST_GeomFromWKB(_BINARY :geometry_wkb))""", {
'id':
index,
'name':
props['RegName'],
'geometry_wkb':
shapely.wkb.dumps(to_multipolygon(geometry))
})
log.info("Updating t1_survey_region (this may take a while)")
session.execute("CALL update_t1_survey_region(NULL)")
session.commit()
def process_database():
session = get_session()
log.info("Step 1/2 - Building list of filtered time series")
# Get year range
min_year = tsx.config.config.getint("processing", "min_year")
max_year = tsx.config.config.getint("processing", "max_year")
min_tssy = tsx.config.config.getint("processing",
"min_time_series_sample_years")
session.execute(
"""CREATE TEMPORARY TABLE tmp_filtered_ts
( INDEX (time_series_id) )
SELECT time_series_id
FROM aggregated_by_year agg
INNER JOIN taxon ON agg.taxon_id = taxon.id
LEFT JOIN data_source ON data_source.taxon_id = agg.taxon_id AND data_source.source_id = agg.source_id
WHERE agg.start_date_y <= COALESCE(data_source.end_year, :max_year)
AND agg.start_date_y >= COALESCE(data_source.start_year, :min_year)
AND NOT data_source.exclude_from_analysis
AND COALESCE(agg.search_type_id, 0) != 6
AND COALESCE(taxon.max_status_id, 0) NOT IN (0,1,7)
AND region_id IS NOT NULL
AND COALESCE(data_source.data_agreement_id, -1) NOT IN (0)
AND COALESCE(data_source.standardisation_of_method_effort_id, -1) NOT IN (0, 1)
AND COALESCE(data_source.consistency_of_monitoring_id, -1) NOT IN (0, 1)
AND experimental_design_type_id = 1
GROUP BY agg.time_series_id
HAVING MAX(value) > 0
AND COUNT(DISTINCT start_date_y) >= :min_tssy;
""", {
'min_year': min_year,
'max_year': max_year,
'min_tssy': min_tssy
})
log.info("Step 2/2 - Updating aggregated_by_year table")
session.execute(
"""UPDATE aggregated_by_year agg
LEFT JOIN data_source ON data_source.taxon_id = agg.taxon_id AND data_source.source_id = agg.source_id
SET agg.include_in_analysis =
agg.time_series_id IN (SELECT time_series_id FROM tmp_filtered_ts)
AND agg.start_date_y <= COALESCE(data_source.end_year, :max_year)
AND agg.start_date_y >= COALESCE(data_source.start_year, :min_year)
""", {
'min_year': min_year,
'max_year': max_year
})
session.execute("""DROP TABLE tmp_filtered_ts""")
log.info("Done")
def main():
logging.basicConfig(
stream=sys.stdout,
level=logging.INFO,
format='%(asctime)-15s %(name)s %(levelname)-8s %(message)s')
parser = argparse.ArgumentParser(description='Import incidental sightings')
parser.add_argument('filename',
type=str,
help='Incidental sightings file (CSV)')
args = parser.parse_args()
session = get_session()
spno_map = {
spno: taxon_id
for spno, taxon_id in session.execute("""SELECT spno, taxon.id
FROM taxon, taxon_level
WHERE taxon_level.id = taxon_level_id
AND taxon_level.description = 'sp'""").fetchall()
}
session.execute("""DELETE FROM incidental_sighting""")
with open(args.filename) as f:
reader = csv.DictReader(f)
row_count = sum(1 for row in reader)
with open(args.filename) as f:
reader = csv.DictReader(f)
rows = []
def flush_rows():
session.execute(
"INSERT INTO incidental_sighting (taxon_id, coords) VALUES (:taxon_id, ST_GeomFromWKB(_BINARY :coords))",
rows)
del rows[:]
for row in tqdm(reader, total=row_count):
spno = int(row['SpNo'])
x = float(row['Longitude'])
y = float(row['Latitude'])
if spno in spno_map:
rows.append({
'taxon_id': spno_map[spno],
'coords': shapely.wkb.dumps(Point(x, y))
})
if len(rows) > 4000:
flush_rows()
flush_rows()
session.commit()
def test_db():
"""
Test the db conneciton
"""
print("Testing DB Connection")
print()
session = get_session()
# list all units
for u in session.query(Unit).all():
print("%d: %s" % (u.id, u.description))
print()
print("DB Connection successful")
def process_database(commit = False):
session = get_session()
if not (is_empty(session, "t2_processed_survey") and is_empty(session, "t2_survey_site")):
log.error("Existing outputs found - please drop and recreate these tables first: t2_processed_sighting, t2_processed_survey, t2_survey_site")
return
process_sites(session)
process_grid(session)
if commit:
log.info("Committing changes")
session.commit()
else:
log.info("Rolling back changes (dry-run only)")
session.rollback()
def get_taxon_sites(taxon_id):
session = get_session()
try:
return session.execute("""SELECT DISTINCT t2_survey_site.site_id, taxon_id
FROM taxon_presence_alpha_hull_subdiv alpha STRAIGHT_JOIN t2_survey USE INDEX (coords), t2_survey_site
WHERE ST_Contains(alpha.geometry, t2_survey.coords)
AND t2_survey_site.survey_id = t2_survey.id
AND taxon_id = :taxon_id
AND alpha.range_id = 1""", {
'taxon_id': taxon_id
}).fetchall()
except:
log.exception("Exception getting sites for taxon")
raise
finally:
session.close()
def clear_database():
# Clears out all derived data from the database
session = get_session()
statements = [
"SET FOREIGN_KEY_CHECKS = 0;", "TRUNCATE taxon_presence_alpha_hull;",
"TRUNCATE taxon_presence_alpha_hull_subdiv;",
"TRUNCATE t2_ultrataxon_sighting;", "TRUNCATE t2_processed_survey;",
"TRUNCATE t2_processed_sighting;", "TRUNCATE t2_survey_site;",
"TRUNCATE aggregated_by_year;", "TRUNCATE aggregated_by_month;",
"SET FOREIGN_KEY_CHECKS = 1;"
]
for sql in tqdm(statements):
session.execute(sql)
session.commit()
def get_source():
session = get_session()
rows = session.execute(
"""SELECT id, source_type_id, provider, description FROM source ORDER BY description"""
).fetchall()
res_info = []
for id, source_type_id, provider, description in rows:
res_info.append({
'id':
int(id),
'source_type_id':
None if source_type_id == None else int(source_type_id),
'provider':
provider,
'description':
description
})
session.close()
return jsonify(res_info)
def main():
logging.basicConfig(stream=sys.stdout, level=logging.INFO, format='%(asctime)-15s %(name)s %(levelname)-8s %(message)s')
parser = argparse.ArgumentParser(description='Import Type 2 sites from shapefile')
parser.add_argument('filename', type=str, help='Shapefile containing type 2 site polygons')
args = parser.parse_args()
session = get_session()
with fiona.open(args.filename, encoding = 'Windows-1252') as shp:
reproject = reproject_fn(pyproj.Proj(shp.crs), pyproj.Proj('+init=EPSG:4326'))
for feature in tqdm(shp):
props = feature['properties']
geometry = to_multipolygon(reproject(shape(feature['geometry'])))
session.execute("""INSERT INTO t2_site (search_type_id, geometry)
VALUES (:search_type_id, ST_GeomFromWKB(_BINARY :geometry_wkb))""", {
'search_type_id': props['SiteType'],
'geometry_wkb': shapely.wkb.dumps(geometry)
})
session.commit()
def get_intensity():
filtered_data = get_filtered_data()
if len(filtered_data) == 0:
return json.dumps([])
dat = filtered_data.to_dict()
source = request.args.get('source', type=str)
if source == 'lpi_wide_table':
lats = dat['SurveysCentroidLatitude']
longs = dat['SurveysCentroidLongitude']
counts = dat['SurveyCount']
ids = lats.keys()
return json.dumps([[lats[id], longs[id], counts[id]] for id in ids])
else: # get it from database
ids = dat['TimeSeriesID'].values()
session = get_session()
result = session.execute(
"""SELECT time_series_id, start_date_y as Year, ST_X(centroid_coords) as Latitude,
ST_Y(centroid_coords) as Longitude, SUM(survey_count) as Count
FROM aggregated_by_year
WHERE include_in_analysis
AND time_series_id in %s
GROUP BY time_series_id, Year, Latitude, Longitude""" % str(tuple(ids)))
values = pd.DataFrame.from_records(data=result.fetchall(),
columns=result.keys()).to_dict()
session.close()
# years = values['Year']
lats = values['Latitude']
longs = values['Longitude']
counts = values['Count']
years = values['Year']
timeSeriesIDs = {id: [] for id in ids}
results = {}
for k, v in values['time_series_id'].iteritems():
timeSeriesIDs[v].append(k)
return json.dumps([[
lats[v[0]], longs[v[0]], [[years[i], int(counts[i])] for i in v]
] for k, v in timeSeriesIDs.iteritems() if len(v) > 0])
def process_spno(spno):
session = get_session()
try:
# Get raw points from DB
raw_points = get_species_points(session, spno)
if len(raw_points) < 4:
# Not enough points to create an alpha hull
return
# Read points from database
points = [reproject(p, db_proj, working_proj) for p in raw_points]
# Generate alpha shape
alpha_shp = make_alpha_hull(
points = points,
coastal_shape = coastal_shape,
thinning_distance = tsx.config.config.getfloat('processing.alpha_hull', 'thinning_distance'),
alpha = tsx.config.config.getfloat('processing.alpha_hull', 'alpha'),
hullbuffer_distance = tsx.config.config.getfloat('processing.alpha_hull', 'hullbuffer_distance'),
isolatedbuffer_distance = tsx.config.config.getfloat('processing.alpha_hull', 'isolatedbuffer_distance'))
# Convert back to DB projection
alpha_shp = reproject(alpha_shp, working_proj, db_proj)
# Clean up geometry
alpha_shp = alpha_shp.buffer(0)
# Get range polygons to intersect with alpha shape
for taxon_id, range_id, breeding_range_id, geom_wkb in get_species_range_polygons(session, spno):
# Intersect and insert into DB
geom = shapely.wkb.loads(binascii.unhexlify(geom_wkb)).buffer(0)
geom = to_multipolygon(geom.intersection(alpha_shp)) # slow
if len(geom) > 0:
session.execute("""INSERT INTO taxon_presence_alpha_hull (taxon_id, range_id, breeding_range_id, geometry)
VALUES (:taxon_id, :range_id, :breeding_range_id, ST_GeomFromWKB(_BINARY :geom_wkb))""", {
'taxon_id': taxon_id,
'range_id': range_id,
'breeding_range_id': breeding_range_id,
'geom_wkb': shapely.wkb.dumps(geom)
}
)
# We also subdivide the geometries into small pieces and insert this into the database. This allows for much faster
# spatial queries in the database.
for subgeom in subdivide_geometry(geom, max_points = 100):
session.execute("""INSERT INTO taxon_presence_alpha_hull_subdiv (taxon_id, range_id, breeding_range_id, geometry)
VALUES (:taxon_id, :range_id, :breeding_range_id, ST_GeomFromWKB(_BINARY :geom_wkb))""", {
'taxon_id': taxon_id,
'range_id': range_id,
'breeding_range_id': breeding_range_id,
'geom_wkb': shapely.wkb.dumps(subgeom)
}
)
if commit:
session.commit()
except:
log.exception("Exception processing alpha hull")
raise
finally:
session.close()
def process_database(species = None, commit = False):
"""
Generates alpha hulls from raw sighting data in the database
Intersects alpha hulls with range layers, and inserts the result back into the database
"""
session = get_session()
if species is None:
species = get_all_spno(session)
if commit:
for spno in species:
session.execute("""
DELETE FROM taxon_presence_alpha_hull
WHERE taxon_id IN (SELECT id FROM taxon WHERE spno = :spno)
""", { 'spno': spno })
session.execute("""
DELETE FROM taxon_presence_alpha_hull_subdiv
WHERE taxon_id IN (SELECT id FROM taxon WHERE spno = :spno)
""", { 'spno': spno })
session.commit()
db_proj = pyproj.Proj('+init=EPSG:4326') # Database always uses WGS84
working_proj = pyproj.Proj('+init=EPSG:3112') # GDA94 / Geoscience Australia Lambert - so that we can buffer in metres
# Load coastal shapefile
coastal_shape_filename = tsx.config.config.get("processing.alpha_hull", "coastal_shp")
with fiona.open(coastal_shape_filename, 'r') as coastal_shape:
# Convert from fiona dictionary to shapely geometry and reproject
coastal_shape = reproject(shape(coastal_shape[0]['geometry']), pyproj.Proj(coastal_shape.crs), working_proj)
# Simplify coastal boundary - makes things run ~20X faster
log.info("Simplifying coastal boundary")
coastal_shape = coastal_shape.buffer(10000).simplify(10000)
log.info("Generating alpha shapes")
# Process a single species.
# This gets run off the main thread.
def process_spno(spno):
session = get_session()
try:
# Get raw points from DB
raw_points = get_species_points(session, spno)
if len(raw_points) < 4:
# Not enough points to create an alpha hull
return
# Read points from database
points = [reproject(p, db_proj, working_proj) for p in raw_points]
# Generate alpha shape
alpha_shp = make_alpha_hull(
points = points,
coastal_shape = coastal_shape,
thinning_distance = tsx.config.config.getfloat('processing.alpha_hull', 'thinning_distance'),
alpha = tsx.config.config.getfloat('processing.alpha_hull', 'alpha'),
hullbuffer_distance = tsx.config.config.getfloat('processing.alpha_hull', 'hullbuffer_distance'),
isolatedbuffer_distance = tsx.config.config.getfloat('processing.alpha_hull', 'isolatedbuffer_distance'))
# Convert back to DB projection
alpha_shp = reproject(alpha_shp, working_proj, db_proj)
# Clean up geometry
alpha_shp = alpha_shp.buffer(0)
# Get range polygons to intersect with alpha shape
for taxon_id, range_id, breeding_range_id, geom_wkb in get_species_range_polygons(session, spno):
# Intersect and insert into DB
geom = shapely.wkb.loads(binascii.unhexlify(geom_wkb)).buffer(0)
geom = to_multipolygon(geom.intersection(alpha_shp)) # slow
if len(geom) > 0:
session.execute("""INSERT INTO taxon_presence_alpha_hull (taxon_id, range_id, breeding_range_id, geometry)
VALUES (:taxon_id, :range_id, :breeding_range_id, ST_GeomFromWKB(_BINARY :geom_wkb))""", {
'taxon_id': taxon_id,
'range_id': range_id,
'breeding_range_id': breeding_range_id,
'geom_wkb': shapely.wkb.dumps(geom)
}
)
# We also subdivide the geometries into small pieces and insert this into the database. This allows for much faster
# spatial queries in the database.
for subgeom in subdivide_geometry(geom, max_points = 100):
session.execute("""INSERT INTO taxon_presence_alpha_hull_subdiv (taxon_id, range_id, breeding_range_id, geometry)
VALUES (:taxon_id, :range_id, :breeding_range_id, ST_GeomFromWKB(_BINARY :geom_wkb))""", {
'taxon_id': taxon_id,
'range_id': range_id,
'breeding_range_id': breeding_range_id,
'geom_wkb': shapely.wkb.dumps(subgeom)
}
)
if commit:
session.commit()
except:
log.exception("Exception processing alpha hull")
raise
finally:
session.close()
# This is important because we are about to spawn child processes, and this stops them attempting to share the
# same database connection pool
session.close()
tsx.db.connect.engine.dispose()
# Process all the species in parallel
for result, error in tqdm(run_parallel(process_spno, species, use_processes = True), total = len(species)):
if error:
print error
def process(taxon_id, coastal_shape, data_type, commit):
session = get_session()
try:
# Load core range geometry
core_range_geom = reproject(
get_core_range_geometry(session, taxon_id),
to_working_transformer).buffer(0).intersection(coastal_shape)
for source_id in get_source_ids(session, data_type, taxon_id):
log.info("Processing taxon_id: %s, source_id: %s" %
(taxon_id, source_id))
# Get raw points from DB
raw_points = get_raw_points(session, data_type, taxon_id,
source_id)
empty = len(raw_points) < 4
if empty:
log.info(
"Taxon %s: not enough points to create alpha hull (%s)" %
(taxon_id, len(raw_points)))
if not empty:
# Read points from database
points = [
reproject(p, to_working_transformer) for p in raw_points
]
# Generate alpha shape
alpha_shp = make_alpha_hull(
points=points,
coastal_shape=None,
thinning_distance=tsx.config.config.getfloat(
'processing.alpha_hull', 'thinning_distance'),
alpha=tsx.config.config.getfloat('processing.alpha_hull',
'alpha'),
hullbuffer_distance=tsx.config.config.getfloat(
'processing.alpha_hull', 'hullbuffer_distance'),
isolatedbuffer_distance=tsx.config.config.getfloat(
'processing.alpha_hull', 'isolatedbuffer_distance'))
# Clean up geometry
alpha_shp = alpha_shp.buffer(0)
if core_range_geom.area == 0:
log.info("Core range geometry area is zero")
empty = True
else:
# Intersect alpha hull with core range
intersected_alpha = to_multipolygon(
core_range_geom.intersection(alpha_shp))
empty = intersected_alpha.is_empty
if empty:
session.execute(
"""INSERT INTO taxon_source_alpha_hull (source_id, taxon_id, data_type, core_range_area_in_m2, alpha_hull_area_in_m2)
VALUES (:source_id, :taxon_id, :data_type, 0, 0)""", {
'source_id': source_id,
'taxon_id': taxon_id,
'data_type': data_type
})
else:
session.execute(
"""INSERT INTO taxon_source_alpha_hull (source_id, taxon_id, data_type, geometry, core_range_area_in_m2, alpha_hull_area_in_m2)
VALUES (:source_id, :taxon_id, :data_type, ST_GeomFromWKB(_BINARY :geom_wkb), :core_range_area, :alpha_hull_area)""",
{
'source_id':
source_id,
'taxon_id':
taxon_id,
'data_type':
data_type,
'geom_wkb':
shapely.wkb.dumps(
reproject(intersected_alpha, to_db_transformer)),
'core_range_area':
core_range_geom.area,
'alpha_hull_area':
intersected_alpha.area
})
if commit:
session.commit()
except:
log.exception("Exception processing alpha hull")
raise
finally:
session.close()
def process_taxon(taxon_id, commit):
try:
session = get_session()
taxon = session.query(Taxon).get(taxon_id)
for range_id, breeding_range_id, geom_wkb in get_taxon_range_polygons(
session, taxon.id):
if not taxon.ultrataxon:
log.info("Skipping range polygon for non-ultrataxon: %s" %
taxon.id)
continue
# tqdm.write("Taxon: %s, range: %s" % (taxon.id, range_id))
cache = {}
geom = shapely.wkb.loads(binascii.unhexlify(geom_wkb)).buffer(
0) # Ensure valid
bounds_wkb = shapely.wkb.dumps(Polygon.from_bounds(*geom.bounds))
# Get all sightings matching that taxon
q = session.execute(
"""SELECT t2_sighting.id, ST_X(coords), ST_Y(coords)
FROM t2_sighting, t2_survey
WHERE t2_sighting.survey_id = t2_survey.id
AND t2_sighting.taxon_id = :taxon_id
AND t2_sighting.taxon_id NOT IN (SELECT taxon_id FROM t2_ultrataxon_sighting WHERE sighting_id = t2_sighting.id)
AND MBRContains(ST_GeomFromWKB(_BINARY :bounds_wkb), coords) """,
{
'taxon_id': taxon.id,
'bounds_wkb': bounds_wkb
})
records = []
for sighting_id, x, y in q.fetchall():
if point_intersects_geom(geom, x, y, cache):
records.append(
T2UltrataxonSighting(sighting_id=sighting_id,
taxon_id=taxon.id,
range_id=range_id,
generated_subspecies=False))
if taxon.taxon_level.description == 'ssp':
# Get parent taxa (species) sightings
q = session.execute(
"""SELECT t2_sighting.id, ST_X(coords), ST_Y(coords)
FROM t2_sighting, t2_survey, taxon taxon_ssp, taxon taxon_sp
WHERE t2_sighting.survey_id = t2_survey.id
AND t2_sighting.taxon_id = taxon_sp.id
AND taxon_ssp.id NOT IN (SELECT taxon_id FROM t2_ultrataxon_sighting WHERE sighting_id = t2_sighting.id)
AND taxon_sp.taxon_level_id = (SELECT id FROM taxon_level WHERE description = 'sp')
AND taxon_sp.spno = taxon_ssp.spno
AND taxon_ssp.id = :taxon_id
AND MBRContains(ST_GeomFromWKB(_BINARY :bounds_wkb), coords) """,
{
'taxon_id': taxon.id,
'bounds_wkb': bounds_wkb
})
for sighting_id, x, y in q.fetchall():
if point_intersects_geom(geom, x, y, cache):
records.append(
T2UltrataxonSighting(sighting_id=sighting_id,
taxon_id=taxon.id,
range_id=range_id,
generated_subspecies=True))
session.bulk_save_objects(records)
if commit:
session.commit()
except Exception as e:
log.exception('Exception in range and ultrataxon processing')
raise
finally:
session.close()
def build_filter_string():
filter_str = ""
#spno
filters = []
if request.args.has_key('spno'):
_sp_no = request.args.get('spno', type=int)
filters.append("SpNo=='%d'" % (_sp_no))
if request.args.has_key('datatype'):
_sp_no = request.args.get('datatype', type=int)
filters.append("DataType=='%d'" % (_sp_no))
#state
if request.args.has_key('state'):
_stateList = request.args.get('state', type=str).split('+')
filters.append("(%s)" %
" or ".join(["State=='%s'" % s for s in _stateList]))
#searchtypedesc
if request.args.has_key('searchtype'):
_search_type = request.args.get('searchtype', type=int)
# find in database
session = get_session()
_search_type_desc = session.execute(
"""SELECT * FROM search_type WHERE id = :searchtypeid""", {
'searchtypeid': _search_type
}).fetchone()['description']
filters.append("SearchTypeDesc=='%s'" % (_search_type_desc))
session.close()
#subibra
if request.args.has_key('subibra'):
_subibra = request.args.get('subibra', type=str)
filters.append("SubIBRA=='%s'" % (_subibra))
#sourceid
if request.args.has_key('sourceid'):
_sourceid = request.args.get('sourceid', type=int)
filters.append("SourceID=='%d'" % (_sourceid))
# Functional group
if request.args.has_key('group'):
_group = request.args.get('group', type=str)
filters.append("FunctionalGroup=='%s'" % (_group))
# functional subgroup
if request.args.has_key('subgroup'):
_subgroup = request.args.get('subgroup', type=str)
filters.append("FunctionalSubGroup=='%s'" % (_subgroup))
# status/statusauth
if request.args.has_key('status') and request.args.has_key(
'statusauth'): #IUCN, EPBC, BirdLifeAustralia, Max
_statusList = request.args.get('status', type=str).split('+')
_statusauth = request.args.get('statusauth', type=str)
filters.append("(%s)" % " or ".join(
["%sStatus=='%s'" % (_statusauth, s) for s in _statusList]))
# national priority
if request.args.has_key('priority'):
filters.append("NationalPriorityTaxa=='%d'" %
(request.args.get('priority', type=int)))
if len(filters) > 0:
return " and ".join(filters)
else:
return None
def get_stats(filtered_data):
df = filtered_data
years = [col for col in df.columns if col.isdigit()]
int_years = [int(year) for year in years]
year_df = df.loc[:, years] * 0 + int_years
# Time series length
ts_length = df[
'TimeSeriesLength'] # year_df.max(axis = 1) - year_df.min(axis = 1) + 1
# Time series sample years
ts_years = df['TimeSeriesSampleYears'] # (year_df * 0 + 1).sum(axis = 1)
n_sources = df['SourceDesc'].nunique()
n_taxa = df['TaxonID'].nunique()
grouped_by_taxon = df.groupby('TaxonID').agg({
'TimeSeriesLength':
np.mean,
'SourceDesc':
lambda x: x.nunique(),
'TimeSeriesID':
lambda x: x.nunique(),
'SpatialRepresentativeness':
np.mean
}).rename(
columns={
'TimeSeriesLength': 'ts_length_mean',
'SourceDesc': 'num_sources',
'TimeSeriesID': 'num_ts',
'SpatialRepresentativeness': 'spatial_rep'
})
session = get_session()
result = session.execute(
"""SELECT
id AS 'TaxonID',
common_name,
scientific_name,
bird_group,
bird_sub_group,
(SELECT description FROM taxon_status WHERE taxon_status.id = aust_status_id) AS aust_status,
(SELECT description FROM taxon_status WHERE taxon_status.id = epbc_status_id) AS epbc_status
FROM taxon
WHERE GREATEST(COALESCE(aust_status_id, 0), COALESCE(epbc_status_id, 0), COALESCE(iucn_status_id, 0)) NOT IN (0,1,7)
AND (ultrataxon OR taxon.id IN :taxon_ids)""",
{'taxon_ids': list(df['TaxonID'].unique())})
session.close()
all_taxa = pd.DataFrame.from_records(data=result.fetchall(),
index='TaxonID',
columns=result.keys())
joined = all_taxa.join(grouped_by_taxon, how='outer').sort_values(
['bird_group', 'bird_sub_group', 'common_name'], na_position='first')
joined = joined.reset_index().rename(columns={'TaxonID': 'taxon_id'})
joined['spatial_rep'] *= 100
taxa_with_data = joined.query('num_ts > 0')
taxa_without_data = joined.query('num_ts != num_ts').drop(
columns=['ts_length_mean', 'num_sources', 'num_ts', 'spatial_rep'])
return {
'num_sources': df['SourceDesc'].nunique(),
'num_taxa': df['TaxonID'].nunique(),
'ts_length': {
'mean': ts_length.mean(),
'stddev': ts_length.std()
},
'ts_years': {
'mean': ts_years.mean(),
'stddev': ts_years.std()
},
'taxa_with_data': taxa_with_data.to_dict(orient='records'),
'taxa_without_data': taxa_without_data.to_dict(orient='records')
}