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cv_analysis.py
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"""Global CV Analysis."""
import argparse
import bisect
import collections
import datetime
import gzip
import hashlib
import logging
import math
import multiprocessing
import os
import shutil
import tempfile
import threading
from ecoshard import geoprocessing
from ecoshard import taskgraph
from osgeo import gdal
from osgeo import ogr
from osgeo import osr
import ecoshard
import numpy
import retrying
import rtree
import shapely.geometry
import shapely.strtree
import shapely.wkt
gdal.SetCacheMax(2**25)
BARRIER_REEFS = False
SHORE_POINT_SAMPLE_DISTANCE = 500.0
WORKSPACE_DIR = 'global_cv_workspace'
CHURN_DIR = os.path.join(WORKSPACE_DIR, 'cn')
ECOSHARD_DIR = os.path.join(WORKSPACE_DIR, 'es')
TARGET_NODATA = -1
TARGET_CV_VECTOR_PATH = os.path.join(
WORKSPACE_DIR, 'global_cv_analysis_result.gpkg')
# [minx, miny, maxx, maxy].
GLOBAL_AOI_WGS84_BB = [-179, -65, 180, 77]
RELIEF_SAMPLE_DISTANCE = 5000.0
N_FETCH_RAYS = 16 # this is hardcoded because of WWIII fields
MAX_FETCH_DISTANCE = 60000
M_PER_DEGREE = 111300.0
ECOSHARD_BUCKET_URL = (
'https://storage.googleapis.com/critical-natural-capital-ecoshards/'
'cv_layers/')
#some of the following datasets seem to be missing from that folder; they
#can all be found here: gs://ecoshard-root/key_datasets/cv_layers
EMPTY_RASTER_URL = (
ECOSHARD_BUCKET_URL + 'empty_md5_f8f71e20668060bda7567ca33149a45c.tif')
GLOBAL_POLYGON_URL = (
ECOSHARD_BUCKET_URL +
'ipbes-cv_global_polygon_simplified_geometries_'
'md5_653118dde775057e24de52542b01eaee.gpkg')
BUFFER_VECTOR_URL = (
ECOSHARD_BUCKET_URL +
'buffered_global_shore_5km_md5_a68e1049c1c03673add014cd29b7b368.gpkg')
SHORE_GRID_URL = (
ECOSHARD_BUCKET_URL +
'shore_grid_md5_07aea173cf373474c096f1d5e3463c2f.gpkg')
GLOBAL_WWIII_GZ_URL = (
ECOSHARD_BUCKET_URL +
'wave_watch_iii_md5_c8bb1ce4739e0a27ee608303c217ab5b.gpkg.gz')
GLOBAL_DEM_RASTER_URL = (
ECOSHARD_BUCKET_URL +
'global_dem_md5_22c5c09ac4c4c722c844ab331b34996c.tif')
SLR_RASTER_URL = (
ECOSHARD_BUCKET_URL +
'MSL_Map_MERGED_Global_AVISO_NoGIA_Adjust_'
'md5_3072845759841d0b2523d00fe9518fee.tif')
GLOBAL_GEOMORPHOLOGY_VECTOR_URL = (
ECOSHARD_BUCKET_URL +
'geomorphology_md5_e65eff55840e7a80cfcb11fdad2d02d7.gpkg')
# The reefs raster is not in wgs84 but the script below projects it so
GLOBAL_REEFS_RASTER_URL = (
ECOSHARD_BUCKET_URL +
'ipbes-cv_reef_wgs84_compressed_md5_96d95cc4f2c5348394eccff9e8b84e6b.tif')
GLOBAL_SEAGRASS_RASTER_URL = (
ECOSHARD_BUCKET_URL +
'ipbes-cv_seagrass_md5_a9cc6d922d2e74a14f74b4107c94a0d6.tif')
GLOBAL_SALTMARSH_RASTER_URL = (
ECOSHARD_BUCKET_URL +
'ipbes-cv_saltmarsh_md5_203d8600fd4b6df91f53f66f2a011bcd.tif')
GLOBAL_MESOAMERICAN_BARRIER_REEF = (
ECOSHARD_BUCKET_URL +
'mesoamerican_barrier_reef_md5_4e8964bf9bf3d0f5fdddebd204eb3b32.tif')
GLOBAL_NEW_CALEDONIAN_BARRIER_REEF = (
ECOSHARD_BUCKET_URL +
'new_caledonian_barrier_reef_md5_ab1e8e54304a916101b9a53baa47c908.tif')
GLOBAL_GREAT_BARRIER_REEF = (
ECOSHARD_BUCKET_URL +
'great_barrier_reef_md5_68ec7a55480f0d8a6445ccfde21b48e0.tif')
GLOBAL_KEYS_BARRIER_REEF = (
ECOSHARD_BUCKET_URL +
'keys_barrier_reef_md5_21a5374ffd7237c6ccf629a8d78b3b51.tif')
GLOBAL_DATA_URL_MAP = {
'geomorphology': GLOBAL_GEOMORPHOLOGY_VECTOR_URL,
'mangroves_forest': GLOBAL_MANGROVES_RASTER_URL,
'reefs': GLOBAL_REEFS_RASTER_URL,
'seagrass': GLOBAL_SEAGRASS_RASTER_URL,
'saltmarsh_wetland': GLOBAL_SALTMARSH_RASTER_URL,
'dem': GLOBAL_DEM_RASTER_URL,
'slr': SLR_RASTER_URL,
'landmass': GLOBAL_POLYGON_URL,
'shore_grid': SHORE_GRID_URL,
'global_wwiii_vector_path': GLOBAL_WWIII_GZ_URL,
}
HAB_FIELDS = [
'4_500',
'2_2000',
'mangroves_forest',
'saltmarsh_wetland',
'seagrass',
'reefs_all',
]
REEF_FIELDS = [
'reefs',
]
# this is used to evaluate habitat value
FINAL_HAB_FIELDS = REEF_FIELDS + [
'mangroves_forest',
'saltmarsh_wetland',
'seagrass',
'4_500',
'2_2000',
]
SEDTYPE_TO_RISK = {
0: 5, # unknown
1: 5, # sandy
2: 1, # unerodable
3: 5, # muddy
4: 1, # coral/mangrove
}
# This dictionary maps landcode id to (risk, dist) tuples
LULC_CODE_TO_HAB_MAP = {
0: (0, None),
10: (0, None),
11: (0, None),
12: (0, None),
20: (0, None),
30: (0, None),
40: (4, 500),
50: (1, 2000),
51: (1, 2000),
52: (2, 2000),
60: (1, 2000),
61: (1, 2000),
62: (2, 2000),
70: (1, 2000),
71: (1, 2000),
72: (2, 2000),
80: (1, 2000),
81: (1, 2000),
82: (2, 2000),
90: (1, 2000),
91: (1, 2000),
92: (2, 2000),
100: (1, 2000),
110: (2, 2000),
120: (2, 2000),
121: (2, 2000),
122: (2, 2000),
130: (2, 2000),
140: (2, 2000),
150: (4, 500),
151: (4, 500),
152: (4, 500),
153: (4, 500),
160: (1, 2000),
161: (1, 2000),
170: (1, 2000),
171: (1, 2000),
180: (2, 1000),
181: (2, 1000),
190: (0, None),
200: (0, None),
201: (0, None),
202: (0, None),
210: (0, None),
220: (0, None),
}
logging.basicConfig(
level=logging.DEBUG,
format=(
'%(asctime)s (%(relativeCreated)d) %(levelname)s %(name)s'
' [%(pathname)s.%(funcName)s:%(lineno)d] %(message)s'),
filename='globalcvlog.txt')
LOGGER = logging.getLogger(__name__)
STOP_SENTINEL = 'STOP'
HABITAT_VECTOR_PATH_MAP = {
'reefs': (
os.path.join(
ECOSHARD_DIR, os.path.basename(GLOBAL_DATA_URL_MAP['reefs'])),
1, 2000.0),
'mangroves_forest': (
os.path.join(
ECOSHARD_DIR, os.path.basename(
GLOBAL_DATA_URL_MAP['mangroves_forest'])),
1, 2000.0),
'saltmarsh_wetland': (
os.path.join(
ECOSHARD_DIR, os.path.basename(
GLOBAL_DATA_URL_MAP['saltmarsh_wetland'])),
2, 1000.0),
'seagrass': (
os.path.join(
ECOSHARD_DIR, os.path.basename(GLOBAL_DATA_URL_MAP['seagrass'])),
4, 500.0),
}
if BARRIER_REEFS:
HABITAT_VECTOR_PATH_MAP.update({
'mesoamerican_barrier_reef': (
os.path.join(
ECOSHARD_DIR, os.path.basename(
GLOBAL_DATA_URL_MAP['mesoamerican_barrier_reef'])),
1, 35000.0),
'new_caledonian_barrier_reef': (
os.path.join(
ECOSHARD_DIR, os.path.basename(
GLOBAL_DATA_URL_MAP['new_caledonian_barrier_reef'])),
1, 20000.0),
'great_barrier_reef': (
os.path.join(
ECOSHARD_DIR, os.path.basename(
GLOBAL_DATA_URL_MAP['great_barrier_reef'])),
1, 35000.0),
'keys_barrier_reef': (
os.path.join(
ECOSHARD_DIR, os.path.basename(
GLOBAL_DATA_URL_MAP['keys_barrier_reef'])),
1, 25000.0),
})
def download_and_ungzip(url, target_path, buffer_size=2**20):
"""Download `url` to `target_dir` and touch `target_token_path`."""
gzipfile_path = os.path.join(
os.path.dirname(target_path), os.path.basename(url))
ecoshard.download_url(url, gzipfile_path)
with gzip.open(gzipfile_path, 'rb') as gzip_file:
with open(target_path, 'wb') as target_file:
while True:
content = gzip_file.read(buffer_size)
if content:
target_file.write(content)
else:
break
def build_rtree(vector_path):
"""Build an rtree that can be queried for nearest neighbors.
Parameters:
vector_path (str): path to vector of geometry to build into
r tree.
Returns:
rtree.Index object that will return shapely geometry objects with
a field_val_map field that contains the 'fieldname'->value pairs
from the original vector. The main object will also have a
`field_name_type_list` field which contains original
fieldname/field type pairs
"""
geometry_prep_list = []
vector = gdal.OpenEx(vector_path, gdal.OF_VECTOR)
layer = vector.GetLayer()
layer_defn = layer.GetLayerDefn()
field_name_type_list = []
for index in range(layer_defn.GetFieldCount()):
field_name = layer_defn.GetFieldDefn(index).GetName()
field_type = layer_defn.GetFieldDefn(index).GetType()
field_name_type_list.append((field_name, field_type))
LOGGER.debug('loop through features for rtree')
for index, feature in enumerate(layer):
feature_geom = feature.GetGeometryRef().Clone()
feature_geom_shapely = shapely.wkb.loads(
bytes(feature_geom.ExportToWkb()))
field_val_map = {}
for field_name, _ in field_name_type_list:
field_val_map[field_name] = (
feature.GetField(field_name))
geometry_prep_list.append(
(index, feature_geom_shapely.bounds, field_val_map))
LOGGER.debug('constructing the tree')
r_tree = rtree.index.Index(geometry_prep_list)
LOGGER.debug('all done')
r_tree.field_name_type_list = field_name_type_list
return r_tree
def build_strtree(vector_path):
"""Build an rtree that generates geom and preped geometry.
Parameters:
vector_path (str): path to vector of geometry to build into
r tree.
Returns:
strtree.STRtree object that will return shapely geometry objects
with a .prep field that is prepared geomtry for fast testing,
a .geom field that is the base gdal geometry, and a field_val_map
field that contains the 'fieldname'->value pairs from the original
vector. The main object will also have a `field_name_type_list`
field which contains original fieldname/field type pairs
"""
geometry_prep_list = []
vector = gdal.OpenEx(vector_path, gdal.OF_VECTOR)
layer = vector.GetLayer()
layer_defn = layer.GetLayerDefn()
field_name_type_list = []
for index in range(layer_defn.GetFieldCount()):
field_name = layer_defn.GetFieldDefn(index).GetName()
field_type = layer_defn.GetFieldDefn(index).GetType()
field_name_type_list.append((field_name, field_type))
LOGGER.debug('loop through features for rtree')
for index, feature in enumerate(layer):
feature_geom = feature.GetGeometryRef().Clone()
feature_geom_shapely = shapely.wkb.loads(
bytes(feature_geom.ExportToWkb()))
feature_geom_shapely.prep = shapely.prepared.prep(feature_geom_shapely)
feature_geom_shapely.geom = feature_geom
feature_geom_shapely.id = index
feature_geom_shapely.field_val_map = {}
for field_name, _ in field_name_type_list:
feature_geom_shapely.field_val_map[field_name] = (
feature.GetField(field_name))
geometry_prep_list.append(feature_geom_shapely)
LOGGER.debug('constructing the tree')
r_tree = shapely.strtree.STRtree(geometry_prep_list)
LOGGER.debug('all done')
r_tree.field_name_type_list = field_name_type_list
return r_tree
def cv_grid_worker(
bb_work_queue,
cv_point_complete_queue,
global_landmass_vector_path,
geomorphology_vector_path,
slr_raster_path,
global_dem_raster_path,
wwiii_vector_path,
habitat_raster_path_map,
grid_workspace_dir,
):
"""Worker process to calculate CV for a grid.
Parameters:
bb_work_queue (multiprocessing.Queue): contains
[minx, miny, maxx, maxy] bounding box values to be processed or
`STOP_SENTINEL` values to indicate the worker should be terminated.
cv_point_complete_queue (multiprocessing.Queue): this queue is used to
pass completed CV point vectors for further processing. It will be
terminated by `STOP_SENTINEL`.
global_landmass_vector_path (str): path to global landmass vector. Used
for intersecting rays to determine shoreline exposure.
geomorphology_vector_path (str): path to line geometry geomorphology
layer that has a field called 'SEDTYPE'
global_dem_raster_path (str): path to a global dem/bathymetry raster.
slr_raster_path (str): path to a sea level rise raster.
wwiii_vector_path (str): path to wave watch III dataset that has
fields REI_PCT[degree] and REI_V[degree] for degrees 0-360 in 22.5
degree increments.
habitat_raster_path_map (dict): mapt a habitat id to a
(raster path, risk, dist(m)) tuple. These are the raster versions
of habitats to use in Rhab.
grid_workspace_dir (str): path to workspace to use to handle grid
Returns:
None.
"""
LOGGER.info('build geomorphology rtree')
geomorphology_strtree = build_strtree(geomorphology_vector_path)
geomorphology_proj_wkt = geoprocessing.get_vector_info(
geomorphology_vector_path)['projection_wkt']
gegeomorphology_proj = osr.SpatialReference()
gegeomorphology_proj.ImportFromWkt(geomorphology_proj_wkt)
LOGGER.info('build landmass rtree')
landmass_strtree = build_strtree(global_landmass_vector_path)
LOGGER.info('build wwiii rtree')
wwiii_rtree = build_rtree(wwiii_vector_path)
target_pixel_size = [
SHORE_POINT_SAMPLE_DISTANCE / 4,
-SHORE_POINT_SAMPLE_DISTANCE / 4]
while True:
payload = bb_work_queue.get()
if payload == STOP_SENTINEL:
LOGGER.debug('stopping')
# put it back so others can stop
bb_work_queue.put(STOP_SENTINEL)
break
else:
LOGGER.debug('running')
# otherwise payload is the bounding box
index, (lng_min, lat_min, lng_max, lat_max) = payload
bounding_box_list = [lng_min, lat_min, lng_max, lat_max]
buffered_bounding_box_list = [
lng_min-0.1, lat_min-0.1, lng_max+0.1, lat_max+0.1]
# create workspace
workspace_dir = os.path.join(
grid_workspace_dir, '%d_%s_%s_%s_%s' % (
index, lng_min, lat_min, lng_max, lat_max))
try:
os.makedirs(workspace_dir)
except OSError:
pass
# task_graph = taskgraph.TaskGraph(workspace_dir, -1)
utm_srs = calculate_utm_srs((lng_min+lng_max)/2, (lat_min+lat_max)/2)
wgs84_srs = osr.SpatialReference()
wgs84_srs.ImportFromEPSG(4326)
try:
local_geomorphology_vector_path = os.path.join(
workspace_dir, 'geomorphology.gpkg')
clip_geometry(
bounding_box_list, wgs84_srs, utm_srs,
ogr.wkbMultiLineString, geomorphology_strtree,
local_geomorphology_vector_path)
shore_point_vector_path = os.path.join(
workspace_dir, 'shore_points.gpkg')
sample_line_to_points(
local_geomorphology_vector_path, shore_point_vector_path,
SHORE_POINT_SAMPLE_DISTANCE)
local_landmass_vector_path = os.path.join(
workspace_dir, 'landmass.gpkg')
clip_geometry(
buffered_bounding_box_list, wgs84_srs, utm_srs,
ogr.wkbPolygon, landmass_strtree,
local_landmass_vector_path)
landmass_boundary_vector_path = os.path.join(
workspace_dir, 'landmass_boundary.gpkg')
vector_to_lines(
local_landmass_vector_path, landmass_boundary_vector_path)
local_dem_path = os.path.join(
workspace_dir, 'dem.tif')
clip_and_reproject_raster(
global_dem_raster_path, local_dem_path, utm_srs.ExportToWkt(),
bounding_box_list, RELIEF_SAMPLE_DISTANCE, 'bilinear', True,
target_pixel_size)
local_slr_path = os.path.join(
workspace_dir, 'slr.tif')
clip_and_reproject_raster(
global_dem_raster_path, local_slr_path, utm_srs.ExportToWkt(),
bounding_box_list, 0, 'bilinear', True,
target_pixel_size)
# Rrelief
LOGGER.info('calculate relief on %s', workspace_dir)
calculate_relief(
shore_point_vector_path, local_dem_path, 'relief')
LOGGER.info('calculate rhab on %s', workspace_dir)
# Rhab
calculate_rhab(
shore_point_vector_path, habitat_raster_path_map, 'Rhab',
target_pixel_size)
# Rslr
calculate_slr(shore_point_vector_path, local_slr_path, 'slr')
# wind and wave power
calculate_wind_and_wave(
shore_point_vector_path, local_landmass_vector_path,
landmass_boundary_vector_path,
local_dem_path, wwiii_rtree, 'rei', 'ew')
# Rsurge
calculate_surge(shore_point_vector_path, local_dem_path, 'surge')
# Rgeomorphology
calculate_geomorphology(
shore_point_vector_path, local_geomorphology_vector_path,
'Rgeomorphology')
LOGGER.info('completed %s', shore_point_vector_path)
cv_point_complete_queue.put(shore_point_vector_path)
except Exception:
LOGGER.exception('error on %s, removing workspace', payload)
retrying_rmtree(workspace_dir)
def make_shore_kernel(kernel_path):
"""Make a 3x3 raster with a 9 in the middle and 1s on the outside."""
driver = gdal.GetDriverByName('GTiff')
kernel_raster = driver.Create(
kernel_path.encode('utf-8'), 3, 3, 1,
gdal.GDT_Byte)
# Make some kind of geotransform, it doesn't matter what but
# will make GIS libraries behave better if it's all defined
kernel_raster.SetGeoTransform([0, 1, 0, 0, 0, -1])
srs = osr.SpatialReference()
srs.SetWellKnownGeogCS('WGS84')
kernel_raster.SetProjection(srs.ExportToWkt())
kernel_band = kernel_raster.GetRasterBand(1)
kernel_band.SetNoDataValue(127)
kernel_band.WriteArray(numpy.array([[1, 1, 1], [1, 9, 1], [1, 1, 1]]))
def calculate_geomorphology(
shore_point_vector_path, geomorphology_vector_path,
geomorphology_fieldname):
"""Sample the geomorphology vector path for the closest line to each point.
Parameters:
shore_point_vector_path (str): path to a point shapefile to
for relief point analysis.
geomorphology_vector_path (str): path to a vector of lines that
contains the integer field 'SEDTYPE'.
geomorphology_fieldname (str): fieldname to add to
`shore_point_vector_path`.
Returns:
None.
"""
shore_point_vector = gdal.OpenEx(
shore_point_vector_path, gdal.OF_VECTOR | gdal.GA_Update)
shore_point_layer = shore_point_vector.GetLayer()
shore_point_layer.CreateField(
ogr.FieldDefn(geomorphology_fieldname, ogr.OFTReal))
geomorphology_strtree = build_strtree(geomorphology_vector_path)
shore_point_layer.StartTransaction()
for shore_point_feature in shore_point_layer:
shore_point_geom = shapely.wkb.loads(
bytes(shore_point_feature.GetGeometryRef().ExportToWkb()))
min_dist = MAX_FETCH_DISTANCE
geo_risk = 5
for line in geomorphology_strtree.query(shore_point_geom.buffer(500)):
cur_dist = line.distance(shore_point_geom)
if cur_dist < min_dist:
min_dist = cur_dist
geo_risk = line.field_val_map['Rgeo']
shore_point_feature.SetField(geomorphology_fieldname, geo_risk)
shore_point_layer.SetFeature(shore_point_feature)
shore_point_layer.CommitTransaction()
shore_point_layer = None
shore_point_vector = None
def calculate_surge(
shore_point_vector_path, bathymetry_raster_path, surge_fieldname):
"""Calculate surge potential as distance to continental shelf (-150m).
Parameters:
base_shore_point_vector_path (string): path to a point shapefile to
for relief point analysis.
global_dem_path (string): path to a DEM raster projected in wgs84.
surge_fieldname (str): fieldname to add to `shore_point_vector_path`
workspace_dir (string): path to a directory to make local calculations
in
target_surge_point_vector_path (string): path to output vector.
after completion will a value for closest distance to continental
shelf called 'surge'.
Returns:
None.
"""
shore_point_vector = gdal.OpenEx(
shore_point_vector_path, gdal.OF_VECTOR | gdal.GA_Update)
shore_point_layer = shore_point_vector.GetLayer()
shore_point_layer.CreateField(ogr.FieldDefn(surge_fieldname, ogr.OFTReal))
shelf_nodata = 2
bathymetry_nodata = geoprocessing.get_raster_info(
bathymetry_raster_path)['nodata'][0]
def mask_shelf(depth_array):
valid_mask = ~numpy.isclose(depth_array, bathymetry_nodata)
result_array = numpy.empty(
depth_array.shape, dtype=numpy.int16)
result_array[:] = shelf_nodata
result_array[valid_mask] = 0
result_array[depth_array < -150] = 1
return result_array
workspace_dir = os.path.dirname(shore_point_vector_path)
shelf_mask_path = os.path.join(workspace_dir, 'shelf_mask.tif')
geoprocessing.raster_calculator(
[(bathymetry_raster_path, 1)], mask_shelf,
shelf_mask_path, gdal.GDT_Byte, shelf_nodata)
# convolve to find edges
# grid shoreline from raster
shelf_kernel_path = os.path.join(workspace_dir, 'shelf_kernel.tif')
shelf_convoultion_raster_path = os.path.join(
workspace_dir, 'shelf_convolution.tif')
make_shore_kernel(shelf_kernel_path)
geoprocessing.convolve_2d(
(shelf_mask_path, 1), (shelf_kernel_path, 1),
shelf_convoultion_raster_path, target_datatype=gdal.GDT_Byte,
target_nodata=255)
nodata = geoprocessing.get_raster_info(
shelf_convoultion_raster_path)['nodata'][0]
def _shelf_mask_op(shelf_convolution):
"""Mask values on land that border the continental shelf."""
result = numpy.empty(shelf_convolution.shape, dtype=numpy.uint8)
result[:] = nodata
valid_mask = shelf_convolution != nodata
# If a pixel is on land, it gets at least a 9, but if it's all on
# land it gets an 17 (8 neighboring pixels), so we search between 9
# and 17 to determine a shore pixel
result[valid_mask] = numpy.where(
(shelf_convolution[valid_mask] >= 9) &
(shelf_convolution[valid_mask] < 17), 1, nodata)
return result
shelf_edge_raster_path = os.path.join(workspace_dir, 'shelf_edge.tif')
geoprocessing.raster_calculator(
[(shelf_convoultion_raster_path, 1)], _shelf_mask_op,
shelf_edge_raster_path, gdal.GDT_Byte, nodata)
shore_geotransform = geoprocessing.get_raster_info(
shelf_edge_raster_path)['geotransform']
shelf_rtree = rtree.index.Index()
for offset_info, data_block in geoprocessing.iterblocks(
(shelf_edge_raster_path, 1)):
row_indexes, col_indexes = numpy.mgrid[
offset_info['yoff']:offset_info['yoff']+offset_info['win_ysize'],
offset_info['xoff']:offset_info['xoff']+offset_info['win_xsize']]
valid_mask = data_block == 1
x_coordinates = (
shore_geotransform[0] +
shore_geotransform[1] * (col_indexes[valid_mask] + 0.5) +
shore_geotransform[2] * (row_indexes[valid_mask] + 0.5))
y_coordinates = (
shore_geotransform[3] +
shore_geotransform[4] * (col_indexes[valid_mask] + 0.5) +
shore_geotransform[5] * (row_indexes[valid_mask] + 0.5))
for x_coord, y_coord in zip(x_coordinates, y_coordinates):
shelf_rtree.insert(
0, [x_coord, y_coord, x_coord, y_coord],
obj=shapely.geometry.Point(x_coord, y_coord))
shore_point_layer.StartTransaction()
for point_feature in shore_point_layer:
point_geometry = point_feature.GetGeometryRef()
point_shapely = shapely.wkb.loads(bytes(point_geometry.ExportToWkb()))
nearest_point = list(shelf_rtree.nearest(
(point_geometry.GetX(),
point_geometry.GetY(),
point_geometry.GetX(),
point_geometry.GetY()),
objects='raw', num_results=1))
if len(nearest_point) > 0:
distance = nearest_point[0].distance(point_shapely)
point_feature.SetField(surge_fieldname, float(distance))
else:
# so far away it's essentially not an issue
point_feature.SetField(surge_fieldname, MAX_FETCH_DISTANCE)
shore_point_layer.SetFeature(point_feature)
shore_point_layer.CommitTransaction()
shore_point_layer.SyncToDisk()
shore_point_layer = None
shore_point_vector = None
def calculate_wind_and_wave(
shore_point_vector_path, landmass_vector_path,
landmass_boundary_vector_path, bathymetry_raster_path,
wwiii_rtree, wind_fieldname, wave_fieldname):
"""Calculate wind exposure for given points.
Parameters:
shore_point_vector_path (str): path to a point vector, this value will
be modified to hold the total wind exposure at this point.
landmass_vector_path (str): path to a vector indicating landmass that
will block wind exposure.
landmass_boundary_vector_path (str): path to a string vector containing
the perimeter of `landmass_vector_path`.
bathymetry_raster_path (str): path to a raster indicating bathymetry
values. (negative is deeper).
wwiii_rtree (str): path to an r_tree that can find the nearest point
in lat/lng whose object has values 'REI_PCT', 'REI_V',
'WavP_[DIR]', 'WavPPCT', 'V10PCT_[DIR]'.
wind_fieldname (str): fieldname to add to `shore_point_vector_path` for
wind power.
wave_fieldname (str): fieldname to add to `shore_point_vector_path` for
wave power.
Returns:
None
"""
gpkg_driver = ogr.GetDriverByName('gpkg')
temp_workspace_dir = tempfile.mkdtemp(
dir=os.path.dirname(shore_point_vector_path),
prefix='calculate_rwind_')
temp_fetch_rays_vector = gpkg_driver.CreateDataSource(
os.path.join(temp_workspace_dir, 'fetch_rays.gpkg'))
layer_name = 'fetch_rays'
shore_point_projection_wkt = geoprocessing.get_vector_info(
shore_point_vector_path)['projection_wkt']
shore_point_srs = osr.SpatialReference()
shore_point_srs.ImportFromWkt(shore_point_projection_wkt)
shore_point_srs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
temp_fetch_rays_layer = (
temp_fetch_rays_vector.CreateLayer(
str(layer_name), shore_point_srs, ogr.wkbLineString))
temp_fetch_rays_layer.CreateField(ogr.FieldDefn(
'fetch_dist', ogr.OFTReal))
temp_fetch_rays_layer.CreateField(ogr.FieldDefn(
'direction', ogr.OFTReal))
temp_fetch_rays_defn = temp_fetch_rays_layer.GetLayerDefn()
# These WWIII fields are the only ones needed for wind & wave equations
# Copy them to a new vector which also gets more fields added with
# computed values.
target_shore_point_vector = gdal.OpenEx(
shore_point_vector_path, gdal.OF_VECTOR | gdal.GA_Update)
target_shore_point_layer = target_shore_point_vector.GetLayer()
target_shore_point_layer.CreateField(
ogr.FieldDefn(wind_fieldname, ogr.OFTReal))
target_shore_point_layer.CreateField(
ogr.FieldDefn(wave_fieldname, ogr.OFTReal))
for ray_index in range(N_FETCH_RAYS):
compass_degree = int(ray_index * 360 / N_FETCH_RAYS)
target_shore_point_layer.CreateField(
ogr.FieldDefn('fdist_%d' % compass_degree, ogr.OFTReal))
target_shore_point_layer.CreateField(
ogr.FieldDefn('fdepth_%d' % compass_degree, ogr.OFTReal))
# Iterate over every shore point
LOGGER.info("Casting rays and extracting bathymetry values")
bathy_raster = gdal.OpenEx(
bathymetry_raster_path, gdal.OF_RASTER | gdal.GA_ReadOnly)
bathy_band = bathy_raster.GetRasterBand(1)
bathy_raster_info = geoprocessing.get_raster_info(bathymetry_raster_path)
bathy_gt = bathy_raster_info['geotransform']
bathy_inv_gt = gdal.InvGeoTransform(bathy_gt)
landmass_vector = gdal.OpenEx(landmass_vector_path, gdal.OF_VECTOR)
landmass_layer = landmass_vector.GetLayer()
landmass_geom_list = [
shapely.wkb.loads(bytes(f.GetGeometryRef().ExportToWkb()))
for f in landmass_layer]
if landmass_geom_list:
landmass_union_geom = shapely.ops.cascaded_union(landmass_geom_list)
else:
landmass_union_geom = shapely.Polygon()
landmass_layer = None
landmass_vector = None
landmass_union_geom_prep = shapely.prepared.prep(landmass_union_geom)
landmass_boundary_vector = gdal.OpenEx(
landmass_boundary_vector_path, gdal.OF_VECTOR)
landmass_boundary_layer = landmass_boundary_vector.GetLayer()
landmass_boundary_geom_list = [
shapely.wkb.loads(bytes(f.GetGeometryRef().ExportToWkb()))
for f in landmass_boundary_layer]
if landmass_boundary_geom_list:
landmass_boundary_union_geom = shapely.ops.cascaded_union(
landmass_boundary_geom_list)
else:
landmass_boundary_union_geom = shapely.Polygon()
landmass_boundary_layer = None
landmass_boundary_vector = None
landmass_boundary_union_geom_prep = shapely.prepared.prep(
landmass_boundary_union_geom)
landmass_boundary_strtree = build_strtree(landmass_boundary_vector_path)
target_shore_point_layer.StartTransaction()
temp_fetch_rays_layer.StartTransaction()
# make a transfomer for local points to lat/lng for wwiii_rtree
wgs84_srs = osr.SpatialReference()
wgs84_srs.ImportFromEPSG(4326)
wgs84_srs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
base_to_target_transform = osr.CoordinateTransformation(
shore_point_srs, wgs84_srs)
for shore_point_feature in target_shore_point_layer:
shore_point_geom = shore_point_feature.GetGeometryRef().Clone()
_ = shore_point_geom.Transform(base_to_target_transform)
wwiii_point = next(wwiii_rtree.nearest(
(shore_point_geom.GetX(), shore_point_geom.GetY()), 1,
objects='raw'))
rei_value = 0.0
height_list = []
period_list = []
e_local = 0.0
e_ocean = 0.0
shore_point_geometry = shore_point_feature.GetGeometryRef()
shapely_point = shapely.wkb.loads(
bytes(shore_point_geometry.ExportToWkb()))
if landmass_union_geom_prep.contains(shapely_point):
new_point = shapely.ops.nearest_points(
landmass_boundary_union_geom, shapely_point)[0]
shore_point_geometry = ogr.CreateGeometryFromWkb(new_point.wkb)
# Iterate over every ray direction
for sample_index in range(N_FETCH_RAYS):
compass_degree = int(sample_index * 360 / N_FETCH_RAYS)
compass_theta = float(sample_index) / N_FETCH_RAYS * 360
# wwiii_point should be closest point to shore point
rei_pct = wwiii_point[
'REI_PCT%d' % int(compass_theta)]
rei_v = wwiii_point[
'REI_V%d' % int(compass_theta)]
cartesian_theta = -(compass_theta - 90)
# Determine the direction the ray will point
delta_x = math.cos(cartesian_theta * math.pi / 180)
delta_y = math.sin(cartesian_theta * math.pi / 180)
# Start a ray offset from the shore point
# so that rays start outside of the landmass.
# Shore points are interpolated onto the coastline,
# but floating point error results in points being just
# barely inside/outside the landmass.
offset = 10
point_a_x = (
shore_point_geometry.GetX() + delta_x * offset)
point_a_y = (
shore_point_geometry.GetY() + delta_y * offset)
origin_point = shapely.geometry.Point(point_a_x, point_a_y)
if landmass_union_geom_prep.intersects(origin_point):
# the origin is inside the landmass, skip
continue
point_b_x = point_a_x + delta_x * (
MAX_FETCH_DISTANCE)
point_b_y = point_a_y + delta_y * (
MAX_FETCH_DISTANCE)
# build ray geometry so we can intersect it later
ray_geometry = ogr.Geometry(ogr.wkbLineString)
ray_geometry.AddPoint(point_a_x, point_a_y)
ray_geometry.AddPoint(point_b_x, point_b_y)
# keep a shapely version of the ray so we can do fast intersection
# with it and the entire landmass
ray_point_origin_shapely = shapely.geometry.Point(
point_a_x, point_a_y)
if not landmass_boundary_union_geom_prep.intersects(
ray_point_origin_shapely):
# the origin is in ocean, so we'll get a ray length > 0.0
# This algorithm searches for intersections, if one is found
# the ray updates and a smaller intersection set is determined
# by experimentation I've found this is significant, but not
# an order of magnitude, faster than looping through all
# original possible intersections. Since this algorithm
# will be run for a long time, it's worth the additional
# complexity
tested_indexes = set()
while True:
intersection = False
ray_envelope = ray_geometry.GetEnvelope()
for landmass_line in landmass_boundary_strtree.query(
shapely.geometry.box(
*[ray_envelope[i] for i in [0, 2, 1, 3]])):
if landmass_line.id in tested_indexes:
continue
tested_indexes.add(landmass_line.id)
if ray_geometry.Intersects(landmass_line.geom):
intersection_point = ray_geometry.Intersection(
landmass_line.geom)
# offset the dist with smallest_feature_size
# update the endpoint of the ray
ray_geometry = ogr.Geometry(ogr.wkbLineString)
ray_geometry.AddPoint(point_a_x, point_a_y)
ray_geometry.AddPoint(
intersection_point.GetX(),
intersection_point.GetY())
intersection = True
break
if not intersection:
break
ray_step_loc = 0.0
bathy_values = []
# walk along ray
ray_shapely = shapely.wkb.loads(
bytes(ray_geometry.ExportToWkb()))
while ray_step_loc < ray_shapely.length:
sample_point = ray_shapely.interpolate(ray_step_loc)
ray_step_loc += SHORE_POINT_SAMPLE_DISTANCE/4
pixel_x, pixel_y = [int(x) for x in gdal.ApplyGeoTransform(
bathy_inv_gt,
sample_point.coords[0][0], sample_point.coords[0][1])]
if (pixel_x < 0 or pixel_y < 0 or
pixel_x >= bathy_band.XSize or
pixel_y >= bathy_band.YSize):
continue
bathy_values.append(
bathy_band.ReadAsArray(
pixel_x, pixel_y, 1, 1)[0][0])
if bathy_values:
avg_bathy_value = numpy.mean(bathy_values)
else:
avg_bathy_value = 0.0
# when we get here, we have the final ray geometry
ray_feature = ogr.Feature(temp_fetch_rays_defn)
ray_feature.SetField('fetch_dist', ray_shapely.length)
ray_feature.SetField('direction', compass_degree)
ray_feature.SetGeometry(ray_geometry)
temp_fetch_rays_layer.CreateFeature(ray_feature)
rei_value += ray_shapely.length * rei_pct * rei_v
ray_length = ray_geometry.Length()
ray_feature = None
ray_geometry = None
shore_point_feature.SetField(
'fdist_%d' % compass_degree, ray_length)
shore_point_feature.SetField(
'fdepth_%d' % compass_degree, float(avg_bathy_value))
velocity = wwiii_point['V10PCT_%d' % compass_degree]
occurrence = wwiii_point['REI_PCT%d' % compass_degree]
height = compute_wave_height(
velocity, ray_shapely.length, avg_bathy_value)
height_list.append(height)
period = compute_wave_period(
velocity, ray_shapely.length, avg_bathy_value)
period_list.append(period)
power = 0.5 * float(height)**2 * float(period) # UG Eq. 8
e_local += power * occurrence # UG Eq. 9
if intersection:
e_ocean += (
wwiii_point['WavP_%d' % compass_degree] *
wwiii_point['WavPPCT%d' % compass_degree])
ray_feature = None
ray_geometry = None
rei_value += ray_length * rei_pct * rei_v
shore_point_feature.SetField(wind_fieldname, rei_value)
shore_point_feature.SetField(wave_fieldname, max(e_ocean, e_local))
target_shore_point_layer.SetFeature(shore_point_feature)
shore_point_geometry = None
target_shore_point_layer.CommitTransaction()
target_shore_point_layer.SyncToDisk()
target_shore_point_layer = None
target_shore_point_vector = None
temp_fetch_rays_layer.CommitTransaction()
temp_fetch_rays_layer.SyncToDisk()
temp_fetch_rays_layer = None
temp_fetch_rays_vector = None