def validate_min_lt_max(min, max):
try:
min = float(min)
max = float(max)
except ValueError:
raise error_handler.InvalidUsage(
"Specified min and max values must be numbers.")
if not min < max:
raise error_handler.InvalidUsage(
"Specified min must be less than max.")
def get_msfd_data():
"""
Get Crucial data. Either groundwater_declining_trend, or evaporation_deficit dataset must be provided.
See datasets_visualization_parameters.json for possible bands to request as band
:return:
"""
r = request.get_json()
dataset = r.get("dataset", None)
image_id = r.get("imageId", None)
band = r.get("band", None)
function = r.get("function", None)
start_date = r.get("startDate", None)
end_date = r.get("endDate", None)
image_num_limit = r.get("limit", None)
min = r.get("min", None)
max = r.get("max", None)
if not band:
msg = f"band is a required parameter"
logger.error(msg)
raise error_handler.InvalidUsage(msg)
if dataset:
source = "projects/dgds-gee/msfd/" + dataset
elif image_id:
source = image_id
else:
msg = f"dataset or image_id is a required parameter"
logger.error(msg)
raise error_handler.InvalidUsage(msg)
image_info = dgds_functions.get_dgds_data(
source=source,
dataset=dataset,
image_id=image_id,
band=band,
function=function,
start_date=start_date,
end_date=end_date,
image_num_limit=image_num_limit,
min=min,
max=max,
)
if not image_info:
raise error_handler.InvalidUsage("No images returned.")
return Response(json.dumps(image_info),
status=200,
mimetype="application/json")
def get_chasm_data():
"""
Get metocean data. dataset must be provided.
:return:
"""
r = request.get_json()
dataset = r.get("dataset", None)
band = r["band"]
image_id = r.get("imageId", None)
function = r.get("function", None)
start_date = r.get("startDate", None)
end_date = r.get("endDate", None)
image_num_limit = r.get("limit", None)
min = r.get("min", None)
max = r.get("max", None)
source = None
# Can provide either dataset and/or image_id
if not (dataset or image_id):
msg = f"dataset or imageId required."
logger.error(msg)
raise error_handler.InvalidUsage(msg)
if dataset:
source = "projects/dgds-gee/chasm/" + dataset
elif image_id:
image_location_parameters = image_id.split("/")
source = ("/").join(image_location_parameters[:-1])
image_info = dgds_functions.get_dgds_data(
source=source,
dataset=dataset,
image_id=image_id,
band=band,
function=function,
start_date=start_date,
end_date=end_date,
image_num_limit=image_num_limit,
min=min,
max=max,
)
if not image_info:
raise error_handler.InvalidUsage("No images returned.")
return Response(json.dumps(image_info),
status=200,
mimetype="application/json")
def get_gebco_data():
r = request.get_json()
dataset = r.get("dataset", "gebco")
band = r.get("band", "elevation")
image_id = r.get("imageId", None)
start_date = r.get("startDate", None)
end_date = r.get("endDate", None)
image_num_limit = r.get("limit", None)
min = r.get("min", None)
max = r.get("max", None)
if dataset:
source = "projects/dgds-gee/bathymetry/" + dataset + "/2019"
if image_id:
source = image_id
image_info = dgds_functions.get_dgds_data(
source=source,
dataset=dataset,
image_id=image_id,
band=band,
start_date=start_date,
end_date=end_date,
image_num_limit=image_num_limit,
min=min,
max=max,
)
if not image_info:
raise error_handler.InvalidUsage("No images returned.")
return Response(json.dumps(image_info),
status=200,
mimetype="application/json")
def get_glossis_data():
"""
Get GLOSSIS data. Either currents, wind, or waterlevel dataset must be provided.
If waterlevel dataset is requested, must specify if band water_level_surge, water_level,
or astronomical_tide is requested
:return:
"""
r = request.get_json()
dataset = r.get("dataset", None)
image_id = r.get("imageId", None)
band = r.get("band", None)
function = r.get("function", None)
start_date = r.get("startDate", None)
end_date = r.get("endDate", None)
image_num_limit = r.get("limit", None)
min = r.get("min", None)
max = r.get("max", None)
if not (dataset or image_id):
msg = f"dataset or imageId required."
logger.error(msg)
raise error_handler.InvalidUsage(msg)
if dataset:
source = "projects/dgds-gee/glossis/" + dataset
if image_id:
image_location_parameters = image_id.split("/")
source = ("/").join(image_location_parameters[:-1])
image_info = dgds_functions.get_dgds_data(
source=source,
dataset=dataset,
image_id=image_id,
band=band,
function=function,
start_date=start_date,
end_date=end_date,
image_num_limit=image_num_limit,
min=min,
max=max,
)
if not image_info:
raise error_handler.InvalidUsage("No images returned.")
return Response(json.dumps(image_info),
status=200,
mimetype="application/json")
def get_image_collection_info():
r = request.get_json()
source = r['source']
start_date = r.get('startDate', None)
end_date = r.get('endDate', None)
image_num_limit = r.get('limit', None)
info = dgds_functions.get_image_collection_info(source, start_date,
end_date, image_num_limit)
if not info:
raise error_handler.InvalidUsage('No images returned.')
return Response(json.dumps(info), status=200, mimetype='application/json')
def get_gll_dtm_data():
r = request.get_json()
band = r.get("band", "elevation")
imageid = "users/maartenpronk/gll_dtm/gll_dtm_v1"
image_info = dgds_functions.get_dgds_data(
source=None,
image_id=imageid,
band=band,
)
if not image_info:
raise error_handler.InvalidUsage("No images returned.")
return Response(json.dumps(image_info),
status=200,
mimetype="application/json")
def filter_liwo_collection_v1(collection_path, id_key, liwo_ids, band,
reducer):
"""
Create combined max image from collection. Version 1 based on unnamed single
and multi band images.
:param collection_path: Path to Earth Engine Image Collection
:param id_key: Metadata key name for unique ids
:param scenario_ids: List of scenario ids to combine
:param band: band of image to select
:param reducer: reducer operation by which to combine images
:return: combined image
"""
# Filter based on breach location
collection = ee.ImageCollection(collection_path)
# TODO: how to make this generic, consider GraphQL
collection = collection.filter(ee.Filter.inList(id_key, liwo_ids))
collection = collection.map(lambda im: im.set('bandNames', im.bandNames()))
n_selected = collection.size().getInfo()
if band != 'waterdepth':
collection = collection.filterMetadata('bandNames', 'equals',
['b1', 'b2', 'b3', 'b4', 'b5'])
n_filtered = collection.size().getInfo()
if n_selected != n_filtered:
logging.warning('missing images, selected %s, filtered %s', n_selected,
n_filtered)
# Filter based on band name (characteristic to display)
collection = collection.select(band)
n_images = collection.size().getInfo()
msg = 'No images available for breach locations: %s' % (liwo_ids, )
logger.debug(msg)
if not n_images:
raise error_handler.InvalidUsage(msg)
# get max image
reduce_func = getattr(ee.Reducer, reducer)()
image = ee.Image(collection.reduce(reduce_func))
# clip image to region and show only values greater than 0 (no-data value given in images) .clip(region)
image = image.mask(image.gt(0))
return image
def filter_liwo_collection_v2(collection_path, id_key, scenario_ids, band,
reducer):
"""
Create combined max image from collection. Version 2 based on named image bands
:param collection_path: Path to Earth Engine Image Collection
:param id_key: Metadata key name for unique ids
:param scenario_ids: List of scenario ids to combine
:param band: band of image to select
:param reducer: reducer operation by which to combine images
:return: combined image
"""
# Filter based on scenario id, band
scenarios = ee.ImageCollection(collection_path)
scenarios = scenarios.filter(ee.Filter.inList(id_key, scenario_ids))
scenarios = scenarios.filter(
ee.Filter.listContains("system:band_names", band))
scenarios = scenarios.select(band)
n_selected = scenarios.size().getInfo()
if n_selected == 0:
msg = 'No images available for breach locations: %s' % (scenario_ids, )
logger.debug(msg)
raise error_handler.InvalidUsage(msg)
# raise ValueError("No images with band {} in scenario_ids {}".format(band, scenario_ids))
if len(scenario_ids) != n_selected:
logging.info("collection {}, missing {} scenarios for band {}".format(
collection_path,
len(scenario_ids) - n_selected, band))
bounds = scenarios.geometry().bounds()
# reduce image
reduce_func = getattr(ee.Reducer, reducer)()
if reducer == 'min':
# Aankomstijden <= 0 should not be included in the aggregated minimum
scenarios = scenarios.map(lambda i: i.mask(i.gt(0)))
image = ee.Image(scenarios.reduce(reduce_func))
if reducer == 'max':
# Do not display any values <= 0 (Some no data values assigned as 0 and not -9999).
image = image.mask(image.gt(0))
# reclip by bounds
image = image.clip(bounds)
return image
def validate_min_lt_max(min, max):
if not min < max:
raise error_handler.InvalidUsage(
'Specified min must be less than max.')
def get_feature_info():
"""
Get image value at point
:return:
"""
r = request.get_json()
image_id = r['imageId']
bbox = r['bbox']
datasets = r.get('datasets', None)
band = r.get('band', None)
function = r.get('function', None)
info_format = r.get('info_format', 'JSON')
if function == 'mosaic_elevation_datasets':
if not datasets:
msg = f'datsets list expected for function {function}'
raise error_handler.InvalidUsage(msg)
image = ee.Image(
dgds_functions.mosaic_elevation_datasets(datasets).select(
'elevation'))
else:
image = ee.Image(image_id)
image_location_parameters = image_id.split('/')
source = ('/').join(image_location_parameters[:-1])
data_params = dgds_functions.get_dgds_source_vis_params(
source, image_id)
if band:
band_name = data_params['bandNames'][band]
image = image.select(band_name)
if function:
assert (function in data_params.get('function', None)) or \
(function == data_params['function'].get(band, None)), \
f'{function} not an option.'
image = dgds_functions.apply_image_operation(image, function,
data_params, band)
image = image.rename('value')
# TODO: get scale of image, or load default scale from parameters
value = (image.sample(**{
'region': ee.Geometry(bbox),
'geometries': True,
'scale': 10
}).first().getInfo())
# if no data at point, value will be None.
if not value:
# return Feature with value None (standard return format from GEE)
value = {
"geometry": bbox,
"id": "0",
"properties": {
"value": None
},
"type": "Feature"
}
else:
value['properties']['value'] = round(value['properties']['value'], 2)
if info_format == 'JSON':
value = value['properties']
return Response(json.dumps(value), status=200, mimetype='application/json')
def get_liwo_scenarios():
r = request.get_json()
# Currently 'liwo' only option
variable = 'liwo'
# name of breach location as string
liwo_ids = r['liwo_ids']
# band name as string
band = r['band']
raster_assets = {
'liwo': 'users/rogersckw9/liwo/liwo-scenarios-03-2019'
}
bands = {
'waterdepth': 'b1',
'velocity': 'b2',
'riserate': 'b3',
'damage': 'b4',
'fatalities': 'b5'
}
reducers = {
'waterdepth': 'max',
'velocity': 'max',
'riserate': 'max',
'damage': 'max',
'fatalities': 'max'
}
# for now use max as a reducer
assert band in reducers
assert band in bands
reducer = reducers[band]
styles = {
'waterdepth': {
'sld_style': '\
<RasterSymbolizer>\
<ColorMap type="intervals">\
<ColorMapEntry color="#FFFFFF" opacity="0.01" quantity="0.01999"/>\
<ColorMapEntry color="#CEFEFE" opacity="1.0" quantity="0.5" label="< 0.5"/>\
<ColorMapEntry color="#94bff7" opacity="1.0" quantity="1" label="0.5 - 1.0"/>\
<ColorMapEntry color="#278ef4" opacity="1.0" quantity="1.5" label="1.0 - 1.5"/>\
<ColorMapEntry color="#0000cc" opacity="1.0" quantity="2.0" label="1.5 - 2.0"/>\
<ColorMapEntry color="#4A0177" opacity="1.0" quantity="5" label="2.0 - 5.0"/>\
<ColorMapEntry color="#73004c" opacity="1.0" quantity="9999" label="> 5.0"/>\
</ColorMap>\
</RasterSymbolizer>'
},
'velocity': {
'sld_style': '\
<RasterSymbolizer>\
<ColorMap type="intervals">\
<ColorMapEntry color="#FFFFFF" opacity="0.01" quantity="0.01"/>\
<ColorMapEntry color="#FAD7FE" opacity="1.0" quantity="0.5" label="< 0.5"/>\
<ColorMapEntry color="#E95CF5" opacity="1.0" quantity="1" label="0.5 - 1.0"/>\
<ColorMapEntry color="#CB00DB" opacity="1.0" quantity="2" label="1.0 - 2.0"/>\
<ColorMapEntry color="#8100B1" opacity="1.0" quantity="4" label="2.0 - 4.0"/>\
<ColorMapEntry color="#8100D2" opacity="1.0" quantity="1000" label="> 4.0"/>\
</ColorMap>\
</RasterSymbolizer>'
},
'riserate': {
'sld_style': '\
<RasterSymbolizer>\
<ColorMap type="intervals">\
<ColorMapEntry color="#FFFFFF" opacity="0.01" quantity="0.01"/>\
<ColorMapEntry color="#FFF5E6" opacity="1.0" quantity="0.25" label="< 0.25"/>\
<ColorMapEntry color="#FFD2A8" opacity="1.0" quantity="0.5" label="0.25 - 0.5"/>\
<ColorMapEntry color="#FFAD66" opacity="1.0" quantity="1" label="0.5 - 1.0"/>\
<ColorMapEntry color="#EB7515" opacity="1.0" quantity="2" label="1.0 - 2.0"/>\
<ColorMapEntry color="#B05500" opacity="1.0" quantity="1000000" label="> 2.0"/>\
</ColorMap>\
</RasterSymbolizer>'
},
'damage': {
'sld_style': '\
<RasterSymbolizer>\
<ColorMap type="intervals">\
<ColorMapEntry color="#FFFFFF" opacity="0.01" quantity="0.01"/>\
<ColorMapEntry color="#499b1b" opacity="1.0" quantity="10000" label="< 10.000"/>\
<ColorMapEntry color="#61f033" opacity="1.0" quantity="100000" label="10.000 - 100.000"/>\
<ColorMapEntry color="#ffbb33" opacity="1.0" quantity="1000000" label="100.000 - 1.000.000"/>\
<ColorMapEntry color="#ff3333" opacity="1.0" quantity="5000000" label="1.000.000 - 5.000.000"/>\
<ColorMapEntry color="#8f3333" opacity="1.0" quantity="1000000000000000" label="> 5.000.000"/>\
</ColorMap>\
</RasterSymbolizer>'
},
'fatalities': {
'sld_style': '\
<RasterSymbolizer>\
<ColorMap type="intervals">\
<ColorMapEntry color="#FFFFFF" opacity="0.01" quantity="0.0001"/>\
<ColorMapEntry color="#499b1b" opacity="1.0" quantity="0.1" label="< 0.1"/>\
<ColorMapEntry color="#61f033" opacity="1.0" quantity="0.3" label="0.1 - 0.3"/>\
<ColorMapEntry color="#ffbb33" opacity="1.0" quantity="1" label="0.3 - 1"/>\
<ColorMapEntry color="#ff3333" opacity="1.0" quantity="3" label="1 - 3"/>\
<ColorMapEntry color="#8f3333" opacity="1.0" quantity="10000" label="> 3"/>\
</ColorMap>\
</RasterSymbolizer>'
}
}
# Filter based on breach location
collection = ee.ImageCollection(raster_assets[variable])
# TODO: how to make this generic, consider GraphQL
collection = collection.filter(
ee.Filter.inList('LIWO_ID', liwo_ids)
)
collection = collection.map(
lambda im: im.set('bandNames', im.bandNames())
)
n_selected = collection.size().getInfo()
collection = collection.filterMetadata('bandNames', 'equals', ['b1', 'b2', 'b3', 'b4', 'b5'])
n_filtered = collection.size().getInfo()
if n_selected != n_filtered:
logging.warning('missing images, selected %s, filtered %s', n_selected, n_filtered)
# Filter based on band name (characteristic to display)
collection = collection.select(bands[band])
n_images = collection.size().getInfo()
msg = 'No images available for breach locations: %s' % (liwo_ids, )
logger.debug(msg)
if not n_images:
raise error_handler.InvalidUsage(msg)
# get max image
reduce_func = getattr(ee.Reducer, reducer)()
image = ee.Image(collection.reduce(reduce_func))
# clip image to region and mask all 0 values (no-data value given in images) .clip(region)
image = image.mask(image.neq(0))
def generate_image_info(im, params):
"""generate url and tokens for image"""
im = ee.Image(im)
# some images are scaled to a factor of 10.
if params.get('scale') == 'log':
im = im.log10()
im = im.sldStyle(params.get('sld_style'))
m = im.getMapId()
mapid = m.get('mapid')
token = m.get('token')
url = 'https://earthengine.googleapis.com/map/{mapid}/{{z}}/{{x}}/{{y}}?token={token}'.format(
mapid=mapid,
token=token
)
result = {
'mapid': mapid,
'token': token,
'url': url
}
return result
def export_image_response(image, region, info):
"""create export response for image"""
url = image.getDownloadURL({
'name': 'export',
'format': 'tif',
'crs': info['crs'],
'scale': info['scale'],
'region': json.dumps(region.bounds(info['scale']).getInfo())
})
result = {'export_url': url}
return result
# TODO: generate visualization params for map ids
params = styles[band]
info = generate_image_info(image, params)
info['variable'] = variable
info['liwo_ids'] = liwo_ids
info['band'] = band
# # Following needed for export:
# # Specify region over which to compute
# export is True or None/False
if r.get('export'):
region = ee.Geometry(r['region'])
# scale of pixels for export, in meters
info['scale'] = float(r['scale'])
# coordinate system for export projection
info['crs'] = r['crs']
extra_info = export_image_response(image, region, info)
info.update(extra_info)
return Response(
json.dumps(info),
status=200,
mimetype='application/json'
)
