def test_store_and_restore_and_discard(self):
cache_store = TracingCacheStore()
cache = Cache(store=cache_store, capacity=1000)
self.assertIs(cache.store, cache_store)
self.assertEqual(cache.size, 0)
self.assertEqual(cache.max_size, 750)
cache_store.trace = ''
cache.put_value('k1', 'x')
self.assertEqual(cache.get_value('k1'), 'x')
self.assertEqual(cache.size, 100)
self.assertEqual(cache_store.trace, 'store(k1, x);restore(k1, S/x);')
cache_store.trace = ''
cache.remove_value('k1')
self.assertEqual(cache.size, 0)
self.assertEqual(cache_store.trace, 'discard(k1, S/x);')
cache_store.trace = ''
cache_store.trace = ''
cache.put_value('k1', 'x')
cache.put_value('k1', 'xx')
self.assertEqual(cache.get_value('k1'), 'xx')
self.assertEqual(cache.size, 200)
self.assertEqual(
cache_store.trace,
'store(k1, x);discard(k1, S/x);store(k1, xx);restore(k1, S/xx);')
cache_store.trace = ''
cache.remove_value('k1')
self.assertEqual(cache.size, 0)
self.assertEqual(cache_store.trace, 'discard(k1, S/xx);')
cache_store.trace = ''
cache.put_value('k1', 'x')
cache.put_value('k2', 'xxx')
cache.put_value('k3', 'xx')
self.assertEqual(cache.get_value('k1'), 'x')
self.assertEqual(cache.get_value('k2'), 'xxx')
self.assertEqual(cache.get_value('k3'), 'xx')
self.assertEqual(cache.size, 600)
self.assertEqual(
cache_store.trace, 'store(k1, x);store(k2, xxx);store(k3, xx);'
'restore(k1, S/x);restore(k2, S/xxx);restore(k3, S/xx);')
cache_store.trace = ''
cache.put_value('k4', 'xxxx')
self.assertEqual(cache.size, 600)
self.assertEqual(
cache_store.trace,
'store(k4, xxxx);discard(k1, S/x);discard(k2, S/xxx);')
cache_store.trace = ''
cache.clear()
self.assertEqual(cache.size, 0)
class ServiceContext:
def __init__(self,
prefix: str = None,
base_dir: str = None,
config: Config = None,
trace_perf: bool = DEFAULT_TRACE_PERF,
tile_comp_mode: int = None,
tile_cache_capacity: int = None,
ml_dataset_openers: Dict[str,
MultiLevelDatasetOpener] = None):
self._prefix = normalize_prefix(prefix)
self._base_dir = os.path.abspath(base_dir or '')
self._config = config if config is not None else dict()
self._config_mtime = 0.0
self._place_group_cache = dict()
self._feature_index = 0
self._ml_dataset_openers = ml_dataset_openers
self._tile_comp_mode = tile_comp_mode
self._trace_perf = trace_perf
self._lock = threading.RLock()
self._dataset_cache = dict(
) # contains tuples of form (MultiLevelDataset, ds_descriptor)
self._image_cache = dict()
if tile_cache_capacity and tile_cache_capacity > 0:
self._tile_cache = Cache(MemoryCacheStore(),
capacity=tile_cache_capacity,
threshold=0.75)
else:
self._tile_cache = None
@property
def config(self) -> Config:
return self._config
@config.setter
def config(self, config: Config):
if self._config:
with self._lock:
# Close all datasets
for ml_dataset, _ in self._dataset_cache.values():
# noinspection PyBroadException
try:
ml_dataset.close()
except Exception:
pass
# Clear all caches
if self._dataset_cache:
self._dataset_cache.clear()
if self._image_cache:
self._image_cache.clear()
if self._tile_cache:
self._tile_cache.clear()
if self._place_group_cache:
self._place_group_cache.clear()
self._config = config
@property
def config_mtime(self) -> float:
return self._config_mtime
@config_mtime.setter
def config_mtime(self, value: float):
self._config_mtime = value
@property
def base_dir(self) -> str:
return self._base_dir
@property
def tile_comp_mode(self) -> int:
return self._tile_comp_mode
@property
def dataset_cache(
self) -> Dict[str, Tuple[MultiLevelDataset, Dict[str, Any]]]:
return self._dataset_cache
@property
def image_cache(self) -> Dict[str, Any]:
return self._image_cache
@property
def tile_cache(self) -> Optional[Cache]:
return self._tile_cache
@property
def trace_perf(self) -> bool:
return self._trace_perf
def get_service_url(self, base_url, *path: str):
if self._prefix:
return base_url + '/' + self._prefix + '/' + '/'.join(path)
else:
return base_url + '/' + '/'.join(path)
def get_ml_dataset(self, ds_id: str) -> MultiLevelDataset:
ml_dataset, _ = self._get_dataset_entry(ds_id)
return ml_dataset
def get_dataset(self,
ds_id: str,
expected_var_names: Collection[str] = None) -> xr.Dataset:
ml_dataset, _ = self._get_dataset_entry(ds_id)
dataset = ml_dataset.base_dataset
if expected_var_names:
for var_name in expected_var_names:
if var_name not in dataset:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"'
)
return dataset
def get_variable_for_z(self, ds_id: str, var_name: str,
z_index: int) -> xr.DataArray:
ml_dataset = self.get_ml_dataset(ds_id)
dataset = ml_dataset.get_dataset(ml_dataset.num_levels - 1 - z_index)
if var_name not in dataset:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"')
return dataset[var_name]
def get_dataset_descriptors(self):
dataset_descriptors = self._config.get('Datasets')
if not dataset_descriptors:
raise ServiceConfigError(f"No datasets configured")
return dataset_descriptors
def get_dataset_descriptor(self, ds_id: str) -> Dict[str, Any]:
dataset_descriptors = self.get_dataset_descriptors()
if not dataset_descriptors:
raise ServiceConfigError(f"No datasets configured")
dataset_descriptor = self.find_dataset_descriptor(
dataset_descriptors, ds_id)
if dataset_descriptor is None:
raise ServiceResourceNotFoundError(f'Dataset "{ds_id}" not found')
return dataset_descriptor
def get_s3_bucket_mapping(self):
s3_bucket_mapping = {}
for descriptor in self.get_dataset_descriptors():
ds_id = descriptor.get('Identifier')
file_system = descriptor.get('FileSystem', 'local')
if file_system == 'local':
local_path = descriptor.get('Path')
if not os.path.isabs(local_path):
local_path = os.path.join(self.base_dir, local_path)
local_path = os.path.normpath(local_path)
if os.path.isdir(local_path):
s3_bucket_mapping[ds_id] = local_path
return s3_bucket_mapping
def get_tile_grid(self, ds_id: str) -> TileGrid:
ml_dataset, _ = self._get_dataset_entry(ds_id)
return ml_dataset.tile_grid
def get_color_mapping(self, ds_id: str, var_name: str):
cmap_cbar, cmap_vmin, cmap_vmax = DEFAULT_CMAP_CBAR, DEFAULT_CMAP_VMIN, DEFAULT_CMAP_VMAX
dataset_descriptor = self.get_dataset_descriptor(ds_id)
style_name = dataset_descriptor.get('Style', 'default')
styles = self._config.get('Styles')
if styles:
style = None
for s in styles:
if style_name == s['Identifier']:
style = s
break
# TODO: check color_mappings is not None
if style:
color_mappings = style.get('ColorMappings')
if color_mappings:
# TODO: check color_mappings is not None
color_mapping = color_mappings.get(var_name)
if color_mapping:
cmap_vmin, cmap_vmax = color_mapping.get(
'ValueRange', (cmap_vmin, cmap_vmax))
if color_mapping.get('ColorFile') is not None:
cmap_cbar = color_mapping.get(
'ColorFile', cmap_cbar)
else:
cmap_cbar = color_mapping.get(
'ColorBar', cmap_cbar)
cmap_cbar, _ = get_cmap(cmap_cbar)
return cmap_cbar, cmap_vmin, cmap_vmax
else:
ds = self.get_dataset(ds_id, expected_var_names=[var_name])
var = ds[var_name]
cmap_cbar = var.attrs.get('color_bar_name', cmap_cbar)
cmap_vmin = var.attrs.get('color_value_min', cmap_vmin)
cmap_vmax = var.attrs.get('color_value_max', cmap_vmax)
_LOG.warning(
f'color mapping for variable {var_name!r} of dataset {ds_id!r} undefined: using defaults'
)
return cmap_cbar, cmap_vmin, cmap_vmax
def _get_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, Dict[str, Any]]:
if ds_id not in self._dataset_cache:
with self._lock:
self._dataset_cache[ds_id] = self._create_dataset_entry(ds_id)
return self._dataset_cache[ds_id]
def _create_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, Dict[str, Any]]:
dataset_descriptor = self.get_dataset_descriptor(ds_id)
ml_dataset = self._open_ml_dataset(dataset_descriptor)
return ml_dataset, dataset_descriptor
def _open_ml_dataset(
self, dataset_descriptor: DatasetDescriptor) -> MultiLevelDataset:
fs_type = dataset_descriptor.get('FileSystem', 'local')
if self._ml_dataset_openers and fs_type in self._ml_dataset_openers:
ml_dataset_opener = self._ml_dataset_openers[fs_type]
elif fs_type in _DEFAULT_MULTI_LEVEL_DATASET_OPENERS:
ml_dataset_opener = _DEFAULT_MULTI_LEVEL_DATASET_OPENERS[fs_type]
else:
ds_id = dataset_descriptor.get('Identifier')
raise ServiceConfigError(
f"Invalid fs={fs_type!r} in dataset descriptor {ds_id!r}")
return ml_dataset_opener(self, dataset_descriptor)
def get_legend_label(self, ds_name: str, var_name: str):
dataset = self.get_dataset(ds_name)
if var_name in dataset:
ds = self.get_dataset(ds_name)
units = ds[var_name].units
return units
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_name}"')
def get_dataset_place_groups(self,
ds_id: str,
load_features=False) -> List[Dict]:
dataset_descriptor = self.get_dataset_descriptor(ds_id)
place_group_id_prefix = f"DS-{ds_id}-"
place_groups = []
for k, v in self._place_group_cache.items():
if k.startswith(place_group_id_prefix):
place_groups.append(v)
if place_groups:
return place_groups
place_groups = self._load_place_groups(dataset_descriptor.get(
"PlaceGroups", []),
is_global=False,
load_features=load_features)
for place_group in place_groups:
self._place_group_cache[place_group_id_prefix +
place_group["id"]] = place_group
return place_groups
def get_dataset_place_group(self,
ds_id: str,
place_group_id: str,
load_features=False) -> Dict:
place_groups = self.get_dataset_place_groups(ds_id,
load_features=False)
for place_group in place_groups:
if place_group_id == place_group['id']:
if load_features:
self._load_place_group_features(place_group)
return place_group
raise ServiceResourceNotFoundError(
f'Place group "{place_group_id}" not found')
def get_global_place_groups(self, load_features=False) -> List[Dict]:
return self._load_place_groups(self._config.get("PlaceGroups", []),
is_global=True,
load_features=load_features)
def get_global_place_group(self,
place_group_id: str,
load_features: bool = False) -> Dict:
place_group_descriptor = self._get_place_group_descriptor(
place_group_id)
return self._load_place_group(place_group_descriptor,
is_global=True,
load_features=load_features)
def _get_place_group_descriptor(self, place_group_id: str) -> Dict:
place_group_descriptors = self._config.get("PlaceGroups", [])
for place_group_descriptor in place_group_descriptors:
if place_group_descriptor['Identifier'] == place_group_id:
return place_group_descriptor
raise ServiceResourceNotFoundError(
f'Place group "{place_group_id}" not found')
def _load_place_groups(self,
place_group_descriptors: Dict,
is_global: bool = False,
load_features: bool = False) -> List[Dict]:
place_groups = []
for place_group_descriptor in place_group_descriptors:
place_group = self._load_place_group(place_group_descriptor,
is_global=is_global,
load_features=load_features)
place_groups.append(place_group)
return place_groups
def _load_place_group(self,
place_group_descriptor: Dict[str, Any],
is_global: bool = False,
load_features: bool = False) -> Dict[str, Any]:
place_group_id = place_group_descriptor.get("PlaceGroupRef")
if place_group_id:
if is_global:
raise ServiceError(
"'PlaceGroupRef' cannot be used in a global place group")
if len(place_group_descriptor) > 1:
raise ServiceError(
"'PlaceGroupRef' if present, must be the only entry in a 'PlaceGroups' item"
)
return self.get_global_place_group(place_group_id,
load_features=load_features)
place_group_id = place_group_descriptor.get("Identifier")
if not place_group_id:
raise ServiceError(
"Missing 'Identifier' entry in a 'PlaceGroups' item")
if place_group_id in self._place_group_cache:
place_group = self._place_group_cache[place_group_id]
else:
place_group_title = place_group_descriptor.get(
"Title", place_group_id)
place_path_wc = place_group_descriptor.get("Path")
if not place_path_wc:
raise ServiceError(
"Missing 'Path' entry in a 'PlaceGroups' item")
if not os.path.isabs(place_path_wc):
place_path_wc = os.path.join(self._base_dir, place_path_wc)
source_paths = glob.glob(place_path_wc)
source_encoding = place_group_descriptor.get(
"CharacterEncoding", "utf-8")
property_mapping = place_group_descriptor.get("PropertyMapping")
place_group = dict(type="FeatureCollection",
features=None,
id=place_group_id,
title=place_group_title,
propertyMapping=property_mapping,
sourcePaths=source_paths,
sourceEncoding=source_encoding)
sub_place_group_configs = place_group_descriptor.get("Places")
if sub_place_group_configs:
raise ServiceError(
"Invalid 'Places' entry in a 'PlaceGroups' item: not implemented yet"
)
# sub_place_group_descriptors = place_group_config.get("Places")
# if sub_place_group_descriptors:
# sub_place_groups = self._load_place_groups(sub_place_group_descriptors)
# place_group["placeGroups"] = sub_place_groups
self._place_group_cache[place_group_id] = place_group
if load_features:
self._load_place_group_features(place_group)
return place_group
def _load_place_group_features(
self, place_group: Dict[str, Any]) -> List[Dict[str, Any]]:
features = place_group.get('features')
if features is not None:
return features
source_files = place_group['sourcePaths']
source_encoding = place_group['sourceEncoding']
features = []
for source_file in source_files:
with fiona.open(source_file,
encoding=source_encoding) as feature_collection:
for feature in feature_collection:
self._remove_feature_id(feature)
feature["id"] = str(self._feature_index)
self._feature_index += 1
features.append(feature)
place_group['features'] = features
return features
@classmethod
def _remove_feature_id(cls, feature: Dict):
cls._remove_id(feature)
if "properties" in feature:
cls._remove_id(feature["properties"])
@classmethod
def _remove_id(cls, properties: Dict):
if "id" in properties:
del properties["id"]
if "ID" in properties:
del properties["ID"]
def get_dataset_and_coord_variable(self, ds_name: str, dim_name: str):
ds = self.get_dataset(ds_name)
if dim_name not in ds.coords:
raise ServiceResourceNotFoundError(
f'Dimension {dim_name!r} has no coordinates in dataset {ds_name!r}'
)
return ds, ds.coords[dim_name]
@classmethod
def get_var_indexers(cls, ds_name: str, var_name: str, var: xr.DataArray,
dim_names: List[str],
params: RequestParams) -> Dict[str, Any]:
var_indexers = dict()
for dim_name in dim_names:
if dim_name not in var.coords:
raise ServiceBadRequestError(
f'dimension {dim_name!r} of variable {var_name!r} of dataset {ds_name!r} has no coordinates'
)
coord_var = var.coords[dim_name]
dim_value_str = params.get_query_argument(dim_name, None)
try:
if dim_value_str is None:
var_indexers[dim_name] = coord_var.values[0]
elif dim_value_str == 'current':
var_indexers[dim_name] = coord_var.values[-1]
elif np.issubdtype(coord_var.dtype, np.floating):
var_indexers[dim_name] = float(dim_value_str)
elif np.issubdtype(coord_var.dtype, np.integer):
var_indexers[dim_name] = int(dim_value_str)
elif np.issubdtype(coord_var.dtype, np.datetime64):
if '/' in dim_value_str:
date_str_1, date_str_2 = dim_value_str.split(
'/', maxsplit=1)
var_indexer_1 = pd.to_datetime(date_str_1)
var_indexer_2 = pd.to_datetime(date_str_2)
var_indexers[dim_name] = var_indexer_1 + (
var_indexer_2 - var_indexer_1) / 2
else:
date_str = dim_value_str
var_indexers[dim_name] = pd.to_datetime(date_str)
else:
raise ValueError(
f'unable to convert value {dim_value_str!r} to {coord_var.dtype!r}'
)
except ValueError as e:
raise ServiceBadRequestError(
f'{dim_value_str!r} is not a valid value for dimension {dim_name!r} '
f'of variable {var_name!r} of dataset {ds_name!r}') from e
return var_indexers
@classmethod
def find_dataset_descriptor(cls, dataset_descriptors: List[Dict[str, Any]],
ds_name: str) -> Optional[Dict[str, Any]]:
# Note: can be optimized by dict/key lookup
return next(
(dsd
for dsd in dataset_descriptors if dsd['Identifier'] == ds_name),
None)
class ServiceContext:
def __init__(self,
prefix: str = None,
base_dir: str = None,
config: Config = None,
trace_perf: bool = DEFAULT_TRACE_PERF,
tile_comp_mode: int = None,
tile_cache_capacity: int = None,
ml_dataset_openers: Dict[str,
MultiLevelDatasetOpener] = None):
self._prefix = normalize_prefix(prefix)
self._base_dir = os.path.abspath(base_dir or '')
self._config = config if config is not None else dict()
self._config_mtime = 0.0
self._place_group_cache = dict()
self._feature_index = 0
self._ml_dataset_openers = ml_dataset_openers
self._tile_comp_mode = tile_comp_mode
self._trace_perf = trace_perf
self._lock = threading.RLock()
self._dataset_cache = dict(
) # contains tuples of form (MultiLevelDataset, ds_descriptor)
self._image_cache = dict()
if tile_cache_capacity and tile_cache_capacity > 0:
self._tile_cache = Cache(MemoryCacheStore(),
capacity=tile_cache_capacity,
threshold=0.75)
else:
self._tile_cache = None
@property
def config(self) -> Config:
return self._config
@config.setter
def config(self, config: Config):
if self._config:
with self._lock:
# Close all datasets
for ml_dataset, _ in self._dataset_cache.values():
# noinspection PyBroadException
try:
ml_dataset.close()
except Exception:
pass
# Clear all caches
if self._dataset_cache:
self._dataset_cache.clear()
if self._image_cache:
self._image_cache.clear()
if self._tile_cache:
self._tile_cache.clear()
if self._place_group_cache:
self._place_group_cache.clear()
self._config = config
@property
def config_mtime(self) -> float:
return self._config_mtime
@config_mtime.setter
def config_mtime(self, value: float):
self._config_mtime = value
@property
def base_dir(self) -> str:
return self._base_dir
@property
def tile_comp_mode(self) -> int:
return self._tile_comp_mode
@property
def dataset_cache(
self) -> Dict[str, Tuple[MultiLevelDataset, Dict[str, Any]]]:
return self._dataset_cache
@property
def image_cache(self) -> Dict[str, Any]:
return self._image_cache
@property
def tile_cache(self) -> Optional[Cache]:
return self._tile_cache
@property
def trace_perf(self) -> bool:
return self._trace_perf
@property
def access_control(self) -> Dict[str, Any]:
return dict(self._config.get('AccessControl', {}))
@property
def required_scopes(self) -> List[str]:
return self.access_control.get('RequiredScopes', [])
def get_required_dataset_scopes(
self, dataset_descriptor: DatasetDescriptor) -> Set[str]:
return self._get_required_scopes(dataset_descriptor, 'read:dataset',
'Dataset',
dataset_descriptor['Identifier'])
def get_required_variable_scopes(self,
dataset_descriptor: DatasetDescriptor,
var_name: str) -> Set[str]:
return self._get_required_scopes(dataset_descriptor, 'read:variable',
'Variable', var_name)
def _get_required_scopes(self, dataset_descriptor: DatasetDescriptor,
base_scope: str, value_name: str,
value: str) -> Set[str]:
base_scope_prefix = base_scope + ':'
pattern_scope = base_scope_prefix + '{' + value_name + '}'
dataset_access_control = dataset_descriptor.get('AccessControl', {})
dataset_required_scopes = dataset_access_control.get(
'RequiredScopes', [])
dataset_required_scopes = set(self.required_scopes +
dataset_required_scopes)
dataset_required_scopes = {
scope
for scope in dataset_required_scopes
if scope == base_scope or scope.startswith(base_scope_prefix)
}
if pattern_scope in dataset_required_scopes:
dataset_required_scopes.remove(pattern_scope)
dataset_required_scopes.add(base_scope_prefix + value)
return dataset_required_scopes
def get_service_url(self, base_url, *path: str):
if self._prefix:
return base_url + '/' + self._prefix + '/' + '/'.join(path)
else:
return base_url + '/' + '/'.join(path)
def get_ml_dataset(self, ds_id: str) -> MultiLevelDataset:
ml_dataset, _ = self._get_dataset_entry(ds_id)
return ml_dataset
def set_ml_dataset(self, ml_dataset: MultiLevelDataset):
self._set_dataset_entry(
(ml_dataset, dict(Identifier=ml_dataset.ds_id, Hidden=True)))
def get_dataset(self,
ds_id: str,
expected_var_names: Collection[str] = None) -> xr.Dataset:
ml_dataset, _ = self._get_dataset_entry(ds_id)
dataset = ml_dataset.base_dataset
if expected_var_names:
for var_name in expected_var_names:
if var_name not in dataset:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"'
)
return dataset
def get_variable_for_z(self, ds_id: str, var_name: str,
z_index: int) -> xr.DataArray:
ml_dataset = self.get_ml_dataset(ds_id)
index = ml_dataset.num_levels - 1 - z_index
if index < 0 or index >= ml_dataset.num_levels:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" has no z-index {z_index} in dataset "{ds_id}"'
)
dataset = ml_dataset.get_dataset(index)
if var_name not in dataset:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"')
return dataset[var_name]
def get_dataset_descriptors(self):
dataset_descriptors = self._config.get('Datasets')
if not dataset_descriptors:
raise ServiceConfigError(f"No datasets configured")
return dataset_descriptors
def get_dataset_descriptor(self, ds_id: str) -> Dict[str, Any]:
dataset_descriptors = self.get_dataset_descriptors()
if not dataset_descriptors:
raise ServiceConfigError(f"No datasets configured")
dataset_descriptor = self.find_dataset_descriptor(
dataset_descriptors, ds_id)
if dataset_descriptor is None:
raise ServiceResourceNotFoundError(f'Dataset "{ds_id}" not found')
return dataset_descriptor
def get_s3_bucket_mapping(self):
s3_bucket_mapping = {}
for descriptor in self.get_dataset_descriptors():
ds_id = descriptor.get('Identifier')
file_system = descriptor.get('FileSystem', 'local')
if file_system == 'local':
local_path = self.get_descriptor_path(
descriptor, f'dataset descriptor {ds_id}')
local_path = os.path.normpath(local_path)
if os.path.isdir(local_path):
s3_bucket_mapping[ds_id] = local_path
return s3_bucket_mapping
def get_tile_grid(self, ds_id: str) -> TileGrid:
ml_dataset, _ = self._get_dataset_entry(ds_id)
return ml_dataset.tile_grid
def get_rgb_color_mapping(
self, ds_id: str, norm_range: Tuple[float, float] = (0., 1.)
) -> Tuple[List[Optional[str]], List[Tuple[float, float]]]:
var_names = [None, None, None]
norm_ranges = [norm_range, norm_range, norm_range]
color_mappings = self.get_color_mappings(ds_id)
if color_mappings:
rgb_mapping = color_mappings.get('rgb')
if rgb_mapping:
components = 'Red', 'Green', 'Blue'
for i in range(3):
c = components[i]
c_descriptor = rgb_mapping.get(c, {})
var_name = c_descriptor.get('Variable')
norm_vmin, norm_vmax = c_descriptor.get(
'ValueRange', norm_range)
var_names[i] = var_name
norm_ranges[i] = norm_vmin, norm_vmax
return var_names, norm_ranges
def get_color_mapping(self, ds_id: str,
var_name: str) -> Tuple[str, Tuple[float, float]]:
cmap_name = None
cmap_vmin, cmap_vmax = None, None
color_mappings = self.get_color_mappings(ds_id)
if color_mappings:
color_mapping = color_mappings.get(var_name)
if color_mapping:
cmap_vmin, cmap_vmax = color_mapping.get(
'ValueRange', (None, None))
if color_mapping.get('ColorFile') is not None:
cmap_name = color_mapping.get('ColorFile', cmap_name)
else:
cmap_name = color_mapping.get('ColorBar', cmap_name)
cmap_name, _ = get_cmap(cmap_name)
cmap_range = cmap_vmin, cmap_vmax
if cmap_name is not None and None not in cmap_range:
# noinspection PyTypeChecker
return cmap_name, cmap_range
ds = self.get_dataset(ds_id, expected_var_names=[var_name])
var = ds[var_name]
valid_range = get_var_valid_range(var)
return get_var_cmap_params(var, cmap_name, cmap_range, valid_range)
def get_style(self, ds_id: str):
dataset_descriptor = self.get_dataset_descriptor(ds_id)
style_name = dataset_descriptor.get('Style', 'default')
styles = self._config.get('Styles')
if styles:
for style in styles:
if style_name == style['Identifier']:
return style
return None
def get_color_mappings(self,
ds_id: str) -> Optional[Dict[str, Dict[str, Any]]]:
style = self.get_style(ds_id)
if style:
return style.get('ColorMappings')
return None
def _get_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, DatasetDescriptor]:
if ds_id not in self._dataset_cache:
with self._lock:
self._set_dataset_entry(self._create_dataset_entry(ds_id))
return self._dataset_cache[ds_id]
def _set_dataset_entry(self, dataset_entry: Tuple[MultiLevelDataset,
DatasetDescriptor]):
ml_dataset, dataset_descriptor = dataset_entry
self._dataset_cache[ml_dataset.ds_id] = ml_dataset, dataset_descriptor
def _create_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, Dict[str, Any]]:
dataset_descriptor = self.get_dataset_descriptor(ds_id)
ml_dataset = self._open_ml_dataset(dataset_descriptor)
return ml_dataset, dataset_descriptor
def _open_ml_dataset(
self, dataset_descriptor: DatasetDescriptor) -> MultiLevelDataset:
ds_id = dataset_descriptor.get('Identifier')
fs_type = dataset_descriptor.get('FileSystem', 'local')
if self._ml_dataset_openers and fs_type in self._ml_dataset_openers:
ml_dataset_opener = self._ml_dataset_openers[fs_type]
elif fs_type in _DEFAULT_MULTI_LEVEL_DATASET_OPENERS:
ml_dataset_opener = _DEFAULT_MULTI_LEVEL_DATASET_OPENERS[fs_type]
else:
raise ServiceConfigError(
f"Invalid fs={fs_type!r} in dataset descriptor {ds_id!r}")
ml_dataset = ml_dataset_opener(self, dataset_descriptor)
augmentation = dataset_descriptor.get('Augmentation')
if augmentation:
script_path = self.get_descriptor_path(
augmentation, f"'Augmentation' of dataset descriptor {ds_id}")
input_parameters = augmentation.get('InputParameters')
callable_name = augmentation.get('Function', COMPUTE_VARIABLES)
ml_dataset = augment_ml_dataset(ml_dataset,
script_path,
callable_name,
self.get_ml_dataset,
self.set_ml_dataset,
input_parameters=input_parameters,
exception_type=ServiceConfigError)
return ml_dataset
def get_legend_label(self, ds_id: str, var_name: str):
dataset = self.get_dataset(ds_id)
if var_name in dataset:
ds = self.get_dataset(ds_id)
units = ds[var_name].units
return units
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"')
def get_dataset_place_groups(self,
ds_id: str,
base_url: str,
load_features=False) -> List[Dict]:
dataset_descriptor = self.get_dataset_descriptor(ds_id)
place_group_id_prefix = f"DS-{ds_id}-"
place_groups = []
for k, v in self._place_group_cache.items():
if k.startswith(place_group_id_prefix):
place_groups.append(v)
if place_groups:
return place_groups
place_groups = self._load_place_groups(dataset_descriptor.get(
"PlaceGroups", []),
base_url,
is_global=False,
load_features=load_features)
for place_group in place_groups:
self._place_group_cache[place_group_id_prefix +
place_group["id"]] = place_group
return place_groups
def get_dataset_place_group(self,
ds_id: str,
place_group_id: str,
base_url: str,
load_features=False) -> Dict:
place_groups = self.get_dataset_place_groups(ds_id,
base_url,
load_features=False)
for place_group in place_groups:
if place_group_id == place_group['id']:
if load_features:
self._load_place_group_features(place_group)
return place_group
raise ServiceResourceNotFoundError(
f'Place group "{place_group_id}" not found')
def get_global_place_groups(self,
base_url: str,
load_features=False) -> List[Dict]:
return self._load_place_groups(self._config.get("PlaceGroups", []),
base_url,
is_global=True,
load_features=load_features)
def get_global_place_group(self,
place_group_id: str,
base_url: str,
load_features: bool = False) -> Dict:
place_group_descriptor = self._get_place_group_descriptor(
place_group_id)
return self._load_place_group(place_group_descriptor,
base_url,
is_global=True,
load_features=load_features)
def _get_place_group_descriptor(self, place_group_id: str) -> Dict:
place_group_descriptors = self._config.get("PlaceGroups", [])
for place_group_descriptor in place_group_descriptors:
if place_group_descriptor['Identifier'] == place_group_id:
return place_group_descriptor
raise ServiceResourceNotFoundError(
f'Place group "{place_group_id}" not found')
def _load_place_groups(self,
place_group_descriptors: Dict,
base_url: str,
is_global: bool = False,
load_features: bool = False) -> List[Dict]:
place_groups = []
for place_group_descriptor in place_group_descriptors:
place_group = self._load_place_group(place_group_descriptor,
base_url,
is_global=is_global,
load_features=load_features)
place_groups.append(place_group)
return place_groups
def _load_place_group(self,
place_group_descriptor: Dict[str, Any],
base_url: str,
is_global: bool = False,
load_features: bool = False) -> Dict[str, Any]:
place_group_id = place_group_descriptor.get("PlaceGroupRef")
if place_group_id:
if is_global:
raise ServiceConfigError(
"'PlaceGroupRef' cannot be used in a global place group")
if len(place_group_descriptor) > 1:
raise ServiceConfigError(
"'PlaceGroupRef' if present, must be the only entry in a 'PlaceGroups' item"
)
return self.get_global_place_group(place_group_id,
base_url,
load_features=load_features)
place_group_id = place_group_descriptor.get("Identifier")
if not place_group_id:
raise ServiceConfigError(
"Missing 'Identifier' entry in a 'PlaceGroups' item")
if place_group_id in self._place_group_cache:
place_group = self._place_group_cache[place_group_id]
else:
place_group_title = place_group_descriptor.get(
"Title", place_group_id)
place_path_wc = self.get_descriptor_path(place_group_descriptor,
f"'PlaceGroups' item")
source_paths = glob.glob(place_path_wc)
source_encoding = place_group_descriptor.get(
"CharacterEncoding", "utf-8")
join = None
place_join = place_group_descriptor.get("Join")
if isinstance(place_join, dict):
join_path = self.get_descriptor_path(
place_join, "'Join' of a 'PlaceGroups' item")
join_property = place_join.get("Property")
if not join_property:
raise ServiceError(
"Missing 'Property' entry in 'Join' of a 'PlaceGroups' item"
)
join_encoding = place_join.get("CharacterEncoding", "utf-8")
join = dict(path=join_path,
property=join_property,
encoding=join_encoding)
property_mapping = place_group_descriptor.get("PropertyMapping")
if property_mapping:
property_mapping = dict(property_mapping)
for key, value in property_mapping.items():
if isinstance(value, str) and '${base_url}' in value:
property_mapping[key] = value.replace(
'${base_url}', base_url)
place_group = dict(type="FeatureCollection",
features=None,
id=place_group_id,
title=place_group_title,
propertyMapping=property_mapping,
sourcePaths=source_paths,
sourceEncoding=source_encoding,
join=join)
sub_place_group_configs = place_group_descriptor.get("Places")
if sub_place_group_configs:
raise ServiceConfigError(
"Invalid 'Places' entry in a 'PlaceGroups' item: not implemented yet"
)
# sub_place_group_descriptors = place_group_config.get("Places")
# if sub_place_group_descriptors:
# sub_place_groups = self._load_place_groups(sub_place_group_descriptors)
# place_group["placeGroups"] = sub_place_groups
self._place_group_cache[place_group_id] = place_group
if load_features:
self._load_place_group_features(place_group)
return place_group
def _load_place_group_features(
self, place_group: Dict[str, Any]) -> List[Dict[str, Any]]:
features = place_group.get('features')
if features is not None:
return features
source_files = place_group['sourcePaths']
source_encoding = place_group['sourceEncoding']
features = []
for source_file in source_files:
with fiona.open(source_file,
encoding=source_encoding) as feature_collection:
for feature in feature_collection:
self._remove_feature_id(feature)
feature["id"] = str(self._feature_index)
self._feature_index += 1
features.append(feature)
join = place_group['join']
if join:
join_path = join['path']
join_property = join['property']
join_encoding = join['encoding']
with fiona.open(join_path,
encoding=join_encoding) as feature_collection:
indexed_join_features = self._get_indexed_features(
feature_collection, join_property)
for feature in features:
properties = feature.get('properties')
if isinstance(properties,
dict) and join_property in properties:
join_value = properties[join_property]
join_feature = indexed_join_features.get(join_value)
if join_feature:
join_properties = join_feature.get('properties')
if join_properties:
properties.update(join_properties)
feature['properties'] = properties
place_group['features'] = features
return features
@classmethod
def _get_indexed_features(cls, features: Sequence[Dict[str, Any]],
property_name: str) -> Dict[Any, Any]:
feature_index = {}
for feature in features:
properties = feature.get('properties')
if properties and property_name in properties:
property_value = properties[property_name]
feature_index[property_value] = feature
return feature_index
@classmethod
def _remove_feature_id(cls, feature: Dict):
cls._remove_id(feature)
@classmethod
def _remove_id(cls, properties: Dict):
if "id" in properties:
del properties["id"]
if "ID" in properties:
del properties["ID"]
def get_dataset_and_coord_variable(self, ds_name: str, dim_name: str):
ds = self.get_dataset(ds_name)
if dim_name not in ds.coords:
raise ServiceResourceNotFoundError(
f'Dimension {dim_name!r} has no coordinates in dataset {ds_name!r}'
)
return ds, ds.coords[dim_name]
@classmethod
def get_var_indexers(cls, ds_name: str, var_name: str, var: xr.DataArray,
dim_names: List[str],
params: RequestParams) -> Dict[str, Any]:
var_indexers = dict()
for dim_name in dim_names:
if dim_name not in var.coords:
raise ServiceBadRequestError(
f'dimension {dim_name!r} of variable {var_name!r} of dataset {ds_name!r} has no coordinates'
)
coord_var = var.coords[dim_name]
dim_value_str = params.get_query_argument(dim_name, None)
try:
if dim_value_str is None:
var_indexers[dim_name] = coord_var.values[0]
elif dim_value_str == 'current':
var_indexers[dim_name] = coord_var.values[-1]
elif np.issubdtype(coord_var.dtype, np.floating):
var_indexers[dim_name] = float(dim_value_str)
elif np.issubdtype(coord_var.dtype, np.integer):
var_indexers[dim_name] = int(dim_value_str)
elif np.issubdtype(coord_var.dtype, np.datetime64):
if '/' in dim_value_str:
date_str_1, date_str_2 = dim_value_str.split(
'/', maxsplit=1)
var_indexer_1 = pd.to_datetime(date_str_1)
var_indexer_2 = pd.to_datetime(date_str_2)
var_indexers[dim_name] = var_indexer_1 + (
var_indexer_2 - var_indexer_1) / 2
else:
date_str = dim_value_str
var_indexers[dim_name] = pd.to_datetime(date_str)
else:
raise ValueError(
f'unable to convert value {dim_value_str!r} to {coord_var.dtype!r}'
)
except ValueError as e:
raise ServiceBadRequestError(
f'{dim_value_str!r} is not a valid value for dimension {dim_name!r} '
f'of variable {var_name!r} of dataset {ds_name!r}') from e
return var_indexers
@classmethod
def find_dataset_descriptor(cls, dataset_descriptors: List[Dict[str, Any]],
ds_name: str) -> Optional[Dict[str, Any]]:
# Note: can be optimized by dict/key lookup
return next(
(dsd
for dsd in dataset_descriptors if dsd['Identifier'] == ds_name),
None)
def get_descriptor_path(self,
descriptor: Dict[str, Any],
descriptor_name: str,
path_entry_name: str = 'Path',
is_url: bool = False) -> str:
path = descriptor.get(path_entry_name)
if not path:
raise ServiceError(
f"Missing entry {path_entry_name!r} in {descriptor_name}")
if not is_url and not os.path.isabs(path):
path = os.path.join(self._base_dir, path)
return path
class ServiceContext:
def __init__(self,
prefix: str = None,
base_dir: str = None,
config: Config = None,
data_store_pool: DataStorePool = None,
trace_perf: bool = DEFAULT_TRACE_PERF,
tile_comp_mode: int = None,
tile_cache_capacity: int = None,
ml_dataset_openers: Dict[str,
MultiLevelDatasetOpener] = None):
self._prefix = normalize_prefix(prefix)
self._base_dir = os.path.abspath(base_dir or '')
self._config = config if config is not None else dict()
self._config_mtime = 0.0
self._place_group_cache = dict()
self._feature_index = 0
self._ml_dataset_openers = ml_dataset_openers
self._tile_comp_mode = tile_comp_mode
self._trace_perf = trace_perf
self._lock = threading.RLock()
# contains tuples of form (MultiLevelDataset, dataset_config)
self._dataset_cache = dict()
# cache for all dataset configs
self._dataset_configs: Optional[List[DatasetConfigDict]] = None
self._image_cache = dict()
self._data_store_pool = data_store_pool or None
if tile_cache_capacity:
self._tile_cache = Cache(MemoryCacheStore(),
capacity=tile_cache_capacity,
threshold=0.75)
else:
self._tile_cache = None
@property
def config(self) -> Config:
return self._config
@config.setter
def config(self, config: Config):
if self._config:
with self._lock:
# Close all datasets
for ml_dataset, _ in self._dataset_cache.values():
# noinspection PyBroadException
try:
ml_dataset.close()
except Exception:
pass
# Clear all caches
if self._dataset_cache:
self._dataset_cache.clear()
if self._image_cache:
self._image_cache.clear()
if self._tile_cache:
self._tile_cache.clear()
if self._place_group_cache:
self._place_group_cache.clear()
if self._data_store_pool:
self._data_store_pool.remove_all_store_configs()
self._dataset_configs = None
self._config = config
@property
def config_mtime(self) -> float:
return self._config_mtime
@config_mtime.setter
def config_mtime(self, value: float):
self._config_mtime = value
@property
def base_dir(self) -> str:
return self._base_dir
@property
def tile_comp_mode(self) -> int:
return self._tile_comp_mode
@property
def dataset_cache(
self) -> Dict[str, Tuple[MultiLevelDataset, Dict[str, Any]]]:
return self._dataset_cache
@property
def image_cache(self) -> Dict[str, Any]:
return self._image_cache
@property
def tile_cache(self) -> Optional[Cache]:
return self._tile_cache
@property
def trace_perf(self) -> bool:
return self._trace_perf
@property
def measure_time(self):
return measure_time_cm(disabled=not self.trace_perf, logger=LOG)
@property
def access_control(self) -> Dict[str, Any]:
return dict(self._config.get('AccessControl', {}))
@property
def required_scopes(self) -> List[str]:
return self.access_control.get('RequiredScopes', [])
def get_required_dataset_scopes(
self, dataset_config: DatasetConfigDict) -> Set[str]:
return self._get_required_scopes(dataset_config, 'read:dataset',
'Dataset',
dataset_config['Identifier'])
def get_required_variable_scopes(self, dataset_config: DatasetConfigDict,
var_name: str) -> Set[str]:
return self._get_required_scopes(dataset_config, 'read:variable',
'Variable', var_name)
def _get_required_scopes(self, dataset_config: DatasetConfigDict,
base_scope: str, value_name: str,
value: str) -> Set[str]:
base_scope_prefix = base_scope + ':'
pattern_scope = base_scope_prefix + '{' + value_name + '}'
dataset_access_control = dataset_config.get('AccessControl', {})
dataset_required_scopes = dataset_access_control.get(
'RequiredScopes', [])
dataset_required_scopes = set(self.required_scopes +
dataset_required_scopes)
dataset_required_scopes = {
scope
for scope in dataset_required_scopes
if scope == base_scope or scope.startswith(base_scope_prefix)
}
if pattern_scope in dataset_required_scopes:
dataset_required_scopes.remove(pattern_scope)
dataset_required_scopes.add(base_scope_prefix + value)
return dataset_required_scopes
def get_service_url(self, base_url, *path: str):
# noinspection PyTypeChecker
path_comp = '/'.join(path)
if self._prefix:
return base_url + self._prefix + '/' + path_comp
else:
return base_url + '/' + path_comp
def get_ml_dataset(self, ds_id: str) -> MultiLevelDataset:
ml_dataset, _ = self._get_dataset_entry(ds_id)
return ml_dataset
def set_ml_dataset(self, ml_dataset: MultiLevelDataset):
self._set_dataset_entry(
(ml_dataset, dict(Identifier=ml_dataset.ds_id, Hidden=True)))
def get_dataset(self,
ds_id: str,
expected_var_names: Collection[str] = None) -> xr.Dataset:
ml_dataset, _ = self._get_dataset_entry(ds_id)
dataset = ml_dataset.base_dataset
if expected_var_names:
for var_name in expected_var_names:
if var_name not in dataset:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"'
)
return dataset
def get_time_series_dataset(self,
ds_id: str,
var_name: str = None) -> xr.Dataset:
dataset_config = self.get_dataset_config(ds_id)
ts_ds_name = dataset_config.get('TimeSeriesDataset', ds_id)
try:
# Try to get more efficient, time-chunked dataset
return self.get_dataset(
ts_ds_name,
expected_var_names=[var_name] if var_name else None)
except ServiceResourceNotFoundError:
# This happens, if the dataset pointed to by 'TimeSeriesDataset'
# does not contain the variable given by var_name.
return self.get_dataset(
ds_id, expected_var_names=[var_name] if var_name else None)
def get_variable_for_z(self, ds_id: str, var_name: str,
z_index: int) -> xr.DataArray:
ml_dataset = self.get_ml_dataset(ds_id)
index = ml_dataset.num_levels - 1 - z_index
if index < 0 or index >= ml_dataset.num_levels:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" has no z-index {z_index} in dataset "{ds_id}"'
)
dataset = ml_dataset.get_dataset(index)
if var_name not in dataset:
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"')
return dataset[var_name]
def get_dataset_configs(self) -> List[DatasetConfigDict]:
if self._dataset_configs is None:
with self._lock:
dataset_configs = self._config.get('Datasets', [])
dataset_configs += \
self.get_dataset_configs_from_stores()
self._dataset_configs = dataset_configs
self._maybe_assign_store_instance_ids()
return self._dataset_configs
def _maybe_assign_store_instance_ids(self):
assignable_dataset_configs = [
dc for dc in self._dataset_configs if 'StoreInstanceId' not in dc
and dc.get('FileSystem', 'file') in NON_MEMORY_FILE_SYSTEMS
]
# split into sublists according to file system and non-root store params
config_lists = []
for config in assignable_dataset_configs:
store_params = self._get_other_store_params_than_root(config)
file_system = config.get('FileSystem', 'file')
appended = False
for config_list in config_lists:
if config_list[0] == file_system and \
config_list[1] == store_params:
config_list[2].append(config)
appended = True
break
if not appended:
config_lists.append((file_system, store_params, [config]))
data_store_pool = self.get_data_store_pool()
if not data_store_pool:
data_store_pool = self._data_store_pool = DataStorePool()
for file_system, store_params, config_list in config_lists:
# Retrieve paths per configuration
paths = [dc['Path'] for dc in config_list]
list.sort(paths)
# Determine common prefixes of paths (and call them roots)
prefixes = _get_common_prefixes(paths)
if len(prefixes) < 1:
roots = ['']
else:
# perform further step to merge prefixes with same start
prefixes = list(set(prefixes))
prefixes.sort()
roots = []
root_candidate = prefixes[0]
for root in prefixes[1:]:
common_root = os.path.commonprefix([root_candidate, root])
if _is_not_empty(common_root):
root_candidate = common_root
else:
roots.append(root_candidate)
root_candidate = root
roots.append(root_candidate)
for root in roots:
# ensure root does not end with full or partial directory
# or file name
while not root.endswith("/") and not root.endswith("\\") and \
len(root) > 0:
root = root[:-1]
if root.endswith("/") or root.endswith("\\"):
root = root[:-1]
abs_root = root
# For local file systems: Determine absolute root from base dir
fs_protocol = FS_TYPE_TO_PROTOCOL.get(file_system, file_system)
if fs_protocol == 'file' and not os.path.isabs(abs_root):
abs_root = os.path.join(self._base_dir, abs_root)
abs_root = os.path.normpath(abs_root)
store_params_for_root = store_params.copy()
store_params_for_root['root'] = abs_root
# See if there already is a store with this configuration
data_store_config = DataStoreConfig(
store_id=fs_protocol, store_params=store_params_for_root)
store_instance_id = data_store_pool.\
get_store_instance_id(data_store_config)
if not store_instance_id:
# Create new store with new unique store instance id
counter = 1
while data_store_pool.has_store_instance(
f'{fs_protocol}_{counter}'):
counter += 1
store_instance_id = f'{fs_protocol}_{counter}'
data_store_pool.add_store_config(store_instance_id,
data_store_config)
for config in config_list:
if config['Path'].startswith(root):
config['StoreInstanceId'] = store_instance_id
new_path = config['Path'][len(root):]
while new_path.startswith("/") or \
new_path.startswith("\\"):
new_path = new_path[1:]
config['Path'] = new_path
def _get_other_store_params_than_root(self,
dataset_config: DatasetConfigDict) \
-> Dict:
if FS_TYPE_TO_PROTOCOL.get(dataset_config.get('FileSystem',
'file')) != 's3':
return {}
storage_options = dict()
if 'Anonymous' in dataset_config:
storage_options['anon'] = dataset_config['Anonymous']
client_kwargs = dict()
if 'Endpoint' in dataset_config:
client_kwargs['endpoint_url'] = dataset_config['Endpoint']
if 'Region' in dataset_config:
client_kwargs['region_name'] = dataset_config['Region']
storage_options['client_kwargs'] = client_kwargs
store_params = dict(storage_options=storage_options)
return store_params
def get_dataset_configs_from_stores(self) \
-> List[DatasetConfigDict]:
data_store_pool = self.get_data_store_pool()
if data_store_pool is None:
return []
all_dataset_configs: List[DatasetConfigDict] = []
for store_instance_id in data_store_pool.store_instance_ids:
LOG.info(f'scanning store {store_instance_id!r}')
data_store_config = data_store_pool.get_store_config(
store_instance_id)
data_store = data_store_pool.get_store(store_instance_id)
store_dataset_ids = data_store.get_data_ids(data_type=DATASET_TYPE)
for store_dataset_id in store_dataset_ids:
dataset_config_base = {}
store_dataset_configs: List[DatasetConfigDict] \
= data_store_config.user_data
if store_dataset_configs:
for store_dataset_config in store_dataset_configs:
dataset_id_pattern = store_dataset_config.get(
'Path', '*')
if fnmatch.fnmatch(store_dataset_id,
dataset_id_pattern):
dataset_config_base = store_dataset_config
break
else:
dataset_config_base = None
if dataset_config_base is not None:
LOG.debug(f'selected dataset {store_dataset_id!r}')
dataset_config = dict(StoreInstanceId=store_instance_id,
**dataset_config_base)
dataset_config['Path'] = store_dataset_id
dataset_config['Identifier'] = \
f'{store_instance_id}{STORE_DS_ID_SEPARATOR}' \
f'{store_dataset_id}'
all_dataset_configs.append(dataset_config)
# Just for testing:
debug_file = 'all_dataset_configs.json'
with open(debug_file, 'w') as stream:
json.dump(all_dataset_configs, stream)
LOG.debug(f'wrote file {debug_file!r}')
return all_dataset_configs
def new_dataset_metadata(self, store_instance_id: str,
dataset_id: str) -> Optional[DatasetDescriptor]:
data_store = self._data_store_pool.get_store(store_instance_id)
dataset_metadata = data_store.describe_data(dataset_id,
data_type='dataset')
if dataset_metadata.crs is not None:
crs = pyproj.CRS.from_string(dataset_metadata.crs)
if not crs.is_geographic:
LOG.warn(f'ignoring dataset {dataset_id!r} from'
f' store instance {store_instance_id!r}'
f' because it uses a non-geographic CRS')
return None
# noinspection PyTypeChecker
return dataset_metadata
def get_data_store_pool(self) -> Optional[DataStorePool]:
data_store_configs = self._config.get('DataStores', [])
if not data_store_configs or self._data_store_pool:
return self._data_store_pool
if not isinstance(data_store_configs, list):
raise ServiceConfigError('DataStores must be a list')
store_configs: Dict[str, DataStoreConfig] = {}
for data_store_config_dict in data_store_configs:
store_instance_id = data_store_config_dict.get('Identifier')
store_id = data_store_config_dict.get('StoreId')
store_params = data_store_config_dict.get('StoreParams', {})
dataset_configs = data_store_config_dict.get('Datasets')
store_config = DataStoreConfig(store_id,
store_params=store_params,
user_data=dataset_configs)
store_configs[store_instance_id] = store_config
self._data_store_pool = DataStorePool(store_configs)
return self._data_store_pool
def get_dataset_config(self, ds_id: str) -> Dict[str, Any]:
dataset_configs = self.get_dataset_configs()
dataset_config = self.find_dataset_config(dataset_configs, ds_id)
if dataset_config is None:
raise ServiceResourceNotFoundError(f'Dataset "{ds_id}" not found')
return dataset_config
def get_s3_bucket_mapping(self):
s3_bucket_mapping = {}
for dataset_config in self.get_dataset_configs():
ds_id = dataset_config.get('Identifier')
protocol = FS_TYPE_TO_PROTOCOL.get(
dataset_config.get('FileSystem', 'file'))
if protocol == 'file':
store_instance_id = dataset_config.get('StoreInstanceId')
if store_instance_id:
data_store_pool = self.get_data_store_pool()
store_root = data_store_pool.get_store_config(
store_instance_id). \
store_params.get('root')
data_id = dataset_config.get('Path')
local_path = os.path.join(store_root, data_id)
else:
local_path = self.get_config_path(
dataset_config, f'dataset configuration'
f' {ds_id!r}')
local_path = os.path.normpath(local_path)
if os.path.isdir(local_path):
s3_bucket_mapping[ds_id] = local_path
return s3_bucket_mapping
def get_tile_grid(self, ds_id: str) -> TileGrid:
ml_dataset, _ = self._get_dataset_entry(ds_id)
return ml_dataset.tile_grid
def get_rgb_color_mapping(
self, ds_id: str, norm_range: Tuple[float, float] = (0., 1.)
) -> Tuple[List[Optional[str]], List[Tuple[float, float]]]:
var_names = [None, None, None]
norm_ranges = [norm_range, norm_range, norm_range]
color_mappings = self.get_color_mappings(ds_id)
if color_mappings:
rgb_mapping = color_mappings.get('rgb')
if rgb_mapping:
components = 'Red', 'Green', 'Blue'
for i in range(3):
component = components[i]
component_config = rgb_mapping.get(component, {})
var_name = component_config.get('Variable')
norm_vmin, norm_vmax = component_config.get(
'ValueRange', norm_range)
var_names[i] = var_name
norm_ranges[i] = norm_vmin, norm_vmax
return var_names, norm_ranges
def get_color_mapping(self, ds_id: str,
var_name: str) -> Tuple[str, Tuple[float, float]]:
cmap_name = None
cmap_vmin, cmap_vmax = None, None
color_mappings = self.get_color_mappings(ds_id)
if color_mappings:
color_mapping = color_mappings.get(var_name)
if color_mapping:
cmap_vmin, cmap_vmax = color_mapping.get(
'ValueRange', (None, None))
if color_mapping.get('ColorFile') is not None:
cmap_name = color_mapping.get('ColorFile', cmap_name)
else:
cmap_name = color_mapping.get('ColorBar', cmap_name)
cmap_name, _ = get_cmap(cmap_name)
cmap_range = cmap_vmin, cmap_vmax
if cmap_name is not None and None not in cmap_range:
# noinspection PyTypeChecker
return cmap_name, cmap_range
ds = self.get_dataset(ds_id, expected_var_names=[var_name])
var = ds[var_name]
valid_range = get_var_valid_range(var)
return get_var_cmap_params(var, cmap_name, cmap_range, valid_range)
def get_style(self, ds_id: str):
dataset_config = self.get_dataset_config(ds_id)
style_name = dataset_config.get('Style', 'default')
styles = self._config.get('Styles')
if styles:
for style in styles:
if style_name == style['Identifier']:
return style
return None
def get_color_mappings(self,
ds_id: str) -> Optional[Dict[str, Dict[str, Any]]]:
style = self.get_style(ds_id)
if style:
return style.get('ColorMappings')
return None
def _get_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, DatasetConfigDict]:
if ds_id not in self._dataset_cache:
with self._lock:
self._set_dataset_entry(self._create_dataset_entry(ds_id))
return self._dataset_cache[ds_id]
def _set_dataset_entry(self, dataset_entry: Tuple[MultiLevelDataset,
DatasetConfigDict]):
ml_dataset, dataset_config = dataset_entry
self._dataset_cache[ml_dataset.ds_id] = ml_dataset, dataset_config
def _create_dataset_entry(
self, ds_id: str) -> Tuple[MultiLevelDataset, Dict[str, Any]]:
dataset_config = self.get_dataset_config(ds_id)
ml_dataset = self._open_ml_dataset(dataset_config)
return ml_dataset, dataset_config
def _open_ml_dataset(self, dataset_config: DatasetConfigDict) \
-> MultiLevelDataset:
ds_id: str = dataset_config.get('Identifier')
store_instance_id = dataset_config.get('StoreInstanceId')
if store_instance_id:
data_store_pool = self.get_data_store_pool()
data_store = data_store_pool.get_store(store_instance_id)
data_id = dataset_config.get('Path')
open_params = dataset_config.get('StoreOpenParams') or {}
# Inject chunk_cache_capacity into open parameters
chunk_cache_capacity = self.get_dataset_chunk_cache_capacity(
dataset_config)
if chunk_cache_capacity \
and (data_id.endswith('.zarr')
or data_id.endswith('.levels')) \
and 'cache_size' not in open_params:
open_params['cache_size'] = chunk_cache_capacity
with self.measure_time(tag=f"opened dataset {ds_id!r}"
f" from data store"
f" {store_instance_id!r}"):
dataset = data_store.open_data(data_id, **open_params)
if isinstance(dataset, MultiLevelDataset):
ml_dataset = dataset
else:
cube, _, _ = decode_cube(dataset,
normalize=True,
force_non_empty=True,
force_geographic=True)
ml_dataset = BaseMultiLevelDataset(cube, ds_id=ds_id)
else:
fs_type = dataset_config.get('FileSystem')
if fs_type != 'memory':
raise ServiceConfigError(f"Invalid FileSystem {fs_type!r}"
f" in dataset configuration"
f" {ds_id!r}")
with self.measure_time(tag=f"opened dataset {ds_id!r}"
f" from {fs_type!r}"):
ml_dataset = _open_ml_dataset_from_python_code(
self, dataset_config)
augmentation = dataset_config.get('Augmentation')
if augmentation:
script_path = self.get_config_path(
augmentation,
f"'Augmentation' of dataset configuration {ds_id}")
input_parameters = augmentation.get('InputParameters')
callable_name = augmentation.get('Function', COMPUTE_VARIABLES)
ml_dataset = augment_ml_dataset(ml_dataset,
script_path,
callable_name,
self.get_ml_dataset,
self.set_ml_dataset,
input_parameters=input_parameters,
exception_type=ServiceConfigError)
return ml_dataset
def get_legend_label(self, ds_id: str, var_name: str):
dataset = self.get_dataset(ds_id)
if var_name in dataset:
ds = self.get_dataset(ds_id)
units = ds[var_name].units
return units
raise ServiceResourceNotFoundError(
f'Variable "{var_name}" not found in dataset "{ds_id}"')
def get_dataset_place_groups(self,
ds_id: str,
base_url: str,
load_features=False) -> List[Dict]:
dataset_config = self.get_dataset_config(ds_id)
place_group_id_prefix = f"DS-{ds_id}-"
place_groups = []
for k, v in self._place_group_cache.items():
if k.startswith(place_group_id_prefix):
place_groups.append(v)
if place_groups:
return place_groups
place_groups = self._load_place_groups(dataset_config.get(
"PlaceGroups", []),
base_url,
is_global=False,
load_features=load_features)
for place_group in place_groups:
self._place_group_cache[place_group_id_prefix +
place_group["id"]] = place_group
return place_groups
def get_dataset_place_group(self,
ds_id: str,
place_group_id: str,
base_url: str,
load_features=False) -> Dict:
place_groups = self.get_dataset_place_groups(ds_id,
base_url,
load_features=False)
for place_group in place_groups:
if place_group_id == place_group['id']:
if load_features:
self._load_place_group_features(place_group)
return place_group
raise ServiceResourceNotFoundError(
f'Place group "{place_group_id}" not found')
def get_global_place_groups(self,
base_url: str,
load_features=False) -> List[Dict]:
return self._load_place_groups(self._config.get("PlaceGroups", []),
base_url,
is_global=True,
load_features=load_features)
def get_global_place_group(self,
place_group_id: str,
base_url: str,
load_features: bool = False) -> Dict:
place_group_config = self._get_place_group_config(place_group_id)
return self._load_place_group(place_group_config,
base_url,
is_global=True,
load_features=load_features)
def _get_place_group_config(self, place_group_id: str) -> Dict:
place_group_configs = self._config.get("PlaceGroups", [])
for place_group_config in place_group_configs:
if place_group_config['Identifier'] == place_group_id:
return place_group_config
raise ServiceResourceNotFoundError(
f'Place group "{place_group_id}" not found')
def _load_place_groups(self,
place_group_configs: Dict,
base_url: str,
is_global: bool = False,
load_features: bool = False) -> List[Dict]:
place_groups = []
for place_group_config in place_group_configs:
place_group = self._load_place_group(place_group_config,
base_url,
is_global=is_global,
load_features=load_features)
place_groups.append(place_group)
return place_groups
def _load_place_group(self,
place_group_config: Dict[str, Any],
base_url: str,
is_global: bool = False,
load_features: bool = False) -> Dict[str, Any]:
place_group_id = place_group_config.get("PlaceGroupRef")
if place_group_id:
if is_global:
raise ServiceConfigError(
"'PlaceGroupRef' cannot be used in a global place group")
if len(place_group_config) > 1:
raise ServiceConfigError(
"'PlaceGroupRef' if present, must be the only entry in a 'PlaceGroups' item"
)
return self.get_global_place_group(place_group_id,
base_url,
load_features=load_features)
place_group_id = place_group_config.get("Identifier")
if not place_group_id:
raise ServiceConfigError(
"Missing 'Identifier' entry in a 'PlaceGroups' item")
if place_group_id in self._place_group_cache:
place_group = self._place_group_cache[place_group_id]
else:
place_group_title = place_group_config.get("Title", place_group_id)
place_path_wc = self.get_config_path(place_group_config,
f"'PlaceGroups' item")
source_paths = glob.glob(place_path_wc)
source_encoding = place_group_config.get("CharacterEncoding",
"utf-8")
join = None
place_join = place_group_config.get("Join")
if isinstance(place_join, dict):
join_path = self.get_config_path(
place_join, "'Join' of a 'PlaceGroups' item")
join_property = place_join.get("Property")
if not join_property:
raise ServiceError(
"Missing 'Property' entry in 'Join' of a 'PlaceGroups' item"
)
join_encoding = place_join.get("CharacterEncoding", "utf-8")
join = dict(path=join_path,
property=join_property,
encoding=join_encoding)
property_mapping = place_group_config.get("PropertyMapping")
if property_mapping:
property_mapping = dict(property_mapping)
for key, value in property_mapping.items():
if isinstance(value, str) and '${base_url}' in value:
property_mapping[key] = value.replace(
'${base_url}', base_url)
place_group = dict(type="FeatureCollection",
features=None,
id=place_group_id,
title=place_group_title,
propertyMapping=property_mapping,
sourcePaths=source_paths,
sourceEncoding=source_encoding,
join=join)
sub_place_group_configs = place_group_config.get("Places")
if sub_place_group_configs:
raise ServiceConfigError(
"Invalid 'Places' entry in a 'PlaceGroups' item: not implemented yet"
)
# sub_place_group_configs = place_group_config.get("Places")
# if sub_place_group_configs:
# sub_place_groups = self._load_place_groups(sub_place_group_configs)
# place_group["placeGroups"] = sub_place_groups
self._place_group_cache[place_group_id] = place_group
if load_features:
self._load_place_group_features(place_group)
return place_group
def _load_place_group_features(
self, place_group: Dict[str, Any]) -> List[Dict[str, Any]]:
features = place_group.get('features')
if features is not None:
return features
source_files = place_group['sourcePaths']
source_encoding = place_group['sourceEncoding']
features = []
for source_file in source_files:
with fiona.open(source_file,
encoding=source_encoding) as feature_collection:
for feature in feature_collection:
self._remove_feature_id(feature)
feature["id"] = str(self._feature_index)
self._feature_index += 1
features.append(feature)
join = place_group['join']
if join:
join_path = join['path']
join_property = join['property']
join_encoding = join['encoding']
with fiona.open(join_path,
encoding=join_encoding) as feature_collection:
indexed_join_features = self._get_indexed_features(
feature_collection, join_property)
for feature in features:
properties = feature.get('properties')
if isinstance(properties,
dict) and join_property in properties:
join_value = properties[join_property]
join_feature = indexed_join_features.get(join_value)
if join_feature:
join_properties = join_feature.get('properties')
if join_properties:
properties.update(join_properties)
feature['properties'] = properties
place_group['features'] = features
return features
@classmethod
def _get_indexed_features(cls, features: Sequence[Dict[str, Any]],
property_name: str) -> Dict[Any, Any]:
feature_index = {}
for feature in features:
properties = feature.get('properties')
if properties and property_name in properties:
property_value = properties[property_name]
feature_index[property_value] = feature
return feature_index
@classmethod
def _remove_feature_id(cls, feature: Dict):
cls._remove_id(feature)
@classmethod
def _remove_id(cls, properties: Dict):
if "id" in properties:
del properties["id"]
if "ID" in properties:
del properties["ID"]
def get_dataset_and_coord_variable(self, ds_name: str, dim_name: str):
ds = self.get_dataset(ds_name)
if dim_name not in ds.coords:
raise ServiceResourceNotFoundError(
f'Dimension {dim_name!r} has no coordinates in dataset {ds_name!r}'
)
return ds, ds.coords[dim_name]
@classmethod
def get_var_indexers(cls, ds_name: str, var_name: str, var: xr.DataArray,
dim_names: List[str],
params: RequestParams) -> Dict[str, Any]:
var_indexers = dict()
for dim_name in dim_names:
if dim_name not in var.coords:
raise ServiceBadRequestError(
f'dimension {dim_name!r} of variable {var_name!r} of dataset {ds_name!r} has no coordinates'
)
coord_var = var.coords[dim_name]
dim_value_str = params.get_query_argument(dim_name, None)
try:
if dim_value_str is None:
var_indexers[dim_name] = coord_var.values[0]
elif dim_value_str == 'current':
var_indexers[dim_name] = coord_var.values[-1]
elif np.issubdtype(coord_var.dtype, np.floating):
var_indexers[dim_name] = float(dim_value_str)
elif np.issubdtype(coord_var.dtype, np.integer):
var_indexers[dim_name] = int(dim_value_str)
elif np.issubdtype(coord_var.dtype, np.datetime64):
if '/' in dim_value_str:
date_str_1, date_str_2 = dim_value_str.split(
'/', maxsplit=1)
var_indexer_1 = pd.to_datetime(date_str_1)
var_indexer_2 = pd.to_datetime(date_str_2)
var_indexers[dim_name] = var_indexer_1 + (
var_indexer_2 - var_indexer_1) / 2
else:
date_str = dim_value_str
var_indexers[dim_name] = pd.to_datetime(date_str)
else:
raise ValueError(
f'unable to convert value {dim_value_str!r} to {coord_var.dtype!r}'
)
except ValueError as e:
raise ServiceBadRequestError(
f'{dim_value_str!r} is not a valid value for dimension {dim_name!r} '
f'of variable {var_name!r} of dataset {ds_name!r}') from e
return var_indexers
@classmethod
def find_dataset_config(cls, dataset_configs: List[Dict[str, Any]],
ds_name: str) -> Optional[Dict[str, Any]]:
# Note: can be optimized by dict/key lookup
return next(
(dsd for dsd in dataset_configs if dsd['Identifier'] == ds_name),
None)
def get_config_path(self,
config: Dict[str, Any],
config_name: str,
path_entry_name: str = 'Path',
is_url: bool = False) -> str:
path = config.get(path_entry_name)
if not path:
raise ServiceError(
f"Missing entry {path_entry_name!r} in {config_name}")
if not is_url and not os.path.isabs(path):
path = os.path.join(self._base_dir, path)
return path
def get_dataset_chunk_cache_capacity(
self, dataset_config: DatasetConfigDict) -> Optional[int]:
cache_size = self.get_chunk_cache_capacity(dataset_config,
'ChunkCacheSize')
if cache_size is None:
cache_size = self.get_chunk_cache_capacity(
self.config, 'DatasetChunkCacheSize')
return cache_size
@classmethod
def get_chunk_cache_capacity(cls, config: Dict[str, Any],
cache_size_key: str) -> Optional[int]:
cache_size = config.get(cache_size_key, None)
if not cache_size:
return None
elif isinstance(cache_size, str):
try:
cache_size = parse_mem_size(cache_size)
except ValueError:
raise ServiceConfigError(f'Invalid {cache_size_key}')
elif not isinstance(cache_size, int) or cache_size < 0:
raise ServiceConfigError(f'Invalid {cache_size_key}')
return cache_size