fork/cellxgene
1
0
Fork 0
mirror of https://github.com/chanzuckerberg/cellxgene.git synced 2026-09-16 05:07:55 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
f2fbeff511556d072e4eae08d6a61686fa16b1be
cellxgene/server/common/app_config.py
Bruce Martin f2fbeff511 add support for corpora default_embedding field (#1696) 
* fix mispelling

* re-implement re-embedding

* always load base embedding to fetch counts

* format

* lint

* fix tests

* lint

* fix accept handling

* test log

* more debug

* more

* more

* more

* more

* remove logging

* logging

* jsonify

* remove debugging logs

* lint

* clean up errors a bit

* fix issue found in PR review

* add support for corpora default_embedding

* fix botched merge

* PR review

* PR review
2020-07-31 07:36:48 -07:00

863 lines
39 KiB
Python
Raw Blame History

from server import display_version as cellxgene_display_version
from flatten_dict import flatten, unflatten
import os
from os.path import splitext, basename, isdir
import sys
from urllib.parse import urlparse, quote_plus
import yaml
import copy
from server.common.default_config import get_default_config
from server.common.errors import ConfigurationError, DatasetAccessError, OntologyLoadFailure
from server.data_common.matrix_loader import MatrixDataLoader, MatrixDataCacheManager, MatrixDataType
from server.common.utils import find_available_port, is_port_available
import warnings
from server.common.annotations import AnnotationsLocalFile
from server.common.utils import custom_format_warning
import server.compute.diffexp_cxg as diffexp_tiledb
from server.common.data_locator import discover_s3_region_name
from server.auth.auth import AuthTypeFactory
DEFAULT_SERVER_PORT = 5005
# anything bigger than this will generate a special message
BIG_FILE_SIZE_THRESHOLD = 100 * 2 ** 20 # 100MB
class AppFeature(object):
def __init__(self, path, available=False, method="POST", extra={}):
self.path = path
self.available = available
self.method = method
self.extra = extra
for k, v in extra.items():
setattr(self, k, v)
def todict(self):
d = dict(available=self.available, method=self.method, path=self.path)
d.update(self.extra)
return d
class AppConfig(object):
"""AppConfig stores all the configuration for cellxgene. The configuration is divided into two main parts:
server attributes, and dataset attributes. The server_config contains attributes that refer to the server process
as a whole. The default_dataset_config referes to attributes that are associated with the features and
presentations of a dataset. The dataset config attributes can be overridden depending on the url by which the
dataset was accessed. These are stored in dataroot_config.
AppConfig has methods to initialize, modify, and access the configuration.
"""
def __init__(self):
# the default configuration (see default_config.py)
self.default_config = get_default_config()
# the server configuration
self.server_config = ServerConfig(self, self.default_config["server"])
# the dataset config, unless overridden by an entry in dataroot_config
self.default_dataset_config = DatasetConfig(None, self, self.default_config["dataset"])
# a dictionary of keys to DatasetConfig objects. Each key must exist in the multi_dataset__dataroot
# attribute of the server_config.
self.dataroot_config = {}
# Set to true when config_completed is called
self.is_completed = False
def get_dataset_config(self, dataroot_key):
if self.server_config.single_dataset__datapath:
return self.default_dataset_config
else:
return self.dataroot_config.get(dataroot_key, self.default_dataset_config)
def check_config(self):
"""Verify all the attributes have been checked"""
if not self.is_completed:
raise ConfigurationError("The configuration has not been completed")
self.server_config.check_config()
self.default_dataset_config.check_config()
for dataset_config in self.dataroot_config.values():
dataset_config.check_config()
def update_server_config(self, **kw):
self.server_config.update(**kw)
self.is_complete = False
def update_default_dataset_config(self, **kw):
self.default_dataset_config.update(**kw)
# update all the other dataset configs, if any
for value in self.dataroot_config.values():
value.update(**kw)
self.is_complete = False
def update_from_config_file(self, config_file):
with open(config_file) as fyaml:
config = yaml.load(fyaml, Loader=yaml.FullLoader)
self.server_config.update_from_config(config["server"], "server")
self.default_dataset_config.update_from_config(config["dataset"], "dataset")
per_dataset_config = config.get("per_dataset_config", {})
for key, dataroot_config in per_dataset_config.items():
self.add_dataroot_config(key, **dataroot_config)
self.is_complete = False
def write_config(self, config_file):
"""output the config to a yaml file"""
server = self.server_config.create_mapping(self.server_config.default_config)
dataset = self.default_dataset_config.create_mapping(self.default_dataset_config.default_config)
config = dict(server={}, dataset={})
for attrname in server.keys():
config["server__" + attrname] = getattr(self.server_config, attrname)
for attrname in dataset.keys():
config["dataset__" + attrname] = getattr(self.default_dataset_config, attrname)
if self.dataroot_config:
config["per_dataset_config"] = {}
for dataroot_tag, dataroot_config in self.dataroot_config.items():
dataset = dataroot_config.create_mapping(dataroot_config.default_config)
for attrname in dataset.keys():
config[f"per_dataset_config__{dataroot_tag}__" + attrname] = getattr(dataroot_config, attrname)
config = unflatten(config, splitter=lambda key: key.split("__"))
yaml.dump(config, open(config_file, "w"))
def changes_from_default(self):
"""Return all the attribute that are different from the default"""
diff_server = self.server_config.changes_from_default()
diff_dataset = self.default_dataset_config.changes_from_default()
diff = dict(server=diff_server, dataset=diff_dataset)
return diff
def add_dataroot_config(self, dataroot_tag, **kw):
"""Create a new dataset config object based on the default dataset config, and kw parameters"""
if dataroot_tag in self.dataroot_config:
raise ConfigurationError(f"dataroot config already exists: {dataroot_tag}")
if type(self.server_config.multi_dataset__dataroot) != dict:
raise ConfigurationError("The server__multi_dataset__dataroot must be a dictionary")
if dataroot_tag not in self.server_config.multi_dataset__dataroot:
raise ConfigurationError(f"The dataroot_tag ({dataroot_tag}) not found in server__multi_dataset__dataroot")
self.is_completed = False
self.dataroot_config[dataroot_tag] = DatasetConfig(dataroot_tag, self, self.default_config["dataset"])
flat_config = self.default_dataset_config.create_mapping(self.default_dataset_config.default_config)
config = {key: value[1] for key, value in flat_config.items()}
self.dataroot_config[dataroot_tag].update(**config)
self.dataroot_config[dataroot_tag].update_from_config(kw, dataroot_tag)
def complete_config(self, messagefn=None):
"""The configure options are checked, and any additional setup based on the config
parameters is done"""
if messagefn is None:
def noop(message):
pass
messagefn = noop
# TODO: to give better error messages we can add a mapping between where each config
# attribute originated (e.g. command line argument or config file), then in the error
# messages we can give correct context for attributes with bad value.
context = dict(messagefn=messagefn)
self.server_config.complete_config(context)
self.default_dataset_config.complete_config(context)
for dataroot_config in self.dataroot_config.values():
dataroot_config.complete_config(context)
self.is_completed = True
self.check_config()
def get_matrix_data_cache_manager(self):
return self.server_config.matrix_data_cache_manager
def is_multi_dataset(self):
return self.server_config.multi_dataset__dataroot is not None
def get_title(self, data_adaptor):
return (
self.server_config.single_dataset__title
if self.server_config.single_dataset__title
else data_adaptor.get_title()
)
def get_about(self, data_adaptor):
return (
self.server_config.single_dataset__about
if self.server_config.single_dataset__about
else data_adaptor.get_about()
)
def get_client_config(self, data_adaptor):
"""
Return the configuration as required by the /config REST route
"""
server_config = self.server_config
dataset_config = data_adaptor.dataset_config
annotation = dataset_config.user_annotations
auth = server_config.auth
# FIXME The current set of config is not consistently presented:
# we have camalCase, hyphen-text, and underscore_text
# make sure the configuration has been checked.
self.check_config()
# features
features = [f.todict() for f in data_adaptor.get_features(annotation)]
# display_names
title = self.get_title(data_adaptor)
about = self.get_about(data_adaptor)
display_names = dict(engine=data_adaptor.get_name(), dataset=title)
# library_versions
library_versions = {}
library_versions.update(data_adaptor.get_library_versions())
library_versions["cellxgene"] = cellxgene_display_version
# links
links = {"about-dataset": about}
# parameters
parameters = {
"layout": dataset_config.embeddings__names,
"max-category-items": dataset_config.presentation__max_categories,
"obs_names": server_config.single_dataset__obs_names,
"var_names": server_config.single_dataset__var_names,
"diffexp_lfc_cutoff": dataset_config.diffexp__lfc_cutoff,
"backed": server_config.adaptor__anndata_adaptor__backed,
"disable-diffexp": not dataset_config.diffexp__enable,
"enable-reembedding": dataset_config.embeddings__enable_reembedding,
"annotations": False,
"annotations_file": None,
"annotations_dir": None,
"annotations_cell_ontology_enabled": False,
"annotations_cell_ontology_obopath": None,
"annotations_cell_ontology_terms": None,
"custom_colors": dataset_config.presentation__custom_colors,
"diffexp-may-be-slow": False,
"about_legal_tos": dataset_config.app__about_legal_tos,
"about_legal_privacy": dataset_config.app__about_legal_privacy,
}
# corpora dataset_props
# TODO/Note: putting info from the dataset into the /config is not ideal.
# However, it is definitely not part of /schema, and we do not have a top-level
# route for data properties. Consider creating one at some point.
corpora_props = data_adaptor.get_corpora_props()
if corpora_props and "default_embedding" in corpora_props:
default_embedding = corpora_props["default_embedding"]
if isinstance(default_embedding, str) and default_embedding.startswith("X_"):
default_embedding = default_embedding[2:] # drop X_ prefix
if default_embedding in data_adaptor.get_embedding_names():
parameters["default_embedding"] = default_embedding
data_adaptor.update_parameters(parameters)
if annotation:
annotation.update_parameters(parameters, data_adaptor)
# gather it all together
c = {}
config = c["config"] = {}
config["features"] = features
config["displayNames"] = display_names
config["library_versions"] = library_versions
config["links"] = links
config["parameters"] = parameters
config["corpora_props"] = corpora_props
config["limits"] = {
"column_request_max": server_config.limits__column_request_max,
"diffexp_cellcount_max": server_config.limits__diffexp_cellcount_max,
}
if dataset_config.app__authentication_enable and auth.is_valid():
config["authentication"] = {
"is_authenticated": auth.is_authenticated(),
"requires_client_login": auth.requires_client_login(),
"username": auth.get_username(),
}
if auth.requires_client_login():
config["authentication"].update({
"login": auth.get_login_url(data_adaptor),
"logout" : auth.get_logout_url(data_adaptor),
})
return c
class BaseConfig(object):
"""This class handles the mechanics of updating and checking attributes.
Derived classes are expected to store the actual attributes"""
def __init__(self, app_config, default_config, dictval_cases={}):
# reference back to the app_config
self.app_config = app_config
# the complete set of attribute and their default values (unflattened)
self.default_config = default_config
# attributes where the value may be a dict (and therefore are not flattened)
self.dictval_cases = dictval_cases
# used to make sure every attribute value is checked
self.attr_checked = {k: False for k in self.create_mapping(default_config).keys()}
def create_mapping(self, config):
"""Create a mapping from attribute names to (location in the config tree, value)"""
dc = copy.deepcopy(config)
mapping = {}
# special cases where the value could be a dict.
# If its value is not None, the entry is added to the mapping, and not included
# in the flattening below.
for dictval_case in self.dictval_cases:
cur = dc
for part in dictval_case[:-1]:
cur = cur.get(part, {})
val = cur.get(dictval_case[-1])
if val is not None:
key = "__".join(dictval_case)
mapping[key] = (dictval_case, val)
del cur[dictval_case[-1]]
flat_config = flatten(dc)
for key, value in flat_config.items():
# name of the attribute
attr = "__".join(key)
mapping[attr] = (key, value)
return mapping
def check_attr(self, attrname, vtype):
val = getattr(self, attrname)
if type(vtype) in (list, tuple):
if type(val) not in vtype:
tnames = ",".join([x.__name__ for x in vtype])
raise ConfigurationError(
f"Invalid type for attribute: {attrname}, expected types ({tnames}), got {type(val).__name__}"
)
else:
if type(val) != vtype:
raise ConfigurationError(
f"Invalid type for attribute: {attrname}, "
f"expected type {vtype.__name__}, got {type(val).__name__}"
)
self.attr_checked[attrname] = True
def check_config(self):
mapping = self.create_mapping(self.default_config)
for key in mapping.keys():
if not self.attr_checked[key]:
raise ConfigurationError(f"The attr '{key}' has not been checked")
def update(self, **kw):
for key, value in kw.items():
if not hasattr(self, key):
raise ConfigurationError(f"unknown config parameter {key}.")
try:
if type(value) == tuple:
# convert tuple values to list values
value = list(value)
setattr(self, key, value)
except KeyError:
raise ConfigurationError(f"Unable to set config parameter {key}.")
self.attr_checked[key] = False
def update_from_config(self, config, prefix):
mapping = self.create_mapping(config)
for attr, (key, value) in mapping.items():
if not hasattr(self, attr):
raise ConfigurationError(f"Unknown key from config file: {prefix}__{attr}")
try:
setattr(self, attr, value)
except KeyError:
raise ConfigurationError(f"Unable to set config attribute: {prefix}__{attr}")
self.attr_checked[attr] = False
def changes_from_default(self):
"""Return all the attribute that are different from the default"""
mapping = self.create_mapping(self.default_config)
diff = []
for attrname, (key, defval) in mapping.items():
curval = getattr(self, attrname)
if curval != defval:
diff.append((attrname, curval, defval))
return diff
class ServerConfig(BaseConfig):
"""Manages the config attribute associated with the server."""
def __init__(self, app_config, default_config):
dictval_cases = [
("app", "csp_directives"),
("authentication", "params"),
("adaptor", "cxg_adaptor", "tiledb_ctx"),
("multi_dataset", "dataroot"),
]
super().__init__(app_config, default_config, dictval_cases)
dc = default_config
try:
self.app__verbose = dc["app"]["verbose"]
self.app__debug = dc["app"]["debug"]
self.app__host = dc["app"]["host"]
self.app__port = dc["app"]["port"]
self.app__open_browser = dc["app"]["open_browser"]
self.app__force_https = dc["app"]["force_https"]
self.app__flask_secret_key = dc["app"]["flask_secret_key"]
self.app__generate_cache_control_headers = dc["app"]["generate_cache_control_headers"]
self.app__server_timing_headers = dc["app"]["server_timing_headers"]
self.app__csp_directives = dc["app"]["csp_directives"]
self.authentication__type = dc["authentication"]["type"]
self.authentication__params = dc["authentication"]["params"]
self.multi_dataset__dataroot = dc["multi_dataset"]["dataroot"]
self.multi_dataset__index = dc["multi_dataset"]["index"]
self.multi_dataset__allowed_matrix_types = dc["multi_dataset"]["allowed_matrix_types"]
self.multi_dataset__matrix_cache__max_datasets = dc["multi_dataset"]["matrix_cache"]["max_datasets"]
self.multi_dataset__matrix_cache__timelimit_s = dc["multi_dataset"]["matrix_cache"]["timelimit_s"]
self.single_dataset__datapath = dc["single_dataset"]["datapath"]
self.single_dataset__obs_names = dc["single_dataset"]["obs_names"]
self.single_dataset__var_names = dc["single_dataset"]["var_names"]
self.single_dataset__about = dc["single_dataset"]["about"]
self.single_dataset__title = dc["single_dataset"]["title"]
self.diffexp__alg_cxg__max_workers = dc["diffexp"]["alg_cxg"]["max_workers"]
self.diffexp__alg_cxg__cpu_multiplier = dc["diffexp"]["alg_cxg"]["cpu_multiplier"]
self.diffexp__alg_cxg__target_workunit = dc["diffexp"]["alg_cxg"]["target_workunit"]
self.data_locator__s3__region_name = dc["data_locator"]["s3"]["region_name"]
self.adaptor__cxg_adaptor__tiledb_ctx = dc["adaptor"]["cxg_adaptor"]["tiledb_ctx"]
self.adaptor__anndata_adaptor__backed = dc["adaptor"]["anndata_adaptor"]["backed"]
self.limits__diffexp_cellcount_max = dc["limits"]["diffexp_cellcount_max"]
self.limits__column_request_max = dc["limits"]["column_request_max"]
except KeyError as e:
raise ConfigurationError(f"Unexpected config: {str(e)}")
# The matrix data cache manager is created during the complete_config and stored here.
self.matrix_data_cache_manager = None
# The authentication object (BCM -- better name)
self.auth = None
def complete_config(self, context):
self.handle_app(context)
self.handle_authentication(context)
self.handle_data_locator(context)
self.handle_adaptor(context) # may depend on data_locator
self.handle_single_dataset(context) # may depend on adaptor
self.handle_multi_dataset(context) # may depend on adaptor
self.handle_diffexp(context)
self.handle_limits(context)
self.check_config()
def handle_app(self, context):
self.check_attr("app__verbose", bool)
self.check_attr("app__debug", bool)
self.check_attr("app__host", str)
self.check_attr("app__port", (type(None), int))
self.check_attr("app__open_browser", bool)
self.check_attr("app__force_https", bool)
self.check_attr("app__flask_secret_key", (type(None), str))
self.check_attr("app__generate_cache_control_headers", bool)
self.check_attr("app__server_timing_headers", bool)
self.check_attr("app__csp_directives", (type(None), dict))
if self.app__port:
try:
if not is_port_available(self.app__host, self.app__port):
raise ConfigurationError(
f"The port selected {self.app__port} is in use, please configure an open port."
)
except OverflowError:
raise ConfigurationError(f"Invalid port: {self.app__port}")
else:
try:
default_server_port = int(os.environ.get("CXG_SERVER_PORT", DEFAULT_SERVER_PORT))
except ValueError:
raise ConfigurationError(
"Invalid port from environment variable CXG_SERVER_PORT: " + os.environ.get("CXG_SERVER_PORT")
)
try:
self.app__port = find_available_port(self.app__host, default_server_port)
except OverflowError:
raise ConfigurationError(f"Invalid port: {default_server_port}")
if self.app__debug:
context["messagefn"]("in debug mode, setting verbose=True and open_browser=False")
self.app__verbose = True
self.app__open_browser = False
else:
warnings.formatwarning = custom_format_warning
if not self.app__verbose:
sys.tracebacklimit = 0
# secret key:
# first, from CXG_SECRET_KEY environment variable
# second, from config file
self.app__flask_secret_key = os.environ.get("CXG_SECRET_KEY", self.app__flask_secret_key)
# CSP Directives are a dict of string: list(string) or string: string
if self.app__csp_directives is not None:
for k, v in self.app__csp_directives.items():
if not isinstance(k, str):
raise ConfigurationError("CSP directive names must be a string.")
if isinstance(v, list):
for policy in v:
if not isinstance(policy, str):
raise ConfigurationError("CSP directive value must be a string or list of strings.")
elif not isinstance(v, str):
raise ConfigurationError("CSP directive value must be a string or list of strings.")
def handle_authentication(self, context):
self.check_attr("authentication__type", (type(None), str))
self.check_attr("authentication__params", (type(None), dict))
self.auth = AuthTypeFactory.create(self.authentication__type)
if self.auth is None:
raise ConfigurationError(f"Unknown authentication type: {self.authentication__type}")
self.auth.set_params(self.authentication__params)
def handle_data_locator(self, context):
self.check_attr("data_locator__s3__region_name", (type(None), bool, str))
if self.data_locator__s3__region_name is True:
path = self.single_dataset__datapath or self.multi_dataset__dataroot
if type(path) == dict:
# if multi_dataset__dataroot is a dict, then use the first key
# that is in s3. NOTE: it is not supported to have dataroots
# in different regions.
paths = [val.get("dataroot") for val in path.values()]
for path in paths:
if path.startswith("s3://"):
break
if path.startswith("s3://"):
region_name = discover_s3_region_name(path)
if region_name is None:
raise ConfigurationError(f"Unable to discover s3 region name from {path}")
else:
region_name = None
self.data_locator__s3__region_name = region_name
def handle_single_dataset(self, context):
self.check_attr("single_dataset__datapath", (str, type(None)))
self.check_attr("single_dataset__title", (str, type(None)))
self.check_attr("single_dataset__about", (str, type(None)))
self.check_attr("single_dataset__obs_names", (str, type(None)))
self.check_attr("single_dataset__var_names", (str, type(None)))
if self.single_dataset__datapath is None:
if self.multi_dataset__dataroot is None:
# TODO: change the error message once dataroot is fully supported
raise ConfigurationError("missing datapath")
return
else:
if self.multi_dataset__dataroot is not None:
raise ConfigurationError("must supply only one of datapath or dataroot")
# create the matrix data cache manager:
if self.matrix_data_cache_manager is None:
self.matrix_data_cache_manager = MatrixDataCacheManager(max_cached=1, timelimit_s=None)
# preload this data set
matrix_data_loader = MatrixDataLoader(self.single_dataset__datapath, app_config=self.app_config)
try:
matrix_data_loader.pre_load_validation()
except DatasetAccessError as e:
raise ConfigurationError(str(e))
file_size = matrix_data_loader.file_size()
file_basename = basename(self.single_dataset__datapath)
if file_size > BIG_FILE_SIZE_THRESHOLD:
context["messagefn"](f"Loading data from {file_basename}, this may take a while...")
else:
context["messagefn"](f"Loading data from {file_basename}.")
if self.single_dataset__about:
def url_check(url):
try:
result = urlparse(url)
if all([result.scheme, result.netloc]):
return True
else:
return False
except ValueError:
return False
if not url_check(self.single_dataset__about):
raise ConfigurationError(
"Must provide an absolute URL for --about. (Example format: http://example.com)"
)
def handle_multi_dataset(self, context):
self.check_attr("multi_dataset__dataroot", (type(None), dict, str))
self.check_attr("multi_dataset__index", (type(None), bool, str))
self.check_attr("multi_dataset__allowed_matrix_types", list)
self.check_attr("multi_dataset__matrix_cache__max_datasets", int)
self.check_attr("multi_dataset__matrix_cache__timelimit_s", (type(None), int, float))
if self.multi_dataset__dataroot is None:
return
if type(self.multi_dataset__dataroot) == str:
default_dict = dict(base_url="d", dataroot=self.multi_dataset__dataroot)
self.multi_dataset__dataroot = dict(d=default_dict)
for tag, dataroot_dict in self.multi_dataset__dataroot.items():
if "base_url" not in dataroot_dict:
raise ConfigurationError(f"error in multi_dataset__dataroot: missing base_url for tag {tag}")
if "dataroot" not in dataroot_dict:
raise ConfigurationError(f"error in multi_dataset__dataroot: missing dataroot, for tag {tag}")
base_url = dataroot_dict["base_url"]
# sanity check for well formed base urls
bad = False
if type(base_url) != str:
bad = True
elif os.path.normpath(base_url) != base_url:
bad = True
else:
base_url_parts = base_url.split("/")
if [quote_plus(part) for part in base_url_parts] != base_url_parts:
bad = True
if ".." in base_url_parts:
bad = True
if bad:
raise ConfigurationError(f"error in multi_dataset__dataroot base_url {base_url} for tag {tag}")
# verify all the base_urls are unique
base_urls = [d["base_url"] for d in self.multi_dataset__dataroot.values()]
if len(base_urls) > len(set(base_urls)):
raise ConfigurationError("error in multi_dataset__dataroot: base_urls must be unique")
# error checking
for mtype in self.multi_dataset__allowed_matrix_types:
try:
MatrixDataType(mtype)
except ValueError:
raise ConfigurationError(f'Invalid matrix type in "allowed_matrix_types": {mtype}')
# create the matrix data cache manager:
if self.matrix_data_cache_manager is None:
self.matrix_data_cache_manager = MatrixDataCacheManager(
max_cached=self.multi_dataset__matrix_cache__max_datasets,
timelimit_s=self.multi_dataset__matrix_cache__timelimit_s,
)
def handle_diffexp(self, context):
self.check_attr("diffexp__alg_cxg__max_workers", (str, int))
self.check_attr("diffexp__alg_cxg__cpu_multiplier", int)
self.check_attr("diffexp__alg_cxg__target_workunit", int)
max_workers = self.diffexp__alg_cxg__max_workers
cpu_multiplier = self.diffexp__alg_cxg__cpu_multiplier
cpu_count = os.cpu_count()
max_workers = min(max_workers, cpu_multiplier * cpu_count)
diffexp_tiledb.set_config(max_workers, self.diffexp__alg_cxg__target_workunit)
def handle_adaptor(self, context):
# cxg
self.check_attr("adaptor__cxg_adaptor__tiledb_ctx", dict)
regionkey = "vfs.s3.region"
if regionkey not in self.adaptor__cxg_adaptor__tiledb_ctx:
if type(self.data_locator__s3__region_name) == str:
self.adaptor__cxg_adaptor__tiledb_ctx[regionkey] = self.data_locator__s3__region_name
from server.data_cxg.cxg_adaptor import CxgAdaptor
CxgAdaptor.set_tiledb_context(self.adaptor__cxg_adaptor__tiledb_ctx)
# anndata
self.check_attr("adaptor__anndata_adaptor__backed", bool)
def handle_limits(self, context):
self.check_attr("limits__diffexp_cellcount_max", (type(None), int))
self.check_attr("limits__column_request_max", (type(None), int))
def exceeds_limit(self, limit_name, value):
limit_value = getattr(self, "limits__" + limit_name, None)
if limit_value is None: # disabled
return False
return value > limit_value
class DatasetConfig(BaseConfig):
"""Manages the config attribute associated with a dataset."""
def __init__(self, tag, app_config, default_config):
super().__init__(app_config, default_config)
self.tag = tag
dc = default_config
try:
self.app__scripts = dc["app"]["scripts"]
self.app__inline_scripts = dc["app"]["inline_scripts"]
self.app__about_legal_tos = dc["app"]["about_legal_tos"]
self.app__about_legal_privacy = dc["app"]["about_legal_privacy"]
self.app__authentication_enable = dc["app"]["authentication_enable"]
self.presentation__max_categories = dc["presentation"]["max_categories"]
self.presentation__custom_colors = dc["presentation"]["custom_colors"]
self.user_annotations__enable = dc["user_annotations"]["enable"]
self.user_annotations__type = dc["user_annotations"]["type"]
self.user_annotations__local_file_csv__directory = dc["user_annotations"]["local_file_csv"]["directory"]
self.user_annotations__local_file_csv__file = dc["user_annotations"]["local_file_csv"]["file"]
self.user_annotations__ontology__enable = dc["user_annotations"]["ontology"]["enable"]
self.user_annotations__ontology__obo_location = dc["user_annotations"]["ontology"]["obo_location"]
self.embeddings__names = dc["embeddings"]["names"]
self.embeddings__enable_reembedding = dc["embeddings"]["enable_reembedding"]
self.diffexp__enable = dc["diffexp"]["enable"]
self.diffexp__lfc_cutoff = dc["diffexp"]["lfc_cutoff"]
self.diffexp__top_n = dc["diffexp"]["top_n"]
except KeyError as e:
raise ConfigurationError(f"Unexpected config: {str(e)}")
# The annotation object is created during complete_config and stored here.
self.user_annotations = None
def complete_config(self, context):
self.handle_app(context)
self.handle_presentation(context)
self.handle_user_annotations(context)
self.handle_embeddings(context)
self.handle_diffexp(context)
def handle_app(self, context):
self.check_attr("app__scripts", list)
self.check_attr("app__inline_scripts", list)
self.check_attr("app__about_legal_tos", (type(None), str))
self.check_attr("app__about_legal_privacy", (type(None), str))
self.check_attr("app__authentication_enable", bool)
# scripts can be string (filename) or dict (attributes). Convert string to dict.
scripts = []
for s in self.app__scripts:
if isinstance(s, str):
scripts.append({"src": s})
elif isinstance(s, dict) and isinstance(s["src"], str):
scripts.append(s)
else:
raise ConfigurationError("Scripts must be string or dict")
self.app__scripts = scripts
def handle_presentation(self, context):
self.check_attr("presentation__max_categories", int)
self.check_attr("presentation__custom_colors", bool)
def handle_user_annotations(self, context):
self.check_attr("user_annotations__enable", bool)
self.check_attr("user_annotations__type", str)
self.check_attr("user_annotations__local_file_csv__directory", (type(None), str))
self.check_attr("user_annotations__local_file_csv__file", (type(None), str))
self.check_attr("user_annotations__ontology__enable", bool)
self.check_attr("user_annotations__ontology__obo_location", (type(None), str))
if self.user_annotations__enable:
server_config = self.app_config.server_config
if not self.app__authentication_enable:
raise ConfigurationError("user annotations requires authentication to be enabled")
if not server_config.auth.is_valid():
auth_type = server_config.authentication__type
raise ConfigurationError(f"authentication method {auth_type} is not compatible with user annotations")
# TODO, replace this with a factory pattern once we have more than one way
# to do annotations. currently only local_file_csv
if self.user_annotations__type != "local_file_csv":
raise ConfigurationError('The only annotation type support is "local_file_csv"')
dirname = self.user_annotations__local_file_csv__directory
filename = self.user_annotations__local_file_csv__file
if filename is not None and dirname is not None:
raise ConfigurationError("'annotations-file' and 'annotations-dir' may not be used together.")
if filename is not None:
lf_name, lf_ext = splitext(filename)
if lf_ext and lf_ext != ".csv":
raise ConfigurationError(f"annotation file type must be .csv: {filename}")
if dirname is not None and not isdir(dirname):
try:
os.mkdir(dirname)
except OSError:
raise ConfigurationError("Unable to create directory specified by --annotations-dir")
self.user_annotations = AnnotationsLocalFile(dirname, filename)
# if the user has specified a fixed label file, go ahead and validate it
# so that we can remove errors early in the process.
server_config = self.app_config.server_config
if server_config.single_dataset__datapath and self.user_annotations__local_file_csv__file:
with server_config.matrix_data_cache_manager.data_adaptor(
self.tag, server_config.single_dataset__datapath, self.app_config
) as data_adaptor:
data_adaptor.check_new_labels(self.user_annotations.read_labels(data_adaptor))
if self.user_annotations__ontology__enable or self.user_annotations__ontology__obo_location:
try:
self.user_annotations.load_ontology(self.user_annotations__ontology__obo_location)
except OntologyLoadFailure as e:
raise ConfigurationError("Unable to load ontology terms\n" + str(e))
else:
if self.user_annotations__type == "local_file_csv":
dirname = self.user_annotations__local_file_csv__directory
filename = self.user_annotations__local_file_csv__file
if filename is not None:
context["messsagefn"]("Warning: --annotations-file ignored as annotations are disabled.")
if dirname is not None:
context["messagefn"]("Warning: --annotations-dir ignored as annotations are disabled.")
if self.user_annotations__ontology__enable:
context["messagefn"](
"Warning: --experimental-annotations-ontology" " ignored as annotations are disabled."
)
if self.user_annotations__ontology__obo_location is not None:
context["messagefn"](
"Warning: --experimental-annotations-ontology-obo" " ignored as annotations are disabled."
)
def handle_embeddings(self, context):
self.check_attr("embeddings__names", list)
self.check_attr("embeddings__enable_reembedding", bool)
server_config = self.app_config.server_config
if server_config.single_dataset__datapath:
if self.embeddings__enable_reembedding:
matrix_data_loader = MatrixDataLoader(
server_config.single_dataset__datapath, app_config=self.app_config
)
if matrix_data_loader.matrix_data_type != MatrixDataType.H5AD:
raise ConfigurationError("'enable-reembedding is only supported with H5AD files.")
if server_config.adaptor__anndata_adaptor__backed:
raise ConfigurationError("enable-reembedding is not supported when run in --backed mode.")
def handle_diffexp(self, context):
self.check_attr("diffexp__enable", bool)
self.check_attr("diffexp__lfc_cutoff", float)
self.check_attr("diffexp__top_n", int)
server_config = self.app_config.server_config
if server_config.single_dataset__datapath:
with server_config.matrix_data_cache_manager.data_adaptor(
self.tag, server_config.single_dataset__datapath, self.app_config
) as data_adaptor:
if self.diffexp__enable and data_adaptor.parameters.get("diffexp_may_be_slow", False):
context["messagefn"](
"CAUTION: due to the size of your dataset, "
"running differential expression may take longer or fail."
)
Reference in New Issue View Git Blame Copy Permalink