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cellxgene/server/common/app_config.py
bmccandless f69d141336 refactor config to support different config options for datasets in different dataroots. (#1596) 
This will give us the ability to specify different config options for
different dataroots.

the key of the dataroot dictionary is no longer the same as the dataroot_url.
Previously key==dataroot_url, and now those are separated.

Added an "is_multi_dataset" function to simplify logic where it branched on single vs multi.

Simplified the rest.py interface by no longer passing in the user annotations object, since
that can be retrieved from the dataset.
2020-07-10 16:21:40 -07:00

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from server import __version__ as cellxgene_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
DEFAULT_SERVER_PORT = int(os.environ.get("CXG_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
# 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_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,
}
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["limits"] = {
"column_request_max": server_config.limits__column_request_max,
"diffexp_cellcount_max": server_config.limits__diffexp_cellcount_max,
}
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"),
("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.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
def complete_config(self, context):
self.handle_app(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:
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."
)
else:
self.app__port = find_available_port(self.app__host, 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_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.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))
# 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:
# 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)
if self.app_config.server_config.single_dataset__datapath:
if self.embeddings__enable_reembedding:
matrix_data_loader = MatrixDataLoader(self.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 self.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."
)
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