mirror of
https://github.com/chanzuckerberg/cellxgene.git
synced 2026-09-16 21:37:59 +08:00
171 lines
7.1 KiB
Python
171 lines
7.1 KiB
Python
import yaml
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from flatten_dict import unflatten
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from server.default_config import get_default_config
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from server.common.config.dataset_config import DatasetConfig
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from server.common.config.server_config import ServerConfig
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from server.common.config.external_config import ExternalConfig
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from server.common.errors import ConfigurationError
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class AppConfig(object):
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"""
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AppConfig stores all the configuration for cellxgene.
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AppConfig contains one or more DatasetConfig(s) and one ServerConfig.
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The server_config contains attributes that refer to the server process as a whole.
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The dataset_config refers to attributes that are associated with the features and
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presentations of a dataset.
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AppConfig has methods to initialize, modify, and access the configuration.
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"""
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def __init__(self):
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# the default configuration (see default_config.py)
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# TODO @madison -- if we always read from the default config (hard coded path) can we set those values as
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# defaults within the config class?
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self.default_config = get_default_config()
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# the server configuration
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self.server_config = ServerConfig(self, self.default_config["server"])
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# the dataset config
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self.dataset_config = DatasetConfig(None, self, self.default_config["dataset"])
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# external config
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self.external_config = ExternalConfig(self, self.default_config["external"])
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# Set to true when config_completed is called
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self.is_completed = False
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def get_dataset_config(self):
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return self.dataset_config
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def check_config(self):
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"""Verify all the attributes in the config have been type checked"""
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if not self.is_completed:
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raise ConfigurationError("The configuration has not been completed")
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self.server_config.check_config()
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self.dataset_config.check_config()
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self.external_config.check_config()
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def update_server_config(self, **kw):
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self.server_config.update(**kw)
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self.is_complete = False
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def update_dataset_config(self, **kw):
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self.dataset_config.update(**kw)
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self.is_complete = False
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def update_single_config_from_path_and_value(self, path, value):
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"""Update a single config parameter with the value.
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Path is a list of string, that gives a path to the config parameter to be updated.
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For example, path may be ["server","app","port"].
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"""
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self.is_complete = False
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if not isinstance(path, list):
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raise ConfigurationError(f"path must be a list of strings, got '{str(path)}'")
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for part in path:
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if not isinstance(part, str):
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raise ConfigurationError(f"path must be a list of strings, got '{str(path)}'")
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if len(path) < 1 or path[0] not in ("server", "dataset"):
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raise ConfigurationError("path must start with 'server', or 'dataset'")
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if path[0] == "server":
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attr = "__".join(path[1:])
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try:
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self.update_server_config(**{attr: value})
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except ConfigurationError:
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raise ConfigurationError(f"unknown config parameter at path: '{str(path)}'")
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elif path[0] == "dataset":
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attr = "__".join(path[1:])
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try:
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self.update_dataset_config(**{attr: value})
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except ConfigurationError:
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raise ConfigurationError(f"unknown config parameter at path: '{str(path)}'")
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def update_from_config_file(self, config_file):
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try:
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with open(config_file) as yml_file:
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config = yaml.safe_load(yml_file)
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except yaml.YAMLError as e:
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raise ConfigurationError(f"The specified config file contained an error: {e}")
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except OSError as e:
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raise ConfigurationError(f"Issue retrieving the specified config file: {e}")
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if config.get("server"):
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self.server_config.update_from_config(config["server"], "server")
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if config.get("dataset"):
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self.dataset_config.update_from_config(config["dataset"], "dataset")
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if config.get("external"):
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self.external_config.update_from_config(config["external"], "external")
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self.is_complete = False
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def config_to_dict(self):
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"""return the configuration as an unflattened dict"""
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server = self.server_config.create_mapping(self.server_config.default_config)
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dataset = self.dataset_config.create_mapping(self.dataset_config.default_config)
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external = self.external_config.create_mapping(self.external_config.default_config)
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config = dict(server={}, dataset={})
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for attrname in server.keys():
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config["server__" + attrname] = getattr(self.server_config, attrname)
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for attrname in dataset.keys():
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config["dataset__" + attrname] = getattr(self.dataset_config, attrname)
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for attrname in external.keys():
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config["external__" + attrname] = getattr(self.external_config, attrname)
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config = unflatten(config, splitter=lambda key: key.split("__"))
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return config
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def write_config(self, config_file):
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"""output the config to a yaml file"""
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config = self.config_to_dict()
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yaml.dump(config, open(config_file, "w"))
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def changes_from_default(self):
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"""Return all the attribute that are different from the default"""
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diff_server = self.server_config.changes_from_default()
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diff_dataset = self.dataset_config.changes_from_default()
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diff_external = self.external.changes_from_default()
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diff = dict(server=diff_server, dataset=diff_dataset, external=diff_external)
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return diff
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def complete_config(self, messagefn=None):
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"""The configure options are checked, and any additional setup based on the config
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parameters is done"""
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if messagefn is None:
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def noop(message):
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pass
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messagefn = noop
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# TODO: to give better error messages we can add a mapping between where each config
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# attribute originated (e.g. command line argument or config file), then in the error
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# messages we can give correct context for attributes with bad value.
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context = dict(messagefn=messagefn)
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# complete config for external_config first, since this may update values in the other sections
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self.external_config.complete_config(context)
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self.server_config.complete_config(context)
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self.dataset_config.complete_config(context)
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self.is_completed = True
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self.check_config()
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def get_matrix_data_cache_manager(self):
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return self.server_config.matrix_data_cache_manager
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def get_title(self, data_adaptor):
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return (
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self.server_config.single_dataset__title
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if self.server_config.single_dataset__title
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else data_adaptor.get_title()
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)
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def get_about(self, data_adaptor):
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return (
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self.server_config.single_dataset__about
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if self.server_config.single_dataset__about
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else data_adaptor.get_about()
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)
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