`doctor`#

Implementation for conda doctor subcommand. Adds various environment and package checks to detect issues or possible environment corruption.

Classes#

`PrefixData`	The PrefixData class aims to be the representation of the state
`CondaSubcommand`	Return type to use when defining a conda subcommand plugin hook.

Functions#

`add_parser_help`(→ None)	So we can use consistent capitalization and periods in the help. You must
`add_parser_prefix`(→ argparse._MutuallyExclusiveGroup)
`add_parser_verbose`(→ None)
`configure_parser`(parser)
`execute`(→ None)	Run registered health_check plugins.
`conda_subcommands`()

Attributes#

`context`
`hookimpl`	Decorator used to mark plugin hook implementations

context#

add_parser_help(p: argparse.ArgumentParser) → None#: So we can use consistent capitalization and periods in the help. You must use the add_help=False argument to ArgumentParser or add_parser to use this. Add this first to be consistent with the default argparse output.

add_parser_prefix(p: argparse.ArgumentParser, prefix_required: bool = False) → argparse._MutuallyExclusiveGroup#

add_parser_verbose(parser: argparse.ArgumentParser | argparse._ArgumentGroup) → None#

class PrefixData(prefix_path: str | os.PathLike[str] | pathlib.Path, interoperability: bool | None = None)#

The PrefixData class aims to be the representation of the state of a conda environment on disk. The directory where the environment lives is called prefix.

This class supports different types of tasks:

Reading and querying conda-meta/*.json files as PrefixRecord objects
Reading and writing environment-specific configuration (env vars, state file, nonadmin markers, etc)
Existence checks and validations of name, path, and magic files / markers
Exposing non-conda packages installed in prefix as PrefixRecord, via the plugin system

property name: str#

Returns the name of the environment, if available.

If the environment doesn't live in one the configured envs_dirs, an empty string is returned. The construct prefix_data.name or prefix_data.prefix_path can be helpful in those cases.

property is_writable: bool | None | conda.auxlib._Null#

Check whether the configured path is writable. This is assessed by checking whether conda-meta/history is writable. It if is, it is assumed that the rest of the directory tree is writable too.

Note: The value is cached in the instance. Use .assert_writable() for a non- cached check.

property _pip_interop_enabled#

property _prefix_records: dict[str, conda.models.records.PrefixRecord] | None#

property _python_pkg_record: conda.models.records.PrefixRecord | None#: Return the prefix record for the package python.

_cache_: dict[tuple[pathlib.Path, bool | None], PrefixData]#

classmethod from_name(name: str, **kwargs) → PrefixData#

Creates a PrefixData instance from an environment name.

The name will be validated with PrefixData.validate_name() if it does not exist.

Parameters:: name -- The name of the environment. Must not contain path separators (/, ).
Raises:: CondaValueError -- If name contains a path separator.

classmethod from_context(validate: bool = False) → PrefixData#

Creates a PrefixData instance from the path specified by context.target_prefix.

The path and name will be validated with PrefixData.validate_path() and PrefixData.validate_name(), respectively, if validate is True.

Parameters:: validate -- Whether the path and name should be validated. Useful for environments about to be created.

__eq__(other: Any) → bool#: Return self==value.

exists() → bool#: Check whether the PrefixData path exists and is a directory.

is_environment() → bool#

Check whether the PrefixData path is a valida conda environment.

This is assessed by checking if conda-meta/history marker file exists.

is_frozen() → bool#

Check whether the environment is marked as frozen, as per CEP 22.

This is assessed by checking if conda-meta/frozen marker file exists.

is_base() → bool#: Check whether the configured path refers to the base environment.

assert_exists() → None#

Check whether the environment path exists.

Raises:: EnvironmentLocationNotFound -- If the check returns False.

assert_environment() → None#

Check whether the environment path exists and is a valid conda environment.

Raises:: DirectoryNotACondaEnvironmentError -- If the check returns False.

assert_writable() → None#

Check whether the environment path is a valid conda environment and is writable.

Raises:: EnvironmentNotWritableError -- If the check returns False.

assert_not_frozen() → None#

Check whether the environment path is a valid conda environment and is not marked as frozen (as per CEP 22).

Raises:: EnvironmentIsFrozenError -- If the environment is marked as frozen.

validate_path(expand_path: bool = False) → None#

Validate the path of the environment.

It runs the following checks:

Make sure the path does not contain : or ; (OS-dependent).
Disallow immediately nested environments (e.g. $CONDA_ROOT and $CONDA_ROOT/my-env).
Warn if there are spaces in the path.

Parameters:: expand_path -- Whether to process ~ and environment variables in the string. The expanded value will replace .prefix_path.
Raises:: CondaValueError -- If the environment contains :, ;, or is nested.

validate_name(allow_base: bool = False) → None#

Validate the name of the environment.

Parameters:: allow_base -- Whether to allow base as a valid name.
Raises:: CondaValueError -- If the name is protected, or if it contains disallowed characters (/, ` , `:, #).

load() → None#

reload() → PrefixData#

_get_json_fn(prefix_record: conda.models.records.PrefixRecord) → str#

insert(prefix_record: conda.models.records.PrefixRecord, remove_auth: bool = True) → None#

remove(package_name: str) → None#

get(package_name: str, default: T = NULL) → conda.models.records.PackageRecord | T#

iter_records() → collections.abc.Iterable[conda.models.records.PrefixRecord]#

iter_records_sorted() → collections.abc.Iterable[conda.models.records.PrefixRecord]#

all_subdir_urls() → set[str]#

query(package_ref_or_match_spec: conda.models.records.PackageRecord | conda.models.match_spec.MatchSpec | str) → collections.abc.Iterable[conda.models.records.PrefixRecord]#

_load_single_record(prefix_record_json_path: conda.common.path.PathType) → None#

_load_site_packages() → dict[str, conda.models.records.PrefixRecord]#

_get_environment_state_file() → dict[str, dict[str, str]]#

_write_environment_state_file(state: dict[str, dict[str, str]]) → None#

get_environment_env_vars() → dict[str, str] | dict[bytes, bytes]#

set_environment_env_vars(env_vars: dict[str, str]) → dict[str, str] | None#

unset_environment_env_vars(env_vars: dict[str, str]) → dict[str, str] | None#

set_nonadmin() → None#: Creates $PREFIX/.nonadmin if sys.prefix/.nonadmin exists (on Windows).

class CondaSubcommand#

Return type to use when defining a conda subcommand plugin hook.

For details on how this is used, see conda_subcommands().

Parameters:

name -- Subcommand name (e.g., conda my-subcommand-name).
summary -- Subcommand summary, will be shown in conda --help.
action -- Callable that will be run when the subcommand is invoked.
configure_parser -- Callable that will be run when the subcommand parser is initialized.

name: str#

summary: str#

action: Callable[[argparse.Namespace | tuple[str]], int | None]#

configure_parser: Callable[[argparse.ArgumentParser], None] | None#

hookimpl#: Decorator used to mark plugin hook implementations

configure_parser(parser: argparse.ArgumentParser)#

execute(args: argparse.Namespace) → None#: Run registered health_check plugins.

conda_subcommands()#

doctor#

Classes#

Functions#

Attributes#

`doctor`#