from __future__ import annotations
import contextlib
import json
import logging
import os
import time
import warnings
from pathlib import Path
from typing import TYPE_CHECKING, Any, Literal
import numpy as np
from qcodes.dataset.data_set_protocol import (
SPECS,
BaseDataSet,
CompletedError,
ParameterData,
)
from qcodes.dataset.descriptions.dependencies import InterDependencies_
from qcodes.dataset.descriptions.rundescriber import RunDescriber
from qcodes.dataset.descriptions.versioning.converters import new_to_old
from qcodes.dataset.export_config import DataExportType
from qcodes.dataset.guids import generate_guid
from qcodes.dataset.linked_datasets.links import Link, links_to_str
from qcodes.dataset.sqlite.connection import ConnectionPlus, atomic
from qcodes.dataset.sqlite.database import conn_from_dbpath_or_conn
from qcodes.dataset.sqlite.queries import (
RUNS_TABLE_COLUMNS,
add_data_to_dynamic_columns,
add_parameter,
create_run,
get_experiment_name_from_experiment_id,
get_raw_run_attributes,
get_runid_from_guid,
get_sample_name_from_experiment_id,
mark_run_complete,
set_run_timestamp,
update_parent_datasets,
update_run_description,
)
from qcodes.utils import NumpyJSONEncoder
from .data_set_cache import DataSetCacheDeferred, DataSetCacheInMem
from .dataset_helpers import _add_run_to_runs_table
from .descriptions.versioning import serialization as serial
from .experiment_settings import get_default_experiment_id
from .exporters.export_info import ExportInfo
from .linked_datasets.links import str_to_links
if TYPE_CHECKING:
from collections.abc import Callable, Mapping, Sequence
import pandas as pd
import xarray as xr
from qcodes.dataset.descriptions.param_spec import ParamSpec, ParamSpecBase
from qcodes.dataset.descriptions.versioning.rundescribertypes import Shapes
from ..parameters import ParameterBase
log = logging.getLogger(__name__)
class DataSetInMem(BaseDataSet):
def __init__(
self,
run_id: int,
captured_run_id: int,
counter: int,
captured_counter: int,
name: str,
exp_id: int,
exp_name: str,
sample_name: str,
guid: str,
path_to_db: str | None,
run_timestamp_raw: float | None,
completed_timestamp_raw: float | None,
snapshot: str | None = None,
metadata: Mapping[str, Any] | None = None,
rundescriber: RunDescriber | None = None,
parent_dataset_links: Sequence[Link] | None = None,
export_info: ExportInfo | None = None,
) -> None:
"""Note that the constructor is considered private.
A ``DataSetInMem``
should be constructed either using one of the load functions
(``load_by_run_spec``, ``load_from_netcdf`` ...)
or using the measurement context manager.
"""
self._run_id = run_id
self._captured_run_id = captured_run_id
self._counter = counter
self._captured_counter = captured_counter
self._name = name
self._exp_id = exp_id
self._exp_name = exp_name
self._sample_name = sample_name
self._guid = guid
self._cache = DataSetCacheInMem(self)
self._run_timestamp_raw = run_timestamp_raw
self._completed_timestamp_raw = completed_timestamp_raw
self._path_to_db = path_to_db
if metadata is None:
self._metadata = {}
else:
self._metadata = dict(metadata)
if rundescriber is None:
interdeps = InterDependencies_()
rundescriber = RunDescriber(interdeps, shapes=None)
self._rundescriber = rundescriber
if parent_dataset_links is not None:
self._parent_dataset_links = list(parent_dataset_links)
else:
self._parent_dataset_links = []
if export_info is not None:
self._export_info = export_info
else:
self._export_info = ExportInfo({})
self._metadata["export_info"] = self._export_info.to_str()
self._snapshot_raw_data = snapshot
def _dataset_is_in_runs_table(self, path_to_db: str | Path | None = None) -> bool:
"""
Does this run exist in the given db
"""
if isinstance(path_to_db, Path):
path_to_db = str(path_to_db)
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=path_to_db)
) as conn:
run_id = get_runid_from_guid(conn, self.guid)
return run_id is not None
def write_metadata_to_db(self, path_to_db: str | Path | None = None) -> None:
from .experiment_container import load_or_create_experiment
if path_to_db is None:
path_to_db = self.path_to_db
if self._dataset_is_in_runs_table(path_to_db=path_to_db):
return
if isinstance(path_to_db, Path):
path_to_db = str(path_to_db)
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=path_to_db)
) as conn:
with atomic(conn) as aconn:
exp = load_or_create_experiment(
conn=aconn,
experiment_name=self.exp_name,
sample_name=self.sample_name,
load_last_duplicate=True,
)
counter, run_id, _ = _add_run_to_runs_table(
self, aconn, exp.exp_id, create_run_table=False
)
self._exp_id = exp.exp_id
self._run_id = run_id
self._counter = counter
self._path_to_db = conn.path_to_dbfile
@classmethod
def _create_new_run(
cls,
name: str,
path_to_db: Path | str | None = None,
exp_id: int | None = None,
) -> DataSetInMem:
if path_to_db is not None:
path_to_db = str(path_to_db)
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=path_to_db)
) as conn:
if exp_id is None:
exp_id = get_default_experiment_id(conn)
name = name or "dataset"
sample_name = get_sample_name_from_experiment_id(conn, exp_id)
exp_name = get_experiment_name_from_experiment_id(conn, exp_id)
guid = generate_guid()
run_counter, run_id, _ = create_run(
conn, exp_id, name, guid=guid, parameters=None, create_run_table=False
)
ds = cls(
run_id=run_id,
captured_run_id=run_id,
counter=run_counter,
captured_counter=run_counter,
name=name,
exp_id=exp_id,
exp_name=exp_name,
sample_name=sample_name,
guid=guid,
path_to_db=conn.path_to_dbfile,
run_timestamp_raw=None,
completed_timestamp_raw=None,
metadata=None,
)
return ds
@classmethod
def _load_from_netcdf(
cls, path: Path | str, path_to_db: Path | str | None = None
) -> DataSetInMem:
"""
Create a in memory dataset from a netcdf file.
The netcdf file is expected to contain a QCoDeS dataset that
has been exported using the QCoDeS netcdf export functions.
Args:
path: Path to the netcdf file to import.
path_to_db: Optional path to a database where this dataset may be
exported to. If not supplied the path can be given at export time
or the dataset exported to the default db as set in the QCoDeS config.
Returns:
The loaded dataset.
"""
# in the code below floats and ints loaded from attributes are explicitly casted
# this is due to some older versions of qcodes writing them with a different backend
# reading them back results in a numpy array of one element
import xarray as xr
with xr.open_dataset(path, engine="h5netcdf") as loaded_data:
parent_dataset_links = str_to_links(
loaded_data.attrs.get("parent_dataset_links", "[]")
)
if path_to_db is not None:
path_to_db = str(path_to_db)
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=path_to_db)
) as conn:
run_data = get_raw_run_attributes(conn, guid=loaded_data.guid)
path_to_db = conn.path_to_dbfile
if run_data is not None:
run_id = run_data["run_id"]
counter = run_data["counter"]
else:
run_id = int(loaded_data.captured_run_id)
counter = int(loaded_data.captured_counter)
path = str(path)
path = os.path.abspath(path)
export_info = ExportInfo.from_str(loaded_data.attrs.get("export_info", ""))
export_info.export_paths["nc"] = path
non_metadata = {
"run_timestamp_raw",
"completed_timestamp_raw",
"ds_name",
"exp_name",
"sample_name",
"export_info",
"parent_dataset_links",
}
metadata_keys = (
set(loaded_data.attrs.keys()) - set(RUNS_TABLE_COLUMNS) - non_metadata
)
metadata = {}
for key in metadata_keys:
data = loaded_data.attrs[key]
if isinstance(data, np.ndarray) and data.size == 1:
data = data[0]
metadata[str(key)] = data
ds = cls(
run_id=run_id,
captured_run_id=int(loaded_data.captured_run_id),
counter=counter,
captured_counter=int(loaded_data.captured_counter),
name=loaded_data.ds_name,
exp_id=0,
exp_name=loaded_data.exp_name,
sample_name=loaded_data.sample_name,
guid=loaded_data.guid,
path_to_db=path_to_db,
run_timestamp_raw=float(loaded_data.run_timestamp_raw),
completed_timestamp_raw=float(loaded_data.completed_timestamp_raw),
metadata=metadata,
rundescriber=serial.from_json_to_current(loaded_data.run_description),
parent_dataset_links=parent_dataset_links,
export_info=export_info,
snapshot=loaded_data.snapshot,
)
ds._cache = DataSetCacheDeferred(ds, path)
return ds
@classmethod
def _load_from_db(cls, conn: ConnectionPlus, guid: str) -> DataSetInMem:
run_attributes = get_raw_run_attributes(conn, guid)
if run_attributes is None:
raise RuntimeError(
f"Could not find the requested run with GUID: {guid} in the db"
)
metadata = run_attributes["metadata"]
export_info_str = metadata.get("export_info", "")
export_info = ExportInfo.from_str(export_info_str)
ds = cls(
run_id=run_attributes["run_id"],
captured_run_id=run_attributes["captured_run_id"],
counter=run_attributes["counter"],
captured_counter=run_attributes["captured_counter"],
name=run_attributes["name"],
exp_id=run_attributes["experiment"]["exp_id"],
exp_name=run_attributes["experiment"]["name"],
sample_name=run_attributes["experiment"]["sample_name"],
guid=guid,
path_to_db=conn.path_to_dbfile,
run_timestamp_raw=run_attributes["run_timestamp"],
completed_timestamp_raw=run_attributes["completed_timestamp"],
metadata=metadata,
rundescriber=serial.from_json_to_current(run_attributes["run_description"]),
parent_dataset_links=str_to_links(run_attributes["parent_dataset_links"]),
export_info=export_info,
snapshot=run_attributes["snapshot"],
)
xr_path_temp = export_info.export_paths.get("nc")
xr_path = Path(xr_path_temp) if xr_path_temp is not None else None
cls._set_cache_from_netcdf(ds, xr_path)
return ds
@classmethod
def _set_cache_from_netcdf(cls, ds: DataSetInMem, xr_path: Path | None) -> bool:
success = True
if xr_path is not None and xr_path.is_file():
ds._cache = DataSetCacheDeferred(ds, xr_path)
elif xr_path is not None and not xr_path.is_file():
success = False
warnings.warn(
f"Could not load raw data for dataset with guid : {ds.guid} from location {xr_path}"
)
else:
warnings.warn(f"No raw data stored for dataset with guid : {ds.guid}")
success = False
return success
def set_netcdf_location(self, path: str | Path) -> None:
"""
Change the location that a DataSetInMem refers to and
load the raw data into the cache from this location.
This may be useful if loading the dataset from a database raises a warning
since the location of the raw data has moved. If this is the case you may
be able to use this method to update the metadata in the database to refer to
the new location.
"""
if isinstance(path, str):
path = Path(path)
data_loaded = self._set_cache_from_netcdf(self, path)
if data_loaded:
export_info = self.export_info
export_info.export_paths["nc"] = str(path)
self._set_export_info(export_info)
else:
raise FileNotFoundError(f"Could not load a netcdf file from {path}")
@staticmethod
def _from_xarray_dataset_to_qcodes_raw_data(
xr_data: xr.Dataset,
) -> dict[str, dict[str, np.ndarray]]:
output: dict[str, dict[str, np.ndarray]] = {}
for datavar in xr_data.data_vars:
output[str(datavar)] = {}
data = xr_data[datavar]
output[str(datavar)][str(datavar)] = data.data
all_coords = []
for index_name in data.dims:
index = data.indexes[index_name]
coords = {name: data.coords[name] for name in index.names}
all_coords.append(coords)
if len(all_coords) > 1:
# if there are more than on index this cannot be a multiindex dataset
# so we can expand the data
coords_unexpanded = []
for coord_name in data.dims:
coords_unexpanded.append(xr_data[coord_name].data)
coords_arrays = np.meshgrid(*coords_unexpanded, indexing="ij")
for coord_name, coord_array in zip(data.dims, coords_arrays):
output[str(datavar)][str(coord_name)] = coord_array
elif len(all_coords) == 1:
# this is either a multiindex or a single regular index
# in both cases we do not need to reshape the data
coords = all_coords[0]
for coord_name, coord in coords.items():
output[str(datavar)][str(coord_name)] = coord.data
return output
def prepare(
self,
*,
snapshot: Mapping[Any, Any],
interdeps: InterDependencies_,
shapes: Shapes | None = None,
parent_datasets: Sequence[Mapping[Any, Any]] = (),
write_in_background: bool = False,
) -> None:
if not self.pristine:
raise RuntimeError("Cannot prepare a dataset that is not pristine.")
self.add_snapshot(json.dumps({"station": snapshot}, cls=NumpyJSONEncoder))
if interdeps == InterDependencies_():
raise RuntimeError("No parameters supplied")
self._set_interdependencies(interdeps, shapes)
links = [Link(head=self.guid, **pdict) for pdict in parent_datasets]
self._set_parent_dataset_links(links)
if self.pristine:
self._perform_start_actions()
self.cache.prepare()
@property
def pristine(self) -> bool:
"""Is this :class:`.DataSetInMem` pristine?
A pristine :class:`.DataSetInMem` has not yet been started,
meaning that parameters can still be added and removed, but results
can not be added.
"""
return self._run_timestamp_raw is None and self._completed_timestamp_raw is None
@property
def running(self) -> bool:
"""
Is this :class:`.DataSetInMem` currently running?
A running :class:`.DataSetInMem` has been started,
but not yet completed.
"""
return (
self._run_timestamp_raw is not None
and self._completed_timestamp_raw is None
)
@property
def completed(self) -> bool:
"""
Is this :class:`.DataSetInMem` completed?
A completed :class:`.DataSetInMem` may not be modified in
any way.
"""
return self._completed_timestamp_raw is not None
def mark_completed(self) -> None:
"""
Mark :class:`.DataSetInMem` as complete and thus read only and notify the subscribers
"""
if self.completed:
return
if self.pristine:
raise RuntimeError(
"Can not mark a dataset as complete before it "
"has been marked as started."
)
self._complete(True)
@property
def run_id(self) -> int:
return self._run_id
@property
def captured_run_id(self) -> int:
return self._captured_run_id
@property
def counter(self) -> int:
return self._counter
@property
def captured_counter(self) -> int:
return self._captured_counter
@property
def guid(self) -> str:
return self._guid
@property
def number_of_results(self) -> int:
return self.__len__()
@property
def name(self) -> str:
return self._name
@property
def exp_name(self) -> str:
return self._exp_name
@property
def exp_id(self) -> int:
return self._exp_id
@property
def sample_name(self) -> str:
return self._sample_name
@property
def path_to_db(self) -> str | None:
return self._path_to_db
@property
def run_timestamp_raw(self) -> float | None:
"""
Returns run timestamp as number of seconds since the Epoch
The run timestamp is the moment when the measurement for this run
started.
"""
return self._run_timestamp_raw
@property
def completed_timestamp_raw(self) -> float | None:
"""
Returns timestamp when measurement run was completed
as number of seconds since the Epoch
If the run (or the dataset) is not completed, then returns None.
"""
return self._completed_timestamp_raw
@property
def snapshot(self) -> dict[str, Any] | None:
"""Snapshot of the run as dictionary (or None)."""
snapshot_json = self._snapshot_raw
if snapshot_json is not None:
return json.loads(snapshot_json)
else:
return None
def add_snapshot(self, snapshot: str, overwrite: bool = False) -> None:
"""
Adds a snapshot to this run.
Args:
snapshot: the raw JSON dump of the snapshot
overwrite: force overwrite an existing snapshot
"""
if self.snapshot is None or overwrite:
self._add_to_dyn_column_if_in_db("snapshot", snapshot)
self._snapshot_raw_data = snapshot
elif self.snapshot is not None and not overwrite:
log.warning(
"This dataset already has a snapshot. Use overwrite"
"=True to overwrite that"
)
@property
def _snapshot_raw(self) -> str | None:
"""Snapshot of the run as a JSON-formatted string (or None)."""
return self._snapshot_raw_data
def add_metadata(self, tag: str, metadata: Any) -> None:
"""
Adds metadata to the :class:`.DataSet`.
The metadata is stored under the provided tag.
Note that None is not allowed as a metadata value.
Args:
tag: represents the key in the metadata dictionary
metadata: actual metadata
"""
self._metadata[tag] = metadata
self._add_to_dyn_column_if_in_db(tag, metadata)
self._add_metadata_to_netcdf_if_nc_exported(tag, metadata)
def _add_to_dyn_column_if_in_db(self, tag: str, data: Any) -> None:
if self._dataset_is_in_runs_table():
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=self._path_to_db)
) as conn:
with atomic(conn) as aconn:
add_data_to_dynamic_columns(aconn, self.run_id, {tag: data})
@property
def metadata(self) -> dict[str, Any]:
return self._metadata
@property
def paramspecs(self) -> dict[str, ParamSpec]:
return {ps.name: ps for ps in self._get_paramspecs()}
@property
def description(self) -> RunDescriber:
return self._rundescriber
@property
def parent_dataset_links(self) -> list[Link]:
"""
Return a list of Link objects. Each Link object describes a link from
this dataset to one of its parent datasets
"""
return self._parent_dataset_links
@property
def cache(self) -> DataSetCacheInMem:
return self._cache
@property
def export_info(self) -> ExportInfo:
return self._export_info
def _set_export_info(self, export_info: ExportInfo) -> None:
tag = "export_info"
metadata = export_info.to_str()
self._metadata[tag] = metadata
self._add_to_dyn_column_if_in_db(tag, metadata)
self._export_info = export_info
def _enqueue_results(self, result_dict: Mapping[ParamSpecBase, np.ndarray]) -> None:
"""
Enqueue the results, for this dataset directly into cache
Before we can enqueue the results, all values of the results dict
must have the same length. We enqueue each parameter tree separately,
effectively mimicking making one call to add_results per parameter
tree.
Deal with 'numeric' type parameters. If a 'numeric' top level parameter
has non-scalar shape, it must be unrolled into a list of dicts of
single values (database).
"""
self._raise_if_not_writable()
interdeps = self._rundescriber.interdeps
toplevel_params = set(interdeps.dependencies).intersection(set(result_dict))
new_results: dict[str, dict[str, np.ndarray]] = {}
for toplevel_param in toplevel_params:
inff_params = set(interdeps.inferences.get(toplevel_param, ()))
deps_params = set(interdeps.dependencies.get(toplevel_param, ()))
all_params = inff_params.union(deps_params).union({toplevel_param})
new_results[toplevel_param.name] = {}
new_results[toplevel_param.name][
toplevel_param.name
] = self._reshape_array_for_cache(
toplevel_param, result_dict[toplevel_param]
)
for param in all_params:
if param is not toplevel_param:
new_results[toplevel_param.name][
param.name
] = self._reshape_array_for_cache(param, result_dict[param])
# Finally, handle standalone parameters
standalones = set(interdeps.standalones).intersection(set(result_dict))
if standalones:
for st in standalones:
new_results[st.name] = {
st.name: self._reshape_array_for_cache(st, result_dict[st])
}
self.cache.add_data(new_results)
def _flush_data_to_database(self, block: bool = False) -> None:
pass
# not part of the protocol specified api
def _set_parent_dataset_links(self, links: list[Link]) -> None:
"""
Assign one or more links to parent datasets to this dataset. It is an
error to assign links to a non-pristine dataset
Args:
links: The links to assign to this dataset
"""
if not self.pristine:
raise RuntimeError(
"Can not set parent dataset links on a dataset "
"that has been started."
)
if not all(isinstance(link, Link) for link in links):
raise ValueError("Invalid input. Did not receive a list of Links")
for link in links:
if link.head != self.guid:
raise ValueError(
"Invalid input. All links must point to this dataset. "
"Got link(s) with head(s) pointing to another dataset."
)
self._parent_dataset_links = links
def _set_interdependencies(
self, interdeps: InterDependencies_, shapes: Shapes | None = None
) -> None:
"""
Set the interdependencies object (which holds all added
parameters and their relationships) of this dataset and
optionally the shapes object that holds information about
the shape of the data to be measured.
"""
if not isinstance(interdeps, InterDependencies_):
raise TypeError(
f"Wrong input type. Expected InterDepencies_, got {type(interdeps)}"
)
if not self.pristine:
mssg = "Can not set interdependencies on a DataSet that has been started."
raise RuntimeError(mssg)
self._rundescriber = RunDescriber(interdeps, shapes=shapes)
def _get_paramspecs(self) -> SPECS:
old_interdeps = new_to_old(self.description.interdeps)
return list(old_interdeps.paramspecs)
def _complete(self, value: bool) -> None:
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=self._path_to_db)
) as conn:
if value:
self._completed_timestamp_raw = time.time()
mark_run_complete(conn, self.run_id, self._completed_timestamp_raw)
def _perform_start_actions(self) -> None:
"""
Perform the actions that must take place once the run has been started
"""
with contextlib.closing(
conn_from_dbpath_or_conn(conn=None, path_to_db=self._path_to_db)
) as conn:
paramspecs = new_to_old(self.description.interdeps).paramspecs
for spec in paramspecs:
add_parameter(
spec, conn=conn, run_id=self.run_id, insert_into_results_table=False
)
desc_str = serial.to_json_for_storage(self.description)
update_run_description(conn, self.run_id, desc_str)
self._run_timestamp_raw = time.time()
set_run_timestamp(conn, self.run_id, self._run_timestamp_raw)
pdl_str = links_to_str(self._parent_dataset_links)
update_parent_datasets(conn, self.run_id, pdl_str)
def _raise_if_not_writable(self) -> None:
if self.pristine:
raise RuntimeError(
"This DataSet has not been marked as started. "
"Please mark the DataSet as started before "
"adding results to it."
)
if self.completed:
raise CompletedError(
"This DataSet is complete, no further results can be added to it."
)
def __len__(self) -> int:
"""
The in memory dataset does not have a concept of sqlite rows
so the length is represented by the number of all datapoints,
summing across parameter trees.
"""
values: list[int] = []
for sub_dataset in self.cache.data().values():
subvals = tuple(val.size for val in sub_dataset.values() if val is not None)
values.extend(subvals)
if len(values):
return sum(values)
else:
return 0
def __repr__(self) -> str:
out = []
heading = f"{self.name} #{self.run_id}@memory"
out.append(heading)
out.append("-" * len(heading))
ps = self.description.interdeps.paramspecs
if len(ps) > 0:
for p in ps:
out.append(f"{p.name} - {p.type}")
return "\n".join(out)
@property
def _parameters(self) -> str | None:
psnames = [ps.name for ps in self.description.interdeps.paramspecs]
if len(psnames) > 0:
return ",".join(psnames)
else:
return None
def to_xarray_dataarray_dict(
self,
*params: str | ParamSpec | ParameterBase,
start: int | None = None,
end: int | None = None,
use_multi_index: Literal["auto", "always", "never"] = "auto",
) -> dict[str, xr.DataArray]:
self._warn_if_set(*params, start=start, end=end)
return self.cache.to_xarray_dataarray_dict()
def to_xarray_dataset(
self,
*params: str | ParamSpec | ParameterBase,
start: int | None = None,
end: int | None = None,
use_multi_index: Literal["auto", "always", "never"] = "auto",
) -> xr.Dataset:
self._warn_if_set(*params, start=start, end=end)
return self.cache.to_xarray_dataset()
def to_pandas_dataframe_dict(
self,
*params: str | ParamSpec | ParameterBase,
start: int | None = None,
end: int | None = None,
) -> dict[str, pd.DataFrame]:
self._warn_if_set(*params, start=start, end=end)
return self.cache.to_pandas_dataframe_dict()
def to_pandas_dataframe(
self,
*params: str | ParamSpec | ParameterBase,
start: int | None = None,
end: int | None = None,
) -> pd.DataFrame:
self._warn_if_set(*params, start=start, end=end)
return self.cache.to_pandas_dataframe()
def get_parameter_data(
self,
*params: str | ParamSpec | ParameterBase,
start: int | None = None,
end: int | None = None,
callback: Callable[[float], None] | None = None,
) -> ParameterData:
self._warn_if_set(*params, start=start, end=end)
return self.cache.data()
@staticmethod
def _warn_if_set(
*params: str | ParamSpec | ParameterBase,
start: int | None = None,
end: int | None,
) -> None:
if len(params) > 0 or start is not None or end is not None:
warnings.warn(
"Passing params, start or stop to to_xarray_... and "
"to_pandas_... methods has no effect for DataSetInMem. "
"This will be an error in the future."
)
[docs]
def load_from_netcdf(
path: Path | str, path_to_db: Path | str | None = None
) -> DataSetInMem:
"""
Create a in memory dataset from a netcdf file.
The netcdf file is expected to contain a QCoDeS dataset that
has been exported using the QCoDeS netcdf export functions.
Args:
path: Path to the netcdf file to import.
path_to_db: Optional path to a database where this dataset may be
exported to. If not supplied the path can be given at export time
or the dataset exported to the default db as set in the QCoDeS config.
Returns:
The loaded dataset.
"""
return DataSetInMem._load_from_netcdf(path=path, path_to_db=path_to_db)
[docs]
def load_from_file(
path: Path | str, path_to_db: Path | str | None = None
) -> DataSetInMem:
"""
Create an in-memory dataset from a file.
The file is expected to contain a QCoDeS dataset that
has been exported using the QCoDeS export functions.
Currently, this only supports loading from netcdf files.
Args:
path: Path to the file to import.
path_to_db: Optional path to a database where this dataset may be
exported to. If not supplied the path can be given at export time
or the dataset exported to the default db as set in the QCoDeS config.
Returns:
The loaded dataset.
"""
path = Path(path)
if not path.is_file():
raise FileNotFoundError(f"File {path} not found.")
if DataExportType.NETCDF.value == path.suffix.replace(".", ""):
return DataSetInMem._load_from_netcdf(path=path, path_to_db=path_to_db)
else:
raise ValueError(
f"Loading file of type {path.suffix} is not supported."
"Please refer to https://github.com/QCoDeS/Qcodes/issues"
"to find existing or create a new feature request."
)