Using datasets

A dataset is a relatively homogeneous collection of records that allows for submission and management of a large number of computations.

A dataset is made up of entries, specifications, and records. It can be thought of as a table, where the entries are rows of the table, and specifications are the columns. A cell within the table (intersection between a row /entry and column/specification) is a record.

Below is an example of this analogy, where the records are identified by their ID. For example, record 18263 is an HF/sto-3g computation on water, and record 23210 is an MP2/cc-pvdz computation on ethanol.

Entry	HF/sto-3g	B3LYP/def2-tzvp	MP2/cc-pvdz
water	18263	18277	18295
methane	19722	19642	19867
ethanol	20212	20931	23210

Using a dataset allows for control of entire rows and columns of the table, and even the entire table itself. As an example, you can add a new specification, and then easily submit computations for that specification that apply to all the existing entries.

Dataset entries, specifications, and records are dependent on the type of dataset; different dataset types have different types for these items. That is, entries in a Singlepoint Dataset are different than entries in an Optimization Dataset, and the same is true for specifications.

Dataset Limitations

A dataset can only contain one type of calculation. For example, you can have a Singlepoint Dataset or a Optimization Dataset, but not a dataset that contains both single point and optimization calculations.

A specification should work for all entries in the dataset. There is some limited ability to override keywords on a per-entry basis, but there is no way to assign a different basis for a particular entry.

Retrieving Datasets

Note

When retrieving a dataset, only a limited about of data is downloaded first. After that, operations such as retrieving entries or records will require contacting the server. This is generally done transparently.

Datasets have unique ID, and a unique name. The unique name only applies to datasets of the same type, so two datasets can have the same name as long as they are of different types. The names are not case sensitive.

You can retrieve a dataset with via its ID with get_dataset_by_id() and its name with get_dataset()

PYTHON

>>> ds = client.get_dataset_by_id(123)
>>> print(ds.id, ds.dataset_type, ds.name)
123 singlepoint Organic molecule energies

>>> ds = client.get_dataset("optimization", "Diatomic geometries")
>>> print(ds.id, ds.dataset_type, ds.name)
52 optimization Diatomic geometries

Adding Datasets

Datasets can be created on a server with the add_dataset() function of the PortalClient. This function returns the dataset:

PYTHON

>>> ds = client.add_dataset("optimization", "Optimization of important molecules")
>>> print(ds.id)
27

The add_dataset() takes several optional arguments, including some for descriptions of the dataset as well as default priority and tags.

Dataset Metadata

Datasets have some useful metadata and properties

• name, description, tagline, group, and tags are user-defined metadata that categorize this dataset among the other datasets

• default_tag and default_priority are the defaults used when submitting new computations (can be overridden in submit(), see Submitting Computations).

• provenance is a user-defined dictionary with any provenance or source information

• metadata is a user-defined dictionary with any other metadata the user wants to attach to the dataset

This metadata is created when the dataset is constructed on the server, but can be changed with set_name(), set_description(), set_metadata(), and so on.

PYTHON

>>> print(ds.description)
Optimization of diatomic molecules at different levels of theory

>>> ds.set_description("A new description")

>>> # It has been changed on the server
>>> ds = client.get_dataset_by_id(1)
>>> print(ds.description)
A new description

Status

The status() returns a dictionary describing the status of the computations. This is indexed by specification

PYTHON

>>> ds.status()
{'pbe0/sto-3g': {<RecordStatusEnum.complete: 'complete'>: 4,
<RecordStatusEnum.error: 'error'>: 1},
 'b3lyp/def2-tzvp': {<RecordStatusEnum.error: 'error'>: 1,
<RecordStatusEnum.complete: 'complete'>: 4},
 'pbe/6-31g': {<RecordStatusEnum.complete: 'complete'>: 3,
<RecordStatusEnum.error: 'error'>: 2}}

If you are in an interactive session or notebook, or just want a prettier version, you can use status_table() returns a table as a string, and print_status() prints a table of the statuses.

PYTHON

>>> ds.print_status_()
    specification    complete    error    invalid
-----------------  ----------  -------  ---------
    pbe/def2-tzvp           3        2
      pbe/sto-3g           4        1
      pbe0/6-31g           4        1
    pbe0/6-31g**           4        1
pbe0/aug-cc-pvtz           3        1          1
  pbe0/def2-tzvp           4        1
      pbe0/sto-3g           4        1

Note

The status is computed on the server, and does require download all the records. This does mean the the status may reflect changes to records that have been retrieved, and so may be out of sync with any local caching.

Specifications and Entries

The specifications of the dataset are available with the .specification_names and .specifications properties. .specifications returns a dictionary, with the key being the name of the specification.

PYTHON

>>> print(ds.specification_names)
['hf/sto-3g', 'hf/def2-tzvp']

>>> print(ds.specifications['hf/sto-3g'])
name='hf/sto-3g' specification=OptimizationSpecification(program='geometric',
qc_specification=QCSpecification(program='psi4', driver=<SinglepointDriver.deferred: 'deferred'>, method='hf',
basis='sto-3g', keywords={'maxiter': 100}, protocols=AtomicResultProtocols(wavefunction=<WavefunctionProtocolEnum.none: 'none'>,
stdout=True, error_correction=ErrorCorrectionProtocol(default_policy=True, policies=None),
native_files=<NativeFilesProtocolEnum.none: 'none'>)), keywords={},
protocols=OptimizationProtocols(trajectory=<TrajectoryProtocolEnum.all: 'all'>)) description=None

Entries are slightly different. Since it is expected that a dataset may have many entries, only the names are accessible all at once

PYTHON

>>> print(ds.entry_names)
['H2', 'N2', 'O2', 'F2', 'Hg2']

You can obtain a full entry from its name with get_entry():

PYTHON

>>> print(ds.get_entry)
OptimizationDatasetEntry(name='H2', initial_molecule=Molecule(name='H2', formula='H2', hash='7746e69'),
additional_keywords={}, attributes={}, comment=None)

If you need to get all entries, you may iterate over the entries with iterate_entries():

PYTHON

>>> for x in ds.iterate_entries():
...    print(x.initial_molecule)
Molecule(name='H2', formula='H2', hash='7746e69')
Molecule(name='N2', formula='N2', hash='609abf3')
Molecule(name='O2', formula='O2', hash='018caee')
Molecule(name='F2', formula='F2', hash='7ffa835')
Molecule(name='Hg2', formula='Hg2', hash='a67cb93')

iterate_entries() can also be restricted to only iterate over certain entry names.

PYTHON

>>> for x in ds.iterate_entries(entry_names=['H2', 'O2']):
...    print(x.initial_molecule)
Molecule(name='H2', formula='H2', hash='7746e69')
Molecule(name='O2', formula='O2', hash='018caee')

Retrieving Records

A single records can be retrieved by entry name and specification name with get_record()

PYTHON

>>> rec = ds.get_record('H2', 'hf/sto-3g')
>>> print(rec)
<OptimizationRecord id=3 status=complete>

>>> print(rec.final_molecule)
Molecule(name='H2', formula='H2', hash='6c7a0a9')

Multiple records (or all records) can be obtained by using the iterator returned from iterate_records(). The iterator return a tuple of three values - the entry name, specification name, and then the full record.

PYTHON

>>> for e_name, s_name, record in ds.iterate_records():
...   print(e_name, s_name, record.id, record.status)
H2 hf/sto-3g 3 RecordStatusEnum.complete
N2 hf/sto-3g 1 RecordStatusEnum.complete
O2 hf/sto-3g 4 RecordStatusEnum.complete
F2 hf/sto-3g 5 RecordStatusEnum.complete
Hg2 hf/sto-3g 2 RecordStatusEnum.error
H2 hf/def2-tzvp 8 RecordStatusEnum.complete
N2 hf/def2-tzvp 9 RecordStatusEnum.complete
O2 hf/def2-tzvp 6 RecordStatusEnum.complete

iterate_records() also has filtering options, including by entry name, specification name, and status

PYTHON

>>> for e_name, s_name, record in ds.iterate_records(status='error'):
...   print(e_name, s_name, record.id, record.status)
Hg2 hf/sto-3g 2 RecordStatusEnum.error
Hg2 hf/def2-tzvp 10 RecordStatusEnum.error
Hg2 hf/6-31g 15 RecordStatusEnum.error
Hg2 hf/6-31g** 17 RecordStatusEnum.error

If the record was previously retrieved, it won’t be retrieved again unless it has been updated on the server. This can be overridden with force_refetch=True which will always download a fresh record.

Manipulating Entries and Specifications

Entries and specifications can be added with add_entries, add_entry, and add_specification. The details of these functions depend on the type of dataset - see Computation Types. The following examples are for an optimization dataset.

Both entries and specifications are given descriptive names, which can be use later in other functions (like get_record()).

When entries or specifications are added, the changes are reflected immediately on the server.

First, we add some entries to this dataset. For an optimization dataset, an entry corresponds to an unoptimized ‘initial’ molecule. Adding entries returns metadata.

PYTHON

>>> from qcportal.molecules import Molecule
>>> from qcportal.optimization import OptimizationDatasetEntry

>>> mol = Molecule(symbols=['C', 'O'], geometry=[0.0, 0.0, 0.0, 0.0, 0.0, 2.0])
>>> meta = ds.add_entry("carbon monoxide", mol)
>>> print(meta)
InsertMetadata(error_description=None, errors=[], inserted_idx=[0], existing_idx=[])

>>> # Can also create lots of entries and add them at once
>>> mol2 = Molecule(symbols=['F', 'F'], geometry=[0.0, 0.0, 0.0, 0.0, 0.0, 2.0])
>>> mol3 = Molecule(symbols=['Br', 'Br'], geometry=[0.0, 0.0, 0.0, 0.0, 0.0, 2.0])
>>> entry2 = OptimizationDatasetEntry(name='difluorine', initial_molecule=mol2)
>>> entry3 = OptimizationDatasetEntry(name='dibromine', initial_molecule=mol3)
>>> meta = ds.add_entries([entry2, entry3])
>>> print(meta)
InsertMetadata(error_description=None, errors=[], inserted_idx=[0, 1], existing_idx=[])

Now our dataset has three entries

PYTHON

>>> print(ds.entry_names)
['carbon monoxide', 'difluorine', 'dibromine']

Next, we will add some specifications. For an optimization dataset, this is an OptimizationSpecification.

PYTHON

>>> from qcportal.singlepoint import SinglepointSpecification
>>> from qcportal.optimization import OptimizationSpecification

>>> # Use geometric, compute gradients with psi4. Optimize with b3lyp/def2-tzvp
>>> spec = OptimizationSpecification(
...   program='geometric',
...   qc_specification=QCSpecification(
...     program='psi4',
...     driver='deferred',
...     method='b3lyp',
...     basis='def2-tzvp',
...   )
... )

>>> meta = ds.add_specification(name='psi4/b3lyp/def2-tzvp', specification=spec)
>>> print(meta)
InsertMetadata(error_description=None, errors=[], inserted_idx=[0], existing_idx=[])

Submitting Computations

Adding entries and specifications does not immediately create the underlying records. To do that, we use submit().

With no arguments, this will create missing records for all entries and specifications, using the default tag and priority of the dataset. However, you may also submit only certain entries and specifications, or change the tag and priority.

PYTHON

>>> ds.submit() # Create everything

>>> # Submit missing difluorine computations with a special tag
>>> ds.submit(['difluorine'], tag='special_tag')

>>> # Submit dibromine hf/sto-3g computation at a high priority
>>> ds.submit(['dibromine'], ['hf/sto-3g'], priority='high')

Renaming and Deleting Entries and Specifications

Entries and specifications can be renamed and deleted. Deletion can also optionally delete the underlying record.

rename_entries() and rename_specification() take a dictionary of old name to new name

PYTHON

>>> ds.rename_entries({'difluorine': 'F2 molecule'})
>>> ent = ds.get_entry('F2 molecule')
>>> print(ent.initial_molecule)
initial_molecule=Molecule(name='F2', formula='F2', hash='7ffa835')

Entries and specifications are deleted with delete_entries() and delete_specification(). Note that deleting entries and specifications by default do not delete the records

PYTHON

>>> # Keeps any records, but removes from dataset
>>> ds.delete_entries(['carbon monoxide'])

>>> # Deletes the records too
>>> ds.delete_specification('hf/sto-3g', delete_records=True)

Record Management

Records that belong to the dataset can be managed via the usual client methods (see Managing Records). However, datasets have convenient methods for management, which use entry and specification names rather than record.

• modify_records()

• reset_records()

• cancel_records() and uncancel_records()

• invalidate_records() and uninvalidate_records()

These functions are similar to the client counterparts, but instead use entry and specification names.

In addition, individual records can be removed from a dataset (and optionally deleted) with remove_records().

PYTHON

>>> # Reset carbon monoxide records
>>> ds.reset_records(entry_names=['carbon monoxide'])

>>> # Cancel pbe0/def2-qzvp computations
>>> ds.cancel_records(specification_names=['pbe0/def2-qzvp'])