def listfamilies(element, with_description):
"""
Print on screen the list of installed PSF-pseudo families.
"""
from aiida import is_dbenv_loaded, load_dbenv
if not is_dbenv_loaded():
load_dbenv()
from aiida.orm import DataFactory
from aiida_siesta.data.psf import PSFGROUP_TYPE
PsfData = DataFactory('siesta.psf')
from aiida.orm.querybuilder import QueryBuilder
from aiida.orm.group import Group
qb = QueryBuilder()
qb.append(PsfData, tag='psfdata')
if element:
qb.add_filter(PsfData, {'attributes.element': {'in': element}})
qb.append(
Group,
group_of='psfdata',
tag='group',
project=["name", "description"],
filters={
"type": {
'==': PSFGROUP_TYPE
}
})
qb.distinct()
if qb.count() > 0:
for res in qb.dict():
group_name = res.get("group").get("name")
group_desc = res.get("group").get("description")
qb = QueryBuilder()
qb.append(
Group, tag='thisgroup', filters={
"name": {
'like': group_name
}
})
qb.append(PsfData, project=["id"], member_of='thisgroup')
if with_description:
description_string = ": {}".format(group_desc)
else:
description_string = ""
click.echo("* {} [{} pseudos]{}".format(group_name,
qb.count(),
description_string))
else:
click.echo("No valid PSF pseudopotential family found.", err=True)
def get_types(self):
"""
return available distinct types of nodes from database
"""
from aiida.orm.querybuilder import QueryBuilder
qb = QueryBuilder()
qb.append(self._aiida_class, project=["type"])
qb_response = qb.distinct().all()
results = {}
if len(qb_response) > 0:
for ntype in qb_response:
ntype = ntype[0]
ntype_parts = ntype.split(".")
if len(ntype_parts) > 0:
dict_key = ntype_parts[0]
if dict_key not in results.keys():
results[dict_key] = []
results[dict_key].append(ntype)
for key, values in results.items():
results[key] = sorted(values)
return results
def listfamilies(elements, with_description):
"""
Print on screen the list of upf families installed
"""
from aiida.orm import DataFactory
from aiida.orm.data.upf import UPFGROUP_TYPE
# pylint: disable=invalid-name
UpfData = DataFactory('upf')
from aiida.orm.querybuilder import QueryBuilder
from aiida.orm.group import Group
qb = QueryBuilder()
qb.append(UpfData, tag='upfdata')
if elements is not None:
qb.add_filter(UpfData, {'attributes.element': {'in': elements}})
qb.append(Group,
group_of='upfdata',
tag='group',
project=["name", "description"],
filters={"type": {
'==': UPFGROUP_TYPE
}})
qb.distinct()
if qb.count() > 0:
for res in qb.dict():
group_name = res.get("group").get("name")
group_desc = res.get("group").get("description")
qb = QueryBuilder()
qb.append(Group,
tag='thisgroup',
filters={"name": {
'like': group_name
}})
qb.append(UpfData, project=["id"], member_of='thisgroup')
if with_description:
description_string = ": {}".format(group_desc)
else:
description_string = ""
echo.echo_success("* {} [{} pseudos]{}".format(
group_name, qb.count(), description_string))
else:
echo.echo_warning("No valid UPF pseudopotential family found.")
def get_subtree(pk, level=0):
qb = QueryBuilder()
qb.append(cls=WorkCalculation,
filters={'id': pk},
tag='workcalculation')
qb.append(
cls=WorkCalculation,
project=['id'],
# In the future, we should specify here the type of link
# for now, CALL links are the only ones allowing calc-calc
# (we here really want instead to follow CALL links)
output_of='workcalculation',
tag='subworkchains')
result = list(itertools.chain(*qb.distinct().all()))
# This will return a single flat list of tuples, where the first element
# corresponds to the WorkChain pk and the second element is an integer
# that represents its level of nesting within the chain
return [(pk, level)] + list(
itertools.chain(
*[get_subtree(subpk, level=level + 1) for subpk in result]))
def query(datatype, project, past_days, group_pks, all_users):
"""
Perform the query
"""
import datetime
from aiida.orm.implementation import Group
from aiida.orm.user import User
from aiida.orm.backend import construct_backend
from aiida.orm.querybuilder import QueryBuilder
from aiida.utils import timezone
backend = construct_backend()
qbl = QueryBuilder()
if all_users is False:
user = backend.users.get_automatic_user()
qbl.append(User, tag="creator", filters={"email": user.email})
else:
qbl.append(User, tag="creator")
# If there is a time restriction
data_filters = {}
if past_days is not None:
now = timezone.now()
n_days_ago = now - datetime.timedelta(days=past_days)
data_filters.update({"ctime": {'>=': n_days_ago}})
qbl.append(datatype, tag="data", created_by="creator", filters=data_filters, project=project)
# If there is a group restriction
if group_pks is not None:
group_filters = dict()
group_filters.update({"id": {"in": group_pks}})
qbl.append(Group, tag="group", filters=group_filters, group_of="data")
qbl.order_by({datatype: {'ctime': 'asc'}})
object_list = qbl.distinct()
return object_list.all()
def query_jobcalculations_by_computer_user_state(
self,
state,
computer=None,
user=None,
only_computer_user_pairs=False,
only_enabled=True,
limit=None):
"""
Filter all calculations with a given state.
Issue a warning if the state is not in the list of valid states.
:param string state: The state to be used to filter (should be a string among
those defined in aiida.common.datastructures.calc_states)
:param computer: a Django DbComputer entry, or a Computer object, of a
computer in the DbComputer table.
A string for the hostname is also valid.
:param user: a Django entry (or its pk) of a user in the DbUser table;
if present, the results are restricted to calculations of that
specific user
:param bool only_computer_user_pairs: if False (default) return a queryset
where each element is a suitable instance of Node (it should
be an instance of Calculation, if everything goes right!)
If True, return only a list of tuples, where each tuple is
in the format
('dbcomputer__id', 'user__id')
[where the IDs are the IDs of the respective tables]
:param int limit: Limit the number of rows returned
:return: a list of calculation objects matching the filters.
"""
# I assume that calc_states are strings. If this changes in the future,
# update the filter below from dbattributes__tval to the correct field.
from aiida.orm.computer import Computer
from aiida.orm.calculation.job import JobCalculation
from aiida.orm.user import User
from aiida.orm.querybuilder import QueryBuilder
from aiida.common.exceptions import InputValidationError
from aiida.common.datastructures import calc_states
if state not in calc_states:
raise InputValidationError(
"querying for calculation state='{}', but it "
"is not a valid calculation state".format(state))
calcfilter = {'state': {'==': state}}
computerfilter = {"enabled": {'==': True}}
userfilter = {}
if computer is None:
pass
elif isinstance(computer, int):
computerfilter.update({'id': {'==': computer}})
elif isinstance(computer, Computer):
computerfilter.update({'id': {'==': computer.pk}})
else:
try:
computerfilter.update({'id': {'==': computer.id}})
except AttributeError as e:
raise Exception("{} is not a valid computer\n{}".format(
computer, e))
if user is None:
pass
elif isinstance(user, int):
userfilter.update({'id': {'==': user}})
else:
try:
userfilter.update({'id': {'==': int(user.id)}})
# Is that safe?
except:
raise Exception("{} is not a valid user".format(user))
qb = QueryBuilder()
qb.append(type="computer", tag='computer', filters=computerfilter)
qb.append(JobCalculation,
filters=calcfilter,
tag='calc',
has_computer='computer')
qb.append(type="user",
tag='user',
filters=userfilter,
creator_of="calc")
if only_computer_user_pairs:
qb.add_projection("computer", "*")
qb.add_projection("user", "*")
returnresult = qb.distinct().all()
else:
qb.add_projection("calc", "*")
if limit is not None:
qb.limit(limit)
returnresult = qb.all()
returnresult = zip(*returnresult)[0]
return returnresult
def get_bands_and_parents_structure(self, args):
"""
Search for bands and return bands and the closest structure that is a parent of the instance.
This is the backend independent way, can be overriden for performance reason
:returns:
A list of sublists, each latter containing (in order):
pk as string, formula as string, creation date, bandsdata-label
"""
import datetime
from aiida.utils import timezone
from aiida.orm.querybuilder import QueryBuilder
from aiida.backends.utils import get_automatic_user
from aiida.orm.implementation import User
from aiida.orm.implementation import Group
from aiida.orm.data.structure import (get_formula, get_symbols_string)
from aiida.orm.data.array.bands import BandsData
from aiida.orm.data.structure import StructureData
qb = QueryBuilder()
if args.all_users is False:
au = get_automatic_user()
user = User(dbuser=au)
qb.append(User, tag="creator", filters={"email": user.email})
else:
qb.append(User, tag="creator")
bdata_filters = {}
if args.past_days is not None:
now = timezone.now()
n_days_ago = now - datetime.timedelta(days=args.past_days)
bdata_filters.update({"ctime": {'>=': n_days_ago}})
qb.append(BandsData,
tag="bdata",
created_by="creator",
filters=bdata_filters,
project=["id", "label", "ctime"])
group_filters = {}
if args.group_name is not None:
group_filters.update({"name": {"in": args.group_name}})
if args.group_pk is not None:
group_filters.update({"id": {"in": args.group_pk}})
if group_filters:
qb.append(Group,
tag="group",
filters=group_filters,
group_of="bdata")
qb.append(
StructureData,
tag="sdata",
ancestor_of="bdata",
# We don't care about the creator of StructureData
project=["id", "attributes.kinds", "attributes.sites"])
qb.order_by({StructureData: {'ctime': 'desc'}})
list_data = qb.distinct()
entry_list = []
already_visited_bdata = set()
for [bid, blabel, bdate, sid, akinds, asites] in list_data.all():
# We process only one StructureData per BandsData.
# We want to process the closest StructureData to
# every BandsData.
# We hope that the StructureData with the latest
# creation time is the closest one.
# This will be updated when the QueryBuilder supports
# order_by by the distance of two nodes.
if already_visited_bdata.__contains__(bid):
continue
already_visited_bdata.add(bid)
if args.element is not None:
all_symbols = [_["symbols"][0] for _ in akinds]
if not any([s in args.element for s in all_symbols]):
continue
if args.element_only is not None:
all_symbols = [_["symbols"][0] for _ in akinds]
if not all([s in all_symbols for s in args.element_only]):
continue
# We want only the StructureData that have attributes
if akinds is None or asites is None:
continue
symbol_dict = {}
for k in akinds:
symbols = k['symbols']
weights = k['weights']
symbol_dict[k['name']] = get_symbols_string(symbols, weights)
try:
symbol_list = []
for s in asites:
symbol_list.append(symbol_dict[s['kind_name']])
formula = get_formula(symbol_list, mode=args.formulamode)
# If for some reason there is no kind with the name
# referenced by the site
except KeyError:
formula = "<<UNKNOWN>>"
entry_list.append(
[str(bid),
str(formula),
bdate.strftime('%d %b %Y'), blabel])
return entry_list