surf = Entity(name='surface',
desc='Subset of struct: periodicity in 1 or 2 dimensions',
attrs=[
Attr('facet', Varchar, desc="Autodetected facet"),
Attr('vacuum', Decimal, desc='Layer separation (Angstrom)')
],
fks=[FK('struct', id=True)])
rich_objs = [
job, elem, species, struct, cell, atom, calc, struct_comp, bulk, mol, surf
]
################################################################################
# Path Equations
################
b0, m0, s0 = Gen('b0', bulk), Gen('m0', mol), Gen('s0', surf)
st0, a0 = Gen('st0', struct), Gen('a0', atom)
rich_pe = [
PathEQ(Path(atom['number']), Path(atom['element'], elem['atomic_number'])),
PathEQ(Path(b0['struct'], struct['system_type']),
Path(JLit('bulk', String))),
PathEQ(Path(m0['struct'], struct['system_type']),
Path(JLit('molecule', String))),
PathEQ(Path(s0['struct'], struct['system_type']),
Path(JLit('surface', String))),
PathEQ(Path(st0['species'], species['phase']), Path(st0['system_type'])),
PathEQ(Path(gteq(a0['x'], JLit("0.000", Double))),
Path(JLit('true', Boolean)))
]
Readr = Entity(
name='Readr',
desc='A reader',
id='id',
attrs=[
Attr('rname', Varchar, desc='Reader name'),
Attr('borrowed',
Varchar,
desc=
'Comma separated list of books reader has checked out of library')
],
fks=[FK('fav', 'Nov')])
src = Schema('src', [Chap, Nov, Readr])
r1, r2 = [Gen('r%d' % i, Readr) for i in range(1, 3)]
n1, n2, n3 = [Gen('n%d' % i, Nov) for i in range(1, 4)]
n1c1, n1c2, n2c1, n3c1 = [
Gen('n%dc%d' % (x, y), Chap) for x, y in [(1, 1), (1, 2), (2, 1), (3, 1)]
]
one, two, y1, y2, y3 = [JLit(x, Integer) for x in [1, 2, 1924, 1915, 1926]]
# Wrap Python string as a typed Java string
s = lambda x: JLit(x, Varchar) # type: C[[str],JLit]
isrc = Instance({
Readr['fav']: {
r1: n1,
r2: n2
},
Chap['novel_id']: {
where='structure_id = structures.id')
atoms_ind = atoms['id'] - SUBSELECT(
MIN(atoms['id']), tab='atoms A', where='A.structure_id=atoms.structure_id')
xs, ys, zs = [
SUBSELECT(GROUP_CONCAT(atoms[x]),
tab='atoms',
where='structure_id = structures.id') for x in 'xyz'
]
calc_constants = dict(dftcode='vasp', xc='PBE', user='******')
# Generators for CQL expressions
#-------------------------------
s0 = Gen('s0', structs)
l0 = Gen('e0', elems)
# Helpers for CQL expressions
#---------------------------
zero = JLit('0', Int) # java literal
elemcount = countsubstr(s0['comp'], cat(l0['symbol'], JLit(',', String)))
msj_kwargs = ['comp', 'xs', 'ys', 'zs'] + [a + b for a in 'xyz' for b in '123']
msj_args = [structs[x] for x in msj_kwargs]
#######################################
# Section 1: Specification of overlap #
#######################################
count_words = JavaFunc(
'count_words', [String], Integer,
"return 1 + input[0].length() - input[0].replaceAll(' ', '').length()")
Len = JavaFunc('len', [String], Integer, "return input[0].length()")
plus = JavaFunc('plus', [Integer, Integer], Integer,
"return input[0] + input[1]")
matches = JavaFunc('matches', [String, String], Boolean,
"return input[0].matches(input[1])")
cat = JavaFunc('cat', [String, String], String, "return input[0] + input[1]")
funcs = [count_words, Len, plus, matches, cat]
########################################################################
# Map any relevant objs/attrs/relations in source onto paths in target #
########################################################################
chap, nov, readr = Gen('Chap', Chap), Gen('Nov', Nov), Gen('Readr', Readr)
overlap = Overlap(
s1=src,
s2=tar,
paths=[
PathEQ(Path(Nov['title']), Path(Novel['title'])),
PathEQ(Path(Nov['aname']), Path(Novel['wrote'], Author['authorname'])),
PathEQ(Path(Chap['num']), Path(Chapter['num'])),
PathEQ(Path(Chap['novel_id']), Path(Chapter['novel'])),
PathEQ(Path(Readr['rname']), Path(Reader['readername'])),
PathEQ(Path(Readr['fav']), Path(Reader['favorite']))
],
new_attr1=[
NewAttr(Chap, 'n_words', Integer, count_words(chap['text'])),
],