def test_figshare_download():
data_2d = data(dataset="dft_2d")
data_3d = data(dataset="dft_3d")
data_ml = data(dataset="cfid_3d")
data_ff = get_ff_eneleast()
print("2d", len(data_2d))
print("3d", len(data_3d))
print("cfid3d", len(data_ml))
print("ff", len(data_ff))
assert (len(data_2d), len(data_3d), len(data_ml), len(data_ff)) == (
1070,
36099,
35984,
3291,
)
w, ef, atoms = get_wann_electron()
hk = get_hk_tb(w=w)
w, atoms = get_wann_phonon()
w = Webpage(jid="JVASP-1002")
info_mbj = w.get_dft_mbj_dielectric_function()
info_opt = w.get_dft_semilocal_dielectric_function()
info_pdos = w.get_dft_phonon_dos()
info_edos = w.get_dft_electron_dos()
k = w.list_keys()
def test_figshare_download():
data_2d = data(dataset="dft_2d")
data_3d = data(dataset="dft_3d")
data_ml = data(dataset="cfid_3d")
data_ff = get_ff_eneleast()
print("2d", len(data_2d))
print("3d", len(data_3d))
print("cfid3d", len(data_ml))
print("ff", len(data_ff))
assert (len(data_2d), len(data_3d), len(data_ml), len(data_ff)) == (
1070,
36099,
35984,
3291,
)
def test_figshare_download():
data_2d = data(dataset="dft_2d")
# data_3d = data(dataset="dft_3d")
# data_ml = data(dataset="cfid_3d")
data_ff = get_ff_eneleast()
print("2d", len(data_2d))
# print("3d", len(data_3d))
# print("cfid3d", len(data_ml))
print("ff", len(data_ff))
# assert (len(data_2d), len(data_3d), len(data_ml), len(data_ff)) == (
# 1070,
# 36099,
# 35984,
# 3291,
# )
w, ef, atoms = get_wann_electron()
hk = get_hk_tb(w=w)
w, atoms = get_wann_phonon()
w = Webpage(jid="JVASP-1002")
info_mbj = w.get_dft_mbj_dielectric_function()
info_opt = w.get_dft_semilocal_dielectric_function()
info_pdos = w.get_dft_phonon_dos()
info_edos = w.get_dft_electron_dos()
k = w.list_keys()
# dat = data(dataset="megnet")
# dat = data(dataset="mp_3d")
# dat = data(dataset="mp_3d_2020")
# dat = data(dataset="qm9")
# dat = data(dataset="aflow1")
# dat = data(dataset="aflow2")
# dat = data(dataset="oqmd_3d")
# dat = data(dataset="twod_matpd")
# dat = data(dataset="oqmd_3d_no_cfid")
p, n = get_stm_2d_dataset()
def test_dataset():
d = data("dft_2d")
x = []
y = []
z = []
for i in d[0:100]:
if i["formation_energy_peratom"] != "na":
x.append(i["atoms"])
y.append(i["formation_energy_peratom"])
z.append(i["jid"])
s = StructureDataset(x, y, ids=z)
def get_ml_data(
ml_property="formation_energy_peratom",
dataset="cfid_3d",
data_ranges=typical_data_ranges,
):
"""
Provide arrays/pandas-dataframe as input for ML algorithms.
Args:
ml_property: target property to train
data_ranges: range for filtering data
dataset: dataset available in jarvis or other array
Returns:
X, Y , ids
"""
import pandas as pd
if isinstance(dataset, str):
dataml = data(dataset)
df = pd.DataFrame(dataml)
else:
df = pd.DataFrame(dataset)
x = []
y = []
jid = []
df2 = df[["desc", "jid", ml_property]].replace("na", np.nan).dropna()
for ii, i in df2.iterrows():
if data_ranges is None:
if (
len(i["desc"]) == 1557
and float(i[ml_property]) != float("inf")
and i[ml_property] != "na"
):
x.append(i["desc"])
y.append(i[ml_property])
jid.append(i["jid"])
else:
if (
len(i["desc"]) == 1557
and float(i[ml_property]) != float("inf")
and i[ml_property] != "na"
and float(i[ml_property]) <= data_ranges[ml_property][1]
and float(i[ml_property]) >= data_ranges[ml_property][0]
):
x.append(i["desc"])
y.append(i[ml_property])
jid.append(i["jid"])
return np.array(x, dtype="float"), np.array(y, dtype="float"), jid
def test_ldau():
d = data('dft_3d')
for i in d:
if i['jid'] == 'JVASP-29569':
atoms = Atoms.from_dict(i['atoms'])
ld = find_ldau_magmom(atoms=atoms, lsorbit=True)
ld = find_ldau_magmom(atoms=atoms, lsorbit=False)
if i['jid'] == 'JVASP-45':
atoms = Atoms.from_dict(i['atoms'])
ld = find_ldau_magmom(atoms=atoms, lsorbit=True)
assert ld['LDAUU'] == '3.0 0'
ld = find_ldau_magmom(atoms=atoms, lsorbit=False)
def test_dataset():
d = data("dft_2d")
d = pd.DataFrame(d[:100])
graphs = []
for i, row in d.iterrows():
structure = Atoms.from_dict(row["atoms"])
g = Graph.atom_dgl_multigraph(
structure,
atom_features="atomic_number",
compute_line_graph=False,
)
graphs.append(g)
s = StructureDataset(d, graphs, "formation_energy_peratom")
col = s.collate
def test_lat():
box = [[10, 0, 0], [0, 10, 0], [0, 0, 10]]
lat = Lattice(box)
td = lat.to_dict()
fd = Lattice.from_dict(td)
frac_coords = [[0, 0, 0], [0.5, 0.5, 0.5]]
cart_coords = [[0, 0, 0], [5, 5, 5]]
lll = lat._calculate_lll()
# print ('lll',lll[0][0][0])
lll_red = lat.get_lll_reduced_lattice()._lat
# print("lll_educed", lat.get_lll_reduced_lattice()._lat[0][0])
assert (
lat.lat_lengths(),
lat.lat_angles(),
round(lat.inv_lattice()[0][0], 2),
[round(i, 2) for i in lat.lat_angles(radians=True)],
lat.cart_coords(frac_coords)[1][1],
lat.frac_coords(cart_coords)[1][1],
lat.volume,
lat.parameters,
lll[0][0][0],
lll_red[0][0],
) == (
[10.0, 10.0, 10.0],
[90.0, 90.0, 90.0],
0.1,
[1.57, 1.57, 1.57],
5.0,
0.5,
1000.0,
[10.0, 10.0, 10.0, 90.0, 90.0, 90.0],
10.0,
10.0,
)
d = data('dft_3d')
for i in d:
if i['jid'] == 'JVASP-588':
atoms = Atoms.from_dict(i['atoms'])
lll = atoms.lattice._calculate_lll()
assert lll[1][0][0] == -1
def test_dataset():
d = data("dft_2d")
d = pd.DataFrame(d[:100])
graphs = []
for i, row in d.iterrows():
structure = Atoms.from_dict(row["atoms"])
g = Graph.atom_dgl_multigraph(
structure,
atom_features="atomic_number",
compute_line_graph=False,
)
graphs.append(g)
s = StructureDataset(
d, graphs, "formation_energy_peratom", line_graph=True
)
col = s.collate
col1 = s.collate_line_graph
ix = s[0]
sz = len(s)
s_std = s.setup_standardizer([1, 2])
def get_twod_defect_energy(vrun="", jid="", atom=""):
"""Get mono 2D defect formation energy with OptB88vdW data."""
dft2d = data("dft_2d")
def get_enp_jid(jid=""):
for i in dft2d:
if i["jid"] == jid:
return (i["optb88vdw_total_energy"] /
Atoms.from_dict(i["atoms"]).num_atoms)
# dir='JVASP-667_C_C_c'
# tmp=dir.split('_')
# jid=tmp[0]
# atom=tmp[2]
strt = vrun.all_structures[-1]
natoms = strt.num_atoms
fin_en = vrun.final_energy
chem_pot = unary_energy(atom)
bulk_en_pa = get_enp_jid(jid)
Ef = fin_en - (natoms + 1) * bulk_en_pa + chem_pot
return Ef
)
from jarvis.io.phonopy.inputs import PhonopyInputs
from jarvis.analysis.structure.spacegroup import Spacegroup3D
from jarvis.io.wannier.outputs import WannierHam
import requests, tempfile, zipfile, io
from jarvis.io.vasp.outputs import Vasprun
# from phonopy.interface.vasp import read_vasp
from jarvis.analysis.phonon.force_constants import qpoint, read_fc
from jarvis.io.phonopy.outputs import get_phonon_tb
import matplotlib.pyplot as plt
plt.switch_backend("agg")
from jarvis.db.figshare import data
fls = data("raw_files")
fc_file = os.path.join(os.path.dirname(__file__), "FORCE_CONSTANTS")
pos = os.path.join(os.path.dirname(__file__), "POSCAR")
wtb = os.path.join(os.path.dirname(__file__), "phonopyTB_hr.dat")
totdos = os.path.join(os.path.dirname(__file__), "total_dos.dat")
band = os.path.join(os.path.dirname(__file__), "band.yaml")
def test_fc():
fc = read_fc(fc_file)
assert (fc[0][0][0][0]) == 12.960974735
qp = qpoint(force_constant=fc, qpt=[0, 0, 0])
assert (qp[0][0][0][0]) == 12.960974735
def test_wann():
def upload_sample_data(curate_xml=False):
"""
Generate and upload XML files.
Set curate_xml==True to upload all the XML documents.
"""
d = data("dft_2d")
count = 0
for i in d[0:1]:
filname = str(i["jid"]) + str(".xml")
if not os.path.exists(filname):
count = count + 1
energy = (str(i["optb88vdw_total_energy"]) + str(",") +
str(i["formation_energy_peratom"]))
atoms = Atoms.from_dict(i["atoms"])
formula = str(atoms.composition.reduced_formula)
sgp = str(Spacegroup3D(atoms).space_group_symbol)
name = str(i["jid"])
print(name)
# ref = str("")
func = str("OptB88vdW")
elem = ""
species = atoms.elements
for j in species:
elem = str(elem) + str(j) + str("-")
encut = str(i["encut"])
kpoints = (str(i["kpoints_array"][0]) + str("x") +
str(i["kpoints_array"][1]) + str("x") +
str(i["kpoints_array"][2]))
el_tens = "na"
try:
el_tens = str(",".join(
map(str,
np.array(i["elastic_tensor"]).flatten())))
except Exception:
pass
KV = str(i["bulk_modulus_kv"])
GV = str(i["shear_modulus_gv"])
op_eg = str(i["optb88vdw_bandgap"])
mbj_eg = str(i["mbj_bandgap"])
realx_arr = str(i["epsx"])
mrealx_arr = str(i["mepsx"])
realy_arr = str(i["epsy"])
mrealy_arr = str(i["mepsy"])
realz_arr = str(i["epsz"])
mrealz_arr = str(i["mepsz"])
typ = str("2D") # 3D
other = str(
"Citation: 1) DOI:10.1038/s41598-017-05402-0" +
",2) DOI: 10.1038/sdata.2018.82, 3) arXiv:1804.01033v2 ")
struct = atoms.get_string()
data_json(
other=other,
energy=energy,
typ=typ,
formula=formula,
sgp=sgp,
name=name,
func=func,
elem=elem,
encut=encut,
kpoints=kpoints,
el_tens=el_tens,
KV=KV,
GV=GV,
op_eg=op_eg,
mbj_eg=mbj_eg,
realx_arr=realx_arr,
mrealx_arr=mrealx_arr,
realy_arr=realy_arr,
mrealy_arr=mrealy_arr,
realz_arr=realz_arr,
mrealz_arr=mrealz_arr,
struct=struct,
curate_xml=curate_xml,
)
def test_extra_spgs():
from jarvis.db.figshare import data
d = data("dft_3d")
few_spgs = {
"JVASP-4663": 119,
"JVASP-4666": 194,
"JVASP-588": 160,
"JVASP-4669": 191,
"JVASP-4672": 141,
"JVASP-581": 164,
"JVASP-4687": 139,
"JVASP-4693": 62,
"JVASP-4696": 187,
"JVASP-4711": 225,
"JVASP-4714": 2,
"JVASP-4723": 60,
"JVASP-32": 167,
"JVASP-107": 186,
"JVASP-4756": 63,
"JVASP-96": 216,
"JVASP-4334": 123,
"JVASP-4222": 11,
"JVASP-4804": 156,
"JVASP-329": 129,
"JVASP-4852": 163,
"JVASP-155": 61,
"JVASP-4340": 51,
"JVASP-4343": 15,
"JVASP-4361": 14,
"JVASP-137": 72,
"JVASP-4879": 166,
"JVASP-4885": 19,
"JVASP-4894": 31,
"JVASP-4216": 12,
"JVASP-4918": 121,
"JVASP-4948": 25,
"JVASP-4957": 221,
"JVASP-4960": 65,
"JVASP-5059": 189,
"JVASP-5071": 66,
"JVASP-5074": 150,
"JVASP-5086": 74,
"JVASP-4388": 64,
"JVASP-4391": 136,
"JVASP-5155": 127,
"JVASP-5185": 43,
"JVASP-5197": 59,
"JVASP-5212": 29,
"JVASP-164": 176,
"JVASP-5224": 137,
"JVASP-5227": 148,
"JVASP-4234": 193,
"JVASP-5257": 7,
"JVASP-5266": 140,
"JVASP-4397": 33,
"JVASP-5317": 8,
"JVASP-5332": 13,
"JVASP-5353": 16,
"JVASP-5371": 4,
"JVASP-5407": 229,
"JVASP-5416": 147,
"JVASP-5509": 52,
"JVASP-5536": 70,
"JVASP-5560": 71,
"JVASP-5680": 28,
"JVASP-5740": 6,
"JVASP-5839": 162,
"JVASP-5863": 79,
"JVASP-110": 99,
"JVASP-579": 38,
"JVASP-4501": 5,
"JVASP-91": 227,
"JVASP-41": 154,
"JVASP-4516": 96,
"JVASP-4564": 82,
"JVASP-4645": 130,
"JVASP-152": 55,
"JVASP-4792": 88,
"JVASP-5041": 107,
"JVASP-5425": 87,
"JVASP-5464": 115,
"JVASP-5650": 157,
"JVASP-4450": 36,
"JVASP-22520": 152,
"JVASP-22523": 205,
"JVASP-11998": 53,
"JVASP-22528": 58,
"JVASP-22533": 41,
"JVASP-12091": 32,
"JVASP-22541": 215,
"JVASP-22543": 97,
"JVASP-22549": 9,
"JVASP-12103": 138,
"JVASP-22575": 40,
"JVASP-22602": 180,
"JVASP-22611": 182,
"JVASP-12022": 85,
"JVASP-22637": 111,
"JVASP-12139": 10,
"JVASP-22709": 92,
"JVASP-12060": 20,
"JVASP-12064": 1,
"JVASP-12194": 185,
"JVASP-13885": 128,
"JVASP-14096": 56,
"JVASP-14020": 122,
"JVASP-13904": 54,
"JVASP-13783": 135,
"JVASP-14213": 26,
"JVASP-14034": 23,
"JVASP-14158": 113,
"JVASP-14256": 21,
"JVASP-32150": 217,
"JVASP-28392": 224,
"JVASP-32180": 146,
"JVASP-32197": 57,
"JVASP-31813": 67,
"JVASP-31819": 219,
"JVASP-29262": 39,
"JVASP-29281": 102,
"JVASP-31825": 226,
"JVASP-29425": 143,
"JVASP-29441": 42,
"JVASP-29520": 73,
"JVASP-29526": 18,
"JVASP-29555": 149,
"JVASP-29597": 151,
"JVASP-29705": 174,
"JVASP-31921": 199,
"JVASP-33147": 161,
"JVASP-33220": 46,
"JVASP-33344": 114,
"JVASP-30263": 44,
"JVASP-33832": 155,
"JVASP-30458": 69,
"JVASP-30461": 144,
"JVASP-30518": 132,
"JVASP-36540": 198,
"JVASP-36548": 220,
"JVASP-36568": 3,
"JVASP-36573": 145,
"JVASP-35061": 131,
"JVASP-35137": 125,
"JVASP-35222": 204,
"JVASP-35344": 109,
"JVASP-42053": 173,
"JVASP-40216": 91,
"JVASP-38594": 165,
"JVASP-37330": 86,
"JVASP-37573": 84,
"JVASP-36714": 47,
"JVASP-36754": 98,
"JVASP-59313": 230,
"JVASP-46893": 95,
"JVASP-46896": 76,
"JVASP-45779": 30,
"JVASP-45831": 158,
"JVASP-46446": 35,
"JVASP-44393": 159,
"JVASP-44773": 22,
"JVASP-47741": 78,
"JVASP-47811": 181,
"JVASP-48055": 94,
"JVASP-48916": 34,
"JVASP-49907": 190,
"JVASP-50342": 223,
"JVASP-50360": 68,
"JVASP-50431": 37,
"JVASP-52902": 142,
"JVASP-52377": 24,
"JVASP-50791": 214,
"JVASP-54512": 108,
"JVASP-56567": 213,
"JVASP-54867": 126,
"JVASP-55180": 81,
"JVASP-57780": 212,
"JVASP-57807": 118,
"JVASP-57816": 50,
"JVASP-57100": 197,
"JVASP-57138": 116,
"JVASP-58233": 124,
"JVASP-59682": 200,
"JVASP-20180": 206,
"JVASP-21448": 203,
"JVASP-40468": 90,
"JVASP-42914": 17,
"JVASP-21594": 100,
"JVASP-21706": 188,
"JVASP-22783": 218,
"JVASP-24006": 202,
"JVASP-30879": 83,
"JVASP-31186": 49,
"JVASP-21031": 110,
"JVASP-21116": 192,
"JVASP-25245": 134,
"JVASP-7066": 169,
"JVASP-13017": 112,
"JVASP-58953": 105,
"JVASP-8682": 183,
"JVASP-9902": 80,
"JVASP-34882": 208,
"JVASP-34330": 179,
"JVASP-34502": 48,
"JVASP-62982": 178,
}
"""
mem=[]
spgs=[]
pos=[]
info=defaultdict()
for i in d:
a=Atoms.from_dict(i['atoms'])
spg=Spacegroup3D(a).space_group_number
if spg not in spgs:
spgs.append(spg)
pos.append(i['jid'])
info[i['jid']]=spg
print (info)
if len(spgs)==230:
break
"""
for i, j in few_spgs.items():
for ii in d:
if ii["jid"] == i:
a = Atoms.from_dict(ii["atoms"])
spg = Spacegroup3D(a).space_group_number
assert j == spg
lattice_mat = a.lattice_mat
kp = Kpoints3D().automatic_length_mesh(lattice_mat=lattice_mat,
length=40)
sym = kp.high_symm_path(a)._path
# print (ii['jid'],a)
# TODO: following line failing for JVASP-4222
# x, y = kp.interpolated_points(a)
break
