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figshare.py
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"""
Downloads files from Figshare.
Main page: https://figshare.com/authors/Kamal_Choudhary/4445539
"""
import zipfile
import tempfile
import os
import numpy as np
import io
import json
import requests
from jarvis.db.jsonutils import loadjson
from tqdm import tqdm
import matplotlib.image as mpimg
# from jarvis.analysis.stm.tersoff_hamann import TersoffHamannSTM
# from jarvis.io.wannier.outputs import WannierHam
# from jarvis.io.vasp.outputs import Vasprun
# from jarvis.io.vasp.inputs import Poscar
# import matplotlib.pyplot as plt
# plt.switch_backend("agg")
def get_db_info():
"""Get DB info."""
db_info = {
# https://doi.org/10.6084/m9.figshare.6815705
"dft_2d": [
"https://ndownloader.figshare.com/files/38521268",
"d2-12-12-2022.json",
"Obtaining 2D dataset 1.1k ...",
"https://www.nature.com/articles/s41524-020-00440-1"
+ "\nOther versions:https://doi.org/10.6084/m9.figshare.6815705",
],
# https://doi.org/10.6084/m9.figshare.6815699
"dft_3d": [
"https://ndownloader.figshare.com/files/38521619",
"jdft_3d-12-12-2022.json",
"Obtaining 3D dataset 76k ...",
"https://www.nature.com/articles/s41524-020-00440-1"
+ "\nOther versions:https://doi.org/10.6084/m9.figshare.6815699",
],
# https://doi.org/10.6084/m9.figshare.6815705
"dft_2d_2021": [
"https://ndownloader.figshare.com/files/26808917",
"d2-3-12-2021.json",
"Obtaining 2D dataset 1.1k ...",
"https://www.nature.com/articles/s41524-020-00440-1",
],
# https://doi.org/10.6084/m9.figshare.6815699
"dft_3d_2021": [
"https://ndownloader.figshare.com/files/29204826",
"jdft_3d-8-18-2021.json",
"Obtaining 3D dataset 55k ...",
"https://www.nature.com/articles/s41524-020-00440-1",
],
# https://doi.org/10.6084/m9.figshare.6815699
"cfid_3d": [
"https://ndownloader.figshare.com/files/29205201",
"cfid_3d-8-18-2021.json",
"Obtaining 3D dataset 55k ...",
"https://www.nature.com/articles/s41524-020-00440-1"
+ "\nOther versions:https://doi.org/10.6084/m9.figshare.6815699",
],
# https://doi.org/10.6084/m9.figshare.14213522
"jff": [
"https://ndownloader.figshare.com/files/28937793",
# "https://ndownloader.figshare.com/files/26809760",
"jff-7-24-2021.json",
# "jff-3-12-2021.json",
"Obtaining JARVIS-FF 2k ...",
"https://www.nature.com/articles/s41524-020-00440-1",
],
# https://doi.org/10.6084/m9.figshare.21667874
"alignn_ff_db": [
"https://ndownloader.figshare.com/files/38522315",
# "https://ndownloader.figshare.com/files/26809760",
"id_prop.json",
"Obtaining ALIGNN-FF training DB 300k ...",
"https://doi.org/10.1039/D2DD00096B",
],
"mp_3d_2020": [
"https://ndownloader.figshare.com/files/26791259",
"all_mp.json",
"Obtaining Materials Project-3D CFID dataset 127k...",
"https://doi.org/10.1063/1.4812323",
],
# https://doi.org/10.6084/m9.figshare.14177630
"megnet": [
"https://ndownloader.figshare.com/files/26724977",
"megnet.json",
"Obtaining MEGNET-3D CFID dataset 69k...",
"https://pubs.acs.org/doi/10.1021/acs.chemmater.9b01294",
],
# https://doi.org/10.6084/m9.figshare.14745435
"megnet2": [
"https://ndownloader.figshare.com/files/28332741",
"megnet-mp-2019-04-01.json",
"Obtaining MEGNET-3D CFID dataset 133k...",
"https://pubs.acs.org/doi/10.1021/acs.chemmater.9b01294",
],
# https://doi.org/10.6084/m9.figshare.14745327
"edos_pdos": [
"https://ndownloader.figshare.com/files/29216859",
"edos-up_pdos-elast_interp-8-18-2021.json",
"Interpolated electronic total dos spin-up dataset 55k...",
"https://www.nature.com/articles/s41524-020-00440-1",
],
# https://doi.org/10.6084/m9.figshare.13054247
"mp_3d": [
"https://ndownloader.figshare.com/files/24979850",
"CFID_mp_desc_data_84k.json",
"Obtaining Materials Project-3D CFID dataset 84k...",
"https://doi.org/10.1063/1.4812323",
],
# https://doi.org/10.6084/m9.figshare.13055333
"oqmd_3d": [
"https://ndownloader.figshare.com/files/24981170",
"CFID_OQMD_460k.json",
"Obtaining OQMD-3D CFID dataset 460k...",
"https://www.nature.com/articles/npjcompumats201510",
],
# https://doi.org/10.6084/m9.figshare.14206169
"oqmd_3d_no_cfid": [
"https://ndownloader.figshare.com/files/26790182",
"all_oqmd.json",
"Obtaining OQMD-3D dataset 800k...",
"https://www.nature.com/articles/npjcompumats201510",
],
# https://doi.org/10.6084/m9.figshare.14205083
"twod_matpd": [
"https://ndownloader.figshare.com/files/26789006",
"twodmatpd.json",
"Obtaining 2DMatPedia dataset 6k...",
"https://www.nature.com/articles/s41597-019-0097-3",
],
# https://doi.org/10.6084/m9.figshare.14213603
"polymer_genome": [
"https://ndownloader.figshare.com/files/26809907",
"pgnome.json",
"Obtaining Polymer genome 1k...",
"https://www.nature.com/articles/sdata201612",
],
"qm9_std_jctc": [
"https://ndownloader.figshare.com/files/28715319",
"qm9_std_jctc.json",
"Obtaining QM9 standardized dataset 130k,"
+ "From https://doi.org/10.1021/acs.jctc.7b00577,+",
"https://www.nature.com/articles/sdata201422",
],
# https://doi.org/10.6084/m9.figshare.14827584
# Use qm9_std_jctc instaed
"qm9_dgl": [
"https://ndownloader.figshare.com/files/28541196",
"qm9_dgl.json",
"Obtaining QM9 dataset 130k, from DGL...",
"https://www.nature.com/articles/sdata201422",
],
# https://doi.org/10.6084/m9.figshare.14912820.v1
"cod": [
"https://ndownloader.figshare.com/files/28715301",
"cod_db.json",
"Obtaining COD dataset 431k",
"https://doi.org/10.1107/S1600576720016532",
],
# Use qm9_std_jctc instaed
"qm9": [
"https://ndownloader.figshare.com/files/27627596",
"qm9_data_cfid.json",
"Obtaining QM9 dataset 134k...",
"https://www.nature.com/articles/sdata201422",
],
# https://doi.org/10.6084/m9.figshare.15127788
"qe_tb": [
"https://ndownloader.figshare.com/files/29070555",
"jqe_tb_folder.json",
"Obtaining QETB dataset 860k...",
"https://arxiv.org/abs/2112.11585",
],
# https://doi.org/10.6084/m9.figshare.14812050
"omdb": [
"https://ndownloader.figshare.com/files/28501761",
"omdbv1.json",
"Obtaining OMDB dataset 12.5k...",
"https://doi.org/10.1002/qute.201900023",
],
# https://doi.org/10.6084/m9.figshare.14812044
"qmof": [
"https://figshare.com/ndownloader/files/30972640",
"qmof_db.json",
"Obtaining QMOF dataset 20k...",
"https://www.cell.com/matter/fulltext/S2590-2385(21)00070-9",
],
# https://doi.org/10.6084/m9.figshare.15127758
"hmof": [
"https://figshare.com/ndownloader/files/30972655",
"hmof_db_9_18_2021.json",
"Obtaining hMOF dataset 137k...",
"https://doi.org/10.1021/acs.jpcc.6b08729",
],
# https://figshare.com/account/projects/100325/articles/14960157
"c2db": [
"https://ndownloader.figshare.com/files/28682010",
"c2db_atoms.json",
"Obtaining C2DB dataset 3.5k...",
"https://iopscience.iop.org/article/10.1088/2053-1583/aacfc1",
],
# https://doi.org/10.6084/m9.figshare.25256236
"halide_peroskites": [
"https://figshare.com/ndownloader/files/44619562",
"halide_peroskites.json",
"Obtaining halide perovskite dataset229...",
"https://doi.org/10.1039/D1EE02971A",
],
# https://figshare.com/account/projects/100325/articles/14962356
"hopv": [
"https://ndownloader.figshare.com/files/28814184",
"hopv_15.json",
"Obtaining HOPV15 dataset 4.5k...",
"https://www.nature.com/articles/sdata201686",
],
# https://figshare.com/account/projects/100325/articles/14962356
"pdbbind_core": [
"https://ndownloader.figshare.com/files/28874802",
"pdbbind_2015_core.json",
"Obtaining PDBBind dataset 195...",
"https://doi.org/10.1093/bioinformatics/btu626",
],
# https://doi.org/10.6084/m9.figshare.14812038
"pdbbind": [
"https://ndownloader.figshare.com/files/28816368",
"pdbbind_2015.json",
"Obtaining PDBBind dataset 11k...",
"https://doi.org/10.1093/bioinformatics/btu626",
],
# https://doi.org/10.6084/m9.figshare.21713885
"snumat": [
"https://ndownloader.figshare.com/files/38521736",
"snumat.json",
"Obtaining SNUMAT Hybrid functional dataset 10k...",
"https://www.nature.com/articles/s41597-020-00723-8",
],
# https://doi.org/10.6084/m9.figshare.13215308
"aflow2": [
"https://ndownloader.figshare.com/files/25453265",
"CFID_AFLOW2.json",
"Obtaining AFLOW-2 CFID dataset 400k...",
"https://doi.org/10.1016/j.commatsci.2012.02.005",
],
# https://doi.org/10.6084/m9.figshare.14211860
"arXiv": [
"https://ndownloader.figshare.com/files/26804795",
"arXivdataset.json",
"Obtaining arXiv dataset 1.8 million...",
"https://www.kaggle.com/Cornell-University/arxiv",
],
# https://doi.org/10.6084/m9.figshare.14211857
"cord19": [
"https://ndownloader.figshare.com/files/26804798",
"cord19.json",
"Obtaining CORD19 dataset 223k...",
"https://github.com/usnistgov/cord19-cdcs-nist",
],
# https://doi.org/10.6084/m9.figshare.22583677
"ssub": [
"https://figshare.com/ndownloader/files/40084921",
"ssub.json",
"Obtaining SSUB dataset 1726...",
"https://github.com/wolverton-research-group/qmpy",
],
# https://doi.org/10.6084/m9.figshare.22721047
"mlearn": [
# "https://figshare.com/ndownloader/files/40424156",
"https://figshare.com/ndownloader/files/40357663",
"mlearn.json",
"Obtaining mlearn dataset 1730...",
"https://github.com/materialsvirtuallab/mlearn",
],
# https://doi.org/10.6084/m9.figshare.22814318
"foundry_ml_exp_bandgaps": [
"https://figshare.com/ndownloader/files/40557743",
"foundry_ml_exp_bandgaps.json",
"Obtaining foundry_ml_exp_bandgaps dataset 2069...",
"https://foundry-ml.org/#/datasets/10.18126/wg3u-g8vu",
],
# ToFix# https://doi.org/10.6084/m9.figshare.22815926
# "mat_scholar_ner": [
# "https://figshare.com/ndownloader/files/40563593",
# "mat_scholar_ner.json",
# "Obtaining mat_scholar_ner dataset XYZ...",
# "https://pubs.acs.org/doi/10.1021/acs.jcim.9b00470",
# ],
# https://doi.org/10.6084/m9.figshare.22817633
# Contains repeats
"ocp10k": [
"https://figshare.com/ndownloader/files/40566122",
"ocp10k.json",
"Obtaining OCP 10k train dataset, 59886...",
"https://github.com/Open-Catalyst-Project/ocp",
],
# https://doi.org/10.6084/m9.figshare.22817651
"arxiv_summary": [
"https://figshare.com/ndownloader/files/40566137",
"arxiv_summary.json",
"Obtaining arxiv summary cond.mat dataset 137927...",
"https://github.com/usnistgov/chemnlp",
],
# TODO:PubChem
# https://doi.org/10.6084/m9.figshare.22975787
"supercon_chem": [
"https://figshare.com/ndownloader/files/40719260",
"supercon_chem.json",
"Obtaining supercon chem dataset 16414...",
"https://www.nature.com/articles/s41524-018-0085-8",
],
# https://doi.org/10.6084/m9.figshare.22976285
"mag2d_chem": [
"https://figshare.com/ndownloader/files/40720004",
"mag2d_chem.json",
"Obtaining magnetic 2D chem dataset 226...",
"https://doi.org/10.24435/materialscloud:2019.0020/v1",
],
# https://doi.org/10.6084/m9.figshare.23000573
"vacancydb": [
"https://figshare.com/ndownloader/files/40750811",
"vacancydb.json",
"Obtaining vacancy dataset 464...",
"https://doi.org/10.1063/5.0135382",
],
# https://doi.org/10.6084/m9.figshare.25832614
"surfacedb": [
"https://figshare.com/ndownloader/files/46355689",
"surface_db_dd.json",
"Obtaining vacancy dataset 607...",
"https://doi.org/10.1039/D4DD00031E",
],
# https://doi.org/10.6084/m9.figshare.25832614
"interfacedb": [
"https://figshare.com/ndownloader/files/46355692",
"interface_db_dd.json",
"Obtaining vacancy dataset 607...",
"https://doi.org/10.1039/D4DD00031E",
],
# Contains repeats
# https://doi.org/10.6084/m9.figshare.23206193
"ocp100k": [
"https://figshare.com/ndownloader/files/40902845",
"ocp100k.json",
"Obtaining OCP100k dataset 149886...",
"https://github.com/Open-Catalyst-Project/ocp",
],
# https://doi.org/10.6084/m9.figshare.23250629
"ocp_all": [
"https://figshare.com/ndownloader/files/40974599",
"ocp_all.json",
"Obtaining OCPall dataset 510214...",
"https://github.com/Open-Catalyst-Project/ocp",
],
# https://doi.org/10.6084/m9.figshare.23225687
"tinnet_N": [
"https://figshare.com/ndownloader/files/40934285",
"tinnet_N.json",
"Obtaining TinNet Nitrogen dataset 329...",
"https://github.com/hlxin/tinnet",
],
# https://doi.org/10.6084/m9.figshare.23254151
"tinnet_O": [
"https://figshare.com/ndownloader/files/40978943",
"tinnet_O.json",
"Obtaining TinNet Oxygen dataset 747...",
"https://github.com/hlxin/tinnet",
],
# https://doi.org/10.6084/m9.figshare.23254154
"tinnet_OH": [
"https://figshare.com/ndownloader/files/40978949",
"tinnet_OH.json",
"Obtaining TinNet OH dataset 748...",
"https://github.com/hlxin/tinnet",
],
# https://doi.org/10.6084/m9.figshare.23909478
"AGRA_O": [
"https://figshare.com/ndownloader/files/41923284",
"AGRA_O.json",
"Obtaining AGRA Oxygen dataset 1000...",
"https://github.com/Feugmo-Group/AGRA",
],
# https://doi.org/10.6084/m9.figshare.23909478
"AGRA_OH": [
"https://figshare.com/ndownloader/files/41923287",
"AGRA_OH.json",
"Obtaining AGRA OH dataset 875...",
"https://github.com/Feugmo-Group/AGRA",
],
# https://doi.org/10.6084/m9.figshare.23909478
"AGRA_CO": [
"https://figshare.com/ndownloader/files/41923278",
"AGRA_CO.json",
"Obtaining AGRA CO dataset 193...",
"https://github.com/Feugmo-Group/AGRA",
],
# https://doi.org/10.6084/m9.figshare.23909478
"AGRA_CHO": [
"https://figshare.com/ndownloader/files/41923275",
"AGRA_CHO.json",
"Obtaining AGRA Oxygen dataset 214...",
"https://github.com/Feugmo-Group/AGRA",
],
# https://doi.org/10.6084/m9.figshare.23909478
"AGRA_COOH": [
"https://figshare.com/ndownloader/files/41923281",
"AGRA_COOH.json",
"Obtaining AGRA COOH dataset 280...",
"https://github.com/Feugmo-Group/AGRA",
],
# https://doi.org/10.6084/m9.figshare.21370572
"supercon_3d": [
"https://figshare.com/ndownloader/files/38307921",
"jarvis_epc_data_figshare_1058.json",
"Obtaining supercond. Tc dataset 1058...",
"https://www.nature.com/articles/s41524-022-00933-1",
],
# https://doi.org/10.6084/m9.figshare.21370572
"supercon_2d": [
"https://figshare.com/ndownloader/files/38950433",
"jarvis_epc_data_2d.json",
"Obtaining supercond. Tc dataset 161...",
"https://doi.org/10.1021/acs.nanolett.2c04420",
],
# https://doi.org/10.6084/m9.figshare.23267852
"m3gnet_mpf": [
"https://figshare.com/ndownloader/files/41009036",
"m3gnet_mpf.json",
"Obtaining m3gnet_mpf dataset 168917...",
"https://github.com/materialsvirtuallab/m3gnet",
],
# https://doi.org/10.6084/m9.figshare.23267852
"m3gnet_mpf_1.5mil": [
"https://figshare.com/ndownloader/files/47281519",
"id_prop.json",
"Obtaining m3gnet_mpf dataset 1.5mil...",
"https://github.com/materialsvirtuallab/m3gnet",
],
# https://doi.org/10.6084/m9.figshare.23531523
"mxene275": [
"https://figshare.com/ndownloader/files/41266233",
"mxene275.json",
"Obtaining mxene dataset 275...",
"https://cmr.fysik.dtu.dk/c2db/c2db.html",
],
# https://doi.org/10.6084/m9.figshare.26117998
"cccbdb": [
"https://figshare.com/ndownloader/files/47283808",
"cccbdb.json",
"Obtaining CCCBDB dataset 1333...",
"https://cccbdb.nist.gov/",
],
# https://doi.org/10.6084/m9.figshare.27174897
"alex_pbe_hull": [
"https://figshare.com/ndownloader/files/49622718",
"alexandria_convex_hull_pbe_2023.12.29_jarvis_tools.json",
"Obtaining Alexandria_DB PBE on hull 116k...",
"https://alexandria.icams.rub.de/",
],
# https://doi.org/10.6084/m9.figshare.27174897
"alex_pbe_3d_all": [
"https://figshare.com/ndownloader/files/49622946",
"alexandria_pbe_3d_2024.10.1_jarvis_tools.json",
"Obtaining Alexandria_DB PBE 3D all 5 million, large file...",
"https://alexandria.icams.rub.de/",
],
# https://doi.org/10.6084/m9.figshare.27174897
"alex_pbe_2d_all": [
"https://figshare.com/ndownloader/files/49622988",
"alexandria_pbe_2d_2024.10.1_jarvis_tools.json",
"Obtaining Alexandria_DB PBE 2D all 200k...",
"https://alexandria.icams.rub.de/",
],
# https://doi.org/10.6084/m9.figshare.27174897
"alex_pbe_1d_all": [
"https://figshare.com/ndownloader/files/49622991",
"alexandria_pbe_1d_2024.10.1_jarvis_tools.json",
"Obtaining Alexandria_DB PBE 1D all 100k...",
"https://alexandria.icams.rub.de/",
],
# https://doi.org/10.6084/m9.figshare.27174897
"alex_scan_3d_all": [
"https://figshare.com/ndownloader/files/49623090",
"alexandria_scan_3d_2024.10.1_jarvis_tools.json",
"Obtaining Alexandria_DB SCAN 3D all 500k...",
"https://alexandria.icams.rub.de/",
],
# https://doi.org/10.6084/m9.figshare.27174897
"alex_pbesol_3d_all": [
"https://figshare.com/ndownloader/files/49623096",
"alexandria_ps_3d_2024.10.1_jarvis_tools.json",
"Obtaining Alexandria_DB PBEsol 3D all 500k...",
"https://alexandria.icams.rub.de/",
],
# https://doi.org/10.6084/m9.figshare.13154159
"raw_files": [
"https://ndownloader.figshare.com/files/25295732",
"figshare_data-10-28-2020.json",
"Obtaining raw io files 145k...",
"https://www.nature.com/articles/s41524-020-00440-1",
],
}
return db_info
# Format: download_link, filename, message, reference
# Figshare link: https://figshare.com/account/home#/projects/100325
def get_stm_2d_dataset():
"""Get 2D STM image dataset."""
# Ref: https://www.nature.com/articles/s41597-021-00824-y
link_1 = "https://ndownloader.figshare.com/files/21884952"
r_jpg = requests.get(link_1)
z = zipfile.ZipFile(io.BytesIO(r_jpg.content))
link_2 = "https://ndownloader.figshare.com/files/21893379"
r_json = requests.get(link_2).content
latts = json.loads(r_json)
namelist = z.namelist()
pos_bias = []
neg_bias = []
print("Obtaining 2D STM dataset ...")
for i in namelist:
img_str = z.read(i)
values = mpimg.imread(io.BytesIO((img_str)), format="jpg")
# img=Image(values=values)
jid = i.split("/")[-1].split("_")[0]
bias = i.split("/")[-1].split("_")[1].split(".jpg")[0]
lat_system = latts[jid]
if bias == "pos":
info = {}
info["jid"] = jid
info["image_values"] = values
info["lat_type"] = lat_system
pos_bias.append(info)
if bias == "neg":
info = {}
info["jid"] = jid
info["image_values"] = values
info["lat_type"] = lat_system
neg_bias.append(info)
return pos_bias, neg_bias
def get_request_data(
js_tag="jdft_2d-4-26-2020.json",
url="https://ndownloader.figshare.com/files/22471019",
store_dir=None,
):
"""Get data with progress bar."""
zfile = js_tag + ".zip"
if store_dir is None:
path = str(os.path.join(os.path.dirname(__file__), zfile))
else:
path = str(os.path.join(store_dir, zfile))
# path = str(os.path.join(os.path.dirname(__file__), js_tag))
if not os.path.isfile(path):
# zfile = str(os.path.join(os.path.dirname(__file__), "tmp.zip"))
response = requests.get(url, stream=True)
total_size_in_bytes = int(response.headers.get("content-length", 0))
block_size = 1024 # 1 Kibibyte
progress_bar = tqdm(
total=total_size_in_bytes, unit="iB", unit_scale=True
)
with open(path, "wb") as file:
for data in response.iter_content(block_size):
progress_bar.update(len(data))
file.write(data)
progress_bar.close()
# f = open(zfile, "wb")
# f.write(r.content)
# f.close()
print("Loading the zipfile...")
data = json.loads(zipfile.ZipFile(path).read(js_tag))
print("Loading completed.")
# with zipfile.ZipFile(zfile, "r") as zipObj:
# # zipObj.extract(path)
# zipObj.extractall(os.path.join(os.path.dirname(__file__)))
# os.remove(zfile)
# data = loadjson(path)
return data
def data(dataset="dft_2d", store_dir=None):
"""Provide main function to download datasets."""
db_info = get_db_info()
if dataset not in list(db_info.keys()):
raise ValueError("Check DB name options.")
url = db_info[dataset][0]
js_tag = db_info[dataset][1]
message = db_info[dataset][2]
print(message)
message = "Reference:" + db_info[dataset][3]
print(message)
# r = requests.get(url)
# z = zipfile.ZipFile(io.BytesIO(r.content))
# data = json.loads(z.read(js_tag).decode("utf-8"))
# r = requests.get(url)
# z = zipfile.ZipFile(io.BytesIO(r.content))
# wdat = z.read(js_tag).decode("utf-8")
# fd, path = tempfile.mkstemp()
# with os.fdopen(fd, "w") as tmp:
# tmp.write(wdat)
# data = loadjson(path)
# path = str(os.path.join(os.path.dirname(__file__), js_tag))
# if not os.path.isfile(path):
# zfile = str(os.path.join(os.path.dirname(__file__), "tmp.zip"))
# r = requests.get(url)
# f = open(zfile, "wb")
# f.write(r.content)
# f.close()
# with zipfile.ZipFile(zfile, "r") as zipObj:
# # zipObj.extract(path)
# zipObj.extractall(os.path.join(os.path.dirname(__file__)))
# os.remove(zfile)
# data = loadjson(path)
dat = get_request_data(js_tag=js_tag, url=url, store_dir=store_dir)
return dat
def get_jid_data(jid="JVASP-667", dataset="dft_2d"):
"""Get info for a jid and dataset."""
d = data(dataset)
for i in d:
if i["jid"] == jid:
return i
def get_ff_eneleast():
"""Get JARVIS-FF related data."""
jff1 = str(os.path.join(os.path.dirname(__file__), "jff1.json"))
if not os.path.isfile(jff1):
r = requests.get("https://ndownloader.figshare.com/files/10307139")
f = open(jff1, "wb")
f.write(r.content)
f.close()
data_ff1 = loadjson(jff1)
return data_ff1
def make_stm_from_prev_parchg(
jid="JVASP-667", bias="Negative", filename="stm_image.png", min_size=10
):
"""Make STM images from previously calculated PARVHG files for 2D."""
from jarvis.analysis.stm.tersoff_hamann import TersoffHamannSTM
fls = data("raw_files")
for i in fls["STM"]:
zip_name = jid + "_" + bias + ".zip"
if i["name"] == zip_name:
zip_file_url = i["download_url"]
r = requests.get(zip_file_url)
z = zipfile.ZipFile(io.BytesIO(r.content))
pchg = z.read("PARCHG").decode("utf-8")
fd, path = tempfile.mkstemp()
with os.fdopen(fd, "w") as tmp:
tmp.write(pchg)
TH_STM = TersoffHamannSTM(
chg_name=path, min_size=min_size, zcut=None
)
t_height = TH_STM.constant_height(filename=filename)
print("t_height", t_height)
return i
def get_wann_electron(jid="JVASP-816"):
"""Download electron WTBH if available."""
from jarvis.io.wannier.outputs import WannierHam
from jarvis.io.vasp.inputs import Poscar
w = ""
ef = ""
fls = data("raw_files")
for i in fls["WANN"]:
if i["name"].split(".zip")[0] == jid:
r = requests.get(i["download_url"])
z = zipfile.ZipFile(io.BytesIO(r.content))
wdat = z.read("wannier90_hr.dat").decode("utf-8")
js_file = jid + ".json"
js = z.read(js_file).decode("utf-8")
fd, path = tempfile.mkstemp()
with os.fdopen(fd, "w") as tmp:
tmp.write(wdat)
w = WannierHam(path)
fd, path = tempfile.mkstemp()
with os.fdopen(fd, "w") as tmp:
tmp.write(js)
d = loadjson(path)
ef = d["info_mesh"]["efermi"]
fd, path = tempfile.mkstemp()
pos = z.read("POSCAR").decode("utf-8")
with os.fdopen(fd, "w") as tmp:
tmp.write(pos)
atoms = Poscar.from_file(path).atoms
return w, ef, atoms
def get_wann_phonon(jid="JVASP-1002", factor=15.633302):
"""Download phonon WTBH if available."""
# Requires phonopy
from jarvis.io.phonopy.outputs import get_phonon_tb
from jarvis.io.vasp.outputs import Vasprun
from jarvis.io.wannier.outputs import WannierHam
fls = data("raw_files")
for i in fls["FD-ELAST"]:
if isinstance(i, dict):
if i["name"].split(".zip")[0] == jid:
r = requests.get(i["download_url"])
z = zipfile.ZipFile(io.BytesIO(r.content))
vrun_path = z.read("vasprun.xml").decode("utf-8")
fd, path = tempfile.mkstemp()
with os.fdopen(fd, "w") as tmp:
tmp.write(vrun_path)
vrun = Vasprun(path)
fc = vrun.phonon_data()["force_constants"]
atoms = vrun.all_structures[0]
# print(atoms)
# atoms = Atoms.from_poscar(pos)
# print(atoms)
fd, path = tempfile.mkstemp()
get_phonon_tb(fc=fc, atoms=atoms, out_file=path, factor=factor)
# cvn = Spacegroup3D(atoms).conventional_standard_structure
w = WannierHam(path)
return w, atoms
def get_hk_tb(k=np.array([0, 0, 0]), w=[]):
"""Get Wannier TB Hamiltonian."""
nr = w.R.shape[0]
hk = np.zeros((w.nwan, w.nwan), dtype=complex)
kmat = np.tile(k, (nr, 1))
exp_ikr = np.exp(1.0j * 2 * np.pi * np.sum(kmat * w.R, 1))
temp = np.zeros(w.nwan**2, dtype=complex)
for i in range(nr):
temp += exp_ikr[i] * w.HR[i, :]
hk = np.reshape(temp, (w.nwan, w.nwan))
hk = (hk + hk.T.conj()) / 2.0
return hk
"""
QM9 xyz file
>>> def get_val(filename=''):
f=open(filename,'r')
lines=f.read().splitlines()
f.close()
info={}
for i in range(len(attr_index)):
info[attr_index[i]]=float(lines[1].split()[i+2])
return info
"""
"""
if __name__ == "__main__":
data_2d = data(dataset='dft_2d')
print('2d',len(data_2d))
data_3d = data(dataset='dft_3d')
print('3d',len(data_3d))
data_ml = data(dataset='cfid_3d')
print('cfid3d',len(data_ml))
data_ff = get_ff_eneleast()
print ('ff',len(data_ff))
"""