Fully validating pure-python CP2K input file parsers including preprocessing capabilities
Available commands (also available through an API, see below):
cp2klint.. a CP2K input file linterfromcp2k.. create a JSON/YAML configuration file or an AiiDA run script from a CP2K input file (includes validation)tocp2k.. convert a JSON or YAML configuration back to CP2K's input file format (includes validation)cp2kgen.. generate new input files based on a given input file and expressions to change parameters programmaticallycp2kget.. get values from a CP2K input file (most likely a restart file) given a path of sections and attributecp2k-language-server.. a Language Server Protocol implementation for the CP2K input file formatcp2k-datafile-lint.. linters for other CP2K-related data file formats (like pseudos and basissets)
For a description of the JSON/YAML formats used, see below.
- Python 3.9+
- https://pypi.org/project/transitions/
- https://pypi.org/project/pint/
- optional: https://pypi.org/project/ruamel.yaml/ for YAML support
- optional: https://github.com/openlawlibrary/pygls for the cp2k-language-server
For development: https://poetry.eustace.io/ https://pytest.org/
- have a pure-python CP2K input file linter with proper syntax error reporting (context, etc.)
- a final & complete restart file parser
- basis for an AiiDA CP2K project importer
- testbed for alternative import formats (YAML, JSON) for CP2K
- possible testbed for a re-implementation of the CP2K input parser itself
- parser: improve error reporting with context
- preprocessor: don't lose original context when interpolating variables
- parser: parsing the XML is slow (easily 70% of the time), pickle or generate Python code directly instead and keep XML parsing as fallback
- parser: preserve comments when going to/from YAML
You will get most tools using simply:
$ pip install cp2k-input-toolsFor YAML support you should use
$ pip install cp2k-input-tools[yaml]and for the Language Server:
$ pip install cp2k-input-tools[lsp]Generate JSON, YAML or aiida-cp2k run script from a CP2K input file:
$ fromcp2k --help
usage: fromcp2k [-h] [-y] [-c] [-b BASE_DIR] [-t TRAFO] <file>
Convert CP2K input to JSON (default) or YAML
positional arguments:
<file> CP2K input file
optional arguments:
-h, --help show this help message and exit
-y, --yaml output yaml instead of json
-c, --canonical use the canonical output format
-b BASE_DIR, --base-dir BASE_DIR
search path used for relative @include's
-t TRAFO, --trafo TRAFO
transformation applied to key and section names (auto,
upper, lower)Generate an aiida-cp2k template run script:
$ fromcp2k --format aiida-cp2k-calc tests/inputs/test01.inp
Any key transformation function other than 'auto' is ignored when generating an aiida-cp2k run script template
from aiida.engine import run
from aiida.orm import (load_code, Dict, StructureData)
cp2k_code = load_code('...')
# Structure
structure = StructureData(...)
# Parameters
parameters = Dict(
dict={
"FORCE_EVAL": {
"DFT": {
"KPOINTS": {
"FULL_GRID": False,
"PARALLEL_GROUP_SIZE": -1,
"SCHEME": "MONKHORST-PACK 3 3 3",
"SYMMETRY": False,
},
"MGRID": {
"CUTOFF": 1000.0,
"REL_CUTOFF": 100.0,
},
"POISSON": {
"PERIODIC": "XYZ",
},
"PRINT": {
"OVERLAP_CONDITION": {
"_": "ON",
"1-NORM": True,
"DIAGONALIZATION": True,
},
},
"QS": {
"EPS_DEFAULT": 1e-16,
"EXTRAPOLATION": "USE_GUESS",
"METHOD": "GAPW",
},
"SCF": {
"SMEAR": {
"_": True,
"ELECTRONIC_TEMPERATURE": 300.0,
"METHOD": "FERMI_DIRAC",
},
"ADDED_MOS": 40,
"EPS_SCF": 1e-08,
"MAX_SCF": 50,
},
"XC": {
"XC_FUNCTIONAL": {
"_": "PBE",
},
},
"BASIS_SET_FILE_NAME": "./BASIS_SETS",
"POTENTIAL_FILE_NAME": "./POTENTIALS",
},
"SUBSYS": {
"CELL": {
"CELL_REF": {
"A": "4.32947291598 0.0 0.0",
"B": "2.16473645799 3.7494335304 0.0",
"C": "2.16473645799 1.24981118034 3.53499983838",
"PERIODIC": "XYZ",
},
"A": "4.07419 0.0 0.0",
"B": "2.037095 3.52835204 0.0",
"C": "2.037095 1.17611735 3.32656221",
"PERIODIC": "XYZ",
},
"KIND": [
{
"_": "Ge",
"ELEMENT": "Ge",
"POTENTIAL": "ALL-q32",
"BASIS_SET": "ORB pob-TZVP",
},
],
"TOPOLOGY": {
"COORD_FILE_NAME": "./struct.xyz",
"COORD_FILE_FORMAT": "XYZ",
},
},
"METHOD": "QUICKSTEP",
},
"GLOBAL": {
"PRINT_LEVEL": "MEDIUM",
"PROJECT_NAME": "fatman.calc",
"RUN_TYPE": "ENERGY",
},
})
# Construct process builder.
builder = cp2k_code.get_builder()
builder.structure = structure
builder.parameters = parameters
builder.code = cp2k_code
builder.metadata.options.resources = {
"num_machines": 1,
"num_mpiprocs_per_machine": 1,
}
builder.metadata.options.max_wallclock_seconds = 1 * 3 * 60
run(builder)Generate a CP2K input file from a JSON or YAML:
$ tocp2k --help
usage: tocp2k [-h] [-y] <file>
Convert JSON or YAML input to CP2K
positional arguments:
<file> JSON or YAML input file
optional arguments:
-h, --help show this help message and exit
-y, --yamlLint a CP2K input file:
$ cp2klint tests/inputs/unterminated_var.inp
Syntax error: unterminated variable, in tests/inputs/unterminated_var.inp:
line 36: @IF ${HP
~~~~^Generate input files for a CUTOFF convergence study (multiple expressions will be combined as a cartesian product):
$ cp2kgen tests/inputs/NaCl.inp "force_eval/dft/mgrid/cutoff=[800,900,1000]"
Writing 'NaCl-cutoff_800.inp'...
Writing 'NaCl-cutoff_900.inp'...
Writing 'NaCl-cutoff_1000.inp'...
$ diff -Naurb NaCl-cutoff_800.inp NaCl-cutoff_900.inp
--- NaCl-cutoff_800.inp 2019-10-21 18:52:09.994323474 +0200
+++ NaCl-cutoff_900.inp 2019-10-21 18:52:10.680996641 +0200
@@ -69,7 +69,7 @@
POTENTIAL_FILE_NAME ALL_POTENTIALS
&MGRID
REL_CUTOFF 80.0
- CUTOFF 800
+ CUTOFF 900
NGRIDS 6
&END MGRID
&XCGet a value from a CP2K input file, for example a RESTART file generated in a cell optimization:
$ cp2kget tests/inputs/NaCl.inp "force_eval/subsys/cell/a/0"
force_eval/subsys/cell/a/0: 5.64123539364476Convert a CP2K input file to a nested Python dictionary:
from cp2k_input_tools.parser import CP2KInputParser, CP2KInputParserSimplified
canonical = False
if canonical:
parser = CP2KInputParser()
else:
parser = CP2KInputParserSimplified()
with open("project.inp") as fhandle:
tree = parser.parse(fhandle)Convert a nested Python dictionary back to a CP2K input file:
from cp2k_input_tools.generator import CP2KInputGenerator
generator = CP2KInputGenerator()
tree = {"global": {}} # ... the input tree
with open("project.inp", "w") as fhandle:
for line in generator.line_iter(tree):
fhandle.write(f"{line}\n")The executable providing the language server is: cp2k-language-server
For vim you need a plugin to be able to use language servers. One such plugin is ALE for which you can create in its directory the file ale_linters/cp2k/language_server.vim with the content
call ale#Set('cp2k_lsp_executable', 'cp2k-language-server')
function! ale_linters#cp2k#language_server#GetProjectRoot(buffer) abort
let l:git_path = ale#path#FindNearestDirectory(a:buffer, '.git')
return !empty(l:git_path) ? fnamemodify(l:git_path, ':h:h') : ''
endfunction
call ale#linter#Define('cp2k', {
\ 'name': 'language_server',
\ 'lsp': 'stdio',
\ 'executable': {b -> ale#Var(b, 'cp2k_lsp_executable')},
\ 'project_root': function('ale_linters#cp2k#language_server#GetProjectRoot'),
\ 'command': '%e',
\})Afterwards you must set the filetype when editing a CP2K input file to cp2k to get it running. This can be done explicitly using :set filetype=cp2k.
A reference to the CP2K input format can be found here: https://manual.cp2k.org/
For everything except the pre-processor capabilities (@IF/@ENDIF/$var/@SET) there is a canonical one-to-one mapping of the CP2K input format to either JSON or YAML:
- repeatable sections are mapped to dictionaries
- keywords or subsections are key/value entries in sections
- all repeatable elements (sections and keywords) are mapped to lists of their respective mapped datatype
- section parameters are mapped to a special key named
_ - default section keywords are mapped to a special key name
* - sections in JSON or YAML must be prefixed to avoid double definition of a key in case of same name for a section and a keyword (like the
POTENTIALinKIND), to avoid quotation marks, instead of CP2K's&we are using the+ - keyword values are mapped based on their datatypes: a list of values is always mapped to a list of their respective datatypes
The following example input:
&GLOBAL
PRINT_LEVEL MEDIUM
PROJECT test
RUN_TYPE ENERGY
&END GLOBAL
&FORCE_EVAL
METHOD Quickstep
&DFT
BASIS_SET_FILE_NAME "./BASIS_SETS"
POTENTIAL_FILE_NAME ./POTENTIALS
&XC
&XC_FUNCTIONAL PBE
&END XC_FUNCTIONAL
&END XC
&END DFT
&SUBSYS
&CELL
A [angstrom] 4.07419 0.0 0.0
B [angstrom] 2.037095 3.52835204 0.0
C [angstrom] 2.037095 1.17611735 3.32656221
PERIODIC XYZ
&END CELL
&KIND Ge
ELEMENT Ge
POTENTIAL ALL-q32
BASIS_SET ORB pob-TZVP
&END KIND
&TOPOLOGY
COORD_FILE ./struct.xyz
COORD_FILE_FORMAT XYZ
&END TOPOLOGY
&END SUBSYS
&END FORCE_EVAL
would generate the (canonical) JSON:
{
"+global": {
"print_level": "medium",
"project_name": "test",
"run_type": "energy"
},
"+force_eval": [
{
"method": "quickstep",
"+DFT": {
"basis_set_file_name": [
"./BASIS_SETS"
],
"potential_file_name": "./POTENTIALS"
},
"+XC": {
"+xc_functional": {
"_": "PBE"
}
},
"+subsys": {
"cell": {
"A": [ 4.07419, 0, 0 ],
"B": [ 2.037095, 3.52835204, 0 ],
"C": [ 2.037095, 1.17611735, 3.32656221 ],
"periodic": "XYZ"
},
"+kind": [
{
"_": "Ge",
"element": "Ge",
"potential": "ALL-q32",
"basis_set": [
[ "ORB", "pob-TZVP" ]
]
}
],
"+topology": {
"coord_file_name": "./struct.xyz",
"coord_file_format": "XYZ"
}
}
}
]
}Caveats:
- the full input format needs be known and is being loaded from a bundled
cp2k_input.xml - the YAML/JSON is quiet verbose and one has to know exactly which keywords can be repeated
While there is no solution to remedy the first caveat, the second can be solved with the simplified output format
Still based on the canonical format the simplified format relaxes some of the rules
- a section must only be prefixed with a
+if a keyword with the same name is present at the same time in the same section (since we can figure out whether the user wanted to specify the section or the keyword by inspecting the value for the key:dictfor a section) - if a repeated keyword or section contains only one entry, the list can be omitted (in case of ambiguity priority is given to multiple values per keyword rather than keyword repetition)
- sections with default parameters can be formulated as dictionaries, as long as the default parameter values are unique and do not match section keyword or subsection names
the example from before in the simplified format:
{
"global": {
"print_level": "medium",
"project_name": "test",
"run_type": "energy"
},
"force_eval": {
"method": "quickstep",
"DFT": {
"basis_set_file_name": "./BASIS_SETS",
"potential_file_name": "./POTENTIALS"
},
"xc": {
"xc_functional": {
"_": "PBE"
}
},
"subsys": {
"cell": {
"A": [ 4.07419, 0, 0 ],
"B": [ 2.037095, 3.52835204, 0 ],
"C": [ 2.037095, 1.17611735, 3.32656221 ],
"periodic": "XYZ"
},
"kind": {
"_": "Ge",
"element": "Ge",
"potential": "ALL-q32",
"basis_set": [ "ORB", "pob-TZVP" ]
},
"topology": {
"coord_file_name": "./struct.xyz",
"coord_file_format": "XYZ"
}
}
}
}or in YAML (with simplification rule #3 applied):
global:
print_level: medium
project_name: test
run_type: energy
force_eval:
DFT:
basis_set_file_name: ./BASIS_SETS
potential_file_name: ./POTENTIALS
XC:
xc_functional:
_: PBE # this can NOT be simplified since PBE could also be a subsection of xc_functional
method: quickstep
subsys:
cell:
A: [ 4.07419, 0.0, 0.0]
B: [ 2.037095, 3.52835204, 0.0]
C: [ 2.037095, 1.17611735, 3.32656221]
periodic: XYZ
kind:
Ge:
basis_set: [ORB, pob-TZVP]
element: Ge
potential: ALL-q32
topology:
coord_file_format: XYZ
coord_file_name: ./struct.xyz