Add support for xyz files via ASE

abipy/abilab.py

@@ -134,6 +134,8 @@ def _straceback():
     # Vasp files.
     ("POSCAR", Structure),
     (".vasp", Structure),
+    # ASE files
+    (".xyz", Structure),
     #("ZINVCONV.nc", ZinvConvFile),
     #("TETRATEST.nc", TetraTestFile),
     # QE/CP files

abipy/core/structure.py

@@ -290,7 +290,6 @@ def from_file(cls, filepath: str, primitive: bool = False, sort: bool = False) -
             raise ValueError("Cannot find structure in Abinit output file `%s`" % filepath)
 
         elif filepath.endswith(".abivars") or filepath.endswith(".ucell"):
-            #print("in abivars")
             with open(filepath, "rt") as fh:
                 return cls.from_abistring(fh.read())
 
@@ -314,6 +313,15 @@ def from_file(cls, filepath: str, primitive: bool = False, sort: bool = False) -
 
                 if new.__class__ != cls: new.__class__ = cls
 
+        elif filepath.endswith(".xyz"):
+            # ASE extended xyz format.
+            try:
+                from ase.io import read
+            except ImportError:
+                raise RuntimeError("ase is required to read xyz files. Use `pip install ase`")
+            atoms = read(filepath)
+            return cls.as_structure(atoms)
+
         else:
             # Invoke pymatgen and change class. Note that AbinitSpacegroup is missing here.
             new = super().from_file(filepath, primitive=primitive, sort=sort)
@@ -2624,7 +2632,7 @@ def diff_structures(structures, fmt="cif", mode="table", headers=(), file=sys.st
             print(diff, file=file)
 
     else:
-        raise ValueError("Unsupported mode: `%s`" % str(mode))
+        raise ValueError(f"Unsupported {mode=}")
 
 
 def structure2siesta(structure: Structure, verbose=0) -> str:
@@ -2686,7 +2694,6 @@ class StructDiff:
     """
     Print difference among structures.
     """
-
     def __init__(self, labels: list[str], structures):
         """
         Args:

abipy/dynamics/analyzer.py

@@ -21,6 +21,7 @@
 from monty.collections import AttrDict #, dict2namedtuple
 from pymatgen.util.string import latexify
 from pymatgen.core.lattice import Lattice
+from pymatgen.core.composition import Composition
 from abipy.core.mixins import TextFile, NotebookWriter
 from abipy.core.structure import Structure
 from abipy.tools.typing import Figure, PathLike
@@ -46,6 +47,21 @@
 kBoltzEv = 8.617333e-05
 #nCar = 56  # FIXME: Harcoded
 
+
+def common_oxidation_states():
+    oxi2symbols = {
+    +1: ["Li", "Na", "K", "Rb", "Cs", "Fr"],
+    +2: ["Be", "Mg", "Ca", "Sr", "Ba", "Ra"],
+    }
+
+    # map: symbol to oxidation state.
+    symb2oxi = {}
+    for oxi, slist in oxi2symbols.items():
+        for s in slist:
+            symb2oxi[s] = oxi
+    return symb2oxi
+
+
 def read_structure_postac_ucmats(traj_filepath: PathLike, step_skip: int) -> tuple[Structure, np.ndarray, np.ndarray, int]:
     """
     Read all configurations from an ASE trajectory file.
@@ -711,7 +727,7 @@ def get_dw_symbol(self, symbol, t0: float = 0.0, tmax=None, atom_inds=None):
         label = r"Linear fit: D={:.2E} cm$^2$/s, $r^2$={:.2f}".format(naive_d, fit.rvalue**2)
         return AttrDict(naive_d=naive_d, ts=ts, fit=fit, label=label)
 
-    def get_msdtt0_symbol(self, symbol: str, tmax: float, atom_inds=None, nprocs=None) -> Msdtt0:
+    def get_msdtt0_symbol_tmax(self, symbol: str, tmax: float, atom_inds=None, nprocs=None) -> Msdtt0:
         r"""
         Calculates the MSD for every possible pair of time points using the formula:
 
@@ -1571,10 +1587,10 @@ def iter_mdatc(self):
         for itemp, mda in enumerate(self):
             yield mda, mda.temperature, self.temp_cmap(float(itemp) / len(self))
 
-    def get_msdtt0_symbol(self, symbol: str, tmax: float, atom_inds=None, nprocs=None) -> Msdtt0List:
+    def get_msdtt0_symbol_tmax(self, symbol: str, tmax: float, atom_inds=None, nprocs=None) -> Msdtt0List:
         msdtt0_list = Msdtt0List()
         for mda in self:
-            obj = mda.get_msdtt0_symbol(symbol, tmax, atom_inds=atom_inds, nprocs=nprocs)
+            obj = mda.get_msdtt0_symbol_tmax(symbol, tmax, atom_inds=atom_inds, nprocs=nprocs)
             msdtt0_list.append(obj)
 
         return msdtt0_list
@@ -1602,7 +1618,7 @@ def _nrows_ncols_nplots(self, size=None):
     #    data_list = DiffusionDataList()
     #    for it, ((mda, temp), params) in enumerate(zip(self.iter_mdat(), params_list)):
     #        p = AttrDict(**params)
-    #        msq_tt0 = mda.get_msdtt0_symbol(symbol, p.tmax, nprocs=nprocs)
+    #        msq_tt0 = mda.get_msdtt0_symbol_tmax(symbol, p.tmax, nprocs=nprocs)
     #        sigma = msq_tt0.get_sigma_berend(t1=p.t1, t2=p.t2)
     #        data_t = msq_tt0.get_diffusion_with_sigma(p.fit_time_start, p.fit_time_stop, p.block_size1, p.block_size2, sigma)
     #        data_list.append(data_t)
@@ -1781,15 +1797,16 @@ class Entry:
 
     key: str
     symbol: str
-    formula: str
+    #formula: str
+    composition: str
     temps: np.ndarray
     diffusions: np.ndarray
     err_diffusions: np.ndarray
     volumes: np.ndarray
     style: dict
 
     @classmethod
-    def from_file(cls, filepath: PathLike, key, symbol, formula, style) -> Entry:
+    def from_file(cls, filepath: PathLike, key, symbol, composition, style) -> Entry:
 
         if str(filepath).endswith(".dat"):
             try:
@@ -1803,7 +1820,7 @@ def from_file(cls, filepath: PathLike, key, symbol, formula, style) -> Entry:
         else:
             try:
                 # Read data in CSV format. Assuming header with at least the following entries:
-                # T,diffusion,err_diffusion,volume,symbol,formula
+                # T,diffusion,err_diffusion,volume,symbol,composition
                 df = pd.read_csv(filepath, skipinitialspace=True) #, delim_whitespace=True)
 
                 def get_unique(col):
@@ -1813,7 +1830,7 @@ def get_unique(col):
                     return v0
 
                 symbol = get_unique("symbol")
-                formula = get_unique("formula")
+                composition = get_unique("composition")
                 temps = df["T"].values
                 volumes = df["volume"].values
                 diffusions = df["diffusion"].values
@@ -1824,9 +1841,12 @@ def get_unique(col):
             except Exception as exc:
                 raise RuntimeError(f"Exception while reading {filepath=}") from exc
 
+        composition = Composition(composition)
+        #print(composition)
+
         return cls(key=key,
                    symbol=symbol,
-                   formula=formula,
+                   composition=composition,
                    temps=temps,
                    diffusions=diffusions,
                    err_diffusions=err_diffusions,
@@ -1934,7 +1954,7 @@ class ArrheniusPlotter:
 
         from abipy.dynamics.analyzer import ArrheniusPlotter
         symbol = "Li"
-        formula = "c-LLZO"
+        composition = "c-LLZO"
 
         key_path = {
             "matgl-MD":  "diffusion_cLLZO-matgl.dat",
@@ -1948,19 +1968,19 @@ class ArrheniusPlotter:
 
         plotter = ArrheniusPlotter()
         for key, path in key_path.items():
-            plotter.add_entry_from_file(path, key, symbol, formula, style=style_key[key])
+            plotter.add_entry_from_file(path, key, symbol, composition, style=style_key[key])
 
         # temperature grid refined
         thinvt_arange = (0.6, 2.5, 0.01)
         xlims, ylims = (0.5, 2.0), (-7, -4)
 
         plotter.plot(thinvt_arange=thinvt_arange,
-                     xlims=xlims, ylims=ylims, text='LLZO cubic',
-                     savefig=None)
+                     xlims=xlims, ylims=ylims, text='LLZO cubic', savefig=None)
     """
 
     def __init__(self):
         self.entries = []
+        self.sym2oxi = common_oxidation_states()
 
     def __iter__(self):
         return self.entries.__iter__()
@@ -2012,11 +2032,11 @@ def get_min_max_temp(self) -> tuple[float, float]:
         max_temp = max([e.temperatures.max() for e in self])
         return min_temp, max_temp
 
-    def add_entry_from_file(self, filepath: PathLike, key: str, symbol: str, formula: str, style=None) -> Entry:
+    def add_entry_from_file(self, filepath: PathLike, key: str, symbol: str, composition: str, style=None) -> Entry:
         """
         """
         style = style or {}
-        self.append(Entry.from_file(filepath, key, symbol, formula, style))
+        self.append(Entry.from_file(filepath, key, symbol, composition, style))
 
     @add_fig_kwargs
     def plot(self, thinvt_arange=None, what="diffusion", ncar=None, colormap="jet", with_t=True, text=None,
@@ -2058,17 +2078,22 @@ def plot(self, thinvt_arange=None, what="diffusion", ncar=None, colormap="jet",
             if "color" not in my_style and "c" not in my_style:
                 my_style["color"] = cmap(ie / len(self))
 
+            symbol = entry.symbol
+            composition = entry.composition
+
             diff = entry.get_diffusion_data(fit_thinvt=fit_thinvt)
-            label = r"D$_{\mathrm{%s}}$ (%s): " % (entry.symbol, entry.key)
+            label = r"D$_{\mathrm{%s}}$ (%s): " % (symbol, entry.key)
             label += r"E$_\mathrm{a}$=" + str('{:.2F}'.format(diff.e_act)) + 'eV'
 
             if what == "diffusion":
                 data = diff
             else:
                 if ncar is None:
-                    raise ValueError("Computation of conductivity requires the specification of ncar!")
-                if len(self.symbols()) != 1:
-                    raise ValueError(f"Computation of conductivity requires entries with same symbol while {self.symbols()=}!")
+                    if symbol not in self.symb2oxi:
+                        raise ValueError(f"No entry for {symbol=} found in symb2oxi! Please add the oxistate manually!")
+                    ncar = symb2oxi[symbol] * composition[symbol]
+                    print(self.symb2oxi[symbol], composition[symbol])
+
                 sigma_data, tsigma_data = entry.get_conductivity_data(ncar, fit_thinvt=fit_thinvt)
                 data = dict(sigma=sigma_data, tsigma=tsigma_data)[what]
 

abipy/ml/aseml.py

@@ -58,22 +58,22 @@
 # Helper functions
 ###################
 
-def nprocs_for_ntasks(nprocs, ntasks, title=None) -> int:
-    """
-    Return the number of procs to be used in a multiprocessing Pool.
-    If negative or None, use half the procs available in the system.
-    """
-    if nprocs is None or nprocs <= 0:
-        nprocs = max(1, os.cpu_count() // 2)
-    else:
-        nprocs = int(nprocs)
-
-    nprocs = min(nprocs, ntasks)
-    if title is not None:
-        print(title)
-        print(f"Using multiprocessing pool with {nprocs=} for {ntasks=} ...")
-
-    return nprocs
+#def nprocs_for_ntasks(nprocs, ntasks, title=None) -> int:
+#    """
+#    Return the number of procs to be used in a multiprocessing Pool.
+#    If negative or None, use half the procs available in the system.
+#    """
+#    if nprocs is None or nprocs <= 0:
+#        nprocs = max(1, os.cpu_count() // 2)
+#    else:
+#        nprocs = int(nprocs)
+#
+#    nprocs = min(nprocs, ntasks)
+#    if title is not None:
+#        print(title)
+#        print(f"Using multiprocessing pool with {nprocs=} for {ntasks=} ...")
+#
+#    return nprocs
 
 
 _CELLPAR_KEYS = ["a", "b", "c", "angle(b,c)", "angle(a,c)", "angle(a,b)"]
@@ -124,7 +124,7 @@ def fix_atoms(atoms: Atoms,
               fix_inds: list[int] | None = None,
               fix_symbols: list[str] | None = None) -> None:
     """
-    Fix atoms by indices and by symbols.
+    Fix atoms by indices and/or by symbols.
 
     Args:
         atoms: ASE atoms
@@ -347,7 +347,7 @@ def make_square_axes(ax_mat):
 
 class AseResultsComparator(HasPickleIO):
     """
-    This object allows one to compare energies, forces and stressee computed
+    This object allows one to compare energies, forces and stresses computed
     for the same structure but with different methods e.g. results obtained
     with different ML potentials.
     """
@@ -2028,6 +2028,7 @@ def postprocess_images(self, images):
         ef, de = nebtools.get_barrier(fit=False)
         neb_data.update(barrier_without_fit=float(ef),
                         energy_change_without_fit=float(de),
+                        nn_name=str(self.nn_name),
         )
 
         self.write_json("neb_data.json", neb_data, info="JSON document with NEB results",
@@ -2232,15 +2233,15 @@ def run(self) -> None:
         self.write_script("ase_gui.sh", text=f"""\
 # To visualize the results, use:
 
-ase gui {nebtraj_file}@-{self.nimages}
+ase gui "{nebtraj_file}@-{self.nimages}"
 
 # then select `tools->neb` in the gui.
 """, info="Shell script to visualize NEB results with ase gui")
 
         self.write_script("ase_nebplot.sh", text=f"""\
 # This command create a series of plots showing the progression of the neb relaxation
 
-ase nebplot --share-x --share-y --nimages {self.nimages} {nebtraj_file}
+ase nebplot --share-x --share-y --nimages {self.nimages} "{nebtraj_file}"
 """, info="Shell script to create a series of plots showing the progression of the neb relaxation")
 
         self._finalize()
@@ -2621,7 +2622,6 @@ def run(self, nprocs, print_dataframes=True) -> AseResultsComparator:
         results_list = [abi_results]
 
         ntasks = len(abi_results)
-        #nprocs = nprocs_for_ntasks(nprocs, ntasks, title="Begin ML computation")
         nprocs = 1
 
         for nn_name in self.nn_names:
@@ -2775,7 +2775,7 @@ def run(self, steps: int):
         self.dyn.run(steps)
 
 
-class MlCompareNNs(_MlNebBase):
+class MlCompareNNs(MlBase):
     """
     Compare energies, forces and stresses obtaiend with different ML potentials.
     Also profile the time required.
@@ -2821,7 +2821,7 @@ def to_string(self, verbose=0) -> str:
 
     def run(self, print_dataframes=True) -> AseResultsComparator:
         """
-        Run calculations
+        Run calculations.
         """
         workdir = self.workdir
 

abipy/ppcodes/oncv_plotter.py

@@ -92,6 +92,11 @@ def _add_rc_vlines_ax(self, ax, with_lloc=False) -> None:
             color = "magenta" if self.parser.lloc == 4 else "k"
             ax.axvline(self.parser.rc5, lw=2, color=color, ls="--")
 
+    def plotly_atan_logders(self, *args, **kwargs):
+        mpl_fig = self.plot_atan_logders(*args, show=False, **kwargs)
+        from plotly.tools import mpl_to_plotly
+        return mpl_to_plotly(mpl_fig)
+
     @add_fig_kwargs
     def plot_atan_logders(self, ax=None, with_xlabel=True,
                           fontsize: int = 8, **kwargs) -> Figure:

abipy/scripts/abicomp.py

@@ -229,7 +229,7 @@ def abicomp_xrd(options):
     Compare X-ray diffraction plots (requires FILES with structure).
     """
     if len(options.paths) < 2:
-        print("You need more than one structure to compare!")
+        print("You need more than one structures to compare!")
         return 1
 
     structures = [abilab.Structure.from_file(p) for p in options.paths]
@@ -584,7 +584,7 @@ def abicomp_abiwan(options):
 
 
 def abicomp_pseudos(options):
-    """"Compare multiple pseudos Print table to terminal."""
+    """"Compare multiple pseudos and print table to terminal."""
     # Make sure entries in index are unique.
     index = [os.path.basename(p) for p in options.paths]
     if len(index) != len(set(index)): index = [os.path.relpath(p) for p in options.paths]

abipy/scripts/oncv.py

@@ -79,6 +79,8 @@ def oncv_plot(options) -> int:
 
     out_path = _find_oncv_output(options.filepath)
     plotter = OncvPlotter.from_file(out_path)
+    plotter.plotly_atan_logders().show()
+    return 0
 
     plotter.expose(slide_mode=options.slide_mode, slide_timeout=options.slide_timeout,
                    use_web=options.expose_web, verbose=options.verbose)

abipy/tools/plotting.py

@@ -1818,7 +1818,7 @@ def wrapper(*args, **kwargs):
                 ================  ====================================================================
                 title             Title of the plot (Default: None).
                 show              True to show the figure (default: True).
-                hovormode         True to show the hover info (default: False)
+                hovermode         True to show the hover info (default: False)
                 savefig           "abc.png" , "abc.jpeg" or "abc.webp" to save the figure to a file.
                 write_json        Write plotly figure to `write_json` JSON file.
                                   Inside jupyter-lab, one can right-click the `write_json` file from