From bb5497a302a03c2cbc24733ca02374a294f7d082 Mon Sep 17 00:00:00 2001 From: First Coder Date: Sat, 6 Mar 2021 08:14:04 -0800 Subject: [PATCH] Update dev docs --- docs/_modules/index.html | 6 +- .../analysis/diffusion/aimd/clustering.html | 4 +- .../analysis/diffusion/aimd/pathway.html | 4 +- .../pymatgen/analysis/diffusion/aimd/rdf.html | 4 +- .../diffusion/aimd/tests/test_clustering.html | 4 +- .../diffusion/aimd/tests/test_pathway.html | 6 +- .../diffusion/aimd/tests/test_rdf.html | 4 +- .../diffusion/aimd/tests/test_van_hove.html | 6 +- .../analysis/diffusion/aimd/van_hove.html | 6 +- .../pymatgen/analysis/diffusion/analyzer.html | 1028 +++++++++++++++++ .../diffusion/neb/full_path_mapper.html | 4 +- .../pymatgen/analysis/diffusion/neb/io.html | 4 +- .../analysis/diffusion/neb/pathfinder.html | 4 +- .../diffusion/neb/periodic_dijkstra.html | 4 +- .../neb/tests/test_full_path_mapper.html | 4 +- .../analysis/diffusion/neb/tests/test_io.html | 4 +- .../diffusion/neb/tests/test_pathfinder.html | 4 +- .../diffusion/tests/test_analyzer.html | 589 ++++++++++ .../diffusion/utils/parse_entries.html | 4 +- .../analysis/diffusion/utils/supercells.html | 4 +- .../utils/tests/test_parse_entries.html | 4 +- ...matgen.analysis.diffusion.analyzer.rst.txt | 7 + .../pymatgen.analysis.diffusion.rst.txt | 8 + ...ysis.diffusion.tests.test_analyzer.rst.txt | 7 + docs/_static/documentation_options.js | 2 +- docs/_themes/README.html | 4 +- docs/change_log.html | 4 +- docs/doctrees/environment.pickle | Bin 324234 -> 391969 bytes ...n.analysis.diffusion.aimd.van_hove.doctree | Bin 105618 -> 105618 bytes ...matgen.analysis.diffusion.analyzer.doctree | Bin 0 -> 152287 bytes .../pymatgen.analysis.diffusion.doctree | Bin 4022 -> 4460 bytes ...ysis.diffusion.tests.test_analyzer.doctree | Bin 0 -> 14980 bytes docs/genindex.html | 80 +- docs/index.html | 4 +- docs/modules.html | 4 +- docs/objects.inv | Bin 2639 -> 2890 bytes docs/py-modindex.html | 14 +- ...en.analysis.diffusion.aimd.clustering.html | 4 +- docs/pymatgen.analysis.diffusion.aimd.html | 4 +- ...atgen.analysis.diffusion.aimd.pathway.html | 8 +- .../pymatgen.analysis.diffusion.aimd.rdf.html | 4 +- ....diffusion.aimd.tests.test_clustering.html | 4 +- ...sis.diffusion.aimd.tests.test_pathway.html | 4 +- ...nalysis.diffusion.aimd.tests.test_rdf.html | 4 +- ...is.diffusion.aimd.tests.test_van_hove.html | 4 +- ...tgen.analysis.diffusion.aimd.van_hove.html | 8 +- .../pymatgen.analysis.diffusion.analyzer.html | 520 +++++++++ docs/pymatgen.analysis.diffusion.html | 13 +- ...alysis.diffusion.neb.full_path_mapper.html | 4 +- docs/pymatgen.analysis.diffusion.neb.html | 4 +- docs/pymatgen.analysis.diffusion.neb.io.html | 4 +- ...gen.analysis.diffusion.neb.pathfinder.html | 4 +- ...lysis.diffusion.neb.periodic_dijkstra.html | 4 +- ...usion.neb.tests.test_full_path_mapper.html | 4 +- ....analysis.diffusion.neb.tests.test_io.html | 4 +- ...s.diffusion.neb.tests.test_pathfinder.html | 4 +- ...nalysis.diffusion.tests.test_analyzer.html | 147 +++ docs/pymatgen.analysis.diffusion.utils.html | 4 +- ...atgen.analysis.diffusion.utils.maggma.html | 4 +- ...nalysis.diffusion.utils.parse_entries.html | 4 +- ...n.analysis.diffusion.utils.supercells.html | 4 +- ...fusion.utils.tests.test_parse_entries.html | 4 +- docs/pymatgen.analysis.html | 8 +- docs/pymatgen.html | 4 +- docs/pymatgen_diffusion.aimd.html | 4 +- docs/pymatgen_diffusion.html | 4 +- docs/pymatgen_diffusion.neb.html | 4 +- docs/search.html | 4 +- docs/searchindex.js | 2 +- docs_rst/pymatgen.analysis.diffusion.rst | 8 + pymatgen/analysis/diffusion/__init__.py | 2 +- setup.py | 4 +- 72 files changed, 2530 insertions(+), 125 deletions(-) create mode 100644 docs/_modules/pymatgen/analysis/diffusion/analyzer.html create mode 100644 docs/_modules/pymatgen/analysis/diffusion/tests/test_analyzer.html create mode 100644 docs/_sources/pymatgen.analysis.diffusion.analyzer.rst.txt create mode 100644 docs/_sources/pymatgen.analysis.diffusion.tests.test_analyzer.rst.txt create mode 100644 docs/doctrees/pymatgen.analysis.diffusion.analyzer.doctree create mode 100644 docs/doctrees/pymatgen.analysis.diffusion.tests.test_analyzer.doctree create mode 100644 docs/pymatgen.analysis.diffusion.analyzer.html create mode 100644 docs/pymatgen.analysis.diffusion.tests.test_analyzer.html diff --git a/docs/_modules/index.html b/docs/_modules/index.html index 784ccd4f..b180c5aa 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -5,7 +5,7 @@ - Overview: module code — pymatgen-diffusion 2021.3.5 documentation + Overview: module code — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen-diffusion 2021.3.5 documentation »
    • +
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  • Overview: module code
    • @@ -55,6 +55,7 @@

    All modules for which code is available

  • pymatgen.analysis.diffusion.aimd.tests.test_rdf
  • pymatgen.analysis.diffusion.aimd.tests.test_van_hove
  • pymatgen.analysis.diffusion.aimd.van_hove
    • +
  • pymatgen.analysis.diffusion.analyzer
  • pymatgen.analysis.diffusion.neb.full_path_mapper
  • pymatgen.analysis.diffusion.neb.io
  • pymatgen.analysis.diffusion.neb.pathfinder
    • @@ -62,6 +63,7 @@

    All modules for which code is available

  • pymatgen.analysis.diffusion.neb.tests.test_full_path_mapper
  • pymatgen.analysis.diffusion.neb.tests.test_io
  • pymatgen.analysis.diffusion.neb.tests.test_pathfinder
    • +
  • pymatgen.analysis.diffusion.tests.test_analyzer
  • pymatgen.analysis.diffusion.utils.parse_entries
  • pymatgen.analysis.diffusion.utils.supercells
  • pymatgen.analysis.diffusion.utils.tests.test_parse_entries
    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/clustering.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/clustering.html index 63e81d57..99129415 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/clustering.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/clustering.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.clustering — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.clustering — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.clustering
    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/pathway.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/pathway.html index 73d2111f..f03d6094 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/pathway.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/pathway.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.pathway — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.pathway — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.pathway
    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/rdf.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/rdf.html index 59fa6cc3..27c7670b 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/rdf.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/rdf.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.rdf — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.rdf — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.rdf
    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_clustering.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_clustering.html index 7f6718ae..d28fda2e 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_clustering.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_clustering.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_clustering — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_clustering — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.tests.test_clustering
    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_pathway.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_pathway.html index c620b59c..2212a9ab 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_pathway.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_pathway.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_pathway — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_pathway — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.tests.test_pathway
    • @@ -64,7 +64,7 @@

    Source code for pymatgen.analysis.diffusion.aimd.tests.test_pathway

    ProbabilityDensityAnalysis, SiteOccupancyAnalyzer, ) -from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer +from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer from pymatgen.core import Structure tests_dir = os.path.dirname(os.path.abspath(__file__)) diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_rdf.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_rdf.html index 27764f9f..58be5054 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_rdf.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_rdf.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_rdf — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_rdf — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • Module code »
  • pymatgen.analysis.diffusion.aimd.tests.test_rdf
    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_van_hove.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_van_hove.html index 11a9472a..28bcda08 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_van_hove.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/tests/test_van_hove.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_van_hove — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_van_hove — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.tests.test_van_hove
    • @@ -64,7 +64,7 @@

    Source code for pymatgen.analysis.diffusion.aimd.tests.test_van_hove

    from copy import deepcopy from monty.serialization import loadfn -from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer +from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer from pymatgen.analysis.diffusion.aimd.van_hove import ( VanHoveAnalysis, RadialDistributionFunction, diff --git a/docs/_modules/pymatgen/analysis/diffusion/aimd/van_hove.html b/docs/_modules/pymatgen/analysis/diffusion/aimd/van_hove.html index 88eac0af..5997e1ea 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/aimd/van_hove.html +++ b/docs/_modules/pymatgen/analysis/diffusion/aimd/van_hove.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.van_hove — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.van_hove — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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  • pymatgen.analysis.diffusion.aimd.van_hove
    • @@ -65,7 +65,7 @@

    Source code for pymatgen.analysis.diffusion.aimd.van_hove

    import pandas as pds from scipy.stats import norm from pymatgen.core import Structure -from pymatgen.analysis.diffusion_analyzer import DiffusionAnalyzer +from pymatgen.analysis.diffusion.analyzer import DiffusionAnalyzer from pymatgen.util.plotting import pretty_plot from pymatgen.util.typing import ArrayLike diff --git a/docs/_modules/pymatgen/analysis/diffusion/analyzer.html b/docs/_modules/pymatgen/analysis/diffusion/analyzer.html new file mode 100644 index 00000000..9517dab0 --- /dev/null +++ b/docs/_modules/pymatgen/analysis/diffusion/analyzer.html @@ -0,0 +1,1028 @@ + + + + + + + + pymatgen.analysis.diffusion.analyzer — pymatgen-diffusion 2021.3.6 documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    +
    + +

    Source code for pymatgen.analysis.diffusion.analyzer

    +# coding: utf-8
+# Copyright (c) Pymatgen Development Team.
+# Distributed under the terms of the MIT License.
+
+"""
+A module to perform diffusion analyses (e.g. calculating diffusivity from
+mean square displacements etc.). If you use this module, please consider
+citing the following papers::
+
+    Ong, S. P., Mo, Y., Richards, W. D., Miara, L., Lee, H. S., & Ceder, G.
+    (2013). Phase stability, electrochemical stability and ionic conductivity
+    of the Li10+-1MP2X12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family
+    of superionic conductors. Energy & Environmental Science, 6(1), 148.
+    doi:10.1039/c2ee23355j
+
+    Mo, Y., Ong, S. P., & Ceder, G. (2012). First Principles Study of the
+    Li10GeP2S12 Lithium Super Ionic Conductor Material. Chemistry of Materials,
+    24(1), 15-17. doi:10.1021/cm203303y
+"""
+
+
+import multiprocessing
+import warnings
+
+import numpy as np
+import scipy.constants as const
+from monty.json import MSONable
+
+from pymatgen.analysis.structure_matcher import (
+    OrderDisorderElementComparator,
+    StructureMatcher,
+)
+from pymatgen.core.periodic_table import get_el_sp
+from pymatgen.core.structure import Structure
+from pymatgen.io.vasp.outputs import Vasprun
+from pymatgen.util.coord import pbc_diff
+
+__author__ = "Will Richards, Shyue Ping Ong"
+__version__ = "0.2"
+__maintainer__ = "Will Richards"
+__email__ = "wrichard@mit.edu"
+__status__ = "Beta"
+__date__ = "5/2/13"
+
+
+
    [docs]class DiffusionAnalyzer(MSONable):
+    """
+    Class for performing diffusion analysis.
+
+    .. attribute: diffusivity
+
+        Diffusivity in cm^2 / s
+
+    .. attribute: chg_diffusivity
+
+        Charge diffusivity in cm^2 / s
+
+    .. attribute: conductivity
+
+        Conductivity in mS / cm
+
+    .. attribute: chg_conductivity
+
+        Conductivity derived from Nernst-Einstein equation using charge
+        diffusivity, in mS / cm
+
+    .. attribute: diffusivity_components
+
+        A vector with diffusivity in the a, b and c directions in cm^2 / s
+
+    .. attribute: conductivity_components
+
+        A vector with conductivity in the a, b and c directions in mS / cm
+
+    .. attribute: diffusivity_std_dev
+
+        Std dev in diffusivity in cm^2 / s. Note that this makes sense only
+        for non-smoothed analyses.
+
+    .. attribute: chg_diffusivity_std_dev
+
+        Std dev in charge diffusivity in cm^2 / s. Note that this makes sense only
+        for non-smoothed analyses.
+
+    .. attribute: conductivity_std_dev
+
+        Std dev in conductivity in mS / cm. Note that this makes sense only
+        for non-smoothed analyses.
+
+    .. attribute: diffusivity_components_std_dev
+
+        A vector with std dev. in diffusivity in the a, b and c directions in
+        cm^2 / cm. Note that this makes sense only for non-smoothed analyses.
+
+    .. attribute: conductivity_components_std_dev
+
+        A vector with std dev. in conductivity in the a, b and c directions
+        in mS / cm. Note that this makes sense only for non-smoothed analyses.
+
+    .. attribute: max_framework_displacement
+
+        The maximum (drift adjusted) distance of any framework atom from its
+        starting location in A.
+
+    .. attribute: max_ion_displacements
+
+        nions x 1 array of the maximum displacement of each individual ion.
+
+    .. attribute: msd
+
+        nsteps x 1 array of the mean square displacement of specie.
+
+    .. attribute: mscd
+
+        nsteps x 1 array of the mean square charge displacement of specie.
+
+    .. attribute: msd_components
+
+        nsteps x 3 array of the MSD in each lattice direction of specie.
+
+    .. attribute: sq_disp_ions
+
+        The square displacement of all ion (both specie and other ions) as a
+        nions x nsteps array.
+
+    .. attribute: dt
+
+        Time coordinate array.
+
+    .. attribute: haven_ratio
+        Haven ratio defined as diffusivity / chg_diffusivity.
+    """
+
+    def __init__(
+        self,
+        structure,
+        displacements,
+        specie,
+        temperature,
+        time_step,
+        step_skip,
+        smoothed="max",
+        min_obs=30,
+        avg_nsteps=1000,
+        lattices=None,
+    ):
+        """
+        This constructor is meant to be used with pre-processed data.
+        Other convenient constructors are provided as class methods (see
+        from_vaspruns and from_files).
+
+        Given a matrix of displacements (see arguments below for expected
+        format), the diffusivity is given by::
+
+            D = 1 / 2dt * <mean square displacement>
+
+        where d is the dimensionality, t is the time. To obtain a reliable
+        diffusion estimate, a least squares regression of the MSD against
+        time to obtain the slope, which is then related to the diffusivity.
+
+        For traditional analysis, use smoothed=False and weighted=False.
+
+        Args:
+            structure (Structure): Initial structure.
+            displacements (array): Numpy array of with shape [site,
+                time step, axis]
+            specie (Element/Species): Species to calculate diffusivity for as a
+                String. E.g., "Li".
+            temperature (float): Temperature of the diffusion run in Kelvin.
+            time_step (int): Time step between measurements.
+            step_skip (int): Sampling frequency of the displacements (
+                time_step is multiplied by this number to get the real time
+                between measurements)
+            smoothed (str): Whether to smooth the MSD, and what mode to smooth.
+                Supported modes are:
+
+                i. "max", which tries to use the maximum #
+                   of data points for each time origin, subject to a
+                   minimum # of observations given by min_obs, and then
+                   weights the observations based on the variance
+                   accordingly. This is the default.
+                ii. "constant", in which each timestep is averaged over
+                    the number of time_steps given by min_steps.
+                iii. None / False / any other false-like quantity. No
+                   smoothing.
+
+            min_obs (int): Used with smoothed="max". Minimum number of
+                observations to have before including in the MSD vs dt
+                calculation. E.g. If a structure has 10 diffusing atoms,
+                and min_obs = 30, the MSD vs dt will be
+                calculated up to dt = total_run_time / 3, so that each
+                diffusing atom is measured at least 3 uncorrelated times.
+                Only applies in smoothed="max".
+            avg_nsteps (int): Used with smoothed="constant". Determines the
+                number of time steps to average over to get the msd for each
+                timestep. Default of 1000 is usually pretty good.
+            lattices (array): Numpy array of lattice matrix of every step. Used
+                for NPT-AIMD. For NVT-AIMD, the lattice at each time step is
+                set to the lattice in the "structure" argument.
+        """
+        self.structure = structure
+        self.disp = displacements
+        self.specie = specie
+        self.temperature = temperature
+        self.time_step = time_step
+        self.step_skip = step_skip
+        self.min_obs = min_obs
+        self.smoothed = smoothed
+        self.avg_nsteps = avg_nsteps
+        self.lattices = lattices
+
+        if lattices is None:
+            self.lattices = np.array([structure.lattice.matrix.tolist()])
+
+        indices = []
+        framework_indices = []
+        for i, site in enumerate(structure):
+            if site.specie.symbol == specie:
+                indices.append(i)
+            else:
+                framework_indices.append(i)
+        if self.disp.shape[1] < 2:
+            self.diffusivity = 0.0
+            self.conductivity = 0.0
+            self.diffusivity_components = np.array([0.0, 0.0, 0.0])
+            self.conductivity_components = np.array([0.0, 0.0, 0.0])
+            self.max_framework_displacement = 0
+        else:
+            framework_disp = self.disp[framework_indices]
+            drift = np.average(framework_disp, axis=0)[None, :, :]
+
+            # drift corrected position
+            dc = self.disp - drift
+
+            nions, nsteps, dim = dc.shape
+
+            if not smoothed:
+                timesteps = np.arange(0, nsteps)
+            elif smoothed == "constant":
+                if nsteps <= avg_nsteps:
+                    raise ValueError("Not enough data to calculate diffusivity")
+                timesteps = np.arange(0, nsteps - avg_nsteps)
+            else:
+                # limit the number of sampled timesteps to 200
+                min_dt = int(1000 / (self.step_skip * self.time_step))
+                max_dt = min(len(indices) * nsteps // self.min_obs, nsteps)
+                if min_dt >= max_dt:
+                    raise ValueError("Not enough data to calculate diffusivity")
+                timesteps = np.arange(min_dt, max_dt, max(int((max_dt - min_dt) / 200), 1))
+
+            dt = timesteps * self.time_step * self.step_skip
+
+            # calculate the smoothed msd values
+            msd = np.zeros_like(dt, dtype=np.double)
+            sq_disp_ions = np.zeros((len(dc), len(dt)), dtype=np.double)
+            msd_components = np.zeros(dt.shape + (3,))
+
+            # calculate mean square charge displacement
+            mscd = np.zeros_like(msd, dtype=np.double)
+
+            for i, n in enumerate(timesteps):
+                if not smoothed:
+                    dx = dc[:, i : i + 1, :]
+                    dcomponents = dc[:, i : i + 1, :]
+                elif smoothed == "constant":
+                    dx = dc[:, i : i + avg_nsteps, :] - dc[:, 0:avg_nsteps, :]
+                    dcomponents = dc[:, i : i + avg_nsteps, :] - dc[:, 0:avg_nsteps, :]
+                else:
+                    dx = dc[:, n:, :] - dc[:, :-n, :]
+                    dcomponents = dc[:, n:, :] - dc[:, :-n, :]
+
+                # Get msd
+                sq_disp = dx ** 2
+                sq_disp_ions[:, i] = np.average(np.sum(sq_disp, axis=2), axis=1)
+                msd[i] = np.average(sq_disp_ions[:, i][indices])
+
+                msd_components[i] = np.average(dcomponents[indices] ** 2, axis=(0, 1))
+
+                # Get mscd
+                sq_chg_disp = np.sum(dx[indices, :, :], axis=0) ** 2
+                mscd[i] = np.average(np.sum(sq_chg_disp, axis=1), axis=0) / len(indices)
+
+            def weighted_lstsq(a, b):
+                if smoothed == "max":
+                    # For max smoothing, we need to weight by variance.
+                    w_root = (1 / dt) ** 0.5
+                    return np.linalg.lstsq(a * w_root[:, None], b * w_root, rcond=None)
+                return np.linalg.lstsq(a, b, rcond=None)
+
+            # Get self diffusivity
+            m_components = np.zeros(3)
+            m_components_res = np.zeros(3)
+            a = np.ones((len(dt), 2))
+            a[:, 0] = dt
+            for i in range(3):
+                (m, c), res, rank, s = weighted_lstsq(a, msd_components[:, i])
+                m_components[i] = max(m, 1e-15)
+                m_components_res[i] = res[0]
+
+            (m, c), res, rank, s = weighted_lstsq(a, msd)
+            # m shouldn't be negative
+            m = max(m, 1e-15)
+
+            # Get also the charge diffusivity
+            (m_chg, c_chg), res_chg, _, _ = weighted_lstsq(a, mscd)
+            # m shouldn't be negative
+            m_chg = max(m_chg, 1e-15)
+
+            # factor of 10 is to convert from A^2/fs to cm^2/s
+            # factor of 6 is for dimensionality
+            conv_factor = get_conversion_factor(self.structure, self.specie, self.temperature)
+            self.diffusivity = m / 60
+            self.chg_diffusivity = m_chg / 60
+
+            # Calculate the error in the diffusivity using the error in the
+            # slope from the lst sq.
+            # Variance in slope = n * Sum Squared Residuals / (n * Sxx - Sx
+            # ** 2) / (n-2).
+            n = len(dt)
+
+            # Pre-compute the denominator since we will use it later.
+            # We divide dt by 1000 to avoid overflow errors in some systems (
+            # e.g., win). This is subsequently corrected where denom is used.
+            denom = (n * np.sum((dt / 1000) ** 2) - np.sum(dt / 1000) ** 2) * (n - 2)
+            self.diffusivity_std_dev = np.sqrt(n * res[0] / denom) / 60 / 1000
+            self.chg_diffusivity_std_dev = np.sqrt(n * res_chg[0] / denom) / 60 / 1000
+            self.conductivity = self.diffusivity * conv_factor
+            self.chg_conductivity = self.chg_diffusivity * conv_factor
+            self.conductivity_std_dev = self.diffusivity_std_dev * conv_factor
+
+            self.diffusivity_components = m_components / 20
+            self.diffusivity_components_std_dev = np.sqrt(n * m_components_res / denom) / 20 / 1000
+            self.conductivity_components = self.diffusivity_components * conv_factor
+            self.conductivity_components_std_dev = self.diffusivity_components_std_dev * conv_factor
+
+            # Drift and displacement information.
+            self.drift = drift
+            self.corrected_displacements = dc
+            self.max_ion_displacements = np.max(np.sum(dc ** 2, axis=-1) ** 0.5, axis=1)
+            self.max_framework_displacement = np.max(self.max_ion_displacements[framework_indices])
+            self.msd = msd
+            self.mscd = mscd
+            self.haven_ratio = self.diffusivity / self.chg_diffusivity
+            self.sq_disp_ions = sq_disp_ions
+            self.msd_components = msd_components
+            self.dt = dt
+            self.indices = indices
+            self.framework_indices = framework_indices
+
+
    [docs]    def get_drift_corrected_structures(self, start=None, stop=None, step=None):
+        """
+        Returns an iterator for the drift-corrected structures. Use of
+        iterator is to reduce memory usage as # of structures in MD can be
+        huge. You don't often need all the structures all at once.
+
+        Args:
+            start, stop, step (int): applies a start/stop/step to the iterator.
+                Faster than applying it after generation, as it reduces the
+                number of structures created.
+        """
+        coords = np.array(self.structure.cart_coords)
+        species = self.structure.species_and_occu
+        lattices = self.lattices
+        nsites, nsteps, dim = self.corrected_displacements.shape
+
+        for i in range(start or 0, stop or nsteps, step or 1):
+            latt = lattices[0] if len(lattices) == 1 else lattices[i]
+            yield Structure(
+                latt,
+                species,
+                coords + self.corrected_displacements[:, i, :],
+                coords_are_cartesian=True,
+            )
    
+
+
    [docs]    def get_summary_dict(self, include_msd_t=False, include_mscd_t=False):
+        """
+        Provides a summary of diffusion information.
+
+        Args:
+            include_msd_t (bool): Whether to include mean square displace and
+                time data with the data.
+            include_msd_t (bool): Whether to include mean square charge displace and
+                time data with the data.
+
+        Returns:
+            (dict) of diffusion and conductivity data.
+        """
+        d = {
+            "D": self.diffusivity,
+            "D_sigma": self.diffusivity_std_dev,
+            "D_charge": self.chg_diffusivity,
+            "D_charge_sigma": self.chg_diffusivity_std_dev,
+            "S": self.conductivity,
+            "S_sigma": self.conductivity_std_dev,
+            "S_charge": self.chg_conductivity,
+            "D_components": self.diffusivity_components.tolist(),
+            "S_components": self.conductivity_components.tolist(),
+            "D_components_sigma": self.diffusivity_components_std_dev.tolist(),
+            "S_components_sigma": self.conductivity_components_std_dev.tolist(),
+            "specie": str(self.specie),
+            "step_skip": self.step_skip,
+            "time_step": self.time_step,
+            "temperature": self.temperature,
+            "max_framework_displacement": self.max_framework_displacement,
+            "Haven_ratio": self.haven_ratio,
+        }
+        if include_msd_t:
+            d["msd"] = self.msd.tolist()
+            d["msd_components"] = self.msd_components.tolist()
+            d["dt"] = self.dt.tolist()
+        if include_mscd_t:
+            d["mscd"] = self.mscd.tolist()
+        return d
    
+
+
    [docs]    def get_framework_rms_plot(self, plt=None, granularity=200, matching_s=None):
+        """
+        Get the plot of rms framework displacement vs time. Useful for checking
+        for melting, especially if framework atoms can move via paddle-wheel
+        or similar mechanism (which would show up in max framework displacement
+        but doesn't constitute melting).
+
+        Args:
+            plt (matplotlib.pyplot): If plt is supplied, changes will be made
+                to an existing plot. Otherwise, a new plot will be created.
+            granularity (int): Number of structures to match
+            matching_s (Structure): Optionally match to a disordered structure
+                instead of the first structure in the analyzer. Required when
+                a secondary mobile ion is present.
+        Notes:
+            The method doesn't apply to NPT-AIMD simulation analysis.
+        """
+        from pymatgen.util.plotting import pretty_plot
+
+        if self.lattices is not None and len(self.lattices) > 1:
+            warnings.warn("Note the method doesn't apply to NPT-AIMD " "simulation analysis!")
+
+        plt = pretty_plot(12, 8, plt=plt)
+        step = (self.corrected_displacements.shape[1] - 1) // (granularity - 1)
+        f = (matching_s or self.structure).copy()
+        f.remove_species([self.specie])
+        sm = StructureMatcher(
+            primitive_cell=False,
+            stol=0.6,
+            comparator=OrderDisorderElementComparator(),
+            allow_subset=True,
+        )
+        rms = []
+        for s in self.get_drift_corrected_structures(step=step):
+            s.remove_species([self.specie])
+            d = sm.get_rms_dist(f, s)
+            if d:
+                rms.append(d)
+            else:
+                rms.append((1, 1))
+        max_dt = (len(rms) - 1) * step * self.step_skip * self.time_step
+        if max_dt > 100000:
+            plot_dt = np.linspace(0, max_dt / 1000, len(rms))
+            unit = "ps"
+        else:
+            plot_dt = np.linspace(0, max_dt, len(rms))
+            unit = "fs"
+        rms = np.array(rms)
+        plt.plot(plot_dt, rms[:, 0], label="RMS")
+        plt.plot(plot_dt, rms[:, 1], label="max")
+        plt.legend(loc="best")
+        plt.xlabel("Timestep ({})".format(unit))
+        plt.ylabel("normalized distance")
+        plt.tight_layout()
+        return plt
    
+
+
    [docs]    def get_msd_plot(self, plt=None, mode="specie"):
+        """
+        Get the plot of the smoothed msd vs time graph. Useful for
+        checking convergence. This can be written to an image file.
+
+        Args:
+            plt: A plot object. Defaults to None, which means one will be
+                generated.
+            mode (str): Determines type of msd plot. By "species", "sites",
+                or direction (default). If mode = "mscd", the smoothed mscd vs.
+                time will be plotted.
+        """
+        from pymatgen.util.plotting import pretty_plot
+
+        plt = pretty_plot(12, 8, plt=plt)
+        if np.max(self.dt) > 100000:
+            plot_dt = self.dt / 1000
+            unit = "ps"
+        else:
+            plot_dt = self.dt
+            unit = "fs"
+
+        if mode == "species":
+            for sp in sorted(self.structure.composition.keys()):
+                indices = [i for i, site in enumerate(self.structure) if site.specie == sp]
+                sd = np.average(self.sq_disp_ions[indices, :], axis=0)
+                plt.plot(plot_dt, sd, label=sp.__str__())
+            plt.legend(loc=2, prop={"size": 20})
+        elif mode == "sites":
+            for i, site in enumerate(self.structure):
+                sd = self.sq_disp_ions[i, :]
+                plt.plot(plot_dt, sd, label="%s - %d" % (site.specie.__str__(), i))
+            plt.legend(loc=2, prop={"size": 20})
+        elif mode == "mscd":
+            plt.plot(plot_dt, self.mscd, "r")
+            plt.legend(["Overall"], loc=2, prop={"size": 20})
+        else:
+            # Handle default / invalid mode case
+            plt.plot(plot_dt, self.msd, "k")
+            plt.plot(plot_dt, self.msd_components[:, 0], "r")
+            plt.plot(plot_dt, self.msd_components[:, 1], "g")
+            plt.plot(plot_dt, self.msd_components[:, 2], "b")
+            plt.legend(["Overall", "a", "b", "c"], loc=2, prop={"size": 20})
+
+        plt.xlabel("Timestep ({})".format(unit))
+        if mode == "mscd":
+            plt.ylabel("MSCD ($\\AA^2$)")
+        else:
+            plt.ylabel("MSD ($\\AA^2$)")
+        plt.tight_layout()
+        return plt
    
+
+
    [docs]    def plot_msd(self, mode="default"):
+        """
+        Plot the smoothed msd vs time graph. Useful for checking convergence.
+
+        Args:
+            mode (str): Can be "default" (the default, shows only the MSD for
+                the diffusing specie, and its components), "ions" (individual
+                square displacements of all ions), "species" (mean square
+                displacement by specie), or "mscd" (overall mean square charge
+                displacement for diffusing specie).
+        """
+        self.get_msd_plot(mode=mode).show()
    
+
+
    [docs]    def export_msdt(self, filename):
+        """
+        Writes MSD data to a csv file that can be easily plotted in other
+        software.
+
+        Args:
+            filename (str): Filename. Supported formats are csv and dat. If
+                the extension is csv, a csv file is written. Otherwise,
+                a dat format is assumed.
+        """
+        fmt = "csv" if filename.lower().endswith(".csv") else "dat"
+        delimiter = ", " if fmt == "csv" else " "
+        with open(filename, "wt") as f:
+            if fmt == "dat":
+                f.write("# ")
+            f.write(delimiter.join(["t", "MSD", "MSD_a", "MSD_b", "MSD_c", "MSCD"]))
+            f.write("\n")
+            for dt, msd, msdc, mscd in zip(self.dt, self.msd, self.msd_components, self.mscd):
+                f.write(delimiter.join(["%s" % v for v in [dt, msd] + list(msdc) + [mscd]]))
+                f.write("\n")
    
+
+
    [docs]    @classmethod
+    def from_structures(
+        cls, structures, specie, temperature, time_step, step_skip, initial_disp=None, initial_structure=None, **kwargs
+    ):
+        r"""
+        Convenient constructor that takes in a list of Structure objects to
+        perform diffusion analysis.
+
+        Args:
+            structures ([Structure]): list of Structure objects (must be
+                ordered in sequence of run). E.g., you may have performed
+                sequential VASP runs to obtain sufficient statistics.
+            specie (Element/Species): Species to calculate diffusivity for as a
+                String. E.g., "Li".
+            temperature (float): Temperature of the diffusion run in Kelvin.
+            time_step (int): Time step between measurements.
+            step_skip (int): Sampling frequency of the displacements (
+                time_step is multiplied by this number to get the real time
+                between measurements)
+            initial_disp (np.ndarray): Sometimes, you need to iteratively
+                compute estimates of the diffusivity. This supplies an
+                initial displacement that will be added on to the initial
+                displacements. Note that this makes sense only when
+                smoothed=False.
+            initial_structure (Structure): Like initial_disp, this is used
+                for iterative computations of estimates of the diffusivity. You
+                typically need to supply both variables. This stipulates the
+                initial structure from which the current set of displacements
+                are computed.
+            \\*\\*kwargs: kwargs supported by the :class:`DiffusionAnalyzer`_.
+                Examples include smoothed, min_obs, avg_nsteps.
+        """
+        p, l = [], []
+        for i, s in enumerate(structures):
+            if i == 0:
+                structure = s
+            p.append(np.array(s.frac_coords)[:, None])
+            l.append(s.lattice.matrix)
+        if initial_structure is not None:
+            p.insert(0, np.array(initial_structure.frac_coords)[:, None])
+            l.insert(0, initial_structure.lattice.matrix)
+        else:
+            p.insert(0, p[0])
+            l.insert(0, l[0])
+
+        p = np.concatenate(p, axis=1)
+        dp = p[:, 1:] - p[:, :-1]
+        dp = dp - np.round(dp)
+        f_disp = np.cumsum(dp, axis=1)
+        c_disp = []
+        for i in f_disp:
+            c_disp.append([np.dot(d, m) for d, m in zip(i, l[1:])])
+        disp = np.array(c_disp)
+
+        # If is NVT-AIMD, clear lattice data.
+        if np.array_equal(l[0], l[-1]):
+            l = np.array([l[0]])
+        else:
+            l = np.array(l)
+        if initial_disp is not None:
+            disp += initial_disp[:, None, :]
+
+        return cls(structure, disp, specie, temperature, time_step, step_skip=step_skip, lattices=l, **kwargs)
    
+
+
    [docs]    @classmethod
+    def from_vaspruns(cls, vaspruns, specie, initial_disp=None, initial_structure=None, **kwargs):
+        r"""
+        Convenient constructor that takes in a list of Vasprun objects to
+        perform diffusion analysis.
+
+        Args:
+            vaspruns ([Vasprun]): List of Vaspruns (must be ordered  in
+                sequence of MD simulation). E.g., you may have performed
+                sequential VASP runs to obtain sufficient statistics.
+            specie (Element/Species): Species to calculate diffusivity for as a
+                String. E.g., "Li".
+            initial_disp (np.ndarray): Sometimes, you need to iteratively
+                compute estimates of the diffusivity. This supplies an
+                initial displacement that will be added on to the initial
+                displacements. Note that this makes sense only when
+                smoothed=False.
+            initial_structure (Structure): Like initial_disp, this is used
+                for iterative computations of estimates of the diffusivity. You
+                typically need to supply both variables. This stipulates the
+                initial stricture from which the current set of displacements
+                are computed.
+            \\*\\*kwargs: kwargs supported by the :class:`DiffusionAnalyzer`_.
+                Examples include smoothed, min_obs, avg_nsteps.
+        """
+
+        def get_structures(vaspruns):
+            for i, vr in enumerate(vaspruns):
+                if i == 0:
+                    step_skip = vr.ionic_step_skip or 1
+                    final_structure = vr.initial_structure
+                    temperature = vr.parameters["TEEND"]
+                    time_step = vr.parameters["POTIM"]
+                    yield step_skip, temperature, time_step
+                # check that the runs are continuous
+                fdist = pbc_diff(vr.initial_structure.frac_coords, final_structure.frac_coords)
+                if np.any(fdist > 0.001):
+                    raise ValueError("initial and final structures do not " "match.")
+                final_structure = vr.final_structure
+
+                assert (vr.ionic_step_skip or 1) == step_skip
+                for s in vr.ionic_steps:
+                    yield s["structure"]
+
+        s = get_structures(vaspruns)
+        step_skip, temperature, time_step = next(s)
+
+        return cls.from_structures(
+            structures=list(s),
+            specie=specie,
+            temperature=temperature,
+            time_step=time_step,
+            step_skip=step_skip,
+            initial_disp=initial_disp,
+            initial_structure=initial_structure,
+            **kwargs,
+        )
    
+
+
    [docs]    @classmethod
+    def from_files(
+        cls, filepaths, specie, step_skip=10, ncores=None, initial_disp=None, initial_structure=None, **kwargs
+    ):
+        r"""
+        Convenient constructor that takes in a list of vasprun.xml paths to
+        perform diffusion analysis.
+
+        Args:
+            filepaths ([str]): List of paths to vasprun.xml files of runs. (
+                must be ordered in sequence of MD simulation). For example,
+                you may have done sequential VASP runs and they are in run1,
+                run2, run3, etc. You should then pass in
+                ["run1/vasprun.xml", "run2/vasprun.xml", ...].
+            specie (Element/Species): Species to calculate diffusivity for as a
+                String. E.g., "Li".
+            step_skip (int): Sampling frequency of the displacements (
+                time_step is multiplied by this number to get the real time
+                between measurements)
+            ncores (int): Numbers of cores to use for multiprocessing. Can
+                speed up vasprun parsing considerably. Defaults to None,
+                which means serial. It should be noted that if you want to
+                use multiprocessing, the number of ionic steps in all vasprun
+                .xml files should be a multiple of the ionic_step_skip.
+                Otherwise, inconsistent results may arise. Serial mode has no
+                such restrictions.
+            initial_disp (np.ndarray): Sometimes, you need to iteratively
+                compute estimates of the diffusivity. This supplies an
+                initial displacement that will be added on to the initial
+                displacements. Note that this makes sense only when
+                smoothed=False.
+            initial_structure (Structure): Like initial_disp, this is used
+                for iterative computations of estimates of the diffusivity. You
+                typically need to supply both variables. This stipulates the
+                initial structure from which the current set of displacements
+                are computed.
+            \\*\\*kwargs: kwargs supported by the :class:`DiffusionAnalyzer`_.
+                Examples include smoothed, min_obs, avg_nsteps.
+        """
+        if ncores is not None and len(filepaths) > 1:
+            p = multiprocessing.Pool(ncores)
+            vaspruns = p.imap(_get_vasprun, [(fp, step_skip) for fp in filepaths])
+            analyzer = cls.from_vaspruns(
+                vaspruns, specie=specie, initial_disp=initial_disp, initial_structure=initial_structure, **kwargs
+            )
+            p.close()
+            p.join()
+            return analyzer
+
+        def vr(filepaths):
+            offset = 0
+            for p in filepaths:
+                v = Vasprun(p, ionic_step_offset=offset, ionic_step_skip=step_skip)
+                yield v
+                # Recompute offset.
+                offset = (-(v.nionic_steps - offset)) % step_skip
+
+        return cls.from_vaspruns(
+            vr(filepaths), specie=specie, initial_disp=initial_disp, initial_structure=initial_structure, **kwargs
+        )
    
+
+
    [docs]    def as_dict(self):
+        """
+        Returns: MSONable dict
+        """
+        return {
+            "@module": self.__class__.__module__,
+            "@class": self.__class__.__name__,
+            "structure": self.structure.as_dict(),
+            "displacements": self.disp.tolist(),
+            "specie": self.specie,
+            "temperature": self.temperature,
+            "time_step": self.time_step,
+            "step_skip": self.step_skip,
+            "min_obs": self.min_obs,
+            "smoothed": self.smoothed,
+            "avg_nsteps": self.avg_nsteps,
+            "lattices": self.lattices.tolist(),
+        }
    
+
+
    [docs]    @classmethod
+    def from_dict(cls, d):
+        """
+        Args:
+            d (dict): Dict representation
+
+        Returns: DiffusionAnalyzer
+        """
+        structure = Structure.from_dict(d["structure"])
+        return cls(
+            structure,
+            np.array(d["displacements"]),
+            specie=d["specie"],
+            temperature=d["temperature"],
+            time_step=d["time_step"],
+            step_skip=d["step_skip"],
+            min_obs=d["min_obs"],
+            smoothed=d.get("smoothed", "max"),
+            avg_nsteps=d.get("avg_nsteps", 1000),
+            lattices=np.array(d.get("lattices", [d["structure"]["lattice"]["matrix"]])),
+        )
    
+
+
+
    [docs]def get_conversion_factor(structure, species, temperature):
+    """
+    Conversion factor to convert between cm^2/s diffusivity measurements and
+    mS/cm conductivity measurements based on number of atoms of diffusing
+    species. Note that the charge is based on the oxidation state of the
+    species (where available), or else the number of valence electrons
+    (usually a good guess, esp for main group ions).
+
+    Args:
+        structure (Structure): Input structure.
+        species (Element/Species): Diffusing species.
+        temperature (float): Temperature of the diffusion run in Kelvin.
+
+    Returns:
+        Conversion factor.
+        Conductivity (in mS/cm) = Conversion Factor * Diffusivity (in cm^2/s)
+    """
+    df_sp = get_el_sp(species)
+    if hasattr(df_sp, "oxi_state"):
+        z = df_sp.oxi_state
+    else:
+        z = df_sp.full_electronic_structure[-1][2]
+
+    n = structure.composition[species]
+
+    vol = structure.volume * 1e-24  # units cm^3
+    return 1000 * n / (vol * const.N_A) * z ** 2 * (const.N_A * const.e) ** 2 / (const.R * temperature)
    
+
+
+def _get_vasprun(args):
+    """
+    Internal method to support multiprocessing.
+    """
+    return Vasprun(args[0], ionic_step_skip=args[1], parse_dos=False, parse_eigen=False)
+
+
+
    [docs]def fit_arrhenius(temps, diffusivities):
+    """
+    Returns Ea, c, standard error of Ea from the Arrhenius fit:
+        D = c * exp(-Ea/kT)
+
+    Args:
+        temps ([float]): A sequence of temperatures. units: K
+        diffusivities ([float]): A sequence of diffusivities (e.g.,
+            from DiffusionAnalyzer.diffusivity). units: cm^2/s
+    """
+    t_1 = 1 / np.array(temps)
+    logd = np.log(diffusivities)
+    # Do a least squares regression of log(D) vs 1/T
+    a = np.array([t_1, np.ones(len(temps))]).T
+    w, res, _, _ = np.linalg.lstsq(a, logd, rcond=None)
+    w = np.array(w)
+    n = len(temps)
+    if n > 2:
+        std_Ea = (res[0] / (n - 2) / (n * np.var(t_1))) ** 0.5 * const.k / const.e
+    else:
+        std_Ea = None
+    return -w[0] * const.k / const.e, np.exp(w[1]), std_Ea
    
+
+
+
    [docs]def get_extrapolated_diffusivity(temps, diffusivities, new_temp):
+    """
+    Returns (Arrhenius) extrapolated diffusivity at new_temp
+
+    Args:
+        temps ([float]): A sequence of temperatures. units: K
+        diffusivities ([float]): A sequence of diffusivities (e.g.,
+            from DiffusionAnalyzer.diffusivity). units: cm^2/s
+        new_temp (float): desired temperature. units: K
+
+    Returns:
+        (float) Diffusivity at extrapolated temp in mS/cm.
+    """
+    Ea, c, _ = fit_arrhenius(temps, diffusivities)
+    return c * np.exp(-Ea / (const.k / const.e * new_temp))
    
+
+
+
    [docs]def get_extrapolated_conductivity(temps, diffusivities, new_temp, structure, species):
+    """
+    Returns extrapolated mS/cm conductivity.
+
+    Args:
+        temps ([float]): A sequence of temperatures. units: K
+        diffusivities ([float]): A sequence of diffusivities (e.g.,
+            from DiffusionAnalyzer.diffusivity). units: cm^2/s
+        new_temp (float): desired temperature. units: K
+        structure (structure): Structure used for the diffusivity calculation
+        species (string/Species): conducting species
+
+    Returns:
+        (float) Conductivity at extrapolated temp in mS/cm.
+    """
+    return get_extrapolated_diffusivity(temps, diffusivities, new_temp) * get_conversion_factor(
+        structure, species, new_temp
+    )
    
+
+
+
    [docs]def get_arrhenius_plot(temps, diffusivities, diffusivity_errors=None, **kwargs):
+    r"""
+    Returns an Arrhenius plot.
+
+    Args:
+        temps ([float]): A sequence of temperatures.
+        diffusivities ([float]): A sequence of diffusivities (e.g.,
+            from DiffusionAnalyzer.diffusivity).
+        diffusivity_errors ([float]): A sequence of errors for the
+            diffusivities. If None, no error bar is plotted.
+        \\*\\*kwargs:
+            Any keyword args supported by matplotlib.pyplot.plot.
+
+    Returns:
+        A matplotlib.pyplot object. Do plt.show() to show the plot.
+    """
+    Ea, c, _ = fit_arrhenius(temps, diffusivities)
+
+    from pymatgen.util.plotting import pretty_plot
+
+    plt = pretty_plot(12, 8)
+
+    # log10 of the arrhenius fit
+    arr = c * np.exp(-Ea / (const.k / const.e * np.array(temps)))
+
+    t_1 = 1000 / np.array(temps)
+
+    plt.plot(t_1, diffusivities, "ko", t_1, arr, "k--", markersize=10, **kwargs)
+    if diffusivity_errors is not None:
+        n = len(diffusivity_errors)
+        plt.errorbar(
+            t_1[0:n],
+            diffusivities[0:n],
+            yerr=diffusivity_errors,
+            fmt="ko",
+            ecolor="k",
+            capthick=2,
+            linewidth=2,
+        )
+    ax = plt.axes()
+    ax.set_yscale("log")
+    plt.text(
+        0.6,
+        0.85,
+        "E$_a$ = {:.0f} meV".format(Ea * 1000),
+        fontsize=30,
+        transform=plt.axes().transAxes,
+    )
+    plt.ylabel("D (cm$^2$/s)")
+    plt.xlabel("1000/T (K$^{-1}$)")
+    plt.tight_layout()
+    return plt
    
+
    + +
    +
    +
    +
    + +
    +
    + +
    + © Copyright 2016, Materials Virtual Lab. +
    +
    This page uses +Google Analytics to collect statistics. You can disable it by blocking +the JavaScript coming from www.google-analytics.com. + +
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    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/tests/test_analyzer.html b/docs/_modules/pymatgen/analysis/diffusion/tests/test_analyzer.html new file mode 100644 index 00000000..ed431d2c --- /dev/null +++ b/docs/_modules/pymatgen/analysis/diffusion/tests/test_analyzer.html @@ -0,0 +1,589 @@ + + + + + + + + pymatgen.analysis.diffusion.tests.test_analyzer — pymatgen-diffusion 2021.3.6 documentation + + + + + + + + + + + + + + + + + + + +
    +
    +
    +
    + +

    Source code for pymatgen.analysis.diffusion.tests.test_analyzer

    +# coding: utf-8
+# Copyright (c) Pymatgen Development Team.
+# Distributed under the terms of the MIT License.
+
+
+import csv
+import json
+import os
+import random
+import unittest
+
+import numpy as np
+import scipy.constants as const
+
+from pymatgen.analysis.diffusion.analyzer import (
+    DiffusionAnalyzer,
+    fit_arrhenius,
+    get_conversion_factor,
+)
+from pymatgen.core.lattice import Lattice
+from pymatgen.core.structure import Structure
+from pymatgen.util.testing import PymatgenTest
+
+module_dir = os.path.dirname(os.path.abspath(__file__))
+
+
+
    [docs]class FuncTest(PymatgenTest):
+
    [docs]    def test_get_conversion_factor(self):
+        s = PymatgenTest.get_structure("LiFePO4")
+        # large tolerance because scipy constants changed between 0.16.1 and 0.17
+        self.assertAlmostEqual(41370704.343540139, get_conversion_factor(s, "Li", 600), delta=20)
    
+
+
    [docs]    def test_fit_arrhenius(self):
+        Ea = 0.5
+        k = const.k / const.e
+        c = 12
+        temps = np.array([300, 1000, 500])
+        diffusivities = c * np.exp(-Ea / (k * temps))
+        diffusivities *= np.array([1.00601834013, 1.00803236262, 0.98609720824])
+        r = fit_arrhenius(temps, diffusivities)
+        self.assertAlmostEqual(r[0], Ea)
+        self.assertAlmostEqual(r[1], c)
+        self.assertAlmostEqual(r[2], 0.000895566)
+
+        # when not enough values for error estimate
+        r2 = fit_arrhenius([1, 2], [10, 10])
+        self.assertAlmostEqual(r2[0], 0)
+        self.assertAlmostEqual(r2[1], 10)
+        self.assertEqual(r2[2], None)
    
+
+
+
    [docs]class DiffusionAnalyzerTest(PymatgenTest):
+
    [docs]    def test_init(self):
+        # Diffusion vasprun.xmls are rather large. We are only going to use a
+        # very small preprocessed run for testing. Note that the results are
+        # unreliable for short runs.
+        with open(os.path.join(module_dir, "DiffusionAnalyzer.json")) as f:
+            dd = json.load(f)
+
+            d = DiffusionAnalyzer.from_dict(dd)
+            # large tolerance because scipy constants changed between 0.16.1 and 0.17
+            self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4)
+            self.assertAlmostEqual(d.chg_conductivity, 232.8278799754324, 4)
+            self.assertAlmostEqual(d.diffusivity, 1.16083658794e-06, 7)
+            self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7)
+            self.assertAlmostEqual(d.conductivity_std_dev, 0.0097244677795984488, 7)
+            self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7)
+            self.assertAlmostEqual(d.chg_diffusivity_std_dev, 7.20911399729e-10, 5)
+            self.assertAlmostEqual(d.haven_ratio, 0.31854161048867402, 7)
+            self.assertArrayAlmostEqual(d.conductivity_components, [45.7903694, 26.1651956, 150.5406140], 3)
+            self.assertArrayAlmostEqual(
+                d.diffusivity_components,
+                [7.49601236e-07, 4.90254273e-07, 2.24649255e-06],
+            )
+            self.assertArrayAlmostEqual(d.conductivity_components_std_dev, [0.0063566, 0.0180854, 0.0217918])
+            self.assertArrayAlmostEqual(
+                d.diffusivity_components_std_dev,
+                [8.9465670e-09, 2.4931224e-08, 2.2636384e-08],
+            )
+            self.assertArrayAlmostEqual(d.mscd[0:4], [0.69131064, 0.71794072, 0.74315283, 0.76703961])
+
+            self.assertArrayAlmostEqual(
+                d.max_ion_displacements,
+                [
+                    1.4620659693989553,
+                    1.2787303484445025,
+                    3.419618540097756,
+                    2.340104469126246,
+                    2.6080973517594233,
+                    1.3928579365672844,
+                    1.3561505956708932,
+                    1.6699242923686253,
+                    1.0352389639563648,
+                    1.1662520093955808,
+                    1.2322019205885841,
+                    0.8094210554832534,
+                    1.9917808504954169,
+                    1.2684148391206396,
+                    2.392633794162402,
+                    2.566313049232671,
+                    1.3175030435622759,
+                    1.4628945430952793,
+                    1.0984921286753002,
+                    1.2864482076554093,
+                    0.655567027815413,
+                    0.5986961164605746,
+                    0.5639091444309045,
+                    0.6166004192954059,
+                    0.5997911580422605,
+                    0.4374606277579815,
+                    1.1865683960470783,
+                    0.9017064371676591,
+                    0.6644840367853767,
+                    1.0346375380664645,
+                    0.6177630142863979,
+                    0.7952002051914302,
+                    0.7342686123054011,
+                    0.7858047956905577,
+                    0.5570732369065661,
+                    1.0942937746885417,
+                    0.6509372395308788,
+                    1.0876687380413455,
+                    0.7058162184725,
+                    0.8298306317598585,
+                    0.7813913747621343,
+                    0.7337655232056153,
+                    0.9057161616236746,
+                    0.5979093093186919,
+                    0.6830333586985015,
+                    0.7926500894084628,
+                    0.6765180009988608,
+                    0.8555866032968998,
+                    0.713087091642237,
+                    0.7621007695790749,
+                ],
+            )
+
+            self.assertEqual(d.sq_disp_ions.shape, (50, 206))
+            self.assertEqual(d.lattices.shape, (1, 3, 3))
+            self.assertEqual(d.mscd.shape, (206,))
+            self.assertEqual(d.mscd.shape, d.msd.shape)
+            self.assertAlmostEqual(d.max_framework_displacement, 1.18656839605)
+
+            ss = list(d.get_drift_corrected_structures(10, 1000, 20))
+            self.assertEqual(len(ss), 50)
+            n = random.randint(0, 49)
+            n_orig = n * 20 + 10
+            self.assertArrayAlmostEqual(
+                ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :],
+                d.disp[:, n_orig, :],
+            )
+
+            d = DiffusionAnalyzer.from_dict(d.as_dict())
+            self.assertIsInstance(d, DiffusionAnalyzer)
+
+            # Ensure summary dict is json serializable.
+            json.dumps(d.get_summary_dict(include_msd_t=True))
+
+            d = DiffusionAnalyzer(
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed="max",
+            )
+            self.assertAlmostEqual(d.conductivity, 74.165372613735684, 4)
+            self.assertAlmostEqual(d.diffusivity, 1.14606446822e-06, 7)
+            self.assertAlmostEqual(d.haven_ratio, 0.318541610489, 6)
+            self.assertAlmostEqual(d.chg_conductivity, 232.8278799754324, 4)
+            self.assertAlmostEqual(d.chg_diffusivity, 3.64565578208e-06, 7)
+
+            d = DiffusionAnalyzer(
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed=False,
+            )
+            self.assertAlmostEqual(d.conductivity, 27.20479170406027, 4)
+            self.assertAlmostEqual(d.diffusivity, 4.25976905436e-07, 7)
+            self.assertAlmostEqual(d.chg_diffusivity, 1.6666666666666667e-17, 3)
+
+            d = DiffusionAnalyzer(
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed="constant",
+                avg_nsteps=100,
+            )
+
+            self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4)
+            self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7)
+            self.assertAlmostEqual(d.chg_conductivity, 1.06440821953e-09, 4)
+
+            # Can't average over 2000 steps because this is a 1000-step run.
+            self.assertRaises(
+                ValueError,
+                DiffusionAnalyzer,
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed="constant",
+                avg_nsteps=2000,
+            )
+
+            d = DiffusionAnalyzer.from_structures(
+                list(d.get_drift_corrected_structures()),
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed=d.smoothed,
+                avg_nsteps=100,
+            )
+            self.assertAlmostEqual(d.conductivity, 47.404056230438741, 4)
+            self.assertAlmostEqual(d.diffusivity, 7.4226016496716148e-07, 7)
+
+            d.export_msdt("test.csv")
+            with open("test.csv") as f:
+                data = []
+                for row in csv.reader(f):
+                    if row:
+                        data.append(row)
+            data.pop(0)
+            data = np.array(data, dtype=np.float64)
+            self.assertArrayAlmostEqual(data[:, 1], d.msd)
+            self.assertArrayAlmostEqual(data[:, -1], d.mscd)
+            os.remove("test.csv")
    
+
+
    [docs]    def test_init_npt(self):
+        # Diffusion vasprun.xmls are rather large. We are only going to use a
+        # very small preprocessed run for testing. Note that the results are
+        # unreliable for short runs.
+        with open(os.path.join(module_dir, "DiffusionAnalyzer_NPT.json"), "r") as f:
+            dd = json.load(f)
+            d = DiffusionAnalyzer.from_dict(dd)
+            # large tolerance because scipy constants changed between 0.16.1 and 0.17
+            self.assertAlmostEqual(d.conductivity, 499.1504129387108, 4)
+            self.assertAlmostEqual(d.chg_conductivity, 1219.5959181678043, 4)
+            self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7)
+            self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 6)
+            self.assertAlmostEqual(d.conductivity_std_dev, 0.10368477696021029, 7)
+            self.assertAlmostEqual(d.diffusivity_std_dev, 9.1013023085561779e-09, 7)
+            self.assertAlmostEqual(d.chg_diffusivity_std_dev, 1.20834853646e-08, 6)
+            self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7)
+            self.assertArrayAlmostEqual(d.conductivity_components, [455.178101, 602.252644, 440.0210014], 3)
+            self.assertArrayAlmostEqual(
+                d.diffusivity_components,
+                [7.66242570e-06, 1.01382648e-05, 7.40727250e-06],
+            )
+            self.assertArrayAlmostEqual(d.conductivity_components_std_dev, [0.1196577, 0.0973347, 0.1525400])
+            self.assertArrayAlmostEqual(
+                d.diffusivity_components_std_dev,
+                [2.0143072e-09, 1.6385239e-09, 2.5678445e-09],
+            )
+
+            self.assertArrayAlmostEqual(
+                d.max_ion_displacements,
+                [
+                    1.13147881,
+                    0.79899554,
+                    1.04153733,
+                    0.96061850,
+                    0.83039864,
+                    0.70246715,
+                    0.61365911,
+                    0.67965179,
+                    1.91973907,
+                    1.69127386,
+                    1.60568746,
+                    1.35587641,
+                    1.03280378,
+                    0.99202692,
+                    2.03359655,
+                    1.03760269,
+                    1.40228350,
+                    1.36315080,
+                    1.27414979,
+                    1.26742035,
+                    0.88199589,
+                    0.97700804,
+                    1.11323184,
+                    1.00139511,
+                    2.94164403,
+                    0.89438909,
+                    1.41508334,
+                    1.23660358,
+                    0.39322939,
+                    0.54264064,
+                    1.25291806,
+                    0.62869809,
+                    0.40846708,
+                    1.43415505,
+                    0.88891241,
+                    0.56259128,
+                    0.81712740,
+                    0.52700441,
+                    0.51011733,
+                    0.55557882,
+                    0.49131002,
+                    0.66740277,
+                    0.57798671,
+                    0.63521025,
+                    0.50277142,
+                    0.52878021,
+                    0.67803443,
+                    0.81161269,
+                    0.46486345,
+                    0.47132761,
+                    0.74301293,
+                    0.79285519,
+                    0.48789600,
+                    0.61776836,
+                    0.60695847,
+                    0.67767756,
+                    0.70972268,
+                    1.08232442,
+                    0.87871177,
+                    0.84674206,
+                    0.45694693,
+                    0.60417985,
+                    0.61652272,
+                    0.66444583,
+                    0.52211986,
+                    0.56544134,
+                    0.43311443,
+                    0.43027547,
+                    1.10730439,
+                    0.59829728,
+                    0.52270635,
+                    0.72327608,
+                    1.02919775,
+                    0.84423208,
+                    0.61694764,
+                    0.72795752,
+                    0.72957755,
+                    0.55491631,
+                    0.68507454,
+                    0.76745343,
+                    0.96346584,
+                    0.66672645,
+                    1.06810107,
+                    0.65705843,
+                ],
+            )
+
+            self.assertEqual(d.sq_disp_ions.shape, (84, 217))
+            self.assertEqual(d.lattices.shape, (1001, 3, 3))
+            self.assertEqual(d.mscd.shape, (217,))
+            self.assertEqual(d.mscd.shape, d.msd.shape)
+
+            self.assertAlmostEqual(d.max_framework_displacement, 1.43415505156)
+
+            ss = list(d.get_drift_corrected_structures(10, 1000, 20))
+            self.assertEqual(len(ss), 50)
+            n = random.randint(0, 49)
+            n_orig = n * 20 + 10
+            self.assertArrayAlmostEqual(
+                ss[n].cart_coords - d.structure.cart_coords + d.drift[:, n_orig, :],
+                d.disp[:, n_orig, :],
+            )
+
+            d = DiffusionAnalyzer.from_dict(d.as_dict())
+            self.assertIsInstance(d, DiffusionAnalyzer)
+
+            # Ensure summary dict is json serializable.
+            json.dumps(d.get_summary_dict(include_msd_t=True))
+
+            d = DiffusionAnalyzer(
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed="max",
+            )
+            self.assertAlmostEqual(d.conductivity, 499.1504129387108, 4)
+            self.assertAlmostEqual(d.diffusivity, 8.40265434771e-06, 7)
+            self.assertAlmostEqual(d.haven_ratio, 0.409275240679, 7)
+            self.assertAlmostEqual(d.chg_diffusivity, 2.05305709033e-05, 7)
+
+            d = DiffusionAnalyzer(
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed=False,
+            )
+            self.assertAlmostEqual(d.conductivity, 406.5964019770787, 4)
+            self.assertAlmostEqual(d.diffusivity, 6.8446082e-06, 7)
+            self.assertAlmostEqual(d.chg_diffusivity, 1.03585877962e-05, 6)
+            self.assertAlmostEqual(d.haven_ratio, 0.6607665413, 6)
+
+            d = DiffusionAnalyzer(
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed="constant",
+                avg_nsteps=100,
+            )
+
+            self.assertAlmostEqual(d.conductivity, 425.77884571149525, 4)
+            self.assertAlmostEqual(d.diffusivity, 7.167523809142514e-06, 7)
+            self.assertAlmostEqual(d.chg_diffusivity, 9.33480892187e-06, 6)
+            self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 6)
+            self.assertAlmostEqual(d.chg_conductivity, 554.5240271992852, 6)
+
+            # Can't average over 2000 steps because this is a 1000-step run.
+            self.assertRaises(
+                ValueError,
+                DiffusionAnalyzer,
+                d.structure,
+                d.disp,
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed="constant",
+                avg_nsteps=2000,
+            )
+
+            d = DiffusionAnalyzer.from_structures(
+                list(d.get_drift_corrected_structures()),
+                d.specie,
+                d.temperature,
+                d.time_step,
+                d.step_skip,
+                smoothed=d.smoothed,
+                avg_nsteps=100,
+            )
+            self.assertAlmostEqual(d.conductivity, 425.7788457114952, 4)
+            self.assertAlmostEqual(d.diffusivity, 7.1675238091425148e-06, 7)
+            self.assertAlmostEqual(d.haven_ratio, 0.767827586952, 7)
+            self.assertAlmostEqual(d.chg_conductivity, 554.5240271992852, 6)
+
+            d.export_msdt("test.csv")
+            with open("test.csv") as f:
+                data = []
+                for row in csv.reader(f):
+                    if row:
+                        data.append(row)
+            data.pop(0)
+            data = np.array(data, dtype=np.float64)
+            self.assertArrayAlmostEqual(data[:, 1], d.msd)
+            self.assertArrayAlmostEqual(data[:, -1], d.mscd)
+            os.remove("test.csv")
    
+
+
    [docs]    def test_from_structure_NPT(self):
+
+        coords1 = np.array([[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]])
+        coords2 = np.array([[0.0, 0.0, 0.0], [0.6, 0.6, 0.6]])
+        coords3 = np.array([[0.0, 0.0, 0.0], [0.7, 0.7, 0.7]])
+        lattice1 = Lattice.from_parameters(a=2.0, b=2.0, c=2.0, alpha=90, beta=90, gamma=90)
+        lattice2 = Lattice.from_parameters(a=2.1, b=2.1, c=2.1, alpha=90, beta=90, gamma=90)
+        lattice3 = Lattice.from_parameters(a=2.0, b=2.0, c=2.0, alpha=90, beta=90, gamma=90)
+        s1 = Structure(coords=coords1, lattice=lattice1, species=["F", "Li"])
+        s2 = Structure(coords=coords2, lattice=lattice2, species=["F", "Li"])
+        s3 = Structure(coords=coords3, lattice=lattice3, species=["F", "Li"])
+        structures = [s1, s2, s3]
+        d = DiffusionAnalyzer.from_structures(
+            structures,
+            specie="Li",
+            temperature=500.0,
+            time_step=2.0,
+            step_skip=1,
+            smoothed=None,
+        )
+        self.assertArrayAlmostEqual(
+            d.disp[1],
+            np.array([[0.0, 0.0, 0.0], [0.21, 0.21, 0.21], [0.40, 0.40, 0.40]]),
+        )
    
+
+
+if __name__ == "__main__":
+    unittest.main()
+
    + +
    +
    +
    +
    + +
    +
    + +
    + © Copyright 2016, Materials Virtual Lab. +
    +
    This page uses +Google Analytics to collect statistics. You can disable it by blocking +the JavaScript coming from www.google-analytics.com. + +
    + + + \ No newline at end of file diff --git a/docs/_modules/pymatgen/analysis/diffusion/utils/parse_entries.html b/docs/_modules/pymatgen/analysis/diffusion/utils/parse_entries.html index 84b3c4b5..121177f1 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/utils/parse_entries.html +++ b/docs/_modules/pymatgen/analysis/diffusion/utils/parse_entries.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.utils.parse_entries — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.utils.parse_entries — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • diff --git a/docs/_modules/pymatgen/analysis/diffusion/utils/supercells.html b/docs/_modules/pymatgen/analysis/diffusion/utils/supercells.html index ff4211ef..5e664e2c 100644 --- a/docs/_modules/pymatgen/analysis/diffusion/utils/supercells.html +++ b/docs/_modules/pymatgen/analysis/diffusion/utils/supercells.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.utils.supercells — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.utils.supercells — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • diff --git a/docs/_sources/pymatgen.analysis.diffusion.analyzer.rst.txt b/docs/_sources/pymatgen.analysis.diffusion.analyzer.rst.txt new file mode 100644 index 00000000..8156b21f --- /dev/null +++ b/docs/_sources/pymatgen.analysis.diffusion.analyzer.rst.txt @@ -0,0 +1,7 @@ +pymatgen.analysis.diffusion.analyzer module +=========================================== + +.. automodule:: pymatgen.analysis.diffusion.analyzer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_sources/pymatgen.analysis.diffusion.rst.txt b/docs/_sources/pymatgen.analysis.diffusion.rst.txt index 0b7a7582..677e4db1 100644 --- a/docs/_sources/pymatgen.analysis.diffusion.rst.txt +++ b/docs/_sources/pymatgen.analysis.diffusion.rst.txt @@ -11,6 +11,14 @@ Subpackages pymatgen.analysis.diffusion.neb pymatgen.analysis.diffusion.utils +Submodules +---------- + +.. toctree:: + :maxdepth: 7 + + pymatgen.analysis.diffusion.analyzer + Module contents --------------- diff --git a/docs/_sources/pymatgen.analysis.diffusion.tests.test_analyzer.rst.txt b/docs/_sources/pymatgen.analysis.diffusion.tests.test_analyzer.rst.txt new file mode 100644 index 00000000..7a235fcf --- /dev/null +++ b/docs/_sources/pymatgen.analysis.diffusion.tests.test_analyzer.rst.txt @@ -0,0 +1,7 @@ +pymatgen.analysis.diffusion.tests.test\_analyzer module +======================================================= + +.. automodule:: pymatgen.analysis.diffusion.tests.test_analyzer + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index a52b8425..eafa0f47 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '2021.3.5', + VERSION: '2021.3.6', LANGUAGE: 'None', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/_themes/README.html b/docs/_themes/README.html index e87c52bd..ef0057a5 100644 --- a/docs/_themes/README.html +++ b/docs/_themes/README.html @@ -5,7 +5,7 @@ - krTheme Sphinx Style — pymatgen-diffusion 2021.3.5 documentation + krTheme Sphinx Style — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    Navigation

  • modules |
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  • pymatgen-diffusion 2021.3.5 documentation »
    • +
  • pymatgen-diffusion 2021.3.6 documentation »
  • Index
    • @@ -81,6 +81,8 @@

    A

      +
    • as_dict() (DiffusionAnalyzer method) +
    • assign_cost_to_graph() (MigrationGraph method)
    • atom_dist() (EvolutionAnalyzer static method) @@ -111,10 +113,14 @@

      C

      D

      @@ -130,6 +136,8 @@

      E

      + - + - + + + + + +
      +
    • get_conversion_factor() (in module pymatgen.analysis.diffusion.analyzer) +
    • get_coordination_number() (RadialDistributionFunctionFast method)
    • get_df() (EvolutionAnalyzer method) +
      • +
    • get_drift_corrected_structures() (DiffusionAnalyzer method)
    • get_endpoint_dist() (in module pymatgen.analysis.diffusion.neb.io)
    • get_endpoints_from_index() (in module pymatgen.analysis.diffusion.neb.io) +
      • +
    • get_extrapolated_conductivity() (in module pymatgen.analysis.diffusion.analyzer) +
      • +
    • get_extrapolated_diffusivity() (in module pymatgen.analysis.diffusion.analyzer) +
      • +
    • get_framework_rms_plot() (DiffusionAnalyzer method)
    • get_full_structure() (ProbabilityDensityAnalysis method)
      • @@ -199,12 +231,14 @@

      G

    • get_least_chg_path() (ChargeBarrierGraph method)
      • +
    • test_filter_and_merge() (ParseEntriesTest method) +
      • +
    • test_fit_arrhenius() (FuncTest method) +
      • +
    • test_from_structure_NPT() (DiffusionAnalyzerTest method)
    • test_generate_stable_sites() (ProbabilityDensityTest method)
    • test_get_all_sym_sites() (ParseEntriesTest method) +
      • +
    • test_get_conversion_factor() (FuncTest method)
    • test_get_df() (EvolutionAnalyzerTest method)
      • @@ -698,20 +760,24 @@

      T

    • test_idpp_from_ep() (IDPPSolverTest method)
      • +       		diff --git a/docs/modules.html b/docs/modules.html index bdc9aeb6..6e7288c5 100644 --- a/docs/modules.html +++ b/docs/modules.html @@ -5,7 +5,7 @@ - pymatgen — pymatgen-diffusion 2021.3.5 documentation + pymatgen — pymatgen-diffusion 2021.3.6 documentation @@ -44,7 +44,7 @@

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      • diff --git a/docs/objects.inv b/docs/objects.inv index a7d6e6766373364f08f60906848402e54635200a..840db38007e3855f5ce8e1341efc1e90a95ede35 100644 GIT binary patch delta 2785 zcmV<73Lf>(6v`HmO#wEMPCS24m)j^3!0-Jj{NC<0-p%Z7Zj*FpCvPXS{iZXy@@NUm zY$VVEa@zap7lb5Bw(O?RALzqO5}Traf}$uWgv(=rRYuqrGn^kKmD`l=c9o=@ZOY@% z1y8G-Z2qqQezn|ouZsLHw&c6NZr}qpUU==`l*T9^K?bs8Br)9ObJd?8H^yu&m<3{Uha97r${PZhS2}` z;bQ=k(^|v#dakc~ti{;asE=39>U7 zSSdlDD@8(}1Rhl5P{wZYK@X|SjwQs~B4N2$H?V{DS=%JxLHd7gWnmJL?qGAF7J$!# zM(7WmQk>sYX`e@b_ojAYBquvlFa zw>*9KCLmGW=?!L3;6cnD+$1k(?AKG4cR>)t_b+%QG?bm>^Xfq7{?im|@{VbGx`z$n z(*v_oOb>kLAUj}&_WVG+jiaq#l7)1YJREplhr#W3!4!X^8KTY0)J}^EO|Yk#04I*E zc`3G%s6Wd{nxq*Vm`WniH=I}GK?p7o-I;wT-4Ecqh;o~r5M!@-kD{Dyx%bURsR`* zm+z5`1Q35KHIZM?;vcD84+TKIG2uxsHE*%1lW-EafjlpSPXIJOL7``n7+RvlRLGDJ zCZB+eVbBDT_S@h<9SO3DW2|s+Cgb#@Xd$uY@M*la*YTVg_11&sed>|^nj7LDD?s#5 zM>tJZt!aSfsaEK|{Rlio4b=MU+hVDmaRO-5xfFjl8RkN!0hBjqVv~ zTfv0_IAB?diUm+#2S9@EOdeYZk|>e}ZI1f%p1=k9!NqqZ3JLUYb8_f`7*M;ciUNyc zpfOno8M*&_61e0! zHp72fGo*T4(?gS0Tv>fnW>M{oJUfRex-2L{G3D!X#U_u13S5?em}pFRvi9xAJr)`H zpbeT5@lV~`Pb#qN?ulGz#x)lM4}&5mo_b! z+Vnf3Y{x_4exaGDw-rO$+7M!k)3jcZBgKD_qy^2fuuT_7Bk9$Iq#4uBSC0gZn6)hz z0h-L$oLM;o$S$+hbvRW2M9*ZKnR;n2NZ$;3a zm$5WcJ>NWINuq13n#}ByW0pk4l=B4V+EpD)lj)f|JBFgedBG)QMKiPhWKWXss7il~ zL5HKL&wA2CPqn-%pN}CylxYcjmQE5q(i_VShjVhvXt?tg4om-GTPgXeN>;<59JF?$ z9K2f7B457!_VM%Y4<8;_`p79$Peeh2=2j_C+p=2fbv;W<^E_SB63}H6)HOCsSxR0O z)N1T$XOzH}_a`y1sq{q6t29(U)}w#^=u-#b+Lz}Hla+UC1Xz4BKgSPYoG5OF`>Hgb z?;jtZcz#&5D^5zc%}S^l17V)LwgVllDs&+2_GJJHQL!~OchTOjHlA6j@B3=)qBHTy z$t*$=9wR3#1MXSOsopvw@V1E;f(~POsw3Hy{^z?{TfpX{b7}arbWuYcLY9Au7C3KT z=RkrSfRphRGC!ozMv4YT2i!<@dw%O+_0A?v%2(W|2W(2W>bl$nhny32B3%X%_^Loi z((Y{!@BPc5~A8>QhQS(Jd1x2`T@-QQ@ZkGPg#a`R7mxv@W;dRw?f}bJw+G{4Bo;{?7}~~_-ptuPt+KB+sR?5Tl45b<*tUD#kc$=@?s6J3mtn)81P@#0yxl$K>|c6K6bPUS~H*w4vFF&55&rY-IlZy-$^_ z4XUuH1CNHc2|f0_e4EthfRpg#(?cjIOHUMhsl0%Ys#UH7lL{OKt3}YW zpOg3@oCT0=n7)4k$sow{cWBqD@#vnbRXv)2c^nRKA)FZvxFifg*hCDuBElwlwWlWW z6W7=Jn4f&qE)@3Cc~IoB>G`ct4=3`tse4n|{FZ^#o)kp>?T?3F?!P>2q)LBId9r!; zelz^0me_n3U(JV`r*co(%jQWPb8?sLG0O3cmHA@5zpdPQaM`cE`N$S-oA<6) z%`5dk9(C8Sp_;auzMih<28uQe>jp1zLN1MHRJ!Z;K{Y3t8}*$`<#x>{iyGI_t4AyP zjx+M=ouGfN3hUcGU(;%9bXDE^U1t!**>7J#1R9%i|Mh4HSN}(8oJ%_snagn@H0{rz zIF)p!82^bp2-o!=G4qu5VfX3YD?dNvBj|$+?f;Qq#DNO=V0QP>d$JBL6!wyIruISw z@`0e*jciw*{+^XnNf!!p@1Y|=d+p)o)q4~jW`uvyI}rgOqbDfAQjRTT?)(6-H3vi- z%DL+AogD#ioMl-6<(;`MUSxpajlQS~7juQZ^>!-&`?X`dQ!4Oy6qspV9%F)ucmVp@ z?)7A$pQsD?SeDTd`C4uPD&&G`(-Dqfr)6q!=im92t5?I1A z(-LR_xy$?O7lfX+>{6l!)S>p@=XgIE|$@f)wva>PNji@FIe=IwTQZt>IpDYy2mQiX5}ZVpLYMf;kWB-bS2@SRz681_LW4 z=&n%22a4xTH4J6!79Vw&N^D<3yiVemi!~kFYn#dC&;` zj$?|`2P$oO^mkA7WDglM8vQdUwj%~**!+Q5k?o*ebXFSjnscH3qN+*!K$Ihw%t=I{ zbM34crGpsnQJ!+;590|-<;^;cavLj!WTg!iXT-0mHY5vE=($PB@_*#IP&AkYd>9?h zrim#3AV6!qfi-^@Pw!h;D=kSER8@sjPfpSCrTiqzz8>pLX*9TJG#bgKnITwR6E|Tv}jO zis_E;9Arn#(19O`vvJfJOtO$BlgA@ZOEuNR?Eb15-&P`ij$nJPN@DqIhb#hK(H71nwQu7w8auZGh7m(+LaB+aQrqGwLln%g5MZ`)h89e=ZNf$ZqsJ zrPG3DCzHNBt7F!NP=@oo@>RwvI~Sg;eS3evA|ZdDun?4pe+ir)##-5Ro|!=GsYrs& zR>C1E6OV5Ab>L)?Wmuf_WEQD_&~ly^sagFeaiSgM?^LHYH5l6^Z5_nzxi8!=G!dn% z?$g%#5L+C_{sF-6$0jPP0B?-4}D9 zmj-{+l%b3oXIKYgTJVLPWuZ7rPr#kaWr5@=%Yv(=g3y3()zMU$p1G5gFFKrN+(zajpMIEGe{>+xH&jH%p#4#l=O=Vfo(Nv# zyOU24Wop9iPfMbEdS$u(aN1$nPO!aB?gxKM|FE?b{a8e+VNec=-6#h~Yg**X*Wd0x z|NHUNBa5FoW$J|}h|^pv1!`MXi?JfY;@p&{SqKC)*#vc!%~BSV*9Em2d)gZ%u<*VW z1Di@m)ErNxx>(Qpqo)R9Y+If)Ojh2+2r&O-eva?L45GN|?yJ&#et3R<;puVJrf7ea zZmUSB>H~gCUfO~7RuwvuNO9qbLKJLG&5gJBi}hz#YWuEQ8+1nPL@j+)KtDeBhmjJN z0M{($RBs0&@Un>$f_7thtUcM7{`Jko7O?r~TpB(tUF1-@kh!86PU|C*NRT6NGQL9Q zhBR17!N6#T8_8zPZyl`8e*dKXiW`6VfKBOEUYCpDkh2$~NS8qb9>4QR+MJ+tUcdB8 zt{opzjlu5+diI^1#Y@v)-MZ&lo|4z#I^$K!5c}hG*fRY*yI0;zF?Em2cLAJs!n{M9 z^-Y9XY;PZe?;`jvfH{+_Gmj3GC1_8DRPPFZ*gbzQ^qsAz0G)w8{FC6>quqZB4tzE` zBMpB>ItMOgvO0+!%Jsm`Y~UZ9|J5(d3pGX_xQVoo;af+CwVfxqEGM1TNJ5`Ehe{Dl z90c5m>Pb!s!-u!0CiET17RxA(ZN26Vxx8p`s45-UOKwn?n+yPq?x2H_SwC)%q7$LX8gKn-GCjisy%r%Hd;231&;jz`5? zxgH0ezfWp(#F2mU=`IwMrO^JQ9PKuhsX;E1mB_J>r1#{|cx^q|sk+M=rc4J{ip1t1 zhh6J99!lIr(HM_kBp*dEm>ep;ZB>FDQA+I?I~oU-JZ$52Rf1$lQI? zvE_9zWRVjQkrdopTS0$^&eIXDrYAFCw*DHZ?B^tI2xmihJyEVe(i>*o2efP1I5f}I zte(wZ4%>5M2xmqEI%Pfx?al&C6%lruMUT`ZeB$zr5avZDdI*I*_URRQXnKAx)cuJ( zZ0gQbHos*cbs!m$|M>m!=Z7zk8>!--VjgWieBAWEDJ3@F#2A21f@LbinpIy(fji6&2K!p)5vz|()5_e)uk*Udn>qL27I`bdMl1S;6Y}I(31Bar(O!IOJA12}e=w`du7s}m4$AH@v_K}e70{dpfs{M!&(Po(8B*`!g!pe+f zsWptPkF!`E0T9?_#0eD|kJ(vu1u>p2FoGn;(_LMV1hL)><|oLXmjm4l4bhc|5M2)h u(cAGKx)t`JH=;fCrbB~n_%UeMeUSu<=+KY%?Ppf}#eV;6(*7Tssahv - Python Module Index — pymatgen-diffusion 2021.3.5 documentation + Python Module Index — pymatgen-diffusion 2021.3.6 documentation @@ -39,7 +39,7 @@

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    • Python Module Index
      • @@ -116,6 +116,11 @@

      Python Module Index

      		    pymatgen.analysis.diffusion.aimd.van_hove
          + pymatgen.analysis.diffusion.analyzer +
          @@ -156,6 +161,11 @@

      Python Module Index

      		    pymatgen.analysis.diffusion.neb.tests.test_pathfinder
          + pymatgen.analysis.diffusion.tests.test_analyzer +
          diff --git a/docs/pymatgen.analysis.diffusion.aimd.clustering.html b/docs/pymatgen.analysis.diffusion.aimd.clustering.html index bc4fae51..e7a0d93e 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.clustering.html +++ b/docs/pymatgen.analysis.diffusion.aimd.clustering.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.clustering module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.clustering module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen.analysis.diffusion.aimd.clustering module
      • diff --git a/docs/pymatgen.analysis.diffusion.aimd.html b/docs/pymatgen.analysis.diffusion.aimd.html index 59900c1f..4115930f 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.html +++ b/docs/pymatgen.analysis.diffusion.aimd.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd package — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd package — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen.analysis.diffusion.aimd package
      • diff --git a/docs/pymatgen.analysis.diffusion.aimd.pathway.html b/docs/pymatgen.analysis.diffusion.aimd.pathway.html index 26f51810..5919f882 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.pathway.html +++ b/docs/pymatgen.analysis.diffusion.aimd.pathway.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.pathway module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.pathway module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen-diffusion 2021.3.6 documentation »
    • pymatgen.analysis.diffusion.aimd.pathway module
      • @@ -85,7 +85,7 @@

      Navigation

      Parameters
        -

      • diffusion_analyzer (DiffusionAnalyzer) – A +

      • diffusion_analyzer (DiffusionAnalyzer) – A pymatgen.analysis.diffusion_analyzer.DiffusionAnalyzer object

      • interval (float) – the interval between two nearest grid points (in Angstrom)

        • @@ -193,7 +193,7 @@

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        • coords_ref (nested list of floats) – Fractional coordinates of a list of reference sites at which the site occupancy will be computed.

          • -

        • diffusion_analyzer (DiffusionAnalyzer) – A +

        • diffusion_analyzer (DiffusionAnalyzer) – A pymatgen.analysis.diffusion_analyzer.DiffusionAnalyzer object

        • species (list of str) – list of species that are of interest.

        diff --git a/docs/pymatgen.analysis.diffusion.aimd.rdf.html b/docs/pymatgen.analysis.diffusion.aimd.rdf.html index edae97dd..878d68e8 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.rdf.html +++ b/docs/pymatgen.analysis.diffusion.aimd.rdf.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.rdf module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.rdf module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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      • modules |
        • -
      • pymatgen-diffusion 2021.3.5 documentation »
        • +
      • pymatgen-diffusion 2021.3.6 documentation »
      • pymatgen.analysis.diffusion.aimd.rdf module
      diff --git a/docs/pymatgen.analysis.diffusion.aimd.tests.test_clustering.html b/docs/pymatgen.analysis.diffusion.aimd.tests.test_clustering.html index d4a4a37b..ec822d79 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.tests.test_clustering.html +++ b/docs/pymatgen.analysis.diffusion.aimd.tests.test_clustering.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_clustering module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_clustering module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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      • +
    • pymatgen-diffusion 2021.3.6 documentation »
    • pymatgen.analysis.diffusion.aimd.tests.test_clustering module
      • diff --git a/docs/pymatgen.analysis.diffusion.aimd.tests.test_pathway.html b/docs/pymatgen.analysis.diffusion.aimd.tests.test_pathway.html index 05ade338..ad091fbd 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.tests.test_pathway.html +++ b/docs/pymatgen.analysis.diffusion.aimd.tests.test_pathway.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_pathway module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_pathway module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • modules |
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    • pymatgen-diffusion 2021.3.5 documentation »
      • +
    • pymatgen-diffusion 2021.3.6 documentation »
    • pymatgen.analysis.diffusion.aimd.tests.test_pathway module
      • diff --git a/docs/pymatgen.analysis.diffusion.aimd.tests.test_rdf.html b/docs/pymatgen.analysis.diffusion.aimd.tests.test_rdf.html index cb73b5bc..8e00d05f 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.tests.test_rdf.html +++ b/docs/pymatgen.analysis.diffusion.aimd.tests.test_rdf.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_rdf module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_rdf module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen-diffusion 2021.3.5 documentation »
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    • pymatgen.analysis.diffusion.aimd.tests.test_rdf module
      • diff --git a/docs/pymatgen.analysis.diffusion.aimd.tests.test_van_hove.html b/docs/pymatgen.analysis.diffusion.aimd.tests.test_van_hove.html index 4edfb13b..69aff122 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.tests.test_van_hove.html +++ b/docs/pymatgen.analysis.diffusion.aimd.tests.test_van_hove.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.tests.test_van_hove module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.tests.test_van_hove module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen.analysis.diffusion.aimd.tests.test_van_hove module
      • diff --git a/docs/pymatgen.analysis.diffusion.aimd.van_hove.html b/docs/pymatgen.analysis.diffusion.aimd.van_hove.html index 89e90fea..70be629b 100644 --- a/docs/pymatgen.analysis.diffusion.aimd.van_hove.html +++ b/docs/pymatgen.analysis.diffusion.aimd.van_hove.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.aimd.van_hove module — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.aimd.van_hove module — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen.analysis.diffusion.aimd.van_hove module
      • @@ -260,7 +260,7 @@

      Navigation

      -class VanHoveAnalysis(diffusion_analyzer: pymatgen.analysis.diffusion_analyzer.DiffusionAnalyzer, avg_nsteps: int = 50, ngrid: int = 101, rmax: float = 10.0, step_skip: int = 50, sigma: float = 0.1, cell_range: int = 1, species: Union[Tuple, List] = ('Li', 'Na'), reference_species: Optional[Union[Tuple, List]] = None, indices: Optional[List] = None)[source]
      +class VanHoveAnalysis(diffusion_analyzer: pymatgen.analysis.diffusion.analyzer.DiffusionAnalyzer, avg_nsteps: int = 50, ngrid: int = 101, rmax: float = 10.0, step_skip: int = 50, sigma: float = 0.1, cell_range: int = 1, species: Union[Tuple, List] = ('Li', 'Na'), reference_species: Optional[Union[Tuple, List]] = None, indices: Optional[List] = None)[source]

      Bases: object

      Class for van Hove function analysis. In particular, self-part (Gs) and distinct-part (Gd) of the van Hove correlation function G(r,t) @@ -273,7 +273,7 @@

      Navigation

      Parameters
        -

      • diffusion_analyzer (DiffusionAnalyzer) – A +

      • diffusion_analyzer (DiffusionAnalyzer) – A pymatgen.analysis.diffusion_analyzer.DiffusionAnalyzer object

      • avg_nsteps (int) – Number of t0 used for statistical average

      • ngrid (int) – Number of radial grid points

        • diff --git a/docs/pymatgen.analysis.diffusion.analyzer.html b/docs/pymatgen.analysis.diffusion.analyzer.html new file mode 100644 index 00000000..c2dd93eb --- /dev/null +++ b/docs/pymatgen.analysis.diffusion.analyzer.html @@ -0,0 +1,520 @@ + + + + + + + + pymatgen.analysis.diffusion.analyzer module — pymatgen-diffusion 2021.3.6 documentation + + + + + + + + + + + + + + + + + + + +
        +
        +
        +
        + +
        +

        pymatgen.analysis.diffusion.analyzer module

        +

        A module to perform diffusion analyses (e.g. calculating diffusivity from +mean square displacements etc.). If you use this module, please consider +citing the following papers:

        +
        Ong, S. P., Mo, Y., Richards, W. D., Miara, L., Lee, H. S., & Ceder, G.
+(2013). Phase stability, electrochemical stability and ionic conductivity
+of the Li10+-1MP2X12 (M = Ge, Si, Sn, Al or P, and X = O, S or Se) family
+of superionic conductors. Energy & Environmental Science, 6(1), 148.
+doi:10.1039/c2ee23355j
+
+Mo, Y., Ong, S. P., & Ceder, G. (2012). First Principles Study of the
+Li10GeP2S12 Lithium Super Ionic Conductor Material. Chemistry of Materials,
+24(1), 15-17. doi:10.1021/cm203303y
+
        +
        +
        +
        +class DiffusionAnalyzer(structure, displacements, specie, temperature, time_step, step_skip, smoothed='max', min_obs=30, avg_nsteps=1000, lattices=None)[source]
        +

        Bases: monty.json.MSONable

        +

        Class for performing diffusion analysis.

        +

        This constructor is meant to be used with pre-processed data. +Other convenient constructors are provided as class methods (see +from_vaspruns and from_files).

        +

        Given a matrix of displacements (see arguments below for expected +format), the diffusivity is given by:

        +
        D = 1 / 2dt * <mean square displacement>
+
        +
        +

        where d is the dimensionality, t is the time. To obtain a reliable +diffusion estimate, a least squares regression of the MSD against +time to obtain the slope, which is then related to the diffusivity.

        +

        For traditional analysis, use smoothed=False and weighted=False.

        +
        +
        Parameters
        +
          +

        • structure (Structure) – Initial structure.

          • +

        • displacements (array) – Numpy array of with shape [site, +time step, axis]

          • +

        • specie (Element/Species) – Species to calculate diffusivity for as a +String. E.g., “Li”.

          • +

        • temperature (float) – Temperature of the diffusion run in Kelvin.

          • +

        • time_step (int) – Time step between measurements.

          • +

        • step_skip (int) – Sampling frequency of the displacements ( +time_step is multiplied by this number to get the real time +between measurements)

          • +

        • smoothed (str) –

          Whether to smooth the MSD, and what mode to smooth. +Supported modes are:

          +
            +

          1. ”max”, which tries to use the maximum # +of data points for each time origin, subject to a +minimum # of observations given by min_obs, and then +weights the observations based on the variance +accordingly. This is the default.

            2. +

          2. ”constant”, in which each timestep is averaged over +the number of time_steps given by min_steps.

            3. +

          3. None / False / any other false-like quantity. No

            4. +
          +
          +

          smoothing.

          +
          +

          • +

        • min_obs (int) – Used with smoothed=”max”. Minimum number of +observations to have before including in the MSD vs dt +calculation. E.g. If a structure has 10 diffusing atoms, +and min_obs = 30, the MSD vs dt will be +calculated up to dt = total_run_time / 3, so that each +diffusing atom is measured at least 3 uncorrelated times. +Only applies in smoothed=”max”.

          • +

        • avg_nsteps (int) – Used with smoothed=”constant”. Determines the +number of time steps to average over to get the msd for each +timestep. Default of 1000 is usually pretty good.

          • +

        • lattices (array) – Numpy array of lattice matrix of every step. Used +for NPT-AIMD. For NVT-AIMD, the lattice at each time step is +set to the lattice in the “structure” argument.

          • +
        +
        +
        +
        +
        +as_dict()[source]
        +

        Returns: MSONable dict

        +
        + +
        +
        +export_msdt(filename)[source]
        +

        Writes MSD data to a csv file that can be easily plotted in other +software.

        +
        +
        Parameters
        +

        filename (str) – Filename. Supported formats are csv and dat. If +the extension is csv, a csv file is written. Otherwise, +a dat format is assumed.

        +
        +
        +
        + +
        +
        +classmethod from_dict(d)[source]
        +
        +
        Parameters
        +

        d (dict) – Dict representation

        +
        +
        +

        Returns: DiffusionAnalyzer

        +
        + +
        +
        +classmethod from_files(filepaths, specie, step_skip=10, ncores=None, initial_disp=None, initial_structure=None, **kwargs)[source]
        +

        Convenient constructor that takes in a list of vasprun.xml paths to +perform diffusion analysis.

        +
        +
        Parameters
        +
          +

        • filepaths ([str]) – List of paths to vasprun.xml files of runs. ( +must be ordered in sequence of MD simulation). For example, +you may have done sequential VASP runs and they are in run1, +run2, run3, etc. You should then pass in +[“run1/vasprun.xml”, “run2/vasprun.xml”, …].

          • +

        • specie (Element/Species) – Species to calculate diffusivity for as a +String. E.g., “Li”.

          • +

        • step_skip (int) – Sampling frequency of the displacements ( +time_step is multiplied by this number to get the real time +between measurements)

          • +

        • ncores (int) – Numbers of cores to use for multiprocessing. Can +speed up vasprun parsing considerably. Defaults to None, +which means serial. It should be noted that if you want to +use multiprocessing, the number of ionic steps in all vasprun +.xml files should be a multiple of the ionic_step_skip. +Otherwise, inconsistent results may arise. Serial mode has no +such restrictions.

          • +

        • initial_disp (np.ndarray) – Sometimes, you need to iteratively +compute estimates of the diffusivity. This supplies an +initial displacement that will be added on to the initial +displacements. Note that this makes sense only when +smoothed=False.

          • +

        • initial_structure (Structure) – Like initial_disp, this is used +for iterative computations of estimates of the diffusivity. You +typically need to supply both variables. This stipulates the +initial structure from which the current set of displacements +are computed.

          • +

        • \*\*kwargs – kwargs supported by the :class:`DiffusionAnalyzer`_. +Examples include smoothed, min_obs, avg_nsteps.

          • +
        +
        +
        +
        + +
        +
        +classmethod from_structures(structures, specie, temperature, time_step, step_skip, initial_disp=None, initial_structure=None, **kwargs)[source]
        +

        Convenient constructor that takes in a list of Structure objects to +perform diffusion analysis.

        +
        +
        Parameters
        +
          +

        • structures ([Structure]) – list of Structure objects (must be +ordered in sequence of run). E.g., you may have performed +sequential VASP runs to obtain sufficient statistics.

          • +

        • specie (Element/Species) – Species to calculate diffusivity for as a +String. E.g., “Li”.

          • +

        • temperature (float) – Temperature of the diffusion run in Kelvin.

          • +

        • time_step (int) – Time step between measurements.

          • +

        • step_skip (int) – Sampling frequency of the displacements ( +time_step is multiplied by this number to get the real time +between measurements)

          • +

        • initial_disp (np.ndarray) – Sometimes, you need to iteratively +compute estimates of the diffusivity. This supplies an +initial displacement that will be added on to the initial +displacements. Note that this makes sense only when +smoothed=False.

          • +

        • initial_structure (Structure) – Like initial_disp, this is used +for iterative computations of estimates of the diffusivity. You +typically need to supply both variables. This stipulates the +initial structure from which the current set of displacements +are computed.

          • +

        • \*\*kwargs – kwargs supported by the :class:`DiffusionAnalyzer`_. +Examples include smoothed, min_obs, avg_nsteps.

          • +
        +
        +
        +
        + +
        +
        +classmethod from_vaspruns(vaspruns, specie, initial_disp=None, initial_structure=None, **kwargs)[source]
        +

        Convenient constructor that takes in a list of Vasprun objects to +perform diffusion analysis.

        +
        +
        Parameters
        +
          +

        • vaspruns ([Vasprun]) – List of Vaspruns (must be ordered in +sequence of MD simulation). E.g., you may have performed +sequential VASP runs to obtain sufficient statistics.

          • +

        • specie (Element/Species) – Species to calculate diffusivity for as a +String. E.g., “Li”.

          • +

        • initial_disp (np.ndarray) – Sometimes, you need to iteratively +compute estimates of the diffusivity. This supplies an +initial displacement that will be added on to the initial +displacements. Note that this makes sense only when +smoothed=False.

          • +

        • initial_structure (Structure) – Like initial_disp, this is used +for iterative computations of estimates of the diffusivity. You +typically need to supply both variables. This stipulates the +initial stricture from which the current set of displacements +are computed.

          • +

        • \*\*kwargs – kwargs supported by the :class:`DiffusionAnalyzer`_. +Examples include smoothed, min_obs, avg_nsteps.

          • +
        +
        +
        +
        + +
        +
        +get_drift_corrected_structures(start=None, stop=None, step=None)[source]
        +

        Returns an iterator for the drift-corrected structures. Use of +iterator is to reduce memory usage as # of structures in MD can be +huge. You don’t often need all the structures all at once.

        +
        +
        Parameters
        +
          +

        • start (int) – applies a start/stop/step to the iterator. +Faster than applying it after generation, as it reduces the +number of structures created.

          • +

        • stop (int) – applies a start/stop/step to the iterator. +Faster than applying it after generation, as it reduces the +number of structures created.

          • +

        • step (int) – applies a start/stop/step to the iterator. +Faster than applying it after generation, as it reduces the +number of structures created.

          • +
        +
        +
        +
        + +
        +
        +get_framework_rms_plot(plt=None, granularity=200, matching_s=None)[source]
        +

        Get the plot of rms framework displacement vs time. Useful for checking +for melting, especially if framework atoms can move via paddle-wheel +or similar mechanism (which would show up in max framework displacement +but doesn’t constitute melting).

        +
        +
        Parameters
        +
          +

        • plt (matplotlib.pyplot) – If plt is supplied, changes will be made +to an existing plot. Otherwise, a new plot will be created.

          • +

        • granularity (int) – Number of structures to match

          • +

        • matching_s (Structure) – Optionally match to a disordered structure +instead of the first structure in the analyzer. Required when +a secondary mobile ion is present.

          • +
        +
        +
        +

        Notes

        +

        The method doesn’t apply to NPT-AIMD simulation analysis.

        +
        + +
        +
        +get_msd_plot(plt=None, mode='specie')[source]
        +

        Get the plot of the smoothed msd vs time graph. Useful for +checking convergence. This can be written to an image file.

        +
        +
        Parameters
        +
          +

        • plt – A plot object. Defaults to None, which means one will be +generated.

          • +

        • mode (str) – Determines type of msd plot. By “species”, “sites”, +or direction (default). If mode = “mscd”, the smoothed mscd vs. +time will be plotted.

          • +
        +
        +
        +
        + +
        +
        +get_summary_dict(include_msd_t=False, include_mscd_t=False)[source]
        +

        Provides a summary of diffusion information.

        +
        +
        Parameters
        +
          +

        • include_msd_t (bool) – Whether to include mean square displace and +time data with the data.

          • +

        • include_msd_t – Whether to include mean square charge displace and +time data with the data.

          • +
        +
        +
        Returns
        +

        (dict) of diffusion and conductivity data.

        +
        +
        +
        + +
        +
        +plot_msd(mode='default')[source]
        +

        Plot the smoothed msd vs time graph. Useful for checking convergence.

        +
        +
        Parameters
        +

        mode (str) – Can be “default” (the default, shows only the MSD for +the diffusing specie, and its components), “ions” (individual +square displacements of all ions), “species” (mean square +displacement by specie), or “mscd” (overall mean square charge +displacement for diffusing specie).

        +
        +
        +
        + +
        + +
        +
        +fit_arrhenius(temps, diffusivities)[source]
        +
        +
        Returns Ea, c, standard error of Ea from the Arrhenius fit:

        D = c * exp(-Ea/kT)

        +
        +
        +
        +
        Parameters
        +
          +

        • temps ([float]) – A sequence of temperatures. units: K

          • +

        • diffusivities ([float]) – A sequence of diffusivities (e.g., +from DiffusionAnalyzer.diffusivity). units: cm^2/s

          • +
        +
        +
        +
        + +
        +
        +get_arrhenius_plot(temps, diffusivities, diffusivity_errors=None, **kwargs)[source]
        +

        Returns an Arrhenius plot.

        +
        +
        Parameters
        +
          +

        • temps ([float]) – A sequence of temperatures.

          • +

        • diffusivities ([float]) – A sequence of diffusivities (e.g., +from DiffusionAnalyzer.diffusivity).

          • +

        • diffusivity_errors ([float]) – A sequence of errors for the +diffusivities. If None, no error bar is plotted.

          • +

        • \*\*kwargs – Any keyword args supported by matplotlib.pyplot.plot.

          • +
        +
        +
        Returns
        +

        A matplotlib.pyplot object. Do plt.show() to show the plot.

        +
        +
        +
        + +
        +
        +get_conversion_factor(structure, species, temperature)[source]
        +

        Conversion factor to convert between cm^2/s diffusivity measurements and +mS/cm conductivity measurements based on number of atoms of diffusing +species. Note that the charge is based on the oxidation state of the +species (where available), or else the number of valence electrons +(usually a good guess, esp for main group ions).

        +
        +
        Parameters
        +
          +

        • structure (Structure) – Input structure.

          • +

        • species (Element/Species) – Diffusing species.

          • +

        • temperature (float) – Temperature of the diffusion run in Kelvin.

          • +
        +
        +
        Returns
        +

        Conversion factor. +Conductivity (in mS/cm) = Conversion Factor * Diffusivity (in cm^2/s)

        +
        +
        +
        + +
        +
        +get_extrapolated_conductivity(temps, diffusivities, new_temp, structure, species)[source]
        +

        Returns extrapolated mS/cm conductivity.

        +
        +
        Parameters
        +
          +

        • temps ([float]) – A sequence of temperatures. units: K

          • +

        • diffusivities ([float]) – A sequence of diffusivities (e.g., +from DiffusionAnalyzer.diffusivity). units: cm^2/s

          • +

        • new_temp (float) – desired temperature. units: K

          • +

        • structure (structure) – Structure used for the diffusivity calculation

          • +

        • species (string/Species) – conducting species

          • +
        +
        +
        Returns
        +

        (float) Conductivity at extrapolated temp in mS/cm.

        +
        +
        +
        + +
        +
        +get_extrapolated_diffusivity(temps, diffusivities, new_temp)[source]
        +

        Returns (Arrhenius) extrapolated diffusivity at new_temp

        +
        +
        Parameters
        +
          +

        • temps ([float]) – A sequence of temperatures. units: K

          • +

        • diffusivities ([float]) – A sequence of diffusivities (e.g., +from DiffusionAnalyzer.diffusivity). units: cm^2/s

          • +

        • new_temp (float) – desired temperature. units: K

          • +
        +
        +
        Returns
        +

        (float) Diffusivity at extrapolated temp in mS/cm.

        +
        +
        +
        + +
        + + +
        +
        +
        +
        + +
        +
        + +
        + © Copyright 2016, Materials Virtual Lab. +
        +
        This page uses +Google Analytics to collect statistics. You can disable it by blocking +the JavaScript coming from www.google-analytics.com. + +
        + + + \ No newline at end of file diff --git a/docs/pymatgen.analysis.diffusion.html b/docs/pymatgen.analysis.diffusion.html index 1bf288b6..d2296ccb 100644 --- a/docs/pymatgen.analysis.diffusion.html +++ b/docs/pymatgen.analysis.diffusion.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion package — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion package — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

        Navigation

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      • pymatgen-diffusion 2021.3.5 documentation »
        • +
      • pymatgen-diffusion 2021.3.6 documentation »
      • pymatgen.analysis.diffusion package
      @@ -96,6 +96,14 @@

      Subpackages +

      Submodules

      +
      + +
      +

      Module contents

      Package for diffusion analysis.

      @@ -113,6 +121,7 @@

      Table of Contents

      diff --git a/docs/pymatgen.analysis.diffusion.neb.html b/docs/pymatgen.analysis.diffusion.neb.html index e6f1e614..4c20efd3 100644 --- a/docs/pymatgen.analysis.diffusion.neb.html +++ b/docs/pymatgen.analysis.diffusion.neb.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.neb package — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.neb package — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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    • pymatgen-diffusion 2021.3.5 documentation »
      • +
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      +
      +
      +
      + +
      +

      pymatgen.analysis.diffusion.tests.test_analyzer module

      +
      +
      +class DiffusionAnalyzerTest(methodName='runTest')[source]
      +

      Bases: pymatgen.util.testing.PymatgenTest

      +

      Create an instance of the class that will use the named test +method when executed. Raises a ValueError if the instance does +not have a method with the specified name.

      +
      +
      +test_from_structure_NPT()[source]
      +
      + +
      +
      +test_init()[source]
      +
      + +
      +
      +test_init_npt()[source]
      +
      + +
      + +
      +
      +class FuncTest(methodName='runTest')[source]
      +

      Bases: pymatgen.util.testing.PymatgenTest

      +

      Create an instance of the class that will use the named test +method when executed. Raises a ValueError if the instance does +not have a method with the specified name.

      +
      +
      +test_fit_arrhenius()[source]
      +
      + +
      +
      +test_get_conversion_factor()[source]
      +
      + +
      + +
      + + +
      +
      +
      +
      + +
      +
      + +
      + © Copyright 2016, Materials Virtual Lab. +
      +
      This page uses +Google Analytics to collect statistics. You can disable it by blocking +the JavaScript coming from www.google-analytics.com. + +
      + + + \ No newline at end of file diff --git a/docs/pymatgen.analysis.diffusion.utils.html b/docs/pymatgen.analysis.diffusion.utils.html index 851398aa..a380bdf6 100644 --- a/docs/pymatgen.analysis.diffusion.utils.html +++ b/docs/pymatgen.analysis.diffusion.utils.html @@ -5,7 +5,7 @@ - pymatgen.analysis.diffusion.utils package — pymatgen-diffusion 2021.3.5 documentation + pymatgen.analysis.diffusion.utils package — pymatgen-diffusion 2021.3.6 documentation @@ -36,7 +36,7 @@

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Sphinx Style","Change Log","Introduction","pymatgen","pymatgen namespace","pymatgen.analysis namespace","pymatgen.analysis.diffusion package","pymatgen.analysis.diffusion.aimd package","pymatgen.analysis.diffusion.aimd.clustering module","pymatgen.analysis.diffusion.aimd.pathway module","pymatgen.analysis.diffusion.aimd.rdf module","pymatgen.analysis.diffusion.aimd.tests.test_clustering module","pymatgen.analysis.diffusion.aimd.tests.test_pathway module","pymatgen.analysis.diffusion.aimd.tests.test_rdf module","pymatgen.analysis.diffusion.aimd.tests.test_van_hove module","pymatgen.analysis.diffusion.aimd.van_hove module","pymatgen.analysis.diffusion.analyzer module","pymatgen.analysis.diffusion.neb package","pymatgen.analysis.diffusion.neb.full_path_mapper module","pymatgen.analysis.diffusion.neb.io module","pymatgen.analysis.diffusion.neb.pathfinder module","pymatgen.analysis.diffusion.neb.periodic_dijkstra module","pymatgen.analysis.diffusion.neb.tests.test_full_path_mapper module","pymatgen.analysis.diffusion.neb.tests.test_io module","pymatgen.analysis.diffusion.neb.tests.test_pathfinder module","pymatgen.analysis.diffusion.tests.test_analyzer module","pymatgen.analysis.diffusion.utils package","pymatgen.analysis.diffusion.utils.maggma module","pymatgen.analysis.diffusion.utils.parse_entries module","pymatgen.analysis.diffusion.utils.supercells module","pymatgen.analysis.diffusion.utils.tests.test_parse_entries module","pymatgen_diffusion package","pymatgen_diffusion.aimd package","pymatgen_diffusion.neb package"],titleterms:{acknowledg:2,aimd:[7,8,9,10,11,12,13,14,15,32],analysi:[5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],analyz:16,api:2,chang:[1,2],cite:2,cluster:[8,32],content:[6,7,17,26,31,32,33],contribut:2,copyright:2,diffus:[6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],document:2,exhaust:2,featur:2,full_path_mapp:[18,33],indic:2,introduct:2,krtheme:0,licens:2,log:[1,2],maggma:27,modul:[6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],namespac:[4,5],neb:[17,18,19,20,21,22,23,24,33],non:2,our:2,packag:[6,7,17,26,31,32,33],parse_entri:28,pathfind:[20,33],pathwai:[9,32],periodic_dijkstra:[21,33],polici:2,pymatgen:[3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30],pymatgen_diffus:[31,32,33],rdf:[10,32],sphinx:0,style:0,submodul:[6,7,17,26,32,33],subpackag:[5,6,7,17,26,31,32,33],supercel:29,tabl:2,test:[11,12,13,14,22,23,24,25,30],test_analyz:25,test_clust:11,test_full_path_mapp:22,test_io:23,test_parse_entri:30,test_pathfind:24,test_pathwai:12,test_rdf:13,test_van_hov:14,util:[26,27,28,29,30],v2018:1,v2019:1,v2021:1,van_hov:[15,32]}}) \ No newline at end of file diff --git a/docs_rst/pymatgen.analysis.diffusion.rst b/docs_rst/pymatgen.analysis.diffusion.rst index 0b7a7582..677e4db1 100644 --- a/docs_rst/pymatgen.analysis.diffusion.rst +++ b/docs_rst/pymatgen.analysis.diffusion.rst @@ -11,6 +11,14 @@ Subpackages pymatgen.analysis.diffusion.neb pymatgen.analysis.diffusion.utils +Submodules +---------- + +.. toctree:: + :maxdepth: 7 + + pymatgen.analysis.diffusion.analyzer + Module contents --------------- diff --git a/pymatgen/analysis/diffusion/__init__.py b/pymatgen/analysis/diffusion/__init__.py index 445a4f7f..f7248225 100644 --- a/pymatgen/analysis/diffusion/__init__.py +++ b/pymatgen/analysis/diffusion/__init__.py @@ -4,4 +4,4 @@ __author__ = "Materials Virtual Lab" __email__ = "ongsp@eng.ucsd.edu" -__version__ = "2021.3.5" +__version__ = "2021.3.6" \ No newline at end of file diff --git a/setup.py b/setup.py index 5b3bfeb7..96da65cb 100644 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ setup( name="pymatgen-analysis-diffusion", packages=find_namespace_packages(include=["pymatgen.analysis.*"]), - version="2021.3.5", + version="2021.3.6", install_requires=["pymatgen>=2022.0.3", "joblib"], extras_require={}, package_data={}, @@ -40,4 +40,4 @@ "Topic :: Scientific/Engineering :: Chemistry", "Topic :: Software Development :: Libraries :: Python Modules", ], -) +) \ No newline at end of file