encyclopedia.py 47.8 KB
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# Copyright 2018 Markus Scheidgen
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an"AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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"""
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The encyclopedia API of the nomad@FAIRDI APIs.
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"""
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import re
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import math
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import numpy as np
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from flask_restplus import Resource, abort, fields, marshal
from flask import request
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from elasticsearch_dsl import Search, Q, A
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from elasticsearch_dsl.utils import AttrDict
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from nomad import config, files
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from nomad.units import ureg
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from nomad.atomutils import get_hill_decomposition
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from nomad.datamodel.datamodel import EntryArchive
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from .api import api
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from .common import enable_gzip
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ns = api.namespace("encyclopedia", description="Access encyclopedia metadata.")
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re_formula = re.compile(r"([A-Z][a-z]?)(\d*)")

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material_prop_map = {
    # General
    "material_id": "encyclopedia.material.material_id",
    "formula": "encyclopedia.material.formula",
    "formula_reduced": "encyclopedia.material.formula_reduced",
    "system_type": "encyclopedia.material.material_type",
    # Bulk only
    "has_free_wyckoff_parameters": "encyclopedia.material.bulk.has_free_wyckoff_parameters",
    "strukturbericht_designation": "encyclopedia.material.bulk.strukturbericht_designation",
    "material_name": "encyclopedia.material.material_name",
    "bravais_lattice": "encyclopedia.material.bulk.bravais_lattice",
    "crystal_system": "encyclopedia.material.bulk.crystal_system",
    "point_group": "encyclopedia.material.bulk.point_group",
    "space_group_number": "encyclopedia.material.bulk.space_group_number",
    "space_group_international_short_symbol": "encyclopedia.material.bulk.space_group_international_short_symbol",
    "structure_prototype": "encyclopedia.material.bulk.structure_prototype",
    "structure_type": "encyclopedia.material.bulk.structure_type",
}
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def rgetattr(obj, attr_name):
    """Used to perform attribute access based on a (possibly nested) attribute
    name given as string.
    """
    try:
        for attr in attr_name.split("."):
            obj = obj[attr]
    except KeyError:
        return None
    return obj


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def get_es_doc_values(es_doc, mapping, keys=None):
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    """Used to form a material definition for "materials/<material_id>" from
    the given ElasticSearch root document.
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    """
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    if keys is None:
        keys = mapping.keys()

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    result = {}
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    for key in keys:
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        es_key = mapping[key]
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        value = rgetattr(es_doc, es_key)
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        result[key] = value
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    return result


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def get_enc_filter():
    """Returns a shared term filter that will leave out unpublished, embargoed
    or invalid entries.
    """
    return [
        Q("term", published=True),
        Q("term", with_embargo=False),
        Q("term", encyclopedia__status="success"),
    ]


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material_query = api.parser()
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material_query.add_argument(
    "property",
    type=str,
    choices=tuple(material_prop_map.keys()),
    help="Optional single property to retrieve for the given material. If not specified, all properties will be returned.",
    location="args"
)
material_result = api.model("material_result", {
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    # General
    "material_id": fields.String,
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    "formula": fields.String,
    "formula_reduced": fields.String,
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    "system_type": fields.String,
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    "n_matches": fields.Integer,
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    # Bulk only
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    "has_free_wyckoff_parameters": fields.Boolean,
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    "strukturbericht_designation": fields.String,
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    "material_name": fields.String,
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    "bravais_lattice": fields.String,
    "crystal_system": fields.String,
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    "point_group": fields.String,
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    "space_group_number": fields.Integer,
    "space_group_international_short_symbol": fields.String,
    "structure_prototype": fields.String,
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    "structure_type": fields.String,
})
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@ns.route("/materials/<string:material_id>")
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class EncMaterialResource(Resource):
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    @api.response(404, "The material does not exist")
    @api.response(200, "Metadata send", fields.Raw)
    @api.doc("material/<material_id>")
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    @api.expect(material_query)
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    @api.marshal_with(material_result, skip_none=True)
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    def get(self, material_id):
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        """Used to retrieve basic information related to the specified
        material.
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        """
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        # Parse request arguments
        args = material_query.parse_args()
        prop = args.get("property", None)
        if prop is not None:
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            keys = [prop]
            es_keys = [material_prop_map[prop]]
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        else:
            keys = list(material_prop_map.keys())
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            es_keys = list(material_prop_map.values())
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        # Find the first public entry with this material id and take
        # information from there. In principle all other entries should have
        # the same information.
        s = Search(index=config.elastic.index_name)
        query = Q(
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            "bool",
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            filter=get_enc_filter() + [
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                Q("term", encyclopedia__material__material_id=material_id),
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            ]
        )
        s = s.query(query)
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        # Only one representative entry is returned by collapsing the results.
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        s = s.extra(**{
            "_source": {"includes": es_keys},
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            "size": 1,
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            "collapse": {"field": "encyclopedia.material.material_id"},
        })
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        response = s.execute()

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        # No such material
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        if len(response) == 0:
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            abort(404, message="There is no material {}".format(material_id))
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        # Add values from ES entry
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        entry = response[0]
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        result = get_es_doc_values(entry, material_prop_map, keys)
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        return result, 200


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range_query = api.model("range_query", {
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    "max": fields.Float,
    "min": fields.Float,
})
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materials_after = api.model("materials_after", {
    "materials": fields.String,
})
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materials_query = api.model("materials_input", {
    "search_by": fields.Nested(api.model("search_query", {
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        "exclusive": fields.Boolean(default=False),
        "formula": fields.String,
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        "element": fields.String,
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        "page": fields.Integer(default=1),
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        "after": fields.Nested(materials_after, allow_null=True),
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        "per_page": fields.Integer(default=25),
        "pagination": fields.Boolean,
    })),
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    "material_name": fields.List(fields.String),
    "structure_type": fields.List(fields.String),
    "space_group_number": fields.List(fields.Integer),
    "system_type": fields.List(fields.String),
    "crystal_system": fields.List(fields.String),
    "band_gap": fields.Nested(range_query, description="Band gap range in eV."),
    "band_gap_direct": fields.Boolean,
    "has_band_structure": fields.Boolean,
    "has_dos": fields.Boolean,
    "has_fermi_surface": fields.Boolean,
    "has_thermal_properties": fields.Boolean,
    "functional_type": fields.List(fields.String),
    "basis_set_type": fields.List(fields.String),
    "code_name": fields.List(fields.String),
    "mass_density": fields.Nested(range_query, description="Mass density range in kg / m ** 3."),
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})
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pages_result = api.model("page_info", {
    "per_page": fields.Integer,
    "total": fields.Integer,
    "page": fields.Integer,
    "pages": fields.Integer,
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    "after": fields.Nested(materials_after),
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})

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materials_result = api.model("materials_result", {
    "total_results": fields.Integer(allow_null=False),
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    "results": fields.List(fields.Nested(material_result, skip_none=True)),
    "pages": fields.Nested(pages_result, skip_none=True),
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    "es_query": fields.String(allow_null=False),
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})


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@ns.route("/materials")
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class EncMaterialsResource(Resource):
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    @api.response(404, "No materials found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
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    @api.expect(materials_query, validate=False)
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    @api.marshal_with(materials_result, skip_none=True)
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    @api.doc("materials")
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    def post(self):
        """Used to query a list of materials with the given search options.
        """
        # Get query parameters as json
        try:
            data = marshal(request.get_json(), materials_query)
        except Exception as e:
            abort(400, message=str(e))

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        filters = get_enc_filter()
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        must_nots = []
        musts = []

        def add_terms_filter(source, target, query_type="terms"):
            if data[source]:
                filters.append(Q(query_type, **{target: data[source]}))

        add_terms_filter("material_name", "encyclopedia.material.material_name")
        add_terms_filter("structure_type", "encyclopedia.material.bulk.structure_type")
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        add_terms_filter("space_group_number", "encyclopedia.material.bulk.space_group_number")
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        add_terms_filter("system_type", "encyclopedia.material.material_type")
        add_terms_filter("crystal_system", "encyclopedia.material.bulk.crystal_system")
        add_terms_filter("band_gap_direct", "encyclopedia.properties.band_gap_direct", query_type="term")
        add_terms_filter("functional_type", "encyclopedia.method.functional_type")
        add_terms_filter("basis_set_type", "dft.basis_set")
        add_terms_filter("code_name", "dft.code_name")

        # Add exists filters
        def add_exists_filter(source, target):
            param = data[source]
            if param is not None:
                query = Q("exists", field=target)
                if param is True:
                    filters.append(query)
                elif param is False:
                    must_nots.append(query)

        add_exists_filter("has_thermal_properties", "encyclopedia.properties.thermodynamical_properties")
        add_exists_filter("has_band_structure", "encyclopedia.properties.electronic_band_structure")
        add_exists_filter("has_dos", "encyclopedia.properties.electronic_dos")
        add_exists_filter("has_fermi_surface", "encyclopedia.properties.fermi_surface")

        # Add range filters
        def add_range_filter(source, target, source_unit=None, target_unit=None):
            param = data[source]
            query_dict = {}
            if param["min"] is not None:
                if source_unit is None and target_unit is None:
                    gte = param["min"]
                else:
                    gte = (param["min"] * source_unit).to(target_unit).magnitude
                query_dict["gte"] = gte
            if param["max"] is not None:
                if source_unit is None and target_unit is None:
                    lte = param["max"]
                else:
                    lte = (param["max"] * source_unit).to(target_unit).magnitude
                query_dict["lte"] = lte
            if len(query_dict) != 0:
                query = Q("range", **{target: query_dict})
                filters.append(query)

        add_range_filter("band_gap", "encyclopedia.properties.band_gap", ureg.eV, ureg.J)
        add_range_filter("mass_density", "encyclopedia.properties.mass_density")
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        # Create query for elements or formula
        search_by = data["search_by"]
        formula = search_by["formula"]
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        elements = search_by["element"]
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        exclusive = search_by["exclusive"]

        if formula is not None:
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            # Here we determine a list of atom types. The types may occur
            # multiple times and at multiple places.
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            element_list = []
            matches = re_formula.finditer(formula)
            for match in matches:
                groups = match.groups()
                symbol = groups[0]
                count = groups[1]
                if symbol != "":
                    if count == "":
                        element_list.append(symbol)
                    else:
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                        element_list += [symbol] * int(count)

            # The given list of species is reformatted with the Hill system
            # into a query string. The counts are reduced by the greatest
            # common divisor.
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            names, reduced_counts = get_hill_decomposition(element_list, reduced=True)
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            query_string = []
            for name, count in zip(names, reduced_counts):
                if count == 1:
                    query_string.append(name)
                else:
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                    query_string.append("{}{}".format(name, int(count)))
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            query_string = " ".join(query_string)
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            # With exclusive search we look for exact match
            if exclusive:
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                filters.append(Q("term", **{"encyclopedia.material.species_and_counts.keyword": query_string}))
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            # With non-exclusive search we look for match that includes at
            # least all parts of the formula, possibly even more.
            else:
                musts.append(Q(
                    "match",
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                    encyclopedia__material__species_and_counts={"query": query_string, "operator": "and"}
                ))
        elif elements is not None:
            # The given list of species is reformatted with the Hill system into a query string
            species, _ = get_hill_decomposition(elements.split(","))
            query_string = " ".join(species)

            # With exclusive search we look for exact match
            if exclusive:
                filters.append(Q("term", **{"encyclopedia.material.species.keyword": query_string}))
            # With non-exclusive search we look for match that includes at
            # least all species, possibly even more.
            else:
                musts.append(Q(
                    "match",
                    encyclopedia__material__species={"query": query_string, "operator": "and"}
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                ))

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        page = search_by["page"]
        per_page = search_by["per_page"]
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        after = search_by["after"]
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        bool_query = Q(
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            "bool",
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            filter=filters,
            must_not=must_nots,
            must=musts,
        )
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        # The top query filters out entries based on the user query
        s = Search(index=config.elastic.index_name)
        s = s.query(bool_query)

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        # 1: The paginated approach: No way to know the amount of materials,
        # but can return aggregation results in a quick fashion including
        # the number of calculation entries per material.
        mode = "collapse"
        if mode == "aggregation":
            # The materials are grouped by using three aggregations:
            # "Composite" to enable scrolling, "Terms" to enable selecting
            # by material_id and "Top Hits" to fetch a single
            # representative material document. Unnecessary fields are
            # filtered to reduce data transfer.
            terms_agg = A("terms", field="encyclopedia.material.material_id")
            composite_kwargs = {"sources": {"materials": terms_agg}, "size": per_page}

            # The number of matched materials is only requested on the first
            # search, not for each page.
            if after is not None:
                composite_kwargs["after"] = after
            else:
                cardinality_agg = A("cardinality", field="encyclopedia.material.material_id", precision_threshold=1000)
                s.aggs.metric("n_materials", cardinality_agg)

            composite_agg = A("composite", **composite_kwargs)
            composite_agg.metric("representative", A(
                "top_hits",
                size=1,
                _source={"includes": list(material_prop_map.values())},
            ))
            s.aggs.bucket("materials", composite_agg)

            # We ignore the top level hits and sort by reduced material formula.
            s = s.extra(**{
                "size": 0,
            })

            response = s.execute()
            materials = response.aggs.materials.buckets
            if len(materials) == 0:
                abort(404, message="No materials found for the given search criteria or pagination.")
            after_new = response.aggs.materials["after_key"]

            # Gather results from aggregations
            result_list = []
            materials = response.aggs.materials.buckets
            keys = list(material_prop_map.keys())
            for material in materials:
                representative = material["representative"][0]
                mat_dict = get_es_doc_values(representative, material_prop_map, keys)
                mat_dict["n_matches"] = material.doc_count
                result_list.append(mat_dict)

            # Page information is incomplete for aggregations
            pages = {
                "page": page,
                "per_page": per_page,
                "after": after_new,
            }
            if after is None:
                n_materials = response.aggs.n_materials.value
                pages["total"] = n_materials

        # 2. Collapse approach. Quickly provides a list of materials
        # corresponding to the query, offers full pagination, doesn"t include
        # the number of matches per material.
        elif mode == "collapse":
            s = Search(index=config.elastic.index_name)
            s = s.query(bool_query)

            # Add cardinality aggregation that gives out the total number of materials
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            cardinality_agg = A("cardinality", field="encyclopedia.material.material_id", precision_threshold=1000)
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            s.aggs.metric("n_materials", cardinality_agg)
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            s = s.extra(**{
                "collapse": {"field": "encyclopedia.material.material_id"},
                "size": per_page,
                "from": (page - 1) * per_page,
                "sort": [{"encyclopedia.material.formula_reduced": {"order": "asc"}}],
                "explain": True,
            })

            # Execute query
            response = s.execute()

            # No matches
            if len(response) == 0:
                abort(404, message="No materials found for the given search criteria or pagination.")

            # Gather number of entries per material with a separate query
            material_ids = [x.encyclopedia.material.material_id for x in response]
            s = Search(index=config.elastic.index_name)
            bool_query = Q(
                "bool",
                filter=Q("terms", encyclopedia__material__material_id=material_ids),
            )
            s2 = s.query(bool_query)
            s2.aggs.bucket("n_matches", A("terms", field="encyclopedia.material.material_id"))
            response2 = s2.execute()
            matmap = {x.key: x.doc_count for x in response2.aggs.n_matches}
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            # Loop over materials
            result_list = []
            keys = list(material_prop_map.keys())
            for material in response:
                # Get values from the collapsed doc
                mat_result = get_es_doc_values(material, material_prop_map, keys)
                mat_id = material.encyclopedia.material.material_id
                mat_result["n_matches"] = matmap[mat_id]
                result_list.append(mat_result)

            # Full page information available for collapse
            pages = {
                "page": page,
                "per_page": per_page,
                "pages": math.ceil(response.hits.total / per_page),
                "total": response.aggs.n_materials.value,
            }
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        result = {
            "results": result_list,
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            "pages": pages,
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        }
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        return result, 200
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groups_result = api.model("groups_result", {
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    "groups_eos": fields.Raw,
    "groups_par": fields.Raw,
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})


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@ns.route("/materials/<string:material_id>/groups")
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class EncGroupsResource(Resource):
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    @api.response(404, "Material not found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
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    @api.marshal_with(groups_result)
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    @api.doc("enc_materials")
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    def get(self, material_id):
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        """Returns a summary of the calculation groups that were identified for
        this material.
        """
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        # Find entries for the given material, which have EOS or parameter
        # variation hashes set.
        bool_query = Q(
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            "bool",
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            filter=get_enc_filter() + [Q("term", encyclopedia__material__material_id=material_id)],
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            must=[
                Q("exists", field="encyclopedia.properties.energies.energy_total"),
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                Q("exists", field="encyclopedia.material.idealized_structure.cell_volume"),
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            ],
            should=[
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                Q("exists", field="encyclopedia.method.group_eos_id"),
                Q("exists", field="encyclopedia.method.group_parametervariation_id"),
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            ],
            minimum_should_match=1,  # At least one of the should query must match
        )
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        s = Search(index=config.elastic.index_name)
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        s = s.query(bool_query)

        # Bucket the calculations by the group hashes. Only create a bucket if an
        # above-minimum number of documents are found.
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        group_eos_bucket = A("terms", field="encyclopedia.method.group_eos_id", min_doc_count=4)
        group_param_bucket = A("terms", field="encyclopedia.method.group_parametervariation_id", min_doc_count=2)
        calc_aggregation = A(
            "top_hits",
            _source={"includes": ["calc_id"]},
            sort=[{"encyclopedia.properties.energies.energy_total": {"order": "asc"}}],
            size=100,
        )
        group_eos_bucket.bucket("calculations", calc_aggregation)
        group_param_bucket.bucket("calculations", calc_aggregation)
        s.aggs.bucket("groups_eos", group_eos_bucket)
        s.aggs.bucket("groups_param", group_param_bucket)

        # We ignore the top level hits
        s = s.extra(**{
            "size": 0,
        })

        # Collect information for each group from the aggregations
        response = s.execute()
        groups_eos = {group.key: [calc.calc_id for calc in group.calculations.hits] for group in response.aggs.groups_eos.buckets}
        groups_param = {group.key: [calc.calc_id for calc in group.calculations.hits] for group in response.aggs.groups_param.buckets}

        # Return results
        result = {
            "groups_eos": groups_eos,
            "groups_par": groups_param,
        }

        return result, 200


group_result = api.model("group_result", {
    "calculations": fields.List(fields.String),
    "energies": fields.List(fields.Float),
    "volumes": fields.List(fields.Float),
})
group_source = {
    "includes": [
        "calc_id",
        "encyclopedia.properties.energies.energy_total",
        "encyclopedia.material.idealized_structure.cell_volume",
    ]
}


@ns.route("/materials/<string:material_id>/groups/<string:group_type>/<string:group_id>")
class EncGroupResource(Resource):
    @api.response(404, "Group not found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
    @api.marshal_with(group_result)
    @api.doc("enc_group")
    def get(self, material_id, group_type, group_id):
        """Used to query detailed information for a specific calculation group.
        """
        # Find entries for the given material, which have EOS or parameter
        # variation hashes set.
        if group_type == "eos":
            group_id_source = "encyclopedia.method.group_eos_id"
        elif group_type == "par":
            group_id_source = "encyclopedia.method.group_parametervariation_id"
        else:
            abort(400, message="Unsupported group type.")

        bool_query = Q(
            "bool",
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            filter=get_enc_filter() + [
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                Q("term", encyclopedia__material__material_id=material_id),
                Q("term", **{group_id_source: group_id}),
            ],
        )

        s = Search(index=config.elastic.index_name)
        s = s.query(bool_query)
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        # calc_id and energy should be extracted for each matched document. The
        # documents are sorted by energy so that the minimum energy one can be
        # easily extracted. A maximum request size is set in order to limit the
        # result size. ES also has an index-level property
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        # "index.max_inner_result_window" that limits the number of results
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        # that an inner result can contain.
        energy_aggregation = A(
            "top_hits",
            _source=group_source,
            sort=[{"encyclopedia.properties.energies.energy_total": {"order": "asc"}}],
            size=100,
        )
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        s.aggs.bucket("groups_eos", energy_aggregation)
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        # We ignore the top level hits
        s = s.extra(**{
            "size": 0,
        })
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        # Collect information for each group from the aggregations
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        response = s.execute()
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        hits = response.aggs.groups_eos.hits
        calculations = [doc.calc_id for doc in hits]
        energies = [doc.encyclopedia.properties.energies.energy_total for doc in hits]
        volumes = [doc.encyclopedia.material.idealized_structure.cell_volume for doc in hits]
        group_dict = {
            "calculations": calculations,
            "energies": energies,
            "volumes": volumes,
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        }
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        return group_dict, 200
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suggestions_map = {
    "code_name": "dft.code_name",
    "structure_type": "encyclopedia.material.bulk.structure_type",
}
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suggestions_query = api.parser()
suggestions_query.add_argument(
    "property",
    type=str,
    choices=("code_name", "structure_type"),
    help="The property name for which suggestions are returned.",
    location="args"
)
suggestions_result = api.model("suggestions_result", {
    "code_name": fields.List(fields.String),
    "structure_type": fields.List(fields.String),
})


@ns.route("/suggestions")
class EncSuggestionsResource(Resource):
    @api.response(404, "Suggestion not found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
    @api.expect(suggestions_query, validate=False)
    @api.marshal_with(suggestions_result, skip_none=True)
    @api.doc("enc_suggestions")
    def get(self):

        # Parse request arguments
        args = suggestions_query.parse_args()
        prop = args.get("property", None)

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        # Use aggregation to return all unique terms for the requested field.
        # Without using composite aggregations there is a size limit for the
        # number of aggregation buckets. This should, however, not be a problem
        # since the number of unique values is low for all supported properties.
        s = Search(index=config.elastic.index_name)
        query = Q(
            "bool",
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            filter=get_enc_filter()
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        )
        s = s.query(query)
        s = s.extra(**{
            "size": 0,
        })

        terms_agg = A("terms", field=suggestions_map[prop])
        s.aggs.bucket("suggestions", terms_agg)

        # Gather unique values into a list
        response = s.execute()
        suggestions = [x.key for x in response.aggs.suggestions.buckets]

        return {prop: suggestions}, 200
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calc_prop_map = {
    "calc_id": "calc_id",
    "code_name": "dft.code_name",
    "code_version": "dft.code_version",
    "functional_type": "encyclopedia.method.functional_type",
    "basis_set_type": "dft.basis_set",
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    "core_electron_treatment": "encyclopedia.method.core_electron_treatment",
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    "run_type": "encyclopedia.calculation.calculation_type",
    "has_dos": "encyclopedia.properties.electronic_dos",
    "has_band_structure": "encyclopedia.properties.electronic_band_structure",
    "has_thermal_properties": "encyclopedia.properties.thermodynamical_properties",
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    "has_phonon_dos": "encyclopedia.properties.phonon_dos",
    "has_phonon_band_structure": "encyclopedia.properties.phonon_band_structure",
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}
calculation_result = api.model("calculation_result", {
    "calc_id": fields.String,
    "code_name": fields.String,
    "code_version": fields.String,
    "functional_type": fields.String,
    "basis_set_type": fields.String,
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    "core_electron_treatment": fields.String,
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    "run_type": fields.String,
    "has_dos": fields.Boolean,
    "has_band_structure": fields.Boolean,
    "has_thermal_properties": fields.Boolean,
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    "has_phonon_dos": fields.Boolean,
    "has_phonon_band_structure": fields.Boolean,
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})
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representatives_result = api.model("representatives_result", {
    "idealized_structure": fields.String,
    "electronic_band_structure": fields.String,
    "electronic_dos": fields.String,
    "thermodynamical_properties": fields.String,
})
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calculations_result = api.model("calculations_result", {
    "total_results": fields.Integer,
    "pages": fields.Nested(pages_result),
    "results": fields.List(fields.Nested(calculation_result)),
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    "representatives": fields.Nested(representatives_result, skip_none=True),
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})


@ns.route("/materials/<string:material_id>/calculations")
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class EncCalculationsResource(Resource):
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    @enable_gzip()
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    @api.response(404, "Suggestion not found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
    @api.doc("enc_calculations")
    def get(self, material_id):
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        """Used to return all calculations related to the given material. Also
        returns a representative calculation for each property shown in the
        overview page.
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        """
        s = Search(index=config.elastic.index_name)
        query = Q(
            "bool",
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            filter=get_enc_filter() + [
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                Q("term", encyclopedia__material__material_id=material_id),
            ]
        )
        s = s.query(query)

        # The query is filtered already on the ES side so we don"t need to
        # transfer so much data.
        s = s.extra(**{
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            "_source": {"includes": list(calc_prop_map.values()) + ["dft.xc_functional"]},
            "size": 10000,
            "from": 0,
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        })
        response = s.execute()

        # No such material
        if len(response) == 0:
            abort(404, message="There is no material {}".format(material_id))

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        # Add representative properties. It might be possible to write a custom
        # ES scoring mechanism or aggregation to also perform the selection.
        representatives = {}

        def calc_score(entry):
            """Custom scoring function used to sort results by their
            "quality". Currently built to mimic the scoring that was used
            in the old Encyclopedia GUI.
            """
            score = 0
            functional_score = {
                "GGA": 100
            }
            code_score = {
                "FHI-aims": 3,
                "VASP": 2,
                "Quantum Espresso": 1,
            }
            code_name = entry.dft.code_name
            functional = entry.dft.xc_functional
            has_dos = rgetattr(entry, "encyclopedia.properties.electronic_band_structure") is not None
            has_bs = rgetattr(entry, "encyclopedia.properties.electronic_dos") is not None
            score += functional_score.get(functional, 0)
            score += code_score.get(code_name, 0)
            if has_dos and has_bs:
                score += 10

            return score

        # The calculations are first sorted by "quality"
        sorted_calc = sorted(response, key=lambda x: calc_score(x), reverse=True)

        # Get the requested representative properties
        representatives["idealized_structure"] = sorted_calc[0].calc_id
        thermo_found = False
        bs_found = False
        dos_found = False
        for calc in sorted_calc:
            if rgetattr(calc, "encyclopedia.properties.thermodynamical_properties") is not None:
                representatives["thermodynamical_properties"] = calc.calc_id
                thermo_found = True
            if rgetattr(calc, "encyclopedia.properties.electronic_band_structure") is not None:
                representatives["electronic_band_structure"] = calc.calc_id
                bs_found = True
            if rgetattr(calc, "encyclopedia.properties.electronic_dos") is not None:
                representatives["electronic_dos"] = calc.calc_id
                dos_found = True
            if thermo_found and bs_found and dos_found:
                break

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        # Create result JSON
        results = []
        for entry in response:
            calc_dict = get_es_doc_values(entry, calc_prop_map)
            calc_dict["has_dos"] = calc_dict["has_dos"] is not None
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            calc_dict["has_band_structure"] = calc_dict["has_band_structure"] is not None
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            calc_dict["has_thermal_properties"] = calc_dict["has_thermal_properties"] is not None
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            calc_dict["has_phonon_dos"] = calc_dict["has_phonon_dos"] is not None
            calc_dict["has_phonon_band_structure"] = calc_dict["has_phonon_band_structure"] is not None
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            results.append(calc_dict)

        result = {
            "total_results": len(results),
            "results": results,
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            "representatives": representatives,
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        }

        return result, 200


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histogram = api.model("histogram", {
    "occurrences": fields.List(fields.Integer),
    "values": fields.List(fields.Float),
})
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statistics_query = api.model("statistics_query", {
    "calculations": fields.List(fields.String),
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    "properties": fields.List(fields.String),
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    "n_histogram_bins": fields.Integer,
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})
statistics = api.model("statistics", {
    "min": fields.Float,
    "max": fields.Float,
    "avg": fields.Float,
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    "histogram": fields.Nested(histogram, skip_none=True)
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})
statistics_result = api.model("statistics_result", {
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    "cell_volume": fields.Nested(statistics, skip_none=True),
    "atomic_density": fields.Nested(statistics, skip_none=True),
    "mass_density": fields.Nested(statistics, skip_none=True),
    "lattice_a": fields.Nested(statistics, skip_none=True),
    "lattice_b": fields.Nested(statistics, skip_none=True),
    "lattice_c": fields.Nested(statistics, skip_none=True),
    "alpha": fields.Nested(statistics, skip_none=True),
    "beta": fields.Nested(statistics, skip_none=True),
    "gamma": fields.Nested(statistics, skip_none=True),
    "band_gap": fields.Nested(statistics, skip_none=True),
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})
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property_map = {
    "cell_volume": "encyclopedia.material.idealized_structure.cell_volume",
    "atomic_density": "encyclopedia.properties.atomic_density",
    "mass_density": "encyclopedia.properties.mass_density",
    "lattice_a": "encyclopedia.material.idealized_structure.lattice_parameters.a",
    "lattice_b": "encyclopedia.material.idealized_structure.lattice_parameters.b",
    "lattice_c": "encyclopedia.material.idealized_structure.lattice_parameters.c",
    "alpha": "encyclopedia.material.idealized_structure.lattice_parameters.alpha",
    "beta": "encyclopedia.material.idealized_structure.lattice_parameters.beta",
    "gamma": "encyclopedia.material.idealized_structure.lattice_parameters.gamma",
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    "band_gap": "encyclopedia.properties.band_gap",
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}
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@ns.route("/materials/<string:material_id>/statistics")
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class EncStatisticsResource(Resource):
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    @api.response(404, "Suggestion not found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
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    @api.expect(statistics_query, validate=False)
    @api.marshal_with(statistics_result, skip_none=True)
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    @api.doc("enc_statistics")
    def post(self, material_id):
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        """Used to return statistics related to the specified material and
        calculations.
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        """
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        # Get query parameters as json
        try:
            data = marshal(request.get_json(), statistics_query)
        except Exception as e:
            abort(400, message=str(e))

        # Find entries for the given material.
        bool_query = Q(
            "bool",
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            filter=get_enc_filter() + [
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                Q("term", encyclopedia__material__material_id=material_id),
                Q("terms", calc_id=data["calculations"]),
            ]
        )

        s = Search(index=config.elastic.index_name)
        s = s.query(bool_query)
        s = s.extra(**{
            "size": 0,
        })

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        # Add statistics aggregations for each requested property
        properties = data["properties"]
        for prop in properties:
            stats_agg = A("stats", field=property_map[prop])
            s.aggs.bucket("{}_stats".format(prop), stats_agg)

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        # No hits on the top query level
        response = s.execute()
        if response.hits.total == 0:
            abort(404, message="Could not find matching calculations.")

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        # Run a second query that creates histograms with fixed size buckets
        # based on the min and max from previous query. Might make more sense
        # to use the mean and sigma to define the range?
        s = Search(index=config.elastic.index_name)
        s = s.query(bool_query)
        s = s.extra(**{
            "size": 0,
        })
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        n_bins = data["n_histogram_bins"]
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        for prop in properties:
            stats = getattr(response.aggs, "{}_stats".format(prop))
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            if stats.count == 0:
                continue
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            interval = (stats.max * 1.001 - stats.min) / n_bins
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            if interval == 0:
                interval = 1
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            hist_agg = A("histogram", field=property_map[prop], interval=interval, offset=stats.min, min_doc_count=0)
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            s.aggs.bucket("{}_hist".format(prop), hist_agg)
        response_hist = s.execute()

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        # Return results
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        result = {}
        for prop in properties:
            stats = getattr(response.aggs, "{}_stats".format(prop))
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            if stats.count == 0:
                continue
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            hist = getattr(response_hist.aggs, "{}_hist".format(prop))
            occurrences = [x.doc_count for x in hist.buckets]
            values = [x.key for x in hist.buckets]
            result[prop] = {
                "min": stats.min,
                "max": stats.max,
                "avg": stats.avg,
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                "histogram": {
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                    "occurrences": occurrences,
                    "values": values,
                }
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            }
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        return result, 200
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wyckoff_variables_result = api.model("wyckoff_variables_result", {
    "x": fields.Float,
    "y": fields.Float,
    "z": fields.Float,
})
wyckoff_set_result = api.model("wyckoff_set_result", {
    "wyckoff_letter": fields.String,
    "indices": fields.List(fields.Integer),
    "element": fields.String,
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    "variables": fields.Nested(wyckoff_variables_result, skip_none=True),
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})
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lattice_parameters = api.model("lattice_parameters", {
    "a": fields.Float,
    "b": fields.Float,
    "c": fields.Float,
    "alpha": fields.Float,
    "beta": fields.Float,
    "gamma": fields.Float,
})
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idealized_structure_result = api.model("idealized_structure_result", {
    "atom_labels": fields.List(fields.String),
    "atom_positions": fields.List(fields.List(fields.Float)),
    "lattice_vectors": fields.List(fields.List(fields.Float)),
    "lattice_vectors_primitive": fields.List(fields.List(fields.Float)),
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    "lattice_parameters": fields.Nested(lattice_parameters, skip_none=True),
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    "periodicity": fields.List(fields.Boolean),
    "number_of_atoms": fields.Integer,
    "cell_volume": fields.Float,
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    "wyckoff_sets": fields.List(fields.Nested(wyckoff_set_result, skip_none=True)),
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})

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calculation_property_map = {
    "lattice_parameters": {
        "es_source": "encyclopedia.material.idealized_structure.lattice_parameters"
    },
    "energies": {
        "es_source": "encyclopedia.properties.energies",
    },
    "mass_density": {
        "es_source": "encyclopedia.properties.mass_density",
    },
    "atomic_density": {
        "es_source": "encyclopedia.properties.atomic_density",
    },
    "cell_volume": {
        "es_source": "encyclopedia.material.idealized_structure.cell_volume"
    },
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    "band_gap": {
        "es_source": "encyclopedia.properties.band_gap"
    },
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    "electronic_band_structure": {
        "es_source": "encyclopedia.properties.electronic_band_structure"
    },
    "electronic_dos": {
        "es_source": "encyclopedia.properties.electronic_dos"
    },
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    "phonon_band_structure": {
        "es_source": "encyclopedia.properties.phonon_band_structure"
    },
    "phonon_dos": {
        "es_source": "encyclopedia.properties.phonon_dos"
    },
    "thermodynamical_properties": {
        "es_source": "encyclopedia.properties.thermodynamical_properties"
    },
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    "wyckoff_sets": {
        "arch_source": "section_metadata/encyclopedia/material/idealized_structure/wyckoff_sets"
    },
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    "idealized_structure": {
        "arch_source": "section_metadata/encyclopedia/material/idealized_structure"
    },
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}

calculation_property_query = api.model("calculation_query", {
    "properties": fields.List(fields.String),
})
energies = api.model("energies", {
    "energy_total": fields.Float,
    "energy_total_T0": fields.Float,
    "energy_free": fields.Float,
})
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electronic_band_structure = api.model("electronic_band_structure", {
    "reciprocal_cell": fields.List(fields.List(fields.Float)),
    "brillouin_zone": fields.Raw,
    "section_k_band_segment": fields.Raw,
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    "section_band_gap": fields.Raw,
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})
electronic_dos = api.model("electronic_dos", {
    "dos_energies": fields.List(fields.Float),
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    "dos_values": fields.List(fields.List(fields.Float)),
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})
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calculation_property_result = api.model("calculation_property_result", {
    "lattice_parameters": fields.Nested(lattice_parameters, skip_none=True),
    "energies": fields.Nested(energies, skip_none=True),
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    "mass_density": fields.Float,
    "atomic_density": fields.Float,
    "cell_volume": fields.Float,
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    "wyckoff_sets": fields.Nested(wyckoff_set_result, skip_none=True),
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    "idealized_structure": fields.Nested(idealized_structure_result, skip_none=True),
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    "band_gap": fields.Float,
    "electronic_band_structure": fields.Nested(electronic_band_structure, skip_none=True),
    "electronic_dos": fields.Nested(electronic_dos, skip_none=True),
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    "phonon_band_structure": fields.Raw,
    "phonon_dos": fields.Raw,
    "thermodynamical_properties": fields.Raw,
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})


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@ns.route("/materials/<string:material_id>/calculations/<string:calc_id>")
class EncCalculationResource(Resource):
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    @enable_gzip()
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    @api.response(404, "Material or calculation not found")
    @api.response(400, "Bad request")
    @api.response(200, "Metadata send", fields.Raw)
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    @api.expect(calculation_property_query, validate=False)
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    @api.marshal_with(calculation_property_result, skip_none=True)
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    @api.doc("enc_calculation")
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    def post(self, material_id, calc_id):
        """Used to return calculation details. Some properties are not
        available in the ES index and are instead read from the Archive
        directly.
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        """
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        # Get query parameters as json
        try:
            data = marshal(request.get_json(), calculation_property_query)
        except Exception as e:
            abort(400, message=str(e))

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        s = Search(index=config.elastic.index_name)
        query = Q(
            "bool",
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            filter=get_enc_filter() + [
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                Q("term", encyclopedia__material__material_id=material_id),
                Q("term", calc_id=calc_id),
            ]
        )
        s = s.query(query)

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        # Create dictionaries for requested properties
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        references = []
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        properties = data["properties"]
        arch_properties = {}
        es_properties = {}
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        ref_properties = set((
            "electronic_dos",
            "electronic_band_structure",
            "thermodynamical_properties",
            "phonon_dos",
            "phonon_band_structure",
        ))
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        for prop in properties:
            es_source = calculation_property_map[prop].get("es_source")
            if es_source is not None:
                es_properties[prop] = es_source
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                if prop in ref_properties:
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                    references.append(prop)
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            arch_source = calculation_property_map[prop].get("arch_source")
            if arch_source is not None:
                arch_properties[prop] = arch_source

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        # The query is filtered already on the ES side so we don't need to
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        # transfer so much data.
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        sources = [
            "upload_id",
            "calc_id",
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            "encyclopedia",
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        ]
        sources += list(es_properties.values())

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        s = s.extra(**{
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            "_source": {"includes": sources},
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            "size": 1,
        })

        response = s.execute()

        # No such material
        if len(response) == 0:
            abort(404, message="There is no material {} with calculation {}".format(material_id, calc_id))

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        # Add references that are to be read from the archive
        for ref in references:
            arch_path = response[0]
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            arch_path = rgetattr(arch_path, es_properties[ref])
            if arch_path is not None:
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                arch_properties[ref] = arch_path
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            del es_properties[ref]

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        # If any of the requested properties require data from the Archive, the
        # file is opened and read.
        result = {}
        if len(arch_properties) != 0:
            entry = response[0]
            upload_id = entry.upload_id
            calc_id = entry.calc_id
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            root = read_archive(
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                upload_id,
                calc_id,
            )

            # Add results from archive
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            for key, arch_path in arch_properties.items():
                value = root[arch_path]

                # Save derived properties and turn into dict
                if key == "thermodynamical_properties":
                    specific_heat_capacity = value.specific_heat_capacity.magnitude.tolist()
                    specific_free_energy = value.specific_vibrational_free_energy_at_constant_volume.magnitude.tolist()
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                if isinstance(value, list):
                    value = [x.m_to_dict() for x in value]
                else:
                    value = value.m_to_dict()
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                if key == "thermodynamical_properties":
                    value["specific_heat_capacity"] = specific_heat_capacity
                    value["specific_vibrational_free_energy_at_constant_volume"] = specific_free_energy
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                # DOS results are simplified.
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                if key == "electronic_dos":
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                    if "dos_energies_normalized" in value:
                        value["dos_energies"] = value["dos_energies_normalized"]
                        del value["dos_energies_normalized"]
                    if "dos_values_normalized" in value:
                        value["dos_values"] = value["dos_values_normalized"]
                        del value["dos_values_normalized"]
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                # Pre-calculate k-path length to be used as x-coordinate in
                # plots. If the VBM and CBM information is needed later, it
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                # can be added as indices along the path. The exact k-points
                # and occupations are removed to save some bandwidth.
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                if key == "electronic_band_structure" or key == "phonon_band_structure":
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                    segments = value["section_k_band_segment"]
                    k_path_length = 0
                    for segment in segments:
                        k_points = np.array(segment["band_k_points"])
                        segment_length = np.linalg.norm(k_points[-1, :] - k_points[0, :])
                        k_path_distances = k_path_length + np.linalg.norm(k_points - k_points[0, :], axis=1)
                        k_path_length += segment_length
                        segment["k_path_distances"] = k_path_distances.tolist()
                        del segment["band_k_points"]
                        if "band_occupations" in segment:
                            del segment["band_occupations"]

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                result[key] = value

        # Add results from ES
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        for prop, es_source in es_properties.items():
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            value = rgetattr(response[0], es_source)
            if value is not None:
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                if isinstance(value, AttrDict):
                    value = value.to_dict()
                result[prop] = value
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        return result, 200


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def read_archive(upload_id: str, calc_id: str) -> EntryArchive:
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    """Used to read data from the archive.
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    Args:
        upload_id: Upload id.
        calc_id: Calculation id.

    Returns:
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        MSection: The section_run as MSection
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        For each path, a dictionary containing the path as key and the returned
        section as value.
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    """
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    upload_files = files.PublicUploadFiles(upload_id)
    with upload_files.read_archive(calc_id, access="public") as archive