""" invenio_tools.recordpubli

"""
import re


from .base import (ARXIV,
                   ARXIV_PDF,
                   REG_ARXIV_NUMBER,
                   REG_YEAR)

from .exception import RecordException
from filters import CLEAN_COLLABORATION
from numpy import NaN
from pandas import concat, DataFrame
from plugin_dbui import as_list, CLEAN_SPACES
from .record import Record


AUTHOR_FORMATS = [
    "First, Last",
    "F. Last",
    "Last",
    "Last, First",
    "Last F."]

# decode publication reference:
# Phys. Rev. Lett. 113, 032001 (2014)
# Eur. Phys. J. C (2014) 74:2883
_ref1 = r"(?P<p>[A-Za-z\. ]+) +(?P<v>\d+),? +(?P<c>[\d-]+) +\((?P<y>[\d]+)\)"
_ref2 = r"(?P<p>[A-Za-z\. ]+) +\((?P<y>\d+)\) +(?P<v>[\d]+):(?P<c>[\d-]+)"
DECODE_REF = [re.compile(_ref1), re.compile(_ref2)]

MSG_INVALID_FMT = "Invalid format for author"

# the keys containing paper reference
PAPER_REFERENCE_KEYS = {"pagination", "title", "volume", "year"}

# extract initial of a first name
REG_INITIAL = initial = r"^(\w+)\.?(\-)* *(\w+)*\.?$"


def to_initial(x, y, z):
    """Help function to extract initial from a first name split in x, y and z:

        Albert                (x="Albert", y="", z="")
        Antonio Augusto       (x="Antonio", y="", z="Augusto")
        Jean-Pierre           (x="Jean", y="-", z="Pierre")

    Args:
        x (str): first part
        y (str): separator
        z (str): second part

    Returns:
        str

    """
    if z == "":
        return "%s." % x[0:1]

    if y == "":
        return "%s. %s." % (x[0:1], z[0:1])
    else:
        return "%s.%s%s." % (x[0:1], y[0:1], z[0:1])


def to_str(x):
    return ("|".join(x) if isinstance(x, list) else x)


class RecordPubli(Record):
    """The record describes an article, preprint, proceeding, report and talk.
    The main ``field`` and ``subfield`` are::

         +---------------------------------+----------------------------------+
         | field                           | subfield                         |
         +---------------------------------+----------------------------------+
         | FIXME_OAI (inspire)             | id                               |
         | abstract                        |                                  |
         | accelerator_experiment          |                                  |
         | agency_code (cds)               |                                  |
         | authors                         | INSPIRE_number, affiliation,     |
         |                                 | control_number, first_name,      |
         |                                 | full_name, last_name,            |
         |                                 | relator_name (phd director)      |
         | base (cds)                      |                                  |
         | collection                      |                                  |
         | comment                         |                                  |
         | copyright_status (cds)          |                                  |
         | corporate_name                  | collaboration                    |
         | creation_date                   |                                  |
         | doi                             |                                  |
         | email_message (cds)             |                                  |
         | filenames                       |                                  |
         | files                           | comment, description, eformat,   |
         |                                 | full_name, full_path, magic,     |
         |                                 | name, path, size, status,        |
         |                                 | subformat, superformat, type,    |
         |                                 | url, version                     |
         | filetypes                       |                                  |
         | imprint                         |                                  |
         | keywords                        |                                  |
         | language (cds)                  |                                  |
         | license                         |                                  |
         | number_of_authors               |                                  |
         | number_of_citations             |                                  |
         | number_of_comments              |                                  |
         | number_of_reviews               |                                  |
         | oai (cds)                       | value                            |
         | other_report_number (cds)       |                                  |
         | persistent_identifiers_keys     |                                  |
         | physical_description            |                                  |
         | prepublication                  | date, publisher_name, place      |
         | primary_report_number           |                                  |
         | publication_info                | pagination, title, volume, year  |
         | recid                           | none                             |
         | reference (inspire)             |                                  |
         | report_number (cds)             | internal, report_number          |
         | source_of_acquisition (inspire) |                                  |
         | status_week (cds)               |                                  |
         | subject                         |                                  |
         | system_control_number           | institute, value or canceled     |
         | thesaurus_terms                 |                                  |
         | title                           | title                            |
         | title_additional (inspire)      |                                  |
         | url (cds)                       | description, url                 |
         | version_id                      |                                  |
         +---------------------------------+----------------------------------+

    """
    def __init__(self, *args):

        self._last_fmt_author = "Last, First"

        Record.__init__(self, *args)
        self._process_authors()
        self._process_publication_info()

    def _process_authors(self):
        """Convert authors information into DataFrame:

        Authors and their affiliations are stored in DataFrame with the
        following structure:

            +---------------+--------------------------------+
            | column        |                                |
            +---------------+--------------------------------+
            | affiliation   | value separated by "|"         |
            | first_name    | first name                     |
            | fmt_name      | formated name                  |
            | full_name     | Last, First                    |
            | last_name     | family name                    |
            | relator_name  | equal to dir. for phd director |
            +---------------+--------------------------------+

        Note:
            After running this method, the field ``authors`` is always defined.
            It contains one entry with empty strings when the field does not
            exist.

        """
        if u"authors" not in self:
            cols = ["affiliation",
                    "first_name",
                    "fmt_name",
                    "full_name",
                    "last_name"]
            self[u"authors"] = DataFrame([[""]*len(cols)], columns=cols)
            return

        data = self[u"authors"]
        data = (data if isinstance(data, list) else [data])

        df = DataFrame(data)

        # drop useless columns
        refcols = ["affiliation",
                   "first_name",
                   "full_name",
                   "last_name",
                   "relator_name"]

        columns = df.columns
        df = df.drop(columns.difference(refcols), axis="columns")

        # protection -- affiliation not defined
        if "affiliation" not in columns:
            dfa = DataFrame([""]*len(df), columns=["affiliation"])
            df = concat([df, dfa], axis="columns")

        # convert list of affiliation to string separated by |
        df.affiliation = (df.affiliation
                          .fillna("")
                          .apply(lambda x: to_str(x)))

        # add the column fmt_name
        df["fmt_name"] = df.full_name

        # replace
        self[u"authors"] = df

    def _process_publication_info(self):
        """Convert publication_info into DataFrame:

            Note:
                * the field is a list when there are erratum
                * in some case the subfield year is a list (cds 1951625)

        publication information are stored in DataFrame with the
        following structure:

            +------------+--------------------------------+
            | column     |                                |
            +------------+--------------------------------+
            | title      | abbreviation of the publisher  |
            | volume     | volume                         |
            | year       | year of publication            |
            | pagination | page number or ranges          |
            +------------+--------------------------------+

        Note:
            * After running this method, the field ``publication_info``
              is always defined. It contains one entry with empty strings
              when the field does not exist.

            * In order to deal with erratum entry are sorter by year
              and volume.

        """
        if u"publication_info" not in self:
            cols = ["title",
                    "volume",
                    "year",
                    "pagination"]

            self[u"publication_info"] = \
                DataFrame([[""]*len(cols)], columns=cols)

            return

        data = self[u"publication_info"]
        data = (data if isinstance(data, list) else [data])

        df = DataFrame(data)

        # protection -- list of year, e.g. [2014, 2014] (cds 1951625)
        df["year"] = \
            df.year.apply(
                lambda x: (", ".join(set(x)) if isinstance(x, list) else x))

        # erratum -- sort by year and volume
        columns = df.columns
        if set(["year", "volume"]).issubset(columns):
            df = df.sort_values(["year", "volume"])

        elif "year" in columns:
            df = df.sort_values("year")

        # replace
        self[u"publication_info"] = df

    def authors(self, sep=", ", sort=False):
        """The author(s) signing the publication.

        Args:
            sep (str):
                string separating author names. The default is the comma.

            sort (bool):
                sort authors by family name when true otherwise use the
                order of authors at the creation of the record

        Returns:
            str:
                * Author names are separated by the ``sep`` argument.
                * The string is empty when there is no authors.

        """
        li = self.authors_as_list(sort=sort)
        return sep.join(li)

    def authors_as_list(self, sort=False):
        """The list of author(s) signing the publication.

        Args:
            sort (bool):
                sort authors by family name when true otherwise use the
                order of authors at the creation of the record

        Returns:
            list:
                the list is empty when authors are not defined.

        """
        df = self[u"authors"]

        if sort:
            li = (df[["last_name", "fmt_name"]]
                  .sort_values(by="last_name")
                  .fmt_name
                  .tolist())

        else:
            li = (df.fmt_name
                  .sort_index()
                  .tolist())

        if len(li) == 1 and li[0] == "":
            li = []

        return li

    def collaboration(self):
        """The collaboration(s) signing the publication.

        Returns:
            str:
                * names of collaboration are separated by a comma.
                * The filter CLEAN_COLLABORATION is applied.

        """
        li = self._get(u"corporate_name", u"collaboration", force_list=True)
        return CLEAN_COLLABORATION(", ".join(li))

    def find_affiliation(self, pattern):
        """Find affiliation matching the regular expression *pattern*.

        Args:
            pattern (str):
                regular expression defining the affiliation keys.
                It has to be build for an exact match namely containing
                start and end of string. This is reuqired to separate
                `Ecole Plolytechnique` from `Ecole Polytechnique, Lausanne`.

        Returns:
            str:
                - the affiliation or the first one when several are found.
                - empty string when nothing is found.

        """
        df = self[u"authors"]

        # modify the pattern to capture group
        pattern = "(%s)" % pattern

        data = (df.affiliation.str.extract(pattern, expand=False)
                .dropna())

        return (data[0] if len(data) > 0 else "")

    def find_authors(self, pattern, sep=", ", sort=False):
        """Find authors containing the regular expression *pattern*.
        The search is performed on the formatted name.

        Args:
            pattern (str):
                regular expression defining the author name(s).

            sep (unicode):
                string separating author names. The default is the comma.

            sort (bool):
                sort authors by family name when true otherwise use the
                order of authors at the creation of the record

        Returns:
            str:
                * Author names are separated by ``sep`` argument.
                * The string is empty when nothing is found.

        """
        df = self[u"authors"]

        query = df.fmt_name.str.contains(pattern)

        if sort:
            data = (df.loc[query, ["last_name", "fmt_name"]]
                    .sort_values(by="last_name")
                    .fmt_name)

        else:
            data = (df.loc[query, ["fmt_name"]]
                    .sort_index()
                    .fmt_name)

        return ("" if len(data) == 0 else sep.join(data))

    def find_authors_by_affiliation(self, pattern, sep=", ", sort=False):
        """Find authors belonging to a given institute(s) defined by a regular
        expression.

        Args:
            pattern (str):
                regular expression defining the affiliation keys
                for the institute(s).

            sep (unicode):
                string separating author names. The default is the comma.

            sort (bool):
                sort authors by family name when true otherwise use the
                order of authors at the creation of the record

        Returns:
            str:
                * Author names are separated by the ``sep`` argument.
                * Author are sorted according to their family name.
                * Empty string when authors are not found.

        """
        df = self[u"authors"]

        query = df.affiliation.str.contains(pattern)

        if sort:
            data = (df.loc[query, ["last_name", "fmt_name"]]
                    .sort_values(by="last_name")
                    .fmt_name)

        else:
            data = (df.loc[query, ["fmt_name"]]
                    .sort_index()
                    .fmt_name)

        return (sep.join(data) if len(data) > 0 else "")

    def first_author(self):
        """The name of the first author.

        Returns:
            str:
                - Empty string when the first author is not defined.

        """
        return self[u"authors"].fmt_name.iloc[0]

    def first_author_institutes(self):
        """The institute(s) associated to the first author.

        Note:
            Search is performed via the affiliation defined by the "u" key
            of the author field.

        Returns:
            str:
                - names are separated by ``|``.
                - The string is empty when institutes are not defined.

        """
        val = self[u"authors"].affiliation.iloc[0]
        return ("" if val == NaN else val)

    def institutes(self):
        """The list of institute signing the publication.

        Note:
            Name of institute are given by the affiliation defined by
            the "affiliation" key of the author field.

        Returns:
            list:
                the list is sort in alphabetic order.

        """
        df = self[u"authors"]

        # expand multi-affiliation (one per column)
        df = df.affiliation.str.split("|", expand=True)

        # merge all columns into a single one,
        # sort and remove duplicate entries
        li = [df[el].dropna() for el in df.columns]
        df = (concat(li, ignore_index=True)
              .sort_values()
              .unique())

        return df.tolist()

    def is_affiliations(self):
        """``True`` when affiliations are defined for authors.

        Note:
            This is a fast algorithm checking that the ``affiliation`` field
            exists. To check that the affiliation is defined for all authors,
            uses the method :func:`is_affiliation_for_all`.

        Returns:
            bool:

        """
        df = self[u"authors"]

        if len(df) == 1 and df.affiliation.iloc[0] == "":
            return False

        return True

    def is_affiliation_for_all(self):
        """``True`` when affiliation are defined for all authors.

        Return:
            bool:

        """
        df = self[u"authors"]

        query = df.affiliation.isin(["", NaN])
        return df.affiliation[query].size == 0

    def is_authors(self):
        """``True`` when authors are defined.

        Returns:
            bool:

        """
        df = self[u"authors"]

        cols = {"first_name", "full_name", "last_name"}
        if len(df.columns.intersection(cols)) != 3:
            return False

        if len(df) == 1 and df.full_name.iloc[0] == "":
            return False

        return True

    def is_published(self):
        """``True`` is the record is published and contains a complet set
        of publication infromation (title, volume, year and pagination).

        Returns:
            bool:

        """
        df = self[u"publication_info"]

        query = \
            (df.title.str.len() > 0) \
            & (df.volume.str.len() > 0) \
            & (df.year.str.len() > 0) \
            & (df.pagination.str.len() > 0)

        return len(df[query]) > 0

    def is_with_erratum(self):
        """``True`` when the record contains erratum data.

        Returns:
            bool

        """
        df = self[u"publication_info"]
        return len(df) > 1

    def paper_editor(self):
        """The abbreviated version of the review, *e.g* Phys Lett B.

        Returns:
            unicode:
                * Empty string when not defined.

        """
        df = self[u"publication_info"]
        return df.title.iloc[0]

    def paper_pages(self):
        """The page number / range when the record is published in a review.

        Returns:
            unicode:
                * The format is "45-67" or "234".
                * Empty string when not defined.

        """
        df = self[u"publication_info"]
        return df.pagination.iloc[0]

    def paper_reference(self):
        """The full reference for a publication published in a review.

        Returns:
            unicode:
                * The format is "Phys Lett B 456 2010 5-6".
                * The string is empty when the publication is not
                  published in a review.

        """
        paper = self[u"publication_info"].iloc[0]

        li = [paper.title,
              paper.volume,
              paper.year,
              paper.pagination]

        return u" ".join(li).strip()

    def paper_url(self):
        """The URL of the preprint.

        Note:
            Many others URL exists mainly those related to open access.

        Returns:
            unicode:
                the string is empty when no URLs are found.

        """
        # depends on the store
        # start with CDS looking for the field `url`
        if u"url" in self:
            data = self[u"url"]

            li = (data if isinstance(data, list) else [data])
            li = [di[u"url"] for di in li if di[u"description"] == u"Preprint"]

            if len(li) == 1:
                return li[0]

        # scan the list of files
        # work for both stores.
        pdf = "%s.pdf" % self.preprint_number()

        li = self._get(u"files", u"url", force_list=True)
        li = [el for el in li if el.endswith(pdf)]

        if len(li) == 1:
            return li[0]

        return u""

    def paper_volume(self):
        """The volume number when the record is published in a review.

        Returns:
            unicode:
                - Empty string when nothing is found.

        """
        df = self[u"publication_info"]
        return df.volume.iloc[0]

    def paper_year(self):
        """The year of the publication.

        Returns:
            unicode:
                - Empty string if the year is not defined.

        """
        df = self[u"publication_info"]
        return df.year.iloc[0]

    def preprint_number(self):
        """The ArXiv preprint number.

        Returns:
            str: empty string when it is not defined.

        """
        if u"primary_report_number" not in self:
            return

        data = self[u"primary_report_number"]
        data = (data if isinstance(data, list) else [data])

        li = [el for el in data if el.startswith(ARXIV)]
        if len(li) == 1:
                return li[0]

        return u""

    def reformat_authors(self, fmt="Last, First"):
        """Reformat names of authors.

        The default formatting for cds/invenio record is ``Last, First``.

        Args:
            fmt (str):
                define the new format for author names.
                Possible values are "First, Last", "F. Last", "Last",
                "Last, First" and "Last F."

        Raises:
            RecordException:
                * the argument ``fmt`` is not valid.

        """
        if fmt not in AUTHOR_FORMATS:
            raise RecordException(MSG_INVALID_FMT)

        if fmt == self._last_fmt_author:
            return

        self._last_fmt_author = fmt

        df = self[u"authors"]

        # ....................................................................
        #
        # Compute initial for the first name
        #
        if fmt in ("F. Last", "Last F."):

            dfm = (df.first_name.str.extract(REG_INITIAL, expand=True)
                   .fillna(""))

            df["initial"] = dfm.apply(
                lambda x: to_initial(x[0], x[1], x[2]), axis="columns")

        # ....................................................................
        #
        # Format
        #
        if fmt == "Last, First":
            df["fmt_name"] = df.last_name + ", " + df.first_name

        elif fmt == "First, Last":
            df["fmt_name"] = df.first_name + ", " + df.last_name

        elif fmt == "F. Last":
            df["fmt_name"] = df.initial + " " + df.last_name

        elif fmt == "Last":
            df["fmt_name"] = df.last_name

        elif fmt == "Last F.":
            df["fmt_name"] = df.last_name + " " + df.initial

        # ....................................................................
        #
        # Clean initial column
        #
        if fmt in ("F. Last", "Last F."):
            df = df.drop("initial", axis="columns")

    def report_number(self):
        """The report number(s) associated to the publication.

        Returns:
            str:
                - Numbers are separated by a comma
                - Number are sorted in alphabetic order.
                - Empty string when not defined.

        """
        # CDS
        if u"report_number" in self:

            data = self[u"report_number"]
            data = (data if isinstance(data, list) else [data])

            li = []
            [li.extend(di.itervalues()) for di in data]

            return ", ".join(sorted(li))

        # INSPIRE
        if u"primary_report_number" in self:

            data = self[u"primary_report_number"]
            data = (data if isinstance(data, list) else [data])

            li = [el for el in data if not el.startswith(ARXIV)]

            return ", ".join(sorted(li))

        return u""

    def submitted(self):
        """The date of submission.

        Returns:
            unicode:
                * format are"YYYY-MM", "YYYY-MM-DD", "DD MMM YYYY", *etc.*
                * Empty sring when not defined.

        """
        return self._get(u"prepublication", u"date")

    def title(self):
        """The title of the publication.

        Returns:
            unicode:
                * Empty string when not defined.
                * The filter CLEAN_SPACES is applied.

        """
        return CLEAN_SPACES(self._get(u"title", u"title"))