from abc import abstractmethod

import pandas as pd

from openomics.database.base import Database, Annotatable

__all__ = ['DiseaseAssociation', 'MalaCards', 'DisGeNet', 'HMDD', 'LncRNADisease']

class DiseaseAssociation(Database):

    def __init__(self, path, file_resources=None, **kwargs):

        """

        Args:

            path:

            file_resources:

            **kwargs:

        """

        super().__init__(path, file_resources, **kwargs)

    @abstractmethod

    def get_disease_assocs(self, index="gene_name"):

        """

        Args:

            index:

        """

        return self.data.groupby(index)[Annotatable.DISEASE_ASSOCIATIONS_COL].unique()

class MalaCards(DiseaseAssociation):

    """Loads the MalaCards database from "http://zdzlab.einstein.yu.edu/1/hedd/" .

    Default path: "http://zdzlab.einstein.yu.edu/1/hedd/" .

    Default file_resources: {

        "MalaCards.csv": "download.action.php?filename=DataDownload/MalaCards.csv",

    }

    """

    COLUMNS_RENAME_DICT = {

        "geneSymbol": "gene_name",

        "maladyMainName": Annotatable.DISEASE_ASSOCIATIONS_COL

    }

    def __init__(self, path="http://zdzlab.einstein.yu.edu/1/hedd/", file_resources=None,

                 col_rename=COLUMNS_RENAME_DICT, **kwargs):

        """

        Args:

            path:

            file_resources:

            col_rename:

            **kwargs:

        """

        if file_resources is None:

            file_resources = {}

            file_resources["MalaCards.csv"] = "download.action.php?filename=DataDownload/MalaCards.csv"

        super().__init__(path, file_resources, col_rename=col_rename, **kwargs)

    def load_dataframe(self, file_resources, blocksize=None):

        # type: (dict, int) -> pd.DataFrame

        """

        Args:

            file_resources:

            blocksize:

        """

        df = pd.read_csv(file_resources["MalaCards.csv"])

        return df

class DisGeNet(DiseaseAssociation):

    """Loads the  database from  .

    Default path:  .

    Default file_resources: {

        "": "",

        "": "",

        "": "",

    }

    """

    COLUMNS_RENAME_DICT = {"geneSymbol": "gene_name",

                           "diseaseName": Annotatable.DISEASE_ASSOCIATIONS_COL}

    def __init__(self, path="https://www.disgenet.org/static/disgenet_ap1/files/downloads/",

                 file_resources=None, curated=True, col_rename=COLUMNS_RENAME_DICT,

                 **kwargs):

        """

        Args:

            path:

            file_resources:

            curated:

            col_rename:

            **kwargs:

        """

        if file_resources is None:

            file_resources = {}

            file_resources["curated_gene_disease_associations.tsv"] = "curated_gene_disease_associations.tsv.gz"

            file_resources["all_gene_disease_associations.tsv"] = "all_gene_disease_associations.tsv.gz"

        self.curated = curated

        super().__init__(path, file_resources, col_rename=col_rename, **kwargs)

    def load_dataframe(self, file_resources, blocksize=None):

        """

        Args:

            file_resources:

            blocksize:

        """

        if self.curated:

            df = pd.read_table(file_resources["curated_gene_disease_associations.tsv"],

                               usecols=["geneSymbol", "diseaseName", "score"])

        else:

            df = pd.read_table(file_resources["all_gene_disease_associations.tsv"],

                               usecols=["geneSymbol", "diseaseName", "score"])

        df["diseaseName"] = df["diseaseName"].str.lower()

        return df

class HMDD(DiseaseAssociation):

    """Loads the HMDD database from "http://www.cuilab.cn/static/hmdd3" .

    Default path: "http://www.cuilab.cn/static/hmdd3/data/" .

    Default file_resources: {

        "alldata.txt": "alldata.txt",

    }

    """

    COLUMNS_RENAME_DICT = {

        "mir": "gene_name",

        "disease": Annotatable.DISEASE_ASSOCIATIONS_COL

    }

    def __init__(self, path="http://www.cuilab.cn/static/hmdd3/data/",

                 file_resources=None, col_rename=COLUMNS_RENAME_DICT,

                 **kwargs):

        """

        Args:

            path:

            file_resources:

            col_rename:

            **kwargs:

        """

        if file_resources is None:

            file_resources = {}

            file_resources["alldata.txt"] = "alldata.txt"

        super().__init__(path, file_resources, col_rename=col_rename, **kwargs)

    def load_dataframe(self, file_resources, blocksize=None):

        """

        Args:

            file_resources:

            blocksize:

        """

        df = pd.read_csv(file_resources["alldata.txt"], sep="\t", encoding="unicode_escape")

        df["disease"] = df["disease"].str.lower()

        return df

class LncRNADisease(DiseaseAssociation):

    COLUMNS_RENAME_DICT = {

        "LncRNA name": "gene_name",

        "Disease name": Annotatable.DISEASE_ASSOCIATIONS_COL

    }

    def __init__(self, path="http://www.cuilab.cn/files/images/ldd/",

                 file_resources=None, species="Human", col_rename=COLUMNS_RENAME_DICT,

                 **kwargs):

        """

        Args:

            path:

            file_resources:

            species:

            col_rename:

            **kwargs:

        """

        if file_resources is None:

            file_resources = {}

            file_resources["data_v2017.txt"] = "data_v2017.txt"

        self.species = species

        super().__init__(path, file_resources, col_rename=col_rename, **kwargs)

    def load_dataframe(self, file_resources, blocksize=None):

        """

        Args:

            file_resources:

            blocksize:

        """

        df = pd.read_csv(self.file_resources["data_v2017.txt"], header=None, sep="\t", encoding="unicode_escape")

        df.columns = ["LncRNA name", "Disease name", "Dysfunction type", "Description", "Chr",

                      "Start", "End", "Strand", "Species", "Alias", "Sequence", "Reference"]

        df = df[df["Species"] == self.species]

        df["Disease name"] = df["Disease name"].str.lower()

        return df