import re

import requests
from bs4 import BeautifulSoup
from mnemon import mnc
import pandas as pd

from .utils import SearchableDataFrame, get_re, EXPIRE

BASE_URL = "http://stats.oecd.org/sdmx-json"


def get_index(ds):
    """Converts the index to a DatetimeIndex"""
    v = [pd.Period(k["id"]).start_time for k in
         ds["structure"]["dimensions"]["observation"][0]["values"]]
    idx = pd.DatetimeIndex(v)
    idx.name = "d"
    return idx


def pk(ds, ks):
    """Converts things like 0:2:0:1 into a dictionary matching
    what the numbers mean."""
    a = [int(i) for i in ks.split(":")]
    key = ds["structure"]["dimensions"]["series"]
    n = [k["name"].lower() for k in key]

    # this should be configurable. right now it's just 'id': 'value',
    # could be 'name': 'value', etc.
    l = [[v["id"] for v in k["values"]] for k in key]

    return {n[i]: l[i][a[i]] for i in range(len(a))}


def parseds(ds):
    if len(ds["dataSets"]) > 1:
        print(len(ds["dataSets"]))
    s = ds["dataSets"][0]["series"]

    df = None

    for k in s:
        kt = pk(ds, k)

        o = s[k]["observations"]
        l = {int(i): o[i][0] for i in o}
        idx = get_index(ds)
        _df = (
            pd.Series(l).to_frame("value").reindex(range(0, len(idx)))
            .assign(dt=idx).assign(**kt).set_index("dt")
        )
        df = _df if df is None else pd.concat((df, _df))

    return df


class OECDims:
    def __iter__(self):
        return self._keys.__iter__()

    def __getitem__(self, i):
        return self._d[i]

    def __repr__(self):
        return "OECDims(\"{}\", {})".format(self._name, self._keys)

    def __call__(self, pattern=None):
        df = None
        for k in self:
            _df = self._d[k](pattern).assign(dim=k)
            df = _df if df is None else pd.concat([df, _df])
        return df[["dim", "id", "name"]]

    def __init__(self, md, name=""):
        self._keys = []
        self._d = {}
        self._name = "\"\"" if len(name) == 0 else name
        for x in md:
            if "role" in x and x["role"] == "TIME_PERIOD":
                continue
            attr = x["name"].lower()
            self._keys.append(attr)
            self._d[attr] = SearchableDataFrame(x["values"],
                                                columns=["id", "name"])
            self.__setattr__(attr, self._d[attr])


def oecd_inds(pattern=None, force=False, expire=EXPIRE, df=True):
    """No way to get the indicators without scraping."""
    key = "OECD|inds"
    mn = mnc(expire=EXPIRE)
    if key in mn and not force:
        l = mn[key]
    else:
        t = requests.get(BASE_URL + "/").text
        soup = BeautifulSoup(t, "html.parser")
        l = {i["value"].strip(): i.text.strip() for i in
             list(soup.find(id="Datasets").children)[2:] if i != "\n"}
        mn[key] = l
        mn.expire(key, expire)
    if pattern is not None and pattern != "":
        rxp = get_re(pattern)
        l = {k: l[k] for k in l if
             re.match(rxp, k) or re.match(rxp, l[k])}
    if df:
        d = [{"id": k, "desc": l[k]} for k in l]
        return pd.DataFrame(d, columns=["id", "desc"])
    else:
        return l


def oecd_md(pattern, force=False, expire=EXPIRE):
    idxs = oecd_inds(pattern, df=False)
    if len(idxs) == 0:
        raise ValueError("Empty selection")
    elif len(idxs) > 1:
        raise ValueError("Selection is too wide")

    idx = list(idxs.keys())[0]
    key = "OECD|{}|md".format(idx)

    mn = mnc(exp=EXPIRE)

    if key not in mn or force:
        j = requests.get("{}/metadata/{}".format(BASE_URL, idx)).json()
        mn[key] = j
        mn.expire(key, expire)
    else:
        j = mn[key]

    return j


def oecd_dims(pattern, force=False, expire=EXPIRE):
    md = oecd_md(pattern, force, expire)
    name = md["structure"]["name"]
    return OECDims(md["structure"]["dimensions"]["observation"], name)


def _pmise(x):
    return "+".join(x["id"])


def oecd_dataset(idx, rawstr=None, force=False, expire=EXPIRE, raw=False,
                 **kwargs):
    if rawstr is None:
        if kwargs is None:
            raise ValueError(
                "If `rawstr` is None you need to set kwargs according "
                "to the dimension.")
        dims = oecd_dims(idx)

        rawstr = ".".join([_pmise(dims[l](kwargs[l])) for l in dims])

    key = "OECD|{}|data".format(rawstr)

    mn = mnc(exp=EXPIRE)
    if key not in mn or force:
        j = requests.get(
            "{}/data/{}/{}/all".format(BASE_URL, idx, rawstr)).json()
        mn[key] = j
        mn.expire(key, expire)
    else:
        j = mn[key]
    if raw:
        return j
    else:
        return parseds(j).assign(ind=idx.upper())

import re

import requests
from bs4 import BeautifulSoup
from mnemon import mnc
import pandas as pd

from .utils import SearchableDataFrame, get_re, EXPIRE

BASE_URL = "http://stats.oecd.org/sdmx-json"


def get_index(ds):
    """Converts the index to a DatetimeIndex"""
    v = [pd.Period(k["id"]).start_time for k in
         ds["structure"]["dimensions"]["observation"][0]["values"]]
    idx = pd.DatetimeIndex(v)
    idx.name = "d"
    return idx


def pk(ds, ks):
    """Converts things like 0:2:0:1 into a dictionary matching
    what the numbers mean."""
    a = [int(i) for i in ks.split(":")]
    key = ds["structure"]["dimensions"]["series"]
    n = [k["name"].lower() for k in key]

    # this should be configurable. right now it's just 'id': 'value',
    # could be 'name': 'value', etc.
    l = [[v["id"] for v in k["values"]] for k in key]

    return {n[i]: l[i][a[i]] for i in range(len(a))}


def parseds(ds):
    if len(ds["dataSets"]) > 1:
        print(len(ds["dataSets"]))
    s = ds["dataSets"][0]["series"]

    df = None

    for k in s:
        kt = pk(ds, k)

        o = s[k]["observations"]
        l = {int(i): o[i][0] for i in o}
        idx = get_index(ds)
        _df = (
            pd.Series(l).to_frame("value").reindex(range(0, len(idx)))
            .assign(dt=idx).assign(**kt).set_index("dt")
        )
        df = _df if df is None else pd.concat((df, _df))

    return df


class OECDims:
    def __iter__(self):
        return self._keys.__iter__()

    def __getitem__(self, i):
        return self._d[i]

    def __repr__(self):
        return "OECDims(\"{}\", {})".format(self._name, self._keys)

    def __call__(self, pattern=None):
        df = None
        for k in self:
            _df = self._d[k](pattern).assign(dim=k)
            df = _df if df is None else pd.concat([df, _df])
        return df[["dim", "id", "name"]]

    def __init__(self, md, name=""):
        self._keys = []
        self._d = {}
        self._name = "\"\"" if len(name) == 0 else name
        for x in md:
            if "role" in x and x["role"] == "TIME_PERIOD":
                continue
            attr = x["name"].lower()
            self._keys.append(attr)
            self._d[attr] = SearchableDataFrame(x["values"],
                                                columns=["id", "name"])
            self.__setattr__(attr, self._d[attr])


def oecd_inds(pattern=None, force=False, expire=EXPIRE, df=True):
    """No way to get the indicators without scraping."""
    key = "OECD|inds"
    mn = mnc(expire=EXPIRE)
    if key in mn and not force:
        l = mn[key]
    else:
        t = requests.get(BASE_URL + "/").text
        soup = BeautifulSoup(t, "html.parser")
        l = {i["value"].strip(): i.text.strip() for i in
             list(soup.find(id="Datasets").children)[2:] if i != "\n"}
        mn[key] = l
        mn.expire(key, expire)
    if pattern is not None and pattern != "":
        rxp = get_re(pattern)
        l = {k: l[k] for k in l if
             re.match(rxp, k) or re.match(rxp, l[k])}
    if df:
        d = [{"id": k, "desc": l[k]} for k in l]
        return pd.DataFrame(d, columns=["id", "desc"])
    else:
        return l


def oecd_md(pattern, force=False, expire=EXPIRE):
    idxs = oecd_inds(pattern, df=False)
    if len(idxs) == 0:
        raise ValueError("Empty selection")
    elif len(idxs) > 1:
        raise ValueError("Selection is too wide")

    idx = list(idxs.keys())[0]
    key = "OECD|{}|md".format(idx)

    mn = mnc(exp=EXPIRE)

    if key not in mn or force:
        j = requests.get("{}/metadata/{}".format(BASE_URL, idx)).json()
        mn[key] = j
        mn.expire(key, expire)
    else:
        j = mn[key]

    return j


def oecd_dims(pattern, force=False, expire=EXPIRE):
    md = oecd_md(pattern, force, expire)
    name = md["structure"]["name"]
    return OECDims(md["structure"]["dimensions"]["observation"], name)


def _pmise(x):
    return "+".join(x["id"])


def oecd_dataset(idx, rawstr=None, force=False, expire=EXPIRE, raw=False,
                 **kwargs):
    if rawstr is None:
        if kwargs is None:
            raise ValueError(
                "If `rawstr` is None you need to set kwargs according "
                "to the dimension.")
        dims = oecd_dims(idx)

        rawstr = ".".join([_pmise(dims[l](kwargs[l])) for l in dims])

    key = "OECD|{}|data".format(rawstr)

    mn = mnc(exp=EXPIRE)
    if key not in mn or force:
        j = requests.get(
            "{}/data/{}/{}/all".format(BASE_URL, idx, rawstr)).json()
        mn[key] = j
        mn.expire(key, expire)
    else:
        j = mn[key]
    if raw:
        return j
    else:
        return parseds(j).assign(ind=idx.upper())

Converts the index to a DatetimeIndex Converts things like 0:2:0:1 into a dictionary matching     what the numbers mean. # this should be configurable. right now it's just 'id': 'value', # could be 'name': 'value', etc. No way to get the indicators without scraping.