from catalyst.exchange.utils.exchange_utils import transform_candles_to_df, \
    forward_fill_df_if_needed, get_candles_df

from catalyst.testing.fixtures import WithLogger, ZiplineTestCase
from pandas import Timestamp, DataFrame, concat

import numpy as np


class TestExchangeUtils(WithLogger, ZiplineTestCase):
    @classmethod
    def get_specific_field_from_df(cls, df, field, asset):
        new_df = DataFrame(df[field])
        new_df.columns = [asset]
        new_df.index.name = None
        return new_df

    def test_get_candles_df(self):
        asset = 'btc_usdt'
        asset2 = 'eth_usdt'

        # test forward fill in the end
        candles = [{'high': 595, 'volume': 10, 'low': 594,
                    'close': 595, 'open': 594,
                    'last_traded': Timestamp('2018-03-01 09:45:00+0000',
                                             tz='UTC')
                    },
                   {'high': 594, 'volume': 108, 'low': 592,
                    'close': 593, 'open': 592,
                    'last_traded': Timestamp('2018-03-01 09:50:00+0000',
                                             tz='UTC')
                    }]

        expected = [{'high': 595.0, 'volume': 10.0, 'low': 594.0,
                     'close': 595.0, 'open': 594.0,
                     'last_traded': Timestamp('2018-03-01 09:45:00+0000',
                                              tz='UTC')
                     },
                    {'high': 594.0, 'volume': 108.0, 'low': 592.0,
                     'close': 593.0, 'open': 592.0,
                     'last_traded': Timestamp('2018-03-01 09:50:00+0000',
                                              tz='UTC')
                     },
                    {'high': 593.0, 'volume': 0.0, 'low': 593.0,
                     'close': 593.0, 'open': 593.0,
                     'last_traded': Timestamp('2018-03-01 09:55:00+0000',
                                              tz='UTC')
                     }]

        periods = [Timestamp('2018-03-01 09:45:00+0000', tz='UTC'),
                   Timestamp('2018-03-01 09:50:00+0000', tz='UTC'),
                   Timestamp('2018-03-01 09:55:00+0000', tz='UTC')]

        observed_df = forward_fill_df_if_needed(
            transform_candles_to_df(candles),
            periods)
        expected_df = transform_candles_to_df(expected)

        assert (expected_df.equals(observed_df))

        for field in ['volume', 'open', 'close', 'high', 'low']:
            field_dt = self.get_specific_field_from_df(expected_df,
                                                       field,
                                                       asset)
            assert (field_dt.equals(get_candles_df({asset: candles},
                                                   field, '5T', 3,
                                                   end_dt=periods[2])))

        # test forward fill in the middle
        candles = [{'high': 595, 'volume': 10, 'low': 594,
                    'close': 595, 'open': 594,
                    'last_traded': Timestamp('2018-03-01 09:45:00+0000',
                                             tz='UTC')
                    },
                   {'high': 594, 'volume': 108, 'low': 592,
                    'close': 593, 'open': 592,
                    'last_traded': Timestamp('2018-03-01 09:55:00+0000',
                                             tz='UTC')
                    }]

        expected = [{'high': 595.0, 'volume': 10.0, 'low': 594.0,
                     'close': 595.0, 'open': 594.0,
                     'last_traded': Timestamp('2018-03-01 09:45:00+0000',
                                              tz='UTC')
                     },
                    {'high': 595.0, 'volume': 0.0, 'low': 595.0,
                     'close': 595.0, 'open': 595.0,
                     'last_traded': Timestamp('2018-03-01 09:50:00+0000',
                                              tz='UTC')
                     },
                    {'high': 594.0, 'volume': 108.0, 'low': 592.0,
                     'close': 593.0, 'open': 592.0,
                     'last_traded': Timestamp('2018-03-01 09:55:00+0000',
                                              tz='UTC')
                     }]

        df = transform_candles_to_df(candles)
        observed_df = forward_fill_df_if_needed(df, periods)
        expected_df = transform_candles_to_df(expected)

        assert (expected_df.equals(observed_df))

        for field in ['volume', 'open', 'close', 'high', 'low']:
            # test several assets as well
            observed_df = get_candles_df({asset: candles,
                                          asset2: candles},
                                         field, '5T', 3,
                                         end_dt=periods[2])

            field_dt_a1 = self.get_specific_field_from_df(expected_df,
                                                          field,
                                                          asset)
            field_dt_a2 = self.get_specific_field_from_df(expected_df,
                                                          field,
                                                          asset2)

            assert(observed_df.equals(concat([field_dt_a1, field_dt_a2],
                                             axis=1)))

        # test "forward fill" at the beginning
        candles = [{'high': 595, 'volume': 10, 'low': 594,
                    'close': 595, 'open': 594,
                    'last_traded': Timestamp('2018-03-01 09:50:00+0000',
                                             tz='UTC')
                    },
                   {'high': 594, 'volume': 108, 'low': 592,
                    'close': 593, 'open': 592,
                    'last_traded': Timestamp('2018-03-01 09:55:00+0000',
                                             tz='UTC')
                    }]

        expected = [{'high': np.NaN, 'volume': 0.0, 'low': np.NaN,
                     'close': np.NaN, 'open': np.NaN,
                     'last_traded': Timestamp('2018-03-01 09:45:00+0000',
                                              tz='UTC')
                     },
                    {'high': 595, 'volume': 10, 'low': 594,
                     'close': 595, 'open': 594,
                     'last_traded': Timestamp('2018-03-01 09:50:00+0000',
                                              tz='UTC')
                     },
                    {'high': 594, 'volume': 108, 'low': 592,
                     'close': 593, 'open': 592,
                     'last_traded': Timestamp('2018-03-01 09:55:00+0000',
                                              tz='UTC')
                     }]

        df = transform_candles_to_df(candles)
        observed_df = forward_fill_df_if_needed(df, periods)
        expected_df = transform_candles_to_df(expected)

        assert (expected_df.equals(observed_df))
        # Not the same due to dropna - commenting out for now
        """
                for field in ['volume', 'open', 'close', 'high', 'low']:
            field_dt = self.get_specific_field_from_df(observed_df,
                                                       field,
                                                       asset)
            assert(field_dt.equals(get_candles_df({asset:candles},
                                                  field, '5T', 3,
                                                  end_dt=periods[2])))
        """