Source code for factorytx.test_utils

"""This module includes helper functions for writing unit tests for FactoryTX
components.
"""

import contextlib
import io
from typing import Dict, Generator, List, Tuple

import dateutil.parser
import pandas as pd
import testfixtures

from factorytx.base import Transform, Transmit
from factorytx.dataflow import InputStreamId
from factorytx.config import get_component_class, TRANSFORM_INFO
from factorytx.validation import ValidationMessage, has_errors


_MINUS_INFINITY = float('-inf')


def _normalize_record(record_dict: dict) -> List[Tuple]:
    return sorted((k, v if v == v else _MINUS_INFINITY) for k, v in record_dict.items())


def normalize_frame(df: pd.DataFrame) -> List[List[tuple]]:
    """Converts a DataFrame to a normalized form for comparisons."""
    return sorted(_normalize_record(d) for d in df.to_dict(orient='records'))


def dataframe_to_csv(df: pd.DataFrame) -> str:
    """Converts a DataFrame to a CSV for assertions. Columns are written in
    lexicographic order.
    """
    df = df.sort_index(axis=1, inplace=False)
    buf = io.StringIO()
    df.to_csv(buf, index=False)
    return buf.getvalue()


def csv_string_to_dataframe(multiline_string: str) -> io.StringIO:
    """Converts the CSV string buffer into a Pandas DataFrame."""
    df = pd.read_csv(io.StringIO(multiline_string), header=0, index_col=None)
    return df


def csv_file_to_dataframe(filename: str) -> pd.DataFrame:
    """Converts a CSV file into a Pandas DataFrame."""
    df = pd.read_csv(filename, header=0, index_col=None)
    return df


def load_transform(config: Dict) -> Transform:
    """From the configuration, retrieve the transform object from the Registry,
    validate the configuration, and return the configured transform.
    """

    transform_cls = get_component_class(TRANSFORM_INFO, config)
    validation_results = transform_cls.clean(config, {})
    assert not has_errors(validation_results)
    transform = transform_cls(config, {})
    return transform


class MemoryTransmit(Transmit):
    """The memory transmit stores data frames in its `received` attribute."""

    @staticmethod
    def clean(config: dict, root_config: dict) -> List[ValidationMessage]:
        return []

    def __init__(self, config: dict, root_config: dict) -> None:
        super().__init__(config, root_config)
        self.processed: List[Tuple[InputStreamId, pd.DataFrame]] = []

    @property
    def received(self) -> List[pd.DataFrame]:
        return [df for (_, df) in self.processed]

    def clear(self) -> None:
        self.processed = []

    def process(self, input_stream_id: InputStreamId, input_frame: pd.DataFrame) -> None:
        self.processed.append((input_stream_id, input_frame))

    def purge(self, streams: List[InputStreamId]) -> None:
        pass

    @staticmethod
    def create(filter_stream: List[str]) -> Transmit:
        return MemoryTransmit({
            'transmit_name': 'my fake transmit',
            'filter_stream': filter_stream,
        }, {})


@contextlib.contextmanager
def freeze_time(module_name: str,
                timestamp: str) -> Generator[None, None, None]:
    """Freeze time by mocking datetime, so tests can be executed in a specific
    time context.
    Example:
        >>> with freeze_time('my_module', "1985-10-26 01:21:00.000"):
        >>>     datetime.now()
        datetime(1985, 10, 26, 1, 21, 0)
    """
    d = dateutil.parser.parse(timestamp)
    fake_datetime = testfixtures.test_datetime(d.year, d.month, d.day, d.hour, d.minute,
                                               d.second, d.microsecond, d.tzinfo, delta=0)
    fake_time = testfixtures.test_time(d.year, d.month, d.day, d.hour, d.minute,
                                       d.second, d.microsecond, delta=0)
    with testfixtures.Replacer() as replacer:
        replacer.replace(f'{module_name}.datetime', fake_datetime)
        replacer.replace(f'time.time', fake_time)
        yield