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Make Scenario independent from CaseStudy
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| import logging | ||
| from abc import ABC | ||
| from typing import List, Tuple, Union | ||
|
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| import pandas as pd | ||
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| from draf import helper as hp | ||
|
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| logger = logging.getLogger(__name__) | ||
| logger.setLevel(level=logging.WARN) | ||
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| class DateTimeHandler(ABC): | ||
| @property | ||
| def step_width(self) -> float: | ||
| """Returns the step width of the current datetimeindex. | ||
| e.g. 0.25 for a frequency of 15min.""" | ||
| return hp.get_step_width(self.freq) | ||
|
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| @property | ||
| def dt_info(self) -> str: | ||
| """Get an info string of the chosen time horizon of the case study.""" | ||
| t1_str = f"{self.dtindex_custom[0].day_name()}, {self.dtindex_custom[0]}" | ||
| t2_str = f"{self.dtindex_custom[-1].day_name()}, {self.dtindex_custom[-1]}" | ||
| return ( | ||
| f"t1 = {self._t1:<5} ({t1_str}),\n" | ||
| f"t2 = {self._t2:<5} ({t2_str})\n" | ||
| f"Length = {self.dtindex_custom.size}" | ||
| ) | ||
|
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| def _set_dtindex(self) -> None: | ||
| self.dtindex = hp.make_datetimeindex(year=self.year, freq=self.freq) | ||
| self.dtindex_custom = self.dtindex | ||
|
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||
| @property | ||
| def steps_per_day(self): | ||
| steps_per_hour = 60 / hp.int_from_freq(self.freq) | ||
| return steps_per_hour * 24 | ||
|
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||
| @property | ||
| def freq_unit(self): | ||
| if self.freq == "15min": | ||
| return "1/4 h" | ||
| elif self.freq == "30min": | ||
| return "1/2 h" | ||
| elif self.freq == "60min": | ||
| return "h" | ||
|
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| def get_T(self) -> List: | ||
| """Returns the set T from dtindex configuration.""" | ||
| return list(range(self._t1, self._t2 + 1)) | ||
|
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| def trim_to_datetimeindex( | ||
| self, data: Union[pd.DataFrame, pd.Series] | ||
| ) -> Union[pd.DataFrame, pd.Series]: | ||
| return data[self._t1 : self._t2 + 1] | ||
|
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| def _set_year(self, year: int) -> None: | ||
| assert year in range(1980, 2100) | ||
| self.year = year | ||
| self._set_dtindex() | ||
| self._t1 = 0 | ||
| self._t2 = self.dtindex.size - 1 # =8759 for a normal year | ||
|
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| def _get_datetime_int_loc_from_string(self, s: str) -> int: | ||
| return self.dtindex.get_loc(f"{self.year}-{s}") | ||
|
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| def _get_integer_locations(self, start, steps, end) -> Tuple[int, int]: | ||
| t1 = self._get_datetime_int_loc_from_string(start) if isinstance(start, str) else start | ||
| if steps is not None and end is None: | ||
| assert t1 + steps < self.dtindex.size, "Too many steps are given." | ||
| t2 = t1 + steps - 1 | ||
| elif steps is None and end is not None: | ||
| t2 = self._get_datetime_int_loc_from_string(end) if isinstance(end, str) else end | ||
| elif steps is None and end is None: | ||
| t2 = self.dtindex.size - 1 | ||
| else: | ||
| raise ValueError("One of steps or end must be given.") | ||
| return t1, t2 | ||
|
|
||
| def dated( | ||
| self, df: Union[pd.Series, pd.DataFrame], activated=True | ||
| ) -> Union[pd.Series, pd.DataFrame]: | ||
| """Add datetime index to a data entity. | ||
| The frequency and year are taken from the CaseStudy or the Scenario object. | ||
| Args: | ||
| df: A pandas data entity. | ||
| activated: If False, the df is returned without modification. | ||
| """ | ||
| try: | ||
| dtindex_to_use = self.dtindex[df.index.min() : df.index.max() + 1] | ||
| if activated: | ||
| if isinstance(df, pd.DataFrame): | ||
| return df.set_index(dtindex_to_use) | ||
| elif isinstance(df, pd.Series): | ||
| return df.set_axis(dtindex_to_use) | ||
| else: | ||
| return df | ||
| except TypeError as e: | ||
| Logger.warning(f"Dated function could not add date-time index to data: {e}") | ||
| return df |
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