Utils

Utilities functions

Utils

Polars

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filter_tranges_df

 filter_tranges_df (df:polars.dataframe.frame.DataFrame,
                    tranges:Tuple[list,list], time_col:str='time')

- Filter data by time ranges

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filter_tranges

 filter_tranges (time:polars.series.series.Series,
                 tranges:Tuple[list,list])

- Filter data by time ranges, return the indices of the time that are in the time ranges (left inclusive, right exclusive)

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DataFrame.plot

 DataFrame.plot (*args, **kwargs)

Partition the dataset by time

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partition_data_by_time

 partition_data_by_time
                         (df:polars.lazyframe.frame.LazyFrame|polars.dataf
                         rame.frame.DataFrame, method)

*Partition the dataset by time

Args: df: Input DataFrame. method: The method to partition the data.

Returns: Partitioned DataFrame.*

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partition_data_by_year_month

 partition_data_by_year_month (df:polars.dataframe.frame.DataFrame)

*Partition the dataset by year

Args: df: Input DataFrame.

Returns: Partitioned DataFrame.*

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partition_data_by_year

 partition_data_by_year (df:polars.dataframe.frame.DataFrame)

*Partition the dataset by year

Args: df: Input DataFrame.

Returns: Partitioned DataFrame.*

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partition_data_by_ts

 partition_data_by_ts (df:polars.dataframe.frame.DataFrame,
                       ts:datetime.timedelta)

*Partition the dataset by time

Args: df: Input DataFrame. ts: Time interval.

Returns: Partitioned DataFrame.*

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concat_partitions

 concat_partitions (partitioned_input:Dict[str,Callable])

*Concatenate input partitions into one DataFrame.

Args: partitioned_input: A dictionary with partition ids as keys and load functions as values.*

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concat_df

 concat_df (dfs:list[typing.Union[polars.dataframe.frame.DataFrame,polars.
            lazyframe.frame.LazyFrame,pandas.core.frame.DataFrame]])

Concatenate a list of DataFrames into one DataFrame.

Resample data

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format_timedelta

 format_timedelta (time)

Format timedelta to timedelta

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resample

 resample
           (df:polars.lazyframe.frame.LazyFrame|polars.dataframe.frame.Dat
           aFrame, every:datetime.timedelta,
           period:datetime.timedelta=None, offset:datetime.timedelta=None,
           shift:datetime.timedelta=None, time_column='time')

Resample the DataFrame

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resample

 resample
           (df:polars.lazyframe.frame.LazyFrame|polars.dataframe.frame.Dat
           aFrame, every:datetime.timedelta,
           period:datetime.timedelta=None, offset:datetime.timedelta=None,
           shift:datetime.timedelta=None, time_column='time')

Resample the DataFrame

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resample

 resample
           (df:polars.lazyframe.frame.LazyFrame|polars.dataframe.frame.Dat
           aFrame, every:datetime.timedelta,
           period:datetime.timedelta=None, offset:datetime.timedelta=None,
           shift:datetime.timedelta=None, time_column='time')

Resample the DataFrame

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calc_vec_mag

 calc_vec_mag (vec)

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df2ts

 df2ts (df:Union[pandas.core.frame.DataFrame,polars.dataframe.frame.DataFr
        ame,polars.lazyframe.frame.LazyFrame], cols=None, time_col='time',
        attrs=None, name=None)

Convert DataFrame to TimeSeries

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check_fgm

 check_fgm (vec:xarray.core.dataarray.DataArray)