code: def pandas_describe_timeseries_1d(
      config: Settings, series: pd.Series, summary: dict
  ) -> Tuple[Settings, pd.Series, dict]:
      """Describe a timeseries.
  
      Args:
          config: report Settings object
          series: The Series to describe.
          summary: The dict containing the series description so far.
  
      Returns:
          A dict containing calculated series description values.
      """
      config, series, stats = describe_numeric_1d(
          config, series, summary
      )
  
      stats["seasonal"] = seasonality_test(series)[
          "seasonality_presence"
      ]
      is_stationary, p_value = stationarity_test(config, series)
      stats["stationary"] = is_stationary and not stats["seasonal"]
      stats["addfuller"] = p_value
      stats["series"] = series
      stats["gap_stats"] = compute_gap_stats(series)
  
      return config, series, stats
  
file_path: ydata-profiling/src/ydata_profiling/model/pandas/describe_timeseries_pandas.py
imports: from typing import Any, Dict, Tuple
  import numpy
  import pandas
  from scipy.fft import _pocketfft
  from scipy.signal import find_peaks
  from statsmodels.tsa.stattools import adfuller
  from ydata_profiling.config import Settings
  from ydata_profiling.model.summary_algorithms import describe_numeric_1d, describe_timeseries_1d, series_handle_nulls, series_hashable
label_desc: Use image node_id 8 for calling a global function with example usage: pandas_describe_timeseries_1d(config, series, summary) and returns: config,  series,  stats