class_docstr: Class holding the content for each time series in the "Time Series" section
class_docstr_tok: ["Class","holding","the","content","for","each","time","series","in","the","``","Time","Series","''","section"]
code: def __init__(
      self, name: str, result: Dict[str, Any], ser: pd.Series
  ) -> None:
      # Summary
      self.name = name
      self.mean = result["mean"]
      self.std = result["std"]
      self.minimum = result["minimum"]
      self.maximum = result["maximum"]
      self.zeros = result["zeros"]
      self.missing = result["missing"]
      self.infinite = result["infinite"]
  
      fmt_num, div_num = ju.format_number(result["sample"].mean())
      fmt_date = ju.format_date_freq(result["freq"])
      self.sample = pd.DataFrame(
          [
              [
                  fmt_num.format(val / div_num)
                  for val in result["sample"].T
              ]
          ],
          columns=[
              fmt_date.format(dt) for dt in result["sample"].index
          ],
      )
      self.sample.insert(self.sample.shape[1] // 2, "...", "...")
  
      self.plot_ts = TS(
          pd.DataFrame(ser), figsize=(5, 2.3), legend=False
      )
  
      # Tabs
      self.tabs = {
          "Stats": TabTSStatistics(name, result, ser),
          "Distribution": TabTSDistribution(name, result, ser),
          "Features": TabTSFeatures(name, result, ser),
          "Components": TabTSComponents(name, result, ser),
          "Correlation": TabTSAutoCorrelation(name, result, ser),
          "Lags": TabTSLagPlots(name, result, ser),
          "Seasonal": TabTSSeasonality(name, result, ser),
          "Smoothing": TabTSSmoothing(name, result, ser),
      }
  
imports: from typing import Dict, Any, Optional
  from pprint import pformat
  import pandas
  import yaml
  import tslumen
  from tslumen.misc import lazyproperty
  from tslumen.scheduling import Scheduler
  from tslumen.profile import BundledResult
  from tslumen import jinja_utils
  from tslumen.report.html.base import HtmlBlock
  from tslumen.plot.static import TS, TSStack, Distribution, BoxPlot, LagCorrelation, LagMatrix, Radar, Heatmap, ScatterMatrix, GrangerMatrix, GrangerGraph
label_desc: Use image node_id 1 to create a new SubTimeSeries object from inherited base classes: HtmlBlock with example: obj = SubTimeSeries(name, result, ser)