text: """TA Overlap View"""
  __docformat__ = "numpy"
  
  import logging
  import os
  from typing import List, Optional
  
  import matplotlib.pyplot as plt
  import mplfinance as mpf
  import pandas as pd
  from pandas.plotting import register_matplotlib_converters
  
  from gamestonk_terminal.config_terminal import theme
  from gamestonk_terminal.common.technical_analysis import overlap_model
  from gamestonk_terminal.config_plot import PLOT_DPI
  from gamestonk_terminal.decorators import log_start_end
  from gamestonk_terminal.helper_funcs import export_data, plot_autoscale, reindex_dates
  from gamestonk_terminal.rich_config import console
  
  logger = logging.getLogger(__name__)
  
  register_matplotlib_converters()
  
  
  @log_start_end(log=logger)
  def view_ma(
      series: pd.Series,
      length: List[int] = None,
      offset: int = 0,
      ma_type: str = "EMA",
      s_ticker: str = "",
      export: str = "",
      external_axes: Optional[List[plt.Axes]] = None,
  ) -> None:
      """Plots MA technical indicator
  
      Parameters
      ----------
      series : pd.Series
          Series of prices
      length : List[int]
          Length of EMA window
      ma_type: str
          Type of moving average.  Either "EMA" "ZLMA" or "SMA"
      s_ticker : str
          Ticker
      export : str
          Format to export data
      external_axes : Optional[List[plt.Axes]], optional
          External axes (1 axis is expected in the list), by default None
      """
      # Define a dataframe for adding EMA series to it
      price_df = pd.DataFrame(series)
  
      l_legend = [s_ticker]
      if not length:
          length = [20, 50]
  
      for win in length:
          if ma_type == "EMA":
              df_ta = overlap_model.ema(series, win, offset)
              l_legend.append(f"EMA {win}")
          elif ma_type == "SMA":
              df_ta = overlap_model.sma(series, win, offset)
              l_legend.append(f"SMA {win}")
          elif ma_type == "WMA":
              df_ta = overlap_model.wma(series, win, offset)
              l_legend.append(f"WMA {win}")
          elif ma_type == "HMA":
              df_ta = overlap_model.hma(series, win, offset)
              l_legend.append(f"HMA {win}")
          elif ma_type == "ZLMA":
              df_ta = overlap_model.zlma(series, win, offset)
              l_legend.append(f"ZLMA {win}")
          price_df = price_df.join(df_ta)
  
      plot_data = reindex_dates(price_df)
  
      # This plot has 1 axis
      if external_axes is None:
          _, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
      else:
          if len(external_axes) != 1:
              console.print("[red]Expected list of one axis item./n[/red]")
              return
          (ax,) = external_axes
  
      ax.plot(plot_data.index, plot_data.iloc[:, 1].values)
      ax.set_xlim([plot_data.index[0], plot_data.index[-1]])
      ax.set_ylabel(f"{s_ticker} Price")
      for idx in range(2, plot_data.shape[1]):
          ax.plot(plot_data.iloc[:, idx])
  
      ax.set_title(f"{s_ticker} {ma_type.upper()}")
      ax.legend(l_legend)
      theme.style_primary_axis(
          ax,
          data_index=plot_data.index.to_list(),
          tick_labels=plot_data["date"].to_list(),
      )
  
      if external_axes is None:
          theme.visualize_output()
  
      export_data(
          export,
          os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
          f"{ma_type.lower()}{'_'.join([str(win) for win in length])}",
          price_df,
      )
  
  
  @log_start_end(log=logger)
  def view_vwap(
      s_ticker: str,
      ohlc: pd.DataFrame,
      offset: int = 0,
      s_interval: str = "",
      export: str = "",
      external_axes: Optional[List[plt.Axes]] = None,
  ):
      """Plots EMA technical indicator
  
      Parameters
      ----------
      s_ticker : str
          Ticker
      ohlc : pd.DataFrame
          Dataframe of prices
      offset : int
          Offset variable
      s_interval : str
          Interval of data
      export : str
          Format to export data
      external_axes : Optional[List[plt.Axes]], optional
          External axes (3 axes are expected in the list), by default None
      """
  
      ohlc.index = ohlc.index.tz_localize(None)
      ohlc["Day"] = [idx.date() for idx in ohlc.index]
      day_df = ohlc[ohlc.Day == ohlc.Day[-1]]
      df_vwap = overlap_model.vwap(day_df, offset)
  
      candle_chart_kwargs = {
          "type": "candle",
          "style": theme.mpf_style,
          "volume": True,
          "xrotation": theme.xticks_rotation,
          "scale_padding": {"left": 0.3, "right": 1.2, "top": 0.8, "bottom": 0.8},
          "update_width_config": {
              "candle_linewidth": 0.6,
              "candle_width": 0.8,
              "volume_linewidth": 0.8,
              "volume_width": 0.8,
          },
          "warn_too_much_data": 10000,
      }
      # This plot has 2 axes
      if external_axes is None:
          candle_chart_kwargs["returnfig"] = True
          candle_chart_kwargs["figratio"] = (10, 7)
          candle_chart_kwargs["figscale"] = 1.10
          candle_chart_kwargs["figsize"] = plot_autoscale()
          candle_chart_kwargs["addplot"] = mpf.make_addplot(
              df_vwap, width=theme.line_width
          )
          fig, _ = mpf.plot(day_df, **candle_chart_kwargs)
          fig.suptitle(
              f"{s_ticker} {s_interval} VWAP",
              x=0.055,
              y=0.965,
              horizontalalignment="left",
          )
          theme.visualize_output(force_tight_layout=False)
      else:
          if len(external_axes) != 3:
              console.print("[red]Expected list of 3 axis items./n[/red]")
              return
          (ax1, ax2, ax3) = external_axes
          candle_chart_kwargs["ax"] = ax1
          candle_chart_kwargs["volume"] = ax2
          candle_chart_kwargs["addplot"] = mpf.make_addplot(
              df_vwap, width=theme.line_width, ax=ax3
          )
          mpf.plot(day_df, **candle_chart_kwargs)
  
      export_data(
          export,
          os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
          "VWAP",
          df_vwap,
      )
  