code: def time_series_plot(
      y,
      lags=31,
      title="Original Time Series",
      chart_type="line",
      chart_freq="years",
  ):
      """
      Plot a Time Series along with how it will look after differencing and what its
      AR/MA lags will be by viewing the ACF and PACF, along with its histogram.
      You just need to provide the time series (y) as a Series. Index is assumed
      to be Pandas datetime. It assumes that you want to see default lags of 31.
      But you can modify it to suit.
      """
  
      y = copy.deepcopy(y)
      if chart_freq in ["MS", "M", "SM", "BM", "CBM", "SMS", "BMS"]:
          chart_time = "months"
      elif chart_freq in ["D", "B", "C"]:
          chart_time = "days"
      elif chart_freq in ["W"]:
          chart_time = "weeks"
      elif chart_freq in ["Q", "BQ", "QS", "BQS"]:
          chart_time = "quarters"
      elif chart_freq in ["A,Y", "BA,BY", "AS,YS", "BAS,YAS"]:
          chart_time = "years"
      elif chart_freq in ["BH", "H", "h"]:
          chart_time = "hours"
      elif chart_freq in ["T,min"]:
          chart_time = "minutes"
      elif chart_freq in [
          "S",
          "L,milliseconds",
          "U,microseconds",
          "N,nanoseconds",
      ]:
          chart_time = "seconds"
      else:
          print("chart frequency not known. Continuing...")
          return
      colors = cycle("byrcmgkbyrcmgkbyrcmgkbyrcmgkbyr")
      fig = plt.figure(figsize=(20, 20))
      grid = plt.GridSpec(3, 2, wspace=0.5, hspace=0.5)
      fig.subplots_adjust(hspace=1)
      ########## Use the gridspec function ##############
      ts_ax = plt.subplot(grid[0, 0:])
      diff_ax = plt.subplot(grid[1, 0])
      hist_ax = plt.subplot(grid[1, 1])
      acf_ax = plt.subplot(grid[2, 0])
      pacf_ax = plt.subplot(grid[2, 1])
      ### Draw multiple kinds of graphs here to each subplot axis ###
  
      if (
          type(y) == dask.dataframe.core.DataFrame
          or type(y) == dask.dataframe.core.Series
      ):
          y = y.head(len(y))  ## this converts it into a pandas Series
      if chart_type == "line":
          y.plot(ax=ts_ax, color=next(colors))
      else:
          if chart_time == "years":
              majors = mdates.YearLocator()  # every year
              minors = mdates.MonthLocator()  # every month
              majorsFmt = mdates.DateFormatter("%Y")
          elif chart_time == "months":
              majors = mdates.YearLocator()  # every year
              minors = mdates.MonthLocator()  # every month
              majorsFmt = mdates.DateFormatter("\n\n\n%b\n%Y")
          elif chart_time == "weeks":
              majors = mdates.MonthLocator()
              minors = mdates.WeekdayLocator(byweekday=(1), interval=1)
              majorsFmt = mdates.DateFormatter("\n\n\n%b\n%Y")
          elif chart_time == "days":
              majors = mdates.DayLocator(
                  bymonthday=None, interval=1, tz=None
              )
              minors = mdates.HourLocator(
                  byhour=None, interval=1, tz=None
              )
              majorsFmt = mdates.DateFormatter("\n\n\n%d\n%b")
          else:
              majors = mdates.YearLocator()  # every year
              minors = mdates.MonthLocator()  # every month
              majorsFmt = mdates.DateFormatter("\n\n\n%b\n%Y")
          try:
              #### this works in most cases but in some cases, it gives an error
              ts_ax.bar(
                  y.index,
                  height=y,
                  width=20,
                  color=list(
                      (y > 0).astype(int).map({1: "g", 0: "r"}).values
                  ),
              )
          except:
              #### In some cases where y is a dataframe, this might work.
              yindex = y.index
              yvalues = y.values.ravel()
              ts_ax.bar(
                  yindex,
                  height=yvalues,
                  width=20,
                  color=list(
                      using_where((yvalues > 0).astype(int)).ravel()
                  ),
              )
          ts_ax.xaxis.set_major_locator(majors)
          ts_ax.xaxis.set_major_formatter(majorsFmt)
          ts_ax.xaxis.set_minor_locator(minors)
          ts_ax.format_xdata = mdates.DateFormatter("%Y-%m-%d")
          ts_ax.grid(True)
      #### Now draw the ACF and PACF charts
      ts_ax.set_title(title)
      y.diff(1).plot(ax=diff_ax, color=next(colors))
      diff_ax.set_title("After Differencing = 1")
      y.plot(ax=hist_ax, kind="hist", bins=25, color=next(colors))
      hist_ax.set_title("Histogram for Original Series")
      try:
          if len(y) < lags:
              lags = int(len(y) - 1)
          smt.graphics.plot_acf(y, lags=lags, ax=acf_ax)
          acf_ax.set_title("ACF for Original Series")
      except:
          acf_ax.set_title(
              "Data Error: Could not draw ACF for Original Series"
          )
      try:
          ### the number of lags cannot be greater than 50% of len of y. So limit it.
          if lags >= len(y) * 0.5:
              lags = int(len(y) * 0.5 - 1)
          smt.graphics.plot_pacf(y, lags=lags, ax=pacf_ax)
          pacf_ax.set_title("PACF for Original Series")
      except:
          pacf_ax.set_title(
              "Data Error: Could not draw PACF for Original Series"
          )
      [ax.set_xlim(0) for ax in [acf_ax, pacf_ax]]
      plt.show(block=False)
  
imports: from itertools import cycle
  import numpy
  import pandas
  import matplotlib.dates
  from itertools import cycle
  import matplotlib.pyplot
  import seaborn
  import statsmodels.tsa.api
  import pdb
  import copy
  import dask
  import dask.dataframe
  from .colors import colorful
  import copy
label_desc: Use image node_id 1 for calling a global function with example usage: time_series_plot(y, lags, title, chart_type, chart_freq) without return types