text: from statsmodels.compat.python import lmap
  
  from io import BytesIO
  
  import numpy as np
  import pandas as pd
  from numpy.testing import assert_equal, assert_
  import pytest
  
  import statsmodels.api as sm
  from statsmodels.graphics.tsaplots import (plot_acf, plot_pacf, month_plot,
                                             quarter_plot, seasonal_plot)
  import statsmodels.tsa.arima_process as tsp
  
  
  try:
      import matplotlib.pyplot as plt
  except ImportError:
      pass
  
  
  @pytest.mark.matplotlib
  def test_plot_acf(close_figures):
      # Just test that it runs.
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ar = np.r_[1., -0.9]
      ma = np.r_[1., 0.9]
      armaprocess = tsp.ArmaProcess(ar, ma)
      rs = np.random.RandomState(1234)
      acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
      plot_acf(acf, ax=ax, lags=10)
      plot_acf(acf, ax=ax)
      plot_acf(acf, ax=ax, alpha=None)
  
  
  @pytest.mark.matplotlib
  def test_plot_acf_irregular(close_figures):
      # Just test that it runs.
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ar = np.r_[1., -0.9]
      ma = np.r_[1., 0.9]
      armaprocess = tsp.ArmaProcess(ar, ma)
      rs = np.random.RandomState(1234)
      acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
      plot_acf(acf, ax=ax, lags=np.arange(1, 11))
      plot_acf(acf, ax=ax, lags=10, zero=False)
      plot_acf(acf, ax=ax, alpha=None, zero=False)
  
  
  @pytest.mark.matplotlib
  def test_plot_pacf(close_figures):
      # Just test that it runs.
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ar = np.r_[1., -0.9]
      ma = np.r_[1., 0.9]
      armaprocess = tsp.ArmaProcess(ar, ma)
      rs = np.random.RandomState(1234)
      pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
      plot_pacf(pacf, ax=ax)
      plot_pacf(pacf, ax=ax, alpha=None)
  
  
  @pytest.mark.matplotlib
  def test_plot_pacf_kwargs(close_figures):
      # Just test that it runs.
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ar = np.r_[1., -0.9]
      ma = np.r_[1., 0.9]
      armaprocess = tsp.ArmaProcess(ar, ma)
      rs = np.random.RandomState(1234)
      pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
  
      buff = BytesIO()
      plot_pacf(pacf, ax=ax)
      fig.savefig(buff, format='rgba')
  
      buff_linestyle = BytesIO()
      fig_linestyle = plt.figure()
      ax = fig_linestyle.add_subplot(111)
      plot_pacf(pacf, ax=ax, ls='-')
      fig_linestyle.savefig(buff_linestyle, format='rgba')
  
      buff_with_vlines = BytesIO()
      fig_with_vlines = plt.figure()
      ax = fig_with_vlines.add_subplot(111)
      vlines_kwargs = {'linestyles': 'dashdot'}
      plot_pacf(pacf, ax=ax, vlines_kwargs=vlines_kwargs)
      fig_with_vlines.savefig(buff_with_vlines, format='rgba')
  
      buff.seek(0)
      buff_linestyle.seek(0)
      buff_with_vlines.seek(0)
      plain = buff.read()
      linestyle = buff_linestyle.read()
      with_vlines = buff_with_vlines.read()
  
      assert_(plain != linestyle)
      assert_(with_vlines != plain)
      assert_(linestyle != with_vlines)
  
  
  @pytest.mark.matplotlib
  def test_plot_acf_kwargs(close_figures):
      # Just test that it runs.
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ar = np.r_[1., -0.9]
      ma = np.r_[1., 0.9]
      armaprocess = tsp.ArmaProcess(ar, ma)
      rs = np.random.RandomState(1234)
      acf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
  
      buff = BytesIO()
      plot_acf(acf, ax=ax)
      fig.savefig(buff, format='rgba')
  
      buff_with_vlines = BytesIO()
      fig_with_vlines = plt.figure()
      ax = fig_with_vlines.add_subplot(111)
      vlines_kwargs = {'linestyles': 'dashdot'}
      plot_acf(acf, ax=ax, vlines_kwargs=vlines_kwargs)
      fig_with_vlines.savefig(buff_with_vlines, format='rgba')
  
      buff.seek(0)
      buff_with_vlines.seek(0)
      plain = buff.read()
      with_vlines = buff_with_vlines.read()
  
      assert_(with_vlines != plain)
  
  
  @pytest.mark.matplotlib
  def test_plot_pacf_irregular(close_figures):
      # Just test that it runs.
      fig = plt.figure()
      ax = fig.add_subplot(111)
  
      ar = np.r_[1., -0.9]
      ma = np.r_[1., 0.9]
      armaprocess = tsp.ArmaProcess(ar, ma)
      rs = np.random.RandomState(1234)
      pacf = armaprocess.generate_sample(100, distrvs=rs.standard_normal)
      plot_pacf(pacf, ax=ax, lags=np.arange(1, 11))
      plot_pacf(pacf, ax=ax, lags=10, zero=False)
      plot_pacf(pacf, ax=ax, alpha=None, zero=False)
  
  
  @pytest.mark.matplotlib
  def test_plot_month(close_figures):
      dta = sm.datasets.elnino.load_pandas().data
      dta['YEAR'] = dta.YEAR.astype(int).apply(str)
      dta = dta.set_index('YEAR').T.unstack()
      dates = pd.to_datetime(['-'.join([x[1], x[0]]) for x in dta.index.values])
  
      # test dates argument
      fig = month_plot(dta.values, dates=dates, ylabel='el nino')
  
      # test with a TimeSeries DatetimeIndex with no freq
      dta.index = pd.DatetimeIndex(dates)
      fig = month_plot(dta)
  
      # w freq
      dta.index = pd.DatetimeIndex(dates, freq='MS')
      fig = month_plot(dta)
  
      # test with a TimeSeries PeriodIndex
      dta.index = pd.PeriodIndex(dates, freq='M')
      fig = month_plot(dta)
  
  
  @pytest.mark.matplotlib
  def test_plot_quarter(close_figures):
      dta = sm.datasets.macrodata.load_pandas().data
      dates = lmap('Q'.join, zip(dta.year.astype(int).apply(str),
                                 dta.quarter.astype(int).apply(str)))
      # test dates argument
      quarter_plot(dta.unemp.values, dates)
  
      # test with a DatetimeIndex with no freq
      dta.set_index(pd.to_datetime(dates), inplace=True)
      quarter_plot(dta.unemp)
  
      # w freq
      # see pandas #6631
      dta.index = pd.DatetimeIndex(pd.to_datetime(dates), freq='QS-Oct')
      quarter_plot(dta.unemp)
  
      # w PeriodIndex
      dta.index = pd.PeriodIndex(pd.to_datetime(dates), freq='Q')
      quarter_plot(dta.unemp)
  
  
  @pytest.mark.matplotlib
  def test_seasonal_plot(close_figures):
      rs = np.random.RandomState(1234)
      data = rs.randn(20,12)
      data += 6*np.sin(np.arange(12.0)/11*np.pi)[None,:]
      data = data.ravel()
      months = np.tile(np.arange(1,13),(20,1))
      months = months.ravel()
      df = pd.DataFrame([data,months],index=['data','months']).T
      grouped = df.groupby('months')['data']
      labels = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
      fig = seasonal_plot(grouped, labels)
      ax = fig.get_axes()[0]
      output = [tl.get_text() for tl in ax.get_xticklabels()]
      assert_equal(labels, output)
  