text: """
  This creates Figure 4. Comparison of Experimental verus Predicted Activity across IL2 and IL15 concentrations.
  """
  import numpy as np
  import pandas as pd
  import seaborn as sns
  from scipy.stats import pearsonr
  from .figureCommon import subplotLabel, getSetup, catplot_comparison, nllsq_EC50, global_legend
  from .figure4 import plot_corrcoef, WT_EC50s
  from .figureS5 import plot_exp_v_pred
  from ..imports import import_pstat, import_Rexpr, import_samples_2_15
  
  unkVec_2_15_glob = import_samples_2_15(Traf=False, N=1)  # use one rate
  
  
  def makeFigure():
      """Get a list of the axis objects and create a figure"""
      # Get list of axis objects
      ax, f = getSetup((7, 6), (3, 3), multz={1: 1})
      subplotLabel(ax)
  
      tps = np.array([0.5, 1.0, 2.0, 4.0]) * 60.0
      tpsSC = np.array([0.5, 1.0]) * 60.0
      df = WT_EC50s(unkVec_2_15_glob, Traf=False)
      catplot_comparison(ax[0], df, Mut=False)  # compare experiments to model predictions
      plot_corrcoef(ax[1], tps, unkVec_2_15_glob, Traf=False)  # find correlation coefficients
      global_legend(ax[1], Mut=True, exppred=False)  # add legend subplots A-C
  
      plot_exp_v_pred(ax[2:8], tpsSC, cell_subset=["NK", "CD8+", "T-reg"], Traf=False)  # NK, CD8+, and Treg subplots taken from fig S5
  
      return f
  