with plt.style.context(pubStyle):
    fig, ax = plt.subplots(1,1,figsize=(10,7))
    filters = ("F275W", "F814W", "F814W")

    bestFitResultsA = optimizationResults['bf']['A'][0]
    bestFitResultsB = optimizationResults['bf']['E'][0]

    bestFitOptimizationResultA = optimizationResults['r']['A'][bestFitResultsA[3]][bestFitResultsA[4]]
    bestFitOptimizationResultB = optimizationResults['r']['E'][bestFitResultsB[3]][bestFitResultsB[4]]

    bestFitPhotF1 = optimizationResults['phot'][filters[0]]
    bestFitPhotF2 = optimizationResults['phot'][filters[1]]
    bestFitPhotF3 = optimizationResults['phot'][filters[2]]

    bestFitPhotColor = bestFitPhotF1-bestFitPhotF2
    bestFitPhotMag = bestFitPhotF3

    bestFitAgeA = bestFitOptimizationResultA['opt']['x'][0]
    bestFitDistanceA = bestFitOptimizationResultA['opt']['x'][1]
    bestFitExtinctionA = bestFitOptimizationResultA['opt']['x'][2]

    bestFitAgeB = bestFitOptimizationResultB['opt']['x'][0]
    bestFitDistanceB = bestFitOptimizationResultB['opt']['x'][1]
    bestFitExtinctionB = bestFitOptimizationResultB['opt']['x'][2]
    
    c = 0

    bestFitIsoAptMagA, bestFitIsoAptColorA = iso_at_params(bestFitOptimizationResultA['iso'], bestFitAgeA, bestFitDistanceA-c, bestFitExtinctionA, *filters)
    bestFitIsoAptMagB, bestFitIsoAptColorB = iso_at_params(bestFitOptimizationResultB['iso'], bestFitAgeB, bestFitDistanceB, bestFitExtinctionB, *filters)

    # ax.scatter(bestFitPhotColor, bestFitPhotMag, s=1, alpha=0.01, color='yellow')
    ax.plot(bestFitOptimizationResultA['fiducial'][:, 0], bestFitOptimizationResultA['fiducial'][:, 1], color='black', marker='o', linestyle='', markersize=2)
    ax.plot(bestFitOptimizationResultB['fiducial'][:, 0], bestFitOptimizationResultB['fiducial'][:, 1], color='orange', marker='o', linestyle='', markersize=2)
    ax.plot(bestFitIsoAptColorA, bestFitIsoAptMagA, color='blue', alpha=0.5, label='Population A')
    ax.plot(bestFitIsoAptColorB, bestFitIsoAptMagB, color='red', alpha=0.5, label='Population E')
    ax.set_xlabel(f"{filters[0]} - {filters[1]}", fontsize=27)
    ax.set_ylabel(f"{filters[2]}", fontsize=27)
    ax.invert_yaxis()
    
    ax.annotate(fr"$\mu_{{A}} = {5*np.log10(bestFitDistanceA-c)-5:0.2f}$", (7, 14), fontsize=10)
    ax.annotate(fr"Age$_{{A}} = {bestFitAgeA:0.2f}$ Gyr", (7, 14.5), fontsize=10)
    ax.annotate(fr"$E(B-V)_{{A}} = {bestFitExtinctionA:0.2f}$", (7, 15), fontsize=10)
    ax.annotate(fr"Y$_{{A}} = {bestFitResultsA[3]}$", (7, 15.5), fontsize=10)
    ax.annotate(fr"$\alpha_{{ML,A}} = {bestFitResultsA[4]}$", (7, 16), fontsize=10)
    ax.annotate(fr"$\chi^{{2}}_{{\nu,A}} = {bestFitOptimizationResultA['opt']['fun']:0.4f}$",(7,16.5),fontsize=10)
    
    ax.annotate(fr"$\mu_{{E}} = {5*np.log10(bestFitDistanceB)-5:0.2f}$", (7, 18), fontsize=10)
    ax.annotate(fr"Age$_{{E}} = {bestFitAgeB:0.2f}$ Gyr", (7, 18.5), fontsize=10)
    ax.annotate(fr"$E(B-V)_{{E}} = {bestFitExtinctionB:0.2f}$", (7, 19), fontsize=10)
    ax.annotate(fr"Y$_{{E}} = {bestFitResultsB[3]}$", (7, 19.5), fontsize=10)
    ax.annotate(fr"$\alpha_{{ML,E}} = {bestFitResultsB[4]}$", (7, 20), fontsize=10)
    ax.annotate(fr"$\chi^{{2}}_{{\nu,E}} = {bestFitOptimizationResultB['opt']['fun']:0.4f}$",(7,20.5),fontsize=10)
    
    ax.legend(frameon=False)
    
    plt.savefig("Figures/DualPopFit.pdf")