def create_fig(): # load data or compute model data1D = load_model() data1D = process_data(data1D) nr = 5 nc = len(alt_model_par) + 1 fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) idx = 0 for key in alt_model_par: subplots = np.arange(nr)*nc + 1 + idx idx += 1 ax_list = [fig.add_subplot(nr, nc, x) for x in subplots] imVec = plot_parscan(fig, ax_list, key, data1D) subplots = np.arange(nr)*nc + nc ax_list = [fig.add_subplot(nr, nc, x) for x in subplots] add_color_bars(fig, ax_list, imVec) plt.tight_layout(pad=1, h_pad=2.5, w_pad=1.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model data1D = load_model() data1D = process_data(data1D) # load data or compute model Output, InvPerHost = load_model_single() # set fonts fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) # plot average investment ax = fig.add_subplot(1, 4, 1, projection='3d') plot_3D(ax, data1D, 1) ax.set_title('Birth rate effect $(s_b,s_d)=(1,0)$') ax = fig.add_subplot(1, 4, 2, projection='3d') ax.set_title('Death rate effect $(s_b,s_d)=(0,0.5)$') plot_3D(ax, data1D, 0) ax = fig.add_subplot(1, 4, 3) plot_heatmap(fig, ax, data1D) axs = fig.add_subplot(1, 4, 4) plot_histogram_line(axs, InvPerHost) plt.tight_layout(pad=1, h_pad=2.5, w_pad=0.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model data1D = load_model() data1D = process_data(data1D) fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) #manual set subplot axis bm = 0.15 tm = 0.06 h = [(1 - bm - tm), (1 - bm - tm) * 0.6] lm = 0.05 rm = 0.05 cmh = 0.15 wt = (1 - rm - lm - cmh) wf = 0.6 w = [wf * wt, (1 - wf) * wt] left = [lm, lm + w[0] + cmh] bot = [bm, bm] # plot average investment ax = fig.add_axes([left[0], bot[0], w[0], h[0]], projection='3d') plot_3D(ax, data1D) ax = fig.add_axes([left[1], bot[1], w[1], h[1]]) plot_heatmap(fig, ax, data1D) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model Output, InvPerHost = load_model() # set fonts fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) # plot average investment axs = fig.add_subplot(1, 2, 1) plot_line(axs, Output, "F_mav") axs = fig.add_subplot(1, 2, 2) plot_histogram_line(axs, InvPerHost) plt.tight_layout(pad=0.2, h_pad=0.5, w_pad=0.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # set settings n0 = 1E-4 mig1 = 0.5 * n0 mig_vec_rel = [0.1, 0.5, 2, 10] # setup manual axis for subplots bm = 0.22 tm = 0.06 cm = 0.05 h = (1 - bm - cm - tm) / 2 lm = 0.08 rm = 0 cmh = 0.18 w1 = 0.4 w2 = 1 - w1 - cmh - rm - lm h3 = 0.1 tm3 = 0.05 h2 = 1 - h3 - cm - bm - tm3 fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) ax = fig.add_axes([lm, bm + cm + h, w1, h]) plot_ver_hor_cell(ax, n0, mig1) ax = fig.add_axes([lm, bm, w1, h]) plot_verFracl(ax, n0, mig_vec_rel) ax.annotate('$\\frac{\\theta/\\beta}{n_0/k}=$', xy=(w1 + lm + 0.02, bm + h * 1.2), xycoords='figure fraction', horizontalalignment='left', verticalalignment='top') ax = fig.add_axes([lm + cmh + w1, bm, w2, h2]) cbaxes = fig.add_axes([lm + cmh + w1, h2 + bm + cm, w2, h3]) plot_tauH_heatmap(fig, ax, cbaxes) #plt.tight_layout(pad=0.2, h_pad=0.5, w_pad=0.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model Output = load_model() # set fonts fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) numcost = len(cost_vec) numElTot = Output.shape[0] numElLoc = numElTot/numcost legendLoc = 'lower right' for i in range(numcost): if i == 0: yAxis = True else: yAxis = False if i>1: legendLoc = 'none' startP = int(i*numElLoc) endP = int((i+1)*numElLoc) currOutput = Output[startP:endP,:] # plot average investment axs = fig.add_subplot(1, 5, i+1) plot_line(axs, currOutput, "F_mav", yAxis, maxT_vec[i], legendLoc) axs.set_title('Cost $\\gamma=%.3f$' % cost_vec[i]) # axs = fig.add_subplot(1, 2, 2) # plot_histogram_line(axs, InvPerHost) plt.tight_layout(pad=0.2, h_pad=0.5, w_pad=0.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model data1D = load_model() data1D = process_data(data1D) # settings cost_vec = [0.001, 0.01, 0.1] # setup manual axis for subplots bm = 0.25 tm = 0.02 cm = 0.12 #h = (1 - bm - tm - cm) / 2 h = 1 - bm - tm lm = 0.08 rm = 0.03 cmh = 0.12 w = (1 - cmh - rm - lm) / 2 # set fonts fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) ax = fig.add_axes([lm, bm, w, h]) plot_helper_frac(ax, cost_vec) ax.annotate('$\\gamma=$', xy=(w+lm-0.12, bm+h*0.7), xycoords='figure fraction', horizontalalignment='left', verticalalignment='top') ax = fig.add_axes([lm+cmh+w, bm, w, h]) plot_tau_var(ax, data1D) ax.annotate('$\\sigma=$', xy=(w+lm+cmh+0.05, bm+h), xycoords='figure fraction', horizontalalignment='left', verticalalignment='top') fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model data1D = load_model() data1D = process_data(data1D) fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) # setup manual axis for subplots bm = 0.15 tm = 0.06 cm = 0.13 hf = 0.65 h = [hf*(1 - bm - cm - tm), (1-hf)*(1 - bm - cm - tm)] lm = 0.05 rm = 0.05 cmh = 0.1 w = (1 - cmh - rm - lm)/2 wf = 0.85 wo = (1-wf)*w left = [lm, lm+cmh+w] bot = [bm+h[1]+cm, bm] # plot for different sampling variance for ss in range(2): # plot scatter ax = fig.add_axes([left[ss], bot[0], w, h[0]], projection='3d') plot_3D(ax, data1D, ss) ax.set_title('$\\sigma={}$'.format(sigma_vec[ss]), fontsize=6) # plot heatmap ax = fig.add_axes([left[ss]+wo, bot[1], wf*w, h[1]]) plot_heatmap(fig, ax, data1D, ss) plt.show() #plt.tight_layout(pad=1, h_pad=2.5, w_pad=1.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None
def create_fig(): # load data or compute model data1D = load_model() data1D = process_data(data1D) # set fonts fig = plt.figure() mlsg.set_fig_size_cm(fig, wFig, hFig) # plot average investment ax = fig.add_subplot(1, 3, 1, projection='3d') plot_3D(ax, data1D, 1) ax.set_title('Brith rate effect $s_b=1$') ax = fig.add_subplot(1, 3, 2, projection='3d') ax.set_title('No brith rate effect $s_b=0$') plot_3D(ax, data1D, 0) ax = fig.add_subplot(1, 3, 3) plot_heatmap(fig, ax, data1D) plt.tight_layout(pad=1, h_pad=2.5, w_pad=0.5) fig.savefig(fig_Folder / figureName, format="pdf", transparent=True) return None