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
