def setup_figure():
width = 18.3
height = (1 / 2) * width
lineweight = width / 3
mpl.rcParams['lines.linewidth'] = lineweight
mpl.rcParams['axes.formatter.limits'] = (-3, 6)
# set up multi-panel figure
fig = plt.figure(figsize=(width, height))
gs = gridspec.GridSpec(1, 2)
plot_positions = [
{
"name": "fixed",
"grid_position": np.s_[0, 0],
"sharex": None,
"sharey": None
},
{
"name": "variable",
"grid_position": np.s_[0, 1],
"sharex": "fixed",
"sharey": "fixed"
},
]
plots = setup_multipanel(fig,
plot_positions,
gridspec=gs,
letter_loc=(0.05, 1.07))
return (fig, plots)
def setup_figure():
width = 18.3
height = (1/3) * width
lineweight = width / 3
mpl.rcParams['lines.linewidth'] = lineweight
mpl.rcParams['axes.formatter.limits'] = (-3, 6)
# set up multi-panel figure
fig = plt.figure(figsize=(width, height))
gs = mpl.gridspec.GridSpec(1, 3)
short_col, long_col, exp_long_col = 0, 1, 2
plot_positions = [
{"name": "typical",
"grid_position": np.s_[0, short_col],
"sharex": None,
"sharey": None},
{"name": "prolonged",
"grid_position": np.s_[0, long_col],
"sharex": None,
"sharey": "typical"},
{"name": "prolonged-experienced",
"grid_position": np.s_[0, exp_long_col],
"sharex": "prolonged",
"sharey": "typical"}
]
plots = setup_multipanel(fig,
plot_positions,
gridspec=gs)
return (fig, plots)
def main(drift_data_paths=None,
constant_data_paths=None,
pt_data_paths=None,
both_data_paths=None,
param_file=None,
output_path=None):
## figure styling / setup
width = 18.3
mpl.rcParams['font.size'] = width / 1.5
mpl.rcParams['lines.linewidth'] = width / 2.5
kernel_lw = width / 4.5
mpl.rcParams['ytick.major.pad'] = width / 2
mpl.rcParams['xtick.major.pad'] = width / 2
mpl.rcParams['legend.fontsize'] = width * 0.9
mpl.rcParams['legend.title_fontsize'] = width
mpl.rcParams['legend.handlelength'] = 2
height = width / 2
fig = plt.figure(figsize=(width, height))
nrows = 2
ncols = 4
gs = gridspec.GridSpec(nrows, ncols)
## multipanel setup
change_dist_sim_row = 0
change_dist_ana_row = 1
drift_col, const_col, both_col, pt_col = 0, 1, 2, 3
plot_positions = [{
"name": "change-dist-drift-sim",
"grid_position": np.s_[change_dist_sim_row, drift_col],
"sharex": None,
"sharey": None
}, {
"name": "change-dist-constant-sim",
"grid_position": np.s_[change_dist_sim_row, const_col],
"sharex": "change-dist-drift-sim",
"sharey": "change-dist-drift-sim"
}, {
"name": "change-dist-both-sim",
"grid_position": np.s_[change_dist_sim_row, both_col],
"sharex": "change-dist-drift-sim",
"sharey": "change-dist-drift-sim"
}, {
"name": "change-dist-pt-sim",
"grid_position": np.s_[change_dist_sim_row, pt_col],
"sharex": "change-dist-drift-sim",
"sharey": "change-dist-drift-sim"
}, {
"name": "change-dist-drift-ana",
"grid_position": np.s_[change_dist_ana_row, drift_col],
"sharex": "change-dist-drift-sim",
"sharey": None
}, {
"name": "change-dist-constant-ana",
"grid_position": np.s_[change_dist_ana_row, const_col],
"sharex": "change-dist-drift-ana",
"sharey": "change-dist-drift-ana"
}, {
"name": "change-dist-both-ana",
"grid_position": np.s_[change_dist_ana_row, both_col],
"sharex": "change-dist-drift-ana",
"sharey": "change-dist-drift-ana"
}, {
"name": "change-dist-pt-ana",
"grid_position": np.s_[change_dist_ana_row, pt_col],
"sharex": "change-dist-drift-ana",
"sharey": "change-dist-drift-ana"
}]
letter_loc = (-0.1, 1.15)
plots = setup_multipanel(fig,
plot_positions,
letter_loc=letter_loc,
gridspec=gs)
## parametrization
params = pp.get_params(param_file)
b = int(params.get("DEFAULT_CHAIN_BOTTLENECK"))
v = int(float(params.get("DEFAULT_VB_RATIO")) * b)
f_mut = float(params.get("DEFAULT_F_MUT"))
z_wt = float(params.get("DEFAULT_Z_WT"))
k = float(params.get("CONSTANT_CHAIN_K"))
mu = float(params.get("DEFAULT_MU"))
d_v = float(params.get("DEFAULT_D_V"))
R0 = float(params.get("DEFAULT_R0_WH"))
mut_sd = float(params.get("DEFAULT_CHAIN_MUT_SD"))
mut_mu = float(params.get("DEFAULT_CHAIN_MUT_MEAN"))
print("parsed parameter file: v = {}, b = {}".format(v, b))
print("parsed parameter file: f_mut = {}".format(f_mut))
print("parsed parameter file: z_wt = {}".format(z_wt))
#####################################
# simulated kernel shifts
#####################################
print("plotting simulated kernel shifts...")
plot_change_dists(plots,
drift_data_paths,
constant_data_paths,
both_data_paths,
pt_data_paths,
param_file,
kernel_lw=kernel_lw,
kernel_color=ps.kernel_color,
kernel_alpha=ps.kernel_alpha,
kernel_linestyle=ps.kernel_linestyle,
dash_capstyle='round')
#####################################
# analytical kernel shifts
#####################################
print("plotting analytical kernel shifts...")
ana_phenotypes = np.linspace(-0.35, 0.35, 101)
ana_gen_phen = -0.8 # least immune history
ana_recip_phen = -0.8 # from the sim model
kern_escape = 1
kern_model = 'linear'
print("no immunity...")
plot_kernel_shift_repl(axis=plots['change-dist-drift-ana'],
k=0,
mu=mu,
d_v=d_v,
R0=R0,
bottleneck=b,
t_M=0,
t_transmit=2,
vb_ratio=v / b,
sd_mut=mut_sd,
phenotypes=ana_phenotypes,
generator_phenotype=-99,
z_homotypic=0.95,
recipient_phenotype=-99,
susceptibility_model=kern_model,
escape=kern_escape,
dist_color=ps.inocs_color,
kernel_color=ps.kernel_color,
kernel_alpha=ps.kernel_alpha,
kernel_lw=kernel_lw,
kernel_linestyle=ps.kernel_linestyle,
dist_alpha=ps.dist_alpha,
dash_capstyle='round',
mark_original_phenotype=True)
print("constant recall response...")
plot_kernel_shift_repl(axis=plots['change-dist-constant-ana'],
k=k,
mu=mu,
d_v=d_v,
R0=R0,
bottleneck=b,
t_M=0,
t_transmit=2,
vb_ratio=v / b,
sd_mut=mut_sd,
phenotypes=ana_phenotypes,
generator_phenotype=ana_gen_phen,
z_homotypic=0.95,
recipient_phenotype=-99,
susceptibility_model=kern_model,
escape=kern_escape,
dist_color=ps.inocs_color,
kernel_color=ps.kernel_color,
kernel_alpha=ps.kernel_alpha,
kernel_linestyle=ps.kernel_linestyle,
kernel_lw=kernel_lw,
dist_alpha=ps.dist_alpha,
dash_capstyle='round',
mark_original_phenotype=True)
print("constant recall response with mucosal antibodies...")
plot_kernel_shift_repl(axis=plots['change-dist-both-ana'],
k=k,
mu=mu,
d_v=d_v,
R0=R0,
bottleneck=b,
t_M=0,
t_transmit=2,
vb_ratio=v / b,
sd_mut=mut_sd,
phenotypes=ana_phenotypes,
generator_phenotype=ana_gen_phen,
z_homotypic=0.95,
recipient_phenotype=ana_recip_phen,
susceptibility_model=kern_model,
escape=kern_escape,
dist_color=ps.inocs_color,
kernel_color=ps.kernel_color,
kernel_alpha=ps.kernel_alpha,
kernel_linestyle=ps.kernel_linestyle,
kernel_lw=kernel_lw,
dist_alpha=ps.dist_alpha,
dash_capstyle='round',
mark_original_phenotype=True)
print("realistic recall response with mucosal antibodies...")
plot_kernel_shift_repl(axis=plots['change-dist-pt-ana'],
k=k,
mu=mu,
d_v=d_v,
R0=R0,
bottleneck=b,
t_M=2,
t_transmit=2,
vb_ratio=v / b,
sd_mut=mut_sd,
phenotypes=ana_phenotypes,
generator_phenotype=ana_gen_phen,
z_homotypic=0.95,
recipient_phenotype=ana_recip_phen,
susceptibility_model=kern_model,
escape=kern_escape,
dist_color=ps.inocs_color,
kernel_color=ps.kernel_color,
kernel_alpha=ps.kernel_alpha,
kernel_lw=kernel_lw,
kernel_linestyle=ps.kernel_linestyle,
dist_alpha=ps.dist_alpha,
dash_capstyle='round',
mark_original_phenotype=True)
#####################################
# plot styling
#####################################
for plotname, plot in plots.items():
if 'ana' in plotname:
plot.set_xlabel('antigenic change')
if 'drift-sim' in plotname:
plot.set_ylabel('frequency')
if 'drift-ana' in plotname:
plot.set_ylabel('probability density')
plot.set_ylim(bottom=0)
plot.label_outer()
fig.tight_layout()
# save
fig.savefig(output_path)
return 0
def main(argv):
if len(argv) < 2:
print("USAGE: ./{} <outpath>\n\n" "outpath: path to output figure\n\n")
return 0
outpath = argv[1]
width = 12
height = width
color_low, color_high = 0.3, 0.7
lineweight = width / 2.5
mpl.rcParams['lines.linewidth'] = lineweight
mpl.rcParams['font.size'] = 1.5 * width
mpl.rcParams['legend.fontsize'] = "x-small"
mpl.rcParams['legend.loc'] = "upper left"
fig = plt.figure(figsize=(width, height))
plot_positions = [
{
"name": "varying-selection",
"row": 1,
"col": 1,
"sharex": None,
"sharey": None
},
{
"name": "varying-init-freq",
"row": 1,
"col": 2,
"sharex": "varying-selection",
"sharey": "varying-selection"
},
{
"name": "varying-selection-ongoing",
"row": 2,
"col": 1,
"sharex": "varying-selection",
"sharey": None
},
{
"name": "varying-init-freq-ongoing",
"row": 2,
"col": 2,
"sharex": "varying-selection-ongoing",
"sharey": "varying-selection-ongoing"
},
]
all_plots = pf.add_bounding_subplot(fig, position="111")
plots = pf.setup_multipanel(fig, plot_positions)
times = np.linspace(0, 4, 1000)
deltas = [12, 9, 6, 3]
delta_colors = np.linspace(color_high, color_low, len(deltas))
delta_cmap = plt.cm.Greens
mu = 0.33e-5
R0 = 5
d_v = 4
t_peak = 1.5
t_M = 2
t_emerge = 0
mu_mult_exps = [3, 2, 1, 0]
mu_colors = np.linspace(color_high, color_low, len(mu_mult_exps))
mu_cmap = plt.cm.Blues
for delta, color in zip(deltas, delta_colors):
freqs = [whp.freq_mut_select(t, mu, delta) for t in times]
freqs_ongoing = [
whp.freq_mut_ongoing_piecewise(t, t_emerge, t_M, t_peak, mu, delta,
mu, R0 * d_v) for t in times
]
plots["varying-selection"].plot(times,
freqs,
color=delta_cmap(color),
label="${}$".format(delta))
plots["varying-selection-ongoing"].plot(times,
freqs_ongoing,
color=delta_cmap(color))
plots["varying-selection-ongoing"].axvline(t_M,
color="k",
linestyle="dashed")
delta_fix = 6
for mult_exp, color in zip(mu_mult_exps, mu_colors):
freqs = [
whp.freq_mut_select(t, mu * 10**(mult_exp), delta_fix)
for t in times
]
freqs_ongoing = [
whp.freq_mut_ongoing_piecewise(t, t_emerge, t_M, t_peak,
mu * 10**(mult_exp), delta_fix, mu,
R0 * d_v) for t in times
]
plots["varying-init-freq"].plot(
times,
freqs,
color=mu_cmap(color),
label="$\\mu \\times 10^{}$".format(mult_exp))
plots["varying-init-freq-ongoing"].plot(times,
freqs_ongoing,
color=mu_cmap(color))
plots["varying-init-freq-ongoing"].axvline(t_M,
color="k",
linestyle="dashed")
plots["varying-selection"].legend(title="$\delta$",
handlelength=width / 10)
plots["varying-init-freq"].legend(title="$f_0$", handlelength=width / 20)
freq_ticks = [0, 0.25, 0.5, 0.75, 1]
for plotname, plot in plots.items():
plot.label_outer()
plot.set_xlim([0, 4])
if not "ongoing" in plotname:
plot.set_yticks(freq_ticks)
plot.set_yticklabels(["${}$".format(x) for x in freq_ticks])
else:
plot.set_yscale("log")
all_plots.set_xlabel("time since first new variant (days)")
plots["varying-selection"].set_ylabel("variant frequency $f_m$")
plots["varying-selection-ongoing"].set_ylabel("variant frequency $f_m$")
fig.tight_layout()
fig.savefig(outpath)
return 0
def setup_figure():
# set up multi-panel figure
fig = plt.figure(figsize=(width, height))
n_cols = 5
n_rows = 2
letter_y = 1.15
letter_x = -0.05
gs = gridspec.GridSpec(n_rows, n_cols)
all_plots = pf.add_bounding_subplot(fig, position=gs[:, :])
all_timecourses = pf.add_bounding_subplot(fig, position=gs[:, :3])
plot_positions = [{
"name": "sterilized-abs",
"grid_position": np.s_[0, 0],
"sharex": None,
"sharey": None
}, {
"name": "replication-selected-abs",
"grid_position": np.s_[0, 1],
"sharex": "sterilized-abs",
"sharey": "sterilized-abs"
}, {
"name": "inoculation-selected-abs",
"grid_position": np.s_[0, 2],
"sharex": "sterilized-abs",
"sharey": "sterilized-abs"
}, {
"name": "proportion-plot",
"grid_position": np.s_[:, 3:],
"sharex": None,
"sharey": None,
"letter_loc": (letter_x, letter_y + (1 - letter_y) / 2),
"include_letter": True
}, {
"name": "sterilized-freq",
"grid_position": np.s_[1, 0],
"sharex": "sterilized-abs",
"sharey": None,
"include_letter": False
}, {
"name": "replication-selected-freq",
"grid_position": np.s_[1, 1],
"sharex": "sterilized-abs",
"sharey": "sterilized-freq",
"include_letter": False
}, {
"name": "inoculation-selected-freq",
"grid_position": np.s_[1, 2],
"sharex": "sterilized-abs",
"sharey": "sterilized-freq",
"include_letter": False
}]
plots = pf.setup_multipanel(fig,
plot_positions,
gridspec=gs,
letter_loc=(letter_x, letter_y))
plots['all-plots'] = all_plots
plots['all-timecourses'] = all_timecourses
fig.tight_layout()
return (fig, plots)
def main(param_file=None, output_path=None):
## figure styling / setup
width = 18.3
mpl.rcParams['font.size'] = width / 1.5
mpl.rcParams['lines.linewidth'] = width / 2.5
kernel_lw = width / 4.5
mpl.rcParams['ytick.major.pad'] = width / 2
mpl.rcParams['xtick.major.pad'] = width / 2
mpl.rcParams['legend.fontsize'] = width * 0.9
mpl.rcParams['legend.title_fontsize'] = width
mpl.rcParams['legend.handlelength'] = 2
height = width / 3
fig = plt.figure(figsize=(width, height))
nrows = 1
ncols = 3
gs = gridspec.GridSpec(nrows, ncols)
## multipanel setup
pop_row = 0
e_rate_col, reinoc_col, epi_escape_col = 0, 1, 2
plot_positions = [{
"name": "emergence-rate",
"grid_position": np.s_[pop_row, e_rate_col],
"sharex": None,
"sharey": None
}, {
"name": "reinoculations",
"grid_position": np.s_[pop_row, reinoc_col],
"sharex": None,
"sharey": None
}, {
"name": "epi-escape",
"grid_position": np.s_[pop_row, epi_escape_col],
"sharex": None,
"sharey": None
}]
letter_loc = (-0.1, 1.15)
plots = setup_multipanel(fig,
plot_positions,
letter_loc=letter_loc,
gridspec=gs)
## parametrization
params = pp.get_params(param_file)
b = int(params.get("DEFAULT_BOTTLENECK"))
v = int(float(params.get("DEFAULT_VB_RATIO")) * b)
f_mut = float(params.get("DEFAULT_F_MUT"))
z_wt = float(params.get("DEFAULT_Z_WT"))
k = float(params.get("CONSTANT_CHAIN_K"))
mu = float(params.get("DEFAULT_MU"))
d_v = float(params.get("DEFAULT_D_V"))
R0 = float(params.get("DEFAULT_R0_WH"))
v_mult = 5
epi_sigma = 0.75
escape = 1
print("parsed parameter file: v = {}, b = {}".format(v, b))
print("parsed parameter file: f_mut = {}".format(f_mut))
print("parsed parameter file: z_wt = {}".format(z_wt))
pop_R0s = [1.5, 2, 2.5]
print("Plotting reinoculations vs population immunity...")
plot_reinoculations(axis=plots["reinoculations"],
R0s=pop_R0s,
cmap=plt.cm.Greens,
leg_cmap=plt.cm.Greys)
print("Plotting new chains vs population immunity...")
plot_epidemic_p_inocs(axis=plots["epi-escape"],
f_mut=f_mut,
bottleneck=b,
cross_imm_sigma=epi_sigma,
v=v,
population_R0s=pop_R0s,
escape=escape,
leg_cmap=plt.cm.Greys,
z_wt=z_wt,
legend=True)
print("Plotting new chains vs population immunity with v = {}..."
"".format(v * v_mult))
plot_epidemic_p_inocs(axis=plots["epi-escape"],
cross_imm_sigma=epi_sigma,
f_mut=f_mut,
bottleneck=b,
v=v * v_mult,
population_R0s=pop_R0s,
cmap=plt.cm.Purples,
linestyle='dotted',
escape=escape,
z_wt=z_wt,
legend=False)
print("Plotting emergence vs population immunity...")
mut_neut_prob_list = [0.75, 0.9, 0.99]
plot_inoculation_selection_vs_immunity(axis=plots["emergence-rate"],
f_mut=f_mut,
mut_neut_probs=mut_neut_prob_list,
wt_neut_prob=neutralize_prob_from_z(
z_wt, v, "poisson"),
final_bottleneck=b,
mucus_bottleneck=v,
cmap=plt.cm.Reds,
linestyle="solid",
legend=True,
leg_cmap=plt.cm.Greys,
inoculum_model="poisson",
bottleneck_model="binomial")
plot_inoculation_selection_vs_immunity(axis=plots["emergence-rate"],
f_mut=f_mut,
mut_neut_probs=mut_neut_prob_list,
wt_neut_prob=neutralize_prob_from_z(
z_wt, v * v_mult, "poisson"),
final_bottleneck=b,
mucus_bottleneck=v * v_mult,
cmap=plt.cm.Purples,
linestyle="dotted",
legend=False,
inoculum_model="poisson",
bottleneck_model="binomial")
#####################################
# plot styling
#####################################
imm_level_text = "population fraction immune\nto wild-type"
emergence_y_text = "new variant infections\nper inoculation"
frac_ticks = [0, 0.25, 0.5, 0.75, 1]
frac_tick_labs = ["$0$", "$0.25$", "$0.5$", "$0.75$", "$1$"]
plots["reinoculations"].set_xlabel(imm_level_text)
plots["reinoculations"].set_ylabel("per capita reinoculations")
plots["epi-escape"].set_xlabel(imm_level_text)
plots["epi-escape"].set_ylabel("per capita probability\n"
"of new variant infection")
plots["emergence-rate"].set_xlabel(imm_level_text)
plots["emergence-rate"].set_ylabel(emergence_y_text)
plots["epi-escape"].set_ylim([0, 3e-4])
for plot in plots.values():
plot.set_xlim(left=0, right=1)
plot.set_ylim(bottom=0)
plot.set_xticks(frac_ticks)
plot.set_xticklabels(frac_tick_labs)
for plot in plots.values():
if not type(plot.yaxis._scale) == mpl.scale.LogScale:
plot.set_ylim(bottom=0)
plt.tight_layout()
# save
fig.savefig(output_path)
return 0
def main(output_path=None,
fixed_results_dir=None,
var_results_dir=None,
gen_param_file=None,
empirical_data_file=None,
bottleneck=1,
heatmap_bottleneck=1,
increment=0.05):
if fixed_results_dir is None:
fixed_results_dir = "../../out/within_host_results/minimal_visible"
if var_results_dir is None:
var_results_dir = "../../out/within_host_results/minimal_visvar"
if output_path is None:
output_path = "../../ms/main/figures/figure-wh-dynamics-summary.pdf"
if gen_param_file is None:
gen_param_file = "../../dat/RunParameters.mk"
if empirical_data_file is None:
empirical_data_file = "../../dat/cleaned/cleaned_wh_data.csv"
aspect = .9
width = 18.3
height = width * aspect
lineweight = width / 2.5
mpl.rcParams['lines.linewidth'] = lineweight
mpl.rcParams['font.size'] = width
mpl.rcParams['legend.fontsize'] = "small"
non_trans_alpha = 0.4
params = pp.get_params(gen_param_file)
detection_threshold = float(params["DEFAULT_DETECTION_THRESHOLD"])
detection_limit = float(params["DEFAULT_DETECTION_LIMIT"])
td_50 = float(params["DEFAULT_TD50"])
transmission_cutoff = float(params["DEFAULT_TRANS_CUTOFF"])
transmission_threshold = (-td_50 * np.log(1 - transmission_cutoff) /
np.log(2))
print(transmission_threshold)
R0_wh = pp.get_param("R0_wh", "minimal_visvar", params)
C_max = pp.get_param("C_max", "minimal_visvar", params)
r_w = pp.get_param("r_w", "minimal_visvar", params)
r_m = pp.get_param("r_m", "minimal_visvar", params)
mu = pp.get_param("mu", "minimal_visvar", params)
d_v = pp.get_param("d_v", "minimal_visvar", params)
t_M = pp.get_param("t_M", "minimal_visvar", params)
t_N = pp.get_param("t_N", "minimal_visvar", params)
max_k = 8
max_t_M = 3.5
heatmap_t_final = 3
# set up multi-panel figure
fig = plt.figure(figsize=(width, height))
n_cols = 4
n_rows = 2
height_ratios = [2, 3.5]
ly2_inc = 0.07
ly1 = 1 + 3 * ly2_inc / 2
ly2 = 1 + ly2_inc
lx = -0.05
row_1_loc = (lx, ly1)
row_2_loc = (lx, ly2)
gs = gridspec.GridSpec(n_rows, n_cols, height_ratios=height_ratios)
all_heatmaps = add_bounding_subplot(fig, position=gs[0, 2:])
all_plots = add_bounding_subplot(fig, position=gs[:, :])
all_timecourses = add_bounding_subplot(fig, position=gs[1, :])
plot_positions = [{
"name": "empirical-detect",
"grid_position": np.s_[0, 0],
"sharex": None,
"sharey": None,
"letter_loc": row_1_loc
}, {
"name": "empirical-hist",
"grid_position": np.s_[0, 1],
"sharex": None,
"sharey": None,
"letter_loc": row_1_loc
}, {
"name": "one-percent-heatmap",
"grid_position": np.s_[0, 2],
"sharex": None,
"sharey": None,
"letter_loc": row_1_loc
}, {
"name": "consensus-heatmap",
"grid_position": np.s_[0, 3],
"sharex": "one-percent-heatmap",
"sharey": "one-percent-heatmap",
"letter_loc": row_1_loc
}, {
"name": "naive",
"grid_position": np.s_[1, 0],
"sharex": None,
"sharey": None,
"letter_loc": row_2_loc
}, {
"name": "fixed",
"grid_position": np.s_[1, 1],
"sharex": None,
"sharey": None,
"letter_loc": row_2_loc
}, {
"name": "var",
"grid_position": np.s_[1, 2],
"sharex": None,
"sharey": None,
"letter_loc": row_2_loc
}, {
"name": "inoc",
"grid_position": np.s_[1, 3],
"sharex": None,
"sharey": None,
"letter_loc": row_2_loc
}]
plots = setup_multipanel(fig, plot_positions, gridspec=gs)
fig.tight_layout(rect=(0, 0.1, 1, 1), w_pad=-0.5)
for timecourse in ['naive', 'fixed', 'var', 'inoc']:
inner = gridspec.GridSpecFromSubplotSpec(
2, 1, hspace=0.15, subplot_spec=plots[timecourse])
plots[timecourse + '-abs'] = plt.Subplot(fig, inner[0])
plots[timecourse + '-freq'] = plt.Subplot(
fig,
inner[1],
sharex=plots.get('naive-freq', None),
sharey=plots.get('naive-freq', None))
fig.add_subplot(plots[timecourse + '-abs'])
fig.add_subplot(plots[timecourse + '-freq'])
ax = plots[timecourse]
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['right'].set_color('none')
ax.grid(b=False)
ax.patch.set_alpha(0)
ax.tick_params(labelcolor='w',
grid_alpha=0,
top=False,
bottom=False,
left=False,
right=False)
ax.set_zorder(0)
wt_col = "Vw"
mut_col = "Vm"
cell_col = "C"
col_colors = [ps.wt_color, ps.mut_color, ps.cell_color]
detection_linestyle = "dashed"
detection_color = "black"
empirical_data = pd.read_csv(empirical_data_file)
plot_wh_ngs(empirical_data,
axis=plots['empirical-detect'],
min_freq=0.01,
legend=True,
edgecolor="k")
plot_ngs_hist(empirical_data,
axis=plots['empirical-hist'],
min_freq=0,
legend=True)
sims_to_plot = {
"naive": {
"results-dir": var_results_dir,
"timecourse": "naive",
"activation-time": t_N,
"E_w": False
},
"fixed": {
"results-dir": fixed_results_dir,
"timecourse": "repl_visible",
"activation-time": 0,
"E_w": True
},
"var": {
"results-dir": var_results_dir,
"timecourse": "repl_visible",
"activation-time": t_M,
"E_w": True
},
"inoc": {
"results-dir": var_results_dir,
"timecourse": "inoc_visible",
"activation-time": t_M,
"E_w": True
}
}
for sim_type, metadata in sims_to_plot.items():
for plot_freq, text in zip([False, True], ["abs", "freq"]):
plot_axis_name = "{}-{}".format(sim_type, text)
plot_axis = plots[plot_axis_name]
plot_wh_timecourse(metadata["timecourse"],
bottleneck,
metadata["results-dir"],
wt_col,
mut_col,
col_colors=col_colors,
cell_col=cell_col,
detection_threshold=detection_threshold,
detection_color=detection_color,
detection_limit=detection_limit,
detection_linestyle=detection_linestyle,
transmission_threshold=transmission_threshold,
axis=plot_axis,
t_M=metadata["activation-time"],
E_w=metadata["E_w"],
gen_param_file=gen_param_file,
non_trans_alpha=non_trans_alpha,
frequencies=plot_freq,
analytical_frequencies=True)
plot_axis.set_xlim([0, 8])
plot_axis.set_xticks([0, 2, 4, 6, 8])
if plot_freq:
plot_axis.set_ylim([1e-6, 2])
## remove inner labels
if "naive" not in plot_axis_name:
for label in plot_axis.get_yticklabels():
label.set_visible(False)
if "abs" in plot_axis_name:
for label in plot_axis.get_xticklabels():
label.set_visible(False)
R0_wh = pp.get_param("R0_wh", "minimal_visvar", params)
d_v = pp.get_param("d_v", "minimal_visvar", params)
min_prob = plot_heatmap(axis=plots["consensus-heatmap"],
contour_linewidth=(width / 3) * 0.5,
contour_fontsize="large",
increment=increment,
mu=mu,
f_target=0.5,
t_final=heatmap_t_final,
max_t_M=max_t_M,
max_k=max_k,
R0=R0_wh,
c_w=1,
c_m=0,
d_v=d_v,
bottleneck=heatmap_bottleneck,
contour_levels=[1e-5, 1e-3, 1e-1],
cbar=True)
plot_heatmap(axis=plots["one-percent-heatmap"],
contour_linewidth=(width / 3) * 0.5,
contour_fontsize="large",
increment=increment,
mu=mu,
bottleneck=heatmap_bottleneck,
f_target=0.01,
min_prob=min_prob,
t_final=heatmap_t_final,
R0=R0_wh,
d_v=d_v,
c_w=1,
c_m=0,
max_t_M=max_t_M,
max_k=max_k,
contour_levels=[1e-3, 1e-2, 1e-1],
cbar=False)
star_size = 20
star_marker = '*'
star_facecolor = 'white'
star_edgecolor = 'black'
star_edgewidth = 1.5
plots['empirical-detect'].set_ylabel('number of infections')
plots['empirical-detect'].set_title('observed HA\npolymorphism')
plots['empirical-hist'].set_title('variant within-host\nfrequencies')
plots['empirical-hist'].set_xlabel('variant frequency')
plots['empirical-hist'].set_ylabel('number of variants')
for heatmap_name in ['consensus-heatmap', 'one-percent-heatmap']:
hm = plots[heatmap_name]
escape = 0.25
star_x = 2.5
sterilizing_k = (R0_wh - 1) * d_v
fitness_diff = sterilizing_k * escape
star_y = fitness_diff
hm.plot(star_x,
star_y,
marker=star_marker,
markersize=star_size,
markerfacecolor=star_facecolor,
markeredgewidth=star_edgewidth,
markeredgecolor=star_edgecolor)
hm.set_xlabel("")
hm.grid(b=False)
hm.set_yticks(np.arange(0, 8.5, 2))
hm.set_xticks(np.arange(0, 3.5, 1))
hm.set_xlabel("")
## need to reintroduce labels because
## axis sharing turns them off
hm.xaxis.set_tick_params(labelbottom=True)
## need to reintroduce labels because
## axis sharing turns them off
plots['one-percent-heatmap'].yaxis.set_tick_params(labelleft=True)
all_heatmaps.set_xlabel("time of recall response $t_M$ (days)")
all_heatmaps.set_ylabel('selection strength $\delta$')
all_timecourses.set_xlabel("time (days)", fontsize="xx-large")
plots["naive-abs"].set_title("no recall\nresponse")
plots["fixed-abs"].set_title("constant recall\nresponse")
plots["var-abs"].set_title("recall response at\n48h")
plots["inoc-abs"].set_title("response at 48h,\nvariant inoculated")
plots["consensus-heatmap"].set_title("prob. new variant\n" "at consensus")
plots["one-percent-heatmap"].set_title("prob. new variant\n" "at 1\%")
plots["naive-abs"].set_ylabel("virions, cells")
plots["naive-freq"].set_ylabel("variant frequency")
plots['empirical-hist'].set_xlim([0, 0.5])
# create legend
cells = mlines.Line2D([], [],
color=ps.cell_color,
lw=lineweight,
label='target\ncells')
wt = mlines.Line2D([], [],
color=ps.wt_color,
lw=lineweight,
label='old variant\nvirus')
mut = mlines.Line2D([], [],
color=ps.mut_color,
lw=lineweight,
label='new variant\nvirus')
ngs = mlines.Line2D([], [],
color='black',
lw=lineweight,
linestyle="dashed",
label='NGS detection\nlimit')
analytical = mlines.Line2D([], [],
color='black',
lw=lineweight,
linestyle="dotted",
label='analytical new\nvariant frequency')
antibody = mlines.Line2D([], [],
color=ps.immune_active_color,
lw=2 * lineweight,
alpha=0.25,
label='recall response\nactive')
star_leg = mlines.Line2D([], [],
color='white',
marker=star_marker,
markersize=star_size,
markerfacecolor=star_facecolor,
markeredgewidth=star_edgewidth,
markeredgecolor=star_edgecolor,
label="influenza-like\nparameters")
handles = [cells, wt, mut, antibody, ngs, analytical, star_leg]
labels = [h.get_label() for h in handles]
all_timecourses.legend(handles=handles,
labels=labels,
fontsize='x-large',
loc="center",
bbox_to_anchor=(0.5, -.3),
frameon=False,
ncol=int(np.ceil(len(handles) / 2)))
fig.savefig(output_path)
def setup_figure(n_rows=2, n_cols=3):
aspect = 0.8
width = 18.3
height = width * aspect
lineweight = width / 2.5
mpl.rcParams['lines.linewidth'] = lineweight
mpl.rcParams['font.size'] = width * 0.9
mpl.rcParams['axes.titlesize'] = 'large'
mpl.rcParams['axes.titleweight'] = 'bold'
# set up multi-panel figure
fig = plt.figure(figsize=(width, height))
gs = gridspec.GridSpec(n_rows, n_cols)
all_plots = pf.add_bounding_subplot(fig, position=gs[:, :])
sensitivity_model_names = ['fixed', 'visvar']
row_plots = [
pf.add_bounding_subplot(fig, position=gs[row, :])
for row in range(n_rows)
]
plot_positions = [{
"name": "fixed-any-inf",
"grid_position": np.s_[0, 0],
"sharex": None,
"sharey": None
}, {
"name": "visvar-any-inf",
"grid_position": np.s_[1, 0],
"sharex": "fixed-any-inf",
"sharey": "fixed-any-inf"
}, {
"name": "fixed-mut-inf",
"grid_position": np.s_[0, 1],
"sharex": "fixed-any-inf",
"sharey": None
}, {
"name": "visvar-mut-inf",
"grid_position": np.s_[1, 1],
"sharex": "fixed-any-inf",
"sharey": "fixed-mut-inf"
}, {
"name": "fixed-mut-inf-per",
"grid_position": np.s_[0, 2],
"sharex": "fixed-any-inf",
"sharey": None
}, {
"name": "visvar-mut-inf-per",
"grid_position": np.s_[1, 2],
"sharex": "fixed-any-inf",
"sharey": "fixed-mut-inf-per"
}]
plots = pf.setup_multipanel(fig, plot_positions, gridspec=gs)
for row, name in enumerate(sensitivity_model_names):
plots[name] = row_plots[row]
plots['all'] = all_plots
fig.tight_layout()
return (fig, plots)
def setup_figure():
magnif = 3
width = magnif * 18.3
nrows = 3
ncols = 4
height = width
font_mag = 1
lineweight = width / 3
mpl.rcParams['lines.linewidth'] = lineweight
mpl.rcParams['axes.formatter.limits'] = (-3, 6)
mpl.rcParams['font.size'] *= magnif * font_mag
mpl.rcParams['ytick.major.pad'] = width / 4
mpl.rcParams['xtick.major.pad'] = width / 2
# set up multi-panel figure
fig = plt.figure(figsize=(width, height))
gs = gridspec.GridSpec(nrows, ncols)
schematic_row, inoc_dist_row, selectors_row = 0, 1, 2
prefilter_col, postfilter_col, mut_inf_col = 0, 1, 2
virion_survival_col, cutdown_col, sus_mod_col, opt_sel_col = 0, 1, 2, 3
plot_positions = [{
"name": "bottleneck-schematic",
"grid_position": np.s_[schematic_row, :],
"letter_loc": (-0.0125, 1.1),
"sharex": None,
"sharey": None
}, {
"name":
"virion-survival",
"grid_position":
np.s_[selectors_row, virion_survival_col],
"sharex":
None,
"sharey":
None
}, {
"name": "cutdown",
"grid_position": np.s_[selectors_row, cutdown_col],
"sharex": None,
"sharey": None
}, {
"name": "susceptibility-models",
"grid_position": np.s_[selectors_row, sus_mod_col],
"sharex": None,
"sharey": None
}, {
"name": "optimal-selectors",
"grid_position": np.s_[selectors_row, opt_sel_col],
"sharex": "susceptibility-models",
"sharey": None
}]
plots = setup_multipanel(fig,
plot_positions,
gridspec=gs,
verbose=True,
letters=['a', 'e', 'f', 'g', 'h', 'i'])
distrow = gs[inoc_dist_row, :]
inner_gs = gridspec.GridSpecFromSubplotSpec(1, 3, subplot_spec=distrow)
plots['all_dists'] = add_bounding_subplot(fig, position=distrow)
letters = ['B', 'C', 'D']
for idist, dist in enumerate(
['pre-filter', 'post-filter', 'mutant-creator']):
plotname = dist + '-dist'
plots[plotname] = fig.add_subplot(
inner_gs[idist],
sharex=plots.get('pre-filter-dist', None),
sharey=plots.get('pre-filter-dist', None))
letter_x, letter_y = ps.letter_loc
plots[plotname].text(letter_x,
letter_y,
letters[idist],
transform=plots[plotname].transAxes,
fontsize=ps.letter_size,
fontweight='bold',
va='top')
return (fig, plots)