richard
/
geinf
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Add some outputs for swp2 parsing

master
tforest 2024-02-26 18:55:05 +01:00
parent 7949befb40
commit 14538a747b
1 changed files with 42 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

51
swp2.py
View File

@ -458,6 +458,9 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
return saved_plots
def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax = None, n_ticks = 10, subset = None, theta_scale = False):
recent_limit_years = 500
# recent limit in coal. time
recent_limit = recent_limit_years/tgen*mu
# nb of plot_lines represent the number of epochs stored (len(plot_lines) = #breaks+1)
nb_epochs = len(plot_lines)
# fig 2 & 3
@ -487,8 +490,8 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
# Plotting (fig 3) which is the same but log scale for x
p3, = ax3.plot(x2_plot, y2_plot, linestyle="-", alpha=0.75, lw=2, label = 'swp2', color="black")
lines_fig3.append(p3)
min_x = 0
min_y = 0
min_x = 1
min_y = 1
max_x = 0
max_y = 0
for breaks, plot in enumerate(plot_lines):
@ -502,11 +505,39 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
min_y = min(min_y, min(y2_plot))
max_x = max(max_x, max(x2_plot))
max_y = max(max_y, max(y2_plot))
# skip the base 0 points x_plot[0:3]
t_max_below_limit = 0
t_min_below_limit = 1
recent_change = False
for t in x[1:]:
if t <= recent_limit:
recent_change = True
t_max_below_limit = max(t_max_below_limit, t)
t_min_below_limit = min(t_min_below_limit, t)
Ne_max_below_limit = y[x.index(t_max_below_limit)]
Ne_min_below_limit = y[x.index(t_min_below_limit)]
if recent_change:
print(f"\n{breaks} breaks ; This is below the recent limit of {recent_limit_years} years:\n",
f"t_min (most recent time point under the limit) : {t_min_below_limit/mu*tgen:.1f} t_max (most ancient time point under the limit) : {t_max_below_limit/mu*tgen:.1f}",
f"\nNe_min (effective size at t_min) : {Ne_min_below_limit/(4*mu):.1f} Ne_max (effective size at t_max) : {Ne_max_below_limit/(4*mu):.1f}",
f"\nNe_min/Ne_max = {(Ne_min_below_limit/(4*mu)) / (Ne_max_below_limit/(4*mu)):.1f}",
f"\nEvolution: {((Ne_min_below_limit/(4*mu)) - (Ne_max_below_limit/(4*mu)))/((Ne_max_below_limit/(4*mu)))*100:.1f}%")
else:
print(f"Recent event under {recent_limit_years} years: NA")
# need to compute the last change and when it occured
tmin = x[1]
tmin_plus_1 = x[2]
Ne_min = y[1]
Ne_min_plus_1 = y[2]
print(f"Last was {tmin/mu*tgen:.1f} years ago. And was of {((Ne_min/(4*mu)) - (Ne_min_plus_1/(4*mu)))/(Ne_min_plus_1/(4*mu))*100:.1f}%")
else:
masking_alpha = 0
autoscale = False
ax2.set_autoscale_on(autoscale)
ax3.set_autoscale_on(autoscale)
p2, = ax2.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=masking_alpha, lw=2, label = str(breaks)+' brks')
# Plotting (fig 3) which is the same but log scale for x
p3, = ax3.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=masking_alpha, lw=2, label = str(breaks)+' brks')
@ -514,7 +545,8 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
# store for legend
lines_fig2.append(p2)
lines_fig3.append(p3)
ax3.axvline(x=500/tgen*mu, linestyle="--")
# put the vertical line of the "recent" time limit
ax3.axvline(x=recent_limit, linestyle="--")
if theta_scale:
xlabel = "Theta scaled by N0"
ylabel = "Theta scaled by N0"
@ -527,19 +559,20 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
plt.ylabel(ylabel, fontsize=fnt_size)
#plt.xlim(left=0)
#xlim_val = plt.gca().get_xlim()
x_ticks = list(plt.xticks())[0]
# plt.gca().set_xticks(x_ticks)
plt.xticks(x_ticks)
# plt.gca().set_xlim(xlim_val)
#x_ticks = list(plt.xticks())[0]
plt.xlim(min(min_x,min(swp2_lines[0])), max(max(swp2_lines[0]), max_x))
x_ticks = list(plt.gca().get_xticks())
plt.gca().set_xticks(x_ticks)
# plt.xticks(x_ticks)
# plt.gca().set_xlim(xlim_val)
plt.gca().set_xticklabels([f'{k:.0e}\n{k/(mu):.0e}\n{k/(mu)*tgen:.0e}' for k in x_ticks], fontsize = fnt_size*0.5)
# rescale y to effective pop size
# ylim_val = plt.gca().get_ylim()
plt.ylim(min(min_y,min(swp2_lines[1])), max(max_y+(max_y*0.05), max(swp2_lines[1])+(max(swp2_lines[1])*0.05)))
y_ticks = list(plt.yticks())[0]
# plt.gca().set_yticks(y_ticks)
plt.gca().set_yticks(y_ticks)
# plt.gca().set_ylim(ylim_val)
plt.yticks(y_ticks)
plt.ylim(min(min_y,min(swp2_lines[1])), max(max_y+(max_y*0.05), max(swp2_lines[1])+(max(swp2_lines[1])*0.05)))
plt.gca().set_yticklabels([f'{k/(4*mu):.0e}' for k in y_ticks], fontsize = fnt_size*0.5)
plt.title(title, fontsize=fnt_size)
plt.legend(handles=lines_fig2, loc='best', fontsize = fnt_size*0.5)