Fixing some scaling for swp2 output
tforest
parent
25d7ef0858
commit
1848102140
134
swp2.py
134
swp2.py
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@ -176,7 +176,7 @@ def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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ax1.set_title(title)
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breaks = len(full_dict['all_epochs']['plots'])
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if ax is None:
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plt.savefig(title+'_'+str(breaks+1)+'_epochs.pdf')
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plt.savefig(title+'_best_'+str(breaks+1)+'_epochs.pdf')
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# plot likelihood against nb of breakpoints
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if ax is None:
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fig, ax2 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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@ -299,7 +299,15 @@ def save_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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# number of monomorphic sites
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S0 = L-S
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# print("SFS", SFS_stored)
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# print("S", S, "L", L, "S0=", S0)
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print("S", S, "L", L, "S0=", S0)
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my_n = len(SFS_stored)*2
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print("n=",my_n)
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an = 1
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for i in range(2, my_n):
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an +=1.0/i
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print("an=", an, "theta_w", S/an, "theta_w_p_site", (S/an)/L)
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# compute Ln
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Ln = log_facto(S+S0) - log_facto(S0) + np.log(float(S0)/(S+S0)) * S0
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for xi in range(0, len(SFS_stored)):
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@ -413,6 +421,25 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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cumul = val+cumul
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prop = prop_cumul
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# print("raw stairs", plots[3])
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# ###########
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# time = []
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# for k in plots[0][0]:
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# k = int(k)
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# dt = 2.0/(k*(k-1))
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# time.append(2.0/(k*(k-1)))
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# Ne = []
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# for values in plots:
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# Ne.append(np.array(values[1])/(4*mu))
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# print(time)
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# print(Ne[3])
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lines_fig2 = []
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for epoch, theta in best_epochs.items():
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groups = np.array(list(theta.values()), dtype=object)[:, 1].tolist()
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@ -423,24 +450,33 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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x += group[::-1]
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y += list(np.repeat(thetas[i], len(group)))
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if epoch == 0:
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N0 = y[0]
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# watterson theta
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theta_w = y[0]
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if theta_scale :
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for i in range(len(y)):
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y[i] = y[i]/N0
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for i in range(len(y)):
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y[i] = y[i]/(4*mu)
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x_2 = []
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T = 0
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for i in range(len(x)):
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x[i] = int(x[i])
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# compute the times as: theta_k / (k*(k-1))
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for i in range(0, len(x)):
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T += y[i] / (x[i]*(x[i]-1))
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T += y[i]*2 / (x[i]*(x[i]-1))
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x_2.append(T)
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# Save plotting (fig 2)
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x_2 = [0]+x_2
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y = [y[0]]+y
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# x_2 = [0]+x_2
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# y = [y[0]]+y
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# x2_plot, y2_plot = plot_straight_x_y(x_2, y)
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p2 = x_2, y
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lines_fig2.append(p2)
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# print("breaks=", epoch, "scaled_theta", lines_fig2[10])
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# print(lines_fig2[3][1][0]/(4*mu))
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# print(np.array(lines_fig2[3][1])/lines_fig2[3][1][0])
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# print("size list y=", len(lines_fig2[3][1]))
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#exit(0)
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if input == None:
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saved_plots = {"raw_stairs":plots, "scaled_stairs":lines_fig2,
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"prop":prop}
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@ -458,9 +494,9 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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return saved_plots
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def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax = None, n_ticks = 10, subset = None, theta_scale = False):
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recent_limit_years = 500
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recent_limit_years = 100
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# recent limit in coal. time
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recent_limit = recent_limit_years/tgen*mu
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recent_limit = recent_limit_years/tgen
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# nb of plot_lines represent the number of epochs stored (len(plot_lines) = #breaks+1)
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nb_epochs = len(plot_lines)
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# fig 2 & 3
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@ -480,9 +516,9 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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#plt.figure(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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if swp2_lines:
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for k in range(len(swp2_lines[0])):
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swp2_lines[0][k] = swp2_lines[0][k]/tgen*mu
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swp2_lines[0][k] = swp2_lines[0][k]/tgen
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for k in range(len(swp2_lines[1])):
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swp2_lines[1][k] = swp2_lines[1][k]*4*mu
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swp2_lines[1][k] = swp2_lines[1][k]
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# x2_plot, y2_plot = plot_straight_x_y(swp2_lines[0],swp2_lines[1])
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x2_plot, y2_plot = swp2_lines[0], swp2_lines[1]
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p2, = ax2.plot(x2_plot, y2_plot, linestyle="-", alpha=0.75, lw=2, label = 'swp2', color="black")
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@ -508,14 +544,14 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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# skip the base 0 points x_plot[0:3]
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t_max_below_limit = 0
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t_min_below_limit = 1
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t_min_below_limit = recent_limit
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recent_change = False
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for t in x[1:]:
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if t <= recent_limit:
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recent_change = True
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t_max_below_limit = max(t_max_below_limit, t)
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t_min_below_limit = min(t_min_below_limit, t)
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Ne_max_below_limit = y[x.index(t_max_below_limit)]
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Ne_max_below_limit = y[min(x.index(t_max_below_limit)+1, len(y)-1)]
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Ne_min_below_limit = y[x.index(t_min_below_limit)]
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if recent_change:
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print(f"\n{breaks} breaks ; This is below the recent limit of {recent_limit_years} years:\n",
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@ -547,6 +583,8 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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lines_fig3.append(p3)
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# put the vertical line of the "recent" time limit
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ax3.axvline(x=recent_limit, linestyle="--")
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ax3.axvline(x=recent_limit/2, linestyle="--", color="green")
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if theta_scale:
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xlabel = "Theta scaled by N0"
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ylabel = "Theta scaled by N0"
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@ -557,26 +595,36 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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# if not ax, then use the plt syntax, not ax...
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plt.xlabel(xlabel, fontsize=fnt_size)
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plt.ylabel(ylabel, fontsize=fnt_size)
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#plt.xlim(left=0)
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plt.gca().set_xlim(0, recent_limit * 3)
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if recent_change:
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plt.ylim(Ne_min_below_limit/3, Ne_max_below_limit *3)
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else:
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plt.ylim(y2_plot[0]/3, y2_plot[0])
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# plt.ylim(0, max(max_y+(max_y*0.05), max(swp2_lines[1])+(max(swp2_lines[1])*0.05)))
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#plt.xlim(0, recent_limit * 3)
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#xlim_val = plt.gca().get_xlim()
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#x_ticks = list(plt.xticks())[0]
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plt.xlim(min(min_x,min(swp2_lines[0])), max(max(swp2_lines[0]), max_x))
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x_ticks = list(plt.gca().get_xticks())
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plt.gca().set_xticks(x_ticks)
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x_ticks = list(plt.xticks())[0]
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# plt.xlim(min(min_x,min(swp2_lines[0])), max(max(swp2_lines[0]), max_x))
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# x_ticks = list(plt.gca().get_xticks())
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# plt.gca().set_xticks(x_ticks)
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# plt.xticks(x_ticks)
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# plt.gca().set_xlim(xlim_val)
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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)
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# 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)
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plt.gca().set_xticklabels([f'{k:.1f}\n{k*tgen:.1f}' for k in x_ticks], fontsize = fnt_size*0.5)
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# rescale y to effective pop size
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# ylim_val = plt.gca().get_ylim()
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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)))
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y_ticks = list(plt.yticks())[0]
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plt.gca().set_yticks(y_ticks)
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# 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)))
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# y_ticks = list(plt.yticks())[0]
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# plt.gca().set_yticks(y_ticks)
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# plt.gca().set_ylim(ylim_val)
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plt.yticks(y_ticks)
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plt.gca().set_yticklabels([f'{k/(4*mu):.0e}' for k in y_ticks], fontsize = fnt_size*0.5)
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plt.title(title, fontsize=fnt_size)
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plt.legend(handles=lines_fig2, loc='best', fontsize = fnt_size*0.5)
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plt.text(-0.13, -0.135, 'Coal. time\nGen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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# plt.yticks(y_ticks)
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# plt.gca().set_yticklabels([f'{k/(4*mu):.0e}' for k in y_ticks], fontsize = fnt_size*0.5)
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# plt.title(title, fontsize=fnt_size)
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# plt.legend(handles=lines_fig2, loc='best', fontsize = fnt_size*0.5)
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# # plt.text(-0.13, -0.135, 'Coal. time\nGen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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plt.text(-0.13, -0.135, 'Gen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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plt.subplots_adjust(bottom=0.2) # Adjust the value as needed
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plt.savefig(title+'_plotB_'+str(nb_epochs)+'_epochs.pdf')
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# close fig2 to save memory
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@ -594,16 +642,30 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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ax3.set_xscale('log')
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ax3.set_yscale('log')
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# Scale the x-axis
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x_ticks = list(ax3.get_xticks())
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ax3.set_xticks(x_ticks)
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ax3.set_xlim(min(min(x_ticks), min(swp2_lines[0])), max(max_x, max(swp2_lines[0])))
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ax3.set_xticklabels([f'{k:.0e}\n{k/(mu):.0e}\n{k/(mu)*tgen:.0e}' for k in x_ticks], fontsize = fnt_size*0.5)
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# x_ticks = list(ax3.get_xticks())
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# ax3.set_xticks(x_ticks)
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# x_ticks = [i for i in range(0.1,max(max_x, max(swp2_lines[0]))), ]
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# ax3.set_xticks(x_ticks)
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ax3.set_xlim(0.1, max(max_x, max(swp2_lines[0])))
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x_ticks = ax3.get_xticks()
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# ax3.set_xlim(min(min(x_ticks), min(swp2_lines[0])), max(max_x, max(swp2_lines[0])))
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# ax3.set_xlim(1, max(max_x, max(swp2_lines[0])))
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# ax3.set_xticklabels([f'{k:.0e}\n{k/(mu):.0e}\n{k/(mu)*tgen:.0e}' for k in x_ticks], fontsize = fnt_size*0.5)
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# ax3.set_xticklabels([f'{k/(mu):.0e}\n{k/(mu)*tgen:.0e}' for k in x_ticks], fontsize = fnt_size*0.5)
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ax3.set_xticklabels([f'{k:.0e}\n{k*tgen:.0e}' for k in x_ticks], fontsize = fnt_size*0.5)
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# rescale y to effective pop size
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y_ticks = list(ax3.get_yticks())
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ax3.set_yticks(y_ticks)
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ax3.set_ylim(min(min(y_ticks), min(swp2_lines[1])), max(max_y+(max_y*0.5), max(swp2_lines[1])+(max(swp2_lines[1])*0.5)))
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ax3.set_yticklabels([f'{k/(4*mu):.0e}' for k in y_ticks], fontsize = fnt_size*0.5)
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plt.text(-0.13, -0.135, 'Coal. time\nGen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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# y_ticks = list(ax3.get_yticks())
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# ax3.set_yticks(y_ticks)
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# ax3.set_ylim(min(min(y_ticks), min(swp2_lines[1])), max(max_y+(max_y*0.5), max(swp2_lines[1])+(max(swp2_lines[1])*0.5)))
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# ax3.set_ylim(1, max(max_y, max(swp2_lines[1])))
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ax3.set_ylim(1, max(max_y+(max_y*0.5), max(swp2_lines[1])+(max(swp2_lines[1])*0.5)))
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# ax3.set_yticklabels([f'{k/(4*mu):.0e}' for k in y_ticks], fontsize = fnt_size*0.5)
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# plt.text(-0.13, -0.135, 'Coal. time\nGen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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# plt.text(-0.13, -0.135, 'Gen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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plt.text(-0.13, -0.085, 'Gen. time\nYears', ha='left', va='bottom', transform=ax3.transAxes)
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plt.subplots_adjust(bottom=0.2) # Adjust the value as needed
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if ax is None:
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# nb of plot_lines represent the number of epochs stored (len(plot_lines) = #breaks+1)
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@ -638,7 +700,7 @@ def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10, rescale =
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x,y = plot
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x_plot, y_plot = plot_straight_x_y(x,y)
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p, = ax1.plot(x_plot, y_plot, 'o', linestyle="-", alpha=0.75, lw=2, label = str(breaks)+' brks')
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print("breaks=", breaks, "theta0", y[0])
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# add plot to the list of all plots to superimpose
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plots.append(p)
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x_ticks = x
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