Improve plotting with swp2 control curve
tforest
parent
8c8545a9ff
commit
4aedf5e280
236
swp2.py
236
swp2.py
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@ -126,10 +126,11 @@ def plot_straight_x_y(x,y):
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def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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theta_scale = True, ax = None, input = None, output = None):
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my_dpi = 300
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my_dpi = 500
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L = full_dict["L"]
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if ax is None:
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# intialize figure
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my_dpi = 300
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#my_dpi = 300
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fnt_size = 18
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# plt.rcParams['font.size'] = fnt_size
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fig, ax1 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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@ -139,16 +140,16 @@ def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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ax1 = ax[1][0,0]
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ax1.set_yscale('log')
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ax1.set_xscale('log')
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ax1.grid(True,which="both", linestyle='--', alpha = 0.3)
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plot_handles = []
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best_plot = full_dict['all_epochs']['best']
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p0, = ax1.plot(best_plot[0], best_plot[1], 'o', linestyle = "-",
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p0, = ax1.plot(best_plot[0], best_plot[1], linestyle = "-",
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alpha=1, lw=2, label = str(best_plot[2])+' brks | Lik='+best_plot[3])
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plot_handles.append(p0)
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#ax1.grid(True,which="both", linestyle='--', alpha = 0.3)
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for k, plot_Lk in enumerate(full_dict['all_epochs']['plots']):
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plot_Lk = str(full_dict['all_epochs']['plots'][k][3])
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# plt.rcParams['font.size'] = fnt_size
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p, = ax1.plot(full_dict['all_epochs']['plots'][k][0], full_dict['all_epochs']['plots'][k][1], 'o', linestyle = "--",
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p, = ax1.plot(full_dict['all_epochs']['plots'][k][0], full_dict['all_epochs']['plots'][k][1], linestyle = "-",
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alpha=1/(k+1), lw=1.5, label = str(full_dict['all_epochs']['plots'][k][2])+' brks | Lik='+plot_Lk)
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plot_handles.append(p)
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if theta_scale:
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@ -160,12 +161,20 @@ def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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# ax1.axvline(x=recent_scale_upper_bound)
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else:
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# years
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plt.set_xlabel("Time (years)", fontsize=fnt_size)
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plt.set_ylabel("Individuals (N)", fontsize=fnt_size)
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if ax is not None:
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plt.set_xlabel("Time (years)", fontsize=fnt_size)
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plt.set_ylabel("Effective pop. size (Ne)", fontsize=fnt_size)
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else:
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plt.xlabel("Time (years)", fontsize=fnt_size)
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plt.ylabel("Effective pop. size (Ne)", fontsize=fnt_size)
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# x_ticks = ax1.get_xticks()
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# ax1.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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# ax1.set_xticklabels([f'{k}\n{k/(mu)}\n{k/(mu)*tgen}' for k in x_ticks], fontsize = fnt_size*0.8)
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# plt.rcParams['font.size'] = fnt_size
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# print(fnt_size, "rcParam font.size=", plt.rcParams['font.size'])
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ax1.legend(handles = plot_handles, loc='best', fontsize = fnt_size*0.5)
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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+'_b'+str(breaks)+'.pdf')
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# plot likelihood against nb of breakpoints
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@ -203,8 +212,9 @@ def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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ax3.set_title(title+" AIC")
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if ax is None:
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plt.savefig(title+'_Breakpts_Likelihood_AIC.pdf')
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# return plots
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return ax[0], ax[1]
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else:
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# return plots
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return ax[0], ax[1]
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def save_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_scale = True, input = None, output = None):
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#scenari = {}
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@ -248,10 +258,11 @@ def save_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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# do something with the theta without bp and skip the plotting
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N0 = y[0]
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#continue
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for i in range(len(y)):
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# divide by N0
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y[i] = y[i]/N0
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x[i] = x[i]/N0
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if theta_scale:
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for i in range(len(y)):
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# divide by N0
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y[i] = y[i]/N0
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x[i] = x[i]/N0
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top_plots[greatest_likelihood] = x,y,epoch
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plots_likelihoods = list(top_plots.keys())
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for i in range(len(plots_likelihoods)):
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@ -294,9 +305,10 @@ def save_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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# to return : plots ; Ln_Brks ; AIC_Brks ; best_Ln ; best_AIC
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# 'plots' dict keys: 'best', {epochs}('0', '1',...)
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if input == None:
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saved_plots = {"S":S, "S0":S0, "L":L, "all_epochs":plots, "Ln_Brks":Ln_Brks,
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"AIC_Brks":AIC_Brks, "best_Ln":best_Ln,
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"best_AIC":best_AIC, "best_epoch_by_AIC":selected_brks_nb}
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saved_plots = {"S":S, "S0":S0, "L":L, "mu":mu, "tgen":tgen,
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"all_epochs":plots, "Ln_Brks":Ln_Brks,
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"AIC_Brks":AIC_Brks, "best_Ln":best_Ln,
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"best_AIC":best_AIC, "best_epoch_by_AIC":selected_brks_nb}
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else:
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# if the dict has to be loaded from input
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with open(input, 'r') as json_file:
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@ -304,6 +316,8 @@ def save_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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saved_plots["S"] = S
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saved_plots["S0"] = S0
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saved_plots["L"] = L
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saved_plots["mu"] = mu
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saved_plots["tgen"] = tgen
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saved_plots["all_epochs"] = plots
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saved_plots["Ln_Brks"] = Ln_Brks
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saved_plots["AIC_Brks"] = AIC_Brks
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@ -369,9 +383,10 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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for k in range(2, len(y)+2):
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prop.append(y[k-2] / (k - 1) / sum_theta_i)
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prop = prop[::-1]
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# normalise to N0 (N0 of epoch1)
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for i in range(len(y)):
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y[i] = y[i]/N0
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if theta_scale :
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# normalise to N0 (N0 of epoch1)
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for i in range(len(y)):
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y[i] = y[i]/N0
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# x_plot, y_plot = plot_straight_x_y(x, y)
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p = x, y
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# add plot to the list of all plots to superimpose
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@ -394,8 +409,9 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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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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for i in range(len(y)):
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y[i] = y[i]/N0
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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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x_2 = []
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T = 0
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for i in range(len(x)):
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@ -426,10 +442,10 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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json.dump(saved_plots, json_file)
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return saved_plots
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def plot_scaled_theta(plot_lines, prop, title, ax = None, n_ticks = 10, subset = None):
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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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# fig 2 & 3
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if ax is None:
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my_dpi = 300
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my_dpi = 500
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fnt_size = 18
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fig2, ax2 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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fig3, ax3 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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@ -442,37 +458,79 @@ def plot_scaled_theta(plot_lines, prop, title, ax = None, n_ticks = 10, subset =
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lines_fig2 = []
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lines_fig3 = []
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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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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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# plot_lines = [[swp2_lines[0], swp2_lines[1]]]+plot_lines
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x2_plot, y2_plot = plot_straight_x_y(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')
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lines_fig2.append(p2)
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# Plotting (fig 3) which is the same but log scale for x
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p3, = ax3.plot(x2_plot, y2_plot, linestyle="-", alpha=0.75, lw=2, label = 'swp2')
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lines_fig3.append(p3)
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nb_breaks = len(plot_lines)
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for breaks, plot in enumerate(plot_lines):
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if subset is not None:
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if breaks not in subset :
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# skip if not in subset
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if max(subset) > nb_breaks and breaks == nb_breaks-1:
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if max(subset) > nb_breaks and breaks == nb_breaks:
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pass
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else:
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continue
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x,y=plot
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# y = [k/(4*mu) for k in y]
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# x = [k/(mu)*tgen for k in x]
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x2_plot, y2_plot = plot_straight_x_y(x,y)
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p2, = ax2.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=0.75, lw=2, label = str(breaks)+' brks')
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lines_fig2.append(p2)
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# Plotting (fig 3) which is the same but log scale for x
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p3, = ax3.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=0.75, lw=2, label = str(breaks)+' brks')
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lines_fig3.append(p3)
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ax2.set_xlabel("Relative scale", fontsize=fnt_size)
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ax2.set_ylabel("theta", fontsize=fnt_size)
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ax2.set_title(title, fontsize=fnt_size)
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ax2.legend(handles=lines_fig2, loc='best', fontsize = fnt_size*0.5)
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ax3.axvline(x=500/tgen*mu, linestyle="--")
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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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else:
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xlabel = "Theta scale"
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ylabel = "Theta"
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if ax is None:
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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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x_ticks = list(plt.xticks())[0]
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plt.gca().set_xticks(x_ticks)
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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.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.subplots_adjust(bottom=0.2) # Adjust the value as needed
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# nb of plot_lines represent the number of epochs stored (len(plot_lines) = #breaks+1)
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plt.savefig(title+'_plot2_'+str(len(plot_lines))+'.pdf')
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# close fig2 to save memory
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plt.close(fig2)
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else:
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# when ax subplotting is used
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ax2.set_xlabel(xlabel, fontsize=fnt_size)
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ax2.set_ylabel(ylabel, fontsize=fnt_size)
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ax2.set_title(title, fontsize=fnt_size)
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ax2.legend(handles=lines_fig2, loc='best', fontsize = fnt_size*0.5)
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ax3.set_xscale('log')
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ax3.set_yscale('log')
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ax3.set_xlabel("log Relative scale", fontsize=fnt_size)
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ax3.set_xlabel("time log scale", fontsize=fnt_size)
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ax3.set_ylabel("theta", fontsize=fnt_size)
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ax3.set_title(title, fontsize=fnt_size)
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ax3.legend(handles=lines_fig3, loc='best', fontsize = fnt_size*0.5)
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x_ticks = list(ax3.get_xticks())
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ax3.set_xlim(left=min(x_ticks))
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ax3.set_xticks(x_ticks)
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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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plt.text(-0.13, -0.135, 'Coal. time\nGen. 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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plt.savefig(title+'_plot3_'+str(len(plot_lines))+'_log.pdf')
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@ -480,7 +538,7 @@ def plot_scaled_theta(plot_lines, prop, title, ax = None, n_ticks = 10, subset =
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plt.close(fig3)
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return ax
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def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10):
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def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10, rescale = False, subset = None):
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# multiple fig
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if ax is None:
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# intialize figure 1
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@ -494,7 +552,6 @@ def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10):
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ax1 = ax[0, 0]
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plt.subplots_adjust(wspace=0.3, hspace=0.3)
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plots = []
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for epoch, plot in enumerate(plot_lines):
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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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@ -527,40 +584,103 @@ def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10):
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# return plots
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return ax
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def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale = True, selected_breaks = []):
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def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale = False, selected_breaks = []):
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my_dpi = 300
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save_k_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, output = title+"_plotdata.json")
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save_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, input = title+"_plotdata.json", output = title+"_plotdata.json")
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saved_plots_dict = save_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, output = title+"_plotdata.json")
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nb_of_epochs = len(saved_plots_dict["all_epochs"]["plots"])
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print(nb_of_epochs)
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best_epoch = saved_plots_dict["best_epoch_by_AIC"]
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save_k_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = nb_of_epochs, input = title+"_plotdata.json", output = title+"_plotdata.json")
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with open(title+"_plotdata.json", 'r') as json_file:
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loaded_data = json.load(json_file)
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# plot page 1 of summary
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fig1, ax1 = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
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# fig1.tight_layout()
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# Adjust absolute space between the top and bottom rows
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fig1.subplots_adjust(hspace=0.35) # Adjust this value based on your requirement
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# plot page 2 of summary
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fig2, ax2 = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
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# fig2.tight_layout()
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ax1 = plot_raw_stairs(plot_lines = loaded_data['raw_stairs'],
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prop = loaded_data['prop'], title = title, ax = ax1)
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ax1 = plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'],
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prop = loaded_data['prop'], title = title, ax = ax1, subset=[loaded_data['best_epoch_by_AIC']]+selected_breaks)
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ax2 = plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'],
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prop = loaded_data['prop'], title = title, ax = ax2)
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ax1, ax2 = plot_all_epochs_thetafolder(loaded_data, mu, tgen, title, theta_scale, ax = [ax1, ax2])
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fig1.savefig(title+'_combined_p1.pdf')
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print("Wrote", title+'_combined_p1.pdf')
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fig2.savefig(title+'_combined_p2.pdf')
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print("Wrote", title+'_combined_p2.pdf')
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# START OF COMBINED PLOT CODE
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# # plot page 1 of summary
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# fig1, ax1 = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
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# # fig1.tight_layout()
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# # Adjust absolute space between the top and bottom rows
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# fig1.subplots_adjust(hspace=0.35) # Adjust this value based on your requirement
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# # plot page 2 of summary
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# fig2, ax2 = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
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# # fig2.tight_layout()
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# ax1 = plot_raw_stairs(plot_lines = loaded_data['raw_stairs'],
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# prop = loaded_data['prop'], title = title, ax = ax1)
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# ax1 = plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'],
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# prop = loaded_data['prop'], title = title, ax = ax1, subset=[loaded_data['best_epoch_by_AIC']]+selected_breaks)
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# ax2 = plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'],
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# prop = loaded_data['prop'], title = title, ax = ax2)
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# ax1, ax2 = plot_all_epochs_thetafolder(loaded_data, mu, tgen, title, theta_scale, ax = [ax1, ax2])
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# fig1.savefig(title+'_combined_p1.pdf')
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# print("Wrote", title+'_combined_p1.pdf')
|
||||
# fig2.savefig(title+'_combined_p2.pdf')
|
||||
# print("Wrote", title+'_combined_p2.pdf')
|
||||
|
||||
# END OF COMBINED PLOT CODE
|
||||
|
||||
|
||||
# Start of Parsing real swp2 output
|
||||
folder_splitted = folder_path.split("/")
|
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swp2_summary = "/".join(folder_splitted[:-2])+'/'+folder_splitted[-3]+".final.summary"
|
||||
swp2_vals = parse_stairwayplot_output_summary(stwplt_out = swp2_summary)
|
||||
swp2_x, swp2_y = swp2_vals[0], swp2_vals[1]
|
||||
# End of Parsing real swp2 output
|
||||
plot_raw_stairs(plot_lines = loaded_data['raw_stairs'],
|
||||
prop = loaded_data['prop'], title = title, ax = None)
|
||||
plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'],
|
||||
prop = loaded_data['prop'], title = title, ax = None)
|
||||
plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'], mu = mu, tgen = tgen, subset=[loaded_data['best_epoch_by_AIC']]+selected_breaks,
|
||||
# plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'], subset=list(range(0,3))+[loaded_data['best_epoch_by_AIC']]+selected_breaks,
|
||||
prop = loaded_data['prop'], title = title, swp2_lines = [swp2_x, swp2_y], ax = None)
|
||||
plot_all_epochs_thetafolder(loaded_data, mu, tgen, title, theta_scale, ax = None)
|
||||
|
||||
plt.close(fig1)
|
||||
plt.close(fig2)
|
||||
# plt.close(fig1)
|
||||
# plt.close(fig2)
|
||||
def parse_stairwayplot_output_summary(stwplt_out, xlim = None, ylim = None, title = "default title", plot = False):
|
||||
#col 5
|
||||
year = []
|
||||
# col 6
|
||||
ne_median = []
|
||||
ne_2_5 = []
|
||||
ne_97_5 = []
|
||||
ne_12_5 = []
|
||||
# col 10
|
||||
ne_87_5 = []
|
||||
with open(stwplt_out, "r") as stwplt_stream:
|
||||
for line in stwplt_stream:
|
||||
## Line format
|
||||
# mutation_per_site n_estimation theta_per_site_median theta_per_site_2.5% theta_per_site_97.5% year Ne_median Ne_2.5% Ne_97.5% Ne_12.5% Ne_87.5%
|
||||
if not line.startswith("mutation_per_site"):
|
||||
#not header
|
||||
values = line.strip().split()
|
||||
year.append(float(values[5]))
|
||||
ne_median.append(float(values[6]))
|
||||
ne_2_5.append(float(values[7]))
|
||||
ne_97_5.append(float(values[8]))
|
||||
ne_12_5.append(float(values[9]))
|
||||
ne_87_5.append(float(values[10]))
|
||||
|
||||
vals = [year, ne_median, ne_2_5, ne_97_5, ne_12_5, ne_87_5]
|
||||
if plot :
|
||||
# plot parsed data
|
||||
label = ["Ne median", "Ne 2.5%", "Ne 97.5%", "Ne 12.5%", "Ne 87.5%"]
|
||||
for i in range(1, 5):
|
||||
fig, = plt.plot(year, vals[i], '--', alpha = 0.4)
|
||||
fig.set_label(label[i])
|
||||
# # last plot is median
|
||||
fig, = plt.plot(year, ne_median, 'r-', lw=2)
|
||||
fig.set_label(label[0])
|
||||
plt.legend()
|
||||
plt.ylabel("Individuals (Ne)")
|
||||
plt.xlabel("Time (years)")
|
||||
if xlim:
|
||||
plt.xlim(xlim)
|
||||
if ylim:
|
||||
plt.ylim(ylim)
|
||||
plt.title(title)
|
||||
plt.show()
|
||||
plt.close()
|
||||
return vals
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue