Improve plotting with concordant colours
tforest
parent
4aedf5e280
commit
03ea6f938b
83
swp2.py
83
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+'_b'+str(breaks)+'.pdf')
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plt.savefig(title+'_'+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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@ -184,8 +184,12 @@ def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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else:
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#plt.rcParams['font.size'] = fnt_size
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ax2 = ax[0][0,1]
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ax2.plot(full_dict['Ln_Brks'][0], full_dict['Ln_Brks'][1], 'o', linestyle = "dotted", lw=2)
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# Retrieve the default color cycle from rcParams
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default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
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# Create an array of colors from the default color cycle
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colors = [default_colors[i % len(default_colors)] for i in range(len(full_dict['Ln_Brks'][0]))]
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ax2.plot(full_dict['Ln_Brks'][0], full_dict['Ln_Brks'][1], "--", lw=1, color="black", zorder=1)
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ax2.scatter(full_dict['Ln_Brks'][0], full_dict['Ln_Brks'][1], s=50, c=colors, marker='o', zorder=2)
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ax2.axhline(y=full_dict['best_Ln'], linestyle = "-.", color = "red", label = "$-\log\mathcal{L}$ = "+str(round(full_dict['best_Ln'], 2)))
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ax2.set_yscale('log')
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ax2.set_xlabel("# breakpoints", fontsize=fnt_size)
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@ -202,7 +206,9 @@ def plot_all_epochs_thetafolder(full_dict, mu, tgen, title = "Title",
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#plt.rcParams['font.size'] = fnt_size
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ax3 = ax[1][0,1]
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AIC = full_dict['AIC_Brks']
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ax3.plot(AIC[0], AIC[1], 'o', linestyle = "dotted", lw=2)
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# ax3.plot(AIC[0], AIC[1], 'o', linestyle = "dotted", lw=2)
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ax3.plot(AIC[0], AIC[1], "--", lw=1, color="black", zorder=1)
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ax3.scatter(AIC[0], AIC[1], s=50, c=colors, marker='o', zorder=2)
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ax3.axhline(y=full_dict['best_AIC'], linestyle = "-.", color = "red",
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label = "Min. AIC = "+str(round(full_dict['best_AIC'], 2)))
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ax3.set_yscale('log')
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@ -341,7 +347,7 @@ def save_k_theta(folder_path, mu, tgen, title = "Title", theta_scale = True,
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for file_name in os.listdir(folder_path):
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cpt +=1
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if os.path.isfile(os.path.join(folder_path, file_name)):
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for k in range(breaks_max):
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for k in range(breaks_max+1):
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x,y,likelihood,thetas,sfs,L = parse_stwp_theta_file(folder_path+file_name, breaks = k,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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@ -443,6 +449,8 @@ 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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# 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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if ax is None:
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my_dpi = 500
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@ -463,40 +471,34 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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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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p2, = ax2.plot(x2_plot, y2_plot, linestyle="-", alpha=0.75, lw=2, label = 'swp2', color="black")
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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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p3, = ax3.plot(x2_plot, y2_plot, linestyle="-", alpha=0.75, lw=2, label = 'swp2', color="black")
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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:
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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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if subset is not None:
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if breaks in subset:
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masking_alpha = 0.75
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else:
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masking_alpha = 0
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p2, = ax2.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=masking_alpha, lw=2, label = str(breaks)+' brks')
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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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p3, = ax3.plot(x2_plot, y2_plot, 'o', linestyle="-", alpha=masking_alpha, lw=2, label = str(breaks)+' brks')
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if subset is not None and breaks in subset:
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# store for legend
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lines_fig2.append(p2)
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lines_fig3.append(p3)
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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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xlabel = "t"
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ylabel = r"$\theta$"
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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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@ -509,8 +511,7 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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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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plt.savefig(title+'_plotB_'+str(nb_epochs)+'_epochs.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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@ -533,29 +534,37 @@ def plot_scaled_theta(plot_lines, prop, title, mu, tgen, swp2_lines = None, ax =
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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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plt.savefig(title+'_plotC_'+str(nb_epochs)+'_epochs_log.pdf')
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# close fig3 to save memory
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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, rescale = False, subset = None):
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def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10, rescale = False, subset = None, max_breaks = None):
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if max_breaks:
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nb_breaks = max_breaks
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else:
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nb_breaks = len(plot_lines)+1
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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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my_dpi = 300
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my_dpi = 500
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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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plt.subplots_adjust(bottom=0.2) # Adjust the value as needed
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else:
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fnt_size = 12
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# plt.rcParams['font.size'] = fnt_size
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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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for breaks, plot in enumerate(plot_lines):
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if max_breaks and breaks > max_breaks:
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# stop plotting if it exceeds the limit
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continue
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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(epoch)+' brks')
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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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# add plot to the list of all plots to superimpose
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plots.append(p)
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@ -565,7 +574,7 @@ def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10, rescale =
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#ax.legend(handles=[p0]+plots)
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ax1.set_xlabel("# bin & cumul. prop. of sites", fontsize=fnt_size)
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# Set the x-axis locator to reduce the number of ticks to 10
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ax1.set_ylabel("theta", fontsize=fnt_size)
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ax1.set_ylabel(r'$\theta_k$', fontsize=fnt_size, rotation = 90)
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ax1.set_title(title, fontsize=fnt_size)
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ax1.legend(handles=plots, loc='best', fontsize = fnt_size*0.5)
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ax1.set_xticks(x_ticks)
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@ -579,7 +588,7 @@ def plot_raw_stairs(plot_lines, prop, title, ax = None, n_ticks = 10, rescale =
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ax1.set_xticklabels([f'{values[k]}\n{val:.2f}' for k, val in enumerate(new_prop)], fontsize = fnt_size*0.8)
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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+'_raw'+str(len(plot_lines))+'.pdf')
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plt.savefig(title+'_raw_'+str(nb_breaks)+'_breaks.pdf')
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plt.close(fig)
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# return plots
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return ax
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@ -588,8 +597,8 @@ def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale =
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my_dpi = 300
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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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print("Best epoch based on AIC =", best_epoch)
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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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@ -628,7 +637,7 @@ def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale =
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swp2_x, swp2_y = swp2_vals[0], swp2_vals[1]
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# End of Parsing real swp2 output
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plot_raw_stairs(plot_lines = loaded_data['raw_stairs'],
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prop = loaded_data['prop'], title = title, ax = None)
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prop = loaded_data['prop'], title = title, ax = None, max_breaks = breaks)
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plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'], mu = mu, tgen = tgen, subset=[loaded_data['best_epoch_by_AIC']]+selected_breaks,
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# plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'], subset=list(range(0,3))+[loaded_data['best_epoch_by_AIC']]+selected_breaks,
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prop = loaded_data['prop'], title = title, swp2_lines = [swp2_x, swp2_y], ax = None)
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