Previous plotting system before feb version
tforest
parent
44449033db
commit
8c8545a9ff
202
swp2.py
202
swp2.py
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@ -124,158 +124,6 @@ def plot_straight_x_y(x,y):
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x_1.append(x[-1])
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return x_1, y_1
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def plot_all_epochs_thetafolder_old(folder_path, mu, tgen, title = "Title",
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theta_scale = True, ax = None, input = None, output = None):
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#scenari = {}
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cpt = 0
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epochs = {}
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for file_name in os.listdir(folder_path):
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breaks = 0
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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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x, y, likelihood, theta, sfs, L = parse_stwp_theta_file(folder_path+file_name, breaks = breaks,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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SFS_stored = sfs
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L_stored = L
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while not (x == 0 and y == 0):
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if breaks not in epochs.keys():
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epochs[breaks] = {}
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epochs[breaks][likelihood] = x,y
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breaks += 1
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x,y,likelihood,theta,sfs,L = parse_stwp_theta_file(folder_path+file_name, breaks = breaks,
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tgen = tgen,
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mu = mu, relative_theta_scale = theta_scale)
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if x == 0:
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# last break did not work, then breaks = breaks-1
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breaks -= 1
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print("\n*******\n"+title+"\n--------\n"+"mu="+str(mu)+"\ntgen="+str(tgen)+"\nbreaks="+str(breaks)+"\n*******\n")
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print(cpt, "theta file(s) have been scanned.")
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my_dpi = 300
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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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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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else:
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fnt_size = 12
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# plt.rcParams['font.size'] = fnt_size
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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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brkpt_lik = []
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top_plots = {}
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for epoch, scenari in epochs.items():
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# sort starting by the smallest -log(Likelihood)
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best10_scenari = (sorted(list(scenari.keys())))[:10]
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greatest_likelihood = best10_scenari[0]
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# store the tuple breakpoints and likelihood for later plot
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brkpt_lik.append((epoch, greatest_likelihood))
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x, y = scenari[greatest_likelihood]
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#without breakpoint
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if epoch == 0:
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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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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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plots_likelihoods[i] = float(plots_likelihoods[i])
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best10_plots = sorted(plots_likelihoods)[:10]
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top_plot_lik = str(best10_plots[0])
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plot_handles = []
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# plt.rcParams['font.size'] = fnt_size
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p0, = ax1.plot(top_plots[top_plot_lik][0], top_plots[top_plot_lik][1], 'o', linestyle = "-",
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alpha=1, lw=2, label = str(top_plots[top_plot_lik][2])+' brks | Lik='+top_plot_lik)
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plot_handles.append(p0)
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for k, plot_Lk in enumerate(best10_plots[1:]):
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plot_Lk = str(plot_Lk)
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# plt.rcParams['font.size'] = fnt_size
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p, = ax1.plot(top_plots[plot_Lk][0], top_plots[plot_Lk][1], 'o', linestyle = "--",
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alpha=1/(k+1), lw=1.5, label = str(top_plots[plot_Lk][2])+' brks | Lik='+plot_Lk)
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plot_handles.append(p)
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if theta_scale:
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ax1.set_xlabel("Coal. time", fontsize=fnt_size)
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ax1.set_ylabel("Pop. size scaled by N0", fontsize=fnt_size)
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# recent_scale_lower_bound = 0.01
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# recent_scale_upper_bound = 0.1
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# ax1.axvline(x=recent_scale_lower_bound)
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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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# 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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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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# best possible likelihood from SFS
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# Segregating sites
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S = sum(SFS_stored)
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# Number of kept sites from which the SFS is computed
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L = L_stored
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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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# 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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p_i = SFS_stored[xi] / float(S+S0)
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Ln += np.log(p_i) * SFS_stored[xi] - log_facto(SFS_stored[xi])
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# basic plot likelihood
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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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# plt.rcParams['font.size'] = fnt_size
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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(np.array(brkpt_lik)[:, 0], np.array(brkpt_lik)[:, 1].astype(float), 'o', linestyle = "dotted", lw=2)
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ax2.axhline(y=-Ln, linestyle = "-.", color = "red", label = "$-\log\mathcal{L}$ = "+str(round(-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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ax2.set_ylabel("$-\log\mathcal{L}$", fontsize=fnt_size)
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ax2.legend(loc='best', fontsize = fnt_size*0.5)
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ax2.set_title(title+" Likelihood gain from # breakpoints")
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if ax is None:
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plt.savefig(title+'_Breakpts_Likelihood.pdf')
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# AIC
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if ax is None:
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fig, ax3 = plt.subplots(figsize=(5000/my_dpi, 2800/my_dpi), dpi=my_dpi)
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# plt.rcParams['font.size'] = '18'
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else:
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#plt.rcParams['font.size'] = fnt_size
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ax3 = ax[1][0,1]
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AIC = []
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for brk in np.array(brkpt_lik)[:, 0]:
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brk = int(brk)
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AIC.append((2*brk+1)+2*np.array(brkpt_lik)[brk, 1].astype(float))
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ax3.plot(np.array(brkpt_lik)[:, 0], AIC, 'o', linestyle = "dotted", lw=2)
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# AIC = 2*k - 2ln(L) ; where k is the number of parameters, here brks+1
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AIC_ln = 2*(len(brkpt_lik)+1) - 2*Ln
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ax3.axhline(y=AIC_ln, linestyle = "-.", color = "red",
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label = "Min. AIC = "+str(round(AIC_ln, 2)))
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selected_brks_nb = AIC.index(min(AIC))
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ax3.set_yscale('log')
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ax3.set_xlabel("# breakpoints", fontsize=fnt_size)
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ax3.set_ylabel("AIC")
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ax3.legend(loc='best', fontsize = fnt_size*0.5)
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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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print("S", S)
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# return plots
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return ax[0], ax[1]
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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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@ -442,21 +290,26 @@ def save_all_epochs_thetafolder(folder_path, mu, tgen, title = "Title", theta_sc
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# AIC = 2*k - 2ln(L) ; where k is the number of parameters, here brks+1
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AIC_ln = 2*(len(brkpt_lik)+1) - 2*Ln
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best_AIC = AIC_ln
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selected_brks_nb = AIC.index(min(AIC))
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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 = {"all_epochs":plots, "Ln_Brks":Ln_Brks,
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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}
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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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saved_plots = json.load(json_file)
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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["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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saved_plots["best_Ln"] = best_Ln
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saved_plots["best_AIC"] = best_AIC
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saved_plots["best_epoch_by_AIC"] = selected_brks_nb
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if output == None:
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output = title+"_plotdata.json"
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with open(output, 'w') as json_file:
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@ -573,7 +426,7 @@ 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):
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def plot_scaled_theta(plot_lines, prop, title, ax = None, n_ticks = 10, subset = None):
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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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@ -589,13 +442,21 @@ def plot_scaled_theta(plot_lines, prop, title, ax = None, n_ticks = 10):
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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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for epoch, plot in enumerate(plot_lines):
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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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pass
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else:
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continue
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x,y=plot
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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(epoch)+' brks')
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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(epoch)+' brks')
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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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@ -666,24 +527,9 @@ 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):
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def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale = True, selected_breaks = []):
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my_dpi = 300
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# # Add some extra space for the second axis at the bottom
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# #plt.rcParams['font.size'] = 18
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# fig, axs = plt.subplots(2, 2, figsize=(5000/my_dpi, 2970/my_dpi), dpi=my_dpi)
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# #plt.rcParams['font.size'] = 12
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# ax = plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, ax = axs)
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# ax = plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = axs)
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# # Adjust layout to prevent clipping of titles
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#
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# # Save the entire grid as a single figure
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# plt.savefig(title+'_combined.pdf')
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# plt.clf()
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# # # second call for individual plots
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# # plot_all_epochs_thetafolder(folder_path, mu, tgen, title, theta_scale, ax = None)
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# # plot_test_theta(folder_path, mu, tgen, title, theta_scale, breaks_max = breaks, ax = None)
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# # plt.clf()
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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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@ -699,12 +545,15 @@ def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale =
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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)
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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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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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plot_scaled_theta(plot_lines = loaded_data['scaled_stairs'],
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@ -712,6 +561,7 @@ def combined_plot(folder_path, mu, tgen, breaks, title = "Title", theta_scale =
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plt.close(fig1)
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plt.close(fig2)
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if __name__ == "__main__":
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if len(sys.argv) != 4:
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