translate images from english to spanish

plots/figure_1.py

@@ -32,8 +32,8 @@
 axi = ax[0][1]
 overview.plot_pyramid(ax = axi)
 axi.set_xlim(left=-270000)
-axi.set_xlabel('Cases')
-axi.set_ylabel('Age group')
+axi.set_xlabel('Casos')
+axi.set_ylabel('Grupo de edad')
 axi.set_title('b.')
 axi.legend(loc = 'upper left')  
 
@@ -50,7 +50,7 @@
 results_genomics.plot_prevalence(ax = axi)
 axi.tick_params(axis = 'x', rotation = 90)
 axi.set_xlabel('')
-axi.set_ylabel('Prevalence')
+axi.set_ylabel('Prevalencia')
 axi.set_title('d.', loc = 'left')
 
 fig.savefig(FIG_PATH + 'figure_1.png')

plots/figure_3.py

@@ -48,14 +48,14 @@
 
 ax[0][1].set_xlabel('')
 ax[0][2].set_xlabel('')
-ax[1][0].set_xlabel('Age group')
-ax[1][1].set_xlabel('Age group')
-ax[1][2].set_xlabel('Age group')
-
-ax[0][1].set_ylabel('Hospitalisation Case Ratio')
-ax[0][2].set_ylabel('ICU Case Ratio')
-ax[1][0].set_ylabel('Case Fatality Ratio')
-ax[1][1].set_ylabel('Hospitalisation Fatality Ratio')
-ax[1][2].set_ylabel('ICU Fatality Ratio')
+ax[1][0].set_xlabel('Grupo de edad')
+ax[1][1].set_xlabel('Grupo de edad')
+ax[1][2].set_xlabel('Grupo de edad')
+
+ax[0][1].set_ylabel('Tasa de casos hospitalizados')
+ax[0][2].set_ylabel('Tasa de casos UCI')
+ax[1][0].set_ylabel('Tasa de mortalidad')
+ax[1][1].set_ylabel('Tasa de mortalidad hospitalaria')
+ax[1][2].set_ylabel('Tasa de mortalidad ICU')
 
 fig.savefig(FIG_PATH + 'figure_3.png')

plots/figure_4.py

@@ -28,12 +28,12 @@
 results_severe_outcomes.plot_percentage(ax)
 # results_severe_outcomes.plot_percentage_err(ax)
 
-ax[0].set_ylabel('Hospitalization percentage by age-group')
-ax[1].set_ylabel('ICU percentage by age-group')
-ax[2].set_ylabel('Deaths percentage by age-group')
+ax[0].set_ylabel('Porcentaje de ingreso a hospitalización')
+ax[1].set_ylabel('Porcentaje de ingreso a UCI')
+ax[2].set_ylabel('Porcentaje de fallecidos')
 for axi in ax:
     axi.tick_params(axis='x', labelrotation=90)
-    axi.set_xlabel('Age group')
+    axi.set_xlabel('Grupo de edad')
 ax[0].set_title('a.')
 ax[1].set_title('b.')
 ax[2].set_title('c.')

plots/figure_5.py

@@ -30,10 +30,10 @@
 red.plot_best_model_bar_outcome(3, ax, w =  0.05)
 red.plot_best_model_bar_outcome(4, ax, w =  0.15)
 
-leglabels= ['Wave 1',
-            'Wave 2',
-            'Wave 3',
-            'Wave 4',
+leglabels= ['ola 1',
+            'ola 2',
+            'ola 3',
+            'ola 4',
            ]
 ax.legend(leglabels, bbox_to_anchor=(0.75, -0.15), ncol=5)
 fig.savefig(FIG_PATH + 'figure_5_v0.png')

plots/individual_plots.py

@@ -67,8 +67,8 @@
 fig, ax = plt.subplots(figsize=(7.5, 5))
 overview.plot_pyramid(ax)
 ax.set_xlim(left=-260000)
-ax.set_xlabel('Cases')
-ax.set_ylabel('Age group')
+ax.set_xlabel('Casos')
+ax.set_ylabel('Grupo de edad')
 ax.legend()     
 fig.savefig(FIG_PATH + 'population_pyramid.png')
 
@@ -184,12 +184,12 @@
 
 fig, ax = plt.subplots(1, 3, figsize=(15, 5))
 results_severe_outcomes.plot_percentage_err(ax)
-ax[0].set_ylabel('Hospitalization percentage by age-group')
-ax[1].set_ylabel('ICU percentage by age-group')
-ax[2].set_ylabel('Deaths percentage by age-group')
+ax[0].set_ylabel('Porcentaje de ingreso hospitalización')
+ax[1].set_ylabel('Porcentaje de ingreso a UCI')
+ax[2].set_ylabel('Porcentaje de fallecidos')
 for axi in ax:
     axi.tick_params(axis='x', labelrotation=90)
-    axi.set_xlabel('Age group')
+    axi.set_xlabel('Grupo de edad')
 ax[0].set_title('a.')
 ax[1].set_title('b.')
 ax[2].set_title('c.')

plots/overview.py

@@ -55,11 +55,11 @@
                             columns=df_confirmed_bogota['sex'],
                             normalize="index")*100                
 def plot_pyramid(ax):
-        hist_f = ax.barh(df_female['age_group'], -df_female['counts'], align='center', label='female')
-        hist_m = ax.barh(df_male['age_group'], df_male['counts'], align='center', label='male')
+        hist_f = ax.barh(df_female['age_group'], -df_female['counts'], align='center', label='mujeres')
+        hist_m = ax.barh(df_male['age_group'], df_male['counts'], align='center', label='hombres')
 
-        ax.bar_label(hist_m, labels=['{:.1f}%'.format(prop) for prop in df_prop['M'].round(1)], label_type='edge')
-        ax.bar_label(hist_f, labels=['{:.1f}%'.format(prop) for prop in df_prop['F'].round(1)], label_type='edge')
+        ax.bar_label(hist_m, labels=['{:.1f}%'.format(prop) for prop in df_prop['M'].round(1)], label_type='edge', fontsize = 9)
+        ax.bar_label(hist_f, labels=['{:.1f}%'.format(prop) for prop in df_prop['F'].round(1)], label_type='edge', fontsize = 9)
 
         ticks =  ax.get_xticks()
         ax.set_xticklabels([int(abs(tick)) for tick in ticks])
@@ -83,22 +83,22 @@ def plot_pyramid(ax):
 df_deaths_60p['deaths_cum'] = df_deaths_60p['deaths'].cumsum().values
 
 def plot_cases_death_cum(ax):
-        ln1 = ax.plot(df_cases_all['date'], df_cases_all['cases'], label='cases all')
-        ln2 = ax.plot(df_cases_60p['date'], df_cases_60p['cases'], label='cases 60+')
+        ln1 = ax.plot(df_cases_all['date'], df_cases_all['cases'], label='casos - todos')
+        ln2 = ax.plot(df_cases_60p['date'], df_cases_60p['cases'], label='casos - 60+')
 
         # fig, ax2 =plt.subplots(figsize=(7.5, 5))
         ax_twin = ax.twinx()
 
-        ln3 = ax_twin.plot(df_deaths_all['date'], df_deaths_all['deaths_cum'], label='cumulative deaths all', linestyle='dashed')
-        ln4 = ax_twin.plot(df_deaths_60p['date'], df_deaths_60p['deaths_cum'], label='cumulative deaths 60+', linestyle='dashed')
+        ln3 = ax_twin.plot(df_deaths_all['date'], df_deaths_all['deaths_cum'], label='fallecimientos acum - todos', linestyle='dashed')
+        ln4 = ax_twin.plot(df_deaths_60p['date'], df_deaths_60p['deaths_cum'], label='fallecimientos acum - 60+', linestyle='dashed')
 
         lns = ln1 + ln2 + ln3 + ln4
         labs = [l.get_label() for l in lns]
 
         ax.tick_params(axis='x', rotation=90)
-        ax.set_ylabel('Confirmed cases')
+        ax.set_ylabel('Casos confirmados')
 
-        ax_twin.legend(lns, labs, loc='upper left', frameon=False, fontsize=10)
+        ax_twin.legend(lns, labs, loc='upper left', frameon=False, fontsize=9)
         ax_twin.spines.right.set_visible(True) #This was set as False by default in the .mpstyle file
         ax_twin.tick_params(axis='x', rotation=90)
-        ax_twin.set_ylabel('Deaths')
+        ax_twin.set_ylabel('Fallecimientos')

plots/results_epidemiological_distributions.py

@@ -233,16 +233,16 @@ def plot_best_model_bar_all(dist, ax, w, n, wt=0.1):
            color = colors[n])
     ax.xaxis.set_major_locator(MaxNLocator(integer=True))
     #ax.set_yscale('log')
-    ax.set_ylabel('Average value of delay time (Days)')
-    ax.set_xlabel('Wave')
+    ax.set_ylabel('Media del tiempo de estancia (días)')
+    ax.set_xlabel('Ola')
 
 def plot_best_model_bar_outcome(wave, ax, w, wt=0.1):
     dist = ['onset_hosp', 'onset_icu', 'onset_death', 'hosp_stay', 'icu_stay']
-    xlabels= ['Onset to hospitalisation', 
-            'Onset to ICU entrance',
-            'Onset to death',
-            'Hospital stay',
-            'ICU stay']
+    xlabels= ['FIS - hospitalización', 
+            'FIS - ingreso UCI',
+            'FIS - fallecimiento',
+            'Estancia en hospitalización',
+            'Estancia en UCI']
     mean = []
     err = [[],[]]
     for d in dist:
@@ -259,6 +259,6 @@ def plot_best_model_bar_outcome(wave, ax, w, wt=0.1):
            color = colors[n])
     ax.xaxis.set_major_locator(MaxNLocator(integer=True))
     #ax.set_yscale('log')
-    ax.set_ylabel('Average value of delay time (Days)')
-    ax.set_xlabel('Epidemiological distribution')
+    ax.set_ylabel('Media del tiempos de estancia (días)')
+    ax.set_xlabel('Distribuciones epidemiológicas')
     ax.set_xticks([1,2,3,4,5], xlabels)

plots/results_genomics.py

@@ -61,11 +61,11 @@ def plot_multinomial(ax, limits):
         df_incidence_count = df_incidence_count.sort_values(by = 'week_date')
 
         l1 = ax.bar(df_incidence_count['week_date'], df_incidence_count['cases'], 
-               alpha = 0.3, color = "grey", width=5.3, label = 'Weekly new cases')
-        ax.set_ylabel('Incidence')
+               alpha = 0.3, color = "grey", width=5.3, label = 'Casos nuevos semanales')
+        ax.set_ylabel('Incidencia')
         ax.tick_params(axis='x', rotation=90)
         ax.xaxis.set_major_locator(plt.MaxNLocator(n_ticks))
-        labs = ['Weekly new cases'] 
+        labs = ['Casos nuevos semanales'] 
 
         # Prevalence - Observed        
         ax1 = ax.twinx()
@@ -168,7 +168,7 @@ def plot_multinomial(ax, limits):
         ax1.tick_params(axis='x', rotation=90)
         ax1.xaxis.set_major_locator(plt.MaxNLocator(n_ticks))
         ax1.spines.right.set_visible(True)
-        ax1.set_ylabel('Prevalence', rotation = 270, labelpad = 15)
+        ax1.set_ylabel('Prevalencia', rotation = 270, labelpad = 15)
 
         handles = ax.containers + ax1.get_lines()
         labels1, handles1 = ax1.get_legend_handles_labels()

plots/results_rt.py

@@ -31,7 +31,7 @@
 
 # Instantaneous reproduction number R(t) plot
 def plot_rt(ax):    
-    ln1 = ax.plot(df_rt_all['window_end'], df_rt_all['Mean(R)'], color=colors[4], label = 'all')
+    ln1 = ax.plot(df_rt_all['window_end'], df_rt_all['Mean(R)'], color=colors[4], label = 'todos')
     ln2 = ax.plot(df_rt_60p['window_end'], df_rt_60p['Mean(R)'], color=colors[1], label = '60+')
     ax.axhline(y=1, color='black', linestyle='--', linewidth=3)
 

plots/results_severe_outcomes.py

@@ -38,7 +38,7 @@
 # Auxiliar plot function
 def plot_xyvar(df, ax, n_strat, varx='age_group', vary='percentage'):
     data = df.loc[df[strat]==n_strat]
-    ax.plot(data[varx], data[vary], label=strat+str(n_strat), ls = '-', marker = ".", lw = 1)
+    ax.plot(data[varx], data[vary], label='ola '+str(n_strat), ls = '-', marker = ".", lw = 1)
 
 # Wave cases percentage distribution by age group
 def plot_percentage(ax):
@@ -54,8 +54,8 @@ def plot_counts(ax):
     strat_list = df_ratios[strat].unique()
     for axi in ax:
         axi.tick_params(axis='x', labelrotation=90)
-        axi.set_ylabel('cases')
-        axi.set_xlabel('age group')
+        axi.set_ylabel('casos')
+        axi.set_xlabel('grupo de edad')
     for wave in strat_list:
         plot_xyvar(df_ratios[[strat, 'age_group','hosp']], ax=ax[0], n_strat=wave, vary='hosp')
         plot_xyvar(df_ratios[[strat, 'age_group', 'icu']], ax=ax[1], n_strat=wave, vary='icu')
@@ -123,9 +123,9 @@ def plot_ratios(ax, var, var_name):
         ratio_upper = df_temp[var+'_upper'] - ratio
         yerr = np.array(list(zip(ratio_lower, ratio_upper))).T
         ax.errorbar(df_temp['age_group'], df_temp[var], yerr = yerr,
-                    fmt='o', color = colors[wave-1], label = 'wave '+str(wave),
+                    fmt='o', color = colors[wave-1], label = 'ola '+str(wave),
                     capsize = 5, ls = '-', marker = ".", lw = 1)
-    ax.set_xlabel('age group')
+    ax.set_xlabel('grupo de edad')
     ax.set_ylabel(var_name)
 
 # Plot variable with error bar
@@ -137,7 +137,7 @@ def plot_var_err(df, axi, var):
         data_upper = df_temp[var + '_upper'] - data
         yerr = np.array(list(zip(data_lower, data_upper))).T
         axi.errorbar(df_temp['age_group'], df_temp[var], yerr = yerr,
-                     fmt='o', color = colors[wave-1], label = 'wave '+str(wave),
+                     fmt='o', color = colors[wave-1], label = 'ola '+str(wave),
                      capsize = 5, ls = '-', marker = ".", lw = 1)
 
 # Plot percentages with erros

plots/results_waves.py

@@ -47,21 +47,21 @@
 # Function to plot waves figure
 def plot_waves(ax):
     # Confirmed cases curve
-    ln1 = ax.plot(df_counts['date'], df_counts['cases'], color = colors[0], label = 'confirmed cases')
+    ln1 = ax.plot(df_counts['date'], df_counts['cases'], color = colors[0], label = 'casos confirmados')
     ln2 = ax.plot(df_counts['date'], df_counts['cases_gs'], color= colors[2], ls = '--')
     date_no_dup = []
     for d in range(len(df_waves)):
             ax.axvline(x = df_waves['start_date'].iloc[d], color = 'black', alpha = 0.8, ls = '--')
             ax.axvline(x = df_waves['end_date'].iloc[d], color = 'black', alpha = 0.8, ls = '--')
             date_no_dup.append(d)
-    ax.set_ylabel('Confirmed cases')
+    ax.set_ylabel('Casos confirmados')
     # First difference plot
     ax2 = ax.twinx()
-    ln3 = ax2.plot(df_counts['date'],df_counts['cases_gs_diff'], colors[1], label = 'diff(confirmed cases)')
-    ln4 = ax2.plot(df_counts['date'],df_counts['cases_gs_diff_gs'], colors[2], ls = '--', label = 'gaussian smoothing')
+    ln3 = ax2.plot(df_counts['date'],df_counts['cases_gs_diff'], colors[1], label = 'diff(casos confirmados)')
+    ln4 = ax2.plot(df_counts['date'],df_counts['cases_gs_diff_gs'], colors[2], ls = '--', label = 'suavizamiento gaussiano')
 
     ax2.axhline(y = 0, color = 'black', alpha = 0.8, ls = '--')
-    ax2.set_ylabel('diff(Confirmed cases)')
+    ax2.set_ylabel('diff(Casos confirmados)')
     ax2.spines.right.set_visible(True) #This was set as False by default in the .mpstyle file
 
     ##Legend