some cool plots

some_cool_plots.py

plt.figure(figsize=(8, 8))
df = np.log(variance_trio_df[['var_mean','merged_var']]+1)
df.loc[:,'color'] = 'b'
df.loc[(df.merged_var - df.var_mean) < -1, 'color'] = 'r'
colors = ["b", "r"]
customPalette = sns.set_palette(sns.color_palette(colors))
ax = sns.scatterplot(
    x = 'var_mean', 
    y = 'merged_var',
    data = df,
    hue='color',
    s=10,
    linewidth=0,
    alpha = 0.4,
    palette=customPalette
)
ax.get_legend().remove()
#plt.loglog(x,y)
plt.plot(np.linspace(0,15,num=16), np.linspace(0,15,num=16),linewidth=1.0)
plt.plot(np.linspace(0,15,num=16), np.linspace(0,15,num=16)-1,'--',linewidth=1.0,color='r')
plt.plot(np.linspace(0,15,num=16), np.linspace(0,15,num=16)+1,'--',linewidth=1.0,color='r')
plt.grid(True,which="both",ls="-", color='0.65')
             
plt.xlabel('Mean of variances for transcripts to be merged (log)',fontsize = 14)
plt.ylabel('Variance of the merged group (log)',fontsize = 14)
#plt.legend(frameon=False, loc ='upper left',fontsize=13)
ax.tick_params(axis='x', labelsize=12)
ax.tick_params(axis='y', labelsize=12)
sns.despine()






plt.plot(gibbs_df_salmon.loc[hg_isoform_info['groups'][group_id][0]].values,
         label='{} InfRV: {:0.2f}'.format(hg_isoform_info['groups'][group_id][0], infa),
         marker = 'o',
         linestyle='dashed',
         markersize = 3.,
         linewidth = 1.0
        )





plt.figure(figsize=(8, 8))
ax = sns.scatterplot(x = 'mean_sum',y = 'metric', 
                data = roll_df , 
                     s = 6, 
                     linewidth=0,
                    alpha = 0.2)
#sns.scatterplot(x = 'mean_1',y = 'ratio', data = roll_df, s = 6, linewidth=0)
#plt.hlines(y=-5,xmin=0,xmax = 500000,colors='red')
ax.tick_params(axis='y', labelsize=14)
#ax.tick_params(axis='y', labelsize=14)
transform=ax.get_xaxis_transform()
plt.axhline(-1.8,linewidth=1.0, color = 'r')
#plt.text(pow(10,4),-20.2,"Cut-off from background $\mathcal{B}$",fontsize=13)
plt.ylabel('Change in uncertainty when the pair is collapsed', fontsize = 14)
plt.xlabel('Sum of the means of read counts for individual pair', fontsize = 14)
plt.tick_params(axis='x', labelsize=14)
plt.xscale('log')
sns.despine()