from PyComplexHeatmap import *

#Generate example dataset (random)
df = pd.DataFrame(['GroupA'] * 5 + ['GroupB'] * 5, columns=['AB'])
df['CD'] = ['C'] * 3 + ['D'] * 3 + ['G'] * 4
df['EF'] = ['E'] * 6 + ['F'] * 2 + ['H'] * 2
df['F'] = np.random.normal(0, 1, 10)
df.index = ['sample' + str(i) for i in range(1, df.shape[0] + 1)]
df_box = pd.DataFrame(np.random.randn(10, 4), columns=['Gene' + str(i) for i in range(1, 5)])
df_box.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
df_bar = pd.DataFrame(np.random.uniform(0, 10, (10, 2)), columns=['TMB1', 'TMB2'])
df_bar.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
df_scatter = pd.DataFrame(np.random.uniform(0, 10, 10), columns=['Scatter'])
df_scatter.index = ['sample' + str(i) for i in range(1, df_box.shape[0] + 1)]
df_heatmap = pd.DataFrame(np.random.randn(30, 10), columns=['sample' + str(i) for i in range(1, 11)])
df_heatmap.index = ["Fea" + str(i) for i in range(1, df_heatmap.shape[0] + 1)]
df_heatmap.iloc[1, 2] = np.nan

#Annotate the rows with sample4 > 0.5
df_rows = df_heatmap.apply(lambda x:x.name if x.sample4 > 0.5 else None,axis=1)
df_rows=df_rows.to_frame(name='Selected')
df_rows['XY']=df_rows.index.to_series().apply(lambda x:'A' if int(x.replace('Fea',''))>=15 else 'B')

#Create row annotations
row_ha = HeatmapAnnotation(
                           Scatter=anno_scatterplot(df_heatmap.sample4.apply(lambda x:round(x,2)),
                                            height=12,cmap='jet',legend=False),
                           Bar=anno_barplot(df_heatmap.sample4.apply(lambda x:round(x,2)),
                                            height=16,cmap='rainbow',legend=False),
                           selected=anno_label(df_rows,colors='red',relpos=(-0.05,0.4)),
                           label_kws={'rotation':30,'horizontalalignment':'left','verticalalignment':'bottom'},
                            axis=0,verbose=0)

#Create column annotations
col_ha = HeatmapAnnotation(label=anno_label(df.AB, merge=True,rotation=10),
                           AB=anno_simple(df.AB,add_text=True),axis=1,
                           CD=anno_simple(df.CD,add_text=True),
                           EF=anno_simple(df.EF,add_text=True,
                                            legend_kws={'frameon':True}),
                           G=anno_boxplot(df_box, cmap='jet',legend=False),
                           verbose=0)

plt.figure(figsize=(5.5, 6.5))
cm = ClusterMapPlotter(data=df_heatmap, top_annotation=col_ha,right_annotation=row_ha,
                       col_cluster=True,row_cluster=True,
                       col_split=df.AB,row_split=2, 
                       col_split_gap=0.5,row_split_gap=0.8,
                       label='values',row_dendrogram=True,
                       show_rownames=False,show_colnames=True,
                       tree_kws={'row_cmap': 'Set1'},verbose=0,legend_gap=5,
                       cmap='RdYlBu_r',xticklabels_kws={'labelrotation':-45,'labelcolor':'blue'})
#plt.savefig("example0.pdf", bbox_inches='tight')
plt.show()