mirror of
https://github.com/Priyatham-sai-chand/compara-deep-learning.git
synced 2026-10-05 08:11:34 -07:00
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
import numpy as np
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import pandas as pd
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import os
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import json
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import gc
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from read_data import read_data_homology
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import matplotlib.pyplot as plt
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import seaborn as sns
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from matplotlib.backends.backend_pdf import PdfPages
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a_h=[]
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d_h=[]
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df=pd.read_hdf("negative_dataset.h5",key="ndf")
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ndir="processed/synteny_matrices/"
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nf1="_synteny_matrices_global"
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nf2="_synteny_matrices_local"
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nf3="_indexes"
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lsy={}
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with open("processed/neighbor_genes.json","r") as file:
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lsy=dict(json.load(file))
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print("Neighbor Genes Loaded")
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synteny_matrices_global=np.load(ndir+"negative_dataset"+nf1+str(6)+".npy")
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synteny_matrices_local=np.load(ndir+"negative_dataset"+nf2+str(6)+".npy")
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indexes=np.load(ndir+"negative_dataset"+nf3+str(6)+".npy")
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df=df.loc[indexes]
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print(indexes[0:1000])
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ng=["Levenshtein Distance","Levenshtein Distance Reverse"]
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nl=["Local Alignment Score","Local Alignment Score Reverse"]
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inddict={}
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for i in range(len(indexes)):
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inddict[indexes[i]]=i
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while(1):
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try:
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i=int(input("Enter the index:"))
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except:
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break
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if i in inddict:
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font = {'family': 'sans-serif',
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'color': 'darkturquoise',
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'weight': 'heavy',
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'size': 20,
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}
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pdf=PdfPages(str(i)+".pdf")
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print("Species",df.loc[i].species)
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print("Homology Species",df.loc[i].homology_species)
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print("Gene Stable Id:",df.loc[i].gene_stable_id)
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print("Homology Gene Stable Id:",df.loc[i].homology_gene_stable_id)
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text="Species:"+df.loc[i].species
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text+="\n"+"Gene Stable Id:"+df.loc[i].gene_stable_id
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text+="\n"+"Homology Species:"+df.loc[i].homology_species
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text+="\n"+"Homology Gene Stable Id:"+df.loc[i].homology_gene_stable_id
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text+="\n"+"Homology Type:"+"Non Homology"
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fp=plt.figure(figsize=(10,10))
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fp.text(0.5,0.5,text,ha="center",fontdict=font)
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pdf.savefig()
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plt.close()
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print("Global aligned matrix:")
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g1=df.loc[i].gene_stable_id
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g2=df.loc[i].homology_gene_stable_id
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x=[]
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y=[]
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for n in range(len(lsy[g1]['b'])-1,-1,-1):
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x.append(lsy[g1]['b'][n])
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x.append(g1)
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for k in lsy[g1]['f']:
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x.append(k)
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for n in range(len(lsy[g2]['b'])-1,-1,-1):
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y.append(lsy[g2]['b'][n])
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y.append(g2)
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for k in lsy[g2]['f']:
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y.append(k)
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loc=inddict[i]
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sg=synteny_matrices_global[loc]
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sl=synteny_matrices_local[loc]
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for m in range(sg.shape[-1]):
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matrix=sg[:,:,m]
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print(matrix)
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fig,ax=plt.subplots(figsize=(8,8))
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ax.set_xlabel(str(df.loc[i].homology_species),fontsize=10)
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ax.set_ylabel(str(df.loc[i].species),fontsize=10)
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hmap=sns.heatmap(matrix,xticklabels=y, yticklabels=x,annot=True,ax=ax,linewidths=.5,cmap="YlGnBu",annot_kws={"size": 10})
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hmap.figure.subplots_adjust(left=0.33,bottom=0.33,right=0.79,top=0.79)
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ax.set_title(ng[m])
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#plt.text(1,0.5,text,size=10)
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pdf.savefig()
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plt.show()
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print("Local Alignment Matrix")
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for m in range(sg.shape[-1]):
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matrix=sl[:,:,m]
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print(matrix)
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fig,ax=plt.subplots(figsize=(8,8))
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ax.set_xlabel(df.loc[i].homology_species)
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ax.set_ylabel(df.loc[i].species)
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hmap=sns.heatmap(matrix,xticklabels=y, yticklabels=x,annot=True,ax=ax,linewidths=.5,cmap="YlGnBu",annot_kws={"size": 10})
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hmap.figure.subplots_adjust(left=0.27,bottom=0.29,right=0.92)
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ax.set_title(nl[m])
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pdf.savefig()
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plt.show()
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pdf.close()
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else:
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print("Index not found")
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