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from benchmark import get_cath |
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from benchmark import config |
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import pandas as pd |
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import matplotlib.pyplot as plt |
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from pathlib import Path |
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import numpy as np |
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import seaborn as sns |
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import matplotlib |
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matplotlib.use('Agg') |
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def format_secondary(df): |
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secondary=[[],[],[],[]] |
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for i,chain in df.iterrows(): |
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for structure, residue in zip(list(chain.dssp), list(chain.sequence)): |
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if structure == "H" or structure == "I" or structure == "G": |
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secondary[0].append(residue) |
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elif structure == "E": |
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secondary[1].append(residue) |
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elif structure == "B" or structure == "T" or structure == "S": |
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secondary[2].append(residue) |
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else: |
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secondary[3].append(residue) |
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return secondary |
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def describe_set(dataset,path_to_pdb): |
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plt.ioff() |
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df = get_cath.read_data("cath-domain-description-file.txt") |
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filtered_df = get_cath.filter_with_user_list(df, Path(dataset)) |
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df_with_sequence = get_cath.append_sequence(filtered_df, Path(path_to_pdb)) |
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resolution=df_with_sequence.drop_duplicates(subset=['PDB']).resolution.values |
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fig,ax=plt.subplots(2,5,figsize=(25,10)) |
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hist=np.histogram(resolution,bins=6,range=(0,3)) |
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counts=hist[0]/len(resolution) |
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ax[0][0].bar(range(len(counts)),counts) |
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ax[0][0].set_xlabel(r'Resolution, $\AA$') |
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ax[0][0].set_ylabel('Fraction of structures') |
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ax[0][0].set_xticks([0,1,2,3,4,5]) |
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ax[0][0].set_xticklabels(['[0, 0.5)','[0.5, 1)','[1, 1.5)','[1.5, 2)','[2, 2.5)','[2.5, 3]']) |
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colors = sns.color_palette() |
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arch=filtered_df.drop_duplicates(subset=['class'])['class'].values |
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grouped=filtered_df.groupby(by=['class','architecture']).count() |
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previous_position=0 |
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gs = ax[0, 0].get_gridspec() |
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for a in ax[0, 1:]: |
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a.remove() |
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ax_big = fig.add_subplot(gs[0, 1:]) |
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for x in arch: |
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if x==1 or x==2 or x==3 or x==4: |
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architectures=grouped.loc[x] |
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ax_big.bar(range(previous_position,previous_position+architectures.shape[0]),architectures.PDB.values/filtered_df.shape[0],color=colors[x],label=config.classes[x]) |
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previous_position+=architectures.shape[0] |
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if x==6: |
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architectures=grouped.loc[6] |
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ax_big.bar(range(previous_position,previous_position+architectures.shape[0]),architectures.PDB.values/filtered_df.shape[0],color=colors[4]) |
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cls_arch=[f"{x[0]}.{x[1]}" for x in grouped.index] |
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names=[config.architectures[label] for label in cls_arch] |
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ax_big.set_xticks(range(grouped.shape[0])) |
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ax_big.set_xticklabels(names, rotation=90, fontdict={"horizontalalignment": "center", "size": 12}) |
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ax_big.set_ylabel('Fraction of structures') |
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ax_big.set_title('CATH architectures') |
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ax_big.set_xlim(-0.8,grouped.shape[0]+4) |
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ax_big.legend() |
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secondary=format_secondary(df_with_sequence) |
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ss_types=["Helices", "Sheets", "Structured loops", "Random"] |
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for x in range(len(secondary)): |
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ax[1][x].bar(config.acids,np.unique(secondary[x],return_counts=True)[1]/len(secondary[x])) |
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ax[1][x].set_ylabel('Fraction of structures') |
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ax[1][x].set_xlabel('Amino acids') |
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ax[1][x].set_title(ss_types[x]) |
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all_structures=[x for y in secondary for x in y] |
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ax[1][4].bar(config.acids,np.unique(all_structures,return_counts=True)[1]/len(all_structures)) |
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ax[1][4].set_ylabel('Fraction of structures') |
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ax[1][4].set_xlabel('Amino acids') |
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ax[1][4].set_title('All structures') |
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plt.tight_layout() |
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plt.savefig(dataset+'.pdf') |
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describe_set("/home/s1706179/project/sequence-recovery-benchmark/nmr_benchmark.txt","/home/shared/datasets/pdb/") |
