| import os
|
| import json
|
| import csv
|
| import pandas as pd
|
|
|
| def clean_value(v):
|
| if isinstance(v, str):
|
| return v.replace("\n", ",")
|
| if isinstance(v, list):
|
| return ";".join(map(str, v))
|
| return v
|
|
|
|
|
| def torsion_stats_dict(file_path):
|
| try:
|
| df = pd.read_csv(file_path)
|
| except FileNotFoundError:
|
| return {}
|
|
|
| torsion_cols = [c for c in df.columns if "_phi" in c or "_psi" in c or "_omega" in c]
|
|
|
| grouped = df.groupby("cluster")
|
|
|
| count_df = grouped.size().rename("count")
|
| mean_df = grouped[torsion_cols].mean()
|
| std_df = grouped[torsion_cols].std()
|
|
|
| mean_df.columns = [c + "_mean" for c in mean_df.columns]
|
| std_df.columns = [c + "_std" for c in std_df.columns]
|
|
|
| result_df = pd.concat([count_df, mean_df, std_df], axis=1)
|
|
|
|
|
| result_dict = {}
|
|
|
| for cluster_id, row in result_df.iterrows():
|
|
|
| cluster_key = f"Cluster {int(cluster_id)}"
|
| cluster_data = {}
|
|
|
| for col, val in row.items():
|
|
|
| if col == "count":
|
| cluster_data[col] = int(val)
|
|
|
| else:
|
| cluster_data[col] = round(float(val), 2)
|
|
|
| result_dict[cluster_key] = cluster_data
|
|
|
| return result_dict
|
|
|
|
|
| raw_loc='../raw_data/'
|
|
|
| GS_list = [
|
| d for d in os.listdir(raw_loc)
|
| if "GS" in d and os.path.isdir(os.path.join(raw_loc, d))
|
| ]
|
|
|
| print('GS_list',len(GS_list))
|
|
|
| data_archetype_term_list = ["glytoucan", "ID", "name", "glycam", "iupac", "iupac_extended", "wurcs", "glycoct",
|
| "smiles", "oxford", "mass", "motifs", "termini", "components", "composition", "rot_bonds",
|
| "hbond_donor", "hbond_acceptor", "entropy", "clusters","coverage_clusters","silhouette_scores","coverage_clusters_per_main","pca_variance", "length", "package", "forcefield",
|
| "temperature", "pressure", "salt", ]
|
| data_alpha_term_list = ["glycam", "iupac", "iupac_extended", "glytoucan", "wurcs", ]
|
| data_beta_term_list = ["glycam", "iupac", "iupac_extended", "glytoucan", "wurcs", ]
|
| data_meta_term_list = ["common_names", "description", "keywords"]
|
| glygen_term_list=["number_monosaccharides","species","classification","enzyme","crossref","mass_pme","tool_support","missing_score","glycan_type","byonic","gwb","motifs","subsumption","section_stats","history",]
|
|
|
|
|
|
|
|
|
| header_list=[]
|
| header_list.extend(['SNFG','torsion_table','torsion_analysis'])
|
| header_list.extend(data_archetype_term_list)
|
| header_list.extend(["a_"+i for i in data_alpha_term_list])
|
| header_list.extend(["b_"+i for i in data_beta_term_list])
|
| header_list.extend(data_meta_term_list)
|
| header_list.extend(['calculated_torsion','a_calculated_torsion','b_calculated_torsion',])
|
| header_list.extend(['glycosmos'])
|
| header_list.extend(glygen_term_list)
|
|
|
| output_file = "../data/glycoshape_data.csv"
|
| csvfile=open(output_file, "w", newline="", encoding="utf-8")
|
| writer = csv.writer(csvfile)
|
|
|
|
|
|
|
| with open("../data/glycan_dictionary.json", "r", encoding="utf-8") as f:
|
| calculated_torsion = json.load(f)
|
| f.close()
|
|
|
|
|
|
|
|
|
| writer.writerow(header_list)
|
|
|
| for GS in GS_list:
|
|
|
| GS_loc=raw_loc+GS+'/'
|
| row = []
|
|
|
|
|
| v='./raw_data'+GS+'/'+'snfg.svg'
|
| row.append(clean_value(v))
|
|
|
|
|
|
|
| v = './torsion_data/' + GS + '_torsion_data.txt'
|
| row.append(clean_value(v))
|
|
|
|
|
| v=torsion_stats_dict('.'+v)
|
| row.append(clean_value(v))
|
|
|
|
|
| with open(GS_loc+"data.json", "r", encoding="utf-8") as f:
|
| data = json.load(f)
|
| f.close()
|
|
|
| for i in data_archetype_term_list:
|
| v = data.get('archetype', {}).get(i, "")
|
| row.append(clean_value(v))
|
|
|
| for i in data_alpha_term_list:
|
| v = data.get('alpha', {}).get(i, "")
|
| row.append(clean_value(v))
|
|
|
| for i in data_beta_term_list:
|
| v = data.get('beta', {}).get(i, "")
|
| row.append(clean_value(v))
|
|
|
| for i in data_meta_term_list:
|
| v = data.get('search_meta', {}).get(i, "")
|
| row.append(clean_value(v))
|
|
|
| gtouch=data['archetype']['glytoucan']
|
| a_gtouch=data['alpha']['glytoucan']
|
| b_gtouch=data['beta']['glytoucan']
|
| print(GS,gtouch,a_gtouch,b_gtouch)
|
|
|
|
|
| try:
|
| v=calculated_torsion[gtouch]
|
| except KeyError:
|
| v={}
|
| row.append(clean_value(v))
|
|
|
| try:
|
| v = calculated_torsion[a_gtouch]
|
| except KeyError:
|
| v = {}
|
| row.append(clean_value(v))
|
|
|
| try:
|
| v = calculated_torsion[b_gtouch]
|
| except KeyError:
|
| v = {}
|
| row.append(clean_value(v))
|
|
|
|
|
|
|
| with open(GS_loc+"glycosmos.json", "r", encoding="utf-8") as f:
|
| glycosmos = json.load(f)
|
| f.close()
|
|
|
|
|
| v = glycosmos
|
| row.append(clean_value(v))
|
|
|
|
|
| with open(GS_loc+"glygen.json", "r", encoding="utf-8") as f:
|
| glygen = json.load(f)
|
| f.close()
|
|
|
| for i in glygen_term_list:
|
| try:
|
| v = glygen[i]
|
| except :
|
| v=[]
|
| row.append(clean_value(v))
|
|
|
| writer.writerow(row)
|
|
|
| |
