File size: 5,494 Bytes
bf004e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | 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 {}
# Detect torsion columns automatically
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)
# Convert dataframe → formatted dictionary
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",]
# ---- Build header ----
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)
# ---- load calculated torsion ---
with open("../data/glycan_dictionary.json", "r", encoding="utf-8") as f:
calculated_torsion = json.load(f)
f.close()
# Write header
writer.writerow(header_list)
for GS in GS_list:
#print(GS)
GS_loc=raw_loc+GS+'/'
row = []
### add snfg.svg
v='./raw_data'+GS+'/'+'snfg.svg'
row.append(clean_value(v))
### add raw torsion table
v = './torsion_data/' + GS + '_torsion_data.txt'
row.append(clean_value(v))
## analysis torsion
v=torsion_stats_dict('.'+v)
row.append(clean_value(v))
### load data.json
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)
### load calculated torsion json
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))
### load glycosmos.json
with open(GS_loc+"glycosmos.json", "r", encoding="utf-8") as f:
glycosmos = json.load(f)
f.close()
#print(glycosmos)
v = glycosmos
row.append(clean_value(v))
### load glygen.json
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)
#print(header_list)
#print(row) |