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84ff331 | 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 | import json
import math
import os
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MaxNLocator
THIS_SCRIPT_PATH = os.path.abspath(__file__)
DATA_PATH = os.path.join(os.path.dirname(THIS_SCRIPT_PATH), "data")
OUTPUT_FOLDER = os.path.join(os.path.dirname(THIS_SCRIPT_PATH), "output", "figureS7")
def draw_scatter(name1, name2, x, y, output_path):
fig, ax = plt.subplots(figsize=(2.25, 1.75), dpi=600)
ax.scatter(x, y, alpha=0.4, s=10, edgecolor='none', color="#1f78b4")
ax.xaxis.set_major_locator(MaxNLocator(6))
ax.yaxis.set_major_locator(MaxNLocator(6))
plt.yticks(fontsize=8)
plt.xticks(fontsize=8)
plt.xlabel(name1, fontsize=11)
plt.ylabel(name2, fontsize=11)
p = np.poly1d(np.polyfit(x, y, 1))
x_lin_space = np.linspace(min(x), max(x), 100)
plt.plot(x_lin_space, p(x_lin_space), color="#ff7f00", linewidth=1, linestyle="--")
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['right'].set_visible(False)
def format_func(value, tick_number=None):
num_thousands = 0 if abs(value) < 1000 else math.floor(math.log10(abs(value)) / 3)
value = round(value / 1000 ** num_thousands, 2)
return f'{value:g}' + ' KMGTPEZY'[num_thousands]
ax.xaxis.set_major_formatter(plt.FuncFormatter(format_func))
ax.yaxis.set_major_formatter(plt.FuncFormatter(format_func))
equation = f'$\\rho = {np.corrcoef(x, y)[0][1]:.2f}$'
plt.annotate(equation, xy=(0.05, 0.9), xycoords='axes fraction', fontsize=8,
bbox=dict(boxstyle='square', facecolor='white', edgecolor="black", lw=0.5, pad=0.2))
plt.savefig(output_path, bbox_inches='tight', dpi=300)
def get_duration_json(json_path):
os.makedirs(OUTPUT_FOLDER, exist_ok=True)
json_data = json.load(open(json_path, "r"))
without_dups = {}
for jobname, times in json_data.items():
if "multimer" not in jobname:
continue
if len(times) == 3 and times[0] == "dup":
if len(times[2]) == 3 and times[2][0] == "dup":
if len(times[2][2]) == 3 and times[2][2][0] == "dup":
without_dups[jobname] = times[1] + times[2][1] + times[2][2][1] + times[2][2][2]
else:
without_dups[jobname] = times[1] + times[2][1] + times[2][2]
else:
without_dups[jobname] = times[1] + times[2]
else:
without_dups[jobname] = times
parsed_times = {}
for jobname, times in without_dups.items():
if "dup" in times:
print(jobname, times)
parsed_times[jobname] = [float(i[:-1]) for i in times]
return parsed_times
def main():
# load benchmark info
pdb_to_subunits = {}
benchmark_path = os.path.join(DATA_PATH, "benchmark1")
runtimes_folder = os.path.join(benchmark_path, "runtimes")
for filename in os.listdir(os.path.join(benchmark_path, "pdb_infos")):
jobname = filename.split(".")[0]
if not filename.endswith(".json"):
continue
pdb_to_subunits[jobname] = json.load(open(os.path.join(benchmark_path, "pdb_infos", filename), "rb"))
print("Benchmark size ", len(pdb_to_subunits), "PDB ids: ", list(pdb_to_subunits.keys()))
combfold_results = json.load(open(os.path.join(benchmark_path, "combfold_results.json"), "r"))
combfold_results = {k: v for k, v in combfold_results.items() if v is not None and k in pdb_to_subunits}
combfold_assembly_time = {jobname: round(result["took"], 1) for jobname, result in combfold_results.items()}
simple_afm_durations = get_duration_json(os.path.join(runtimes_folder, "duration_simple.json"))
combfold_afm_durations = get_duration_json(os.path.join(runtimes_folder, "duration_combfold.json"))
merged_afm_durations = {}
for jobname, times in simple_afm_durations.items():
jobname = jobname.split("_")[1]
if jobname not in merged_afm_durations:
merged_afm_durations[jobname] = []
merged_afm_durations[jobname] += times
avg_simple_afm_durations = {k: np.mean(v) for k, v in merged_afm_durations.items() if v}
# print(avg_simple_afm_durations)
print("Average AFM", np.mean(list(avg_simple_afm_durations.values())))
avg_combfold_pairs_durations = {k: np.mean(v) for k, v in combfold_afm_durations.items()
if v and len(k.split("_")) == 4}
avg_combfold_groupss_durations = {k: np.mean(v) for k, v in combfold_afm_durations.items()
if v and len(k.split("_")) != 4}
print("Average CombFold pairs", np.mean(list(avg_combfold_pairs_durations.values())))
print("Average CombFold groups", np.mean(list(avg_combfold_groupss_durations.values())))
combfold_pairs_durations_by_jobname = {}
for af_jobname, avg_model_time in avg_combfold_pairs_durations.items():
jobname = af_jobname.split("_")[1]
if jobname not in combfold_pairs_durations_by_jobname:
combfold_pairs_durations_by_jobname[jobname] = []
combfold_pairs_durations_by_jobname[jobname].append(avg_model_time)
combfold_groups_durations_by_jobname = {}
for af_jobname, avg_model_time in avg_combfold_groupss_durations.items():
jobname = af_jobname.split("_")[1]
if jobname not in combfold_groups_durations_by_jobname:
combfold_groups_durations_by_jobname[jobname] = []
combfold_groups_durations_by_jobname[jobname].append(avg_model_time)
avg_jobname_combfold_pairs_durations = {k: np.mean(v) for k, v in combfold_pairs_durations_by_jobname.items()}
avg_jobname_combfold_groups_durations = {k: np.mean(v) for k, v in combfold_groups_durations_by_jobname.items()}
print("Average CombFold pairs by jobname", np.mean(list(avg_jobname_combfold_pairs_durations.values())))
print("Average CombFold groups by jobname", np.mean(list(avg_jobname_combfold_groups_durations.values())))
total_combfold_pairs_by_jobname = {}
for af_jobname, avg_model_time in avg_combfold_pairs_durations.items():
jobname = af_jobname.split("_")[1]
if jobname not in total_combfold_pairs_by_jobname:
total_combfold_pairs_by_jobname[jobname] = 0
total_combfold_pairs_by_jobname[jobname] += avg_model_time
print("Average Total CombFold pairs", np.mean(list(total_combfold_pairs_by_jobname.values())))
total_combfold_groups_by_jobname = {}
for af_jobname, avg_model_time in avg_combfold_groupss_durations.items():
jobname = af_jobname.split("_")[1]
if jobname not in total_combfold_groups_by_jobname:
total_combfold_groups_by_jobname[jobname] = 0
total_combfold_groups_by_jobname[jobname] += avg_model_time
print("Average Total CombFold groups", np.mean(list(total_combfold_groups_by_jobname.values())))
with open(os.path.join(runtimes_folder, "runtime_summary.csv"), "w") as f:
f.write("PDB ID, #subunits, #unique subunits, AFM runtime, CombFold pairs average, CombFold groups average, "
"CombFold pairs total, CombFold Groups total, CombFold assembly runtime\n")
for jobname in sorted(list(pdb_to_subunits.keys())):
total_chain_num = sum([len(i["chain_names"]) for i in pdb_to_subunits[jobname].values()])
f.write(f"{jobname}, {total_chain_num}, {len(pdb_to_subunits[jobname])}, "
f"{avg_simple_afm_durations.get(jobname, '-')}, "
f"{avg_jobname_combfold_pairs_durations.get(jobname, '-')}, "
f"{avg_jobname_combfold_groups_durations.get(jobname, '-')}, "
f"{total_combfold_pairs_by_jobname.get(jobname, '-')}, "
f"{total_combfold_groups_by_jobname.get(jobname, '-')}, "
f"{combfold_assembly_time.get(jobname, '-')}\n")
unique_subunits = []
total_combfold_time = []
for jobname in sorted(list(pdb_to_subunits.keys())):
unique_subunits.append(len(pdb_to_subunits[jobname]))
total_combfold_time.append(total_combfold_pairs_by_jobname.get(jobname, 0)
+ total_combfold_groups_by_jobname.get(jobname, 0)
+ combfold_assembly_time.get(jobname, 0))
draw_scatter("Unique subunits", "Runtime (sec)", unique_subunits, total_combfold_time,
os.path.join(OUTPUT_FOLDER, "FigS7.png"))
if __name__ == "__main__":
main() |