def leaderboard(m, n, exp_spectrum):
my_bldg_blocks = convolution(m, exp_spectrum)
leader_board = my_bldg_blocks
leader_peptide = "0"
while len(leader_board) != 0:
dict_cyclic_peptide = collections.defaultdict(list)
leader_board = branch(leader_board)
iter_candidate = list(leader_board)
for peptide in iter_candidate:
cyclic_theo = cyclic_spectrum(peptide)
max_cyclic_theo = max(cyclic_theo)
max_exp_spectrum = max(exp_spectrum)
if max_cyclic_theo > max_exp_spectrum:
leader_board.remove(peptide)
elif max_cyclic_theo <= max_exp_spectrum:
score_peptide = scoring(cyclic_theo, exp_spectrum)
dict_cyclic_peptide[score_peptide].append(peptide)
leader_cyclic = cyclic_spectrum(leader_peptide)
score_leader = scoring(leader_cyclic, exp_spectrum)
if score_peptide > score_leader:
leader_peptide = peptide
top_N_list = get_top_N_scores(dict_cyclic_peptide, n)
leader_board = get_top_scorers(dict_cyclic_peptide, top_N_list, n)
return leader_peptide
def leaderboard(n, exp_spectrum):
leader_board = [
57, 71, 87, 97, 99, 101, 103, 113, 114, 115, 128, 129, 131, 137, 147,
156, 163, 186
]
leader_peptide = "0"
while len(leader_board) != 0:
dict_cyclic_peptide = collections.defaultdict(list)
leader_board = branch(leader_board)
iter_candidate = list(leader_board)
for peptide in iter_candidate:
cyclic_theo = cyclic_spectrum(peptide)
max_cyclic_theo = max(cyclic_theo)
max_exp_spectrum = max(exp_spectrum)
if max_cyclic_theo > max_exp_spectrum:
leader_board.remove(peptide)
elif max_cyclic_theo <= max_exp_spectrum:
score_peptide = scoring(cyclic_theo, exp_spectrum)
dict_cyclic_peptide[score_peptide].append(peptide)
leader_cyclic = cyclic_spectrum(leader_peptide)
score_leader = scoring(leader_cyclic, exp_spectrum)
if score_peptide > score_leader:
leader_peptide = peptide
top_N_list = get_top_N_scores(dict_cyclic_peptide, n)
leader_board = get_top_scorers(dict_cyclic_peptide, top_N_list, n)
return leader_peptide
def leaderboard(m_value, n_value, exp_spectrum):
building_blocks = sorted(convolution(m_value,
exp_spectrum)) # List of Integers
candidates = list(building_blocks) # Copy of Building_Blocks
max_exp = max(building_blocks)
leader = '0' # String
while candidates:
d = collections.defaultdict(list)
candidates = branch(building_blocks,
candidates) # List of Strings (Str-Str)
candidate_copy = list(candidates)
for peptide in candidate_copy:
testing_peptide = peptide
split_peptide = testing_peptide.split('-')
candidate_cyclic_spec = cyclic_spectrum(
split_peptide) # Returns a list of integers
max_theo = max(candidate_cyclic_spec)
if max_theo > max_exp:
candidate_copy.remove(testing_peptide)
else:
theo_score = scoring(candidate_cyclic_spec, exp_spectrum)
leader_peptide = leader.split('-')
leader_cyclic_spec = cyclic_spectrum(
leader_peptide) # Returns a list of integers
leader_score = scoring(leader_cyclic_spec, exp_spectrum)
if theo_score > leader_score:
leader = testing_peptide
d[theo_score].append(testing_peptide)
top_n_scores = get_top_N_scores(d, n_value)
candidates = get_top_scorers(d, top_n_scores, n_value)
return leader
def restore():
names = []
counter = 1
print('Genes restored')
while True:
names = repopulate()
scoring(names)
selection()
gc.collect()
print('Generation passed (this session): '+str(counter))
counter+=1
def genetics():
names = generate_networks(args)
counter = 1
print('New population created')
while True:
gc.collect()
scoring(names)
selection()
names = repopulate()
print('Generation passed: '+str(counter))
counter+=1
def add_scoring(self):
def close():
self.root.deiconify()
self.root.state("zoomed")
self.temp_root.destroy()
def running():
if self.new_text_box.t1 == None:
self.temp_root.after(self.normal_hitrate, running)
return
elif self.new_text_box.t1.isAlive():
self.temp_root.after(self.execution_hitrate, running)
return
else:
self.new_text_box.show_message()
self.new_text_box.execute_button['state'] = 'active'
self.temp_root.after(self.normal_hitrate, running)
self.new_text_box.t1 = None
self.root.withdraw()
self.temp_root = tk.Tk()
self.new_text_box = scoring(self.temp_root, width=80, height=20, label="scoring")
self.new_text_box.text_box_info = pickle.load(open(os.path.join(self.info["dump_path"],
self.info["account_name"] + "_info.pickle"), "rb"))
self.new_text_box.popup_button.grid_forget()
self.temp_root.after(self.normal_hitrate, running)
self.temp_root.resizable(0, 0)
self.temp_root.protocol("WM_DELETE_WINDOW", close)
self.temp_root.mainloop()
def main(num):
"""num: is target(protein pocket) number
"""
target = func.init_pcd(data_dir_pass + data_list["pdb_name"][num] + ".ply")
score = []
# screening
# data in "dud38"
for ligand in data_list["ligand_name"]:
source = func.init_pcd(data_dir_pass + ligand + ".ply")
# docking
docking.docking(target, source)
# scoring
score.append(scoring.scoring(source, target))
# score.append(docking.docking(target, source).inlier_rmse)
"""
# save pose
address = "result/target"+str(num)+"/withligand"+str(i)+".ply"
two_pcd = source.pcd_full_points + target.pcd_full_points
o3d.io.write_point_cloud(address, two_pcd)
i = i + 1
"""
# screening
# data in "decoy"
for ligands in decoy_list["decoy"]:
for ligand in ligands["ligand"]:
# load ligand data
source = func.init_pcd(data_dir_pass + "decoy/" + ligand + ".ply")
# docking
docking.docking(target, source)
# scoring
score.append(scoring.scoring(source, target))
return np.argsort(np.array(score))
Add the scores for every nc/nc pair to text files that have two NC ids
in the first column.
'''
import os
import sys
from scoring import scoring
try:
f=sys.argv[1]
except:
sys.exit(sys.argv[0] + " <file of nc/nc> ")
score = scoring()
wanted = ['species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom']
first = True
with open(f, 'r') as fin:
for l in fin:
l=l.strip()
if first and not l.startswith('NC'):
print(l + "\t" + "\t".join(wanted))
first = False
continue
first = False
p=l.split("\t")
if not p[0].startswith('NC'):
sys.stderr.write("Phage " + p[0] + " does not seem to be an NC id\n")
continue
print_question(i, current_quiz, choices)
# gets the users answer for the current question
user_answer = get_user_answer(choices)
# stop the timer when user enters a valid answer and find the difference to get time taken
end_time = time.time()
time_taken = end_time - start_time
clear()
# reprints the question for review, showing what the correct answer was and what answer the user gave
print_topic_and_question_number(quiz_topic, i, current_quiz)
print_question_review(i, current_quiz, choices, user_answer, time_taken)
# calls the scoring function and updates variables with the values returned
score_data = scoring(current_quiz[5][i], user_answer, time_taken, score_data)
# prints current score info as part of the review screen
print_current_score(score_data)
# print a different continue message depending on if it's the last question or not
print("\n\n")
if i + 1 != len(current_quiz[0]):
if input("Press enter to continue to the next question ") == "quit":
exit_quiz()
else:
if input("Press enter to continue to your results ") == "quit":
exit_quiz()
# get avg answer time for correctly answered questions
avg_time = get_avg_time(score_data[3], score_data[2])
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 26 14:03:17 2021
@author: Alan Lin
"""
from scoring import scoring
with open("C:/Users/User/Machine_Learning/openaifab/Line_bot/test/foodlinebot/question_list.txt", "r",encoding="utf-8") as f: #開啟檔案
Q = f.read() #讀取檔案
Qdict = eval(Q[6:]) #str轉dict
Q_name = list(Qdict.keys()) #列出全部問題之name
ans={"Wanfang": {"PerMonth": {"q1": "0", "q2": "0", "q3": "0", "q4": "Test", "q5": "0", "q6": "0", "a": "1", "b": "1", "c": "1", "d": "1", "e": "1", "f": "1", "q7": "0", "q8": "0", "1": "2400", "2": "30", "3": "0900", "4": "8", "5": "3", "6A": "0", "6-1-a": "3", "6-1-b": "1", "6-2.1-a": "2", "6-2.1-b": "0", "6-2.2-a": "0", "6B": "0", "6-3": "2", "6-3-A": "0", "6-3-B": "0", "6-3-C": "1", "6-4": "0", "6-5.1": "1", "6-5.2": "3", "6-5.2-A": "1", "6-5.2-B": "1", "6-5.2-C": "1", "6-5.2-D": "0", "6-6": "0", "6-7": "0", "6-8": "0", "6-9": "0", "7": "1", "8": "1", "9": "2", "10": "1", "11_1": "1", "11_2": "0", "DSM_5_1": "3", "DSM_5_2": "1", "12": "0", "13": "0", "13_1": "1", "13_2": "1", "13_3": "1", "13_4": "2", "13_5": "2", "13_6": "2", "13_7": "1", "13_8": "1", "14": "0", "15": "0", "16": "3", "17": "3", "18": "3", "19": "3", "20": "3", "21": "3", "22": "3", "23": "3", "24": "3", "25": "3", "26": "3", "27": "3", "28": "3", "29": "3", "gender": "1", "birthday": "1996/1/1", "height": "170", "weight": "50", "neck circumference": "14", "education": "5", "Profession": "4", "marital": "0"}}}
score_list={}
hos="Wanfang"
qnaire="PerMonth"
score=''
ans[hos][qnaire]['score'] = scoring(ans,hos,qnaire,Q_name) # 回傳分數
for key, value in ans[hos][qnaire]['score'].items():
temp = key+': '+value+'\n'
score += temp
# for i in range(len(score)):
print('score: \n',score)
# message.append(TextSendMessage(text=score)) #顯示各參數值
import os
import sys
import re
from scoring import scoring
try:
f=sys.argv[1]
except:
sys.exit(sys.argv[0] + " file to parse?. Probably longest_hits_NCIDs.txt")
scoringO = scoring()
matches={}
lengths=set()
with open(f, 'r') as fin:
for l in fin:
p=l.strip().split("\t")
p[2] = int(p[2])
if p[2] > 100:
p[2] = 101
lengths.add(p[2])
if p[0] not in matches:
matches[p[0]]={}
matches[p[0]][p[1]]=p[2]
correct={}
incorrect={}
taxalevels = ['species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom']
from flask import Flask, url_for, flash ,redirect, render_template, request, abort
from scoring import scoring
import json
app = Flask(__name__)
app.secret_key = "dsfajkl23@!fesjkl#"
scr = scoring()
@app.route("/")
def index():
rankings = scr.read_rank()
return render_template("index.html", rankings = rankings)
@app.route("/upload", methods=["POST"])
def upload():
if request.method == "POST":
s_id = (request.form["register_name"])
f = request.files["csv_file"]
user_ip = request.environ.get('HTTP_X_REAL_IP', request.remote_addr)
if f.headers["Content-Type"]=="text/csv":
path = f"./reported/{s_id}.csv"
f.save(path)
message = scr.write(path, s_id, f.filename, user_ip)
flash(message)
else:
flash("CSV파일이 아닙니다. CSV파일을 제출해주세요.")
else:
abort(404)
return redirect(url_for("index"))
'''
Add the scores for every nc/nc pair to text files that have two NC ids
in the first column.
'''
import os
import sys
from scoring import scoring
try:
f = sys.argv[1]
except:
sys.exit(sys.argv[0] + " <file of nc/nc> ")
score = scoring()
wanted = [
'species', 'genus', 'family', 'order', 'class', 'phylum', 'superkingdom'
]
first = True
with open(f, 'r') as fin:
for l in fin:
l = l.strip()
if first and not l.startswith('NC'):
print(l + "\t" + "\t".join(wanted))
first = False
continue
first = False
p = l.split("\t")
if not p[0].startswith('NC'):
sys.stderr.write("Phage " + p[0] +