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compare_method_pps.py
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Wed, May 1, 19:20

compare_method_pps.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Sep 13 11:51:15 2022
@author: trostele
"""
# Python version 3.6.13
################################################################################################################################################################################################################################################################
# import all necessary packages
import pandas as pd
import rdkit # rdkit is only supported before Python 3.7
from rdkit import Chem
from rdkit.Chem.MolStandardize import rdMolStandardize
from rdkit.Chem import Descriptors
from rdkit.Chem.rdMolDescriptors import CalcMolFormula
import pickle
################################################################################################################################################################################################################################################################
# script to compare predicted TPs with found TPs from different methods
################################################################################################################################################################################################################################################################
# INPUT
found_tp_smiles_input = "./input/found_TP_SMILES.txt"
# contains SMILES of all TPs that were found in samples
pickle_file_data_dict = "./input/data_dict_com_with_CAS.pickle"
# pickle file location of combined dictionary with all predictions made by different methods
package_method_1 = "EAWAG_BBD-PPS_round_2"
package_method_2 = "EAWAG_BBD-PPS_round_2b"
package_method_5 = "enviPath-BBD_1"
package_method_6 = "enviPath-BBD+SOIL_2"
package_method_7 = "enviPath-BBD+SLUDGE_3"
package_method_8 = "enviPath-BBD+SOIL+SLUDGE_4"
# enter used package (source name in combined data dict)
# same input as in "get_mass_list_from_prediction.py"
#!!!
# I used two rounds of predictions with the EAWAG/BBD-PPS, so it combines them
# the script only works with 7 methods, needs to be updated!
#!!!
################################################################################################################################################################################################################################################################
# FUNCTIONS
def canonicalize_smiles(smiles):
mol = Chem.MolFromSmiles(smiles) # creates mol object from SMILES
uncharger = rdMolStandardize.Uncharger() # easier to access
uncharged = uncharger.uncharge(mol) # protonates or deprotonates the mol object
new_smiles = rdkit.Chem.rdmolfiles.MolToSmiles(uncharged) # converts mol object to canonical SMILES
can_smiles = Chem.CanonSmiles(new_smiles)
return can_smiles
def do_pickle(d, pickle_file):
with open(pickle_file, 'wb') as handle:
pickle.dump(d, handle, protocol=pickle.HIGHEST_PROTOCOL)
def get_pickle(pickle_file):
with open(pickle_file, 'rb') as handle:
d = pickle.load(handle)
return d
def export(data_dict, smi_list):
df_name_list_com = []
df_smi_list_com = []
df_ID_list_com = []
df_Formula_list_com = []
df_MolWeight_list_com = []
df_name_parent_list_com = []
df_inchikey_list_com = []
df_source_list_com = []
df_alt_parent_list_com = []
# add all the data to the lists
for parent in data_dict:
df_name_list_com.append(data_dict[parent]["code_parent"][0])
df_smi_list_com.append(parent)
df_ID_list_com.append(data_dict[parent]["ID_parent"])
df_MolWeight_list_com.append(data_dict[parent]["mass_parent"][0])
df_Formula_list_com.append(data_dict[parent]["Formula_parent"])
df_name_parent_list_com.append(data_dict[parent]["name"][0])
df_inchikey_list_com.append(data_dict[parent]["inchi_parent"])
df_source_list_com.append("")
df_alt_parent_list_com.append([])
for tp in data_dict[parent]["TP_dict"]:
if tp in predicted_and_found:
df_name_list_com.append(data_dict[parent]["TP_dict"][tp]["code"])
df_smi_list_com.append(tp)
df_ID_list_com.append(data_dict[parent]["TP_dict"][tp]["CAS"])
df_MolWeight_list_com.append(data_dict[parent]["TP_dict"][tp]["mass"])
df_Formula_list_com.append(data_dict[parent]["TP_dict"][tp]["Formula"])
df_name_parent_list_com.append(data_dict[parent]["name"][0])
df_inchikey_list_com.append(data_dict[parent]["TP_dict"][tp]["InchiKey"])
df_source_list_com.append(data_dict[parent]["TP_dict"][tp]["source_list"])
df_alt_parent_list_com.append(data_dict[parent]["TP_dict"][tp]["alternative_parent"])
df_complete_dict = {"Name of parent":df_name_parent_list_com,"SMILES": df_smi_list_com,
"Name": df_name_list_com,
"Source": df_source_list_com,
"CAS": df_ID_list_com,"Formula": df_Formula_list_com,
"MolWeight": df_MolWeight_list_com, "InchiKey":df_inchikey_list_com, "Alternative parent": df_alt_parent_list_com}
df_complete = pd.DataFrame.from_dict(df_complete_dict)
df_complete.to_csv("./output/predicted_and_found_TPs.csv", index = False, sep = ",")
################################################################################################################################################################################################################################################################
# export overview of predicted TPs that were also found (on txt file) as csv file
found_tp_smiles = []
SMILES_comp_file = open(found_tp_smiles_input)
for line in SMILES_comp_file:
found_tp_smiles.append(line.rstrip())
found_tp_smiles_canon = []
for tp in found_tp_smiles:
found_tp_smiles_canon.append(canonicalize_smiles(tp))
data_dict = get_pickle(pickle_file_data_dict)
Bar = 'CCS(=O)(=O)N1CC(CC#N)(n2cc(-c3ncnc4[nH]ccc34)cn2)C1'
Abe = 'CCN1CCN(Cc2ccc(Nc3ncc(F)c(-c4cc(F)c5nc(C)n(C(C)C)c5c4)n3)nc2)CC1'
# remove Abe and Bar from dict because they were not spiked
data_dict.pop(Abe)
data_dict.pop(Bar)
predicted_tp_smiles = []
for parent in data_dict:
for tp in data_dict[parent]["TP_dict"]:
predicted_tp_smiles.append(tp)
predicted_and_found = []
for smi in found_tp_smiles_canon:
if smi in predicted_tp_smiles:
predicted_and_found.append(smi)
export(data_dict, predicted_and_found)
################################################################################################################################################################################################################################################################
# export precision (= found/predicted) for each method as tsv file
source_list_nest = []
for parent in data_dict:
for tp in data_dict[parent]["TP_dict"]:
if tp in predicted_and_found:
source_list_nest.append(data_dict[parent]["TP_dict"][tp]["source_list"])
source_list = []
for sublist in source_list_nest:
for item in sublist:
source_list.append(item)
pps = []
envi_updated = []
envi_soil_updated = []
envi_sludge = []
envi_soil_sludge = []
for source in source_list:
if "EAWAG_BBD-PPS" in source:
pps.append(source)
if package_method_5 in source:
envi_updated.append(source)
if package_method_6 in source:
envi_soil_updated.append(source)
if package_method_7 in source:
envi_sludge.append(source)
if package_method_8 in source:
envi_soil_sludge.append(source)
pps_predicted = []
envi_updated_predicted = []
envi_soil_updated_predicted = []
envi_sludge_predicted = []
envi_soil_sludge_predicted = []
for parent in data_dict:
for tp in data_dict[parent]["TP_dict"]:
if package_method_1 in data_dict[parent]["TP_dict"][tp]["source_list"] or package_method_2 in data_dict[parent]["TP_dict"][tp]["source_list"]:
pps_predicted.append(tp)
if package_method_5 in data_dict[parent]["TP_dict"][tp]["source_list"]:
envi_updated_predicted.append(tp)
if package_method_6 in data_dict[parent]["TP_dict"][tp]["source_list"]:
envi_soil_updated_predicted.append(tp)
if package_method_7 in data_dict[parent]["TP_dict"][tp]["source_list"]:
envi_sludge_predicted.append(tp)
if package_method_8 in data_dict[parent]["TP_dict"][tp]["source_list"]:
envi_soil_sludge_predicted.append(tp)
string_precision = "Precision of methods:" + "\n" + "Prediction Method" + "\t" + "Found" + "\t" + "Predicted" + "\t" + "Precision" + "\n" + package_method_1 + "\t" + str(len(pps))+ "\t" + str(len(pps_predicted))+ "\t" + str(round(len(pps)/len(pps_predicted)*100, 2)) + "%\n"+ package_method_5+ "\t" + str(len(envi_updated))+ "\t" + str(len(envi_updated_predicted))+ "\t" + str(round(len(envi_updated)/len(envi_updated_predicted)*100, 2)) + "%\n"+ package_method_6 + "\t"+ str(len(envi_soil_updated))+ "\t" + str(len(envi_soil_updated_predicted))+ "\t" + str(round(len(envi_soil_updated)/len(envi_soil_updated_predicted)*100, 2)) + "%\n"+ package_method_7+ "\t" + str(len(envi_sludge))+ "\t" + str(len(envi_sludge_predicted))+ "\t" + str(round(len(envi_sludge)/len(envi_sludge_predicted)*100, 2)) + "%\n"+ package_method_8+ "\t" + str(len(envi_soil_sludge))+ "\t" + str(len(envi_soil_sludge_predicted))+ "\t" + str(round(len(envi_soil_sludge)/len(envi_soil_sludge_predicted)*100, 2)) + "%\n"
with open("./output/precision_of_methods.tsv", 'w') as t:
t.write(string_precision)
################################################################################################################################################################################################################################################################
# export how many times a subset of methods were used to predict found TPs as tsv
source_dict = {}
for source in source_list_nest:
key = " ".join(source)
source_dict[key] = 0
for source in source_list_nest:
key = " ".join(source)
source_dict[key] += 1
tp_num = 0
for key in source_dict:
tp_num += source_dict[key]
string_sources = "Sources of TPs\t" + "Times used\n"
for key in source_dict:
string_sources += key + "\t"
string_sources += str(source_dict[key]) + "\n"
with open("./output/sources_of_TPs.tsv", 'w') as s:
s.write(string_sources)
################################################################################################################################################################################################################################################################
# check how many TPs were for a given parent for each method and export as tsv
string_tp_tsv = "Number of predicted TPs per parent (without considering overlap): \n" + "Parent\t" + package_method_1 + "\t" + package_method_5 + "\t" + package_method_6 + "\t" + package_method_7 + "\t" + package_method_8 + "\t" + "Total predicted TPs" + "\t" + "Found" + "\t" + "Overall Precision" + "\n"
for parent in data_dict:
list_tp = []
list_pps = []
list_envi_updated = []
list_envi_soil_updated = []
list_envi_sludge = []
list_envi_soil_sludge = []
found_tp_per_parent = []
for tp in data_dict[parent]["TP_dict"]:
list_tp.append(tp)
if package_method_1 in data_dict[parent]["TP_dict"][tp]["source_list"] or package_method_2 in data_dict[parent]["TP_dict"][tp]["source_list"]:
list_pps.append(tp)
if package_method_5 in data_dict[parent]["TP_dict"][tp]["source_list"]:
list_envi_updated.append(tp)
if package_method_6 in data_dict[parent]["TP_dict"][tp]["source_list"]:
list_envi_soil_updated.append(tp)
if package_method_7 in data_dict[parent]["TP_dict"][tp]["source_list"]:
list_envi_sludge.append(tp)
if package_method_8 in data_dict[parent]["TP_dict"][tp]["source_list"]:
list_envi_soil_sludge.append(tp)
if tp in found_tp_smiles_canon:
found_tp_per_parent.append(tp)
string_tp_tsv += data_dict[parent]["name"][0] + "\t" + str(len(list_pps)) + "\t"+ str(len(list_envi_updated)) + "\t" + str(len(list_envi_soil_updated)) + "\t"+ str(len(list_envi_sludge)) + "\t"+ str(len(list_envi_soil_sludge)) + "\t" + str(len(list_tp)) + "\t" + str(len(found_tp_per_parent)) + "\t" + str(round(len(found_tp_per_parent)/len(list_tp)*100, 2)) + "%" + "\n"
list_tp.clear()
list_pps.clear()
list_envi_updated.clear()
list_envi_soil_updated.clear()
list_envi_sludge.clear()
list_envi_soil_sludge.clear()
with open("./output/number_of_predicted_TPs_per_parent.tsv", 'w') as h:
h.write(string_tp_tsv)
################################################################################################################################################################################################################################################################
# get combined probabilities of TPs that were predicted and found and only predicted but not found
predicted_and_found_combined_probability = []
only_predicted_combined_probability = []
for parent in data_dict:
for tp in data_dict[parent]["TP_dict"]:
if data_dict.get(parent, {}).get("TP_dict", {}).get(tp, {}).get("combined_prob") is not None:
if tp in predicted_and_found:
predicted_and_found_combined_probability += ((data_dict[parent]["TP_dict"][tp]["combined_prob"]))
else:
only_predicted_combined_probability += ((data_dict[parent]["TP_dict"][tp]["combined_prob"]))
predicted_and_found_combined_probability_float = []
only_predicted_combined_probability_float = []
for prob in predicted_and_found_combined_probability:
predicted_and_found_combined_probability_float.append(float(prob))
for prob in only_predicted_combined_probability:
only_predicted_combined_probability_float.append(float(prob))
do_pickle(predicted_and_found_combined_probability_float, "predicted_and_found_combined_probability.pickle")
do_pickle(only_predicted_combined_probability_float, "only_predicted_combined_probability.pickle")
################################################################################################################################################################################################################################################################
list_all = []
for parent in data_dict:
for tp in data_dict[parent]["TP_dict"]:
if package_method_1 in data_dict[parent]["TP_dict"][tp]["source_list"] or package_method_2 in data_dict[parent]["TP_dict"][tp]["source_list"]:
if package_method_5 in data_dict[parent]["TP_dict"][tp]["source_list"]:
if package_method_6 in data_dict[parent]["TP_dict"][tp]["source_list"]:
if package_method_7 in data_dict[parent]["TP_dict"][tp]["source_list"]:
if package_method_8 in data_dict[parent]["TP_dict"][tp]["source_list"]:
list_all.append(tp)
# print(list_all)
################################################################################################################################################################################################################################################################
# check which rules were used to predict:
rule_list = []
for parent in data_dict:
for tp in data_dict[parent]["TP_dict"]:
rule = data_dict[parent]["TP_dict"][tp]["rule_list"][0]
rule_split = rule.split(",")
rule_list += rule_split
# the bt rule classification is not final, those are only the most important rules
add_O_rules = ["bt0063", "bt0023", "bt0003", "bt0242", "bt0243", "bt0193", "bt0014", "bt0259", "bt0374", "bt0005", "bt0332"]
add_H2O_rules = ["bt0067", "bt0350", "bt0430", "bt0024", "bt0021", "bt0020", "bt0389", "bt0373", "bt0391"]
desat_rules = ["bt0002", "bt0001"]
add_O_list = []
add_H2O_list = []
desat_list = []
for rule in rule_list:
if rule in add_O_rules:
add_O_list.append(rule)
if rule in add_H2O_rules:
add_H2O_list.append(rule)
if rule in desat_rules:
desat_list.append(rule)
print("Oxygen addition: ", len(add_O_list), ", water addition: ", len(add_H2O_list), ", desat: ", len(desat_list))
string_rule = "Rules that were used in the predictions: need to remove all spaces \" \" and '' signs manually \n"
for rule in rule_list:
string_rule += rule
string_rule += "\n"
with open("./output/used_rules.txt", 'w') as p:
p.write(string_rule)
################################################################################################################################################################################################################################################################
t.close()
h.close()
s.close()
print("Script finished successfully")
################################################################################################################################################################################################################################################################
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣀⣠⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣴⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣶⣦⣤⣤⣤⣤⣤⣤⣄⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣾⡿⠟⠛⠛⠛⠛⠋⠉⠉⠉⠉⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠉⠉⠉⠉⠛⣿⣦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢠⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⠀⠀⣴⡄⠀⠀⠀⠀⣠⡄⠀⠀⠀⠀⠀⠀⠶⠀⠀⠀⠀⠀⠈⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⣿⠀⠀⠀⠀⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⠀⠀⠉⠁⠀⠀⠀⠘⠟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⠁⠀⠀⠀⢸⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣤⠀⢀⣶⣿⣷⣦⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⡇⠀⠀⠀⣀⣤⣄⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣤⠀⠀⠀⠀⠀⠀⣸⡇⠀⣸⡿⠀⠀⠉⠻⣿⣦⡀⠀⢰⡿⠀⠀⠀⠀⠀⣸⣿⣁⣴⣾⡿⠟⠛⣿⡄⠀⠀
# ⣴⣿⠿⠿⣿⣶⣦⣄⡀⠀⠀⠀⠀⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠛⠀⠀⠀⠀⠀⠀⠉⠁⠀⣿⡇⠀⠀⠀⠀⠈⠻⣿⣆⠀⠀⠀⠀⠀⠀⠀⣿⣿⣿⠟⠁⠀⠀⠀⣿⡇⠀⠀
# ⢿⣧⠀⠀⠀⠀⠉⠛⢿⣶⣄⠀⠀⣿⣿⠀⠀⠀⠀⠀⠙⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⡀⠀⠀⠀⣿⠇⠀⠀⠀⠀⠀⠀⠈⢻⣷⣤⣤⣤⣤⣤⣼⣿⠟⠁⠀⠀⠀⠀⠀⣿⡇⠀⠀
# ⠈⢿⣧⡀⠀⠀⠀⠀⠀⠈⢻⣷⡄⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠛⠃⠀⠀⢰⣿⡆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⡇⠀⠀
# ⠀⠈⠻⣷⣄⠀⠀⠀⠀⠀⠀⠙⣿⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⡿⠇⠀⠀⠀⠀⠀⠀⠀⠀⠀⣴⣿⠟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠹⣿⣦⡀
# ⠀⠀⠀⠘⢿⣷⣄⠀⠀⠀⠀⠀⠘⣿⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠠⣼⡇⠀⠀⣾⡿⠁⠀⠀⠀⠀⢠⣾⠋⠉⢳⡄⠀⠀⠀⠀⠀⠀⠀⠀⢠⣾⠋⠙⢳⡄⠀⠀⠈⢿⣷
# ⠀⠀⠀⠀⠀⠙⢿⣷⣄⡀⠀⠀⠀⢹⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠿⠁⠀⠀⣿⡇⠀⠀⠀⠀⠀⠸⣿⣶⣤⣾⡇⠀⠀⠀⠀⠀⠀⠀⠀⠸⣿⣧⣤⣾⣿⠀⠀⠀⠘⣿
# ⠀⠀⠀⠀⠀⠀⠀⠈⠻⢿⣦⣄⠀⢸⣿⠀⠀⠀⠀⠀⠀⣾⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⡇⠀⠀⠀⠀⠀⠀⠈⠛⠛⠛⠁⠀⠀⠀⢿⣉⣩⠿⠀⠀⠉⠛⠿⠛⠃⠀⠀⠀⠀⣿
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠿⣿⣾⣿⠀⠀⠀⠀⠀⠀⠉⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢿⣧⠀⠀⠀⠀⠀⠀⠀⠀⠀⡄⠀⠀⠀⠀⠀⢸⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢰⣿
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⢻⣿⠀⠀⠀⠀⠀⠀⠀⠀⣀⠀⠀⠀⠀⠀⣠⡄⠀⠀⠘⣿⣧⡀⠀⠀⠀⠀⠀⠀⠀⢷⣤⣀⣀⣀⣴⠟⢿⣤⣀⣀⣀⣴⠇⠀⠀⠀⠀⢠⣿⡟
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣟⠀⠀⠀⠀⠀⠀⠀⠀⠛⠀⠀⠀⠀⠀⠻⠃⠀⠀⠀⠈⢿⣷⣄⡀⠀⠀⠀⠀⠀⠀⠈⠉⠉⠉⠁⠀⠀⠈⠉⠉⠉⠁⠀⠀⠀⢀⣴⣿⠟⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣽⣿⣶⣦⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣽⣿⣿⣷⣶⣦⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣤⣴⣶⣶⣾⠿⠛⠁⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣼⡟⠀⠈⠉⠉⣻⡟⠛⣻⡿⠛⠛⠛⠛⢿⣿⠿⠛⠛⠛⠛⠛⠛⠛⢻⣿⠏⠉⠉⠉⠉⢻⡟⠛⠛⣻⣿⠋⠉⠉⠙⣿⠉⠉⠉⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣿⣅⠀⠀⣠⣾⠟⠁⠀⣿⠀⠀⠀⢀⣠⣿⠏⠀⠀⠀⠀⠀⠀⠀⠀⢸⣿⡀⠀⠀⠀⣠⣿⠃⠀⠀⣿⡇⠀⠀⠀⢸⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⠻⠿⠿⠛⠁⠀⠀⠀⠻⢿⣶⣾⠿⠛⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠛⢿⣷⣶⡿⠟⠁⠀⠀⠀⠻⣷⣄⣠⣴⣿⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
# ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠈⠛⠛⠉⠀

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