content stringlengths 7 1.05M | fixed_cases stringlengths 1 1.28M |
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#!/usr/bin/env python3
REPLACE_MASK = "@MASK"
BLANK_SPACE = " "
FILE_EXTENSION_SEPARATOR = "."
TAB_SPACE = 4 * BLANK_SPACE
WRITE_MODE = "w"
READ_MODE = "r"
EOL = "\n"
XML_HEADER = "<?xml version=\"1.0\" encoding=\"utf-8\"?>" + 2 * EOL
XML_FILE_EXTENSION = FILE_EXTENSION_SEPARATOR + "xml"
SOLR_XML_ADD_OPEN_TAG = "<add>"
SOLR_XML_ADD_CLOSE_TAG = "</add>"
SOLR_XML_DOCUMENT_OPEN_TAG = "<doc>"
SOLR_XML_DOCUMENT_CLOSE_TAG = "</doc>"
SOLR_XML_FIELD_OPEN_TAG = "<field name=\"" + REPLACE_MASK + "\">"
SOLR_XML_FIELD_CLOSE_TAG = "</field>"
DOCUMENT_FIELD_NAME_INDEX = 0
DOCUMENT_FIELD_VALUE_INDEX = 1
XML_CHARS_TO_ESCAPE_LIST = ["&", "<", ">", "'", "\""]
XML_ESCAPE_DICTIONARY = {"&":"&", "<":"<", ">":">", "'":"'", "\"":"""}
DOCUMENT_IDENTIFIER_FIELD_NAME = "id"
DECODE_ERROR_LOG = REPLACE_MASK + " could not be decoded"
EMPTY_STRING = ""
BACKUP_SYMBOL = "~"
LINUX_PATH_SEPARATOR = "/"
CDATA_OPEN_TAG = "<![CDATA["
CDATA_CLOSE_TAG = "]]>"
CONTROL_CHARS_TO_IGNORE = {}
for i in range(1, 10):
CONTROL_CHARS_TO_IGNORE[i] = None
for i in range(11, 32):
CONTROL_CHARS_TO_IGNORE[i] = None
| replace_mask = '@MASK'
blank_space = ' '
file_extension_separator = '.'
tab_space = 4 * BLANK_SPACE
write_mode = 'w'
read_mode = 'r'
eol = '\n'
xml_header = '<?xml version="1.0" encoding="utf-8"?>' + 2 * EOL
xml_file_extension = FILE_EXTENSION_SEPARATOR + 'xml'
solr_xml_add_open_tag = '<add>'
solr_xml_add_close_tag = '</add>'
solr_xml_document_open_tag = '<doc>'
solr_xml_document_close_tag = '</doc>'
solr_xml_field_open_tag = '<field name="' + REPLACE_MASK + '">'
solr_xml_field_close_tag = '</field>'
document_field_name_index = 0
document_field_value_index = 1
xml_chars_to_escape_list = ['&', '<', '>', "'", '"']
xml_escape_dictionary = {'&': '&', '<': '<', '>': '>', "'": ''', '"': '"'}
document_identifier_field_name = 'id'
decode_error_log = REPLACE_MASK + ' could not be decoded'
empty_string = ''
backup_symbol = '~'
linux_path_separator = '/'
cdata_open_tag = '<![CDATA['
cdata_close_tag = ']]>'
control_chars_to_ignore = {}
for i in range(1, 10):
CONTROL_CHARS_TO_IGNORE[i] = None
for i in range(11, 32):
CONTROL_CHARS_TO_IGNORE[i] = None |
# Generators are functions that create an iterable. They use the special yield syntax.
def gen():
n = 0
while n < 10:
# yielding a value is similiar to returning it
yield n
# The code continues to the next line on the next call to next()
n += 1
for i in gen():
print(i)
| def gen():
n = 0
while n < 10:
yield n
n += 1
for i in gen():
print(i) |
class Restaurant:
def __init__(self, restaurant_name, cuisine_type):
self.name = restaurant_name
self.cuisine = cuisine_type
self.number_served = 0
def describe_restaurant(self):
print(f"The restaurant's name is {self.name}, its cuisine type is {self.cuisine}")
def open_restaurant(self):
print("The restaurant is openning!")
def set_number_served(self, served_number):
self.number_served = served_number
def increment_number_served(self, number):
self.number_served += number
restaurant = Restaurant('KFC', 'noshery')
print(f"There are {restaurant.number_served} customers in this restaurant")
restaurant.set_number_served(10)
print(f"There are {restaurant.number_served} customers in this restaurant")
restaurant.increment_number_served(50)
print(f"There are {restaurant.number_served} customers in this restaurant")
| class Restaurant:
def __init__(self, restaurant_name, cuisine_type):
self.name = restaurant_name
self.cuisine = cuisine_type
self.number_served = 0
def describe_restaurant(self):
print(f"The restaurant's name is {self.name}, its cuisine type is {self.cuisine}")
def open_restaurant(self):
print('The restaurant is openning!')
def set_number_served(self, served_number):
self.number_served = served_number
def increment_number_served(self, number):
self.number_served += number
restaurant = restaurant('KFC', 'noshery')
print(f'There are {restaurant.number_served} customers in this restaurant')
restaurant.set_number_served(10)
print(f'There are {restaurant.number_served} customers in this restaurant')
restaurant.increment_number_served(50)
print(f'There are {restaurant.number_served} customers in this restaurant') |
asset_types = {
'file': {'name': 'file', 'contents':{'suff_list':['']}},
'lastdb': {'name': 'lastdb',
'contents': {
'suff_patt': '[0-9]*\.(prj|suf|bck|ssp|tis|sds|des)$',
}
},
'taxdump': {'name': 'taxdump',
'contents': {
'suff_list': ['/names.dmp', '/nodes.dmp']
}
},
'bwadb': {'name': 'bwadb',
'contents': {
'suff_patt': '\.[a-z]+$'
}
},
'prefix': {'name': 'prefix',
'contents': {'suff_patt': '[^/]*$'}
},
}
def cleanup_asset_types(asset_types):
for name, type_def in asset_types.items():
# add name to def, so we don't have to keep track
type_def['name'] = name
# if suff_xxxx definitions are top level, move to contents
for key in type_def:
if key.startswith('suff_'):
type_def.setdefault('contents', {})[key] = type_def[key]
| asset_types = {'file': {'name': 'file', 'contents': {'suff_list': ['']}}, 'lastdb': {'name': 'lastdb', 'contents': {'suff_patt': '[0-9]*\\.(prj|suf|bck|ssp|tis|sds|des)$'}}, 'taxdump': {'name': 'taxdump', 'contents': {'suff_list': ['/names.dmp', '/nodes.dmp']}}, 'bwadb': {'name': 'bwadb', 'contents': {'suff_patt': '\\.[a-z]+$'}}, 'prefix': {'name': 'prefix', 'contents': {'suff_patt': '[^/]*$'}}}
def cleanup_asset_types(asset_types):
for (name, type_def) in asset_types.items():
type_def['name'] = name
for key in type_def:
if key.startswith('suff_'):
type_def.setdefault('contents', {})[key] = type_def[key] |
class Solution:
def preorderTraversal(self, root: Optional[TreeNode]) -> List[int]:
ans = []
def preorder(root: Optional[TreeNode]) -> None:
if not root:
return
ans.append(root.val)
preorder(root.left)
preorder(root.right)
preorder(root)
return ans
| class Solution:
def preorder_traversal(self, root: Optional[TreeNode]) -> List[int]:
ans = []
def preorder(root: Optional[TreeNode]) -> None:
if not root:
return
ans.append(root.val)
preorder(root.left)
preorder(root.right)
preorder(root)
return ans |
abc = 'abcdefghijklmnopqrstuvwxyz'
xyz = input()
frs = input()
for i in frs:
print(abc[xyz.find(i)], end='')
print()
| abc = 'abcdefghijklmnopqrstuvwxyz'
xyz = input()
frs = input()
for i in frs:
print(abc[xyz.find(i)], end='')
print() |
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
# class Solution:
# def postorderTraversal(self, root: TreeNode) -> List[int]:
# '''
# recursion solution
# '''
# res = []
# if root:
# if root.left:
# res += self.postorderTraversal(root.left)
# if root.right:
# res += self.postorderTraversal(root.right)
# res.append(root.val)
# return res
# class Solution:
# def postorderTraversal(self, root: TreeNode) -> List[int]:
# '''
# iterative solution, preorder reversed
# '''
# res = []
# stack = []
# if root:
# stack.append(root)
# while stack:
# node = stack.pop()
# res.append(node.val)
# if node.left:
# stack.append(node.left)
# if node.right:
# stack.append(node.right)
# return res[::-1]
class Solution:
def postorderTraversal(self, root: TreeNode) -> List[int]:
'''
iterative solution
'''
res = []
if root:
stack = [(root, False)]
while stack:
node, visited = stack.pop()
if visited:
res.append(node.val)
else:
stack.append((node, True))
if node.right:
stack.append((node.right, False))
if node.left:
stack.append((node.left, False))
return res
| class Solution:
def postorder_traversal(self, root: TreeNode) -> List[int]:
"""
iterative solution
"""
res = []
if root:
stack = [(root, False)]
while stack:
(node, visited) = stack.pop()
if visited:
res.append(node.val)
else:
stack.append((node, True))
if node.right:
stack.append((node.right, False))
if node.left:
stack.append((node.left, False))
return res |
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def levelOrder(self, root: Optional[TreeNode]) -> List[List[int]]:
if not root:
return []
## Queue, result intialization
q = deque([root])
result = []
## Traversing the q
while q:
## Intializing level_nodes
level_nodes = []
## Now we want to go through the queue and remove every
## element that are currently in it
## So however many hence
for i in range(len(q)):
node = q.popleft()
## Adding the nodes at that level
level_nodes.append(node.val)
## Add the children of the removed node to the right
if node.left:
q.append(node.left)
if node.right:
q.append(node.right)
## Adding the level nodes to main result
result.append(level_nodes)
return result
| class Solution:
def level_order(self, root: Optional[TreeNode]) -> List[List[int]]:
if not root:
return []
q = deque([root])
result = []
while q:
level_nodes = []
for i in range(len(q)):
node = q.popleft()
level_nodes.append(node.val)
if node.left:
q.append(node.left)
if node.right:
q.append(node.right)
result.append(level_nodes)
return result |
i = 0
while i <= 2:
if i == 1.0:
i = 1
elif i == 2.0:
i = 2
for j in range(1,4):
print("I={0} J={1}".format(i, j + i))
i += 0.2
i = round(i,1)
| i = 0
while i <= 2:
if i == 1.0:
i = 1
elif i == 2.0:
i = 2
for j in range(1, 4):
print('I={0} J={1}'.format(i, j + i))
i += 0.2
i = round(i, 1) |
#-*- coding: utf-8 -*-
'''
Exceptions for steamwatch.
'''
class ConfigurationError(Exception):
pass
# not used in the template - delete if not required.
class ApplicationError(Exception):
'''Base class for errors in the application logic.'''
pass
class GameNotFoundError(ApplicationError):
pass
| """
Exceptions for steamwatch.
"""
class Configurationerror(Exception):
pass
class Applicationerror(Exception):
"""Base class for errors in the application logic."""
pass
class Gamenotfounderror(ApplicationError):
pass |
class Person:
def __init__(self, id: id, name: str, emails: str, categories: str) -> None:
self.id = id
self.name = name
self.emails = self.__no_duplicates(emails)
self.categories = self.__no_duplicates(categories)
def linked_emails(self) -> str:
return ", ".join([f"__{email}__" for email in self.emails.split(", ") if email])
def __no_duplicates(
self, list_as_str: str, sep_in: str = ",", sep_out: str = ", "
) -> str:
return (
sep_out.join({elem.strip() for elem in list_as_str.split(sep_in)})
if list_as_str
else ""
)
| class Person:
def __init__(self, id: id, name: str, emails: str, categories: str) -> None:
self.id = id
self.name = name
self.emails = self.__no_duplicates(emails)
self.categories = self.__no_duplicates(categories)
def linked_emails(self) -> str:
return ', '.join([f'__{email}__' for email in self.emails.split(', ') if email])
def __no_duplicates(self, list_as_str: str, sep_in: str=',', sep_out: str=', ') -> str:
return sep_out.join({elem.strip() for elem in list_as_str.split(sep_in)}) if list_as_str else '' |
# Finding HCF (GCD) and LCM using Recursive Function
# Defining function
def hcf(a,b):
if b==0:
return a
else:
return hcf(b, a%b) # this is recursion as hcf() calls itself
# Reading numbers from user
first = int(input('Enter first number: '))
second = int(input('Enter second number: '))
# Function call & displaying output HCF (GCD)
print('HCF or GCD of %d and %d is %d' %(first, second, hcf(first, second)))
print('LCM of %d and %d is %d' %(first, second, first*second/hcf(first, second))) | def hcf(a, b):
if b == 0:
return a
else:
return hcf(b, a % b)
first = int(input('Enter first number: '))
second = int(input('Enter second number: '))
print('HCF or GCD of %d and %d is %d' % (first, second, hcf(first, second)))
print('LCM of %d and %d is %d' % (first, second, first * second / hcf(first, second))) |
SKILLS = [30010166, 30011167, 30011168, 30011169, 30011170]
ARKARIUM = 2159309
sm.completeQuestNoRewards(parentID)
sm.deleteQuest(parentID)
for i in range(5):
if sm.hasSkill(SKILLS[i]):
sm.removeSkill(SKILLS[i]) # remove the skill
sm.removeNpc(ARKARIUM)
sm.warpInstanceIn(927000070, 0)
| skills = [30010166, 30011167, 30011168, 30011169, 30011170]
arkarium = 2159309
sm.completeQuestNoRewards(parentID)
sm.deleteQuest(parentID)
for i in range(5):
if sm.hasSkill(SKILLS[i]):
sm.removeSkill(SKILLS[i])
sm.removeNpc(ARKARIUM)
sm.warpInstanceIn(927000070, 0) |
key = int(input())
lanes = int(input())
message = []
for symbol in range(lanes):
letter = input()
decrypt_letter = ord(letter) + key
message.append(chr(decrypt_letter))
print(f"{''.join(message)}")
| key = int(input())
lanes = int(input())
message = []
for symbol in range(lanes):
letter = input()
decrypt_letter = ord(letter) + key
message.append(chr(decrypt_letter))
print(f"{''.join(message)}") |
# -*- coding: utf-8 -*-
# created: 2021-07-12
# creator: liguopeng@liguopeng.net
def split(list_obj, count):
return list_obj[:count], list_obj[count:]
| def split(list_obj, count):
return (list_obj[:count], list_obj[count:]) |
# complex() returns a complex number with the value real + imag * 1j or
# converts a string or number to a complex number.
# If the first parameter is a string, it will be interpreted as a complex
# number and the function must be called without a second parameter.
# The second parameter can never be a string.
# Each argument may be any numeric type (including complex). If imag is omitted,
# it defaults to zero and the constructor serves as a numeric conversion like
# int and float. If both arguments are omitted, returns 0j.
print(f"complex(2): {complex(2)}")
print(f"complex(2, 3): {complex(2, 3)}")
print(f"complex('2+3j'): {complex('2+3j')}")
| print(f'complex(2): {complex(2)}')
print(f'complex(2, 3): {complex(2, 3)}')
print(f"complex('2+3j'): {complex('2+3j')}") |
def CompareLists(headA, headB):
currentA=headA
currentB=headB
while currentA!=None or currentB!=None:
if currentA==None:
return 0
elif currentB==None:
return 0
if currentA.data!=currentB.data:
return 0
currentA=currentA.next
currentB=currentB.next
return 1
| def compare_lists(headA, headB):
current_a = headA
current_b = headB
while currentA != None or currentB != None:
if currentA == None:
return 0
elif currentB == None:
return 0
if currentA.data != currentB.data:
return 0
current_a = currentA.next
current_b = currentB.next
return 1 |
for _ in range(int(input())):
n,k=map(int,input().split())
x,y=n-(k-1),n-2*(k-1)
if x%2!=0 and x>0:
print("YES")
print('1 '*(k-1)+str(x))
elif y%2==0 and y>0:
print("YES")
print('2 '*(k-1)+str(y))
else:
print("NO") | for _ in range(int(input())):
(n, k) = map(int, input().split())
(x, y) = (n - (k - 1), n - 2 * (k - 1))
if x % 2 != 0 and x > 0:
print('YES')
print('1 ' * (k - 1) + str(x))
elif y % 2 == 0 and y > 0:
print('YES')
print('2 ' * (k - 1) + str(y))
else:
print('NO') |
class Script:
@staticmethod
def main():
superhero_ranks = dict()
superhero_ranks["Aquaman"] = 1
superhero_ranks["Superman"] = 2
print(str(superhero_ranks))
Script.main() | class Script:
@staticmethod
def main():
superhero_ranks = dict()
superhero_ranks['Aquaman'] = 1
superhero_ranks['Superman'] = 2
print(str(superhero_ranks))
Script.main() |
def test_breadcrumbs(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_with_one_level(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_with_multiple_levels(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_without_the_home_section(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_with_last_breadcrumb_as_current_page(
env, similar, template, expected
):
template = env.from_string(template)
assert similar(template.render(), expected)
| def test_breadcrumbs(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_with_one_level(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_with_multiple_levels(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_without_the_home_section(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected)
def test_breadcrumbs_with_last_breadcrumb_as_current_page(env, similar, template, expected):
template = env.from_string(template)
assert similar(template.render(), expected) |
counts = dict()
print('Enter a line of text:')
line = input('')
words = line.split()
print('Words:', words)
print('Counting...')
for word in words:
counts[word] = counts.get(word, 0) + 1
print('Counts', counts)
| counts = dict()
print('Enter a line of text:')
line = input('')
words = line.split()
print('Words:', words)
print('Counting...')
for word in words:
counts[word] = counts.get(word, 0) + 1
print('Counts', counts) |
##########################################################################################
# Author: Jared L. Ostmeyer
# Date Started: 2016-05-02
# Environment: Python3
# License: See LICENSE
# Purpose: Tools for describing an amino acid sequence as a sequence of Atchley factors.
##########################################################################################
__path = '/'.join(__file__.split('/')[:-1])+'/atchley_factors.csv'
vecs = dict()
with open(__path, 'r') as stream:
for line in stream:
row = line.split(',')
key = row[0]
values = []
for value in row[1:]:
values.append(float(value))
vecs[key] = values
length = len(vecs['A'])
labels = ['I', 'II', 'III', 'IV', 'V']
def features(sequence):
values = []
for aa in sequence:
values += vecs[aa]
return values
| __path = '/'.join(__file__.split('/')[:-1]) + '/atchley_factors.csv'
vecs = dict()
with open(__path, 'r') as stream:
for line in stream:
row = line.split(',')
key = row[0]
values = []
for value in row[1:]:
values.append(float(value))
vecs[key] = values
length = len(vecs['A'])
labels = ['I', 'II', 'III', 'IV', 'V']
def features(sequence):
values = []
for aa in sequence:
values += vecs[aa]
return values |
qa_calcs_all = {
'H': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'HF', 'basis_set': basis_set, 'lambda_limits': '(0, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'He': (
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'HF', 'basis_set': basis_set, 'lambda_limits': '(-1, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Li': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'HF', 'basis_set': basis_set, 'lambda_limits': '(-2, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Be': (
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'B': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'C': (
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'N': (
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'O': (
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'F': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Ne': (
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Na': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Mg': (
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Al': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Si': (
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'P': (
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'S': (
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Cl': (
{'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
'Ar': (
{'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
{'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta},
),
} | qa_calcs_all = {'H': ({'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'HF', 'basis_set': basis_set, 'lambda_limits': '(0, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'He': ({'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'HF', 'basis_set': basis_set, 'lambda_limits': '(-1, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'Li': ({'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'HF', 'basis_set': basis_set, 'lambda_limits': '(-2, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'Be': ({'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'B': ({'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 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'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 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'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'O': ({'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 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'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'F': ({'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 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'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'Si': ({'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 4)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'P': ({'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 3)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'S': ({'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(0, 2)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'Cl': ({'state': 'chrg0.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg-1.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-1, 1)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}), 'Ar': ({'state': 'chrg0.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg0.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-2, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult2', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg1.mult4', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-3, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult3', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta}, {'state': 'chrg2.mult1', 'try_easier_state_if_fail': 'None', 'qc_method': 'CCSD(T)', 'basis_set': basis_set, 'lambda_limits': '(-4, 0)', 'dimer_sep_range': 'None', 'dimer_sep_step': 'None', 'broken_symmetry': 'False', 'force_unrestrict_spin': 'False', 'specific_atom_lambda': 'None', 'max_qats_order': '4', 'lambda_step': '0.25', 'finite_diff_accuracy': finite_diff_accuracy, 'finite_diff_delta': finite_diff_delta})} |
def balsa_example_error_callback(log_record):
try:
# in case formatting is not yet set
asc_time = log_record.asctime
except AttributeError:
asc_time = None
if asc_time is None:
print(f'{log_record.levelname} : "{log_record.msg}"')
else:
print(f"{log_record.levelname} : it's {asc_time}, do you know where your code is?")
| def balsa_example_error_callback(log_record):
try:
asc_time = log_record.asctime
except AttributeError:
asc_time = None
if asc_time is None:
print(f'{log_record.levelname} : "{log_record.msg}"')
else:
print(f"{log_record.levelname} : it's {asc_time}, do you know where your code is?") |
#!/usr/bin/env python3
#finds number of letters and digits
inp = input("Enter key: ")
digit = 0
letter = 0
for i in inp:
if i.isdigit():
digit+=1
elif i.isalpha():
letter+=1
else:
pass
print("Letter = ",letter)
print("Digit = ",digit)
| inp = input('Enter key: ')
digit = 0
letter = 0
for i in inp:
if i.isdigit():
digit += 1
elif i.isalpha():
letter += 1
else:
pass
print('Letter = ', letter)
print('Digit = ', digit) |
def test_comment(client, commentable, real_login):
comment = 'comment here'
commentable.post_comment(comment)
[c] = _get_comments(client, commentable)
assert c['comment'] == comment
assert commentable.refresh().num_comments == 1
def test_delete_comment(client, commentable, real_login):
comment = 'comment here'
commentable.post_comment(comment)
[c] = _get_comments(client, commentable)
client.api.delete('/rest/comments/{}'.format(c['id']))
assert commentable.refresh().num_comments == 0
assert _get_comments(client, commentable) == []
def _get_comments(client, commentable):
returned = client.api.get('/rest/comments', params={
type(commentable).__name__.lower() + '_id': commentable.id
})
return returned['comments']
| def test_comment(client, commentable, real_login):
comment = 'comment here'
commentable.post_comment(comment)
[c] = _get_comments(client, commentable)
assert c['comment'] == comment
assert commentable.refresh().num_comments == 1
def test_delete_comment(client, commentable, real_login):
comment = 'comment here'
commentable.post_comment(comment)
[c] = _get_comments(client, commentable)
client.api.delete('/rest/comments/{}'.format(c['id']))
assert commentable.refresh().num_comments == 0
assert _get_comments(client, commentable) == []
def _get_comments(client, commentable):
returned = client.api.get('/rest/comments', params={type(commentable).__name__.lower() + '_id': commentable.id})
return returned['comments'] |
accuracy_scores = {
'id1': 0.27, 'id2': 0.75, 'id3': 0.61, 'id4': 0.05, 'id5': 0.4,
'id6': 0.67, 'id7': 0.69, 'id8': 0.52, 'id9': 0.7, 'id10': 0.3
}
# store the top 3 values from the dictionary as a list
max_accs = ___
# create an empty list that will hold ids of participants with the highes accuracy
max_ids = ___ # create an empty list
for ___ in ___: # iterate over all keys in the dictionary
if ___ in ___: # check if the value of this key is in top 3
____ # if so, append the list
print(max_ids)
| accuracy_scores = {'id1': 0.27, 'id2': 0.75, 'id3': 0.61, 'id4': 0.05, 'id5': 0.4, 'id6': 0.67, 'id7': 0.69, 'id8': 0.52, 'id9': 0.7, 'id10': 0.3}
max_accs = ___
max_ids = ___
for ___ in ___:
if ___ in ___:
____
print(max_ids) |
class RFIDReaderException(Exception):
pass
class RFIDReaderTypeException(RFIDReaderException):
pass
| class Rfidreaderexception(Exception):
pass
class Rfidreadertypeexception(RFIDReaderException):
pass |
__MAJOR = "0"
__MINOR = "0"
__MICRO = "1.post1"
__VERSION__ = "{}.{}.{}".format(__MAJOR, __MINOR, __MICRO)
| __major = '0'
__minor = '0'
__micro = '1.post1'
__version__ = '{}.{}.{}'.format(__MAJOR, __MINOR, __MICRO) |
class FSM(object):
def __init__(self, instructions):
self.result={}
self.instructions={}
for i in instructions.split("\n"):
temp=i.split("; ")
self.result[temp[0]]=int(temp[-1])
self.instructions[temp[0]]=temp[1].split(", ")
def run_fsm(self, start, sequence):
path=[start]
for i in sequence:
path.append(self.instructions[path[-1]][i])
return (path[-1], self.result[path[-1]], path) | class Fsm(object):
def __init__(self, instructions):
self.result = {}
self.instructions = {}
for i in instructions.split('\n'):
temp = i.split('; ')
self.result[temp[0]] = int(temp[-1])
self.instructions[temp[0]] = temp[1].split(', ')
def run_fsm(self, start, sequence):
path = [start]
for i in sequence:
path.append(self.instructions[path[-1]][i])
return (path[-1], self.result[path[-1]], path) |
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def recOverlap(l1, r1, l2, r2):
# if rectangle is to the left side of one another
if (l1.x >= r2.x) or (l2.x >= r1.x):
print('hi')
return False
# if rectangle is one above the other
if (l1.y <= r2.y) or (l2.y <= r1.y):
print('yo')
return False
return True
if __name__ == "__main__":
l1 = Point(0, 10)
r1 = Point(10, 0)
l2 = Point(5, 5)
r2 = Point(15, 0)
if recOverlap(l1, r1, l2, r2):
print("Overlap")
else:
print('Do not overlap') | class Point:
def __init__(self, x, y):
self.x = x
self.y = y
def rec_overlap(l1, r1, l2, r2):
if l1.x >= r2.x or l2.x >= r1.x:
print('hi')
return False
if l1.y <= r2.y or l2.y <= r1.y:
print('yo')
return False
return True
if __name__ == '__main__':
l1 = point(0, 10)
r1 = point(10, 0)
l2 = point(5, 5)
r2 = point(15, 0)
if rec_overlap(l1, r1, l2, r2):
print('Overlap')
else:
print('Do not overlap') |
# uncompyle6 version 3.7.4
# Python bytecode 3.7 (3394)
# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]
# Embedded file name: T:\InGame\Gameplay\Scripts\Server\postures\posture_errors.py
# Compiled at: 2018-02-21 00:22:03
# Size of source mod 2**32: 1046 bytes
class PostureGraphError(Exception):
pass
class PostureGraphBoundaryConditionError(PostureGraphError):
pass
class PostureGraphMiddlePathError(PostureGraphError):
pass | class Posturegrapherror(Exception):
pass
class Posturegraphboundaryconditionerror(PostureGraphError):
pass
class Posturegraphmiddlepatherror(PostureGraphError):
pass |
#!/usr/bin/python3
# Constants
DEPTH = 256
NUM_REGISTERS = 8
# Read assembly code from code.txt
with open("code.txt", 'r') as f:
lines = f.readlines()
# Initialize machine code
machineCode = ["0000"]
# Initialize maps
# Memory isn't really a register but acts like one
regMap = {'prefix' : 0, 'a' : 1, 'b' : 2, 'c' : 3, 'd' : 4, 'e' : 5, 'f' : 6, 'pc' : NUM_REGISTERS-1, 'memory' : NUM_REGISTERS+4}
jumpsMap = {'equal' : NUM_REGISTERS, 'unequal' : NUM_REGISTERS+1, 'lt' : NUM_REGISTERS+2, 'gt' : NUM_REGISTERS+3}
labels = {}
# Interpret assembly code and generate machine code
# Could be simplified with more dictionaries at a later date
for i in range(0,len(lines)):
line = lines[i].strip()
if (len(line) == 0 or line[0] == "#"):
continue
cols = line.split()
if (("set" == cols[0]) and (len(cols) < 4)):
if cols[2] in labels:
mc = 0x8000 | (regMap[cols[1]] << 8) | labels[cols[2]]
else:
mc = 0x8000 | (regMap[cols[1]] << 8) | int(cols[2])
machineCode.append(f'{mc:04X}')
elif (("set" == cols[0]) and (len(cols) >= 4)):
if cols[2] in labels:
mc = 0x8000 | (jumpsMap[cols[4]] << 8) | labels[cols[2]]
else:
mc = 0x8000 | (jumpsMap[cols[4]] << 8) | int(cols[2])
machineCode.append(f'{mc:04X}')
elif "nop" == cols[0]:
machineCode.append("0000")
elif "inc" == cols[0]:
mc = 0x0089 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "dec" == cols[0]:
mc = 0x008A | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "label" == cols[0]:
labels[cols[1]] = len(machineCode)
elif "copy" == cols[0]:
mc = 0x0000 | (regMap[cols[3]] << 8) | regMap[cols[1]]
machineCode.append(f'{mc:04X}')
elif "add" == cols[0]:
mc = 0x0081 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "sub" == cols[0]:
mc = 0x0082 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "equal" == cols[0]:
mc = 0x0083 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "gt" == cols[0]:
mc = 0x0084 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "lt" == cols[0]:
mc = 0x0085 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "and" == cols[0]:
mc = 0x0086 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "or" == cols[0]:
mc = 0x0087 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "xor" == cols[0]:
mc = 0x0088 | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "shiftleft" == cols[0]:
mc = 0x008B | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
elif "shiftright" == cols[0]:
mc = 0x008C | (regMap[cols[1]] << 8)
machineCode.append(f'{mc:04X}')
else:
print("Unknown: " + line)
# Write machine code to instructions.txt
with open("instructions.txt", 'w') as f:
for line in machineCode:
f.write("%s\n" % line)
for i in range(0, DEPTH - len(machineCode) + 1):
f.write("0000\n") | depth = 256
num_registers = 8
with open('code.txt', 'r') as f:
lines = f.readlines()
machine_code = ['0000']
reg_map = {'prefix': 0, 'a': 1, 'b': 2, 'c': 3, 'd': 4, 'e': 5, 'f': 6, 'pc': NUM_REGISTERS - 1, 'memory': NUM_REGISTERS + 4}
jumps_map = {'equal': NUM_REGISTERS, 'unequal': NUM_REGISTERS + 1, 'lt': NUM_REGISTERS + 2, 'gt': NUM_REGISTERS + 3}
labels = {}
for i in range(0, len(lines)):
line = lines[i].strip()
if len(line) == 0 or line[0] == '#':
continue
cols = line.split()
if 'set' == cols[0] and len(cols) < 4:
if cols[2] in labels:
mc = 32768 | regMap[cols[1]] << 8 | labels[cols[2]]
else:
mc = 32768 | regMap[cols[1]] << 8 | int(cols[2])
machineCode.append(f'{mc:04X}')
elif 'set' == cols[0] and len(cols) >= 4:
if cols[2] in labels:
mc = 32768 | jumpsMap[cols[4]] << 8 | labels[cols[2]]
else:
mc = 32768 | jumpsMap[cols[4]] << 8 | int(cols[2])
machineCode.append(f'{mc:04X}')
elif 'nop' == cols[0]:
machineCode.append('0000')
elif 'inc' == cols[0]:
mc = 137 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'dec' == cols[0]:
mc = 138 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'label' == cols[0]:
labels[cols[1]] = len(machineCode)
elif 'copy' == cols[0]:
mc = 0 | regMap[cols[3]] << 8 | regMap[cols[1]]
machineCode.append(f'{mc:04X}')
elif 'add' == cols[0]:
mc = 129 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'sub' == cols[0]:
mc = 130 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'equal' == cols[0]:
mc = 131 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'gt' == cols[0]:
mc = 132 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'lt' == cols[0]:
mc = 133 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'and' == cols[0]:
mc = 134 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'or' == cols[0]:
mc = 135 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'xor' == cols[0]:
mc = 136 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'shiftleft' == cols[0]:
mc = 139 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
elif 'shiftright' == cols[0]:
mc = 140 | regMap[cols[1]] << 8
machineCode.append(f'{mc:04X}')
else:
print('Unknown: ' + line)
with open('instructions.txt', 'w') as f:
for line in machineCode:
f.write('%s\n' % line)
for i in range(0, DEPTH - len(machineCode) + 1):
f.write('0000\n') |
# coding=utf-8
# Author: Jianghan LI
# Question: 066.Plus_One
# Complexity: O(N)
# Date: 2017-08-02 10:51-10:53, 0 wrong try
class Solution(object):
def plusOne(self, digits):
for i in range(len(digits)):
if digits[-1 - i] < 9:
return digits[:-1 - i] + [digits[-1 - i] + 1] + [0] * i
return [1] + [0] * len(0)
def plusOne(self, digits):
for i in range(len(digits)):
if digits[-1 - i] < 9:
digits[-1 - i] += 1
return digits
digits[-1 - i] = 0
return [1] + digits
def plusOne(self, digits):
return (digits[:-1] + [digits[-1] + 1] if digits[-1] < 9 else self.plusOne(digits[:-1]) + [0]) if digits else [1]
| class Solution(object):
def plus_one(self, digits):
for i in range(len(digits)):
if digits[-1 - i] < 9:
return digits[:-1 - i] + [digits[-1 - i] + 1] + [0] * i
return [1] + [0] * len(0)
def plus_one(self, digits):
for i in range(len(digits)):
if digits[-1 - i] < 9:
digits[-1 - i] += 1
return digits
digits[-1 - i] = 0
return [1] + digits
def plus_one(self, digits):
return (digits[:-1] + [digits[-1] + 1] if digits[-1] < 9 else self.plusOne(digits[:-1]) + [0]) if digits else [1] |
coluna = int(input('Entre com a quantidade de colunas '))
contador = 0
linha = int(input('Entre com a quantidade de linhas '))
for i in range (1, linha + 1):
for i in range (1, coluna + 1):
contador += 1
print(contador, end=' ')
print() | coluna = int(input('Entre com a quantidade de colunas '))
contador = 0
linha = int(input('Entre com a quantidade de linhas '))
for i in range(1, linha + 1):
for i in range(1, coluna + 1):
contador += 1
print(contador, end=' ')
print() |
#!/usr/bin/env python3
#Take input from the user in celsius, convert to Fahrenheit and
#use if logic to give feedback regarding the temperature
def main():
temp_celsius = float(input("What is the temperature in Celsius?: "))
f = convert_celsius_to_fahrenheit(temp_celsius)
if f > 80:
print("It's hot outside with a temperature of " + str(f) + " degrees F")
elif f < 40:
print("It's cold outside with a temperature of " + str(f) + " degrees F")
else:
print("It's " + str(f) + " degrees F outside")
def convert_celsius_to_fahrenheit(temp_celsius):
temp_fahrenheit = temp_celsius * 1.8 + 32
return temp_fahrenheit
main() | def main():
temp_celsius = float(input('What is the temperature in Celsius?: '))
f = convert_celsius_to_fahrenheit(temp_celsius)
if f > 80:
print("It's hot outside with a temperature of " + str(f) + ' degrees F')
elif f < 40:
print("It's cold outside with a temperature of " + str(f) + ' degrees F')
else:
print("It's " + str(f) + ' degrees F outside')
def convert_celsius_to_fahrenheit(temp_celsius):
temp_fahrenheit = temp_celsius * 1.8 + 32
return temp_fahrenheit
main() |
print("Enter Two Numbers, And I'll sum It")
try:
first_num = int(input("\nFirst number - "))
sec_num = int(input("\nSecond number - "))
except ValueError:
print("you have entered wrong value!!")
else:
answer = first_num + sec_num
print(answer) | print("Enter Two Numbers, And I'll sum It")
try:
first_num = int(input('\nFirst number - '))
sec_num = int(input('\nSecond number - '))
except ValueError:
print('you have entered wrong value!!')
else:
answer = first_num + sec_num
print(answer) |
name = "rezutil"
version = "1.4.5"
# build with bez build system
build_command = "python {root}/rezbuild.py"
private_build_requires = ["python-2.7+<4"]
def commands():
env = globals()["env"]
env.PYTHONPATH.prepend("{root}/python")
| name = 'rezutil'
version = '1.4.5'
build_command = 'python {root}/rezbuild.py'
private_build_requires = ['python-2.7+<4']
def commands():
env = globals()['env']
env.PYTHONPATH.prepend('{root}/python') |
# Copyright (c) 2010-2013, Regents of the University of California.
# All rights reserved.
#
# Released under the BSD 3-Clause license as published at the link below.
# https://openwsn.atlassian.net/wiki/display/OW/License
class ParserException(Exception):
GENERIC = 1
TOO_SHORT = 2
WRONG_LENGTH = 3
UNKNOWN_OPTION = 4
NO_KEY = 5
DESERIALIZE = 6
descriptions = {
GENERIC: 'generic parsing error',
TOO_SHORT: 'input too short',
WRONG_LENGTH: 'input of the wrong length',
UNKNOWN_OPTION: 'no parser key',
NO_KEY: 'no key',
DESERIALIZE: 'deserialization error',
}
def __init__(self,errorCode,details=None):
self.errorCode = errorCode
self.details = details
def __str__(self):
try:
output = self.descriptions[self.errorCode]
if self.details:
output += ': ' + str(self.details)
return output
except KeyError:
return "Unknown error: #" + str(self.errorCode) | class Parserexception(Exception):
generic = 1
too_short = 2
wrong_length = 3
unknown_option = 4
no_key = 5
deserialize = 6
descriptions = {GENERIC: 'generic parsing error', TOO_SHORT: 'input too short', WRONG_LENGTH: 'input of the wrong length', UNKNOWN_OPTION: 'no parser key', NO_KEY: 'no key', DESERIALIZE: 'deserialization error'}
def __init__(self, errorCode, details=None):
self.errorCode = errorCode
self.details = details
def __str__(self):
try:
output = self.descriptions[self.errorCode]
if self.details:
output += ': ' + str(self.details)
return output
except KeyError:
return 'Unknown error: #' + str(self.errorCode) |
N = int(input())
ans = N
for i in range(N+1):
cnt = 0
t = i
while t>0:
cnt+=t%6
t//=6
j=N-i
while j>0:
cnt+=j%9
j//=9
ans = min(ans,cnt)
print(ans) | n = int(input())
ans = N
for i in range(N + 1):
cnt = 0
t = i
while t > 0:
cnt += t % 6
t //= 6
j = N - i
while j > 0:
cnt += j % 9
j //= 9
ans = min(ans, cnt)
print(ans) |
# --------------
#Code starts here
def palindrome(num):
while True:
num+=1
if str(num) == str(num)[::-1]:
return num
break
print(palindrome(123))
# --------------
#Code starts here
#Function to find anagram of one word in another
def a_scramble(str_1,str_2):
result=True
for i in (str_2.lower()):
if i not in (str_1.lower()):
result=False
break
str_1=str_1.replace(i,'',1) #Removing the letters from str_1 that are already checked
return (result)
#Code ends here
# --------------
#Code starts here
def check_fib(num):
if num == 0: return False
elif num == 1: return True
else:
A = 1
B = 1
FLIP = True
while(True):
new = A + B
if new > num: return False
elif new == num: return True
else:
if(FLIP):
A = new
FLIP = not FLIP
else:
B = new
FLIP = not FLIP
# --------------
def compress(word):
word = word.lower()
if len(word) == 0:
return None
final_arr = []
index = 0
letter = word[0]
for el in word:
if el == letter and ord(el) == ord(letter):
index += 1
else:
final_arr.append(letter + repr(index))
letter = el
index = 1
final_arr.append(letter + repr(index))
return "".join(final_arr)
print(compress("xxcccdex"))
# --------------
#Code starts here
def k_distinct(string,k):
string = string.lower()
if len(list(set(string))) == k:
return True
else:
return False
print(k_distinct('SUBBOOKKEEPER',8))
#Code ends here
| def palindrome(num):
while True:
num += 1
if str(num) == str(num)[::-1]:
return num
break
print(palindrome(123))
def a_scramble(str_1, str_2):
result = True
for i in str_2.lower():
if i not in str_1.lower():
result = False
break
str_1 = str_1.replace(i, '', 1)
return result
def check_fib(num):
if num == 0:
return False
elif num == 1:
return True
else:
a = 1
b = 1
flip = True
while True:
new = A + B
if new > num:
return False
elif new == num:
return True
elif FLIP:
a = new
flip = not FLIP
else:
b = new
flip = not FLIP
def compress(word):
word = word.lower()
if len(word) == 0:
return None
final_arr = []
index = 0
letter = word[0]
for el in word:
if el == letter and ord(el) == ord(letter):
index += 1
else:
final_arr.append(letter + repr(index))
letter = el
index = 1
final_arr.append(letter + repr(index))
return ''.join(final_arr)
print(compress('xxcccdex'))
def k_distinct(string, k):
string = string.lower()
if len(list(set(string))) == k:
return True
else:
return False
print(k_distinct('SUBBOOKKEEPER', 8)) |
'''Simple logger for information and debugging
Used when connected to the microcontroller (e.g. Pyboard) and monitoring the
code via the REPL
'''
class Logger:
def __init__(self, prestring='JETI EX BUS'):
self.default_prestring = prestring
self.prestring = prestring
def log(self, msg_type, message):
# define different debug levels for print statements to the REPL
header = {'info': self.prestring + ' - INFO: ',
'debug': self.prestring + ' - DEBUG: '}
print(header[msg_type] + message)
def empty(self):
print(' ')
def setPreString(self, prestring):
self.prestring = prestring
def resetPreString(self):
self.prestring = self.default_prestring
| """Simple logger for information and debugging
Used when connected to the microcontroller (e.g. Pyboard) and monitoring the
code via the REPL
"""
class Logger:
def __init__(self, prestring='JETI EX BUS'):
self.default_prestring = prestring
self.prestring = prestring
def log(self, msg_type, message):
header = {'info': self.prestring + ' - INFO: ', 'debug': self.prestring + ' - DEBUG: '}
print(header[msg_type] + message)
def empty(self):
print(' ')
def set_pre_string(self, prestring):
self.prestring = prestring
def reset_pre_string(self):
self.prestring = self.default_prestring |
class MoveLog:
def __init__(self):
self.moves = []
self.count = 0
def add(self, move):
self.moves.append(move)
self.count += 1
def pop(self):
self.count -= 1
return self.moves.pop()
def count(self):
return self.count
def reset(self):
self.moves = []
self.count = 0
| class Movelog:
def __init__(self):
self.moves = []
self.count = 0
def add(self, move):
self.moves.append(move)
self.count += 1
def pop(self):
self.count -= 1
return self.moves.pop()
def count(self):
return self.count
def reset(self):
self.moves = []
self.count = 0 |
t = int(input())
while t:
A = input()
B = input()
f = False;
for i in A:
if i in B:
f = True;
break;
if f == True:
print("Yes")
else:
print("No")
t = t-1 | t = int(input())
while t:
a = input()
b = input()
f = False
for i in A:
if i in B:
f = True
break
if f == True:
print('Yes')
else:
print('No')
t = t - 1 |
k = int(input().split(' ')[1])
words = input().split(' ')
currentLine = []
for word in words:
currentLine.append(word)
if len(''.join(currentLine)) > k:
currentLine.pop()
print(' '.join(currentLine))
currentLine = [word]
print(' '.join(currentLine))
| k = int(input().split(' ')[1])
words = input().split(' ')
current_line = []
for word in words:
currentLine.append(word)
if len(''.join(currentLine)) > k:
currentLine.pop()
print(' '.join(currentLine))
current_line = [word]
print(' '.join(currentLine)) |
#if customizations are required when doing a a linking of the jpackage code to the OS
def main(j,jp,force=True):
recipe=jp.getCodeMgmtRecipe()
recipe.link(force=force)
| def main(j, jp, force=True):
recipe = jp.getCodeMgmtRecipe()
recipe.link(force=force) |
#Python 3.X solution for Easy Challenge #0005
#GitHub: https://github.com/Ashkore
#https://www.reddit.com/user/Ashkoree/
def program(username):
print ("Hello "+username)
def login(username,password):
validuser = False
validpass = False
with open("usernames.txt","r") as usernamefile:
usernamelist = usernamefile.readlines()
#clean up carrige returns
for x in range(len(usernamelist)):
usernamelist[x] = usernamelist[x].replace("\n","")
for usernameinfile in usernamelist:
if username == usernameinfile:
validuser = True
usernameindex = usernamelist.index(username)
with open("passwords.txt","r") as passwordfile:
passwordlist = passwordfile.readlines()
#clean up carrige returns
for x in range(len(passwordlist)):
passwordlist[x] = passwordlist[x].replace("\n","")
if password == passwordlist[usernameindex]:
validpass = True
if validuser and validpass:
return True
else:
return False
username = input("What is your username?")
password = input ("What is your password?")
valid = login(username,password)
if valid:
program(username)
else:
print("Invalid Username or Password.")
#Example of passwords.txt
# admin
# username1
# username2
#Example of usernames.txt
# admin
# password1
# password2
#Even more extra credit
#good ol admin, admin because everyone needs a default admin account... right? :P
| def program(username):
print('Hello ' + username)
def login(username, password):
validuser = False
validpass = False
with open('usernames.txt', 'r') as usernamefile:
usernamelist = usernamefile.readlines()
for x in range(len(usernamelist)):
usernamelist[x] = usernamelist[x].replace('\n', '')
for usernameinfile in usernamelist:
if username == usernameinfile:
validuser = True
usernameindex = usernamelist.index(username)
with open('passwords.txt', 'r') as passwordfile:
passwordlist = passwordfile.readlines()
for x in range(len(passwordlist)):
passwordlist[x] = passwordlist[x].replace('\n', '')
if password == passwordlist[usernameindex]:
validpass = True
if validuser and validpass:
return True
else:
return False
username = input('What is your username?')
password = input('What is your password?')
valid = login(username, password)
if valid:
program(username)
else:
print('Invalid Username or Password.') |
class CommandError(Exception):
@property
def msg(self):
if self.args:
return self.args[0]
return "An error occurred while running this command ..."
class ConverterNotFound(CommandError):
pass
class BadArgumentCount(CommandError):
def __init__(self, *args, func):
super().__init__(*args)
self.func = func
def usage(self, name: str):
return self.func.usage.format(name=name)
class ConversionError(CommandError):
msg_format = "{value} is not a valid value"
def __init__(self, value, *args, msg=None, msg_format=None):
msg = msg or (msg_format or self.msg_format).format(value=value)
super().__init__(msg, value, *args)
class CompanyNotFound(ConversionError):
msg_format = 'Company "{value}" not found'
# class CompanyNotFoundNorInt(ConversionError):
# msg_format = ''
| class Commanderror(Exception):
@property
def msg(self):
if self.args:
return self.args[0]
return 'An error occurred while running this command ...'
class Converternotfound(CommandError):
pass
class Badargumentcount(CommandError):
def __init__(self, *args, func):
super().__init__(*args)
self.func = func
def usage(self, name: str):
return self.func.usage.format(name=name)
class Conversionerror(CommandError):
msg_format = '{value} is not a valid value'
def __init__(self, value, *args, msg=None, msg_format=None):
msg = msg or (msg_format or self.msg_format).format(value=value)
super().__init__(msg, value, *args)
class Companynotfound(ConversionError):
msg_format = 'Company "{value}" not found' |
string = str(input("Enter a string. "))
def encode(string):
if not string:
return ""
x = 1
while x < len(string) and string[0] == string[x]:
x += 1
return string[0]+str(x)+encode(string[x:])
print(encode(string)) | string = str(input('Enter a string. '))
def encode(string):
if not string:
return ''
x = 1
while x < len(string) and string[0] == string[x]:
x += 1
return string[0] + str(x) + encode(string[x:])
print(encode(string)) |
# -*- coding: utf-8 -*-
pad = '<pad>'
unk = '<unk>'
bos = '<bos>'
eos = '<eos>'
| pad = '<pad>'
unk = '<unk>'
bos = '<bos>'
eos = '<eos>' |
# Reading input file
f = open("inputs/day01.txt", "r")
lines = f.readlines()
input_numbers = list(map(lambda x: int(x.replace("\n","")), lines))
def part1(numbers):
last_num = total = 0
first_line = True
for number in numbers:
if first_line:
first_line = False
elif number > last_num:
total += 1
last_num = number
return total
def part2():
numbers = []
idx = 0
while idx < len(input_numbers):
if idx >= 2:
numbers.append(input_numbers[idx] + input_numbers[idx-1] + input_numbers[idx-2])
idx += 1
return part1(numbers)
part1(input_numbers)
part2() | f = open('inputs/day01.txt', 'r')
lines = f.readlines()
input_numbers = list(map(lambda x: int(x.replace('\n', '')), lines))
def part1(numbers):
last_num = total = 0
first_line = True
for number in numbers:
if first_line:
first_line = False
elif number > last_num:
total += 1
last_num = number
return total
def part2():
numbers = []
idx = 0
while idx < len(input_numbers):
if idx >= 2:
numbers.append(input_numbers[idx] + input_numbers[idx - 1] + input_numbers[idx - 2])
idx += 1
return part1(numbers)
part1(input_numbers)
part2() |
class ComplianceAlertingException(Exception):
pass
class AwsClientException(ComplianceAlertingException):
pass
class ClientFactoryException(ComplianceAlertingException):
pass
class FilterConfigException(ComplianceAlertingException):
pass
class MissingConfigException(ComplianceAlertingException):
pass
class InvalidConfigException(ComplianceAlertingException):
pass
class NotificationMappingException(ComplianceAlertingException):
pass
class UnsupportedAuditException(ComplianceAlertingException):
pass
class UnsupportedEventException(ComplianceAlertingException):
pass
| class Compliancealertingexception(Exception):
pass
class Awsclientexception(ComplianceAlertingException):
pass
class Clientfactoryexception(ComplianceAlertingException):
pass
class Filterconfigexception(ComplianceAlertingException):
pass
class Missingconfigexception(ComplianceAlertingException):
pass
class Invalidconfigexception(ComplianceAlertingException):
pass
class Notificationmappingexception(ComplianceAlertingException):
pass
class Unsupportedauditexception(ComplianceAlertingException):
pass
class Unsupportedeventexception(ComplianceAlertingException):
pass |
def keep_while(func, items):
for item in items:
result = func(item)
if result:
yield item
| def keep_while(func, items):
for item in items:
result = func(item)
if result:
yield item |
'''
Created on 30.12.2018
@author: ED
'''
name = "PyTrinamic"
desc = "TRINAMIC's Python Technology Access Package"
def showInfo():
print(name + " - " + desc)
" motor types "
class MotorTypes():
DC = 0
BLDC = 1
DC_BLDC = 2
STEPPER = 3
DC_BLDC_STEPPER = 4 | """
Created on 30.12.2018
@author: ED
"""
name = 'PyTrinamic'
desc = "TRINAMIC's Python Technology Access Package"
def show_info():
print(name + ' - ' + desc)
' motor types '
class Motortypes:
dc = 0
bldc = 1
dc_bldc = 2
stepper = 3
dc_bldc_stepper = 4 |
IMAGE_DIR = './data'
CONTENT_IMAGE_NAME = 'octopus.jpg'
STYLE_IMAGE_NAME = 'hockney.jpg'
SIZE = 400
STEPS = 2000
DISPLAY_INTERVAL = 400
| image_dir = './data'
content_image_name = 'octopus.jpg'
style_image_name = 'hockney.jpg'
size = 400
steps = 2000
display_interval = 400 |
# Definition for a binary tree node.
# class TreeNode:
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution:
def isValidSequence(self, root: TreeNode, arr: List[int]) -> bool:
if not root:
return False
if len(arr)==1 and root.val==arr[0] and root.left==None and root.right==None:
return True
if not arr:
return False
if root.val!=arr[0]:
return False
return self.isValidSequence(root.left, arr[1:]) or self.isValidSequence(root.right, arr[1:])
| class Solution:
def is_valid_sequence(self, root: TreeNode, arr: List[int]) -> bool:
if not root:
return False
if len(arr) == 1 and root.val == arr[0] and (root.left == None) and (root.right == None):
return True
if not arr:
return False
if root.val != arr[0]:
return False
return self.isValidSequence(root.left, arr[1:]) or self.isValidSequence(root.right, arr[1:]) |
def numLines(filename):
'telt het aantal regels in een bestand'
infile = open(filename, 'r')
lineList = infile.readlines()
infile.close()
return len(lineList)
maxNummer = 0
lineCount = 1
kaartnummers = open("kaartnummers.txt", 'a')
with open('kaartnummers.txt', 'r') as kaartnummers: # Dit is hetzelfde als kaartnummers = open("kaartnummers.txt", 'a') en hiermee open je het bestand en noem je het kaartnummers
for line in kaartnummers: #Dit loopje lees het bestand per regel en noemt het "line"
nummers = int(line.split(',')[0])
if nummers > maxNummer:
maxNummer = nummers
lineNummer = lineCount
lineCount += 1
numLines = numLines('kaartnummers.txt')
print('Deze file telt '+ str(numLines) +' regels')
print('Het grootste kaartnummer is: {} en dat staat op regel {}'.format(maxNummer, lineNummer)) | def num_lines(filename):
"""telt het aantal regels in een bestand"""
infile = open(filename, 'r')
line_list = infile.readlines()
infile.close()
return len(lineList)
max_nummer = 0
line_count = 1
kaartnummers = open('kaartnummers.txt', 'a')
with open('kaartnummers.txt', 'r') as kaartnummers:
for line in kaartnummers:
nummers = int(line.split(',')[0])
if nummers > maxNummer:
max_nummer = nummers
line_nummer = lineCount
line_count += 1
num_lines = num_lines('kaartnummers.txt')
print('Deze file telt ' + str(numLines) + ' regels')
print('Het grootste kaartnummer is: {} en dat staat op regel {}'.format(maxNummer, lineNummer)) |
nil = 0
num = 0
max = 1
cap = 'A'
low = 'a'
print('Equality : \t', nil, '= =', num, nil == num)
print('Equality : \t', cap, '= =', low, cap == low)
print('Inequality : \t', nil, '!=', max, nil != max)
| nil = 0
num = 0
max = 1
cap = 'A'
low = 'a'
print('Equality : \t', nil, '= =', num, nil == num)
print('Equality : \t', cap, '= =', low, cap == low)
print('Inequality : \t', nil, '!=', max, nil != max) |
File = open("File PROTEK/Data2.txt", "r")
dataMhs = {}
i = 1
for data in File:
dictsiji = {}
dataDict = data.split("|")
dictsiji['NIM'] = dataDict[0]
dictsiji['Nama'] = dataDict[1]
dictsiji['Alamat'] = dataDict[2].rstrip("\n")
dataMhs[i] = dictsiji
i += 1
print(dataMhs)
| file = open('File PROTEK/Data2.txt', 'r')
data_mhs = {}
i = 1
for data in File:
dictsiji = {}
data_dict = data.split('|')
dictsiji['NIM'] = dataDict[0]
dictsiji['Nama'] = dataDict[1]
dictsiji['Alamat'] = dataDict[2].rstrip('\n')
dataMhs[i] = dictsiji
i += 1
print(dataMhs) |
n, m, x, y = map(int, input().split())
horse_position = [[x,y],[x-1,y-2], [x-1, y+2], [x+1, y-2], [x+1, y+2], [x-2, y-1], [x-2, y+1], [x+2, y-1], [x+2, y+1]]
#print(horse_position)
f = [[0, 1]]
#calculating ways without hourse
for i in range(1, n+1):
f.append([1])
if ([i, 0] in horse_position):
f[i][0] = 0
#print("i = {}".format(i))
#print(f)
for j in range(1, m+1):
if (i == 1):
f[0].append(1) # add 1 to f[0][j]
if ([0, j] in horse_position):
f[0][j] = 0
f[i].append(f[i-1][j] + f[i][j-1]) # f(n,m) = f(n, m-1) + f(n-1, m)
#print("i = {}; j ={}" .format(i, j))
if ([i,j] in horse_position):
f[i][j] = 0
#print(f)
print(f[n][m])
| (n, m, x, y) = map(int, input().split())
horse_position = [[x, y], [x - 1, y - 2], [x - 1, y + 2], [x + 1, y - 2], [x + 1, y + 2], [x - 2, y - 1], [x - 2, y + 1], [x + 2, y - 1], [x + 2, y + 1]]
f = [[0, 1]]
for i in range(1, n + 1):
f.append([1])
if [i, 0] in horse_position:
f[i][0] = 0
for j in range(1, m + 1):
if i == 1:
f[0].append(1)
if [0, j] in horse_position:
f[0][j] = 0
f[i].append(f[i - 1][j] + f[i][j - 1])
if [i, j] in horse_position:
f[i][j] = 0
print(f[n][m]) |
#=================================\CONFIG./=====================================
# default System camera access code = 1, if connect with any external camera put 1,2 and so on with the no of connected cameras.
camera_no = 0
# To count the total number of people (True/False).
People_Counter = True
# Set the threshold value for total violations limit.
Threshold = 15
# Set if GPU should be used for computations; Otherwise uses the CPU by default.
USE_GPU = True
MIN_CONF = 0.3
NMS_THRESH = 0.3
#===============================================================================
| camera_no = 0
people__counter = True
threshold = 15
use_gpu = True
min_conf = 0.3
nms_thresh = 0.3 |
def get_file_type_from_extension(ext):
ext_to_file = {
'py': 'python',
'c': 'c',
'cs': 'csharp',
}
return ext_to_file.get(ext)
| def get_file_type_from_extension(ext):
ext_to_file = {'py': 'python', 'c': 'c', 'cs': 'csharp'}
return ext_to_file.get(ext) |
d = float(input('Insira distancia ser percorrida: '))
if d <= 200:
p = d * 0.50
print(f'A viagem vai custar R${p:.2f}')
else:
p = d * 0.45
print(f'A viagem vai custar R${p:.2f}') | d = float(input('Insira distancia ser percorrida: '))
if d <= 200:
p = d * 0.5
print(f'A viagem vai custar R${p:.2f}')
else:
p = d * 0.45
print(f'A viagem vai custar R${p:.2f}') |
# numeros de casos
entrance = int(input())
# variavel
countC = 0
listaP = []
p1 = 2
p2 = 3
p3 = 5
# calcular media
while countC < entrance:
a1, a2, a3 = map(float, input().split(' '))
mediaP = ((a1 * p1) + (a2 * p2) + (a3 * p3))/(p1 + p2 + p3)
listaP.append(mediaP)
countC = countC + 1
# imprimir media
for i in listaP:
print('{:.1f}'.format(i))
| entrance = int(input())
count_c = 0
lista_p = []
p1 = 2
p2 = 3
p3 = 5
while countC < entrance:
(a1, a2, a3) = map(float, input().split(' '))
media_p = (a1 * p1 + a2 * p2 + a3 * p3) / (p1 + p2 + p3)
listaP.append(mediaP)
count_c = countC + 1
for i in listaP:
print('{:.1f}'.format(i)) |
# https://leetcode.com/problems/longest-common-prefix/
# Write a function to find the longest common prefix string amongst an array of
# strings.
# If there is no common prefix, return an empty string "".
################################################################################
# one-pass -> compare with the first string
class Solution:
def longestCommonPrefix(self, strs: List[str]) -> str:
if not strs or len(strs) == 0: return ''
if len(strs) == 1: return strs[0]
ans = ''
for i in range(len(strs[0])):
char = strs[0][i]
for string in strs[1:]: # compare with other strings
if i >= len(string) or char != string[i]:
return ans
ans += char
return ans
| class Solution:
def longest_common_prefix(self, strs: List[str]) -> str:
if not strs or len(strs) == 0:
return ''
if len(strs) == 1:
return strs[0]
ans = ''
for i in range(len(strs[0])):
char = strs[0][i]
for string in strs[1:]:
if i >= len(string) or char != string[i]:
return ans
ans += char
return ans |
# Problem Statement
#
# Given the root of a binary tree, then value v and depth d, you need to add a
# row of nodes with value v at the given depth d. The root node is at depth 1.
#
# The adding rule is: given a positive integer depth d, for each NOT null tree
# nodes N in depth d-1, create two tree nodes with value v as N's left subtree
# root and right subtree root. And N's original left subtree should be the left
# subtree of the new left subtree root, its original right subtree should be the
# right subtree of the new right subtree root. If depth d is 1 that means there
# is no depth d-1 at all, then create a tree node with value v as the new root
# of the whole original tree, and the original tree is the new root's left subtree.
# //
# Example:
# 4 4
# / \ / \
# 2 6 => 1 1
# / \ / / \
# 3 1 5 2 6
# / \ /
# v = 1 3 1 5
# d = 2
# Definition for a binary tree node.
class TreeNode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def addOneRow(self, root: TreeNode, v: int, d: int) -> TreeNode:
if d == 1:
node = TreeNode(v)
node.left = root
return node
if d == 2:
add_left(v, root)
add_right(v, root)
else:
if root.left is not None:
self.addOneRow(root.left, v, d - 1)
if root.right is not None:
self.addOneRow(root.right, v, d - 1)
return root
def add_left(v: int, root: TreeNode):
node_left = TreeNode(v)
node_left.left = root.left
root.left = node_left
def add_right(v: int, root: TreeNode):
node_right = TreeNode(v)
node_right.right = root.right
root.right = node_right
| class Treenode:
def __init__(self, x):
self.val = x
self.left = None
self.right = None
class Solution:
def add_one_row(self, root: TreeNode, v: int, d: int) -> TreeNode:
if d == 1:
node = tree_node(v)
node.left = root
return node
if d == 2:
add_left(v, root)
add_right(v, root)
else:
if root.left is not None:
self.addOneRow(root.left, v, d - 1)
if root.right is not None:
self.addOneRow(root.right, v, d - 1)
return root
def add_left(v: int, root: TreeNode):
node_left = tree_node(v)
node_left.left = root.left
root.left = node_left
def add_right(v: int, root: TreeNode):
node_right = tree_node(v)
node_right.right = root.right
root.right = node_right |
S = input()
things = []
curr = S[0]
count = 1
for i in range(1,len(S)):
if(S[i] == curr):
count += 1
else:
things.append((count,int(curr)))
count = 1
curr = S[i]
things.append((count,int(curr)))
print(" ".join(str(i) for i in things)) | s = input()
things = []
curr = S[0]
count = 1
for i in range(1, len(S)):
if S[i] == curr:
count += 1
else:
things.append((count, int(curr)))
count = 1
curr = S[i]
things.append((count, int(curr)))
print(' '.join((str(i) for i in things))) |
for _ in range(int(input())):
n = int(input())
pages=list(map(int,input().split()))
m = int(input())
mem = []
ans=0
i=0
while i<n:
if pages[i] not in mem:
if len(mem)==m:
mem.pop(0)
mem.append(pages[i])
else:
mem.append(pages[i])
ans+=1
else:
mem.remove(pages[i])
mem.append(pages[i])
i+=1
print(ans)
| for _ in range(int(input())):
n = int(input())
pages = list(map(int, input().split()))
m = int(input())
mem = []
ans = 0
i = 0
while i < n:
if pages[i] not in mem:
if len(mem) == m:
mem.pop(0)
mem.append(pages[i])
else:
mem.append(pages[i])
ans += 1
else:
mem.remove(pages[i])
mem.append(pages[i])
i += 1
print(ans) |
'''
cardlist.py
Name: Wengel Gemu
Collaborators: None
Date: September 6th, 2019
Description: This program checks user input against a list to see if it is valid.
'''
# this is a list containing all of the valid values for a card
cards = ['A','2','3','4','5','6','7','8','9','10','J','Q','K']
card_value = input("Enter a card value: ")
if card_value in cards:
print("the card is valid")
else:
print("the card is invalid")
# Write some code that prompts the user to enter a
# card value and then checks if it is valid or
# not. Print a message saying whether
# or not the card is valid.
# (hint: think about what operator you would use
# to see if a value is in a list)
| """
cardlist.py
Name: Wengel Gemu
Collaborators: None
Date: September 6th, 2019
Description: This program checks user input against a list to see if it is valid.
"""
cards = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']
card_value = input('Enter a card value: ')
if card_value in cards:
print('the card is valid')
else:
print('the card is invalid') |
def plus_matrix(A,B) :
C = zeroMat(A)
for i in range(len(A)) :
for j in range(len(A[0])) :
C[i][j] = A[i][j] + B[i][j]
return C
def zeroMat(A):
c = []
for i in range(len(A)):
row = []
for _ in range(len(A[i])):
row.append(0)
c.append(row)
return c
# minus
def minus_matrix(A,B) :
C = zeroMat(A)
for i in range(len(A)) :
for j in range(len(A[0])) :
C[i][j] = A[i][j] - B[i][j]
return C
#transpose
def transpose_matrix(A):
n = len(A[0]) ## 2
m = len(A) ###3
arr = []
for i in range(n):
b = []
for j in range(m):
b.append(0)
arr.append(b)
for i in range(n): ### 3
for j in range(m): ### 2
arr[i][j] = A[j][i]
return arr
# mul and power
def mul_matrix(A,B) :
x1 = len(A)
y2 = len(B[0])
C = [ [0]*y2 for i in range(x1) ]
for i in range(len(A)) :
for k in range(len(B[0])) :
sum = 0
for j in range(len(A[0])) :
sum += A[i][j] * B[j][k]
C[i][k] = sum
return C
def power_matrix(A,c) :
tmp = A.copy()
for _ in range(c-1) :
tmp = mul_matrix(tmp,A)
return tmp
# print matrix
def print_matrix(A) :
for i in range(len(A)) :
for j in range(len(A[0])) :
print(f'{A[i][j]:^6}', end = ' ')
print()
A = [[1,2],[3,4],[5,6]]
B = [[7,9,11],[8,10,12]]
C = [[13,14],[15,16]]
D = [[100,50],[20,70]]
c = 2
res = mul_matrix(plus_matrix(A,transpose_matrix(B)), minus_matrix(power_matrix(C,2),D))
print_matrix(res) | def plus_matrix(A, B):
c = zero_mat(A)
for i in range(len(A)):
for j in range(len(A[0])):
C[i][j] = A[i][j] + B[i][j]
return C
def zero_mat(A):
c = []
for i in range(len(A)):
row = []
for _ in range(len(A[i])):
row.append(0)
c.append(row)
return c
def minus_matrix(A, B):
c = zero_mat(A)
for i in range(len(A)):
for j in range(len(A[0])):
C[i][j] = A[i][j] - B[i][j]
return C
def transpose_matrix(A):
n = len(A[0])
m = len(A)
arr = []
for i in range(n):
b = []
for j in range(m):
b.append(0)
arr.append(b)
for i in range(n):
for j in range(m):
arr[i][j] = A[j][i]
return arr
def mul_matrix(A, B):
x1 = len(A)
y2 = len(B[0])
c = [[0] * y2 for i in range(x1)]
for i in range(len(A)):
for k in range(len(B[0])):
sum = 0
for j in range(len(A[0])):
sum += A[i][j] * B[j][k]
C[i][k] = sum
return C
def power_matrix(A, c):
tmp = A.copy()
for _ in range(c - 1):
tmp = mul_matrix(tmp, A)
return tmp
def print_matrix(A):
for i in range(len(A)):
for j in range(len(A[0])):
print(f'{A[i][j]:^6}', end=' ')
print()
a = [[1, 2], [3, 4], [5, 6]]
b = [[7, 9, 11], [8, 10, 12]]
c = [[13, 14], [15, 16]]
d = [[100, 50], [20, 70]]
c = 2
res = mul_matrix(plus_matrix(A, transpose_matrix(B)), minus_matrix(power_matrix(C, 2), D))
print_matrix(res) |
''' List Nesting '''
# List in string
list_str = ['list in string', 'list']
str_nest = list_str
print(str_nest)
# List in List
list0 = ['list', 'in']
list1 = ['list']
list_nest = [list0, list1]
print(list_nest)
# List in Dictionary
list_dict0 = ['list', 'dictionary']
list_dict1 = ['in']
dict_nest = {
'sector': list_dict0[0] + " " + list_dict1[0] + " " + list_dict0[1]
}
print(dict_nest) | """ List Nesting """
list_str = ['list in string', 'list']
str_nest = list_str
print(str_nest)
list0 = ['list', 'in']
list1 = ['list']
list_nest = [list0, list1]
print(list_nest)
list_dict0 = ['list', 'dictionary']
list_dict1 = ['in']
dict_nest = {'sector': list_dict0[0] + ' ' + list_dict1[0] + ' ' + list_dict0[1]}
print(dict_nest) |
class Language(object):
'''
Programming Language mapper that should be subclassed
by any new Language
'''
valid_extensions = []
invalid_extensions = []
ignore_files = []
ignore_dirs = []
def __init__(self):
pass
def load_language( self ):
raise NotImplementedError("load_language should be implemented.... :D")
def add_valid_extension( self, extension ):
self.valid_extensions.append( extension )
def add_invalid_extension( self, extension ):
self.valid_extensions.append( extension )
def add_ignore_file( self, filename ):
if type(filename) is str:
self.ignore_files.append(filename)
if type(filename) is list:
self.add_ignore_files( filename )
def add_ignore_files( self, filenames ):
if type(filenames) is list:
for f in filenames:
self.ignore_files.append(f)
return self.ignore_files
def add_ignore_dir( self, directory ):
if not type(directory) is str:
return
self.ignore_dirs.append( directory )
def add_ignore_dirs( self, directories ):
if not type(directory) is list:
return
for d in directories:
self.ignore_dirs.append(d)
def add_valid_extensions(self, extensions):
if type(extensions) is list:
for ext in extensions:
self.valid_extensions.append( "."+ext if "." not in ext else ext )
def add_invalid_extensions(self, extensions):
if type(extensions) is list:
for ext in extensions:
self.invalid_extensions.append( "."+ext if "." not in ext else ext )
| class Language(object):
"""
Programming Language mapper that should be subclassed
by any new Language
"""
valid_extensions = []
invalid_extensions = []
ignore_files = []
ignore_dirs = []
def __init__(self):
pass
def load_language(self):
raise not_implemented_error('load_language should be implemented.... :D')
def add_valid_extension(self, extension):
self.valid_extensions.append(extension)
def add_invalid_extension(self, extension):
self.valid_extensions.append(extension)
def add_ignore_file(self, filename):
if type(filename) is str:
self.ignore_files.append(filename)
if type(filename) is list:
self.add_ignore_files(filename)
def add_ignore_files(self, filenames):
if type(filenames) is list:
for f in filenames:
self.ignore_files.append(f)
return self.ignore_files
def add_ignore_dir(self, directory):
if not type(directory) is str:
return
self.ignore_dirs.append(directory)
def add_ignore_dirs(self, directories):
if not type(directory) is list:
return
for d in directories:
self.ignore_dirs.append(d)
def add_valid_extensions(self, extensions):
if type(extensions) is list:
for ext in extensions:
self.valid_extensions.append('.' + ext if '.' not in ext else ext)
def add_invalid_extensions(self, extensions):
if type(extensions) is list:
for ext in extensions:
self.invalid_extensions.append('.' + ext if '.' not in ext else ext) |
# mistertribs - experiment 2
# https://youtu.be/jZTu6qttvMU
Scale.default = Scale.minor
Root.default = 0
Clock.bpm = 105
~b1 >> bass(dur=8, oct=4)
###
b1.room = 1
b1.shape = PWhite(1)
###
~d1 >> play('V [ -]', dur=2, room=1, mix=.5)
~k1 >> karp([0,3,6,10], dur=.25, sus=.5, echo=.5, amp=var([0,1],[15,1]), oct=7)
###
k1.room = 1
k1.mix = .5
###
###
b1.fmod = 1
b1.pan = (-1,1)
###
b1.lpf=linvar([0,4000],30)
~s1 >> soprano(dur=8, sus=8, amp=[0,1])
s1.degree = var([0,-2,-3],8)
s1.oct = 6
k1.amp = var([0,1.5],[15,1])
b1.degree = s1.degree
###
~k2 >> karp([0,2,4,7], dur=1/3, sus=.5, echo=.5, amp=var([0,1.5],[5,1]), oct=7, room=1, mix=.5)
k1.every(16, 'reverse')
###
###
s1.degree = (0,4,7)+var([0,-2,-3],8)
s1.oct = (5,6)
###
b1.dur = PRand([16,8,12,4])
b1.amp = .8
~b2 >> blip(PWhite(4), dur=PWhite(.1), amp=linvar([0,1,0,0],[6,2,0,0]))
b2.shape = linvar([0,1],12)
b2.dur = PWhite(.3)/var([1,2,3],1)
###
b2.bits = 6
b2.room = .5
b2.mix = linvar([0,1],18)
###
~d2 >> play('#', dur=32, sus=8, chop=64, bits=4)
d1.chop = [128,64,0]
b1.stop()
d1.degree = 'V[-X]o[-=n-]'
d1.dur = 1
d1.fmod = 1
d1.slide = 3
s1.amp = .75
s1.slide = PWhite(-1,1)
~s1 >> soprano((0,4,7)+var([0,-2,-3],8), dur=8, sus=8, amp=[0,.75], oct=(5,6), slide=PWhite(-1,1))
~b1 >> bass(s1.degree, dur=PRand([16,8,12,4]), fmod=1, oct=4, room=1, shape=PWhite(1), pan=(-1,1), lpf=linvar([0,4000],30), amp=.8)
b2.stop()
s1.stop()
d1.stop()
~b2 >> blip(PWhite(4), dur=PWhite(.3)/var([1,2,3],1), amp=linvar([0,1,0,0],[6,2,0,0]), shape=linvar([0,1],12))
b1.stop()
Clock.clear()
| Scale.default = Scale.minor
Root.default = 0
Clock.bpm = 105
~b1 >> bass(dur=8, oct=4)
b1.room = 1
b1.shape = p_white(1)
~d1 >> play('V [ -]', dur=2, room=1, mix=0.5)
~k1 >> karp([0, 3, 6, 10], dur=0.25, sus=0.5, echo=0.5, amp=var([0, 1], [15, 1]), oct=7)
k1.room = 1
k1.mix = 0.5
b1.fmod = 1
b1.pan = (-1, 1)
b1.lpf = linvar([0, 4000], 30)
~s1 >> soprano(dur=8, sus=8, amp=[0, 1])
s1.degree = var([0, -2, -3], 8)
s1.oct = 6
k1.amp = var([0, 1.5], [15, 1])
b1.degree = s1.degree
~k2 >> karp([0, 2, 4, 7], dur=1 / 3, sus=0.5, echo=0.5, amp=var([0, 1.5], [5, 1]), oct=7, room=1, mix=0.5)
k1.every(16, 'reverse')
s1.degree = (0, 4, 7) + var([0, -2, -3], 8)
s1.oct = (5, 6)
b1.dur = p_rand([16, 8, 12, 4])
b1.amp = 0.8
~b2 >> blip(p_white(4), dur=p_white(0.1), amp=linvar([0, 1, 0, 0], [6, 2, 0, 0]))
b2.shape = linvar([0, 1], 12)
b2.dur = p_white(0.3) / var([1, 2, 3], 1)
b2.bits = 6
b2.room = 0.5
b2.mix = linvar([0, 1], 18)
~d2 >> play('#', dur=32, sus=8, chop=64, bits=4)
d1.chop = [128, 64, 0]
b1.stop()
d1.degree = 'V[-X]o[-=n-]'
d1.dur = 1
d1.fmod = 1
d1.slide = 3
s1.amp = 0.75
s1.slide = p_white(-1, 1)
~s1 >> soprano((0, 4, 7) + var([0, -2, -3], 8), dur=8, sus=8, amp=[0, 0.75], oct=(5, 6), slide=p_white(-1, 1))
~b1 >> bass(s1.degree, dur=p_rand([16, 8, 12, 4]), fmod=1, oct=4, room=1, shape=p_white(1), pan=(-1, 1), lpf=linvar([0, 4000], 30), amp=0.8)
b2.stop()
s1.stop()
d1.stop()
~b2 >> blip(p_white(4), dur=p_white(0.3) / var([1, 2, 3], 1), amp=linvar([0, 1, 0, 0], [6, 2, 0, 0]), shape=linvar([0, 1], 12))
b1.stop()
Clock.clear() |
def main(a, b, c, d):
return 1 if a - c >= 2 and b - d >= 2 else 0
if __name__ == '__main__':
print(main(*map(int, input().split())))
| def main(a, b, c, d):
return 1 if a - c >= 2 and b - d >= 2 else 0
if __name__ == '__main__':
print(main(*map(int, input().split()))) |
def dobro(n):
return n*2
def metade(n):
return n/2
def aumentar(n,v=10):
novo = n
x = (n*v)/100
return novo + x
def diminuir(n,v=13):
novo = n
x = (n*v)/100
return novo - x | def dobro(n):
return n * 2
def metade(n):
return n / 2
def aumentar(n, v=10):
novo = n
x = n * v / 100
return novo + x
def diminuir(n, v=13):
novo = n
x = n * v / 100
return novo - x |
class AbstractRepositoryFile(object):
ADDED = "A"
MODIFIED = "M"
DELETED = "D"
IGNORED = "I"
RENAMED = "R"
UNVERSIONED = "?"
NOT_MODIFIED = " "
CONFLICTED = "C"
STATE_MAP = {
"A": "added",
"M": "modified",
"D": "deleted",
"I": "ignored",
"R": "renamed",
"?": "unversioned",
" ": "not modified",
"C": "conflicted",
}
def __init__(self, workingCopy, status:str, filePath:str):
self.__workingCopy = workingCopy
self.__status = status
self.__filePath = filePath
#
def filePath(self) -> str:
return self.__filePath
#
def status(self) -> str:
return self.__status
#
def statusText(self) -> str:
return self.STATE_MAP[self.__status]
#
def workingCopy(self):
return self.__workingCopy
#
def __str__(self):
return self.__class__.__name__ + "<" + self.STATE_MAP[self.__status] + ": " + repr(self.__filePath) + ">"
#
def __repr__(self):
return self.__class__.__name__ + "<" + self.STATE_MAP[self.__status] + ": " + repr(self.__filePath) + ">"
#
#
| class Abstractrepositoryfile(object):
added = 'A'
modified = 'M'
deleted = 'D'
ignored = 'I'
renamed = 'R'
unversioned = '?'
not_modified = ' '
conflicted = 'C'
state_map = {'A': 'added', 'M': 'modified', 'D': 'deleted', 'I': 'ignored', 'R': 'renamed', '?': 'unversioned', ' ': 'not modified', 'C': 'conflicted'}
def __init__(self, workingCopy, status: str, filePath: str):
self.__workingCopy = workingCopy
self.__status = status
self.__filePath = filePath
def file_path(self) -> str:
return self.__filePath
def status(self) -> str:
return self.__status
def status_text(self) -> str:
return self.STATE_MAP[self.__status]
def working_copy(self):
return self.__workingCopy
def __str__(self):
return self.__class__.__name__ + '<' + self.STATE_MAP[self.__status] + ': ' + repr(self.__filePath) + '>'
def __repr__(self):
return self.__class__.__name__ + '<' + self.STATE_MAP[self.__status] + ': ' + repr(self.__filePath) + '>' |
########################################################################
'''
say something ....
'''
# from epidemix.utils.plot import *
# from epidemix.utils.partition import *
__version__ = "1.1.2"
########################################################################
| """
say something ....
"""
__version__ = '1.1.2' |
f1_scores_001_train = {
'Wake': [0.45828943664803573, 0.47566984186570316, 0.5875755194928342, 0.7704458983749956, 0.8776419969393865,
0.9099441500276573, 0.922647481237028, 0.9350972410673902, 0.9465349405012661, 0.9534392971969388,
0.9572805948925108, 0.9627780979304024, 0.9671772627452403, 0.9684523701540192, 0.9716055990492866,
0.9731932563363663, 0.9741702106414556, 0.9750272331154685, 0.9763683737482866, 0.9776438172104023,
0.9779034872265177, 0.9782498184458969, 0.9790514587703288, 0.9804106722113014, 0.9802716115527991,
0.9813018761692003, 0.9814845932014806],
'REM': [0.21260117157805308, 0.35374807384355084, 0.5103512283642359, 0.7049339859692951, 0.7804678062886637,
0.8131475844498263, 0.8394483436487339, 0.8634476562257954, 0.8858121874421188, 0.898998362634237,
0.9160738813815542, 0.9319316568182859, 0.9472641818444968, 0.9574975686846584, 0.9649491736446937,
0.9709271015844844, 0.9756795855455999, 0.9788204281364097, 0.9807636963996305, 0.9833653715167936,
0.9848436671966083, 0.9857726653549266, 0.987027730685396, 0.9874375662176479, 0.9885864793678666,
0.9896364035519353, 0.99061473486626],
'Non REM': [0.3401838631048861, 0.475220709175341, 0.5374332841107604, 0.6340473156622304, 0.7303350082917914,
0.7759327787793486, 0.8091571662779938, 0.8371759030861152, 0.8542371387331004, 0.8664349470288908,
0.8812739412416539, 0.8957909719398129, 0.9114945790997332, 0.9205330841504897, 0.930866013351348,
0.9387957691035018, 0.9419240026921892, 0.945397904922747, 0.9504453644461172, 0.9537926314588798,
0.9551680485384851, 0.9571398417488634, 0.9595577339564574, 0.962153041039352, 0.9628565892798121,
0.9644391272147212, 0.965803250485636],
'Pre REM': [0.19860624317691192, 0.1884477836851209, 0.161593542507486, 0.32138035252003516, 0.5003342912439154,
0.597080323173599, 0.6383882339403956, 0.6777091347889314, 0.7154756685664183, 0.7457149471099168,
0.7927714646464645, 0.8286919003726221, 0.8679852222991217, 0.8928338793882888, 0.9127351158091093,
0.9285575691722613, 0.9372181620276784, 0.943335761107065, 0.9516857973155537, 0.9570796675253974,
0.9598336604418881, 0.9629075069761112, 0.9667892371446916, 0.9683581857266528, 0.9716692115423892,
0.9725854152074092, 0.9749576790613507],
'Artefakt': [0.5229366001967272, 0.6338351044007543, 0.6570850735373311, 0.6454606333110112, 0.6661511061117361,
0.7106723973046948, 0.8216750921171551, 0.925570332136013, 0.9651614399165265, 0.977290979284187,
0.9842082303329941, 0.9882866708719491, 0.990505776138239, 0.9923858361012373, 0.9937809736663104,
0.9947903504850028, 0.9952559640697606, 0.9958682908334388, 0.9962760940796701, 0.9965954632115481,
0.9968580072178286, 0.9967910694490364, 0.9972373540856032, 0.9972666167329747, 0.9978593391196046,
0.9977720484506494, 0.9978014280989163],
'avg': [0.34652346294092284, 0.425384302594094, 0.49080772960252955, 0.6152536371675134, 0.7109860417750986,
0.7613554467470252, 0.8062632634442612, 0.8478000534608491, 0.8734442750318859, 0.888375706650834,
0.9063216224990356, 0.9214958595866143, 0.9368854044253663, 0.9463405476957387, 0.9547873751041497,
0.9612528093363233, 0.9648495849953367, 0.9676899236230257, 0.9711078651978516, 0.9736953901846043,
0.9749213741242656, 0.9761721803949669, 0.9779327029284953, 0.9791252163855857, 0.9802486461724943,
0.981146974118783, 0.9821323371427286]
}
f1_scores_001_valid = {
'Wake': [0.657405684754522, 0.4611178937310898, 0.843902349955265, 0.855320411392405, 0.885871037659171,
0.9139117987867221, 0.932055717572047, 0.9463877720904695, 0.9570114044125484, 0.9595433464145559,
0.9673044150459424, 0.9634778302470773, 0.9712198478939845, 0.9694530626717489, 0.9703757610181224,
0.9729483335963112, 0.9736898243618004, 0.9754512380346861, 0.9746917814242247, 0.9747490755414686,
0.9774797034945287, 0.9771752369856486, 0.9760045134787286, 0.9766318905963626, 0.9769876682008221,
0.9782155845072908, 0.978478283917023],
'REM': [0.12518034456420268, 0.11241206629307261, 0.2589039585400816, 0.429526563064691, 0.7098027495517035,
0.82624801552302, 0.815959741193386, 0.8383912248628885, 0.8554066130473637, 0.8804795803671787,
0.8694686169227921, 0.8914441629312025, 0.8816533437317216, 0.8909512761020881, 0.8801363378148078,
0.8948485433146827, 0.8946564885496183, 0.895306859205776, 0.9020965570301981, 0.9019078820581999,
0.9002114977888866, 0.9014194050165274, 0.8947568389057751, 0.9020897832817337, 0.9026852028185107,
0.9030291484092209, 0.9097933165926211],
'Non REM': [0.19235930929587544, 0.05094734791291218, 0.5261806039702052, 0.5527151935297515, 0.7126930223617607,
0.7644436245118839, 0.8765320885540361, 0.8957227937195452, 0.9114534991646556, 0.9150781723747001,
0.9210171195724076, 0.9341014545644126, 0.9412280475539623, 0.9429993330254989, 0.9435981463939169,
0.9463633087970351, 0.9507063572149343, 0.9456240555285198, 0.950480413895048, 0.9514810381274992,
0.9523567655424625, 0.9540212443095599, 0.9520783017668742, 0.9555623038769105, 0.9508740589511293,
0.9542678310029782, 0.955841139192567],
'Pre REM': [0.025122883670125617, 0.0, 0.005687203791469194, 0.05966438781852082, 0.24408602150537637,
0.23279847701930925, 0.29683698296836986, 0.36845637583892615, 0.350185873605948, 0.35790219702338766,
0.3778217821782178, 0.4421052631578948, 0.45901639344262296, 0.4576107899807322, 0.4524975514201763,
0.4938271604938272, 0.47925764192139736, 0.45363908275174475, 0.49278707443739184, 0.492874109263658,
0.4843835616438356, 0.4977011494252873, 0.49826989619377154, 0.5121495327102803, 0.47285464098073554,
0.483423749246534, 0.4955116696588868],
'Artefakt': [0.12214863870493008, 0.30966531122927743, 0.31692207368764264, 0.3072642967542504, 0.1799193843898864,
0.2667024416420714, 0.278149386845039, 0.39522437216961714, 0.4789156626506024, 0.4533083059596434,
0.587088915956151, 0.5015259409969481, 0.6417704011065007, 0.5848024316109421, 0.6144964720974985,
0.674123788217748, 0.6778115501519757, 0.6790490341753345, 0.7044728434504792, 0.6750369276218611,
0.7093596059113301, 0.7350993377483444, 0.718241042345277, 0.7056936647955093, 0.7402489626556018,
0.7391304347826086, 0.7164906580016247],
'avg': [0.22444337219793115, 0.18682852383327042, 0.39031923798893275, 0.44089817051192376, 0.5464744430935796,
0.6008208714966013, 0.6399067834265756, 0.6888365077362893, 0.7105946105762235, 0.7132623204278932,
0.7445401699351022, 0.746530930379507, 0.7789776067457584, 0.7691633786782021, 0.7722208537489044,
0.7964222268839208, 0.7952243724399453, 0.7898140539392123, 0.8049057340474682, 0.7992098065225374,
0.8047582268762087, 0.8130832746970735, 0.8078701185380854, 0.8104254350521594, 0.80873010672136,
0.8116133495897264, 0.8112230134725446]
}
f1_scores_001b_train = {
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}
f1_scores_003_train = {
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'Pre REM': [0.1834041799504074, 0.19172531950361177, 0.2620691563697749, 0.4357441721458459, 0.5412528169601871,
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'avg': [0.33865457796639553, 0.43622528847893777, 0.5508377700822115, 0.6486373556855003, 0.7083492411749353,
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0.9691152429258041, 0.9709925106203603, 0.9729553986099916, 0.974736366815948, 0.9761751165057528,
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}
f1_scores_003b_train = {
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0.8919537341959567, 0.9053421143972485, 0.9239945636223252, 0.9369187624389362, 0.9463731838711433,
0.9526025930664054, 0.9581055804746945, 0.9621588230217527, 0.9652561530290787, 0.9667060212514758,
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0.9788884428048752, 0.9799045960475505, 0.9818839756837986, 0.9830390404942909, 0.9843418741292628],
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0.758483732224806, 0.776876982728234, 0.8099952598465915, 0.8446422480662801, 0.8602145790101109,
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0.9468460872339457, 0.9497151321786691, 0.9519047320731936, 0.9541438069356851, 0.9571166531849202],
'Pre REM': [0.19497741293049012, 0.16800126435570542, 0.16935250967481083, 0.3163466554694558, 0.507647510905686,
0.5804237385380916, 0.6210424217673184, 0.6590068080060515, 0.7019758788811907, 0.732017097496438,
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'avg': [0.3637631935500195, 0.4775587563270444, 0.5371538580463169, 0.6179485589781122, 0.6984150300991057,
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}
f1_scores_003c_train = {
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'Pre REM': [0.18374680909068378, 0.2051593398423948, 0.28768268886145515, 0.45042921678046977, 0.5319476438678995,
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0.9550856733274572, 0.9555110375596396, 0.9564939405388125],
'Artefakt': [0.5071941697032795, 0.5788057559692271, 0.6224417939351261, 0.6494646182495345, 0.6565630376882645,
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'avg': [0.37132418751635904, 0.4958002379147034, 0.5807550075093684, 0.6555653419013167, 0.7098027031204946,
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0.9608737475923729, 0.9625844039257707, 0.9637809070808036, 0.9652448656116007, 0.9668265312209874,
0.9677436096248172, 0.9686572293476805, 0.9692693929746119, 0.9696268388057134, 0.970729139963249,
0.9716414723842168, 0.9719695037565466, 0.9725513481720209, 0.973314523312116, 0.9740257268119665,
0.9744482482884139, 0.9746423458023734, 0.9749800713219713],
}
f1_scores_003d_train = {
'Wake': [0.4590079841931025, 0.6067054566706488, 0.6515180926498967, 0.7531546507066651, 0.8600782527546853,
0.892678799973969, 0.9163818154517432, 0.9321424730219051, 0.9453948677143426, 0.9507330316742083,
0.9569370193156459, 0.9609148287869994, 0.9643283480471143, 0.9672967160603614, 0.9684425392937335,
0.9702420134648647, 0.9711349471858957, 0.9714817167226205, 0.9729300086296951, 0.9731129311402136,
0.9737347679367054, 0.9748944658769215, 0.975002499750025, 0.9756219538808467, 0.975020108426453,
0.975939644593919, 0.9753603693672405, 0.9764022120938095, 0.9768413974219754, 0.9762576879542881,
0.9769249298930557, 0.9770600293624352, 0.977179328654035, 0.9781562115006069, 0.9776499245605424],
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0.8891479050365008, 0.8967768132819504, 0.9037667127356582, 0.9092033344529603, 0.9120807778380319,
0.9157758094074526, 0.9185655812816911, 0.9209410540172352, 0.9230169170057205, 0.9261353218018458,
0.9276267450404115, 0.9305781463578536, 0.9318291183232636, 0.935286207634241, 0.9362023808614778,
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0.9477523117770928, 0.9491336539195482, 0.9510748589724043, 0.9532723101663784, 0.9553321698307332],
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0.9010568259604094, 0.9029339839680922, 0.9043182985948216, 0.9070512283055253, 0.9078734858681022,
0.9092296214020857, 0.9101751854440512, 0.9136890138872417, 0.9162252105406139, 0.9181735574738754,
0.9210151061521069, 0.9233422309237749, 0.924147513724061, 0.9262288545879136, 0.9282737517554777],
'Pre REM': [0.2185548365218866, 0.2834966603286042, 0.22596656852964792, 0.3356500929249726, 0.5074289076733317,
0.5936133120374141, 0.6262453332233247, 0.6494031374346368, 0.6783503648111229, 0.7065070275897971,
0.726734592855375, 0.7407255753009582, 0.7523349634505172, 0.7598868036329087, 0.7698874029510995,
0.7723171141110287, 0.7786381788327826, 0.7865395084380103, 0.7904764777445982, 0.7952704402515725,
0.8006290442133942, 0.8057079911876627, 0.8096551586787888, 0.8182630627767958, 0.8232291759399388,
0.8281914841104518, 0.8342934267020058, 0.84214021996054, 0.849311072432201, 0.8547895942693078,
0.8614322057671288, 0.867332189866831, 0.8703270014745424, 0.8759298516091732, 0.8827571624907494],
'Artefakt': [0.5267054047600095, 0.6047960465877532, 0.6243213664942812, 0.6318329568618809, 0.6637899709302325,
0.6970531439606019, 0.7296933003358671, 0.8053531362309633, 0.8780763519662786, 0.9214560305241059,
0.9466757868572915, 0.9600910002313566, 0.9714412605236734, 0.9781185639280223, 0.9825253991291727,
0.9863738051657515, 0.9878619380212925, 0.989535955001697, 0.9906389748464889, 0.9915430123169431,
0.9915214776298942, 0.9926726773357659, 0.9931859214536701, 0.993601506898527, 0.9936022523178486,
0.9937948513774654, 0.9939412769923878, 0.9943694786913891, 0.9944835087991919, 0.9947865596147684,
0.9946090858758049, 0.9951622304254905, 0.9956762113895394, 0.9954718594527363, 0.9952800870173257],
'avg': [0.3818553004085008, 0.5075075239964383, 0.5303485076823864, 0.6091219733771364, 0.7058590571762372,
0.7518200312524692, 0.7786671998597178, 0.8121592618524692, 0.8427088137436345, 0.8637640259759255,
0.8779324536976756, 0.886827231393079, 0.8949162598567154, 0.9000918722877067, 0.904591039620205,
0.9072098624865157, 0.9098819181223259, 0.9128419914198871, 0.9151733220179891, 0.9170504949893118,
0.9189137721561629, 0.9213574529452592, 0.9227981993601139, 0.925964791899187, 0.9271854806827641,
0.9291613972786374, 0.9308018117078681, 0.9338502347295232, 0.9363494341461877, 0.9379725446542786,
0.9403467278930379, 0.942406066899616, 0.9436809828429163, 0.9458118174633616, 0.9478586191309658],
}
f1_scores_003e_train = {
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0.86078777800873, 0.8853923665468657, 0.9058019838206619, 0.9211747490703408, 0.92997397787895,
0.9349235788647163, 0.9412216707798842, 0.9477923641185069, 0.9519023104820634, 0.955170742534119,
0.9575336501832166, 0.9595641602554799, 0.9609826458501537, 0.9631867060333529, 0.9629206349206348,
0.9645036176824151, 0.9661691497134247, 0.9660202326436322, 0.9670435453843709, 0.9675378029570515,
0.9678602556653108, 0.9684258334090291, 0.9685488888787963, 0.969058405890524, 0.969614401257022,
0.9693823325021462, 0.9704055502079513, 0.9708861276688473, 0.9713172450749766],
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0.7862571446517179, 0.8086693579333527, 0.8255767943020638, 0.8430115683671533, 0.851206546455651,
0.8602496304311382, 0.8673794098120464, 0.8795522938033532, 0.8848393589723892, 0.8927059040137474,
0.8973435320446986, 0.9002835900973404, 0.9046655631169149, 0.9064025089879905, 0.9087666388555071,
0.9118953527555679, 0.9157561883178035, 0.9148384233362156, 0.9179896204895823, 0.9194131340357755,
0.9214663695330867, 0.9219112345877437, 0.924543458469448, 0.9263030655415382, 0.9285714285714286,
0.9293286219081272, 0.9312282743518675, 0.9325719709437168, 0.9328426722159316],
'Non REM': [0.300507264697497, 0.4421090572061171, 0.6597646827155024, 0.7055236236678812, 0.7401019528200313,
0.7586434348457018, 0.7674361442821735, 0.7815747453712075, 0.7942405509038265, 0.8198546433378198,
0.8317356164607529, 0.8443212079615648, 0.8559567282611681, 0.8620839259922345, 0.866051279787686,
0.8686454231604955, 0.873909018778101, 0.8767851028815674, 0.8798214256115012, 0.8792239503452247,
0.8826288483024879, 0.8846155977589236, 0.8858539183109686, 0.8869923027439497, 0.8888161144753859,
0.8885134571887451, 0.8919704863524401, 0.8922167356050527, 0.8927944152954886, 0.8944701770814173,
0.8946446892983003, 0.8976302311683525, 0.8979201610197919, 0.8998735268111968],
'Pre REM': [0.17708598684152735, 0.18346998397888983, 0.261995743445874, 0.36977587268291573, 0.527307218690718,
0.5952723666954547, 0.6142764817478683, 0.6376558552266706, 0.6514704072843607, 0.6657877389584707,
0.6727223652520555, 0.683999666138052, 0.7024064115607417, 0.7146690982188189, 0.7267327750089915,
0.7312403267632196, 0.7388780637883294, 0.7495224962974313, 0.7515355920077673, 0.755950808008944,
0.7605616618031336, 0.768707414119529, 0.7714620450046459, 0.7748226055534808, 0.7777195809830781,
0.7808005788770187, 0.7863585740515917, 0.7914337800749244, 0.7931761625408501, 0.7985442474779287,
0.8010663045757246, 0.8087862237741368, 0.8090135581090214, 0.8130139364181918],
'Artefakt': [0.4551118829423982, 0.576196422111491, 0.6030167334433184, 0.6420437922035366, 0.654982332155477,
0.6802976183412642, 0.6909193391185899, 0.7157926184676197, 0.740816533051486, 0.7908454786353096,
0.8346000436568571, 0.8829686197740004, 0.9202791002486459, 0.9446408445955708, 0.9600331531114772,
0.9689011614996186, 0.9749236891928443, 0.9786567986164117, 0.981128790443488, 0.9833688286544047,
0.9852824415654834, 0.9877966101694915, 0.9887047827730971, 0.9894059377150556, 0.9901280062063615,
0.9904680636496747, 0.9914342532861231, 0.991744516554621, 0.9929053234337847, 0.992190455863949,
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'avg': [0.30089267010664467, 0.35946693959917086, 0.5158100853848315, 0.5899636020568773, 0.6969091896900124,
0.7362516685085738, 0.7533387379257702, 0.7732803994376447, 0.7901427617354335, 0.8115336770532402,
0.8268462469331039, 0.8439781148931095, 0.8611973795984833, 0.8716271076522155, 0.8801387708912042,
0.8847328187302498, 0.889511704422419, 0.8941225213524959, 0.89641500461682, 0.898046172156943,
0.9009743844218174, 0.9046089920158344, 0.9053758804137118, 0.9072508023772878, 0.9087229277315305,
0.9098217449827672, 0.9120200763373856, 0.9136974759165686, 0.9148474745404371, 0.9166781420503491,
0.9175449999526901, 0.9202161788644887, 0.9207121690984404, 0.9220835328497714],
}
f1_scores_003c_valid = {
'Wake': [0.7659384013526506, 0.7626023018819246, 0.8090274078921338, 0.7815923818439799, 0.8991182529872271,
0.9366738182573188, 0.9471757697552573, 0.9474741069541323, 0.9574051668920361, 0.9609795049241416,
0.9659506479673353, 0.962255457896229, 0.9678618003822999, 0.969906631131193, 0.9712296327713569,
0.9701819737287682, 0.9736543104672631, 0.9714255307827383, 0.9735028770334587, 0.9738889480976234,
0.9752792334974326, 0.9749991134437392, 0.9721538706650852, 0.9749787955894826, 0.974475245770968,
0.9748384240814519, 0.9745653060139315, 0.976328210952195, 0.976317095653407, 0.976317935264296,
0.9760839976667314, 0.9754706622035669, 0.9759406239457377, 0.9768716624818757, 0.9759673411430599,
0.9763893805309733, 0.9777336899721892, 0.9776074594709144, 0.9780765546844513, 0.9776889894632628,
0.9770368929979317, 0.9773206844578421, 0.9773502837845005],
'REM': [0.5709274281955712, 0.5350080171031535, 0.6886145404663924, 0.5441772493920561, 0.8166269755358303,
0.8460570469798658, 0.8608326908249808, 0.8776026464292664, 0.8877972096679112, 0.8811959087332809,
0.8930491933877714, 0.8827474090631985, 0.8912209889001009, 0.9019607843137255, 0.8975687524910322,
0.8962703962703963, 0.8921493902439024, 0.8981321009050645, 0.8977403293757182, 0.9015664281570296,
0.9015325670498084, 0.8960490985807441, 0.8972762645914396, 0.8968177434908391, 0.8962446767324816,
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0.8994197292069632, 0.8960092095165004, 0.9027993779160187, 0.9053658536585366, 0.9050867277333852,
0.9003875968992248, 0.9058209243859989, 0.9021802045147599, 0.905112316034082, 0.9050583657587549,
0.9063962558502339, 0.9047619047619047, 0.9067747539085119],
'Non REM': [0.5393776676304558, 0.4804003336113428, 0.6571839753673148, 0.5529007771552502, 0.8249040809208232,
0.9071181718327216, 0.9195858003599467, 0.9159701769747142, 0.932116845079184, 0.9343255620316403,
0.9416895410584644, 0.9387776145180832, 0.94497772119669, 0.9386167371178133, 0.9447569150165652,
0.9432627748449592, 0.9437362412327855, 0.9467919023366519, 0.9439656274775581, 0.9436803688052248,
0.9469371030526532, 0.9511996746644978, 0.9488165906601105, 0.9484293461577816, 0.9494099956673548,
0.9506512006512007, 0.9461212399775292, 0.9501812742438456, 0.9496457682638836, 0.9504518225200528,
0.950821342162231, 0.9508572009971002, 0.9522365428354813, 0.9528108121814539, 0.9509972663764301,
0.9532018455610201, 0.952183267940767, 0.952376114398923, 0.9535771145535872, 0.9524123585815027,
0.9533773489080752, 0.9530530682450549, 0.9532743452577137],
'Pre REM': [0.1061139896373057, 0.005859374999999999, 0.27722772277227725, 0.2956785443517817, 0.4224872231686541,
0.5048247841543931, 0.4513163393230255, 0.45925258892390813, 0.4702627939142462, 0.4717861688587188,
0.4915640674874601, 0.49109653233364575, 0.4888076747373229, 0.4519874944171506, 0.49025341130604294,
0.47314814814814815, 0.4515539305301645, 0.4828953891918691, 0.45471349353049906, 0.4505494505494506,
0.4640826873385013, 0.4848484848484848, 0.5010706638115632, 0.4732189287571503, 0.4782157676348548,
0.4928305894848646, 0.45012285012285014, 0.4696189495365603, 0.4726134585289515, 0.4711437565582371,
0.48311688311688317, 0.47132429614181437, 0.48202959830866804, 0.48583877995642705, 0.46892950391644905,
0.4829106945975744, 0.48995111352525794, 0.4854368932038835, 0.48484848484848486, 0.4769647696476965,
0.49418282548476455, 0.4835164835164835, 0.4856661045531198],
'Artefakt': [0.18129079042784624, 0.29403029403029407, 0.3095163806552262, 0.3069306930693069, 0.4288272157564906,
0.40743801652892564, 0.36986301369863017, 0.39365595770638473, 0.44076888690210103,
0.46783625730994155, 0.539031339031339, 0.488, 0.5180467091295117, 0.5583284968078933,
0.5726141078838174, 0.5317417254476398, 0.64, 0.588380716934487, 0.6356902356902357,
0.6030341340075853, 0.6695156695156695, 0.6444906444906445, 0.6214099216710184, 0.6344086021505377,
0.6318607764390897, 0.6420260095824777, 0.6445824706694272, 0.6734397677793904, 0.6553672316384181,
0.670995670995671, 0.6874536005939124, 0.6613255644573925, 0.6715867158671587, 0.6897590361445783,
0.6522366522366522, 0.6764705882352942, 0.6977099236641222, 0.7174959871589085, 0.6944655041698256,
0.7028265851795263, 0.6723891273247495, 0.6878306878306878, 0.693939393939394],
'avg': [0.4327296554487659, 0.415580064325343, 0.5483140054306689, 0.4962559291624749, 0.678392749673805,
0.7204223675506449, 0.7097547227923682, 0.7187910953976812, 0.7376701804910957, 0.7432246803715447,
0.7662569577864741, 0.7525754027622312, 0.7621829788691851, 0.7641600287575552, 0.7752845638937629,
0.7629210036879822, 0.7802187744948232, 0.777525128030162, 0.7811225126214939, 0.7745438659233826,
0.791469452090813, 0.7903174032056222, 0.7881454622798435, 0.7855706832291582, 0.7860412924489497,
0.7925314704348873, 0.7835577597421743, 0.794893690860717, 0.7923952300253139, 0.7940058617876204,
0.7993791105493442, 0.7909973866632749, 0.7969185717746129, 0.8021292288845743, 0.7906434982811953,
0.7978720211648174, 0.8046797838976671, 0.8070193317494778, 0.8032159948580861, 0.8029902137261485,
0.8006764901131509, 0.8012965657623946, 0.8034009762886478]
}
f1_scores_003d_valid = {
'Wake': [0.7986130603483859, 0.7132484651251829, 0.7200546287122085, 0.8459017393027546, 0.8695469798657718,
0.9239532019704433, 0.946169477023992, 0.9518422639100677, 0.9591239834498501, 0.9652665732877076,
0.9669026957000124, 0.9611357839727469, 0.9695176298620272, 0.9709512529609774, 0.9699856058855936,
0.9748308811840967, 0.9709214968800268, 0.9726432991152952, 0.9721896226750596, 0.9755291357893053,
0.9772228126106979, 0.9768060431960847, 0.9760521218524388, 0.9773669110615236, 0.9772831980720842,
0.9772494073523689, 0.9783474979722819, 0.9768146582100071, 0.9784320317061519, 0.9783038869257951,
0.9777439671643904, 0.9777337951509153, 0.9789546347593677, 0.9789578093449999, 0.9780818043624281],
'REM': [0.5452914798206278, 0.6506587335316617, 0.6895439889451866, 0.8186979560938682, 0.8385690938805156,
0.8535791757049891, 0.850438763830599, 0.8682521706304266, 0.8850710900473934, 0.881992337164751,
0.8837390457643622, 0.8749281746791803, 0.8537839823659074, 0.8967691095350669, 0.8978145304193739,
0.8916953693073096, 0.895878101191639, 0.8904716073147257, 0.8836944127708095, 0.8668672433245581,
0.8942962818765817, 0.8856757277809909, 0.8947574334898277, 0.8933951332560836, 0.897951219512195,
0.8996062992125985, 0.8988326848249026, 0.8998644199109045, 0.898820844427539, 0.8989838613269576,
0.8975238095238095, 0.8984674329501916, 0.8969766994030426, 0.9016583108368685, 0.8951157812804047],
'Non REM': [0.4891523067354479, 0.0540278853601859, 0.1305182341650672, 0.587199552822806, 0.6907428571428572,
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0.9332322096398775, 0.9308284860870931, 0.9410338423442014, 0.937810945273632, 0.9321790826836327,
0.9381083754099214, 0.9404299404299404, 0.9405663312934344, 0.9436379604277523, 0.9418409718102804,
0.9440597245462736, 0.945471325659497, 0.9428060073146058, 0.9490803683656979, 0.9451783185271019,
0.9505087172145815, 0.9421833790143249, 0.9413780570123059, 0.9386677708193158, 0.9536666751572886,
0.9376943810906075, 0.9425127491886881, 0.932213526092722, 0.9518109113137385, 0.9472441147023336],
'Pre REM': [0.11554762435868839, 0.04310833806012478, 0.019536019536019536, 0.19884057971014493, 0.2901049475262369,
0.3119266055045872, 0.4058823529411765, 0.46015180265654654, 0.5074626865671641, 0.4852801519468186,
0.4529240978846952, 0.452560873215785, 0.4439764111204718, 0.4619144602851324, 0.44743481917577793,
0.4395509499136442, 0.4887690925426775, 0.47164179104477616, 0.4610081861266696, 0.45450802799505974,
0.4606007836308228, 0.46689895470383275, 0.47687471935339015, 0.48605947955390333, 0.4630669546436285,
0.4860666971219735, 0.443796835970025, 0.44855789926818773, 0.4096586178184846, 0.5220440881763527,
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0.468342644320298, 0.40998363338788874, 0.6127819548872181, 0.49552238805970156, 0.457480673033197,
0.5756327251324308, 0.5678449258836944, 0.5549915397631133, 0.5604584527220631, 0.654130288784419,
0.6385463984425697, 0.684813753581662, 0.6145181476846058, 0.6369087275149901, 0.6212603437301083,
0.6788588149231894, 0.7007407407407407, 0.6398416886543534, 0.6525017135023989, 0.6728307254623044,
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'avg': [0.4282695600234113, 0.3417362826252769, 0.3761232826050297, 0.5885382608168005, 0.5922550182460053,
0.6511937945105577, 0.6895591217135368, 0.7196312356740953, 0.7366312852093768, 0.746993470445415,
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0.7829452002213891, 0.7919331609844135, 0.7810016859389737, 0.7885621239504397, 0.7809480068970236,
0.7984579871649424, 0.7927802277044551, 0.7812913446111518, 0.7756161956547781, 0.8051658474097397,
0.7762665160855221, 0.7916303069834175, 0.7766600279516476, 0.8002169432633593, 0.7883621096375636],
}
f1_scores_003e_valid = {
'Wake': [0.6438713917570349, 0.7011950165268243, 0.19955691888207225, 0.7256888524494158, 0.8306592124600396,
0.8722392442231585, 0.9141733342298124, 0.9472026580372749, 0.9499813826486284, 0.9506403128026688,
0.9569846373704894, 0.9574932170027132, 0.9600573682323412, 0.9625938737834495, 0.9616155169051291,
0.9606813342796311, 0.9616207329361642, 0.9662877786589126, 0.9637677300243206, 0.9588251826869028,
0.9668159212785162, 0.9670955457085402, 0.9668864729994654, 0.9658557481214414, 0.9703093590450608,
0.9734150606772536, 0.9712507778469198, 0.9678038038749089, 0.9710814783332447, 0.9720483641536273,
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0.8655903520715814, 0.861779722830051, 0.8948797517455392, 0.8688035780842341, 0.8110447146095199,
0.8725190839694656, 0.8718544328300426, 0.8674471299093656, 0.8869769260852562, 0.8778403573509419,
0.8507795100222717, 0.8808349146110057, 0.8886726352666406, 0.8436599423631124, 0.8704722169542956,
0.8691999236203933, 0.8851047171657859, 0.8550670640834575, 0.8568249258160238, 0.8698912635920508,
0.8942493245851023, 0.8824640967498112, 0.8915848257269401, 0.8895752895752896, 0.8884652049571019,
0.8881239242685026, 0.8823079862437905, 0.8930072602216279, 0.8841234010534237],
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0.7472225689676695, 0.8032319391634981, 0.904359106018431, 0.8908256631641824, 0.9330246913580246,
0.9306777030522504, 0.9225924954080293, 0.9366189937236342, 0.9388825251368207, 0.9339049103663289,
0.9291829201246171, 0.9200295016331262, 0.9339135873247512, 0.9240140734128027, 0.9245681686123289,
0.933669360076905, 0.9401291584120205, 0.943256958326926, 0.9417662938681064, 0.9378061987519983,
0.9411097359735973, 0.936295227296155, 0.939005849460149, 0.9419070101611413, 0.9433807929245164,
0.9409226343367924, 0.9417220590508579, 0.9346804511278196, 0.9382665048669024],
'Pre REM': [0.03155680224403927, 0.0022271714922048997, 0.007692307692307692, 0.06753812636165578,
0.3008849557522124, 0.34635793535938253, 0.2597325408618128, 0.45390693590869186, 0.35899306822327615,
0.5172964342735498, 0.47064137308039755, 0.47084048027444253, 0.49114631873252557, 0.5261627906976745,
0.49297094657919405, 0.45897542690545606, 0.4284559417946645, 0.4837126282909416, 0.399845619451949,
0.47818499127399644, 0.4614100959532749, 0.4844020797227037, 0.49953746530989834, 0.47193990278391523,
0.4603514787826832, 0.46797153024911037, 0.43229604709840197, 0.4713715046604527, 0.49859418931583893,
0.48171846435100546, 0.4802513464991023, 0.4687083888149135, 0.4553571428571429, 0.4368932038834951],
'Artefakt': [0.2830349531116795, 0.43014394580863674, 0.42789820923656924, 0.40393667094565683, 0.4562078922040424,
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0.4528819762122598, 0.4366505918456817, 0.45462878093492204, 0.4901185770750988, 0.47053231939163487,
0.5118924508790073, 0.4957605985037407, 0.5510662177328844, 0.5651162790697674, 0.5101298701298701,
0.5876662636033858, 0.5446478092068774, 0.595561035758323, 0.5742806811509102, 0.5995055624227441,
0.654295532646048, 0.6175528507367072, 0.5687645687645688, 0.6360655737704919, 0.6419919246298789,
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'avg': [0.2187004933366135, 0.24426606814895244, 0.24195573488921474, 0.42793916802509, 0.6124116712746371,
0.6500818504890743, 0.6534098810434085, 0.7357895346999641, 0.681814520279314, 0.7110354104303037,
0.7367409547369727, 0.7318862434721818, 0.7419797183065577, 0.7609469385556599, 0.7473728101186459,
0.7423023284421966, 0.7373403378957402, 0.764730569454826, 0.7392807288643903, 0.7484360859314787,
0.7637523129064951, 0.7642758620431855, 0.772061799295614, 0.7621335103480793, 0.7675727725189074,
0.7862082368262222, 0.767971799945599, 0.7677061104974039, 0.7874447082312013, 0.7855209502032261,
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}
f1_scores_005b_train = {
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0.9440160977644719, 0.9503148608423141, 0.9578379703395024, 0.9631408961466038, 0.9671228181840438,
0.9698286833088595, 0.9716694830078034, 0.9734237218523942, 0.9752119084604376, 0.9767090390644487,
0.9774589612074769, 0.9791858970435208, 0.980704874738477, 0.9816784841015891, 0.982599684966843,
0.9833402350842544, 0.9840535307237923, 0.984698430130512, 0.9856748419622672, 0.9862769183995215,
0.9870804961834868, 0.9877587259852987, 0.9886263315873108, 0.9892306447142847, 0.989663011729968,
0.9902686366524306, 0.9907959356892991, 0.9913237584781623, 0.9914727427054663, 0.9920273572038347,
0.9922955600742727, 0.9926898432977147, 0.9929614609936386, 0.9932511499540018, 0.9934780635809086,
0.9939334469943211, 0.9940459105193821, 0.9940008097066654, 0.9944160942100098, 0.9944404916705514,
0.9946316233696132, 0.9947269109120309, 0.9949824200683568, 0.9950880085579552, 0.995274964672633],
'REM': [0.07988582374039924, 0.42404723564143854, 0.5958738516090903, 0.6597943888780543, 0.7328192140235052,
0.7749508533006639, 0.8084947195764152, 0.8325194894200292, 0.8468050168455027, 0.8596969611784218,
0.8676976425515256, 0.8762131883977594, 0.8828155072862249, 0.8902285994871515, 0.8945229969120755,
0.8987926424114739, 0.9071303410163095, 0.9135317086306719, 0.9159120310478654, 0.9211638360293919,
0.9230727777328698, 0.9255633840692734, 0.9315754069689224, 0.9331973482916879, 0.9363863581391599,
0.9405327110324293, 0.9413279678068411, 0.9461649938274919, 0.9475408075601374, 0.9498537737114646,
0.9538593527976621, 0.9556917782438028, 0.9589659527584447, 0.9598302991246442, 0.9619075781607034,
0.9631461519851616, 0.9649202978414558, 0.9673866322802493, 0.9689177582134422, 0.9687978714827059,
0.9711308644627655, 0.9710588487972508, 0.9718873345326638, 0.9729163308076736, 0.9751919669226226,
0.9758534554537884, 0.9746770858508553, 0.9769694360740422, 0.9777968926401807, 0.977835093092609],
'Non REM': [0.5618943612905138, 0.8291478985965827, 0.8351290403407666, 0.8656160591835054, 0.8910454883023418,
0.9084527744516703, 0.9137885522269863, 0.9227873691743448, 0.9296582107275982, 0.9353472756421778,
0.9391260510253687, 0.9424340145242077, 0.9450760110390526, 0.9476324026996218, 0.9498594818051601,
0.950987352042073, 0.9535891291027812, 0.95531360243065, 0.9570849086040621, 0.9589790337283501,
0.9603610931565149, 0.9617046011879893, 0.9631225409880732, 0.9649080111149452, 0.9660943628742908,
0.968060969224504, 0.969351291419016, 0.971259162256052, 0.9724208673131626, 0.9735048488928513,
0.9749951780833882, 0.9760603259405912, 0.9773407407946758, 0.9780711975995572, 0.9788402366863905,
0.9795692343563288, 0.9802957100904328, 0.981163011003425, 0.9817435792787802, 0.9825664058797366,
0.9836305834657956, 0.9837942703775017, 0.9841998964644807, 0.9844630244777555, 0.9850174698927007,
0.9854790244471673, 0.9856067348516904, 0.9861282850941987, 0.9864709058801208, 0.987015735847955],
'Pre REM': [0.04629976534616371, 0.10604466992513766, 0.13119008145318245, 0.1380129787039576, 0.222433279513724,
0.29180610000956114, 0.33508158508158503, 0.3600116132778477, 0.3670751712824471, 0.38125948406676785,
0.3949400055808762, 0.4129458146144677, 0.42276273519963287, 0.43649211813734373, 0.44708192039978584,
0.45008460236886627, 0.4781886528542705, 0.48728153659802736, 0.508757014113246, 0.5353322664869873,
0.5456518110499081, 0.5656615587411437, 0.5855737704918033, 0.5941553111147594, 0.6130466413386144,
0.6294169968464463, 0.6407204889031842, 0.6621966794380587, 0.6730249562867588, 0.6831393046646076,
0.7025027546041239, 0.7183890103028411, 0.7284037011118887, 0.7424982554082344, 0.7461484864906711,
0.7543832666871733, 0.7642114914425426, 0.7739070515274481, 0.7840856924254017, 0.7887045212361089,
0.8016704631738801, 0.8012970364225925, 0.8110230275575688, 0.8112098839837276, 0.8253681995792005,
0.8258025712352455, 0.8275295172619939, 0.8327991054789415, 0.8417693335312453, 0.8425360916803095],
'Artefakt': [0.003648939356806361, 0.020145044319097503, 0.04468802698145025, 0.06972690296339337,
0.0707635009310987, 0.09065550906555091, 0.02011173184357542, 0.0030165912518853697, 0.0,
0.006259780907668232, 0.0, 0.0031695721077654522, 0.0030627871362940277, 0.0, 0.0031397174254317118,
0.0124804992199688, 0.04833836858006043, 0.184, 0.250620347394541, 0.338785046728972,
0.3552941176470588, 0.37657864523536166, 0.42045454545454547, 0.43863636363636366, 0.4760869565217391,
0.5283422459893049, 0.5254054054054053, 0.5744016649323622, 0.5806451612903225, 0.6134969325153374,
0.6459378134403209, 0.6804733727810651, 0.7057673509286414, 0.7117031398667935, 0.7431279620853081,
0.7377358490566037, 0.7531219980787703, 0.767097966728281, 0.8, 0.8025594149908593, 0.8120437956204379,
0.8033088235294117, 0.8091324200913242, 0.813466787989081, 0.8324324324324325, 0.8255395683453237,
0.8520499108734403, 0.852863436123348, 0.845601436265709, 0.8571428571428572],
'avg': [0.279786000906734, 0.44484374841901414, 0.4981019900671324, 0.5293635738275823, 0.5700376634901325,
0.6019762669183837, 0.6055582899141753, 0.6152346066927219, 0.6213358590004303, 0.629937263995816,
0.6343184764933261, 0.6412864145304007, 0.6454281525027197, 0.6499130057569109, 0.6542626311213804,
0.6579608114499718, 0.6732864777193884, 0.7041663444795653, 0.7228105570522608, 0.7473719735881089,
0.7535440069341213, 0.762712343991512, 0.7770849388067712, 0.7833143752240047, 0.7955782474546652,
0.8106866838552342, 0.812912775903949, 0.8285297664082553, 0.8325724874329332, 0.8419315743028457,
0.8535127471155851, 0.8642820845915198, 0.8723603008143626, 0.8767151269409391, 0.8844103241253816,
0.885426012431908, 0.8910478681501832, 0.8965032245066084, 0.9055996359743252, 0.907221255434064,
0.9124818307434401, 0.9107009779292277, 0.9140486976705405, 0.9152944242936496, 0.9224901120995016,
0.9214612485702276, 0.9269180319500021, 0.9287485365677774, 0.9293453153750422, 0.9319609484872728]
}
f1_scores_005b_valid = {
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0.9254204031050448, 0.9249679537832095, 0.9430707099333994, 0.9459074225848983, 0.953157261611936,
0.9541666666666667, 0.9567336559177763, 0.9672777414393552, 0.9625220836692339, 0.9696717473859439,
0.9707054707489862, 0.9776465626318009, 0.9808552827837927, 0.9806192058772083, 0.9807722699257231,
0.9796699438202248, 0.9808293725815856, 0.9814246373506869, 0.9815708798759646, 0.9803556694696558,
0.9803756214972155, 0.9765371450866335, 0.980597539543058, 0.9810310513059866, 0.9800680773414747,
0.9805787736180376, 0.9813605561911466, 0.9807949820283318, 0.9807884977062205, 0.9817143058665867,
0.9815140845070423, 0.981657848324515, 0.9815078736693938, 0.981851499453783, 0.981689371376205,
0.9809351443481927, 0.9817649548532732, 0.9810397338168759, 0.9811619161424102, 0.9809051587720223,
0.9812949133436664, 0.9812804061200028, 0.9810630703497265, 0.9809619414945141, 0.9809004572634541],
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0.8884348181446671, 0.8852459016393442, 0.8904705417160934, 0.8691514670896114, 0.8955759266640095,
0.893625419380304, 0.9085534095388254, 0.9128662914806908, 0.918822832312858, 0.908984145625367,
0.9136328427575523, 0.9112541447240103, 0.9135707410972088, 0.9106549364613881, 0.9122137404580153,
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0.9121371517631015, 0.9119411650861233, 0.9107497024990083, 0.9107763615295481, 0.9124709527498064,
0.9141092453194364, 0.9120750293083236, 0.9082371355899043, 0.9107176851323053, 0.9115859635365615,
0.9109483423284502, 0.910136041387239, 0.9141092453194364, 0.9133980582524273, 0.9162313793770555,
0.9117703349282298, 0.9121660580434364, 0.9125358851674641, 0.9099029126213591, 0.9120245870149828],
'Non REM': [0.7628060321142928, 0.6757905583821731, 0.41258687258687254, 0.7493693693693695, 0.8330781863222646,
0.8782678913899263, 0.8675785508970166, 0.9017147026632616, 0.9038968578408844, 0.9183336918333692,
0.9129357505581042, 0.9244289945898309, 0.9409454616857297, 0.9511503792037997, 0.9555733841448127,
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f1_scores_006_train = {
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}
f1_scores_006b_train = {
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}
f1_scores_006c_train = {
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}
f1_scores_006d_train = {
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}
f1_scores_006e_train = {
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0.4052494415487714, 0.4083604273107292, 0.41812488487750965, 0.41624082504877824, 0.43508137432188065,
0.44546601766720745, 0.45021025319853275, 0.45903099394371216, 0.46789641716920893, 0.4756331877729258,
0.46745457752690067, 0.4597333333333333, 0.4759964570416297, 0.4749428521188676, 0.4770982261053746,
0.4679311865902074, 0.4827104435906392, 0.4760391845379931, 0.4764762997616736, 0.4786729857819906,
0.48663755458515284, 0.49109083007388094, 0.4896150169462067, 0.4820118756549075, 0.4813642756680731,
0.49047411918225325, 0.4888579387186629, 0.4947359262159575, 0.500996447448228, 0.49839367890943825,
0.4913710866360247, 0.4970826439083863, 0.4961990324809951, 0.49740124740124736, 0.5011282763409131],
'Artefakt': [0.00356351130171753, 0.0031190434933287122, 0.004226840436773512, 0.003188097768331562,
0.0064516129032258064, 0.003968253968253969, 0.01818181818181818, 0.02506265664160401,
0.07113543091655267, 0.09523809523809523, 0.0951086956521739, 0.12176165803108809, 0.11279229711141678,
0.14993306559571618, 0.09537166900420757, 0.1204481792717087, 0.14068965517241377, 0.1724137931034483,
0.1270718232044199, 0.21963824289405687, 0.20806241872561765, 0.22842639593908626, 0.2860635696821516,
0.30407911001236093, 0.2673521850899743, 0.27877237851662406, 0.28886168910648713, 0.33853541416566624,
0.3325301204819277, 0.29232643118148594, 0.3131067961165049, 0.3159173754556501, 0.33574879227053134,
0.3415204678362573, 0.33176470588235296, 0.35238095238095235, 0.33615477629987905, 0.35885167464114837,
0.36835891381345925, 0.35885167464114837, 0.36823935558112775, 0.35138387484957884,
0.39497716894977175, 0.3824884792626728, 0.3752913752913753, 0.35167464114832536, 0.37875288683602765,
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'avg': [0.2199439791826076, 0.36789895634046715, 0.4180475792068365, 0.47663130120798824, 0.52974117726972,
0.5693535417557392, 0.5901621648283555, 0.6009508206907253, 0.6240160698045863, 0.633588545952523,
0.6399079456428832, 0.6502439074779663, 0.651962172649039, 0.6579908821090427, 0.6526559435278535,
0.6599884587001175, 0.6662453912293488, 0.6753492228129248, 0.6673235726827738, 0.690665141978952,
0.6915302154806866, 0.6976818656221062, 0.7115638609000301, 0.7183462994915594, 0.7124959154208024,
0.7135757288701441, 0.7143651648323184, 0.7281830973940066, 0.7271999243009952, 0.7200071981003724,
0.7226502351767758, 0.7263890896651263, 0.7294758607631511, 0.7308749564279539, 0.7296553453991935,
0.7355121785132835, 0.7333666513367436, 0.7377970978662751, 0.7382495263350284, 0.7363830442770987,
0.7404091514588343, 0.7366781691114491, 0.7466461133136314, 0.7458539496125777, 0.7442442467470081,
0.7381439346790641, 0.7444799327032875, 0.7449270544233794, 0.7519445878400546, 0.752006155540462],
}
f1_scores_006_valid = {
'Wake': [0.8167866560009481, 0.8136802715168392, 0.8140994258756509, 0.9105244888176273, 0.9378243580238592,
0.9364142520356468, 0.9305886243386243, 0.936632061480383, 0.9309808173186215, 0.9560611323376172,
0.9475517775958473, 0.9633948033076151, 0.9724115094372505, 0.9747209168253524, 0.9686261501126107,
0.9734451796661978, 0.9729663948818692, 0.9757008029299901, 0.9722549767635431, 0.9774291604066945,
0.9794556628621598, 0.9780980840217964, 0.9787748663570239, 0.979918689170876, 0.9771176234443998,
0.9791236120354478, 0.9799169949352842, 0.9775249431122004, 0.9800953872687915, 0.9790094422408648,
0.9795731169518435, 0.9786187389096788, 0.9791505520005624, 0.9779627385906687, 0.9793888703414102,
0.9797903268434499, 0.9787316182922121, 0.97926100822864, 0.9787899006215542, 0.9781587435142336,
0.9787084533764316, 0.9787622104137073, 0.9782459836713194, 0.978663672094329, 0.9784162554222793,
0.9782120552943654, 0.9790894885615465, 0.9790546476893339, 0.9790015639002618, 0.9788280769568655],
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0.8527187612531508, 0.853019369540448, 0.8592923952826351, 0.8131370328425821, 0.8704165866769054,
0.8861003861003861, 0.8836750788643533, 0.8981852913085006, 0.9140306617504367, 0.900236593059937,
0.9005040134403584, 0.8816778789077958, 0.8874222138412219, 0.9003167505123906, 0.9093379926227916,
0.9105283455457, 0.9026083693000563, 0.9100136638688268, 0.9112176414189836, 0.908310573546625,
0.9048332708879729, 0.91244418559503, 0.9095074455899198, 0.9068841266831026, 0.9086323957322988,
0.9121531100478468, 0.9072985781990521, 0.9121436938695822, 0.9130602782071099, 0.90905565251115,
0.9086353122590594, 0.8969964989865487, 0.906108597285068, 0.9069148936170213, 0.9056963231091637,
0.912085807316606, 0.9096899224806201, 0.9064609450337513, 0.9088080886642038, 0.9077306733167082,
0.9101681957186545, 0.9072847682119205, 0.9070390206579955, 0.911504424778761, 0.9101773323053199],
'Non REM': [0.7165672383793188, 0.619786681252564, 0.642835524767125, 0.8594011842252262, 0.8968874225912004,
0.8747055159901431, 0.8928038220364287, 0.9001650381159458, 0.8839958918864756, 0.9194789518684303,
0.9029514757378689, 0.9303437515728019, 0.9485185648206026, 0.9528997276429874, 0.9358204866386073,
0.9451228781352926, 0.9510808305989028, 0.9500137256369944, 0.9458158196408102, 0.9508707561631442,
0.9578301227667433, 0.9554248957234033, 0.9587440122388584, 0.9583385462279496, 0.9558459908159661,
0.9560298665057696, 0.9604686678516886, 0.9553216138618847, 0.9579653832567997, 0.9566546402084587,
0.9605180906658666, 0.9579307568438002, 0.9588292780715162, 0.9569868059234593, 0.9587083510918345,
0.9585311016737447, 0.9558184207865738, 0.9579515501443454, 0.9562569213732004, 0.9555729548152452,
0.9566963725367139, 0.9566683442375441, 0.9565610632364393, 0.9571518114579416, 0.9561524738045581,
0.9557268056814746, 0.9571174064687729, 0.9557233524822517, 0.957209980150255, 0.9562797693657558],
'Pre REM': [0.028291621327529923, 0.11320754716981131, 0.07332722273143906, 0.20954162768942938,
0.24297520661157024,
0.374830852503383, 0.40643522438611346, 0.4825018615040953, 0.4912023460410557, 0.44938271604938274,
0.4729392173189009, 0.4696707105719237, 0.44961240310077516, 0.5328413284132841, 0.49404289118347894,
0.5051546391752577, 0.4991816693944353, 0.534850640113798, 0.48787446504992865, 0.5309352517985612,
0.5291479820627802, 0.4963609898107715, 0.5091743119266056, 0.5235378031383737, 0.5072353389185073,
0.4977843426883308, 0.5368344274252371, 0.4996293550778354, 0.5, 0.5147492625368731, 0.5166051660516605,
0.5101156069364161, 0.5263157894736843, 0.5255972696245734, 0.5254237288135594, 0.5198606271777004,
0.4652827487473157, 0.5003503854239664, 0.49184975194897235, 0.5169606512890095, 0.5152590489709015,
0.5218579234972678, 0.521067415730337, 0.5240112994350282, 0.5025197984161267, 0.5095271700776287,
0.5177948360083741, 0.5056022408963585, 0.517193947730399, 0.5139664804469274],
'Artefakt': [0.0, 0.0, 0.0078125, 0.0077972709551656924, 0.0, 0.0, 0.007782101167315175, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.015414258188824664, 0.011627906976744186, 0.16071428571428573, 0.2485875706214689,
0.25850340136054417, 0.36079545454545453, 0.536986301369863, 0.5984251968503937, 0.716612377850163,
0.6029579067121729, 0.6805251641137856, 0.5105189340813464, 0.694006309148265, 0.6697782963827305,
0.7664092664092664, 0.6575654152445962, 0.7413962635201573, 0.6910229645093946, 0.7388137356919875,
0.736441484300666, 0.6842684268426843, 0.676605504587156, 0.7064017660044151, 0.7221644120707598,
0.6920565832426552, 0.6965065502183406, 0.6891133557800225, 0.6882681564245811, 0.7119386637458925,
0.6682464454976303, 0.6936170212765957, 0.7138193688792166, 0.7131782945736432, 0.6627497062279673,
0.7043478260869566, 0.6643274853801169],
'avg': [0.31455364259237617, 0.4525152060424005, 0.4205373998835905, 0.5319534614689576, 0.5672460449665838,
0.6077338763564647, 0.6181258282937859, 0.6357182712766118, 0.623863217617747, 0.6390678773864672,
0.6419085713506006, 0.6494168688633388, 0.6537455537334258, 0.6748985269264121, 0.6628280758366917,
0.6671709234787702, 0.6931242118994577, 0.7193149906286946, 0.7129530826654433, 0.7458737231073292,
0.7827896829214493, 0.7861835071412842, 0.8146638464482955, 0.7951941173336712, 0.8058069381678568,
0.7696580052397735, 0.816734116991101, 0.8023523308049143, 0.8222708327235921, 0.8033222311926183,
0.822049149447475, 0.8089973290796684, 0.8230506098214665, 0.8220097153292956, 0.8113690059201277,
0.8086845745082222, 0.800646210563413, 0.813167190630556, 0.8051736101606807, 0.8105790445891985,
0.810372607596135, 0.811049311410744, 0.814854814283548, 0.8073762634298266, 0.8076872444472535,
0.813490719130268, 0.8148929587648513, 0.8020337935907813, 0.8138515485293267, 0.8047158288909971],
}
f1_scores_006b_valid = {
'Wake': [0.8483756717147045, 0.843067694391589, 0.8097431763215478, 0.7395030552030041, 0.7245968773995393,
0.7863708311349309, 0.8782614091148823, 0.9330533473326333, 0.9523664277362615, 0.9626032557978779,
0.9676380447768946, 0.9692278452580471, 0.9735694302048761, 0.9730005812007961, 0.9760686755430974,
0.9776256909713084, 0.9785753012574986, 0.9752264808362369, 0.9787316797199583, 0.979583934668591,
0.9807347512743892, 0.9812934447866256, 0.9804905633703258, 0.9812645958806693, 0.9807465757037611,
0.9805472086035111, 0.98113539267568, 0.9802157641353622, 0.9789817186567951, 0.9814954749143308,
0.9815831758868504, 0.9808247132502991, 0.98065447376296, 0.9814003413750022, 0.9812050565254848,
0.9805601085672994, 0.9800973176172992, 0.9807394366197183, 0.9811373873873873, 0.9804377300019369,
0.9803148913712727, 0.9805915785398813, 0.9802742616033755, 0.9803115001933692, 0.9800235844905574,
0.979253988940157, 0.980331390537233, 0.9805643738977073, 0.98014456305728, 0.9797592227541538],
'REM': [0.028506640751538713, 0.1037451571244081, 0.017167381974248927, 0.38324796625489604, 0.18653576437587657,
0.640321817321344, 0.8297663459650501, 0.8010752688172044, 0.8768492602958816, 0.9026025236593059,
0.8878957169459962, 0.8954650269023827, 0.9002716336825766, 0.898006580220631, 0.9050814956855225,
0.9037807561512302, 0.9027144838517932, 0.9089154766499422, 0.9086372360844531, 0.9102095750817152,
0.9123345482447727, 0.9123878602786792, 0.9075533346146454, 0.9063965484899643, 0.9086660175267771,
0.911601150527325, 0.9140445126630851, 0.9112887502437121, 0.9110512129380054, 0.9146153846153847,
0.9144838212634822, 0.9148484263371307, 0.9165856503986, 0.9159566227730441, 0.9157140089130015, 0.9140625,
0.9118436182445382, 0.9090560245587107, 0.9124513618677043, 0.9124854142357058, 0.9123890389810884,
0.912842267643656, 0.9131619937694703, 0.9136328427575523, 0.9084249084249085, 0.9108415174273059,
0.9095083221733309, 0.9129676293855399, 0.9098646034816248, 0.9101426918627074],
'Non REM': [0.7583792195355519, 0.8374450075179595, 0.6541232196309332, 0.2657541322314049, 0.171405213915887,
0.4843257837108144, 0.775517595219412, 0.8976739223218096, 0.924012634106868, 0.9401087067993027,
0.9413036205964849, 0.9448023946019989, 0.9533683972116666, 0.9523430108062346, 0.9538407681536307,
0.9596242254647211, 0.9586363749968759, 0.9560154581511239, 0.9635477826358525, 0.9622688906752411,
0.9631638418079095, 0.9631568451490677, 0.9623928633898898, 0.9605325964074866, 0.9626111864917836,
0.9617034083214205, 0.9627658238971604, 0.9615374978075121, 0.9582544132131222, 0.9630484988452656,
0.9622382509296927, 0.9615973110592722, 0.9617409617409618, 0.9623388997542751, 0.9610095779179969,
0.9616635804013813, 0.9603905581559212, 0.9601383874849578, 0.9613718863437225, 0.9611334002006017,
0.9599578767896096, 0.9601423844379825, 0.9600824493489517, 0.9598132483245061, 0.9604494269305043,
0.9596399024316644, 0.960419910593199, 0.9612756264236901, 0.9597708369977637, 0.9593720912681442],
'Pre REM': [0.05490848585690516, 0.08643042350907518, 0.026540284360189573, 0.005141388174807198,
0.005263157894736842, 0.12895662368112545, 0.24899598393574296, 0.3482224247948951, 0.4742729306487696,
0.4868624420401855, 0.44011976047904194, 0.4094488188976378, 0.4018433179723502, 0.46473029045643155,
0.473063973063973, 0.5347593582887701, 0.5408618127786033, 0.5156017830609212, 0.5221971407072987,
0.51818856718634, 0.4977168949771689, 0.4924012158054712, 0.5208333333333334, 0.49101796407185627,
0.5182584269662921, 0.4984709480122324, 0.4962292609351432, 0.5085501858736059, 0.5003436426116838,
0.5277777777777778, 0.5113960113960114, 0.5104844540853217, 0.5136298421807747, 0.5230094959824689,
0.507256392536282, 0.5222929936305734, 0.49333333333333335, 0.5132743362831859, 0.51,
0.5088841506751954,
0.503925767309065, 0.510846745976207, 0.5097493036211699, 0.5025053686471009, 0.4985549132947977,
0.5103734439834026, 0.5007072135785007, 0.5120567375886524, 0.5045422781271838, 0.5058259081562715],
'Artefakt': [0.014939309056956115, 0.007285974499089253, 0.04964539007092198, 0.045801526717557245, 0.0, 0.0,
0.24027072758037224, 0.2546728971962617, 0.2048780487804878, 0.5632183908045977, 0.5625,
0.33753943217665616, 0.5759577278731836, 0.44467425025853147, 0.44720496894409945, 0.6375908618899273,
0.4835479256080114, 0.6745098039215687, 0.6695576756287944, 0.7052441229656419, 0.7475915221579962,
0.6096938775510204, 0.7009708737864078, 0.6697782963827305, 0.7485714285714287, 0.7369498464687819,
0.771760154738878, 0.7202441505595116, 0.7335907335907336, 0.7074527252502781, 0.7601626016260163,
0.7666335650446872, 0.7690839694656489, 0.7683397683397684, 0.7500000000000001, 0.7648183556405354,
0.7449856733524356, 0.6893523600439079, 0.7538940809968847, 0.7542533081285444, 0.7203302373581012,
0.7176220806794057, 0.7533460803059272, 0.738430583501006, 0.7399411187438666, 0.7252747252747253,
0.7475728155339806, 0.7438794726930319, 0.7502448579823702, 0.7397003745318352],
'avg': [0.3410218653831313, 0.3755948514084242, 0.3114438904715683, 0.2878896137163339, 0.21756020271720794,
0.40799501116964293, 0.5945624123630919, 0.6469395720925608, 0.6864758603136537, 0.7710790638202539,
0.7598914285596836, 0.7112967035673445, 0.7610021013889305, 0.746550942588525, 0.7510519762780646,
0.8026761785531914, 0.7728671796985565, 0.8060538005239586, 0.8085343029552714, 0.8150990181155058,
0.8203083116924473, 0.7917866487141729, 0.8144481936989205, 0.8017980002465415, 0.8237707270520085,
0.8178545123866542, 0.8251870289819893, 0.8163672697239408, 0.816444344202068, 0.8188779722806074,
0.8259727722204108, 0.8268776939553423, 0.8283389795097891, 0.8302090256449117, 0.8230370071785531,
0.828679507647958, 0.8181301001407055, 0.810512108998096, 0.8237709433191398, 0.823438800648397,
0.8153835623618273, 0.8164090114554264, 0.8233228177297789, 0.818938708684707, 0.8174787903769267,
0.8170767156114509, 0.8197079304832489, 0.8221487679977244, 0.8209134279292446, 0.8189600577146223],
}
f1_scores_006c_valid = {
'Wake': [0.8618463925523662, 0.8696090635488395, 0.8205569324974487, 0.7489534205924434, 0.8367503692762186,
0.9225929081732976, 0.927912685774947, 0.9501896161760454, 0.9676521254937444, 0.9754575267110971,
0.9746921050758169, 0.9757899343544856, 0.9758854822903542, 0.9773763591722202, 0.9760144337920543,
0.9773163896575428, 0.9786192321239873, 0.9799391452023776, 0.9806678383128296, 0.9806902362812906,
0.9801705682097358, 0.9809206701548544, 0.9802884025721444, 0.980900888249131, 0.9823445204390011,
0.9819735847065652, 0.9809778779766098, 0.9812053981488024, 0.9817900311416857, 0.9820713970912296,
0.9822017604205253, 0.9820073095305032, 0.9812505511949907, 0.981505874914379, 0.9814889975118671,
0.9823347745521926, 0.9822433037682284, 0.9821919740461245, 0.9817028027498679, 0.9827312775330396,
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0.9141513132105058, 0.9157523510971787, 0.915471112834432, 0.9165692007797271, 0.9166666666666666],
'Non REM': [0.7850800327141021, 0.8042562876977353, 0.6236624268120331, 0.2452418369128689, 0.6779871744536055,
0.8819601537161702, 0.8731400859693595, 0.9140811455847254, 0.94530111238972, 0.9550155763239875,
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0.9599779603285914, 0.9616332627909872, 0.9621452669180393, 0.9601102480581307, 0.9615664616543241,
0.9618354952388094, 0.9625034121646774, 0.962448054401209, 0.9626805778491172, 0.9639834189688982,
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0.9640176158542687, 0.9639598997493734, 0.9644025879943044, 0.9634204514071953, 0.9634835676054225,
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'Pre REM': [0.04081632653061224, 0.09162995594713656, 0.10396975425330814, 0.052173913043478265,
0.13922651933701657,
0.34345047923322686, 0.3482889733840304, 0.3218197135636057, 0.4571877454831107, 0.466966966966967,
0.44818871103622576, 0.42482517482517484, 0.47457627118644063, 0.4706827309236948, 0.47385620915032683,
0.5179526355996944, 0.5249813571961224, 0.5063694267515924, 0.5194424064563462, 0.5011286681715575,
0.5239520958083832, 0.505050505050505, 0.5104477611940299, 0.5465838509316769, 0.5408895265423243,
0.5367965367965368, 0.4893292682926829, 0.5277777777777779, 0.5262379896526238, 0.5157593123209169,
0.5290780141843973, 0.5240875912408759, 0.5252225519287834, 0.5260869565217391, 0.5219298245614036,
0.5183098591549294, 0.5313601127554616, 0.5178571428571429, 0.5296995108315863, 0.5375886524822695,
0.5214899713467048, 0.509719222462203, 0.5251550654720882, 0.5288326300984529, 0.5240174672489083,
0.5272601794340924, 0.5249643366619116, 0.5235816814764184, 0.5175627240143369, 0.5237084217975938],
'Artefakt': [0.025590551181102365, 0.0, 0.007782101167315175, 0.0, 0.0, 0.0, 0.35960591133004927,
0.5014691478942214, 0.6120612061206121, 0.6326530612244898, 0.6690223792697291, 0.7137476459510358,
0.6186726659167604, 0.31360000000000005, 0.5985401459854015, 0.6540447504302926, 0.7069943289224953,
0.6589371980676328, 0.5975308641975308, 0.6590909090909091, 0.6564705882352941, 0.613664596273292,
0.7095477386934673, 0.6840236686390533, 0.7198228128460686, 0.6570048309178743, 0.7439703153988868,
0.7392497712717292, 0.7340529931305201, 0.7607699358386802, 0.7497621313035204, 0.7687564234326825,
0.7316620241411328, 0.7395301327885597, 0.7315247895229186, 0.7536496350364964, 0.7575757575757576,
0.7685098406747891, 0.7610953729933899, 0.7452229299363058, 0.7695351137487636, 0.7609359104781281,
0.7587511825922422, 0.7212765957446807, 0.7661141804788214, 0.747983870967742, 0.756385068762279,
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'avg': [0.35213411621693835, 0.46731364943973813, 0.4611033338551119, 0.36412947715437755, 0.47041463395655436,
0.5768813917002631, 0.6695986107820774, 0.7110683196787979, 0.7761473150113044, 0.7859918217268324,
0.7880130298528705, 0.7937682018054499, 0.7856938188423308, 0.7256560224314444, 0.7829973839827589,
0.8047510377164435, 0.8143483914741413, 0.8047370926083595, 0.793691105839553, 0.8022789362540376,
0.8075738405323859, 0.796359193982956, 0.8149346243893948, 0.8179768802728787, 0.8253313884406588,
0.8110956019872303, 0.817769421988114, 0.825541872685762, 0.8247158625742147, 0.8276306813423691,
0.8284356069087003, 0.8306305374736137, 0.823623160131195, 0.8251476895304111, 0.8228903665028803,
0.8264201403735358, 0.8301570833093675, 0.8299126557837966, 0.8299776742660814, 0.8291336572968133,
0.8304699563682085, 0.8260882008623543, 0.8294197253323896, 0.822209883734697, 0.8305334301098689,
0.8268627324572322, 0.8282275800441378, 0.8305953208151162, 0.8271802540825698, 0.8322633260425766],
}
f1_scores_006d_valid = {
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0.8669003695890107, 0.9370517645388056, 0.9352746891697936, 0.9443139422412602, 0.9512332628611699,
0.9596058084061706, 0.9563431276980923, 0.9683043341818055, 0.9646421392991349, 0.9703792498393009,
0.9698324803046448, 0.9778089395267309, 0.9784590475520045, 0.9804859045190537, 0.9803976624607339,
0.9811685006756875, 0.9813919980320149, 0.982907644719399, 0.9818252331341336, 0.9826977401129944,
0.9831315107609329, 0.982827321132621, 0.9833944436609786, 0.983229583649285, 0.983715426189551,
0.9835550138660731, 0.9827537660143602, 0.9844045285151628, 0.9837409868514068, 0.9840230855855855,
0.9843551349060602, 0.9839935270526982, 0.9837593560231607, 0.9821501373748116, 0.9837948284365532,
0.9832056777457663, 0.9835048639503736, 0.9832451499118166, 0.9834230735281136, 0.9829177233023386,
0.9831793497425769, 0.9828767123287672, 0.9840552328147899, 0.9835851454391414, 0.9834640959786088],
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0.8673334591432346, 0.844158539115999, 0.8891891891891892, 0.8882011605415862, 0.8990059642147117,
0.8820690986296081, 0.9067420266451353, 0.9128824790489183, 0.9055056618508396, 0.910737875070768,
0.9139660493827161, 0.9095400988217408, 0.9157057654075547, 0.9173666731783552, 0.917772934416485,
0.9194460698264092, 0.9202312138728324, 0.9184195006880282, 0.9155038759689922, 0.9191176470588236,
0.9186434032542639, 0.9200940070505288, 0.9220524872698785, 0.9224505622334238, 0.9167938931297709,
0.9229867083659109, 0.920591669910471, 0.9210168833689113, 0.9165526675786594, 0.9188869153345174,
0.9183317167798254, 0.9198905822586948, 0.9211698624830525, 0.9191820837390458, 0.9181467181467182,
0.9183673469387755, 0.9206903238316851, 0.922650840751731, 0.9204434934837581, 0.921011673151751],
'Non REM': [0.7532730338801165, 0.4679057867409135, 0.22788448974258457, 0.3711820188641381, 0.6800566661419801,
0.7624413682645006, 0.8951810117477823, 0.8842360795023517, 0.9113554588229374, 0.9140596200634047,
0.9349014077413181, 0.9349888254283586, 0.9491533927003637, 0.9495510367424528, 0.958806000050084,
0.952454793287147, 0.9621138816524004, 0.9606460646064606, 0.9627125385303769, 0.9588965655351958,
0.9654828162827264, 0.9634648198333835, 0.9650294306771529, 0.9651647008951949, 0.966512184182987,
0.9662123296236088, 0.9661306532663317, 0.9658680096736393, 0.9663726571113561, 0.9672421978461307,
0.9676165803108808, 0.9651376146788991, 0.9675303704071746, 0.967563389762896, 0.966498531958142,
0.9675264797507789, 0.9665546808403951, 0.9670263788968825, 0.9656749434543782, 0.9673074997496746,
0.9660842237586423, 0.9665496489468406, 0.9665248936702527, 0.9660503865302343, 0.9650391802290536,
0.9647943831494484, 0.9663247435576683, 0.9672993938789254, 0.9657798486898143, 0.9652676551897461],
'Pre REM': [0.021505376344086023, 0.06759443339960239, 0.0935672514619883, 0.04539722572509458, 0.22198952879581152,
0.2914757103574703, 0.37134778510838834, 0.3889340927583401, 0.4758893280632411, 0.43087362171331633,
0.4683734939759036, 0.4592833876221498, 0.5193855157278712, 0.52356780275562, 0.5024077046548957,
0.47396226415094345, 0.5079617834394905, 0.5380029806259314, 0.570281124497992, 0.47719869706840395,
0.5128205128205128, 0.4984615384615384, 0.5342362678705793, 0.5227447956823439, 0.5481798715203426,
0.5540443808160345, 0.5436893203883496, 0.5397520058351568, 0.5475017593244195, 0.5541310541310541,
0.5331302361005331, 0.5482014388489208, 0.5248447204968943, 0.5354449472096531, 0.5382963493199714,
0.544649446494465, 0.5455871626549964, 0.5345080763582967, 0.5260837619397503, 0.5413313825896123,
0.5384615384615384, 0.5449358059914409, 0.5340236686390533, 0.5347670250896057, 0.5372076541459958,
0.5219818562456385, 0.537117903930131, 0.5443873807776962, 0.5374570446735396, 0.5404644616467277],
'Artefakt': [0.0028011204481792713, 0.2966507177033493, 0.4111986001749781, 0.2077562326869806, 0.22045454545454543,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.007476635514018692, 0.0, 0.03308823529411765,
0.44755244755244755, 0.5278058645096056, 0.6141078838174274, 0.5644768856447689, 0.5688073394495413,
0.7036649214659686, 0.7565789473684211, 0.7504835589941973, 0.7032136105860112, 0.7957610789980732,
0.7575221238938054, 0.7695431472081218, 0.7767527675276754, 0.8007483629560337, 0.7862939585211903,
0.7540425531914894, 0.7909270216962524, 0.7669990933816864, 0.7837837837837839, 0.7870722433460077,
0.7763794772507261, 0.7760368663594469, 0.7521663778162913, 0.7868561278863233, 0.7847358121330725,
0.7728873239436619, 0.7853881278538813, 0.781305114638448, 0.7768595041322315, 0.7890255439924314,
0.7586206896551724, 0.7627416520210896, 0.7765765765765766, 0.76269621421976],
'avg': [0.33512414452129435, 0.4379224777868105, 0.3894777884681461, 0.3367105187176208, 0.5357232837614311,
0.5249010539646664, 0.6050186620968654, 0.6107172339972856, 0.6368830569639998, 0.633420569761528,
0.6460428338533253, 0.6389547759729199, 0.6652064863598459, 0.6666877549705625, 0.6661197837517985,
0.6622813743332923, 0.7604358157632409, 0.7835592872685841, 0.806618622643138, 0.7783415371559741,
0.7884490437222368, 0.8113046753229292, 0.8308916112086214, 0.8275169923768451, 0.823675268163764,
0.8437190740050118, 0.8340801265107881, 0.835395421413185, 0.8378721287163456, 0.8449909376363187,
0.8378478384105883, 0.8340458759568398, 0.8379518256770726, 0.8352397958878133, 0.8378791287554508,
0.8413180025726446, 0.8386213035418575, 0.8364695122013396, 0.8285255776327782, 0.8396353507993363,
0.838163793775769, 0.8375536450182024, 0.8380703405116113, 0.8369455367050895, 0.8360341559912676,
0.8354696960137742, 0.8331260746606848, 0.8362269000488464, 0.836768421772566, 0.8345808200373186],
}
f1_scores_006e_valid = {
'Wake': [0.7950675943209458, 0.7228878089347435, 0.7181377846146023, 0.7200213114130228, 0.8048306299402557,
0.8773713577185368, 0.9502296695167478, 0.9441228588304783, 0.9434503229723575, 0.9593663203374139,
0.9699237650990358, 0.9524197380031222, 0.9677967571317091, 0.9701539983046059, 0.9682602761027697,
0.9724455247851376, 0.972644908524854, 0.9749150632844543, 0.9744882667183836, 0.9766314087922129,
0.9734472571600417, 0.9744921230834006, 0.9737936336000559, 0.9763796096585828, 0.9728787561876873,
0.9777497016915843, 0.9798643530344402, 0.9783493578496737, 0.9797237937941455, 0.9801903144787577,
0.9807573592959064, 0.9773029772329247, 0.9790066924973582, 0.9804728007733544, 0.9795918367346937,
0.9806835590146326, 0.9808418353908019, 0.9817554847903728, 0.9810962421087801, 0.9809015859604653,
0.9813243982012169, 0.9816901408450704, 0.9817682185699942, 0.9816615384615384, 0.9814622467123215,
0.9817629715940186, 0.9817425630903708, 0.9813843825878875, 0.9818861009303637, 0.9806569279062836],
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0.7459177008491183, 0.8603218756209021, 0.8182996851268752, 0.8550444530344028, 0.8856457417033188,
0.9012908124525437, 0.884949215143121, 0.9008104978772673, 0.906949806949807, 0.9082851637764933,
0.9129852744310576, 0.8936248392430646, 0.9097613040947021, 0.9074180563542266, 0.9115958668197475,
0.899768696993061, 0.9106728538283063, 0.911851705777953, 0.9085979860573199, 0.9117238643126363,
0.9137254901960784, 0.9117131163515884, 0.9108758421559191, 0.9157140089130015, 0.9157957010451587,
0.9147738303242089, 0.9135993800852382, 0.9092652982927297, 0.9173489278752437, 0.9105752800455668,
0.9171356491364254, 0.9186752890456594, 0.9190457567461869, 0.9126289161315432, 0.9139123750960799,
0.9137269938650306, 0.9190785587714118, 0.9177387914230019, 0.9157281553398059, 0.9121725731895223,
0.9176516179035071, 0.9174995100921027, 0.9152086137281293, 0.9129586260733801, 0.9174738473459899],
'Non REM': [0.5282551704048937, 0.1159926383842197, 0.015117609030782237, 0.060458786936236394, 0.5554838709677419,
0.7867411191635976, 0.9144646402932641, 0.9120705124821872, 0.9078402209709975, 0.9348232821590589,
0.941084212390276, 0.916157366986798, 0.9364381067961165, 0.9494027580359593, 0.9390051423043633,
0.9457971307334815, 0.9458135541864459, 0.950569203115638, 0.9529523523823809, 0.952114645228941,
0.9552485358161886, 0.9560919018435853, 0.9524695704657479, 0.956071832224036, 0.9521950724931563,
0.9577161729383508, 0.9597605089185481, 0.9591564536829654, 0.9590810120879947, 0.962066823997019,
0.9621338651377291, 0.9559345947444006, 0.9608795832498497, 0.9614988572720196, 0.9583490625393231,
0.9598801647734365, 0.9619664424495511, 0.9627984730157937, 0.9617579337270998, 0.9611109723505756,
0.961355425439143, 0.962885468041648, 0.9621325361235675, 0.9625507595127086, 0.9634167687729738,
0.9627284286106056, 0.9628874692386089, 0.9622509262040653, 0.9630719361675601, 0.9611126452941029],
'Pre REM': [0.05244122965641953, 0.0, 0.002652519893899204, 0.0, 0.16336056009334887, 0.24898785425101216,
0.44827586206896547, 0.35051546391752575, 0.4183333333333333, 0.4750593824228028, 0.5056497175141244,
0.4078125, 0.45998445998445997, 0.5292397660818714, 0.5052950075642966, 0.5098335854765508,
0.4543429844097995, 0.5454545454545455, 0.5255474452554745, 0.5480427046263345, 0.5752380952380952,
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0.5598320503848845, 0.5364775239498895, 0.5487465181058496, 0.5389657683903859, 0.5310899780541332,
0.5402214022140222, 0.5549964054636951, 0.5497564370215727, 0.5373352855051244, 0.5520110957004161,
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'Artefakt': [0.0018083182640144665, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03202846975088968, 0.15140845070422537,
0.46618106139438087, 0.40476190476190477, 0.29787234042553196, 0.5163551401869159, 0.15488215488215487,
0.5104270109235353, 0.27257799671592775, 0.11070110701107011, 0.3735955056179775, 0.48189762796504376,
0.5658436213991769, 0.34948604992657856, 0.5725338491295939, 0.6388625592417062, 0.669683257918552,
0.6457883369330453, 0.7117988394584138, 0.6343115124153499, 0.6703417861080485, 0.7257731958762886,
0.6541617819460727, 0.7350785340314135, 0.6916488222698073, 0.6384872080088987, 0.7076923076923077,
0.7535353535353535, 0.657243816254417, 0.6526576019777504, 0.671604938271605, 0.7429854096520763,
0.7188208616780046, 0.6947368421052632, 0.6721120186697782, 0.7288888888888889, 0.68,
0.7748917748917747, 0.75, 0.741061755146262, 0.7168458781362008, 0.7436743674367436, 0.744920993227991,
0.7553072625698324],
'avg': [0.286728454298802, 0.2975796054927482, 0.2894603021052202, 0.27144228005766347, 0.45660478782719405,
0.531803606396453, 0.6410641034501539, 0.6352833942122584, 0.7181698783410945, 0.7319313262768998,
0.7231641695763024, 0.7355387920639913, 0.6839823953343416, 0.7732346680591557, 0.7186847172927701,
0.6903525244874594, 0.7280043583964283, 0.7725195487828767, 0.7852499484219285, 0.7475741350787628,
0.7952472868673961, 0.807452459027971, 0.8067750530535955, 0.8041924787906204, 0.8171776699950856,
0.8055197084826522, 0.8118072172503412, 0.8225010133283094, 0.8109834311761999, 0.8319732972977387,
0.8197687768516619, 0.8065886415381021, 0.8203283139187034, 0.8327921823663325, 0.8090576856612633,
0.8140279167195793, 0.8185841111085004, 0.8286125296308636, 0.8246100943502557, 0.817925508780554,
0.8119217628458604, 0.8265528917522083, 0.8193271903160518, 0.83691773304548, 0.8288773748359883,
0.8310431737909619, 0.8231835259549095, 0.8274200845043336, 0.8298385857738435, 0.8305911900248513],
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| f1_scores_001_train = {'Wake': [0.45828943664803573, 0.47566984186570316, 0.5875755194928342, 0.7704458983749956, 0.8776419969393865, 0.9099441500276573, 0.922647481237028, 0.9350972410673902, 0.9465349405012661, 0.9534392971969388, 0.9572805948925108, 0.9627780979304024, 0.9671772627452403, 0.9684523701540192, 0.9716055990492866, 0.9731932563363663, 0.9741702106414556, 0.9750272331154685, 0.9763683737482866, 0.9776438172104023, 0.9779034872265177, 0.9782498184458969, 0.9790514587703288, 0.9804106722113014, 0.9802716115527991, 0.9813018761692003, 0.9814845932014806], 'REM': [0.21260117157805308, 0.35374807384355084, 0.5103512283642359, 0.7049339859692951, 0.7804678062886637, 0.8131475844498263, 0.8394483436487339, 0.8634476562257954, 0.8858121874421188, 0.898998362634237, 0.9160738813815542, 0.9319316568182859, 0.9472641818444968, 0.9574975686846584, 0.9649491736446937, 0.9709271015844844, 0.9756795855455999, 0.9788204281364097, 0.9807636963996305, 0.9833653715167936, 0.9848436671966083, 0.9857726653549266, 0.987027730685396, 0.9874375662176479, 0.9885864793678666, 0.9896364035519353, 0.99061473486626], 'Non REM': [0.3401838631048861, 0.475220709175341, 0.5374332841107604, 0.6340473156622304, 0.7303350082917914, 0.7759327787793486, 0.8091571662779938, 0.8371759030861152, 0.8542371387331004, 0.8664349470288908, 0.8812739412416539, 0.8957909719398129, 0.9114945790997332, 0.9205330841504897, 0.930866013351348, 0.9387957691035018, 0.9419240026921892, 0.945397904922747, 0.9504453644461172, 0.9537926314588798, 0.9551680485384851, 0.9571398417488634, 0.9595577339564574, 0.962153041039352, 0.9628565892798121, 0.9644391272147212, 0.965803250485636], 'Pre REM': [0.19860624317691192, 0.1884477836851209, 0.161593542507486, 0.32138035252003516, 0.5003342912439154, 0.597080323173599, 0.6383882339403956, 0.6777091347889314, 0.7154756685664183, 0.7457149471099168, 0.7927714646464645, 0.8286919003726221, 0.8679852222991217, 0.8928338793882888, 0.9127351158091093, 0.9285575691722613, 0.9372181620276784, 0.943335761107065, 0.9516857973155537, 0.9570796675253974, 0.9598336604418881, 0.9629075069761112, 0.9667892371446916, 0.9683581857266528, 0.9716692115423892, 0.9725854152074092, 0.9749576790613507], 'Artefakt': [0.5229366001967272, 0.6338351044007543, 0.6570850735373311, 0.6454606333110112, 0.6661511061117361, 0.7106723973046948, 0.8216750921171551, 0.925570332136013, 0.9651614399165265, 0.977290979284187, 0.9842082303329941, 0.9882866708719491, 0.990505776138239, 0.9923858361012373, 0.9937809736663104, 0.9947903504850028, 0.9952559640697606, 0.9958682908334388, 0.9962760940796701, 0.9965954632115481, 0.9968580072178286, 0.9967910694490364, 0.9972373540856032, 0.9972666167329747, 0.9978593391196046, 0.9977720484506494, 0.9978014280989163], 'avg': [0.34652346294092284, 0.425384302594094, 0.49080772960252955, 0.6152536371675134, 0.7109860417750986, 0.7613554467470252, 0.8062632634442612, 0.8478000534608491, 0.8734442750318859, 0.888375706650834, 0.9063216224990356, 0.9214958595866143, 0.9368854044253663, 0.9463405476957387, 0.9547873751041497, 0.9612528093363233, 0.9648495849953367, 0.9676899236230257, 0.9711078651978516, 0.9736953901846043, 0.9749213741242656, 0.9761721803949669, 0.9779327029284953, 0.9791252163855857, 0.9802486461724943, 0.981146974118783, 0.9821323371427286]}
f1_scores_001_valid = {'Wake': [0.657405684754522, 0.4611178937310898, 0.843902349955265, 0.855320411392405, 0.885871037659171, 0.9139117987867221, 0.932055717572047, 0.9463877720904695, 0.9570114044125484, 0.9595433464145559, 0.9673044150459424, 0.9634778302470773, 0.9712198478939845, 0.9694530626717489, 0.9703757610181224, 0.9729483335963112, 0.9736898243618004, 0.9754512380346861, 0.9746917814242247, 0.9747490755414686, 0.9774797034945287, 0.9771752369856486, 0.9760045134787286, 0.9766318905963626, 0.9769876682008221, 0.9782155845072908, 0.978478283917023], 'REM': [0.12518034456420268, 0.11241206629307261, 0.2589039585400816, 0.429526563064691, 0.7098027495517035, 0.82624801552302, 0.815959741193386, 0.8383912248628885, 0.8554066130473637, 0.8804795803671787, 0.8694686169227921, 0.8914441629312025, 0.8816533437317216, 0.8909512761020881, 0.8801363378148078, 0.8948485433146827, 0.8946564885496183, 0.895306859205776, 0.9020965570301981, 0.9019078820581999, 0.9002114977888866, 0.9014194050165274, 0.8947568389057751, 0.9020897832817337, 0.9026852028185107, 0.9030291484092209, 0.9097933165926211], 'Non REM': [0.19235930929587544, 0.05094734791291218, 0.5261806039702052, 0.5527151935297515, 0.7126930223617607, 0.7644436245118839, 0.8765320885540361, 0.8957227937195452, 0.9114534991646556, 0.9150781723747001, 0.9210171195724076, 0.9341014545644126, 0.9412280475539623, 0.9429993330254989, 0.9435981463939169, 0.9463633087970351, 0.9507063572149343, 0.9456240555285198, 0.950480413895048, 0.9514810381274992, 0.9523567655424625, 0.9540212443095599, 0.9520783017668742, 0.9555623038769105, 0.9508740589511293, 0.9542678310029782, 0.955841139192567], 'Pre REM': [0.025122883670125617, 0.0, 0.005687203791469194, 0.05966438781852082, 0.24408602150537637, 0.23279847701930925, 0.29683698296836986, 0.36845637583892615, 0.350185873605948, 0.35790219702338766, 0.3778217821782178, 0.4421052631578948, 0.45901639344262296, 0.4576107899807322, 0.4524975514201763, 0.4938271604938272, 0.47925764192139736, 0.45363908275174475, 0.49278707443739184, 0.492874109263658, 0.4843835616438356, 0.4977011494252873, 0.49826989619377154, 0.5121495327102803, 0.47285464098073554, 0.483423749246534, 0.4955116696588868], 'Artefakt': [0.12214863870493008, 0.30966531122927743, 0.31692207368764264, 0.3072642967542504, 0.1799193843898864, 0.2667024416420714, 0.278149386845039, 0.39522437216961714, 0.4789156626506024, 0.4533083059596434, 0.587088915956151, 0.5015259409969481, 0.6417704011065007, 0.5848024316109421, 0.6144964720974985, 0.674123788217748, 0.6778115501519757, 0.6790490341753345, 0.7044728434504792, 0.6750369276218611, 0.7093596059113301, 0.7350993377483444, 0.718241042345277, 0.7056936647955093, 0.7402489626556018, 0.7391304347826086, 0.7164906580016247], 'avg': [0.22444337219793115, 0.18682852383327042, 0.39031923798893275, 0.44089817051192376, 0.5464744430935796, 0.6008208714966013, 0.6399067834265756, 0.6888365077362893, 0.7105946105762235, 0.7132623204278932, 0.7445401699351022, 0.746530930379507, 0.7789776067457584, 0.7691633786782021, 0.7722208537489044, 0.7964222268839208, 0.7952243724399453, 0.7898140539392123, 0.8049057340474682, 0.7992098065225374, 0.8047582268762087, 0.8130832746970735, 0.8078701185380854, 0.8104254350521594, 0.80873010672136, 0.8116133495897264, 0.8112230134725446]}
f1_scores_001b_train = {'Wake': [0.8216369980220904, 0.9148960769839647, 0.9262593869783211, 0.9307407224833854, 0.93437170366048, 0.9361000399908336, 0.9392671051507645, 0.9400201522212426, 0.9418583182098613, 0.9437846254933641, 0.946338341153827, 0.947366047470224, 0.9506416673430553, 0.9536365852117317, 0.9544220667476659, 0.9560755262444666, 0.9574754370755671, 0.9591688245954166, 0.9595061192761719, 0.9607198571137889, 0.9619010158824886, 0.9623254215210941, 0.9632624998869199, 0.9635522731181044, 0.9637292094850923, 0.963798692479038, 0.9644366802740792, 0.9649516840398845, 0.9653783125548886, 0.965020268587087, 0.9660741378139082, 0.96624938857588, 0.966527802934251, 0.9665532905493233], 'REM': [0.7614879649890591, 0.8373268950437317, 0.8495166577088742, 0.8565099642697295, 0.8618982536066817, 0.865077676101298, 0.8698590432023432, 0.8729105417891642, 0.8782313756646092, 0.8798407167745147, 0.8843661432401378, 0.8877133079673254, 0.8935266470899431, 0.8974062650676178, 0.9003429061294471, 0.9037400836909708, 0.9074412662469304, 0.9088489483747609, 0.9136049639320211, 0.9160532893077434, 0.9204903945308025, 0.9220649242499485, 0.9274855497949864, 0.9310610510576945, 0.9335377153446659, 0.9375915750915751, 0.9407341310697751, 0.944534277084541, 0.9468975293023822, 0.9514857443194369, 0.952917498894598, 0.9550017517936845, 0.9570201009232268, 0.9595492786173817], 'Non REM': [0.7410094927626485, 0.8106415762384027, 0.8246266402289318, 0.8329492641898353, 0.8406863964188013, 0.8453382895503342, 0.8493045680767863, 0.8539930806625655, 0.8544867556108755, 0.8582889188076507, 0.8609136153572697, 0.8629103075708903, 0.8675721373111749, 0.8700001112631709, 0.8742503643868843, 0.8751135103816733, 0.8809324071899154, 0.8834159853460215, 0.8883650351921429, 0.8933036689998375, 0.896576115330852, 0.8993790358447759, 0.9047234899556027, 0.9094462393529292, 0.9138251845148885, 0.9165407448909715, 0.9195960237486319, 0.9229478077437794, 0.9254691201617989, 0.9279472757229703, 0.9305727633358719, 0.9340084772281172, 0.9347551324823792, 0.9361311893736152], 'Pre REM': [0.5753381066030231, 0.6531545137840069, 0.6639423854992718, 0.6747356480168537, 0.6849267591563796, 0.6943487989537461, 0.7008447600391772, 0.71123301129851, 0.7161349171372642, 0.723404255319149, 0.7289075286415712, 0.7317744996021301, 0.741140639483361, 0.7437676794399561, 0.7549063426922921, 0.7580707265173776, 0.7715358822285547, 0.7753496767947722, 0.7895598710053345, 0.7998757863525263, 0.810965801804322, 0.8188597928521067, 0.8327870151219561, 0.8454052345054489, 0.8554315380902187, 0.8669575986318335, 0.8757138651751545, 0.8838630687105149, 0.8923666810125592, 0.9006305293513857, 0.9053856214253645, 0.9136587506705027, 0.9164822091991458, 0.9226994820670588], 'Artefakt': [0.6938652766639936, 0.7614956514378822, 0.802243083310097, 0.832086492615126, 0.853656827713219, 0.8807399282601607, 0.8989053065632517, 0.9172664322792392, 0.9311935963318795, 0.9459323086477255, 0.9580706087461863, 0.9676724973819959, 0.9757160285830477, 0.98237283536674, 0.9851055788667836, 0.987261516984644, 0.9880737681075107, 0.9903669102397612, 0.9909089146911163, 0.9917719004099512, 0.9928428729367497, 0.9934152467585364, 0.9935917943945166, 0.9937937588487423, 0.9944614190794897, 0.9944220788669544, 0.994568590937306, 0.9947501722448108, 0.9949642451704297, 0.995624290523824, 0.995487583576745, 0.9954585152838427, 0.9960005436154309, 0.9960103672209442], 'avg': [0.718667567808163, 0.7955029426975976, 0.8133176307450991, 0.825404418314986, 0.8351079881111122, 0.8443209465712744, 0.8516361566064645, 0.8590846436501444, 0.8643809925908978, 0.8702501650084807, 0.8757192474277984, 0.8794873319985133, 0.8857194239621166, 0.8894366952698434, 0.8938054517646146, 0.8960522727638264, 0.9010917521696957, 0.9034300690701464, 0.9083889808193574, 0.9123449004367694, 0.916555240097043, 0.9192088842452923, 0.9243700698307963, 0.928651711376584, 0.932197013302871, 0.9358621379920745, 0.9390098582409893, 0.942209401964706, 0.9450151776404117, 0.9481416217009407, 0.9500875210092975, 0.9528753767104053, 0.9541571578308868, 0.9561887215656647]}
f1_scores_003_train = {'Wake': [0.34114242511145293, 0.47037630601418756, 0.6231108635424149, 0.7475570095554086, 0.8430910969227903, 0.885049862822802, 0.9044004997858691, 0.9253318162882266, 0.9369672478644814, 0.9452637857181587, 0.9518877048365375, 0.9519492785387302, 0.9599945681694732, 0.9649082267440543, 0.9660711210217642, 0.9688784923862239, 0.9698137676789205, 0.9716789831108208, 0.9731037362477761, 0.9737947680274393, 0.9744066459283712, 0.9748579152435513, 0.9758808743863597, 0.976034107402032, 0.9767549533601143, 0.9779290625638234, 0.9781986046743861, 0.9787328673867658, 0.9787371221786845, 0.9795699657153236], 'REM': [0.33085111174501175, 0.5190163242660332, 0.6481260289892502, 0.7238209275028948, 0.7667388585547388, 0.8039935174143046, 0.8260626269594878, 0.8558023967343915, 0.8703875107984697, 0.8857655272212258, 0.8967234984307647, 0.90612615524313, 0.9239083706933612, 0.9406622293884351, 0.9519391889833348, 0.9603378095636627, 0.9671704328948467, 0.9715075483733787, 0.974906154021158, 0.9778475254730713, 0.9794804545075535, 0.9824130693399407, 0.9832002787329768, 0.9848179517871484, 0.9866099153274057, 0.9870328571644978, 0.9881570777362148, 0.988027534399067, 0.9889871845366134, 0.9895973713299313], 'Non REM': [0.351980942709495, 0.45674395145063235, 0.6163104861034215, 0.6953867850728838, 0.7299422327657482, 0.7585851814110643, 0.7753320906085256, 0.8150885716625269, 0.8421267105371844, 0.8549725358138507, 0.8629687326137754, 0.8676629264992817, 0.8853304856115107, 0.9006314502987507, 0.9104625137754349, 0.9217028741982889, 0.9277105683633874, 0.9353619105730465, 0.9396988364134153, 0.9436405048550341, 0.9474020819890248, 0.948741792126146, 0.9520946470131885, 0.9544484148276688, 0.955918828151282, 0.9582128867830935, 0.9594037442760933, 0.9609816454649645, 0.9616822429906542, 0.9635761778061385], 'Pre REM': [0.1834041799504074, 0.19172531950361177, 0.2620691563697749, 0.4357441721458459, 0.5412528169601871, 0.5956108512258562, 0.6214464441831721, 0.6589435516086408, 0.6936645551961806, 0.7195463930233275, 0.7393872091262431, 0.7650942263833873, 0.8046556204365137, 0.8448039484507814, 0.8718334048948047, 0.8962313657654856, 0.9123335016115879, 0.925423827499539, 0.9335649743286132, 0.9421271146981219, 0.9483699345643773, 0.9526558218179698, 0.9564608925517026, 0.9615418241162585, 0.9643054975170309, 0.9660984001710631, 0.9684122541015474, 0.9698065519587828, 0.9725783718104497, 0.9744826513147659], 'Artefakt': [0.48589423031561074, 0.543264541160224, 0.6045723154061959, 0.6406778841504691, 0.6607212006712118, 0.6792695774318156, 0.719872537659328, 0.8504288920955587, 0.9366815174834616, 0.9621537185779441, 0.9730542964606038, 0.9768481342092279, 0.98624, 0.9903513880799845, 0.9923686356751719, 0.9933174038891155, 0.994170455870351, 0.9945097124644103, 0.9953544424359049, 0.9960429731174956, 0.9959170976396936, 0.9962939545741939, 0.9971403003657302, 0.996839535946632, 0.9972863881729319, 0.9975876425041827, 0.9974713582696311, 0.9976458685966653, 0.9976656875522789, 0.9979570792069576], 'avg': [0.33865457796639553, 0.43622528847893777, 0.5508377700822115, 0.6486373556855003, 0.7083492411749353, 0.7445017980611686, 0.7694228398392765, 0.8211190456778688, 0.8559655083759555, 0.8735403920709015, 0.8848042882935848, 0.8935361441747514, 0.9120258089821718, 0.9282714485924013, 0.9385349728701021, 0.9480935891605553, 0.9542397452838186, 0.9596963964042391, 0.9633256286893734, 0.9666905772342325, 0.9691152429258041, 0.9709925106203603, 0.9729553986099916, 0.974736366815948, 0.9761751165057528, 0.977372169837332, 0.9783286078115745, 0.9790388935612491, 0.9799301218137361, 0.9810366490746233]}
f1_scores_003b_train = {'Wake': [0.4313997632363146, 0.5472644332254802, 0.6574325037840122, 0.7680884313500437, 0.8588134397026899, 0.8919537341959567, 0.9053421143972485, 0.9239945636223252, 0.9369187624389362, 0.9463731838711433, 0.9526025930664054, 0.9581055804746945, 0.9621588230217527, 0.9652561530290787, 0.9667060212514758, 0.9690848011621572, 0.9707154757203104, 0.9719770370538449, 0.9728229517281478, 0.9745429120148194, 0.9755435029480436, 0.9757504200535854, 0.9767391156230837, 0.9770848849248879, 0.97815974998183], 'REM': [0.31206495709787546, 0.49244859379372, 0.599596254676487, 0.6896992243708981, 0.756718591412559, 0.7945578835884788, 0.8159464658637544, 0.8423975051342513, 0.8704807892564853, 0.8882604071944886, 0.9014192617382694, 0.9152789166736915, 0.9324890002444389, 0.9443083381034153, 0.9544382748916103, 0.9604561003420753, 0.9671201900093336, 0.9706295405029948, 0.9743208941087327, 0.9764104118516274, 0.9788884428048752, 0.9799045960475505, 0.9818839756837986, 0.9830390404942909, 0.9843418741292628], 'Non REM': [0.37274978333539677, 0.5500774066299743, 0.6232983906688321, 0.6736729998291955, 0.7230304243189293, 0.758483732224806, 0.776876982728234, 0.8099952598465915, 0.8446422480662801, 0.8602145790101109, 0.8692816257709545, 0.8812693740103923, 0.894218425021163, 0.9050085771036476, 0.914490898587497, 0.923859481799396, 0.931428311416481, 0.9361149702780877, 0.9406685538209179, 0.9458101131662622, 0.9468460872339457, 0.9497151321786691, 0.9519047320731936, 0.9541438069356851, 0.9571166531849202], 'Pre REM': [0.19497741293049012, 0.16800126435570542, 0.16935250967481083, 0.3163466554694558, 0.507647510905686, 0.5804237385380916, 0.6210424217673184, 0.6590068080060515, 0.7019758788811907, 0.732017097496438, 0.7558063113618669, 0.7864649474536783, 0.8243962909703971, 0.8548676910992195, 0.8804948954122334, 0.8994615054434776, 0.915765265353057, 0.9270872832988168, 0.9351260438844655, 0.9434567493220058, 0.9468353938262967, 0.9513858486803062, 0.9546299269291768, 0.9582474026226702, 0.9622247070834752], 'Artefakt': [0.5076240511500205, 0.630002083630342, 0.6360896314274426, 0.6419354838709677, 0.6458651841556636, 0.6723094958968346, 0.7098380785164273, 0.8135635145581036, 0.9210007727975271, 0.9601728095045227, 0.9740944653723559, 0.9813190855063206, 0.986806774009054, 0.9890350877192983, 0.9916036536240185, 0.9925792101326806, 0.9937048262998368, 0.9944496826307143, 0.9950039851480337, 0.9952844405985358, 0.995848928206873, 0.996140047252822, 0.996479489623247, 0.996859351451213, 0.9966255968414808], 'avg': [0.3637631935500195, 0.4775587563270444, 0.5371538580463169, 0.6179485589781122, 0.6984150300991057, 0.7395457168888335, 0.7658092126545964, 0.8097915302334646, 0.8550036902880841, 0.8774076154153407, 0.8906408514619704, 0.9044875808237555, 0.9200138626533612, 0.9316951694109317, 0.941546748753367, 0.9490882197759574, 0.9557468137598037, 0.9600517027528916, 0.9635884857380596, 0.9671009253906501, 0.9687924710040068, 0.9705792088425866, 0.9723274479865, 0.9738748972857494, 0.9756937162441938]}
f1_scores_003c_train = {'Wake': [0.4466379651005595, 0.5967105208480507, 0.6905195116112043, 0.7632186210321186, 0.8525029797377831, 0.901673584147811, 0.9227019404484612, 0.9377986972508561, 0.9468803155875647, 0.954563166622867, 0.9583437198319427, 0.9611485337143636, 0.965801999156053, 0.967048925153274, 0.9687681580476466, 0.9709119461174325, 0.9717056516054046, 0.9723846181778568, 0.9732127855705962, 0.974165451911593, 0.9744053351747197, 0.97508356950803, 0.9752216247638426, 0.9761454888993859, 0.9760474419576165, 0.9763797410856234, 0.9767382714928017, 0.9773362225220437, 0.9774966372196168, 0.977710334299552, 0.9785559816090283, 0.9788511951005646, 0.9787566555816024, 0.9783548996801396, 0.9794128738828384, 0.9792664636640458, 0.9795714363606624, 0.9793149819863615, 0.9799288398533174, 0.979722392603194, 0.9800211642134043, 0.980373984626288, 0.9801077760511799], 'REM': [0.35829084060139516, 0.5479005299633103, 0.6614117411371289, 0.7207113538845913, 0.770582596655661, 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f1_scores_003d_train = {'Wake': [0.4590079841931025, 0.6067054566706488, 0.6515180926498967, 0.7531546507066651, 0.8600782527546853, 0.892678799973969, 0.9163818154517432, 0.9321424730219051, 0.9453948677143426, 0.9507330316742083, 0.9569370193156459, 0.9609148287869994, 0.9643283480471143, 0.9672967160603614, 0.9684425392937335, 0.9702420134648647, 0.9711349471858957, 0.9714817167226205, 0.9729300086296951, 0.9731129311402136, 0.9737347679367054, 0.9748944658769215, 0.975002499750025, 0.9756219538808467, 0.975020108426453, 0.975939644593919, 0.9753603693672405, 0.9764022120938095, 0.9768413974219754, 0.9762576879542881, 0.9769249298930557, 0.9770600293624352, 0.977179328654035, 0.9781562115006069, 0.9776499245605424], 'REM': [0.33481608598460044, 0.5535281453713322, 0.6063505380854806, 0.6978732361298888, 0.7795728474001595, 0.8117988189036146, 0.8310854593951084, 0.8479374371169852, 0.8629822507087391, 0.8793104770376279, 0.8891479050365008, 0.8967768132819504, 0.9037667127356582, 0.9092033344529603, 0.9120807778380319, 0.9157758094074526, 0.9185655812816911, 0.9209410540172352, 0.9230169170057205, 0.9261353218018458, 0.9276267450404115, 0.9305781463578536, 0.9318291183232636, 0.935286207634241, 0.9362023808614778, 0.9386513849092646, 0.9402388000336553, 0.9426502490146355, 0.9448859815369567, 0.9458553239591528, 0.9477523117770928, 0.9491336539195482, 0.9510748589724043, 0.9532723101663784, 0.9553321698307332], 'Non REM': [0.37019219058290503, 0.4890113110238528, 0.5435859726526254, 0.6270989302622741, 0.718425307122777, 0.7639560813867466, 0.7899300908925455, 0.8259601254578554, 0.8487402335176895, 0.8608135630538888, 0.8701669644235647, 0.8756279393641303, 0.8827100145266146, 0.8859539433642813, 0.8900190788889876, 0.8913405702834811, 0.8932089452899676, 0.8957117229198717, 0.8988042318634425, 0.8991907694359841, 0.9010568259604094, 0.9029339839680922, 0.9043182985948216, 0.9070512283055253, 0.9078734858681022, 0.9092296214020857, 0.9101751854440512, 0.9136890138872417, 0.9162252105406139, 0.9181735574738754, 0.9210151061521069, 0.9233422309237749, 0.924147513724061, 0.9262288545879136, 0.9282737517554777], 'Pre REM': [0.2185548365218866, 0.2834966603286042, 0.22596656852964792, 0.3356500929249726, 0.5074289076733317, 0.5936133120374141, 0.6262453332233247, 0.6494031374346368, 0.6783503648111229, 0.7065070275897971, 0.726734592855375, 0.7407255753009582, 0.7523349634505172, 0.7598868036329087, 0.7698874029510995, 0.7723171141110287, 0.7786381788327826, 0.7865395084380103, 0.7904764777445982, 0.7952704402515725, 0.8006290442133942, 0.8057079911876627, 0.8096551586787888, 0.8182630627767958, 0.8232291759399388, 0.8281914841104518, 0.8342934267020058, 0.84214021996054, 0.849311072432201, 0.8547895942693078, 0.8614322057671288, 0.867332189866831, 0.8703270014745424, 0.8759298516091732, 0.8827571624907494], 'Artefakt': [0.5267054047600095, 0.6047960465877532, 0.6243213664942812, 0.6318329568618809, 0.6637899709302325, 0.6970531439606019, 0.7296933003358671, 0.8053531362309633, 0.8780763519662786, 0.9214560305241059, 0.9466757868572915, 0.9600910002313566, 0.9714412605236734, 0.9781185639280223, 0.9825253991291727, 0.9863738051657515, 0.9878619380212925, 0.989535955001697, 0.9906389748464889, 0.9915430123169431, 0.9915214776298942, 0.9926726773357659, 0.9931859214536701, 0.993601506898527, 0.9936022523178486, 0.9937948513774654, 0.9939412769923878, 0.9943694786913891, 0.9944835087991919, 0.9947865596147684, 0.9946090858758049, 0.9951622304254905, 0.9956762113895394, 0.9954718594527363, 0.9952800870173257], 'avg': [0.3818553004085008, 0.5075075239964383, 0.5303485076823864, 0.6091219733771364, 0.7058590571762372, 0.7518200312524692, 0.7786671998597178, 0.8121592618524692, 0.8427088137436345, 0.8637640259759255, 0.8779324536976756, 0.886827231393079, 0.8949162598567154, 0.9000918722877067, 0.904591039620205, 0.9072098624865157, 0.9098819181223259, 0.9128419914198871, 0.9151733220179891, 0.9170504949893118, 0.9189137721561629, 0.9213574529452592, 0.9227981993601139, 0.925964791899187, 0.9271854806827641, 0.9291613972786374, 0.9308018117078681, 0.9338502347295232, 0.9363494341461877, 0.9379725446542786, 0.9403467278930379, 0.942406066899616, 0.9436809828429163, 0.9458118174633616, 0.9478586191309658]}
f1_scores_003e_train = {'Wake': [0.37558449872610644, 0.4451441684126509, 0.5637551801083839, 0.6495528703902673, 0.8124668546656004, 0.86078777800873, 0.8853923665468657, 0.9058019838206619, 0.9211747490703408, 0.92997397787895, 0.9349235788647163, 0.9412216707798842, 0.9477923641185069, 0.9519023104820634, 0.955170742534119, 0.9575336501832166, 0.9595641602554799, 0.9609826458501537, 0.9631867060333529, 0.9629206349206348, 0.9645036176824151, 0.9661691497134247, 0.9660202326436322, 0.9670435453843709, 0.9675378029570515, 0.9678602556653108, 0.9684258334090291, 0.9685488888787963, 0.969058405890524, 0.969614401257022, 0.9693823325021462, 0.9704055502079513, 0.9708861276688473, 0.9713172450749766], 'REM': [0.1961737173256944, 0.15041506628670548, 0.49051808721107915, 0.5829218513397854, 0.749687590118235, 0.7862571446517179, 0.8086693579333527, 0.8255767943020638, 0.8430115683671533, 0.851206546455651, 0.8602496304311382, 0.8673794098120464, 0.8795522938033532, 0.8848393589723892, 0.8927059040137474, 0.8973435320446986, 0.9002835900973404, 0.9046655631169149, 0.9064025089879905, 0.9087666388555071, 0.9118953527555679, 0.9157561883178035, 0.9148384233362156, 0.9179896204895823, 0.9194131340357755, 0.9214663695330867, 0.9219112345877437, 0.924543458469448, 0.9263030655415382, 0.9285714285714286, 0.9293286219081272, 0.9312282743518675, 0.9325719709437168, 0.9328426722159316], 'Non REM': [0.300507264697497, 0.4421090572061171, 0.6597646827155024, 0.7055236236678812, 0.7401019528200313, 0.7586434348457018, 0.7674361442821735, 0.7815747453712075, 0.7942405509038265, 0.8198546433378198, 0.8317356164607529, 0.8443212079615648, 0.8559567282611681, 0.8620839259922345, 0.866051279787686, 0.8686454231604955, 0.873909018778101, 0.8767851028815674, 0.8798214256115012, 0.8792239503452247, 0.8826288483024879, 0.8846155977589236, 0.8858539183109686, 0.8869923027439497, 0.8888161144753859, 0.8885134571887451, 0.8919704863524401, 0.8922167356050527, 0.8927944152954886, 0.8944701770814173, 0.8946446892983003, 0.8976302311683525, 0.8979201610197919, 0.8998735268111968], 'Pre REM': [0.17708598684152735, 0.18346998397888983, 0.261995743445874, 0.36977587268291573, 0.527307218690718, 0.5952723666954547, 0.6142764817478683, 0.6376558552266706, 0.6514704072843607, 0.6657877389584707, 0.6727223652520555, 0.683999666138052, 0.7024064115607417, 0.7146690982188189, 0.7267327750089915, 0.7312403267632196, 0.7388780637883294, 0.7495224962974313, 0.7515355920077673, 0.755950808008944, 0.7605616618031336, 0.768707414119529, 0.7714620450046459, 0.7748226055534808, 0.7777195809830781, 0.7808005788770187, 0.7863585740515917, 0.7914337800749244, 0.7931761625408501, 0.7985442474779287, 0.8010663045757246, 0.8087862237741368, 0.8090135581090214, 0.8130139364181918], 'Artefakt': [0.4551118829423982, 0.576196422111491, 0.6030167334433184, 0.6420437922035366, 0.654982332155477, 0.6802976183412642, 0.6909193391185899, 0.7157926184676197, 0.740816533051486, 0.7908454786353096, 0.8346000436568571, 0.8829686197740004, 0.9202791002486459, 0.9446408445955708, 0.9600331531114772, 0.9689011614996186, 0.9749236891928443, 0.9786567986164117, 0.981128790443488, 0.9833688286544047, 0.9852824415654834, 0.9877966101694915, 0.9887047827730971, 0.9894059377150556, 0.9901280062063615, 0.9904680636496747, 0.9914342532861231, 0.991744516554621, 0.9929053234337847, 0.992190455863949, 0.9933030514791521, 0.9930306148201354, 0.9931690277508248, 0.9933702837285602], 'avg': [0.30089267010664467, 0.35946693959917086, 0.5158100853848315, 0.5899636020568773, 0.6969091896900124, 0.7362516685085738, 0.7533387379257702, 0.7732803994376447, 0.7901427617354335, 0.8115336770532402, 0.8268462469331039, 0.8439781148931095, 0.8611973795984833, 0.8716271076522155, 0.8801387708912042, 0.8847328187302498, 0.889511704422419, 0.8941225213524959, 0.89641500461682, 0.898046172156943, 0.9009743844218174, 0.9046089920158344, 0.9053758804137118, 0.9072508023772878, 0.9087229277315305, 0.9098217449827672, 0.9120200763373856, 0.9136974759165686, 0.9148474745404371, 0.9166781420503491, 0.9175449999526901, 0.9202161788644887, 0.9207121690984404, 0.9220835328497714]}
f1_scores_003c_valid = {'Wake': [0.7659384013526506, 0.7626023018819246, 0.8090274078921338, 0.7815923818439799, 0.8991182529872271, 0.9366738182573188, 0.9471757697552573, 0.9474741069541323, 0.9574051668920361, 0.9609795049241416, 0.9659506479673353, 0.962255457896229, 0.9678618003822999, 0.969906631131193, 0.9712296327713569, 0.9701819737287682, 0.9736543104672631, 0.9714255307827383, 0.9735028770334587, 0.9738889480976234, 0.9752792334974326, 0.9749991134437392, 0.9721538706650852, 0.9749787955894826, 0.974475245770968, 0.9748384240814519, 0.9745653060139315, 0.976328210952195, 0.976317095653407, 0.976317935264296, 0.9760839976667314, 0.9754706622035669, 0.9759406239457377, 0.9768716624818757, 0.9759673411430599, 0.9763893805309733, 0.9777336899721892, 0.9776074594709144, 0.9780765546844513, 0.9776889894632628, 0.9770368929979317, 0.9773206844578421, 0.9773502837845005], 'REM': [0.5709274281955712, 0.5350080171031535, 0.6886145404663924, 0.5441772493920561, 0.8166269755358303, 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f1_scores_003d_valid = {'Wake': [0.7986130603483859, 0.7132484651251829, 0.7200546287122085, 0.8459017393027546, 0.8695469798657718, 0.9239532019704433, 0.946169477023992, 0.9518422639100677, 0.9591239834498501, 0.9652665732877076, 0.9669026957000124, 0.9611357839727469, 0.9695176298620272, 0.9709512529609774, 0.9699856058855936, 0.9748308811840967, 0.9709214968800268, 0.9726432991152952, 0.9721896226750596, 0.9755291357893053, 0.9772228126106979, 0.9768060431960847, 0.9760521218524388, 0.9773669110615236, 0.9772831980720842, 0.9772494073523689, 0.9783474979722819, 0.9768146582100071, 0.9784320317061519, 0.9783038869257951, 0.9777439671643904, 0.9777337951509153, 0.9789546347593677, 0.9789578093449999, 0.9780818043624281], 'REM': [0.5452914798206278, 0.6506587335316617, 0.6895439889451866, 0.8186979560938682, 0.8385690938805156, 0.8535791757049891, 0.850438763830599, 0.8682521706304266, 0.8850710900473934, 0.881992337164751, 0.8837390457643622, 0.8749281746791803, 0.8537839823659074, 0.8967691095350669, 0.8978145304193739, 0.8916953693073096, 0.895878101191639, 0.8904716073147257, 0.8836944127708095, 0.8668672433245581, 0.8942962818765817, 0.8856757277809909, 0.8947574334898277, 0.8933951332560836, 0.897951219512195, 0.8996062992125985, 0.8988326848249026, 0.8998644199109045, 0.898820844427539, 0.8989838613269576, 0.8975238095238095, 0.8984674329501916, 0.8969766994030426, 0.9016583108368685, 0.8951157812804047], 'Non REM': [0.4891523067354479, 0.0540278853601859, 0.1305182341650672, 0.587199552822806, 0.6907428571428572, 0.8305537799988378, 0.9045191069128381, 0.9229552703023275, 0.9369888620609081, 0.9368451158316217, 0.9332322096398775, 0.9308284860870931, 0.9410338423442014, 0.937810945273632, 0.9321790826836327, 0.9381083754099214, 0.9404299404299404, 0.9405663312934344, 0.9436379604277523, 0.9418409718102804, 0.9440597245462736, 0.945471325659497, 0.9428060073146058, 0.9490803683656979, 0.9451783185271019, 0.9505087172145815, 0.9421833790143249, 0.9413780570123059, 0.9386677708193158, 0.9536666751572886, 0.9376943810906075, 0.9425127491886881, 0.932213526092722, 0.9518109113137385, 0.9472441147023336], 'Pre REM': [0.11554762435868839, 0.04310833806012478, 0.019536019536019536, 0.19884057971014493, 0.2901049475262369, 0.3119266055045872, 0.4058823529411765, 0.46015180265654654, 0.5074626865671641, 0.4852801519468186, 0.4529240978846952, 0.452560873215785, 0.4439764111204718, 0.4619144602851324, 0.44743481917577793, 0.4395509499136442, 0.4887690925426775, 0.47164179104477616, 0.4610081861266696, 0.45450802799505974, 0.4606007836308228, 0.46689895470383275, 0.47687471935339015, 0.48605947955390333, 0.4630669546436285, 0.4860666971219735, 0.443796835970025, 0.44855789926818773, 0.4096586178184846, 0.5220440881763527, 0.40032349373230897, 0.4475770925110132, 0.380952380952381, 0.481203007518797, 0.45883441258094354], 'Artefakt': [0.19274332885390671, 0.24763799104922923, 0.3209635416666667, 0.49205147615442835, 0.2723112128146453, 0.33595620937393084, 0.34078590785907864, 0.39495467087110764, 0.39450980392156865, 0.4655831739961759, 0.468342644320298, 0.40998363338788874, 0.6127819548872181, 0.49552238805970156, 0.457480673033197, 0.5756327251324308, 0.5678449258836944, 0.5549915397631133, 0.5604584527220631, 0.654130288784419, 0.6385463984425697, 0.684813753581662, 0.6145181476846058, 0.6369087275149901, 0.6212603437301083, 0.6788588149231894, 0.7007407407407407, 0.6398416886543534, 0.6525017135023989, 0.6728307254623044, 0.6680469289164941, 0.6918604651162791, 0.6942028985507246, 0.6874546773023931, 0.662534435261708], 'avg': [0.4282695600234113, 0.3417362826252769, 0.3761232826050297, 0.5885382608168005, 0.5922550182460053, 0.6511937945105577, 0.6895591217135368, 0.7196312356740953, 0.7366312852093768, 0.746993470445415, 0.741028138661849, 0.7258873902685388, 0.7642187641159651, 0.752593631222902, 0.740978942239515, 0.7639636601894806, 0.7727687113855957, 0.766062913706269, 0.7641977269444709, 0.7785751335407245, 0.7829452002213891, 0.7919331609844135, 0.7810016859389737, 0.7885621239504397, 0.7809480068970236, 0.7984579871649424, 0.7927802277044551, 0.7812913446111518, 0.7756161956547781, 0.8051658474097397, 0.7762665160855221, 0.7916303069834175, 0.7766600279516476, 0.8002169432633593, 0.7883621096375636]}
f1_scores_003e_valid = {'Wake': [0.6438713917570349, 0.7011950165268243, 0.19955691888207225, 0.7256888524494158, 0.8306592124600396, 0.8722392442231585, 0.9141733342298124, 0.9472026580372749, 0.9499813826486284, 0.9506403128026688, 0.9569846373704894, 0.9574932170027132, 0.9600573682323412, 0.9625938737834495, 0.9616155169051291, 0.9606813342796311, 0.9616207329361642, 0.9662877786589126, 0.9637677300243206, 0.9588251826869028, 0.9668159212785162, 0.9670955457085402, 0.9668864729994654, 0.9658557481214414, 0.9703093590450608, 0.9734150606772536, 0.9712507778469198, 0.9678038038749089, 0.9710814783332447, 0.9720483641536273, 0.9723443483193873, 0.971516633010947, 0.9691018132080105, 0.9728321548195012], 'REM': [0.022836843440751077, 0.028254288597376383, 0.16538663861125724, 0.558894061221611, 0.8439688715953307, 0.8655903520715814, 0.861779722830051, 0.8948797517455392, 0.8688035780842341, 0.8110447146095199, 0.8725190839694656, 0.8718544328300426, 0.8674471299093656, 0.8869769260852562, 0.8778403573509419, 0.8507795100222717, 0.8808349146110057, 0.8886726352666406, 0.8436599423631124, 0.8704722169542956, 0.8691999236203933, 0.8851047171657859, 0.8550670640834575, 0.8568249258160238, 0.8698912635920508, 0.8942493245851023, 0.8824640967498112, 0.8915848257269401, 0.8895752895752896, 0.8884652049571019, 0.8881239242685026, 0.8823079862437905, 0.8930072602216279, 0.8841234010534237], 'Non REM': [0.11220247612956279, 0.05950991831971996, 0.4092446000238673, 0.38363812914711126, 0.6303374243615603, 0.7472225689676695, 0.8032319391634981, 0.904359106018431, 0.8908256631641824, 0.9330246913580246, 0.9306777030522504, 0.9225924954080293, 0.9366189937236342, 0.9388825251368207, 0.9339049103663289, 0.9291829201246171, 0.9200295016331262, 0.9339135873247512, 0.9240140734128027, 0.9245681686123289, 0.933669360076905, 0.9401291584120205, 0.943256958326926, 0.9417662938681064, 0.9378061987519983, 0.9411097359735973, 0.936295227296155, 0.939005849460149, 0.9419070101611413, 0.9433807929245164, 0.9409226343367924, 0.9417220590508579, 0.9346804511278196, 0.9382665048669024], 'Pre REM': [0.03155680224403927, 0.0022271714922048997, 0.007692307692307692, 0.06753812636165578, 0.3008849557522124, 0.34635793535938253, 0.2597325408618128, 0.45390693590869186, 0.35899306822327615, 0.5172964342735498, 0.47064137308039755, 0.47084048027444253, 0.49114631873252557, 0.5261627906976745, 0.49297094657919405, 0.45897542690545606, 0.4284559417946645, 0.4837126282909416, 0.399845619451949, 0.47818499127399644, 0.4614100959532749, 0.4844020797227037, 0.49953746530989834, 0.47193990278391523, 0.4603514787826832, 0.46797153024911037, 0.43229604709840197, 0.4713715046604527, 0.49859418931583893, 0.48171846435100546, 0.4802513464991023, 0.4687083888149135, 0.4553571428571429, 0.4368932038834951], 'Artefakt': [0.2830349531116795, 0.43014394580863674, 0.42789820923656924, 0.40393667094565683, 0.4562078922040424, 0.4189991518235794, 0.4281318681318682, 0.47859922178988334, 0.34046890927624873, 0.3431708991077557, 0.4528819762122598, 0.4366505918456817, 0.45462878093492204, 0.4901185770750988, 0.47053231939163487, 0.5118924508790073, 0.4957605985037407, 0.5510662177328844, 0.5651162790697674, 0.5101298701298701, 0.5876662636033858, 0.5446478092068774, 0.595561035758323, 0.5742806811509102, 0.5995055624227441, 0.654295532646048, 0.6175528507367072, 0.5687645687645688, 0.6360655737704919, 0.6419919246298789, 0.6392447741065409, 0.6308196721311475, 0.6531165311653115, 0.6285714285714286], 'avg': [0.2187004933366135, 0.24426606814895244, 0.24195573488921474, 0.42793916802509, 0.6124116712746371, 0.6500818504890743, 0.6534098810434085, 0.7357895346999641, 0.681814520279314, 0.7110354104303037, 0.7367409547369727, 0.7318862434721818, 0.7419797183065577, 0.7609469385556599, 0.7473728101186459, 0.7423023284421966, 0.7373403378957402, 0.764730569454826, 0.7392807288643903, 0.7484360859314787, 0.7637523129064951, 0.7642758620431855, 0.772061799295614, 0.7621335103480793, 0.7675727725189074, 0.7862082368262222, 0.767971799945599, 0.7677061104974039, 0.7874447082312013, 0.7855209502032261, 0.7841774055060651, 0.7790149478503313, 0.7810526397159825, 0.7721373386389502]}
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f1_scores_006b_train = {'Wake': [0.671187161228611, 0.7736797720349422, 0.8092740042350551, 0.868999069900551, 0.9200443620776722, 0.9455887860467603, 0.9536779285218938, 0.9562648302962027, 0.9604987472711023, 0.9633808324982279, 0.9675940744288097, 0.9688429186228481, 0.9704172663276801, 0.9725349910078974, 0.9744282317019465, 0.9762750362862466, 0.9777015269473535, 0.9785752583528583, 0.979005221753059, 0.9804129013298067, 0.9810565608864766, 0.9815682767576043, 0.9821977364853273, 0.9827205512955298, 0.983597556790886, 0.9840264045811066, 0.9847281462960239, 0.9854598497199104, 0.9858928444486933, 0.9867881593659885, 0.9871719130347385, 0.9877186015384841, 0.9882765383672469, 0.9884020871339179, 0.9891881420230362, 0.9896447636539297, 0.9900251577878801, 0.9902873578719971, 0.9905258949877741, 0.9909232225257076, 0.9913509747937087, 0.9916058707407089, 0.9915487431023912, 0.9919215601640212, 0.9921956627309786, 0.9922458948855275, 0.9928685288275253, 0.9929457588705366, 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f1_scores_006_valid = {'Wake': [0.8167866560009481, 0.8136802715168392, 0.8140994258756509, 0.9105244888176273, 0.9378243580238592, 0.9364142520356468, 0.9305886243386243, 0.936632061480383, 0.9309808173186215, 0.9560611323376172, 0.9475517775958473, 0.9633948033076151, 0.9724115094372505, 0.9747209168253524, 0.9686261501126107, 0.9734451796661978, 0.9729663948818692, 0.9757008029299901, 0.9722549767635431, 0.9774291604066945, 0.9794556628621598, 0.9780980840217964, 0.9787748663570239, 0.979918689170876, 0.9771176234443998, 0.9791236120354478, 0.9799169949352842, 0.9775249431122004, 0.9800953872687915, 0.9790094422408648, 0.9795731169518435, 0.9786187389096788, 0.9791505520005624, 0.9779627385906687, 0.9793888703414102, 0.9797903268434499, 0.9787316182922121, 0.97926100822864, 0.9787899006215542, 0.9781587435142336, 0.9787084533764316, 0.9787622104137073, 0.9782459836713194, 0.978663672094329, 0.9784162554222793, 0.9782120552943654, 0.9790894885615465, 0.9790546476893339, 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f1_scores_006b_valid = {'Wake': [0.8483756717147045, 0.843067694391589, 0.8097431763215478, 0.7395030552030041, 0.7245968773995393, 0.7863708311349309, 0.8782614091148823, 0.9330533473326333, 0.9523664277362615, 0.9626032557978779, 0.9676380447768946, 0.9692278452580471, 0.9735694302048761, 0.9730005812007961, 0.9760686755430974, 0.9776256909713084, 0.9785753012574986, 0.9752264808362369, 0.9787316797199583, 0.979583934668591, 0.9807347512743892, 0.9812934447866256, 0.9804905633703258, 0.9812645958806693, 0.9807465757037611, 0.9805472086035111, 0.98113539267568, 0.9802157641353622, 0.9789817186567951, 0.9814954749143308, 0.9815831758868504, 0.9808247132502991, 0.98065447376296, 0.9814003413750022, 0.9812050565254848, 0.9805601085672994, 0.9800973176172992, 0.9807394366197183, 0.9811373873873873, 0.9804377300019369, 0.9803148913712727, 0.9805915785398813, 0.9802742616033755, 0.9803115001933692, 0.9800235844905574, 0.979253988940157, 0.980331390537233, 0.9805643738977073, 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f1_scores_006c_valid = {'Wake': [0.8618463925523662, 0.8696090635488395, 0.8205569324974487, 0.7489534205924434, 0.8367503692762186, 0.9225929081732976, 0.927912685774947, 0.9501896161760454, 0.9676521254937444, 0.9754575267110971, 0.9746921050758169, 0.9757899343544856, 0.9758854822903542, 0.9773763591722202, 0.9760144337920543, 0.9773163896575428, 0.9786192321239873, 0.9799391452023776, 0.9806678383128296, 0.9806902362812906, 0.9801705682097358, 0.9809206701548544, 0.9802884025721444, 0.980900888249131, 0.9823445204390011, 0.9819735847065652, 0.9809778779766098, 0.9812053981488024, 0.9817900311416857, 0.9820713970912296, 0.9822017604205253, 0.9820073095305032, 0.9812505511949907, 0.981505874914379, 0.9814889975118671, 0.9823347745521926, 0.9822433037682284, 0.9821919740461245, 0.9817028027498679, 0.9827312775330396, 0.9821951133519021, 0.9815147353966409, 0.9823157078124725, 0.9819018836097836, 0.9820801186419731, 0.981888722334891, 0.9814655399588904, 0.9819666074350358, 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f1_scores_006d_valid = {'Wake': [0.8395779402637117, 0.7756621251892282, 0.7311085033161483, 0.7514762000401687, 0.8343441370861628, 0.8669003695890107, 0.9370517645388056, 0.9352746891697936, 0.9443139422412602, 0.9512332628611699, 0.9596058084061706, 0.9563431276980923, 0.9683043341818055, 0.9646421392991349, 0.9703792498393009, 0.9698324803046448, 0.9778089395267309, 0.9784590475520045, 0.9804859045190537, 0.9803976624607339, 0.9811685006756875, 0.9813919980320149, 0.982907644719399, 0.9818252331341336, 0.9826977401129944, 0.9831315107609329, 0.982827321132621, 0.9833944436609786, 0.983229583649285, 0.983715426189551, 0.9835550138660731, 0.9827537660143602, 0.9844045285151628, 0.9837409868514068, 0.9840230855855855, 0.9843551349060602, 0.9839935270526982, 0.9837593560231607, 0.9821501373748116, 0.9837948284365532, 0.9832056777457663, 0.9835048639503736, 0.9832451499118166, 0.9834230735281136, 0.9829177233023386, 0.9831793497425769, 0.9828767123287672, 0.9840552328147899, 0.9835851454391414, 0.9834640959786088], 'REM': [0.05846325167037862, 0.5817993259009592, 0.48363009764503156, 0.30774091627172195, 0.7217715413286561, 0.70368782161235, 0.8215127490893508, 0.8451413085559426, 0.8528565556925605, 0.8709363441697489, 0.8673334591432346, 0.844158539115999, 0.8891891891891892, 0.8882011605415862, 0.8990059642147117, 0.8820690986296081, 0.9067420266451353, 0.9128824790489183, 0.9055056618508396, 0.910737875070768, 0.9139660493827161, 0.9095400988217408, 0.9157057654075547, 0.9173666731783552, 0.917772934416485, 0.9194460698264092, 0.9202312138728324, 0.9184195006880282, 0.9155038759689922, 0.9191176470588236, 0.9186434032542639, 0.9200940070505288, 0.9220524872698785, 0.9224505622334238, 0.9167938931297709, 0.9229867083659109, 0.920591669910471, 0.9210168833689113, 0.9165526675786594, 0.9188869153345174, 0.9183317167798254, 0.9198905822586948, 0.9211698624830525, 0.9191820837390458, 0.9181467181467182, 0.9183673469387755, 0.9206903238316851, 0.922650840751731, 0.9204434934837581, 0.921011673151751], 'Non REM': [0.7532730338801165, 0.4679057867409135, 0.22788448974258457, 0.3711820188641381, 0.6800566661419801, 0.7624413682645006, 0.8951810117477823, 0.8842360795023517, 0.9113554588229374, 0.9140596200634047, 0.9349014077413181, 0.9349888254283586, 0.9491533927003637, 0.9495510367424528, 0.958806000050084, 0.952454793287147, 0.9621138816524004, 0.9606460646064606, 0.9627125385303769, 0.9588965655351958, 0.9654828162827264, 0.9634648198333835, 0.9650294306771529, 0.9651647008951949, 0.966512184182987, 0.9662123296236088, 0.9661306532663317, 0.9658680096736393, 0.9663726571113561, 0.9672421978461307, 0.9676165803108808, 0.9651376146788991, 0.9675303704071746, 0.967563389762896, 0.966498531958142, 0.9675264797507789, 0.9665546808403951, 0.9670263788968825, 0.9656749434543782, 0.9673074997496746, 0.9660842237586423, 0.9665496489468406, 0.9665248936702527, 0.9660503865302343, 0.9650391802290536, 0.9647943831494484, 0.9663247435576683, 0.9672993938789254, 0.9657798486898143, 0.9652676551897461], 'Pre REM': [0.021505376344086023, 0.06759443339960239, 0.0935672514619883, 0.04539722572509458, 0.22198952879581152, 0.2914757103574703, 0.37134778510838834, 0.3889340927583401, 0.4758893280632411, 0.43087362171331633, 0.4683734939759036, 0.4592833876221498, 0.5193855157278712, 0.52356780275562, 0.5024077046548957, 0.47396226415094345, 0.5079617834394905, 0.5380029806259314, 0.570281124497992, 0.47719869706840395, 0.5128205128205128, 0.4984615384615384, 0.5342362678705793, 0.5227447956823439, 0.5481798715203426, 0.5540443808160345, 0.5436893203883496, 0.5397520058351568, 0.5475017593244195, 0.5541310541310541, 0.5331302361005331, 0.5482014388489208, 0.5248447204968943, 0.5354449472096531, 0.5382963493199714, 0.544649446494465, 0.5455871626549964, 0.5345080763582967, 0.5260837619397503, 0.5413313825896123, 0.5384615384615384, 0.5449358059914409, 0.5340236686390533, 0.5347670250896057, 0.5372076541459958, 0.5219818562456385, 0.537117903930131, 0.5443873807776962, 0.5374570446735396, 0.5404644616467277], 'Artefakt': [0.0028011204481792713, 0.2966507177033493, 0.4111986001749781, 0.2077562326869806, 0.22045454545454543, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.007476635514018692, 0.0, 0.03308823529411765, 0.44755244755244755, 0.5278058645096056, 0.6141078838174274, 0.5644768856447689, 0.5688073394495413, 0.7036649214659686, 0.7565789473684211, 0.7504835589941973, 0.7032136105860112, 0.7957610789980732, 0.7575221238938054, 0.7695431472081218, 0.7767527675276754, 0.8007483629560337, 0.7862939585211903, 0.7540425531914894, 0.7909270216962524, 0.7669990933816864, 0.7837837837837839, 0.7870722433460077, 0.7763794772507261, 0.7760368663594469, 0.7521663778162913, 0.7868561278863233, 0.7847358121330725, 0.7728873239436619, 0.7853881278538813, 0.781305114638448, 0.7768595041322315, 0.7890255439924314, 0.7586206896551724, 0.7627416520210896, 0.7765765765765766, 0.76269621421976], 'avg': [0.33512414452129435, 0.4379224777868105, 0.3894777884681461, 0.3367105187176208, 0.5357232837614311, 0.5249010539646664, 0.6050186620968654, 0.6107172339972856, 0.6368830569639998, 0.633420569761528, 0.6460428338533253, 0.6389547759729199, 0.6652064863598459, 0.6666877549705625, 0.6661197837517985, 0.6622813743332923, 0.7604358157632409, 0.7835592872685841, 0.806618622643138, 0.7783415371559741, 0.7884490437222368, 0.8113046753229292, 0.8308916112086214, 0.8275169923768451, 0.823675268163764, 0.8437190740050118, 0.8340801265107881, 0.835395421413185, 0.8378721287163456, 0.8449909376363187, 0.8378478384105883, 0.8340458759568398, 0.8379518256770726, 0.8352397958878133, 0.8378791287554508, 0.8413180025726446, 0.8386213035418575, 0.8364695122013396, 0.8285255776327782, 0.8396353507993363, 0.838163793775769, 0.8375536450182024, 0.8380703405116113, 0.8369455367050895, 0.8360341559912676, 0.8354696960137742, 0.8331260746606848, 0.8362269000488464, 0.836768421772566, 0.8345808200373186]}
f1_scores_006e_valid = {'Wake': [0.7950675943209458, 0.7228878089347435, 0.7181377846146023, 0.7200213114130228, 0.8048306299402557, 0.8773713577185368, 0.9502296695167478, 0.9441228588304783, 0.9434503229723575, 0.9593663203374139, 0.9699237650990358, 0.9524197380031222, 0.9677967571317091, 0.9701539983046059, 0.9682602761027697, 0.9724455247851376, 0.972644908524854, 0.9749150632844543, 0.9744882667183836, 0.9766314087922129, 0.9734472571600417, 0.9744921230834006, 0.9737936336000559, 0.9763796096585828, 0.9728787561876873, 0.9777497016915843, 0.9798643530344402, 0.9783493578496737, 0.9797237937941455, 0.9801903144787577, 0.9807573592959064, 0.9773029772329247, 0.9790066924973582, 0.9804728007733544, 0.9795918367346937, 0.9806835590146326, 0.9808418353908019, 0.9817554847903728, 0.9810962421087801, 0.9809015859604653, 0.9813243982012169, 0.9816901408450704, 0.9817682185699942, 0.9816615384615384, 0.9814622467123215, 0.9817629715940186, 0.9817425630903708, 0.9813843825878875, 0.9818861009303637, 0.9806569279062836], 'REM': [0.05606995884773663, 0.6490175801447777, 0.7113935969868174, 0.5767313019390582, 0.7593488781346239, 0.7459177008491183, 0.8603218756209021, 0.8182996851268752, 0.8550444530344028, 0.8856457417033188, 0.9012908124525437, 0.884949215143121, 0.9008104978772673, 0.906949806949807, 0.9082851637764933, 0.9129852744310576, 0.8936248392430646, 0.9097613040947021, 0.9074180563542266, 0.9115958668197475, 0.899768696993061, 0.9106728538283063, 0.911851705777953, 0.9085979860573199, 0.9117238643126363, 0.9137254901960784, 0.9117131163515884, 0.9108758421559191, 0.9157140089130015, 0.9157957010451587, 0.9147738303242089, 0.9135993800852382, 0.9092652982927297, 0.9173489278752437, 0.9105752800455668, 0.9171356491364254, 0.9186752890456594, 0.9190457567461869, 0.9126289161315432, 0.9139123750960799, 0.9137269938650306, 0.9190785587714118, 0.9177387914230019, 0.9157281553398059, 0.9121725731895223, 0.9176516179035071, 0.9174995100921027, 0.9152086137281293, 0.9129586260733801, 0.9174738473459899], 'Non REM': [0.5282551704048937, 0.1159926383842197, 0.015117609030782237, 0.060458786936236394, 0.5554838709677419, 0.7867411191635976, 0.9144646402932641, 0.9120705124821872, 0.9078402209709975, 0.9348232821590589, 0.941084212390276, 0.916157366986798, 0.9364381067961165, 0.9494027580359593, 0.9390051423043633, 0.9457971307334815, 0.9458135541864459, 0.950569203115638, 0.9529523523823809, 0.952114645228941, 0.9552485358161886, 0.9560919018435853, 0.9524695704657479, 0.956071832224036, 0.9521950724931563, 0.9577161729383508, 0.9597605089185481, 0.9591564536829654, 0.9590810120879947, 0.962066823997019, 0.9621338651377291, 0.9559345947444006, 0.9608795832498497, 0.9614988572720196, 0.9583490625393231, 0.9598801647734365, 0.9619664424495511, 0.9627984730157937, 0.9617579337270998, 0.9611109723505756, 0.961355425439143, 0.962885468041648, 0.9621325361235675, 0.9625507595127086, 0.9634167687729738, 0.9627284286106056, 0.9628874692386089, 0.9622509262040653, 0.9630719361675601, 0.9611126452941029], 'Pre REM': [0.05244122965641953, 0.0, 0.002652519893899204, 0.0, 0.16336056009334887, 0.24898785425101216, 0.44827586206896547, 0.35051546391752575, 0.4183333333333333, 0.4750593824228028, 0.5056497175141244, 0.4078125, 0.45998445998445997, 0.5292397660818714, 0.5052950075642966, 0.5098335854765508, 0.4543429844097995, 0.5454545454545455, 0.5255474452554745, 0.5480427046263345, 0.5752380952380952, 0.557142857142857, 0.5260770975056689, 0.5341246290801186, 0.5372918175235337, 0.5440956651718983, 0.5373563218390804, 0.5383502170767004, 0.546236559139785, 0.5667351129363449, 0.5495300072306579, 0.5476190476190478, 0.5447976878612717, 0.5511049723756907, 0.5395284327323162, 0.5597826086956521, 0.5598320503848845, 0.5364775239498895, 0.5487465181058496, 0.5389657683903859, 0.5310899780541332, 0.5402214022140222, 0.5549964054636951, 0.5497564370215727, 0.5373352855051244, 0.5520110957004161, 0.5369422092172641, 0.5345821325648416, 0.5463552724699221, 0.5384052670080468], 'Artefakt': [0.0018083182640144665, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03202846975088968, 0.15140845070422537, 0.46618106139438087, 0.40476190476190477, 0.29787234042553196, 0.5163551401869159, 0.15488215488215487, 0.5104270109235353, 0.27257799671592775, 0.11070110701107011, 0.3735955056179775, 0.48189762796504376, 0.5658436213991769, 0.34948604992657856, 0.5725338491295939, 0.6388625592417062, 0.669683257918552, 0.6457883369330453, 0.7117988394584138, 0.6343115124153499, 0.6703417861080485, 0.7257731958762886, 0.6541617819460727, 0.7350785340314135, 0.6916488222698073, 0.6384872080088987, 0.7076923076923077, 0.7535353535353535, 0.657243816254417, 0.6526576019777504, 0.671604938271605, 0.7429854096520763, 0.7188208616780046, 0.6947368421052632, 0.6721120186697782, 0.7288888888888889, 0.68, 0.7748917748917747, 0.75, 0.741061755146262, 0.7168458781362008, 0.7436743674367436, 0.744920993227991, 0.7553072625698324], 'avg': [0.286728454298802, 0.2975796054927482, 0.2894603021052202, 0.27144228005766347, 0.45660478782719405, 0.531803606396453, 0.6410641034501539, 0.6352833942122584, 0.7181698783410945, 0.7319313262768998, 0.7231641695763024, 0.7355387920639913, 0.6839823953343416, 0.7732346680591557, 0.7186847172927701, 0.6903525244874594, 0.7280043583964283, 0.7725195487828767, 0.7852499484219285, 0.7475741350787628, 0.7952472868673961, 0.807452459027971, 0.8067750530535955, 0.8041924787906204, 0.8171776699950856, 0.8055197084826522, 0.8118072172503412, 0.8225010133283094, 0.8109834311761999, 0.8319732972977387, 0.8197687768516619, 0.8065886415381021, 0.8203283139187034, 0.8327921823663325, 0.8090576856612633, 0.8140279167195793, 0.8185841111085004, 0.8286125296308636, 0.8246100943502557, 0.817925508780554, 0.8119217628458604, 0.8265528917522083, 0.8193271903160518, 0.83691773304548, 0.8288773748359883, 0.8310431737909619, 0.8231835259549095, 0.8274200845043336, 0.8298385857738435, 0.8305911900248513]} |
# Dynamic programming method
'''
# mainRun(weight,value,capacity,type)
n: number of items
value: value of each item
weight: weight of each item
capacity: capacity of bag
m: memery matrix
type: 'right2left' or 'left2right'
'''
# right to left
def Knapsack(value,weight,capacity,n,m):
jMax = min(weight[n-1]-1,capacity)
for j in range(0,jMax+1):
m[n-1][j] = 0
for j in range(weight[n-1],capacity+1):
m[n-1][j] = value[n-1]
for i in range(n-2,-1,-1):
jMax = min(weight[i]-1,capacity+1)
for j in range(0,jMax+1):
m[i][j] = m[i+1][j]
for j in range(weight[i],capacity+1):
m[i][j] = max(m[i+1][j],m[i+1][j-weight[i]]+value[i])
return m
def Trackback(m,weight,capacity,n,select):
for i in range(0,n-1):
if m[i][capacity] == m[i+1][capacity]:
select[i] = 0
else:
select[i] = 1
capacity = capacity - weight[i]
if m[n-1][capacity] != 0:
select[n-1] = 1
return select
# left to right
def KnapsackL(value,weight,capacity,n,m):
jMax = min(weight[0]-1,capacity)
for j in range(0,jMax+1):
m[0][j] = 0
for j in range(weight[0],capacity+1):
m[0][j] = value[0]
for i in range(1,n,1):
jMax = min(weight[i]-1,capacity+1)
for j in range(0,jMax+1):
m[i][j] = m[i-1][j]
for j in range(weight[i],capacity+1):
m[i][j] = max(m[i-1][j],m[i-1][j-weight[i]]+value[i])
return m
def TrackbackL(m,weight,capacity,n,select):
for i in range(n-1,0,-1):
if m[i][capacity] == m[i-1][capacity]:
select[i] = 0
else:
select[i] = 1
capacity = capacity - weight[i]
if m[0][capacity] != 0:
select[0] = 1
return select
# switch between left2right and right2left
def switchFunc(value,weight,capacity,n,m,Select,type):
if type == 'right2left':
# print('Type: right to left.')
m = Knapsack(value,weight,capacity,n,m)
select = Trackback(m,weight,capacity,n,Select)
else:
# print('Type: left to right.')
m = KnapsackL(value,weight,capacity,n,m)
select = TrackbackL(m,weight,capacity,n,Select)
return m, select
def mainRun(weight = [6,5,4,1,2,3,9,8,7],value = [1,2,3,7,8,9,6,5,4],capacity = 20,type = 'left2right'):
'''
weight = [6,5,4,1,2,3,9,8,7]
value = [1,2,3,7,8,9,6,5,4]
capacity = 20
'''
'''
weight = [3, 5, 1, 4, 2, 6]
value = [2, 3, 4, 2, 5, 1]
capacity = 11
'''
n = len(weight)
try:
n == len(value)
except ValueError:
print("Please check the number of weights and values.")
m = [[-1]*(capacity+1) for _ in range(n)]
select = [0 for _ in range(n)]
(m,select) = switchFunc(value,weight,capacity,n,m,select,type)
maxValue = 0;
for i in range(0,n):
if select[i] == 1:
maxValue = maxValue + value[i]
'''
print("Dymamic programming method is done.")
print("m matrix: ", m)
print("Select: ",select)
print("Maximum value: ",maxValue,"\n")
'''
if __name__ == '__main__':
mainRun(type = 'right2left')
mainRun(type = 'left2rightt')
| """
# mainRun(weight,value,capacity,type)
n: number of items
value: value of each item
weight: weight of each item
capacity: capacity of bag
m: memery matrix
type: 'right2left' or 'left2right'
"""
def knapsack(value, weight, capacity, n, m):
j_max = min(weight[n - 1] - 1, capacity)
for j in range(0, jMax + 1):
m[n - 1][j] = 0
for j in range(weight[n - 1], capacity + 1):
m[n - 1][j] = value[n - 1]
for i in range(n - 2, -1, -1):
j_max = min(weight[i] - 1, capacity + 1)
for j in range(0, jMax + 1):
m[i][j] = m[i + 1][j]
for j in range(weight[i], capacity + 1):
m[i][j] = max(m[i + 1][j], m[i + 1][j - weight[i]] + value[i])
return m
def trackback(m, weight, capacity, n, select):
for i in range(0, n - 1):
if m[i][capacity] == m[i + 1][capacity]:
select[i] = 0
else:
select[i] = 1
capacity = capacity - weight[i]
if m[n - 1][capacity] != 0:
select[n - 1] = 1
return select
def knapsack_l(value, weight, capacity, n, m):
j_max = min(weight[0] - 1, capacity)
for j in range(0, jMax + 1):
m[0][j] = 0
for j in range(weight[0], capacity + 1):
m[0][j] = value[0]
for i in range(1, n, 1):
j_max = min(weight[i] - 1, capacity + 1)
for j in range(0, jMax + 1):
m[i][j] = m[i - 1][j]
for j in range(weight[i], capacity + 1):
m[i][j] = max(m[i - 1][j], m[i - 1][j - weight[i]] + value[i])
return m
def trackback_l(m, weight, capacity, n, select):
for i in range(n - 1, 0, -1):
if m[i][capacity] == m[i - 1][capacity]:
select[i] = 0
else:
select[i] = 1
capacity = capacity - weight[i]
if m[0][capacity] != 0:
select[0] = 1
return select
def switch_func(value, weight, capacity, n, m, Select, type):
if type == 'right2left':
m = knapsack(value, weight, capacity, n, m)
select = trackback(m, weight, capacity, n, Select)
else:
m = knapsack_l(value, weight, capacity, n, m)
select = trackback_l(m, weight, capacity, n, Select)
return (m, select)
def main_run(weight=[6, 5, 4, 1, 2, 3, 9, 8, 7], value=[1, 2, 3, 7, 8, 9, 6, 5, 4], capacity=20, type='left2right'):
"""
weight = [6,5,4,1,2,3,9,8,7]
value = [1,2,3,7,8,9,6,5,4]
capacity = 20
"""
'\n weight = [3, 5, 1, 4, 2, 6]\n value = [2, 3, 4, 2, 5, 1]\n capacity = 11\n '
n = len(weight)
try:
n == len(value)
except ValueError:
print('Please check the number of weights and values.')
m = [[-1] * (capacity + 1) for _ in range(n)]
select = [0 for _ in range(n)]
(m, select) = switch_func(value, weight, capacity, n, m, select, type)
max_value = 0
for i in range(0, n):
if select[i] == 1:
max_value = maxValue + value[i]
'\n print("Dymamic programming method is done.")\n print("m matrix: ", m)\n print("Select: ",select)\n print("Maximum value: ",maxValue,"\n")\n '
if __name__ == '__main__':
main_run(type='right2left')
main_run(type='left2rightt') |
#!/usr/bin/env python2
# Author: Jayden Navarro
# CAPTURE_GROUP: default_project
rgx_default_project = [ 'export CURR_WS=\"(.*)\"' ]
fmt_default_project = 'export CURR_WS=\"%s\"\n'
# CAPTURE_GROUP: projects
rgx_projects = [ r'\"\$CURR_WS\" == \"(.*)\"', 'export CURR_PK=\"(.*)\"',
'export CURR_TYPE=\"(.*)\"' ]
fmt_projects = '''\
if [ "$CURR_WS" == "%s" ]
then
export CURR_PK="%s"
export CURR_TYPE="%s"
fi
'''
| rgx_default_project = ['export CURR_WS="(.*)"']
fmt_default_project = 'export CURR_WS="%s"\n'
rgx_projects = ['\\"\\$CURR_WS\\" == \\"(.*)\\"', 'export CURR_PK="(.*)"', 'export CURR_TYPE="(.*)"']
fmt_projects = 'if [ "$CURR_WS" == "%s" ]\nthen\n export CURR_PK="%s"\n export CURR_TYPE="%s"\nfi\n' |
class Car:
PURCHASE_TYPES = ("LEASE", "CASH")
__sales_list = None
@classmethod
def get_purchase_types(cls):
return cls.PURCHASE_TYPES
@staticmethod
def get_sales_list():
if Car.__sales_list == None:
Car.__sales_list = []
return Car.__sales_list
def __init__(self, maker, model, colour, price, purchase_type):
self.maker = maker
self.model = model
self.colour = colour
self.price = price
self.__secret_cog = "Tshhh"
if (not purchase_type in Car.PURCHASE_TYPES):
raise ValueError(f"{purchase_type} is not a valid purchase type")
else:
self.purchase_type = purchase_type
def get_price(self):
if hasattr(self, "_discount"):
return self.price - (self.price * self._discount)
else:
return self.price
def set_discount(self, amount):
self._discount = amount
class Boat:
def __init__(self, name):
self.name = name
car1 = Car("BMW", "i8", "white", 50000, "CASH")
car2 = Car("Mercedes", "C-class", "black", 28500, "LEASE")
print("Purchase types: ", Car.get_purchase_types())
print(car1.purchase_type)
print(car2.purchase_type)
sales_this_month = Car.get_sales_list()
sales_this_month.append(car1)
sales_this_month.append(car2)
print(sales_this_month)
| class Car:
purchase_types = ('LEASE', 'CASH')
__sales_list = None
@classmethod
def get_purchase_types(cls):
return cls.PURCHASE_TYPES
@staticmethod
def get_sales_list():
if Car.__sales_list == None:
Car.__sales_list = []
return Car.__sales_list
def __init__(self, maker, model, colour, price, purchase_type):
self.maker = maker
self.model = model
self.colour = colour
self.price = price
self.__secret_cog = 'Tshhh'
if not purchase_type in Car.PURCHASE_TYPES:
raise value_error(f'{purchase_type} is not a valid purchase type')
else:
self.purchase_type = purchase_type
def get_price(self):
if hasattr(self, '_discount'):
return self.price - self.price * self._discount
else:
return self.price
def set_discount(self, amount):
self._discount = amount
class Boat:
def __init__(self, name):
self.name = name
car1 = car('BMW', 'i8', 'white', 50000, 'CASH')
car2 = car('Mercedes', 'C-class', 'black', 28500, 'LEASE')
print('Purchase types: ', Car.get_purchase_types())
print(car1.purchase_type)
print(car2.purchase_type)
sales_this_month = Car.get_sales_list()
sales_this_month.append(car1)
sales_this_month.append(car2)
print(sales_this_month) |
def nextGreaterElement(n: int) -> int:
digits = list(reversed([int(d) for d in str(n)]))
result = None
for i in range(len(digits) - 1):
if digits[i] > digits[i+1]:
toSwap = 0
while digits[toSwap] <= digits[i+1]:
toSwap += 1
digits[toSwap], digits[i+1] = digits[i+1], digits[toSwap]
result = digits[0:i+1][::-1] + digits[i+1:len(digits)]
break
if result is None:
return -1
total = int(''.join([str(x) for x in result[::-1]]))
if total >= 2**31:
return -1
else:
return total | def next_greater_element(n: int) -> int:
digits = list(reversed([int(d) for d in str(n)]))
result = None
for i in range(len(digits) - 1):
if digits[i] > digits[i + 1]:
to_swap = 0
while digits[toSwap] <= digits[i + 1]:
to_swap += 1
(digits[toSwap], digits[i + 1]) = (digits[i + 1], digits[toSwap])
result = digits[0:i + 1][::-1] + digits[i + 1:len(digits)]
break
if result is None:
return -1
total = int(''.join([str(x) for x in result[::-1]]))
if total >= 2 ** 31:
return -1
else:
return total |
# sum(of_list) is built in
def mysumli(li):
if li == []:
return 0
else:
return li[0] + mysumli(li[1:])
# print(li[0:-4])
lis = [1, 1, 1]
print(mysumli(lis)) | def mysumli(li):
if li == []:
return 0
else:
return li[0] + mysumli(li[1:])
lis = [1, 1, 1]
print(mysumli(lis)) |
# Determining the Grade using the user's
score = float(input('Enter score between 0.0 and 1.0: '))
if score > 0.0 and score < 1.0:
if score >= 0.9:
print('A')
elif score >= 0.8:
print('B')
elif score >= 0.7:
print('C')
elif score >= 0.6:
print('D')
else:
print('F')
else:
print('Bad score') | score = float(input('Enter score between 0.0 and 1.0: '))
if score > 0.0 and score < 1.0:
if score >= 0.9:
print('A')
elif score >= 0.8:
print('B')
elif score >= 0.7:
print('C')
elif score >= 0.6:
print('D')
else:
print('F')
else:
print('Bad score') |
#
# PySNMP MIB module RIVERSTONE-SYSTEM-RESOURCES-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/RIVERSTONE-SYSTEM-RESOURCES-MIB
# Produced by pysmi-0.3.4 at Mon Apr 29 20:49:28 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
ObjectIdentifier, Integer, OctetString = mibBuilder.importSymbols("ASN1", "ObjectIdentifier", "Integer", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueSizeConstraint, ValueRangeConstraint, SingleValueConstraint, ConstraintsUnion = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "ValueRangeConstraint", "SingleValueConstraint", "ConstraintsUnion")
entPhysicalIndex, = mibBuilder.importSymbols("ENTITY-MIB", "entPhysicalIndex")
riverstoneMibs, = mibBuilder.importSymbols("RIVERSTONE-SMI-MIB", "riverstoneMibs")
ModuleCompliance, NotificationGroup, ObjectGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup", "ObjectGroup")
Counter64, ObjectIdentity, Bits, Counter32, iso, TimeTicks, MibIdentifier, Unsigned32, Gauge32, ModuleIdentity, NotificationType, MibScalar, MibTable, MibTableRow, MibTableColumn, IpAddress, Integer32 = mibBuilder.importSymbols("SNMPv2-SMI", "Counter64", "ObjectIdentity", "Bits", "Counter32", "iso", "TimeTicks", "MibIdentifier", "Unsigned32", "Gauge32", "ModuleIdentity", "NotificationType", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "IpAddress", "Integer32")
TextualConvention, DisplayString = mibBuilder.importSymbols("SNMPv2-TC", "TextualConvention", "DisplayString")
rsSystemResourcesMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 5567, 2, 281))
rsSystemResourcesMIB.setRevisions(('2004-09-14 13:00',))
if mibBuilder.loadTexts: rsSystemResourcesMIB.setLastUpdated('200409141300Z')
if mibBuilder.loadTexts: rsSystemResourcesMIB.setOrganization('Riverstone Networks, Inc')
rsSystemUtilization = ObjectIdentity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5))
if mibBuilder.loadTexts: rsSystemUtilization.setStatus('current')
rsCpuUtl = ObjectIdentity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5))
if mibBuilder.loadTexts: rsCpuUtl.setStatus('current')
rsMemory = ObjectIdentity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10))
if mibBuilder.loadTexts: rsMemory.setStatus('current')
rsUtlSamplingRate = MibScalar((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 25), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(100, 60000))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: rsUtlSamplingRate.setStatus('current')
rsUtlConformance = ObjectIdentity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35))
if mibBuilder.loadTexts: rsUtlConformance.setStatus('current')
rsUtlCPUTable = MibTable((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1), )
if mibBuilder.loadTexts: rsUtlCPUTable.setStatus('current')
rsUtlCPUEntry = MibTableRow((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex"))
if mibBuilder.loadTexts: rsUtlCPUEntry.setStatus('current')
rsUtlCPUSystemUtilization5Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 1), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlCPUSystemUtilization5Sec.setStatus('current')
rsUtlCPUUserUtilization5Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 2), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlCPUUserUtilization5Sec.setStatus('current')
rsUtlCPUSystemUtilization60Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 3), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlCPUSystemUtilization60Sec.setStatus('current')
rsUtlCPUUserUtilization60Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 4), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlCPUUserUtilization60Sec.setStatus('current')
rsUtlCPUSystemUtilization5Min = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 5), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlCPUSystemUtilization5Min.setStatus('current')
rsUtlCPUUserUtilization5Min = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 6), Unsigned32().subtype(subtypeSpec=ValueRangeConstraint(0, 100))).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlCPUUserUtilization5Min.setStatus('current')
rsUtlMemoryTable = MibTable((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1), )
if mibBuilder.loadTexts: rsUtlMemoryTable.setStatus('current')
rsUtlMemoryEntry = MibTableRow((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1), ).setIndexNames((0, "ENTITY-MIB", "entPhysicalIndex"))
if mibBuilder.loadTexts: rsUtlMemoryEntry.setStatus('current')
rsUtlMemoryActivePages5Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 1), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlMemoryActivePages5Sec.setStatus('current')
rsUtlMemoryFreePages5Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 2), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlMemoryFreePages5Sec.setStatus('current')
rsUtlMemoryActivePages60Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 3), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlMemoryActivePages60Sec.setStatus('current')
rsUtlMemoryFreePages60Sec = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 4), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlMemoryFreePages60Sec.setStatus('current')
rsUtlMemoryActivePages5Min = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 5), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlMemoryActivePages5Min.setStatus('current')
rsUtlMemoryFreePages5Min = MibTableColumn((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 6), Unsigned32()).setMaxAccess("readonly")
if mibBuilder.loadTexts: rsUtlMemoryFreePages5Min.setStatus('current')
rsUtlCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 1))
rsUtlGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 2))
rsUtlComplianceV1 = ModuleCompliance((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 1, 1)).setObjects(("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlConfGroupV1"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
rsUtlComplianceV1 = rsUtlComplianceV1.setStatus('current')
rsUtlConfGroupV1 = ObjectGroup((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 2, 1)).setObjects(("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlMemoryActivePages5Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlMemoryFreePages5Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlCPUSystemUtilization5Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlCPUUserUtilization5Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlCPUSystemUtilization60Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlCPUUserUtilization60Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlCPUSystemUtilization5Min"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlCPUUserUtilization5Min"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlMemoryActivePages60Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlMemoryFreePages60Sec"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlMemoryActivePages5Min"), ("RIVERSTONE-SYSTEM-RESOURCES-MIB", "rsUtlMemoryFreePages5Min"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
rsUtlConfGroupV1 = rsUtlConfGroupV1.setStatus('current')
mibBuilder.exportSymbols("RIVERSTONE-SYSTEM-RESOURCES-MIB", rsUtlConfGroupV1=rsUtlConfGroupV1, rsUtlMemoryActivePages5Sec=rsUtlMemoryActivePages5Sec, rsUtlMemoryEntry=rsUtlMemoryEntry, rsMemory=rsMemory, rsUtlCPUSystemUtilization60Sec=rsUtlCPUSystemUtilization60Sec, rsUtlGroups=rsUtlGroups, rsUtlMemoryFreePages5Min=rsUtlMemoryFreePages5Min, rsUtlMemoryActivePages5Min=rsUtlMemoryActivePages5Min, rsUtlConformance=rsUtlConformance, rsUtlMemoryActivePages60Sec=rsUtlMemoryActivePages60Sec, rsUtlComplianceV1=rsUtlComplianceV1, rsUtlCPUEntry=rsUtlCPUEntry, rsUtlMemoryTable=rsUtlMemoryTable, rsCpuUtl=rsCpuUtl, rsUtlCPUUserUtilization60Sec=rsUtlCPUUserUtilization60Sec, rsSystemResourcesMIB=rsSystemResourcesMIB, rsSystemUtilization=rsSystemUtilization, rsUtlSamplingRate=rsUtlSamplingRate, rsUtlCPUUserUtilization5Sec=rsUtlCPUUserUtilization5Sec, rsUtlMemoryFreePages5Sec=rsUtlMemoryFreePages5Sec, rsUtlCPUSystemUtilization5Sec=rsUtlCPUSystemUtilization5Sec, rsUtlCPUUserUtilization5Min=rsUtlCPUUserUtilization5Min, rsUtlCPUTable=rsUtlCPUTable, rsUtlMemoryFreePages60Sec=rsUtlMemoryFreePages60Sec, rsUtlCPUSystemUtilization5Min=rsUtlCPUSystemUtilization5Min, PYSNMP_MODULE_ID=rsSystemResourcesMIB, rsUtlCompliances=rsUtlCompliances)
| (object_identifier, integer, octet_string) = mibBuilder.importSymbols('ASN1', 'ObjectIdentifier', 'Integer', 'OctetString')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_intersection, value_size_constraint, value_range_constraint, single_value_constraint, constraints_union) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsIntersection', 'ValueSizeConstraint', 'ValueRangeConstraint', 'SingleValueConstraint', 'ConstraintsUnion')
(ent_physical_index,) = mibBuilder.importSymbols('ENTITY-MIB', 'entPhysicalIndex')
(riverstone_mibs,) = mibBuilder.importSymbols('RIVERSTONE-SMI-MIB', 'riverstoneMibs')
(module_compliance, notification_group, object_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'NotificationGroup', 'ObjectGroup')
(counter64, object_identity, bits, counter32, iso, time_ticks, mib_identifier, unsigned32, gauge32, module_identity, notification_type, mib_scalar, mib_table, mib_table_row, mib_table_column, ip_address, integer32) = mibBuilder.importSymbols('SNMPv2-SMI', 'Counter64', 'ObjectIdentity', 'Bits', 'Counter32', 'iso', 'TimeTicks', 'MibIdentifier', 'Unsigned32', 'Gauge32', 'ModuleIdentity', 'NotificationType', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'IpAddress', 'Integer32')
(textual_convention, display_string) = mibBuilder.importSymbols('SNMPv2-TC', 'TextualConvention', 'DisplayString')
rs_system_resources_mib = module_identity((1, 3, 6, 1, 4, 1, 5567, 2, 281))
rsSystemResourcesMIB.setRevisions(('2004-09-14 13:00',))
if mibBuilder.loadTexts:
rsSystemResourcesMIB.setLastUpdated('200409141300Z')
if mibBuilder.loadTexts:
rsSystemResourcesMIB.setOrganization('Riverstone Networks, Inc')
rs_system_utilization = object_identity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5))
if mibBuilder.loadTexts:
rsSystemUtilization.setStatus('current')
rs_cpu_utl = object_identity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5))
if mibBuilder.loadTexts:
rsCpuUtl.setStatus('current')
rs_memory = object_identity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10))
if mibBuilder.loadTexts:
rsMemory.setStatus('current')
rs_utl_sampling_rate = mib_scalar((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 25), unsigned32().subtype(subtypeSpec=value_range_constraint(100, 60000))).setMaxAccess('readwrite')
if mibBuilder.loadTexts:
rsUtlSamplingRate.setStatus('current')
rs_utl_conformance = object_identity((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35))
if mibBuilder.loadTexts:
rsUtlConformance.setStatus('current')
rs_utl_cpu_table = mib_table((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1))
if mibBuilder.loadTexts:
rsUtlCPUTable.setStatus('current')
rs_utl_cpu_entry = mib_table_row((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1)).setIndexNames((0, 'ENTITY-MIB', 'entPhysicalIndex'))
if mibBuilder.loadTexts:
rsUtlCPUEntry.setStatus('current')
rs_utl_cpu_system_utilization5_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 1), unsigned32().subtype(subtypeSpec=value_range_constraint(0, 100))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlCPUSystemUtilization5Sec.setStatus('current')
rs_utl_cpu_user_utilization5_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 2), unsigned32().subtype(subtypeSpec=value_range_constraint(0, 100))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlCPUUserUtilization5Sec.setStatus('current')
rs_utl_cpu_system_utilization60_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 3), unsigned32().subtype(subtypeSpec=value_range_constraint(0, 100))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlCPUSystemUtilization60Sec.setStatus('current')
rs_utl_cpu_user_utilization60_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 4), unsigned32().subtype(subtypeSpec=value_range_constraint(0, 100))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlCPUUserUtilization60Sec.setStatus('current')
rs_utl_cpu_system_utilization5_min = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 5), unsigned32().subtype(subtypeSpec=value_range_constraint(0, 100))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlCPUSystemUtilization5Min.setStatus('current')
rs_utl_cpu_user_utilization5_min = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 5, 1, 1, 6), unsigned32().subtype(subtypeSpec=value_range_constraint(0, 100))).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlCPUUserUtilization5Min.setStatus('current')
rs_utl_memory_table = mib_table((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1))
if mibBuilder.loadTexts:
rsUtlMemoryTable.setStatus('current')
rs_utl_memory_entry = mib_table_row((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1)).setIndexNames((0, 'ENTITY-MIB', 'entPhysicalIndex'))
if mibBuilder.loadTexts:
rsUtlMemoryEntry.setStatus('current')
rs_utl_memory_active_pages5_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 1), unsigned32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlMemoryActivePages5Sec.setStatus('current')
rs_utl_memory_free_pages5_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 2), unsigned32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlMemoryFreePages5Sec.setStatus('current')
rs_utl_memory_active_pages60_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 3), unsigned32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlMemoryActivePages60Sec.setStatus('current')
rs_utl_memory_free_pages60_sec = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 4), unsigned32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlMemoryFreePages60Sec.setStatus('current')
rs_utl_memory_active_pages5_min = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 5), unsigned32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlMemoryActivePages5Min.setStatus('current')
rs_utl_memory_free_pages5_min = mib_table_column((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 10, 1, 1, 6), unsigned32()).setMaxAccess('readonly')
if mibBuilder.loadTexts:
rsUtlMemoryFreePages5Min.setStatus('current')
rs_utl_compliances = mib_identifier((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 1))
rs_utl_groups = mib_identifier((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 2))
rs_utl_compliance_v1 = module_compliance((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 1, 1)).setObjects(('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlConfGroupV1'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
rs_utl_compliance_v1 = rsUtlComplianceV1.setStatus('current')
rs_utl_conf_group_v1 = object_group((1, 3, 6, 1, 4, 1, 5567, 2, 281, 5, 35, 2, 1)).setObjects(('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlMemoryActivePages5Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlMemoryFreePages5Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlCPUSystemUtilization5Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlCPUUserUtilization5Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlCPUSystemUtilization60Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlCPUUserUtilization60Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlCPUSystemUtilization5Min'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlCPUUserUtilization5Min'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlMemoryActivePages60Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlMemoryFreePages60Sec'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlMemoryActivePages5Min'), ('RIVERSTONE-SYSTEM-RESOURCES-MIB', 'rsUtlMemoryFreePages5Min'))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
rs_utl_conf_group_v1 = rsUtlConfGroupV1.setStatus('current')
mibBuilder.exportSymbols('RIVERSTONE-SYSTEM-RESOURCES-MIB', rsUtlConfGroupV1=rsUtlConfGroupV1, rsUtlMemoryActivePages5Sec=rsUtlMemoryActivePages5Sec, rsUtlMemoryEntry=rsUtlMemoryEntry, rsMemory=rsMemory, rsUtlCPUSystemUtilization60Sec=rsUtlCPUSystemUtilization60Sec, rsUtlGroups=rsUtlGroups, rsUtlMemoryFreePages5Min=rsUtlMemoryFreePages5Min, rsUtlMemoryActivePages5Min=rsUtlMemoryActivePages5Min, rsUtlConformance=rsUtlConformance, rsUtlMemoryActivePages60Sec=rsUtlMemoryActivePages60Sec, rsUtlComplianceV1=rsUtlComplianceV1, rsUtlCPUEntry=rsUtlCPUEntry, rsUtlMemoryTable=rsUtlMemoryTable, rsCpuUtl=rsCpuUtl, rsUtlCPUUserUtilization60Sec=rsUtlCPUUserUtilization60Sec, rsSystemResourcesMIB=rsSystemResourcesMIB, rsSystemUtilization=rsSystemUtilization, rsUtlSamplingRate=rsUtlSamplingRate, rsUtlCPUUserUtilization5Sec=rsUtlCPUUserUtilization5Sec, rsUtlMemoryFreePages5Sec=rsUtlMemoryFreePages5Sec, rsUtlCPUSystemUtilization5Sec=rsUtlCPUSystemUtilization5Sec, rsUtlCPUUserUtilization5Min=rsUtlCPUUserUtilization5Min, rsUtlCPUTable=rsUtlCPUTable, rsUtlMemoryFreePages60Sec=rsUtlMemoryFreePages60Sec, rsUtlCPUSystemUtilization5Min=rsUtlCPUSystemUtilization5Min, PYSNMP_MODULE_ID=rsSystemResourcesMIB, rsUtlCompliances=rsUtlCompliances) |
string = 'INDIA123DELHI'
#list = list(filter(lambda x: x!= '1' and x!='2' and x!='3',string))
list = list(filter(lambda x: x not in [str(n) for n in range(10)] ,string))
print((''.join(list)))
| string = 'INDIA123DELHI'
list = list(filter(lambda x: x not in [str(n) for n in range(10)], string))
print(''.join(list)) |
def transform(df, index):
df['Latitude'].fillna('0', inplace=True)
df['Longitude'].fillna('0', inplace=True)
return df
| def transform(df, index):
df['Latitude'].fillna('0', inplace=True)
df['Longitude'].fillna('0', inplace=True)
return df |
# GENERATED VERSION FILE
# TIME: Thu Mar 7 20:30:16 2019
__version__ = '0.5.4+a6ee053'
short_version = '0.5.4'
| __version__ = '0.5.4+a6ee053'
short_version = '0.5.4' |
timezone_info = {
"A": "UTC +1",
"ACDT": "UTC +10:30",
"ACST": "UTC +9:30",
"ACT": "UTC -5",
"ACWST": "UTC +8:45",
"ADT": "UTC +4",
"AEDT": "UTC +11",
"AEST": "UTC +10",
"AET": "UTC +10:00 / +11:00",
"AFT": "UTC +4:30",
"AKDT": "UTC -8",
"AKST": "UTC -9",
"ALMT": "UTC +6",
"AMST": "UTC -3",
"AMT": "UTC -4",
"ANAST": "UTC +12",
"ANAT": "UTC +12",
"AQTT": "UTC +5",
"ART": "UTC -3",
"AST": "UTC +3",
"AT": "UTC -4:00 / -3:00",
"AWDT": "UTC +9",
"AWST": "UTC +8",
"AZOST": "UTC +0",
"AZOT": "UTC -1",
"AZST": "UTC +5",
"AZT": "UTC +4",
"AoE": "UTC -12",
"B": "UTC +2",
"BNT": "UTC +8",
"BOT": "UTC -4",
"BRST": "UTC -2",
"BRT": "UTC -3",
"BST": "UTC +6",
"BTT": "UTC +6",
"C": "UTC +3",
"CAST": "UTC +8",
"CAT": "UTC +2",
"CCT": "UTC +6:30",
"CDT": "UTC -5",
"CEST": "UTC +2",
"CET": "UTC +1",
"CHADT": "UTC +13:45",
"CHAST": "UTC +12:45",
"CHOST": "UTC +9",
"CHOT": "UTC +8",
"CHUT": "UTC +10",
"CIDST": "UTC -4",
"CIST": "UTC -5",
"CKT": "UTC -10",
"CLST": "UTC -3",
"CLT": "UTC -4",
"COT": "UTC -5",
"CST": "UTC -6",
"CT": "UTC -6:00 / -5:00",
"CVT": "UTC -1",
"CXT": "UTC +7",
"ChST": "UTC +10",
"D": "UTC +4",
"DAVT": "UTC +7",
"DDUT": "UTC +10",
"E": "UTC +5",
"EASST": "UTC -5",
"EAST": "UTC -6",
"EAT": "UTC +3",
"ECT": "UTC -5",
"EDT": "UTC -4",
"EEST": "UTC +3",
"EET": "UTC +2",
"EGST": "UTC +0",
"EGT": "UTC -1",
"EST": "UTC -5",
"ET": "UTC -5:00 / -4:00",
"F": "UTC +6",
"FET": "UTC +3",
"FJST": "UTC +13",
"FJT": "UTC +12",
"FKST": "UTC -3",
"FKT": "UTC -4",
"FNT": "UTC -2",
"G": "UTC +7",
"GALT": "UTC -6",
"GAMT": "UTC -9",
"GET": "UTC +4",
"GFT": "UTC -3",
"GILT": "UTC +12",
"GMT": "UTC +0",
"GST": "UTC +4",
"GYT": "UTC -4",
"H": "UTC +8",
"HDT": "UTC -9",
"HKT": "UTC +8",
"HOVST": "UTC +8",
"HOVT": "UTC +7",
"HST": "UTC -10",
"I": "UTC +9",
"ICT": "UTC +7",
"IDT": "UTC +3",
"IOT": "UTC +6",
"IRDT": "UTC +4:30",
"IRKST": "UTC +9",
"IRKT": "UTC +8",
"IRST": "UTC +3:30",
"IST": "UTC +5:30",
"JST": "UTC +9",
"K": "UTC +10",
"KGT": "UTC +6",
"KOST": "UTC +11",
"KRAST": "UTC +8",
"KRAT": "UTC +7",
"KST": "UTC +9",
"KUYT": "UTC +4",
"L": "UTC +11",
"LHDT": "UTC +11",
"LHST": "UTC +10:30",
"LINT": "UTC +14",
"M": "UTC +12",
"MAGST": "UTC +12",
"MAGT": "UTC +11",
"MART": "UTC -9:30",
"MAWT": "UTC +5",
"MDT": "UTC -6",
"MHT": "UTC +12",
"MMT": "UTC +6:30",
"MSD": "UTC +4",
"MSK": "UTC +3",
"MST": "UTC -7",
"MT": "UTC -7:00 / -6:00",
"MUT": "UTC +4",
"MVT": "UTC +5",
"MYT": "UTC +8",
"N": "UTC -1",
"NCT": "UTC +11",
"NDT": "UTC -2:30",
"NFT": "UTC +11",
"NOVST": "UTC +7",
"NOVT": "UTC +7",
"NPT": "UTC +5:45",
"NRT": "UTC +12",
"NST": "UTC -3:30",
"NUT": "UTC -11",
"NZDT": "UTC +13",
"NZST": "UTC +12",
"O": "UTC -2",
"OMSST": "UTC +7",
"OMST": "UTC +6",
"ORAT": "UTC +5",
"P": "UTC -3",
"PDT": "UTC -7",
"PET": "UTC -5",
"PETST": "UTC +12",
"PETT": "UTC +12",
"PGT": "UTC +10",
"PHOT": "UTC +13",
"PHT": "UTC +8",
"PKT": "UTC +5",
"PMDT": "UTC -2",
"PMST": "UTC -3",
"PONT": "UTC +11",
"PST": "UTC -8",
"PT": "UTC -8:00 / -7:00",
"PWT": "UTC +9",
"PYST": "UTC -3",
"PYT": "UTC -4",
"Q": "UTC -4",
"QYZT": "UTC +6",
"R": "UTC -5",
"RET": "UTC +4",
"ROTT": "UTC -3",
"S": "UTC -6",
"SAKT": "UTC +11",
"SAMT": "UTC +4",
"SAST": "UTC +2",
"SBT": "UTC +11",
"SCT": "UTC +4",
"SGT": "UTC +8",
"SRET": "UTC +11",
"SRT": "UTC -3",
"SST": "UTC -11",
"SYOT": "UTC +3",
"T": "UTC -7",
"TAHT": "UTC -10",
"TFT": "UTC +5",
"TJT": "UTC +5",
"TKT": "UTC +13",
"TLT": "UTC +9",
"TMT": "UTC +5",
"TOST": "UTC +14",
"TOT": "UTC +13",
"TRT": "UTC +3",
"TVT": "UTC +12",
"U": "UTC -8",
"ULAST": "UTC +9",
"ULAT": "UTC +8",
"UTC": "UTC",
"UYST": "UTC -2",
"UYT": "UTC -3",
"UZT": "UTC +5",
"V": "UTC -9",
"VET": "UTC -4",
"VLAST": "UTC +11",
"VLAT": "UTC +10",
"VOST": "UTC +6",
"VUT": "UTC +11",
"W": "UTC -10",
"WAKT": "UTC +12",
"WARST": "UTC -3",
"WAST": "UTC +2",
"WAT": "UTC +1",
"WEST": "UTC +1",
"WET": "UTC +0",
"WFT": "UTC +12",
"WGST": "UTC -2",
"WGT": "UTC -3",
"WIB": "UTC +7",
"WIT": "UTC +9",
"WITA": "UTC +8",
"WST": "UTC +14",
"WT": "UTC +0",
"X": "UTC -11",
"Y": "UTC -12",
"YAKST": "UTC +10",
"YAKT": "UTC +9",
"YAPT": "UTC +10",
"YEKST": "UTC +6",
"YEKT": "UTC +5",
"Z": "UTC +0"
} | timezone_info = {'A': 'UTC +1', 'ACDT': 'UTC +10:30', 'ACST': 'UTC +9:30', 'ACT': 'UTC -5', 'ACWST': 'UTC +8:45', 'ADT': 'UTC +4', 'AEDT': 'UTC +11', 'AEST': 'UTC +10', 'AET': 'UTC +10:00 / +11:00', 'AFT': 'UTC +4:30', 'AKDT': 'UTC -8', 'AKST': 'UTC -9', 'ALMT': 'UTC +6', 'AMST': 'UTC -3', 'AMT': 'UTC -4', 'ANAST': 'UTC +12', 'ANAT': 'UTC +12', 'AQTT': 'UTC +5', 'ART': 'UTC -3', 'AST': 'UTC +3', 'AT': 'UTC -4:00 / -3:00', 'AWDT': 'UTC +9', 'AWST': 'UTC +8', 'AZOST': 'UTC +0', 'AZOT': 'UTC -1', 'AZST': 'UTC +5', 'AZT': 'UTC +4', 'AoE': 'UTC -12', 'B': 'UTC +2', 'BNT': 'UTC +8', 'BOT': 'UTC -4', 'BRST': 'UTC -2', 'BRT': 'UTC -3', 'BST': 'UTC +6', 'BTT': 'UTC +6', 'C': 'UTC +3', 'CAST': 'UTC +8', 'CAT': 'UTC +2', 'CCT': 'UTC +6:30', 'CDT': 'UTC -5', 'CEST': 'UTC +2', 'CET': 'UTC +1', 'CHADT': 'UTC +13:45', 'CHAST': 'UTC +12:45', 'CHOST': 'UTC +9', 'CHOT': 'UTC +8', 'CHUT': 'UTC +10', 'CIDST': 'UTC -4', 'CIST': 'UTC -5', 'CKT': 'UTC -10', 'CLST': 'UTC -3', 'CLT': 'UTC -4', 'COT': 'UTC -5', 'CST': 'UTC -6', 'CT': 'UTC -6:00 / -5:00', 'CVT': 'UTC -1', 'CXT': 'UTC +7', 'ChST': 'UTC +10', 'D': 'UTC +4', 'DAVT': 'UTC +7', 'DDUT': 'UTC +10', 'E': 'UTC +5', 'EASST': 'UTC -5', 'EAST': 'UTC -6', 'EAT': 'UTC +3', 'ECT': 'UTC -5', 'EDT': 'UTC -4', 'EEST': 'UTC +3', 'EET': 'UTC +2', 'EGST': 'UTC +0', 'EGT': 'UTC -1', 'EST': 'UTC -5', 'ET': 'UTC -5:00 / -4:00', 'F': 'UTC +6', 'FET': 'UTC +3', 'FJST': 'UTC +13', 'FJT': 'UTC +12', 'FKST': 'UTC -3', 'FKT': 'UTC -4', 'FNT': 'UTC -2', 'G': 'UTC +7', 'GALT': 'UTC -6', 'GAMT': 'UTC -9', 'GET': 'UTC +4', 'GFT': 'UTC -3', 'GILT': 'UTC +12', 'GMT': 'UTC +0', 'GST': 'UTC +4', 'GYT': 'UTC -4', 'H': 'UTC +8', 'HDT': 'UTC -9', 'HKT': 'UTC +8', 'HOVST': 'UTC +8', 'HOVT': 'UTC +7', 'HST': 'UTC -10', 'I': 'UTC +9', 'ICT': 'UTC +7', 'IDT': 'UTC +3', 'IOT': 'UTC +6', 'IRDT': 'UTC +4:30', 'IRKST': 'UTC +9', 'IRKT': 'UTC +8', 'IRST': 'UTC +3:30', 'IST': 'UTC +5:30', 'JST': 'UTC +9', 'K': 'UTC +10', 'KGT': 'UTC +6', 'KOST': 'UTC +11', 'KRAST': 'UTC +8', 'KRAT': 'UTC +7', 'KST': 'UTC +9', 'KUYT': 'UTC +4', 'L': 'UTC +11', 'LHDT': 'UTC +11', 'LHST': 'UTC +10:30', 'LINT': 'UTC +14', 'M': 'UTC +12', 'MAGST': 'UTC +12', 'MAGT': 'UTC +11', 'MART': 'UTC -9:30', 'MAWT': 'UTC +5', 'MDT': 'UTC -6', 'MHT': 'UTC +12', 'MMT': 'UTC +6:30', 'MSD': 'UTC +4', 'MSK': 'UTC +3', 'MST': 'UTC -7', 'MT': 'UTC -7:00 / -6:00', 'MUT': 'UTC +4', 'MVT': 'UTC +5', 'MYT': 'UTC +8', 'N': 'UTC -1', 'NCT': 'UTC +11', 'NDT': 'UTC -2:30', 'NFT': 'UTC +11', 'NOVST': 'UTC +7', 'NOVT': 'UTC +7', 'NPT': 'UTC +5:45', 'NRT': 'UTC +12', 'NST': 'UTC -3:30', 'NUT': 'UTC -11', 'NZDT': 'UTC +13', 'NZST': 'UTC +12', 'O': 'UTC -2', 'OMSST': 'UTC +7', 'OMST': 'UTC +6', 'ORAT': 'UTC +5', 'P': 'UTC -3', 'PDT': 'UTC -7', 'PET': 'UTC -5', 'PETST': 'UTC +12', 'PETT': 'UTC +12', 'PGT': 'UTC +10', 'PHOT': 'UTC +13', 'PHT': 'UTC +8', 'PKT': 'UTC +5', 'PMDT': 'UTC -2', 'PMST': 'UTC -3', 'PONT': 'UTC +11', 'PST': 'UTC -8', 'PT': 'UTC -8:00 / -7:00', 'PWT': 'UTC +9', 'PYST': 'UTC -3', 'PYT': 'UTC -4', 'Q': 'UTC -4', 'QYZT': 'UTC +6', 'R': 'UTC -5', 'RET': 'UTC +4', 'ROTT': 'UTC -3', 'S': 'UTC -6', 'SAKT': 'UTC +11', 'SAMT': 'UTC +4', 'SAST': 'UTC +2', 'SBT': 'UTC +11', 'SCT': 'UTC +4', 'SGT': 'UTC +8', 'SRET': 'UTC +11', 'SRT': 'UTC -3', 'SST': 'UTC -11', 'SYOT': 'UTC +3', 'T': 'UTC -7', 'TAHT': 'UTC -10', 'TFT': 'UTC +5', 'TJT': 'UTC +5', 'TKT': 'UTC +13', 'TLT': 'UTC +9', 'TMT': 'UTC +5', 'TOST': 'UTC +14', 'TOT': 'UTC +13', 'TRT': 'UTC +3', 'TVT': 'UTC +12', 'U': 'UTC -8', 'ULAST': 'UTC +9', 'ULAT': 'UTC +8', 'UTC': 'UTC', 'UYST': 'UTC -2', 'UYT': 'UTC -3', 'UZT': 'UTC +5', 'V': 'UTC -9', 'VET': 'UTC -4', 'VLAST': 'UTC +11', 'VLAT': 'UTC +10', 'VOST': 'UTC +6', 'VUT': 'UTC +11', 'W': 'UTC -10', 'WAKT': 'UTC +12', 'WARST': 'UTC -3', 'WAST': 'UTC +2', 'WAT': 'UTC +1', 'WEST': 'UTC +1', 'WET': 'UTC +0', 'WFT': 'UTC +12', 'WGST': 'UTC -2', 'WGT': 'UTC -3', 'WIB': 'UTC +7', 'WIT': 'UTC +9', 'WITA': 'UTC +8', 'WST': 'UTC +14', 'WT': 'UTC +0', 'X': 'UTC -11', 'Y': 'UTC -12', 'YAKST': 'UTC +10', 'YAKT': 'UTC +9', 'YAPT': 'UTC +10', 'YEKST': 'UTC +6', 'YEKT': 'UTC +5', 'Z': 'UTC +0'} |
# open file
func=open("func.txt","r")
# split it with \n
func_list=func.read().split("\n")
new_list=[]
# remove len 0 to 31 and store it in new list
for a in range(len(func_list)):
b=func_list[a][36:]
if len(b)>0:
new_list.append(b)
# short it with sorting algorithm
new_list=sorted(new_list)
# sorting the number behind ')' and print new_list[a]
for a in range(len(new_list)):
b=new_list[a].split(")")
new_list[a]=b[0]
# make new_list[a] to int
for a in range(len(new_list)):
new_list[a]=int(new_list[a])
new_list=sorted(new_list)
#for i in range(len(new_list)):
# print(str(new_list[i])+"\n")
file=open("func.txt","r")
file=file.read()
var_decode=[]
for i in new_list:
# if there exist text i shomewhere in file_list
if file.find(str(i))!=-1:
# find the text and store it in var_decode
print(file[file.find(str(i))+29:file.find(str(i))+30],end="")
#file_run=open("run.txt","r")
#file_run=file_run.read().replace("();","")
#file_run=file_run.split("\n")
#true_decode=[]
#for i in file_run:
# # if there exist text i shomewhere in file_list
# if file.find(str(i))!=-1:
# # find the text and store it in var_decode
# print(file[file.find(str(i)):file.find(str(i))+60])
#var_num=0
#for i in true_decode:
# print(true_decode[var_num][true_decode[var_num].find("'")+1:true_decode[var_num].find(str("'"))+2],end="")
# var_num+=1 | func = open('func.txt', 'r')
func_list = func.read().split('\n')
new_list = []
for a in range(len(func_list)):
b = func_list[a][36:]
if len(b) > 0:
new_list.append(b)
new_list = sorted(new_list)
for a in range(len(new_list)):
b = new_list[a].split(')')
new_list[a] = b[0]
for a in range(len(new_list)):
new_list[a] = int(new_list[a])
new_list = sorted(new_list)
file = open('func.txt', 'r')
file = file.read()
var_decode = []
for i in new_list:
if file.find(str(i)) != -1:
print(file[file.find(str(i)) + 29:file.find(str(i)) + 30], end='') |
#
# PySNMP MIB module CPM-NORTEL-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CPM-NORTEL-MIB
# Produced by pysmi-0.3.4 at Wed May 1 12:27:09 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, ObjectIdentifier, OctetString = mibBuilder.importSymbols("ASN1", "Integer", "ObjectIdentifier", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsUnion, ValueRangeConstraint, ValueSizeConstraint, SingleValueConstraint, ConstraintsIntersection = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsUnion", "ValueRangeConstraint", "ValueSizeConstraint", "SingleValueConstraint", "ConstraintsIntersection")
ModuleCompliance, NotificationGroup = mibBuilder.importSymbols("SNMPv2-CONF", "ModuleCompliance", "NotificationGroup")
MibIdentifier, Integer32, Bits, IpAddress, enterprises, Counter64, NotificationType, ModuleIdentity, TimeTicks, Unsigned32, ObjectIdentity, Gauge32, Counter32, MibScalar, MibTable, MibTableRow, MibTableColumn, iso = mibBuilder.importSymbols("SNMPv2-SMI", "MibIdentifier", "Integer32", "Bits", "IpAddress", "enterprises", "Counter64", "NotificationType", "ModuleIdentity", "TimeTicks", "Unsigned32", "ObjectIdentity", "Gauge32", "Counter32", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "iso")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
nortel = ModuleIdentity((1, 3, 6, 1, 4, 1, 562))
if mibBuilder.loadTexts: nortel.setLastUpdated('9906231337Z')
if mibBuilder.loadTexts: nortel.setOrganization('Nortel Networks')
if mibBuilder.loadTexts: nortel.setContactInfo('Nortel Customer Support Nortel Networks E-Mail: joedev@nortel.ca')
if mibBuilder.loadTexts: nortel.setDescription('')
dialaccess = MibIdentifier((1, 3, 6, 1, 4, 1, 562, 14))
mibBuilder.exportSymbols("CPM-NORTEL-MIB", dialaccess=dialaccess, nortel=nortel, PYSNMP_MODULE_ID=nortel)
| (integer, object_identifier, octet_string) = mibBuilder.importSymbols('ASN1', 'Integer', 'ObjectIdentifier', 'OctetString')
(named_values,) = mibBuilder.importSymbols('ASN1-ENUMERATION', 'NamedValues')
(constraints_union, value_range_constraint, value_size_constraint, single_value_constraint, constraints_intersection) = mibBuilder.importSymbols('ASN1-REFINEMENT', 'ConstraintsUnion', 'ValueRangeConstraint', 'ValueSizeConstraint', 'SingleValueConstraint', 'ConstraintsIntersection')
(module_compliance, notification_group) = mibBuilder.importSymbols('SNMPv2-CONF', 'ModuleCompliance', 'NotificationGroup')
(mib_identifier, integer32, bits, ip_address, enterprises, counter64, notification_type, module_identity, time_ticks, unsigned32, object_identity, gauge32, counter32, mib_scalar, mib_table, mib_table_row, mib_table_column, iso) = mibBuilder.importSymbols('SNMPv2-SMI', 'MibIdentifier', 'Integer32', 'Bits', 'IpAddress', 'enterprises', 'Counter64', 'NotificationType', 'ModuleIdentity', 'TimeTicks', 'Unsigned32', 'ObjectIdentity', 'Gauge32', 'Counter32', 'MibScalar', 'MibTable', 'MibTableRow', 'MibTableColumn', 'iso')
(display_string, textual_convention) = mibBuilder.importSymbols('SNMPv2-TC', 'DisplayString', 'TextualConvention')
nortel = module_identity((1, 3, 6, 1, 4, 1, 562))
if mibBuilder.loadTexts:
nortel.setLastUpdated('9906231337Z')
if mibBuilder.loadTexts:
nortel.setOrganization('Nortel Networks')
if mibBuilder.loadTexts:
nortel.setContactInfo('Nortel Customer Support Nortel Networks E-Mail: joedev@nortel.ca')
if mibBuilder.loadTexts:
nortel.setDescription('')
dialaccess = mib_identifier((1, 3, 6, 1, 4, 1, 562, 14))
mibBuilder.exportSymbols('CPM-NORTEL-MIB', dialaccess=dialaccess, nortel=nortel, PYSNMP_MODULE_ID=nortel) |
#! /usr/bin/python3
input = open("input/01.txt").read()
floor = input.count("(") - input.count(")")
print(floor)
floor = 0
for i, a in enumerate(input):
floor = floor + (1 if a == '(' else - 1)
if floor == -1:
print(i + 1)
break
| input = open('input/01.txt').read()
floor = input.count('(') - input.count(')')
print(floor)
floor = 0
for (i, a) in enumerate(input):
floor = floor + (1 if a == '(' else -1)
if floor == -1:
print(i + 1)
break |
def generateClassId(classIds, firstName, lastName, subjectId):
initials = (firstName[0] + lastName[0: 2]).upper()
classId = f"{subjectId}-{initials}-A"
suffix = ord("B")
while classId in classIds:
classId = classId[0: -1] + chr(suffix)
suffix += 1
return classId
| def generate_class_id(classIds, firstName, lastName, subjectId):
initials = (firstName[0] + lastName[0:2]).upper()
class_id = f'{subjectId}-{initials}-A'
suffix = ord('B')
while classId in classIds:
class_id = classId[0:-1] + chr(suffix)
suffix += 1
return classId |
# These dictionaries are applied to the generated enums dictionary at build time
# Any changes to the API should be made here. attributes.py is code generated
# We are not code genning enums that have been marked as obsolete prior to the initial
# Python API bindings release
# We also do not codegen enums associated with P2P or External Calibration since neither
# are supported in Python
enums_codegen_method = {
'CalADCInput': { 'codegen_method': 'no', }, # Calibration Enum - not supported in Python
}
enums_additional_enums = {
'RelativeTo': {
'values': [
{
'name': 'NIFGEN_VAL_WAVEFORM_POSITION_START',
'value': 0,
},
{
'name': 'NIFGEN_VAL_WAVEFORM_POSITION_CURRENT',
'value': 1,
},
],
},
'TriggerWhen': {
'values': [
{
'name': 'NIFGEN_VAL_ACTIVE_HIGH',
'value': 101,
},
{
'name': 'NIFGEN_VAL_ACTIVE_LOW',
'value': 102,
},
],
},
'ByteOrder': {
'values': [
{
'name': 'NIFGEN_VAL_LITTLE_ENDIAN',
'value': 0,
},
{
'name': 'NIFGEN_VAL_BIG_ENDIAN',
'value': 1,
},
],
},
'Signal': {
'values': [
{
'name': 'NIFGEN_VAL_ONBOARD_REFERENCE_CLOCK',
'value': 1019,
},
{
'name': 'NIFGEN_VAL_SYNC_OUT',
'value': 1002,
},
{
'name': 'NIFGEN_VAL_START_TRIGGER',
'value': 1004,
},
{
'name': 'NIFGEN_VAL_MARKER_EVENT',
'value': 1001,
},
{
'name': 'NIFGEN_VAL_SAMPLE_CLOCK_TIMEBASE',
'value': 1006,
},
{
'name': 'NIFGEN_VAL_SYNCHRONIZATION',
'value': 1007,
},
{
'name': 'NIFGEN_VAL_SAMPLE_CLOCK',
'value': 101,
},
{
'name': 'NIFGEN_VAL_REFERENCE_CLOCK',
'value': 102,
},
{
'name': 'NIFGEN_VAL_SCRIPT_TRIGGER',
'value': 103,
},
{
'name': 'NIFGEN_VAL_READY_FOR_START_EVENT',
'value': 105,
},
{
'name': 'NIFGEN_VAL_STARTED_EVENT',
'value': 106,
},
{
'name': 'NIFGEN_VAL_DONE_EVENT',
'value': 107,
},
{
'name': 'NIFGEN_VAL_DATA_MARKER_EVENT',
'value': 108,
},
],
},
'HardwareState': {
'values': [
{
'name': 'NIFGEN_VAL_IDLE',
'value': 0,
},
{
'name': 'NIFGEN_VAL_WAITING_FOR_START_TRIGGER',
'value': 1,
},
{
'name': 'NIFGEN_VAL_RUNNING',
'value': 2,
},
{
'name': 'NIFGEN_VAL_DONE',
'value': 3,
},
{
'name': 'NIFGEN_VAL_HARDWARE_ERROR',
'value': 4,
},
],
},
}
# TODO(bhaswath): Move this enum together with other enums once Issue #624 is fixed.
replacement_enums = {
'Trigger': {
'values': [
{
'name': 'NIFGEN_VAL_START_TRIGGER',
'value': 1004,
},
{
'name': 'NIFGEN_VAL_SCRIPT_TRIGGER',
'value': 103,
},
],
},
}
| enums_codegen_method = {'CalADCInput': {'codegen_method': 'no'}}
enums_additional_enums = {'RelativeTo': {'values': [{'name': 'NIFGEN_VAL_WAVEFORM_POSITION_START', 'value': 0}, {'name': 'NIFGEN_VAL_WAVEFORM_POSITION_CURRENT', 'value': 1}]}, 'TriggerWhen': {'values': [{'name': 'NIFGEN_VAL_ACTIVE_HIGH', 'value': 101}, {'name': 'NIFGEN_VAL_ACTIVE_LOW', 'value': 102}]}, 'ByteOrder': {'values': [{'name': 'NIFGEN_VAL_LITTLE_ENDIAN', 'value': 0}, {'name': 'NIFGEN_VAL_BIG_ENDIAN', 'value': 1}]}, 'Signal': {'values': [{'name': 'NIFGEN_VAL_ONBOARD_REFERENCE_CLOCK', 'value': 1019}, {'name': 'NIFGEN_VAL_SYNC_OUT', 'value': 1002}, {'name': 'NIFGEN_VAL_START_TRIGGER', 'value': 1004}, {'name': 'NIFGEN_VAL_MARKER_EVENT', 'value': 1001}, {'name': 'NIFGEN_VAL_SAMPLE_CLOCK_TIMEBASE', 'value': 1006}, {'name': 'NIFGEN_VAL_SYNCHRONIZATION', 'value': 1007}, {'name': 'NIFGEN_VAL_SAMPLE_CLOCK', 'value': 101}, {'name': 'NIFGEN_VAL_REFERENCE_CLOCK', 'value': 102}, {'name': 'NIFGEN_VAL_SCRIPT_TRIGGER', 'value': 103}, {'name': 'NIFGEN_VAL_READY_FOR_START_EVENT', 'value': 105}, {'name': 'NIFGEN_VAL_STARTED_EVENT', 'value': 106}, {'name': 'NIFGEN_VAL_DONE_EVENT', 'value': 107}, {'name': 'NIFGEN_VAL_DATA_MARKER_EVENT', 'value': 108}]}, 'HardwareState': {'values': [{'name': 'NIFGEN_VAL_IDLE', 'value': 0}, {'name': 'NIFGEN_VAL_WAITING_FOR_START_TRIGGER', 'value': 1}, {'name': 'NIFGEN_VAL_RUNNING', 'value': 2}, {'name': 'NIFGEN_VAL_DONE', 'value': 3}, {'name': 'NIFGEN_VAL_HARDWARE_ERROR', 'value': 4}]}}
replacement_enums = {'Trigger': {'values': [{'name': 'NIFGEN_VAL_START_TRIGGER', 'value': 1004}, {'name': 'NIFGEN_VAL_SCRIPT_TRIGGER', 'value': 103}]}} |
def product(factor1, factor2):
resultaat = factor1 * factor2
return resultaat
print("Unittest van 'product'")
assert product(9, 3) == 27, "Berekening van 'product' bevat een fout"
assert product(0, 0) == 0, "Fout bij berekenen 0 waarde"
assert product(1000, 1000) == 100000, "Fout bij berekenen grote waarde"
| def product(factor1, factor2):
resultaat = factor1 * factor2
return resultaat
print("Unittest van 'product'")
assert product(9, 3) == 27, "Berekening van 'product' bevat een fout"
assert product(0, 0) == 0, 'Fout bij berekenen 0 waarde'
assert product(1000, 1000) == 100000, 'Fout bij berekenen grote waarde' |
def pallindrome(z):
mid = (len(z)-1)//2
start = 0
last = len(z) - 1
flag = 0
while(start < mid):
if(z[start]== z[last]):
start += 1
last -= 1
else:
flag = 1
break;
if flag == 0:
print("The entered string is pallindrome")
else:
print("The entered string is not pallindrome")
def symmetry(z):
n = len(z)
flag = 0
if n % 2:
mid = n//2 + 1
else:
mid = n//2
start1 = 0
start2 = mid
while(start1 < mid and start2 < n):
if(z[start1] == z[start2]):
start1 = start1 + 1
start2 = start2 + 1
else:
flag = 1
break
if flag == 0:
print("The entered string is symmetrical")
else:
print("The entered string is not symmetrical")
string = 'soumyo amamama'
pallindrome(string)
symmetry(string) | def pallindrome(z):
mid = (len(z) - 1) // 2
start = 0
last = len(z) - 1
flag = 0
while start < mid:
if z[start] == z[last]:
start += 1
last -= 1
else:
flag = 1
break
if flag == 0:
print('The entered string is pallindrome')
else:
print('The entered string is not pallindrome')
def symmetry(z):
n = len(z)
flag = 0
if n % 2:
mid = n // 2 + 1
else:
mid = n // 2
start1 = 0
start2 = mid
while start1 < mid and start2 < n:
if z[start1] == z[start2]:
start1 = start1 + 1
start2 = start2 + 1
else:
flag = 1
break
if flag == 0:
print('The entered string is symmetrical')
else:
print('The entered string is not symmetrical')
string = 'soumyo amamama'
pallindrome(string)
symmetry(string) |
class Student1:
def __init__(self,a=10,b=20):
self.add=a+b
self.sub=a-b
def result(self):
print("Sum is: ",self.add)
print("Sub is: ",self.sub)
| class Student1:
def __init__(self, a=10, b=20):
self.add = a + b
self.sub = a - b
def result(self):
print('Sum is: ', self.add)
print('Sub is: ', self.sub) |
FACTOR_RULES = "rules_factor"
TOPIC_RULES = "rules_topic"
TOPIC_RULE = "topic_rule"
FACTOR_RULE = "factor_rule"
MONITOR_RULES = "monitor_rules"
| factor_rules = 'rules_factor'
topic_rules = 'rules_topic'
topic_rule = 'topic_rule'
factor_rule = 'factor_rule'
monitor_rules = 'monitor_rules' |
var = 0
def foo():
var = 2
print(var)
def fua():
var = 1
def fup():
global var
var = 42
print(foo())
print()
fua()
print(var)
fup()
print(var)
| var = 0
def foo():
var = 2
print(var)
def fua():
var = 1
def fup():
global var
var = 42
print(foo())
print()
fua()
print(var)
fup()
print(var) |
class PaginationKeys(object):
PAGE = 'page'
COUNT = 'count'
TOTAL = 'total'
SHOW = 'show'
DATA = 'data'
ITEMS_PER_PAGE = 25
| class Paginationkeys(object):
page = 'page'
count = 'count'
total = 'total'
show = 'show'
data = 'data'
items_per_page = 25 |
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