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# Generated by Django 3.0 on 2020-11-07 18:26
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
]
operations = [
migrations.CreateModel(
name='Contact',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('first_name', models.CharField(max_length=120, null=True)),
('last_name', models.CharField(max_length=120, null=True)),
('email', models.EmailField(blank=True, max_length=254, null=True)),
('message', models.TextField(blank=True)),
('timestamp', models.DateTimeField(auto_now_add=True)),
],
),
migrations.CreateModel(
name='NewsAndEvent',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('title', models.CharField(max_length=120, null=True)),
('summary', models.TextField(blank=True)),
('posted_as', models.TextField(choices=[('News', 'News'), ('Events', 'Events')], null=True)),
('timestamp', models.DateTimeField(auto_now_add=True)),
],
),
]
|
# Union Find: Union and find techniques are not just for graph.
# They aim to build an array indicating the parent of each element.
# When find, do path compression. When union, do union by rank
# Python Program for union-find algorithm to detect cycle in a undirected graph
# we have one egde for any two vertex i.e 1-2 is either 1-2 or 2-1 but not both
# union by rank and compression tech:
# http://www.geeksforgeeks.org/disjoint-set-data-structures-java-implementation/
from collections import defaultdict
# This class represents a undirected graph using adjacency list representation
class Graph:
def __init__(self, vertices):
self.V = vertices # No. of vertices
self.graph = defaultdict(list) # default dictionary to store graph
# function to add an edge to graph
def add_edge(self, u, v):
self.graph[u].append(v)
# A utility function to find the subset of an element i
# path compression
def find_parent(self, parent, i):
if parent[i] == i:
return i
result = self.find_parent(parent, parent[i])
parent[i] = result
return result
# A utility function to do union of two subsets
# union by rank
def union(self, parent, rank, x, y):
x_set = self.find_parent(parent, x)
y_set = self.find_parent(parent, y)
if x_set == y_set: return
if rank[x_set] < rank[y_set]:
parent[x_set] = y_set
elif rank[x_set] > rank[y_set]:
parent[y_set] = x_set
else:
parent[x_set] = y_set
rank[y_set] += 1
# The main function to check whether a given graph
# contains cycle or not
def is_cyclic(self):
# Allocate memory for creating V subsets and
# Initialize all subsets as single element sets
parent = []
rank = []
for node in range(self.V):
parent.append(node)
rank.append(0)
# Iterate through all edges of graph, find subset of both
# vertices of every edge, if both subsets are same, then
# there is cycle in graph.
for i in self.graph:
for j in self.graph[i]:
x = self.find_parent(parent, i)
y = self.find_parent(parent, j)
if x == y:
return True
self.union(parent, rank, x, y)
# Create a graph given in the above diagram
g = Graph(3)
g.add_edge(0, 1)
g.add_edge(1, 2)
g.add_edge(2, 0)
if g.is_cyclic():
print("Graph contains cycle")
else:
print("Graph does not contain cycle ") |
# Generated by Django 3.1.7 on 2021-07-08 01:11
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('api', '0007_auto_20210703_1138'),
]
operations = [
migrations.RemoveField(
model_name='file',
name='task',
),
migrations.AddField(
model_name='task',
name='file',
field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='api.file', verbose_name='File'),
),
]
|
class Solution(object):
def canPartition(self, nums):
def canReachSum(nums,S):
sums = set([S])
for i in range(len(nums)):
tmp=set()
#print sums
for s in sums:
nv = s-nums[i]
if nv < 0: continue
if nv == 0:
#print i, nums[i]
return True
tmp.add(nv)
sums = sums.union(tmp)
return False
t = sum(nums)
#print nums, t, t/2
if t % 2 == 1: return False
return canReachSum(nums, t/2)
def canPartition1(self, nums):
def canReachSum(nums,S):
if S == 0: return True
if S < 0 or not nums: return False
return canReachSum(nums[1:], S-nums[0]) or canReachSum(nums[1:],S)
t = sum(nums)
print nums, t
if t % 2 == 1: return False
return canReachSum(nums, t/2)
def canPartition2(self, nums):
def canReachSum(nums,S, sl):
rotseed = -1
while rotseed < len(nums):
print sums, rotseed
rotseed += 1
for i in range(len(nums)):
r = (i + rotseed) % len(nums)
sums[i] += nums[r]
if sums[i] == S:
print sums, i
return True
return False
t = sum(nums)
print nums, t, t/2, len(nums)
if t % 2 == 1: return False
sums = [0]*len(nums)
return canReachSum(nums, t/2, sums)
print Solution().canPartition([3,5,6,8])
"""
print Solution().canPartition([5,1 ,5,11])
print Solution().canPartition([1, 5,11 ,5])
print Solution().canPartition([1 ,2,3,5])
print Solution().canPartition([2,2,4,4,6])
print Solution().canPartition([1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,100])
print Solution().canPartition([66,90,7,6,32,16,2,78,69,88,85,26,3,9,58,65,30,96,11,31,99,49,63,83,79,97,20,64,81,80,25,69,9,75,23,70,26,71,25,54,1,40,41,82,32,10,26,33,50,71,5,91,59,96,9,15,46,70,26,32,49,35,80,21,34,95,51,66,17,71,28,88,46,21,31,71,42,2,98,96,40,65,92,43,68,14,98,38,13,77,14,13,60,79,52,46,9,13,25,8])
"""
|
import turtle as t
def ract(hor,ver,col):
t.pendown()
t.pensize()
t.color(col)
t.begin_fill()
for i in range(1,3):
t.forward(hor)
t.right(90)
t.forward(ver)
t.right(90)
t.end_fill()
t.penup()
t.penup()
t.speed(1000)
t.bgcolor("blue")
t.goto(-100,-150)
ract(50,20,"black")
t.goto(-30,-150)
ract(50,20,"black")
t.goto(-25,-50)
ract(15,100,"grey")
t.goto(-70,-50)
ract(15,100,"grey")
t.goto(-90,100)
ract(100,150,"red")
t.goto(-150,70)
ract(60,15,"grey")
t.goto(-150,110)
ract(15,40,"grey")
t.goto(10,70)
ract(60,15,"grey")
t.goto(55,100)
ract(15,40,"grey")
t.goto(-50,120)
ract(15,20,"grey")
t.goto(-85,170)
ract(80,50,"red")
t.goto(-60,160)
ract(30,10,"white")
t.goto(-55,155)
ract(5,5,"black")
t.goto(-40,155)
ract(5,5,"black")
t.goto(-65,140)
ract(40,5,"black")
t.hideturtle()
t.Screen().exitonclick() |
## Without filtering results with VIF, calculate the importance for all the features.
## Works for "first" and "structcoef"
from util_relaimpo import *
from util import loadNpy, loadCsv
def main(x_name, y_name, method, feature_names = []):
# INFO
print("Dataset", x_name.split('_')[0])
print("Method", str(method).split(' ')[1])
# load data
X = loadNpy(['data', 'X', x_name])
Y = loadNpy(['data', 'Y', y_name])
# make dataframe
if feature_names: xdf = pd.DataFrame(data=X, columns=feature_names)
else: xdf = pd.DataFrame(data=X)
print("bootstrapping ...")
coef_boot = bootstrapping(xdf, Y, method)
print(printBootResult(coef_boot, list(xdf.columns), list(xdf.columns)))
feature_names = getFeatureNames(loadCsv(['data', 'X', 'feature_descriptions.csv']))
if __name__ == '__main__':
main('HM_X_ang_vel.npy','HM_MPSCC95.npy', structcoef, feature_names)
main('AF_X_ang_vel.npy', 'AF_MPSCC95.npy', structcoef, feature_names)
main('NFL53_X_ang_vel.npy', 'NFL53_MPSCC95.npy', structcoef, feature_names)
|
#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn import decomposition
corpus = ['To be, or not to be, that is the question',
'Whether tis nobler in the mind to suffer',
'The slings and arrows of outrageous fortune',
'Or to take arms against a sea of troubles',
'And by doing something',
'the the the the the the the'
]
# corpus = [
# 'The dog barked.',
# 'I like chewing gum.',
# 'The cat meowed.'
# ]
vectorizer = CountVectorizer(min_df=1)
dt = vectorizer.fit_transform(corpus)
x = vectorizer.get_feature_names()
a = dt.toarray()
original = a
print('BEFORE APPLYING SVD:\n')
print(a)
transformer = TfidfTransformer(smooth_idf=False)
tfidf = transformer.fit_transform(a).toarray()
u,s,v = np.linalg.svd(tfidf, full_matrices=False)
a = np.dot(u, np.dot(np.diag(s), v))
approximated = a
print('\nAFTER APPLYING SVD:\n')
print(a)
for i in range(len(original)):
similarity = np.dot(original[i], approximated[i]) / (np.linalg.norm(original[i])*np.linalg.norm(approximated[i]))
print ("Similarity for row %d is: %f" % (i, similarity))
|
from Buildings.material import DIRT, BRICKS, WATER, AIR, FENCE, TORCH
fountain = {
"height": -1,
"building": [
[
[DIRT, DIRT, DIRT, DIRT, DIRT, DIRT],
[DIRT, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], DIRT],
[DIRT, BRICKS["STONE"], WATER, WATER, BRICKS["STONE"], DIRT],
[DIRT, BRICKS["STONE"], WATER, WATER, BRICKS["STONE"], DIRT],
[DIRT, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], DIRT],
[DIRT, DIRT, DIRT, DIRT, DIRT, DIRT]
],
[
[BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"]],
[BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"]],
[BRICKS["STONE"], BRICKS["STONE"], WATER, WATER, BRICKS["STONE"], BRICKS["STONE"]],
[BRICKS["STONE"], BRICKS["STONE"], WATER, WATER, BRICKS["STONE"], BRICKS["STONE"]],
[BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"]],
[BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"]]
],
[
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], AIR],
[AIR, BRICKS["STONE"], AIR, AIR, BRICKS["STONE"], AIR],
[AIR, BRICKS["STONE"], AIR, AIR, BRICKS["STONE"], AIR],
[AIR, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], AIR],
[AIR, AIR, AIR, AIR, AIR, AIR]
],
[
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, FENCE, AIR, AIR, FENCE, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, FENCE, AIR, AIR, FENCE, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR]
],
[
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, FENCE, AIR, AIR, FENCE, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, FENCE, AIR, AIR, FENCE, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR]
],
[
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], AIR],
[AIR, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], AIR],
[AIR, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], AIR],
[AIR, BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], BRICKS["STONE"], AIR],
[AIR, AIR, AIR, AIR, AIR, AIR]
],
[
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, TORCH["UP"], AIR, AIR, TORCH["UP"], AIR],
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, AIR, AIR, AIR, AIR, AIR],
[AIR, TORCH["UP"], AIR, AIR, TORCH["UP"], AIR],
[AIR, AIR, AIR, AIR, AIR, AIR]
]
]
}
def generate_fountains():
return [fountain]
|
'''
column_types()
Returns pandas dataframe column names, column object pandas dtypes and column python data types
The `dtype` results from `pandas.Dataframe.info()` are outputs from `pandas.DataFrame.dtypes()` method.
The `dtypes()` method does not differentiate between string iterable python objects (strings, dictionaries, lists, tuples),
it classifies all them as objects data types, the `column_types()` function returns the dataframe column names,
column object pandas dtypes and column python data types
Author: Alex Ricciardi
'''
import pandas as pd
def column_types(df):
'''
Takes the argumnt:
df, pandas DataFrame data type
Returns:
A Dataframe of the inputed DataFrame
column names
column object pandas dtypes
column python data types
'''
# Stores df columns names and columns pandas dtypes
column_n = df[df.columns].dtypes.to_frame().rename(columns={0:'pandas_dtype'})
# Stores df columns python data types
# Uses row indexed `0` values
column_n['python_type'] = [type(df.loc[0][col]).__name__ for col in df.columns]
# Checks column_n `type` values for NoneType
for col in column_n.index:
if column_n.loc[col]['python_type'] == 'NoneType':
# Seach df row with no NaN
for i in range(len(df)):
if df.loc[i][col] != None:
colunm_n.loc[col]['type'] = type(df.loc[i][col]).__name__
break
# If the df column is all NaN values, the function will return a 'NoneType' for that particular column
df_column_names = column_n.reset_index().rename(columns={'index':'Columns'})
return df_column_names |
class Person:
def __init__(self, fname, lname):
self.firstname = fname
self.lastname = lname
def printname(self):
print(self.firstname, self.lastname)
# Use the Person class to create an object, and then execute the printname method:
x = Person("John", "Doe")
x.printname()
""" Tạo class kế thừa từ class Person """
class Student(Person):
pass # khi ko muốn add thêm attribute hay methods nào, kế thừa toàn bộ base class
x = Student("Mike", "Olsen")
x.printname()
class Student(Person):
def __init__(self, fname, lname):
# có self ở đây để còn lưu attribute cho object
Person.__init__(self, fname, lname)
x = Student("John", "Doe")
x.printname()
# thêm attribute
class Student(Person):
def __init__(self, fname, lname, age):
# có self ở đây để còn lưu attribute cho object
# Phải dùng self không sẽ báo lỗi
Person.__init__(self, fname, lname)
self.age = age
def printage(self):
print(self.age)
x = Student("John", "Doe", 15)
x.printname()
x.printage()
# sử dụng super() function sẽ làm cho class con kế thừa tất cả phương thức và thuộc tính của class cha
class Student(Person):
def __init__(self, fname, lname):
super().__init__(fname, lname)
# sử dụng super sẽ không cần tên của class cha, ở đây ko cần dùng self
anh = Student("Huy", "Tran")
anh.printname() |
import pytest
from datetime import timedelta, datetime
from tensorhive.models.Reservation import Reservation
def test_reservation_creation(tables, new_reservation):
new_reservation.save()
def test_interfering_reservation_cannot_be_saved(tables, new_reservation, new_reservation_2):
# Create initial record
new_reservation.save()
offset = timedelta(minutes=5)
with pytest.raises(AssertionError):
# | A A |
new_reservation_2.start = new_reservation.start + offset
new_reservation_2.end = new_reservation.end - offset
new_reservation_2.save()
with pytest.raises(AssertionError):
# A | A |
new_reservation_2.start = new_reservation.start - offset
new_reservation_2.end = new_reservation.end - offset
new_reservation_2.save()
with pytest.raises(AssertionError):
# | A | A
new_reservation_2.start = new_reservation.start + offset
new_reservation_2.end = new_reservation.end + offset
new_reservation_2.save()
with pytest.raises(AssertionError):
# A | | A
new_reservation_2.start = new_reservation.start - offset
new_reservation_2.end = new_reservation.end + offset
new_reservation_2.save()
def test_cancelled_reservation_does_not_cause_interference_with_others(tables, new_reservation, new_reservation_2):
# Create initial record
new_reservation.is_cancelled = True
new_reservation.save()
offset = timedelta(minutes=5)
# | A A |
new_reservation_2.start = new_reservation.start + offset
new_reservation_2.end = new_reservation.end - offset
new_reservation_2.save()
# A | A |
new_reservation_2.start = new_reservation.start - offset
new_reservation_2.end = new_reservation.end - offset
new_reservation_2.save()
# | A | A
new_reservation_2.start = new_reservation.start + offset
new_reservation_2.end = new_reservation.end + offset
new_reservation_2.save()
# A | | A
new_reservation_2.start = new_reservation.start - offset
new_reservation_2.end = new_reservation.end + offset
new_reservation_2.save()
@pytest.mark.usefixtures('faker')
def test_string_time_format_conversion(tables, new_reservation, faker):
# Prepare test data
starts_at_datetime = faker.future_datetime(end_date='+1d')
ends_at_datetime = starts_at_datetime + timedelta(minutes=400)
def cast_dt_to_str(format):
return starts_at_datetime.strftime(format), ends_at_datetime.strftime(format)
# Test invalid format
invalid_format = '%Y_%m_%dT%H:%M:%S.%fZ'
starts_at, ends_at = cast_dt_to_str(invalid_format)
with pytest.raises(ValueError):
new_reservation.start = starts_at
new_reservation.end = ends_at
new_reservation.save()
# Test valid format
valid_format = '%Y-%m-%dT%H:%M:%S.%fZ'
starts_at, ends_at = cast_dt_to_str(valid_format)
new_reservation.start = starts_at
new_reservation.end = ends_at
new_reservation.save()
@pytest.mark.usefixtures('faker')
def test_invalid_reservation_time_range(tables, new_reservation, faker):
with pytest.raises(AssertionError):
# End before start
new_reservation.start = faker.future_datetime(end_date='+1d')
new_reservation.end = faker.past_datetime(start_date='-1d')
new_reservation.save()
with pytest.raises(AssertionError):
# Duration too short (30 min at least required)
new_reservation.start = faker.future_datetime()
new_reservation.end = new_reservation.start + timedelta(minutes=29, seconds=59)
new_reservation.save()
with pytest.raises(AssertionError):
# Duration too long (2 days at last)
new_reservation.start = faker.future_datetime()
new_reservation.end = new_reservation.start + timedelta(days=8, seconds=1)
new_reservation.save()
def test_current_events_will_only_return_non_cancelled_reservations(tables, new_reservation, new_reservation_2):
new_reservation.start = datetime.utcnow() - timedelta(minutes=10)
new_reservation.end = datetime.utcnow() + timedelta(minutes=60)
new_reservation.save()
assert new_reservation in Reservation.current_events()
new_reservation.is_cancelled = True
new_reservation.save()
new_reservation_2.save()
current_events = Reservation.current_events()
assert new_reservation not in current_events
assert new_reservation_2 in current_events
|
"""
This code is intended to start up and safely shut down an RPI with a single-throw switch.
The ST switch is wired to a GPIO of ESP8266 which is pulled up (high).
When switching on, the GPIO goes low, which triggers 'on_press' function - activate the SSR and turn on the LED.
When switching off, the GPIO goes back to high, which triggers 'on_release' function - it sends a pull low signal to
RPI GPIO to execute linux shut down command while monitoring the TxD pin of the RPI, as soon as the TxD goes low,
which means the RPI is shut down safely, the LED will be off.
"""
import machine
import utime
from button import Button
from config import *
power_sw = Button(POWER_SW_PIN)
rpi_ssr = machine.Pin(RPI_SSR_PIN, machine.Pin.OUT, value=0)
rpi_shutdown_cmd = machine.Pin(RPI_SHUTDOWN_PIN, machine.Pin.OUT, value=1)
rpi_is_on = machine.Pin(RPI_STATUS_CHECK_PIN, machine.Pin.IN, None).value
rpi_led = machine.Pin(RPI_POWER_LED_PIN, machine.Pin.OUT, value=0)
def rpi_start_up():
print('Powering On')
rpi_ssr.on()
rpi_led.on()
def rpi_shut_down():
rpi_shutdown_cmd.value(0)
while True:
if not rpi_is_on():
utime.sleep_ms(500)
if not rpi_is_on():
print('Powering Off')
rpi_led.off()
utime.sleep_ms(10000)
rpi_ssr.off()
rpi_shutdown_cmd.value(1)
break
power_sw.on_press(rpi_start_up)
power_sw.on_release(rpi_shut_down)
|
from wtforms import StringField, IntegerField, FloatField, Field, SelectField,ValidationError, FieldList, FormField
from wtforms.validators import DataRequired, length, Email, Regexp, EqualTo
from app.libs.enums import ClientTypeEnum
from app.models.user import User
from app.validators.base import BaseForm as Form
class ClientForm(Form):
account = StringField(validators=[DataRequired(message='不允许为空'), length(
min=5, max=32
)])
secret = StringField()
type = IntegerField(validators=[DataRequired()])
def validate_type(self, value):
try:
client = ClientTypeEnum(value.data)
except ValueError as e:
raise e
self.type.data = client
class UserEmailForm(ClientForm):
account = StringField(validators=[
Email(message='invalidate email')
])
secret = StringField(validators=[
DataRequired(),
# password can only include letters , numbers and "_"
Regexp(r'^[A-Za-z0-9_*&$#@]{6,22}$')
])
secret2=StringField(validators=[
DataRequired(),
# password can only include letters , numbers and "_"
Regexp(r'^[A-Za-z0-9_*&$#@]{6,22}$'),EqualTo("secret")
])
nickname = StringField(validators=[DataRequired(),
length(min=2, max=22)])
def validate_account(self, value):
if User.query.filter_by(email=value.data).first():
raise ValidationError()
class UserPhoneForm(ClientForm):
account = StringField(validators=[
DataRequired(),
Regexp("1\d{10}", message="手机号码格式不正确！")
])
secret = StringField(validators=[
DataRequired(),
# password can only include letters , numbers and "_"
Regexp(r'^[A-Za-z0-9_*&$#@]{6,22}$')
])
def validate_account(self, value):
if User.query.filter_by(phone=value.data).first():
raise ValidationError()
class BookSearchForm(Form):
q = StringField(validators=[DataRequired()])
class TokenForm(Form):
token = StringField(validators=[DataRequired()])
class PassForm(Form):
secret = StringField(validators=[
DataRequired(),
# password can only include letters , numbers and "_"
Regexp(r'^[A-Za-z0-9_*&$#@]{6,22}$')
])
class DetailForm(Form):
wechat_open_id= StringField(validators=[])
nickname= StringField(validators=[])
class Teacher1Form(Form):
school = StringField(validators=[DataRequired()])
name=StringField(validators=[DataRequired()])
class StudentForm(Form):
name = StringField(validators=[DataRequired()])
sno = StringField(validators=[DataRequired()])
school_name = StringField(validators=[DataRequired()])
auth_url = StringField(validators=[DataRequired()])
class preStudentForm(Form):
major= StringField(validators=[])
grade=StringField(validators=[])
classno=StringField(validators=[])
enrolltime=StringField(validators=[
])
class TeacherForm(Form):
head_url = StringField(validators=[])
abstract = StringField(validators=[])
class JsCodeForm(Form):
code= StringField(validators=[])
class SubjectForm(Form):
name = StringField(validators=[DataRequired()])
abstract = StringField(validators=[])
class invitationForm(Form):
invi= StringField(validators=[DataRequired(),length(min=5,max=6)])
class invitation2Form(invitationForm):
token=StringField(validators=[])
class AttendForm(Form):
subject_id=IntegerField(validators=[DataRequired()])
attend_name= StringField(validators=[DataRequired()])
class StudentAttendForm(Form):
code=StringField(validators=[DataRequired() ,Regexp(r'^[0-9]{4}$')])
attend_position=StringField(validators=[DataRequired()])
ip=StringField(validators=[DataRequired(),Regexp(r'(2(5[0-5]{1}|[0-4]\d{1})|[0-1]?\d{1,2})(\.(2(5[0-5]{1}|[0-4]\d{1})|[0-1]?\d{1,2})){3}')])
scl_name=StringField(validators=[DataRequired()])
class TeacherEndAttendForm(Form):
attend_id= IntegerField(validators=[DataRequired()])
class DurationForm (Form):
dat=StringField(validators=[DataRequired()])
subject_id = IntegerField(validators=[DataRequired()])
site= IntegerField(validators=[DataRequired()])
folder_id=IntegerField(validators=[])
class TestForm(Form):
sec_id= StringField(validators=[])
subject_id=IntegerField(validators=[DataRequired()])
# 初始化测试集
class InteractionStartForm(Form):
duration_id = IntegerField(validators=[DataRequired()])
duration_page = IntegerField(validators=[DataRequired()])
name=StringField(validators=[DataRequired()])
class TeCourseForm(Form):
chi_id= IntegerField(validators=[DataRequired()])
kin= IntegerField(validators=[DataRequired()])
class UploadForm(Form):
filetype = IntegerField(validators=[DataRequired()])
class PaperForm(Form):
course_info_id = IntegerField(validators=[DataRequired()])
ware_page = IntegerField(validators=[DataRequired()])
class CosIdForm(Form):
cos_id = IntegerField(validators=[DataRequired()])
class OptionForm(Form):
order_num= IntegerField(validators=[DataRequired()])
opt_cont=StringField(validators=[DataRequired()])
class GetQuesForm(Form):
id = IntegerField(validators=[DataRequired()])
class QuestionForm(Form):
course_id=IntegerField(validators=[DataRequired()])
page=IntegerField(validators=[DataRequired()])
genre=IntegerField(validators=[DataRequired()])
questiontext=StringField(validators=[DataRequired()])
answer=StringField(validators=[])
option=FieldList(FloatField())
class AnswerForm(Form):
paper_id=IntegerField(validators=[DataRequired()])
answer = FieldList(FloatField())
class FileNameForm(Form):
id=IntegerField(validators=[DataRequired()])
name=StringField(validators=[DataRequired()])
class DeleteOneForm(Form):
id=IntegerField(validators=[DataRequired()])
class DeletetwoForm(Form):
id=IntegerField(validators=[DataRequired()])
id2 = IntegerField(validators=[DataRequired()])
class GetOneForm(Form):
id=IntegerField(validators=[DataRequired()])
class GettwoForm(Form):
id=IntegerField(validators=[DataRequired()])
id2 = IntegerField(validators=[DataRequired()])
class NameForm(Form):
name=StringField(validators=[DataRequired()])
|
#!/usr/bin/env python3
"""__init__ pycircuit file"""
__all__ = ["bit", "bitvector", "pin", "ic"]
|
from src.API.blueprints.functions import byRecentDay, byRecentWeek, byTimeperiod, byDate, byRecentYear, byRecentMonth
from flask import Blueprint
airtemp = Blueprint('airtemp', __name__)
value = "temperature_float"
type = "airtemp"
@airtemp.route('/solapi/airtemp/recent/day')
def airTempDay():
return byRecentDay(value, type)
@airtemp.route('/solapi/airtemp/recent/week')
def airTempWeek():
return byRecentWeek(value, type)
@airtemp.route('/solapi/airtemp/recent/month')
def airTempMonth():
return byRecentMonth(value, type)
@airtemp.route('/solapi/airtemp/recent/year')
def airTempYear():
return byRecentYear(value, type)
@airtemp.route('/solapi/airtemp/date')
def airTempDate():
return byDate(value, type)
@airtemp.route('/solapi/airtemp/timeperiod')
def airTempTimeperiod():
return byTimeperiod(value, type)
|
from django.conf.urls import re_path
from .views import *
urlpatterns = [
re_path('contact/', ContactView.as_view(), name='contact'),
re_path('success/', Success.as_view(), name='success'),
# News Letter
#re_path('', newsletter_signup, name='newsletter_signup'),
#re_path(r'^unsubscription/', newsletter_unsubscribe, name='unsubscribe'),
]
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from niveristand._decorators import nivs_rt_sequence, NivsParam
from niveristand.realtimesequencetools import run_py_as_rtseq, save_py_as_rtseq
__all__ = ["NivsParam",
"nivs_rt_sequence",
"run_py_as_rtseq",
"save_py_as_rtseq",
]
|
class ChannelsDb():
def __init__(self, collection):
self.collection = collection
def add(self, object):
if self.collection.find_one({"_id": object._id}) is None:
self.collection.insert_one(object.__dict__)
print(f"Канал '{object.title}' добавлен в БД")
else:
print(f"Канал '{object.title}' уже есть в БД")
|
species(
label = 'C7H9(408)(407)',
structure = SMILES('[CH]1CC=CC=CC1'),
E0 = (267.309,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3862.82,'J/mol'), sigma=(6.55596,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=603.36 K, Pc=31.11 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.05029,0.0548564,-1.71402e-05,-1.22379e-08,7.31431e-12,32264.5,9.34222], Tmin=(100,'K'), Tmax=(1067.28,'K')), NASAPolynomial(coeffs=[12.4249,0.0319753,-1.27385e-05,2.35045e-09,-1.64068e-13,28711.7,-51.544], Tmin=(1067.28,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(267.309,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(RCCJCC)"""),
)
species(
label = 'C7H9(424)(423)',
structure = SMILES('[CH]1C=CCCC=C1'),
E0 = (192.073,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3862.82,'J/mol'), sigma=(6.55596,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=603.36 K, Pc=31.11 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.50916,-0.0350071,0.000330111,-4.45689e-07,1.817e-10,23218.4,21.3615], Tmin=(100,'K'), Tmax=(904.504,'K')), NASAPolynomial(coeffs=[40.5538,-0.0269711,2.44452e-05,-4.92867e-09,3.21553e-13,9125.04,-198.225], Tmin=(904.504,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(192.073,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(C=CCJC=C)"""),
)
species(
label = 'C7H9(449)(448)',
structure = SMILES('[CH]1C=CCC=CC1'),
E0 = (231.461,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3862.82,'J/mol'), sigma=(6.55596,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=603.36 K, Pc=31.11 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.39556,-0.0279134,0.000301615,-4.09558e-07,1.66812e-10,27955.3,20.9914], Tmin=(100,'K'), Tmax=(907.574,'K')), NASAPolynomial(coeffs=[38.3619,-0.0223026,2.10789e-05,-4.22968e-09,2.72784e-13,14667.4,-186.277], Tmin=(907.574,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(231.461,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Allyl_S)"""),
)
species(
label = 'C7H9(452)(451)',
structure = SMILES('[CH]1CC=CC2CC12'),
E0 = (300.5,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3725.22,'J/mol'), sigma=(6.44341,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=581.87 K, Pc=31.6 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.31121,0.0179875,8.31217e-05,-1.10853e-07,4.05188e-11,36219.5,18.9465], Tmin=(100,'K'), Tmax=(997.135,'K')), NASAPolynomial(coeffs=[10.5595,0.0335341,-1.34265e-05,2.61184e-09,-1.92599e-13,32156.8,-32.9466], Tmin=(997.135,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(300.5,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cs_S)"""),
)
species(
label = 'C7H9(453)(452)',
structure = SMILES('[CH2]C1C=CCC=C1'),
E0 = (265.33,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3742.82,'J/mol'), sigma=(6.34669,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=584.62 K, Pc=33.22 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.5923,0.0391468,2.78898e-05,-5.9777e-08,2.46576e-11,32010.9,21.9699], Tmin=(100,'K'), Tmax=(976.761,'K')), NASAPolynomial(coeffs=[11.7085,0.031189,-1.12886e-05,2.04476e-09,-1.44566e-13,28438,-34.7702], Tmin=(976.761,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(265.33,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cyclohexadiene) + radical(Isobutyl)"""),
)
species(
label = 'C7H9(460)(459)',
structure = SMILES('[CH]1CC2C=CCC12'),
E0 = (312.651,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3725.22,'J/mol'), sigma=(6.44341,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=581.87 K, Pc=31.6 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.39033,0.01664,8.50361e-05,-1.11028e-07,4.0121e-11,37677.7,18.8915], Tmin=(100,'K'), Tmax=(1001.43,'K')), NASAPolynomial(coeffs=[9.78379,0.0349878,-1.41629e-05,2.75269e-09,-2.02205e-13,33796.1,-28.7753], Tmin=(1001.43,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(312.651,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane)"""),
)
species(
label = 'C7H9(527)(526)',
structure = SMILES('CC1[CH]C=CC=C1'),
E0 = (153.509,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3742.82,'J/mol'), sigma=(6.34669,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=584.62 K, Pc=33.22 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.72237,0.0364624,3.04702e-05,-5.63404e-08,2.12697e-11,18556.6,18.9349], Tmin=(100,'K'), Tmax=(1041.13,'K')), NASAPolynomial(coeffs=[10.6554,0.0349533,-1.46282e-05,2.80742e-09,-2.01641e-13,14918.2,-33.062], Tmin=(1041.13,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(153.509,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'C7H9(536)(535)',
structure = SMILES('CC1[CH]C2C=CC12'),
E0 = (264.174,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3608.1,'J/mol'), sigma=(6.23584,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=563.58 K, Pc=33.76 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.01942,0.0284648,5.04689e-05,-7.61614e-08,2.82415e-11,31857.3,19.764], Tmin=(100,'K'), Tmax=(1013.49,'K')), NASAPolynomial(coeffs=[9.95731,0.0345687,-1.39666e-05,2.66625e-09,-1.92374e-13,28325.9,-28.1226], Tmin=(1013.49,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(264.174,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutane)"""),
)
species(
label = 'C7H9(680)(679)',
structure = SMILES('[CH]1C=CC2CC2C1'),
E0 = (244.56,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3725.22,'J/mol'), sigma=(6.44341,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=581.87 K, Pc=31.6 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.32125,0.0139708,0.0001034,-1.37277e-07,5.12062e-11,29494.8,15.2464], Tmin=(100,'K'), Tmax=(976.237,'K')), NASAPolynomial(coeffs=[12.5298,0.0303958,-1.13443e-05,2.20506e-09,-1.65895e-13,24725.7,-47.9759], Tmin=(976.237,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(244.56,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(cyclohexene-allyl)"""),
)
species(
label = 'C7H9(681)(680)',
structure = SMILES('[CH2]C1C=CC=CC1'),
E0 = (261.703,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3742.82,'J/mol'), sigma=(6.34669,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=584.62 K, Pc=33.22 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.82631,0.0272134,7.13031e-05,-1.12056e-07,4.50946e-11,31572.6,20.8531], Tmin=(100,'K'), Tmax=(947.537,'K')), NASAPolynomial(coeffs=[14.6403,0.0250914,-7.61175e-06,1.35303e-09,-1.00266e-13,26811.2,-52.5879], Tmin=(947.537,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(261.703,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Isobutyl)"""),
)
species(
label = 'C7H9(682)(681)',
structure = SMILES('C=C1[CH]C=CCC1'),
E0 = (167.661,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3775.14,'J/mol'), sigma=(6.40398,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=589.67 K, Pc=32.62 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.44032,0.00354784,0.0001505,-1.92761e-07,7.19358e-11,20248.9,17.5205], Tmin=(100,'K'), Tmax=(966.749,'K')), NASAPolynomial(coeffs=[15.3585,0.0286008,-1.01769e-05,2.03754e-09,-1.60012e-13,14082.7,-63.3387], Tmin=(966.749,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(167.661,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(C=CCJC=C)"""),
)
species(
label = '[CH2]C=CC=CC=C(878)',
structure = SMILES('[CH2]C=CC=CC=C'),
E0 = (227.723,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2995,3002.5,3010,3017.5,3025,975,981.25,987.5,993.75,1000,1300,1318.75,1337.5,1356.25,1375,400,425,450,475,500,1630,1642.5,1655,1667.5,1680,2950,3100,1380,975,1025,1650,180,180],'cm^-1')),
HinderedRotor(inertia=(1.25006,'amu*angstrom^2'), symmetry=1, barrier=(28.7412,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.25053,'amu*angstrom^2'), symmetry=1, barrier=(28.7522,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.25141,'amu*angstrom^2'), symmetry=1, barrier=(28.7724,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3523.79,'J/mol'), sigma=(5.9032,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=550.41 K, Pc=38.87 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.00079,0.0483141,2.14318e-05,-6.83305e-08,3.15739e-11,27512.9,24.0661], Tmin=(100,'K'), Tmax=(942.067,'K')), NASAPolynomial(coeffs=[17.7907,0.0213084,-6.07919e-06,1.03578e-09,-7.56115e-14,22384.4,-66.3629], Tmin=(942.067,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(227.723,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CC=CCJ)"""),
)
species(
label = 'CH2(S)(21)(22)',
structure = SMILES('[CH2]'),
E0 = (419.862,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1369.36,2789.41,2993.36],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (14.0266,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[4.19195,-0.00230793,8.0509e-06,-6.60123e-09,1.95638e-12,50484.3,-0.754589], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.28556,0.00460255,-1.97412e-06,4.09548e-10,-3.34695e-14,50922.4,8.67684], Tmin=(1000,'K'), Tmax=(3000,'K'))], Tmin=(200,'K'), Tmax=(3000,'K'), E0=(419.862,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(58.2013,'J/(mol*K)'), label="""CH2(S)""", comment="""Thermo library: Klippenstein_Glarborg2016"""),
)
species(
label = 'C6H7(464)(463)',
structure = SMILES('[CH]1C=CC=CC1'),
E0 = (185.261,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (79.1198,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3639.77,'J/mol'), sigma=(6.13475,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=568.52 K, Pc=35.77 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.41084,0.0238203,3.51346e-05,-5.30671e-08,1.90206e-11,22348.5,14.4651], Tmin=(100,'K'), Tmax=(1051.47,'K')), NASAPolynomial(coeffs=[8.26331,0.0294392,-1.26583e-05,2.45535e-09,-1.76993e-13,19576.4,-21.3929], Tmin=(1051.47,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(185.261,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(307.635,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'H(3)(3)',
structure = SMILES('[H]'),
E0 = (211.792,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (1.00794,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1205.6,'J/mol'), sigma=(2.05,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,9.24385e-15,-1.3678e-17,6.66185e-21,-1.00107e-24,25472.7,-0.459566], Tmin=(100,'K'), Tmax=(3459.6,'K')), NASAPolynomial(coeffs=[2.5,9.20456e-12,-3.58608e-15,6.15199e-19,-3.92042e-23,25472.7,-0.459566], Tmin=(3459.6,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(211.792,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""H""", comment="""Thermo library: BurkeH2O2"""),
)
species(
label = 'C7H8(415)(414)',
structure = SMILES('C1=CC=CCC=C1'),
E0 = (165.503,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3872.77,'J/mol'), sigma=(6.33517,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=604.92 K, Pc=34.56 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.976321,0.0470646,2.43918e-05,-7.34711e-08,3.37246e-11,20032,17.1406], Tmin=(100,'K'), Tmax=(948.557,'K')), NASAPolynomial(coeffs=[19.6256,0.016921,-4.63468e-06,8.3158e-10,-6.48659e-14,14312.2,-83.3476], Tmin=(948.557,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(165.503,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3,5-Cycloheptatriene)"""),
)
species(
label = 'C2H2(30)(31)',
structure = SMILES('C#C'),
E0 = (217.784,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([750,770,3400,2100,584.389,584.389,2772.01],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (26.0373,'amu'),
collisionModel = TransportData(shapeIndex=1, epsilon=(1737.73,'J/mol'), sigma=(4.1,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=2.5, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.80868,0.0233616,-3.55172e-05,2.80153e-08,-8.50075e-12,26429,13.9397], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[4.65878,0.00488397,-1.60829e-06,2.46975e-10,-1.38606e-14,25759.4,-3.99838], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(217.784,'kJ/mol'), Cp0=(29.1007,'J/(mol*K)'), CpInf=(87.302,'J/(mol*K)'), label="""C2H2""", comment="""Thermo library: Klippenstein_Glarborg2016"""),
)
species(
label = 'C5H7(211)(210)',
structure = SMILES('[CH2]C=CC=C'),
E0 = (175.951,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,2950,3100,1380,975,1025,1650,180],'cm^-1')),
HinderedRotor(inertia=(1.52628,'amu*angstrom^2'), symmetry=1, barrier=(35.0921,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.5297,'amu*angstrom^2'), symmetry=1, barrier=(35.1709,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (67.1091,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3140.68,'J/mol'), sigma=(5.4037,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=490.57 K, Pc=45.16 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.2987,0.0238036,3.93739e-05,-6.93177e-08,2.88419e-11,21235.8,16.7723], Tmin=(100,'K'), Tmax=(942.053,'K')), NASAPolynomial(coeffs=[11.9106,0.0173605,-5.0926e-06,8.77895e-10,-6.40305e-14,17899.7,-37.1203], Tmin=(942.053,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(175.951,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(274.378,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CC=CCJ)"""),
)
species(
label = 'CH2(17)(18)',
structure = SMILES('[CH2]'),
E0 = (381.563,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([1032.72,2936.3,3459],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (14.0266,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.8328,0.000224446,4.68033e-06,-6.04743e-09,2.59009e-12,45920.8,1.40666], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[3.16229,0.00281798,-7.56235e-07,5.05446e-11,5.65236e-15,46099.1,4.77656], Tmin=(1000,'K'), Tmax=(3000,'K'))], Tmin=(200,'K'), Tmax=(3000,'K'), E0=(381.563,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(58.2013,'J/(mol*K)'), label="""CH2""", comment="""Thermo library: Klippenstein_Glarborg2016"""),
)
species(
label = 'C7H8(436)(435)',
structure = SMILES('C1=CC2C=CC2C1'),
E0 = (260.301,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3734.08,'J/mol'), sigma=(6.22424,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=583.26 K, Pc=35.14 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.91407,0.0215427,8.72675e-05,-1.31996e-07,5.28209e-11,31404.2,19.6757], Tmin=(100,'K'), Tmax=(950.48,'K')), NASAPolynomial(coeffs=[17.2586,0.0185351,-5.14947e-06,9.75875e-10,-7.92279e-14,25706.2,-68.2031], Tmin=(950.48,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(260.301,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_diene_1_5)"""),
)
species(
label = 'C7H8(699)(698)',
structure = SMILES('Cc1ccccc1'),
E0 = (31.5822,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3319.97,'J/mol'), sigma=(5.949e-10,'m'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with fixed Lennard Jones Parameters. This is the fallback method! Try improving transport databases!"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.16224,0.0228472,6.51865e-05,-9.55095e-08,3.6504e-11,3880.32,17.4011], Tmin=(100,'K'), Tmax=(978.375,'K')), NASAPolynomial(coeffs=[11.5979,0.0282381,-1.04878e-05,1.98802e-09,-1.46124e-13,-70.3309,-38.6684], Tmin=(978.375,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(31.5822,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CbHHH) + group(Cb-Cs) + group(Cb-H) + group(Cb-H) + group(Cb-H) + group(Cb-H) + group(Cb-H) + ring(Benzene)"""),
)
species(
label = 'CH3(15)(16)',
structure = SMILES('[CH3]'),
E0 = (136.188,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([604.263,1333.71,1492.19,2836.77,2836.77,3806.92],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (15.0345,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1197.29,'J/mol'), sigma=(3.8,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.65718,0.0021266,5.45839e-06,-6.6181e-09,2.46571e-12,16422.7,1.67354], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.97812,0.00579785,-1.97558e-06,3.07298e-10,-1.79174e-14,16509.5,4.72248], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(136.188,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(83.1447,'J/(mol*K)'), label="""CH3""", comment="""Thermo library: Klippenstein_Glarborg2016"""),
)
species(
label = 'C6H6(468)(467)',
structure = SMILES('c1ccccc1'),
E0 = (68.5201,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (78.1118,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3319.97,'J/mol'), sigma=(5.949e-10,'m'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with fixed Lennard Jones Parameters. This is the fallback method! Try improving transport databases!"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.88775,0.00339716,0.0001009,-1.32851e-07,5.08462e-11,8300.31,11.4552], Tmin=(100,'K'), Tmax=(949.238,'K')), NASAPolynomial(coeffs=[12.521,0.0165025,-4.66524e-06,8.85737e-10,-7.161e-14,4052.17,-47.2613], Tmin=(949.238,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(68.5201,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(282.692,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cb-H) + group(Cb-H) + group(Cb-H) + group(Cb-H) + group(Cb-H) + group(Cb-H) + ring(Benzene)"""),
)
species(
label = 'C7H8(697)(696)',
structure = SMILES('CC1[CH][CH]C=CC=1'),
E0 = (243.094,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3819.49,'J/mol'), sigma=(6.43385,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=596.60 K, Pc=32.54 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.19809,0.0461195,-1.76465e-05,5.35241e-10,5.30964e-13,29296.5,15.4891], Tmin=(100,'K'), Tmax=(1929,'K')), NASAPolynomial(coeffs=[17.6451,0.0270016,-1.28217e-05,2.33811e-09,-1.52447e-13,20934.5,-75.41], Tmin=(1929,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(243.094,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'C6H7(467)(466)',
structure = SMILES('[CH]1CC2C=CC12'),
E0 = (296.996,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (79.1198,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3501.78,'J/mol'), sigma=(6.02237,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=546.97 K, Pc=36.38 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.7024,0.0156163,5.63255e-05,-7.46196e-08,2.67302e-11,35778.4,16.0411], Tmin=(100,'K'), Tmax=(1013.01,'K')), NASAPolynomial(coeffs=[7.74818,0.0286307,-1.17185e-05,2.25842e-09,-1.63935e-13,33066.1,-16.7088], Tmin=(1013.01,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(296.996,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(307.635,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutane)"""),
)
species(
label = 'C7H8(690)(689)',
structure = SMILES('C=C1C=CC=CC1'),
E0 = (169.147,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3784.18,'J/mol'), sigma=(6.18258,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=591.08 K, Pc=36.33 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.88913,0.0328299,3.37063e-05,-5.81883e-08,2.16785e-11,20431.4,16.995], Tmin=(100,'K'), Tmax=(1043.73,'K')), NASAPolynomial(coeffs=[10.5104,0.0329227,-1.40442e-05,2.72618e-09,-1.97113e-13,16827,-33.6119], Tmin=(1043.73,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(169.147,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(13cyclohexadiene5methylene)"""),
)
species(
label = 'C7H8(693)(692)',
structure = SMILES('[CH2]C1=CC=C[CH]C1'),
E0 = (264.261,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(3819.49,'J/mol'), sigma=(6.43385,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with Tc=596.60 K, Pc=32.54 bar (from Joback method)"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.84503,0.0349594,2.76454e-05,-5.16358e-08,1.94794e-11,31871.6,19.5027], Tmin=(100,'K'), Tmax=(1040.47,'K')), NASAPolynomial(coeffs=[9.81315,0.0341423,-1.41611e-05,2.69291e-09,-1.92156e-13,28599.6,-27.0106], Tmin=(1040.47,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(264.261,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CC=CCJ) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'C1C=CC2CC2C=1(1100)',
structure = SMILES('C1C=CC2CC2C=1'),
E0 = (196.057,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.20963,0.0177845,8.68551e-05,-1.22002e-07,4.66536e-11,23664.1,15.7858], Tmin=(100,'K'), Tmax=(970.43,'K')), NASAPolynomial(coeffs=[13.584,0.0253804,-9.09576e-06,1.76513e-09,-1.34199e-13,18891.2,-51.9647], Tmin=(970.43,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(196.057,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + polycyclic(s2_3_6_diene_1_3)"""),
)
species(
label = 'C1=CCC2CC2C=1(1180)',
structure = SMILES('C1=CCC2CC2C=1'),
E0 = (328.236,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[-0.0229152,0.0926059,-0.00011192,7.66938e-08,-2.15028e-11,39618.8,9.97334], Tmin=(100,'K'), Tmax=(864.755,'K')), NASAPolynomial(coeffs=[11.8857,0.0375222,-1.63734e-05,3.03438e-09,-2.08134e-13,37559.2,-45.7484], Tmin=(864.755,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(328.236,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cdd-Cd)CsCsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cdd-CdsCds) + Estimated bicyclic component: polycyclic(s2_3_6_ane) - ring(Cyclohexane) - ring(Cyclopropane) + ring(six-inringtwodouble-12) + ring(Cyclopropane)"""),
)
species(
label = 'C1=CCC2CC2=C1(1188)',
structure = SMILES('C1=CCC2CC2=C1'),
E0 = (274.205,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.87283,0.0252137,7.19311e-05,-1.127e-07,4.5137e-11,33075.4,20.7113], Tmin=(100,'K'), Tmax=(957.121,'K')), NASAPolynomial(coeffs=[15.8374,0.0211044,-6.65199e-06,1.25755e-09,-9.73042e-14,27917.3,-59.0292], Tmin=(957.121,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(274.205,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + polycyclic(s2_3_6_diene_0_2)"""),
)
species(
label = '[CH]1C=CC2C[C]2C1(1181)',
structure = SMILES('[CH]1C=CC2C[C]2C1'),
E0 = (429.983,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.40789,0.0213277,5.70443e-05,-7.64477e-08,2.69286e-11,51784.1,16.7668], Tmin=(100,'K'), Tmax=(1033.28,'K')), NASAPolynomial(coeffs=[7.67331,0.035978,-1.50816e-05,2.90091e-09,-2.08767e-13,48825.8,-17.8581], Tmin=(1033.28,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(429.983,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Tertalkyl) + radical(cyclohexene-allyl)"""),
)
species(
label = '[CH]1C=C[C]2CC2C1(1182)',
structure = SMILES('[CH]1C=C[C]2CC2C1'),
E0 = (376.541,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.59986,0.00856432,0.0001089,-1.40103e-07,5.18997e-11,45357.8,13.1927], Tmin=(100,'K'), Tmax=(971.186,'K')), NASAPolynomial(coeffs=[11.2987,0.0293772,-1.07265e-05,2.06523e-09,-1.55058e-13,40997,-42.2717], Tmin=(971.186,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(376.541,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(cyclohexene-allyl) + radical(Allyl_T)"""),
)
species(
label = '[CH]1C=CC2[CH]C2C1(1183)',
structure = SMILES('[CH]1C=CC2[CH]C2C1'),
E0 = (470.473,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.49088,0.0144824,8.54867e-05,-1.11493e-07,4.05385e-11,56655.7,16.986], Tmin=(100,'K'), Tmax=(995.342,'K')), NASAPolynomial(coeffs=[9.99221,0.0320299,-1.28323e-05,2.50004e-09,-1.84681e-13,52799.9,-31.0364], Tmin=(995.342,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(470.473,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(cyclohexene-allyl) + radical(cyclopropane)"""),
)
species(
label = '[CH]1[CH]C2CC2C=C1(1108)',
structure = SMILES('[CH]1C=C[CH]C2CC12'),
E0 = (383.853,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.49215,0.0109613,0.000103648,-1.34768e-07,4.975e-11,46241,12.4982], Tmin=(100,'K'), Tmax=(980.623,'K')), NASAPolynomial(coeffs=[11.7826,0.0296118,-1.13759e-05,2.23341e-09,-1.68299e-13,41700.1,-46.0035], Tmin=(980.623,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(383.853,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_2) + radical(cyclohexene-allyl) + radical(cyclohexene-allyl)"""),
)
species(
label = '[C]1=C[CH]CC2CC12(1184)',
structure = SMILES('[C]1=C[CH]CC2CC12'),
E0 = (482.402,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.28954,0.0185506,7.87039e-05,-1.08157e-07,4.03178e-11,58098.1,15.8342], Tmin=(100,'K'), Tmax=(986.879,'K')), NASAPolynomial(coeffs=[11.3782,0.0298335,-1.15868e-05,2.2459e-09,-1.66553e-13,53960.9,-39.7658], Tmin=(986.879,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(482.402,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(cyclohexene-allyl) + radical(Cds_S)"""),
)
species(
label = '[C]1[CH]CC2CC2C=1(1185)',
structure = SMILES('[C]1[CH]CC2CC2C=1'),
E0 = (482.402,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.28954,0.0185506,7.87039e-05,-1.08157e-07,4.03178e-11,58098.1,15.8342], Tmin=(100,'K'), Tmax=(986.879,'K')), NASAPolynomial(coeffs=[11.3782,0.0298335,-1.15868e-05,2.2459e-09,-1.66553e-13,53960.9,-39.7658], Tmin=(986.879,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(482.402,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cds_S) + radical(cyclohexene-allyl)"""),
)
species(
label = '[C]1C=CC2CC2C1(1187)',
structure = SMILES('[C]1=C[CH]C2CC2C1'),
E0 = (483.093,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.25478,0.0208484,6.97421e-05,-9.68875e-08,3.58026e-11,58181,15.6242], Tmin=(100,'K'), Tmax=(998.131,'K')), NASAPolynomial(coeffs=[10.6228,0.0314779,-1.26026e-05,2.44172e-09,-1.79415e-13,54310.5,-35.7516], Tmin=(998.131,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(483.093,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_2) + radical(Cds_S) + radical(cyclohexene-allyl)"""),
)
species(
label = '[C]1=CC2CC2CC1(1103)',
structure = SMILES('[C]1=CC2CC2CC1'),
E0 = (343.8,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.08382,0.0238614,6.94659e-05,-9.93345e-08,3.72194e-11,41434.7,17.6793], Tmin=(100,'K'), Tmax=(991.685,'K')), NASAPolynomial(coeffs=[11.3549,0.0322866,-1.25848e-05,2.41655e-09,-1.7727e-13,37342.9,-38.3307], Tmin=(991.685,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(343.8,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cds_S)"""),
)
species(
label = 'C1=CC2C[C]2CC1(1101)',
structure = SMILES('C1=CC2C[C]2CC1'),
E0 = (291.38,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.19854,0.0266724,4.77348e-05,-6.76087e-08,2.38604e-11,35120.9,18.6255], Tmin=(100,'K'), Tmax=(1045.21,'K')), NASAPolynomial(coeffs=[7.72114,0.0383157,-1.60151e-05,3.05672e-09,-2.18276e-13,32176,-16.8271], Tmin=(1045.21,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(291.38,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Tertalkyl)"""),
)
species(
label = '[C]1=CCCC2CC12(1104)',
structure = SMILES('[C]1=CCCC2CC12'),
E0 = (343.8,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.08382,0.0238614,6.94659e-05,-9.93345e-08,3.72194e-11,41434.7,17.6793], Tmin=(100,'K'), Tmax=(991.685,'K')), NASAPolynomial(coeffs=[11.3549,0.0322866,-1.25848e-05,2.41655e-09,-1.7727e-13,37342.9,-38.3307], Tmin=(991.685,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(343.8,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cds_S)"""),
)
species(
label = 'C1=C[C]2CC2CC1(1102)',
structure = SMILES('C1=C[C]2CC2CC1'),
E0 = (237.939,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.39584,0.0138569,9.97166e-05,-1.31334e-07,4.88152e-11,28694.4,15.0316], Tmin=(100,'K'), Tmax=(974.231,'K')), NASAPolynomial(coeffs=[11.2577,0.0318596,-1.17411e-05,2.23974e-09,-1.66091e-13,24386.6,-40.7368], Tmin=(974.231,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(237.939,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Allyl_T)"""),
)
species(
label = '[CH]1C2C=CCCC12(1046)',
structure = SMILES('[CH]1C2C=CCCC12'),
E0 = (331.871,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.28481,0.0197962,7.62455e-05,-1.02678e-07,3.74489e-11,39992.4,18.8324], Tmin=(100,'K'), Tmax=(1000.79,'K')), NASAPolynomial(coeffs=[9.97757,0.0344689,-1.38223e-05,2.66885e-09,-1.95247e-13,36178.1,-29.6506], Tmin=(1000.79,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(331.871,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(cyclopropane)"""),
)
species(
label = '[CH]1C2CC3CC3C12(1186)',
structure = SMILES('[CH]1C2CC3CC3C12'),
E0 = (409.229,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.36907,0.0195077,7.2345e-05,-9.29868e-08,3.22069e-11,49291.6,16.663], Tmin=(100,'K'), Tmax=(1044.85,'K')), NASAPolynomial(coeffs=[8.47781,0.0389869,-1.71582e-05,3.3857e-09,-2.46918e-13,45675.3,-24.2732], Tmin=(1044.85,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(409.229,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + polycyclic(s2_3_5_ane) + polycyclic(s2_3_5_ane) - ring(Cyclopentane) + radical(bicyclo[3.1.0]hexane-C3)"""),
)
species(
label = 'C=CC=CC1[CH]C1(944)',
structure = SMILES('C=CC=CC1[CH]C1'),
E0 = (392.007,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2883.33,3016.67,3150,900,966.667,1033.33,1100,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.48324,0.0364896,4.55082e-05,-8.63459e-08,3.59445e-11,47255.2,25.6467], Tmin=(100,'K'), Tmax=(960.767,'K')), NASAPolynomial(coeffs=[15.7726,0.0238448,-7.8896e-06,1.45709e-09,-1.08433e-13,42347.3,-53.9715], Tmin=(960.767,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(392.007,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(Cyclopropane) + radical(cyclopropane)"""),
)
species(
label = '[CH2]C1C=CC2CC12(1115)',
structure = SMILES('[CH2]C1C=CC2CC12'),
E0 = (318.71,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.99875,0.0218788,8.57864e-05,-1.25886e-07,4.95288e-11,38424.1,20.0243], Tmin=(100,'K'), Tmax=(952.079,'K')), NASAPolynomial(coeffs=[14.4605,0.0254501,-7.95384e-06,1.45253e-09,-1.09229e-13,33516.4,-52.7964], Tmin=(952.079,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(318.71,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_5_ene_1) + radical(Isobutyl)"""),
)
species(
label = 'C1=CCC2C[C]2C1(1189)',
structure = SMILES('C1=CCC2C[C]2C1'),
E0 = (292.071,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.16194,0.0289649,3.89292e-05,-5.67583e-08,1.9626e-11,35203.9,18.4238], Tmin=(100,'K'), Tmax=(1074.37,'K')), NASAPolynomial(coeffs=[7.14409,0.0396746,-1.68735e-05,3.21654e-09,-2.28227e-13,32444.7,-13.8283], Tmin=(1074.37,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(292.071,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_2) + radical(Tertalkyl)"""),
)
species(
label = '[C]1=CCC2CC2C1(1190)',
structure = SMILES('[C]1=CCC2CC2C1'),
E0 = (344.49,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.04804,0.0261697,6.04766e-05,-8.80443e-08,3.27034e-11,41517.6,17.4731], Tmin=(100,'K'), Tmax=(1004.35,'K')), NASAPolynomial(coeffs=[10.6138,0.0339075,-1.35874e-05,2.60929e-09,-1.8988e-13,37686.2,-34.3978], Tmin=(1004.35,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(344.49,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_2) + radical(Cds_S)"""),
)
species(
label = '[CH]1C2CC=CCC12(1098)',
structure = SMILES('[CH]1C2CC=CCC12'),
E0 = (332.561,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.24964,0.0220936,6.73125e-05,-9.14904e-08,3.29913e-11,40075.3,17.9311], Tmin=(100,'K'), Tmax=(1014.63,'K')), NASAPolynomial(coeffs=[9.25357,0.0360624,-1.48098e-05,2.85815e-09,-2.0758e-13,36513.7,-26.5078], Tmin=(1014.63,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(332.561,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_2) + radical(cyclopropane)"""),
)
species(
label = '[CH]1CC2CC=CC12(1038)',
structure = SMILES('[CH]1CC2CC=CC12'),
E0 = (312.651,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.39033,0.01664,8.50361e-05,-1.11028e-07,4.0121e-11,37677.7,18.8915], Tmin=(100,'K'), Tmax=(1001.43,'K')), NASAPolynomial(coeffs=[9.78379,0.0349878,-1.41629e-05,2.75269e-09,-2.02205e-13,33796.1,-28.7753], Tmin=(1001.43,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(312.651,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane)"""),
)
species(
label = '[CH]1C2C=CCC1C2(1114)',
structure = SMILES('[CH]1C2C=CCC1C2'),
E0 = (319.824,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.49849,0.0115915,0.000102703,-1.3198e-07,4.82399e-11,38539.1,18.1235], Tmin=(100,'K'), Tmax=(985.173,'K')), NASAPolynomial(coeffs=[10.7886,0.0326483,-1.26668e-05,2.46624e-09,-1.83686e-13,34250.4,-35.224], Tmin=(985.173,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(319.824,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s3_4_6_ene_1) + radical(cyclobutane)"""),
)
species(
label = '[C]1=CC=CCCC1(845)',
structure = SMILES('[C]1=CC=CCCC1'),
E0 = (310.692,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.768498,0.0589739,-1.82471e-05,-1.82743e-08,1.10431e-11,37494.6,8.95384], Tmin=(100,'K'), Tmax=(1016.06,'K')), NASAPolynomial(coeffs=[15.2699,0.027889,-1.07467e-05,1.99338e-09,-1.41491e-13,33205.4,-67.8437], Tmin=(1016.06,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(310.692,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(Cds_S)"""),
)
species(
label = '[C]1=CCCCC=C1(846)',
structure = SMILES('[C]1=CCCCC=C1'),
E0 = (271.846,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.72053,0.0609687,-2.28133e-05,-1.41996e-08,9.9797e-12,32823.4,8.24802], Tmin=(100,'K'), Tmax=(998.484,'K')), NASAPolynomial(coeffs=[14.8463,0.0285937,-1.05528e-05,1.90163e-09,-1.3276e-13,28795.5,-65.9233], Tmin=(998.484,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(271.846,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(C=CJC=C)"""),
)
species(
label = '[CH]1[CH]CC=CC=C1(1035)',
structure = SMILES('[CH]1C=C[CH]CC=C1'),
E0 = (333.186,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.70798,-0.040239,0.000339088,-4.54205e-07,1.84685e-10,40184,19.5412], Tmin=(100,'K'), Tmax=(904.636,'K')), NASAPolynomial(coeffs=[40.627,-0.029508,2.54911e-05,-5.11056e-09,3.33198e-13,26023.7,-199.942], Tmin=(904.636,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(333.186,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Allyl_S) + radical(C=CCJC=C)"""),
)
species(
label = '[C]1=CC=CC[CH]C1(1036)',
structure = SMILES('[C]1=CC=CC[CH]C1'),
E0 = (505.151,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.03105,0.0591881,-4.04298e-05,1.41996e-08,-2.04422e-12,60867.3,10.5843], Tmin=(100,'K'), Tmax=(1597.2,'K')), NASAPolynomial(coeffs=[13.4726,0.0280297,-1.11676e-05,1.98559e-09,-1.32442e-13,56893,-55.2647], Tmin=(1597.2,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(505.151,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(Cds_S) + radical(RCCJCC)"""),
)
species(
label = '[C]1=CC[CH]CC=C1(1037)',
structure = SMILES('[C]1=CC[CH]CC=C1'),
E0 = (466.304,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.04576,0.0605485,-4.32564e-05,1.66684e-08,-2.68197e-12,56193.3,9.64654], Tmin=(100,'K'), Tmax=(1430.9,'K')), NASAPolynomial(coeffs=[11.6905,0.0307918,-1.20625e-05,2.13497e-09,-1.42743e-13,53147,-45.522], Tmin=(1430.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(466.304,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(RCCJCC) + radical(C=CJC=C)"""),
)
species(
label = '[CH]=CC=CCC=C(920)',
structure = SMILES('[CH]=CC=CCC=C'),
E0 = (387.618,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,3120,650,792.5,1650,2950,3100,1380,975,1025,1650,180,180],'cm^-1')),
HinderedRotor(inertia=(0.801607,'amu*angstrom^2'), symmetry=1, barrier=(18.4305,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.801708,'amu*angstrom^2'), symmetry=1, barrier=(18.4328,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.800374,'amu*angstrom^2'), symmetry=1, barrier=(18.4022,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.852866,0.0562357,-1.12075e-05,-2.77884e-08,1.52379e-11,46744.5,27.1581], Tmin=(100,'K'), Tmax=(982.939,'K')), NASAPolynomial(coeffs=[15.9085,0.0248669,-8.96456e-06,1.63626e-09,-1.16756e-13,42340.4,-52.5649], Tmin=(982.939,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(387.618,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_P)"""),
)
species(
label = '[CH]1CC2C=CC2C1(864)',
structure = SMILES('[CH]1CC2C=CC2C1'),
E0 = (339.607,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.51293,0.0164375,7.81282e-05,-9.97899e-08,3.54206e-11,40913.1,17.1376], Tmin=(100,'K'), Tmax=(1008.55,'K')), NASAPolynomial(coeffs=[7.77001,0.0373506,-1.50897e-05,2.8871e-09,-2.08782e-13,37728.7,-18.7996], Tmin=(1008.55,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(339.607,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + Estimated bicyclic component: polycyclic(s2_4_5_ane) - ring(Cyclopentane) - ring(Cyclobutane) + ring(Cyclopentane) + ring(Cyclobutene) + radical(cyclopentane)"""),
)
species(
label = '[C]1CC=CC=CC1(1039)',
structure = SMILES('[C]1CC=CC=CC1'),
E0 = (521.066,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.767794,0.0572244,-1.23418e-05,-2.8543e-08,1.58476e-11,62798.4,7.69412], Tmin=(100,'K'), Tmax=(985.505,'K')), NASAPolynomial(coeffs=[17.1335,0.0226807,-8.29035e-06,1.543e-09,-1.11912e-13,58024.5,-78.8768], Tmin=(985.505,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(521.066,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(CCJ2_triplet)"""),
)
species(
label = 'C1=CC=CCCC=1(1040)',
structure = SMILES('C1=CC=CCCC=1'),
E0 = (295.75,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.90408,0.0276378,5.9589e-05,-9.39152e-08,3.68174e-11,35662.5,15.3984], Tmin=(100,'K'), Tmax=(974.9,'K')), NASAPolynomial(coeffs=[13.31,0.0267804,-9.77764e-06,1.85702e-09,-1.37535e-13,31255.4,-50.5358], Tmin=(974.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(295.75,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cdd-CdsCds) + ring(1_2_cycloheptadiene)"""),
)
species(
label = '[C]1=CCCC=CC1(1044)',
structure = SMILES('[C]1=CCCC=CC1'),
E0 = (328.19,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.16908,-0.0181441,0.000268059,-3.72022e-07,1.52953e-10,39592.9,24.0778], Tmin=(100,'K'), Tmax=(905.932,'K')), NASAPolynomial(coeffs=[37.0862,-0.0202439,1.97429e-05,-3.99587e-09,2.59571e-13,27026,-175.369], Tmin=(905.932,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(328.19,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Cds_S)"""),
)
species(
label = '[C]1=CCC=CCC1(1045)',
structure = SMILES('[C]1=CCC=CCC1'),
E0 = (328.19,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.16908,-0.0181441,0.000268059,-3.72022e-07,1.52953e-10,39592.9,24.0778], Tmin=(100,'K'), Tmax=(905.932,'K')), NASAPolynomial(coeffs=[37.0862,-0.0202439,1.97429e-05,-3.99587e-09,2.59571e-13,27026,-175.369], Tmin=(905.932,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(328.19,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Cds_S)"""),
)
species(
label = '[C]1=C[CH]C=CCC1(1041)',
structure = SMILES('[C]1=C[CH]C=CCC1'),
E0 = (429.915,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.4818,-0.0304742,0.000305554,-4.16705e-07,1.70846e-10,51821.5,22.6266], Tmin=(100,'K'), Tmax=(902.923,'K')), NASAPolynomial(coeffs=[39.3533,-0.0274524,2.41569e-05,-4.87715e-09,3.20018e-13,38381.5,-189.045], Tmin=(902.923,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(429.915,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Cds_S) + radical(C=CCJC=C)"""),
)
species(
label = '[C]1[CH]C=CCCC=1(1042)',
structure = SMILES('[C]1=CCC[CH]C=C1'),
E0 = (412.959,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.932191,0.055587,-1.33254e-05,-2.33471e-08,1.32157e-11,49788.4,6.38168], Tmin=(100,'K'), Tmax=(987.683,'K')), NASAPolynomial(coeffs=[14.843,0.0261855,-9.58067e-06,1.73707e-09,-1.22548e-13,45726.7,-67.2086], Tmin=(987.683,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(412.959,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(C=CJC=C) + radical(Allyl_S)"""),
)
species(
label = 'C=CC1[CH]C1C=C(1047)',
structure = SMILES('C=CC1[CH]C1C=C'),
E0 = (412.485,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,2750,2950,3150,900,1000,1100,2995,3025,975,1000,1300,1375,400,500,1630,1680,300,800,800,800,800,800,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.69524,0.0323153,5.20481e-05,-8.8819e-08,3.56686e-11,49710,25.5822], Tmin=(100,'K'), Tmax=(970.4,'K')), NASAPolynomial(coeffs=[14.1581,0.0264991,-9.37964e-06,1.75924e-09,-1.29897e-13,45146.3,-45.2211], Tmin=(970.4,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(412.485,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + group(Cds-CdsHH) + ring(Cyclopropane) + radical(cyclopropane)"""),
)
species(
label = '[C]1=C[CH]CCC=C1(1043)',
structure = SMILES('[C]1=C[CH]CCC=C1'),
E0 = (412.959,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.932217,0.0555867,-1.33243e-05,-2.33485e-08,1.32164e-11,49788.4,5.68844], Tmin=(100,'K'), Tmax=(987.677,'K')), NASAPolynomial(coeffs=[14.8429,0.0261857,-9.58077e-06,1.7371e-09,-1.2255e-13,45726.8,-67.9012], Tmin=(987.677,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(412.959,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cycloheptadiene) + radical(Allyl_S) + radical(C=CJC=C)"""),
)
species(
label = '[CH]1CCC2C=CC12(863)',
structure = SMILES('[CH]1CCC2C=CC12'),
E0 = (348.268,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.43619,0.0149235,8.99399e-05,-1.16572e-07,4.22077e-11,41960.5,18.0978], Tmin=(100,'K'), Tmax=(997.638,'K')), NASAPolynomial(coeffs=[10.0487,0.0341372,-1.37297e-05,2.6768e-09,-1.97574e-13,37966.5,-31.015], Tmin=(997.638,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(348.268,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + Estimated bicyclic component: polycyclic(s2_4_5_ane) - ring(Cyclopentane) - ring(Cyclobutane) + ring(Cyclopentane) + ring(Cyclobutene) + radical(Cs_S)"""),
)
species(
label = '[CH]1C=CC2CCC12(1048)',
structure = SMILES('[CH]1C=CC2CCC12'),
E0 = (252.118,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.4256,0.0108296,0.000112132,-1.45541e-07,5.3837e-11,30400.8,14.6164], Tmin=(100,'K'), Tmax=(977.79,'K')), NASAPolynomial(coeffs=[12.3355,0.0309153,-1.16851e-05,2.28892e-09,-1.72853e-13,25564.7,-47.7901], Tmin=(977.79,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(252.118,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclopentene-allyl)"""),
)
species(
label = 'C1=CCC=CCC=1(1094)',
structure = SMILES('C1=CCC=CCC=1'),
E0 = (315.628,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.18463,0.0228973,6.4064e-05,-9.18887e-08,3.43726e-11,38041.8,16.3106], Tmin=(100,'K'), Tmax=(994.126,'K')), NASAPolynomial(coeffs=[10.8394,0.0307169,-1.20778e-05,2.32135e-09,-1.70063e-13,34213.8,-35.9911], Tmin=(994.126,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(315.628,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cdd-CdsCds) + ring(1_2_cycloheptadiene)"""),
)
species(
label = '[C]1=CCC=C[CH]C1(1095)',
structure = SMILES('[C]1=CC[CH]C=CC1'),
E0 = (469.303,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.36799,-0.0233771,0.00027704,-3.80544e-07,1.55941e-10,56558.4,22.2572], Tmin=(100,'K'), Tmax=(906.062,'K')), NASAPolynomial(coeffs=[37.1593,-0.0227806,2.07887e-05,-4.17773e-09,2.71213e-13,43924.7,-177.085], Tmin=(906.062,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(469.303,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Allyl_S) + radical(Cds_S)"""),
)
species(
label = '[C]1=C[CH]CC=CC1(1096)',
structure = SMILES('[C]1=C[CH]CC=CC1'),
E0 = (469.303,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.36799,-0.0233771,0.00027704,-3.80544e-07,1.55941e-10,56558.4,22.2572], Tmin=(100,'K'), Tmax=(906.062,'K')), NASAPolynomial(coeffs=[37.1593,-0.0227806,2.07887e-05,-4.17773e-09,2.71213e-13,43924.7,-177.085], Tmin=(906.062,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(469.303,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Allyl_S) + radical(Cds_S)"""),
)
species(
label = '[C]1[CH]CC=CCC=1(1097)',
structure = SMILES('[C]1[CH]CC=CCC=1'),
E0 = (469.303,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.36799,-0.0233771,0.00027704,-3.80544e-07,1.55941e-10,56558.4,21.5641], Tmin=(100,'K'), Tmax=(906.062,'K')), NASAPolynomial(coeffs=[37.1593,-0.0227806,2.07887e-05,-4.17773e-09,2.71213e-13,43924.7,-177.778], Tmin=(906.062,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(469.303,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cycloheptadiene) + radical(Allyl_S) + radical(Cds_S)"""),
)
species(
label = '[CH]=CCC=CC=C(919)',
structure = SMILES('[CH]=CCC=CC=C'),
E0 = (387.618,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,3120,650,792.5,1650,2950,3100,1380,975,1025,1650,180,180],'cm^-1')),
HinderedRotor(inertia=(0.801607,'amu*angstrom^2'), symmetry=1, barrier=(18.4305,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.801708,'amu*angstrom^2'), symmetry=1, barrier=(18.4328,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.800374,'amu*angstrom^2'), symmetry=1, barrier=(18.4022,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.852866,0.0562357,-1.12075e-05,-2.77884e-08,1.52379e-11,46744.5,27.1581], Tmin=(100,'K'), Tmax=(982.939,'K')), NASAPolynomial(coeffs=[15.9085,0.0248669,-8.96456e-06,1.63626e-09,-1.16756e-13,42340.4,-52.5649], Tmin=(982.939,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(387.618,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_P)"""),
)
species(
label = 'C1=CC2CC2=CC1(1099)',
structure = SMILES('C1=CC2CC2=CC1'),
E0 = (313.593,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.74552,0.0324258,4.32694e-05,-7.68101e-08,3.06009e-11,37812.9,20.3929], Tmin=(100,'K'), Tmax=(989.58,'K')), NASAPolynomial(coeffs=[13.9151,0.0253235,-9.76289e-06,1.89684e-09,-1.41161e-13,33343.5,-48.6035], Tmin=(989.58,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(313.593,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_diene_0_3)"""),
)
species(
label = '[CH]1CC=CC2C[C]12(1105)',
structure = SMILES('[CH]1CC=CC2C[C]12'),
E0 = (485.923,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.39883,0.0252598,3.74302e-05,-5.14632e-08,1.7125e-11,58508.9,20.4681], Tmin=(100,'K'), Tmax=(1104.09,'K')), NASAPolynomial(coeffs=[6.16714,0.0383762,-1.67567e-05,3.21488e-09,-2.2798e-13,56045.2,-5.47374], Tmin=(1104.09,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(485.923,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Tertalkyl) + radical(Cs_S)"""),
)
species(
label = '[CH]1CC=C[C]2CC12(1106)',
structure = SMILES('[CH]1CC=C[C]2CC12'),
E0 = (432.481,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.59031,0.012579,8.86091e-05,-1.13629e-07,4.11738e-11,52082.5,16.8908], Tmin=(100,'K'), Tmax=(991.38,'K')), NASAPolynomial(coeffs=[9.30637,0.0325513,-1.28288e-05,2.47665e-09,-1.8214e-13,48437.8,-27.1182], Tmin=(991.38,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(432.481,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cs_S) + radical(Allyl_T)"""),
)
species(
label = '[CH]1CC=CC2[CH]C12(1107)',
structure = SMILES('[CH]1CC=CC2[CH]C12'),
E0 = (526.413,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.48046,0.018484,6.53641e-05,-8.544e-08,3.00918e-11,63380.5,20.6887], Tmin=(100,'K'), Tmax=(1028.74,'K')), NASAPolynomial(coeffs=[8.13411,0.0349866,-1.48135e-05,2.88362e-09,-2.095e-13,60180.8,-16.645], Tmin=(1028.74,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(526.413,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cs_S) + radical(cyclopropane)"""),
)
species(
label = '[C]1=CC[CH]C2CC12(1109)',
structure = SMILES('[C]1=CC[CH]C2CC12'),
E0 = (538.342,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.27728,0.0225823,5.84322e-05,-8.18408e-08,2.97252e-11,64822.9,19.543], Tmin=(100,'K'), Tmax=(1017.44,'K')), NASAPolynomial(coeffs=[9.48042,0.0328535,-1.36029e-05,2.63742e-09,-1.92015e-13,61359.8,-25.1483], Tmin=(1017.44,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(538.342,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cs_S) + radical(Cds_S)"""),
)
species(
label = '[C]1=CC2CC2[CH]C1(1110)',
structure = SMILES('[C]1=CC2CC2[CH]C1'),
E0 = (538.342,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.27728,0.0225823,5.84322e-05,-8.18408e-08,2.97252e-11,64822.9,19.543], Tmin=(100,'K'), Tmax=(1017.44,'K')), NASAPolynomial(coeffs=[9.48042,0.0328535,-1.36029e-05,2.63742e-09,-1.92015e-13,61359.8,-25.1483], Tmin=(1017.44,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(538.342,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(Cds_S) + radical(Cs_S)"""),
)
species(
label = '[CH]1CC2C1C1CC21(1111)',
structure = SMILES('[CH]1CC2C1C1CC21'),
E0 = (451.657,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.46793,0.0156886,8.43654e-05,-1.07579e-07,3.81175e-11,54392.6,16.5102], Tmin=(100,'K'), Tmax=(1015.13,'K')), NASAPolynomial(coeffs=[8.91875,0.0367299,-1.53767e-05,3.00936e-09,-2.20535e-13,50689.1,-26.4989], Tmin=(1015.13,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(451.657,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + polycyclic(s2_4_4_ane) + polycyclic(s2_3_4_ane) - ring(Cyclobutane) + radical(bicyclo[2.2.0]hexane-secondary)"""),
)
species(
label = '[CH]1C2CC2C2CC12(1112)',
structure = SMILES('[CH]1C2CC2C2CC12'),
E0 = (347.724,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.36907,0.0195077,7.2345e-05,-9.29868e-08,3.22069e-11,41894.3,15.9698], Tmin=(100,'K'), Tmax=(1044.85,'K')), NASAPolynomial(coeffs=[8.47781,0.0389869,-1.71582e-05,3.3857e-09,-2.46918e-13,38278,-24.9663], Tmin=(1044.85,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(347.724,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + polycyclic(s2_3_5_ane) + polycyclic(s2_3_5_ane) - ring(Cyclopentane) + radical(bicyclo[3.1.0]hexane-C5-2)"""),
)
species(
label = '[CH]=CC1CC1C=C(1113)',
structure = SMILES('[CH]=CC1CC1C=C'),
E0 = (433.668,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2883.33,3016.67,3150,900,966.667,1033.33,1100,2995,3025,975,1000,1300,1375,400,500,1630,1680,3120,650,792.5,1650,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.45544,0.0352702,5.35723e-05,-9.76887e-08,4.06809e-11,52268.7,25.1915], Tmin=(100,'K'), Tmax=(954.747,'K')), NASAPolynomial(coeffs=[16.9902,0.0219443,-6.80878e-06,1.25426e-09,-9.53297e-14,46943.3,-61.3892], Tmin=(954.747,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(433.668,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + group(Cds-CdsHH) + ring(Cyclopropane) + radical(Cds_P)"""),
)
species(
label = '[C]1CC=CC2CC12(1116)',
structure = SMILES('[C]1CC=CC2CC12'),
E0 = (554.173,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.06879,0.0222762,7.4826e-05,-1.08951e-07,4.17753e-11,66739.2,17.1644], Tmin=(100,'K'), Tmax=(977.527,'K')), NASAPolynomial(coeffs=[13.3166,0.0269147,-1.00355e-05,1.94444e-09,-1.45903e-13,62119.6,-49.2258], Tmin=(977.527,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(554.173,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_6_ene_1) + radical(CCJ2_triplet)"""),
)
species(
label = 'C=C1C=CCC=C1(1117)',
structure = SMILES('C=C1C=CCC=C1'),
E0 = (153.051,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.921,0.0291054,5.02247e-05,-8.07381e-08,3.12074e-11,18497.3,16.1349], Tmin=(100,'K'), Tmax=(992.58,'K')), NASAPolynomial(coeffs=[12.2027,0.0287016,-1.11705e-05,2.14393e-09,-1.57294e-13,14435.1,-43.573], Tmin=(992.58,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(153.051,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-Cds(Cds-Cds)(Cds-Cds)) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(14cyclohexadiene3methylene)"""),
)
species(
label = 'C[C]1C=CCC=C1(1118)',
structure = SMILES('CC1C=CC[CH]C=1'),
E0 = (147.67,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.54226,0.0440189,4.38914e-06,-2.65201e-08,1.02003e-11,17857.4,18.1894], Tmin=(100,'K'), Tmax=(1131.65,'K')), NASAPolynomial(coeffs=[9.9628,0.0367867,-1.58904e-05,3.02112e-09,-2.12763e-13,14508.8,-29.8506], Tmin=(1131.65,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(147.67,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'CC1[C]=CCC=C1(1119)',
structure = SMILES('CC1[C]=CCC=C1'),
E0 = (298.09,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.52497,0.0422186,1.5228e-05,-4.27823e-08,1.73253e-11,35951.6,20.9058], Tmin=(100,'K'), Tmax=(1028.69,'K')), NASAPolynomial(coeffs=[11.0191,0.0335841,-1.34224e-05,2.51259e-09,-1.78122e-13,32501.8,-32.44], Tmin=(1028.69,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(298.09,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cyclohexadiene) + radical(Cds_S)"""),
)
species(
label = 'CC1C=[C]CC=C1(1120)',
structure = SMILES('CC1C=[C]CC=C1'),
E0 = (298.09,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.52497,0.0422186,1.5228e-05,-4.27823e-08,1.73253e-11,35951.6,20.9058], Tmin=(100,'K'), Tmax=(1028.69,'K')), NASAPolynomial(coeffs=[11.0191,0.0335841,-1.34224e-05,2.51259e-09,-1.78122e-13,32501.8,-32.44], Tmin=(1028.69,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(298.09,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cyclohexadiene) + radical(Cds_S)"""),
)
species(
label = '[CH2][C]1C=CCC=C1(1121)',
structure = SMILES('[CH2]C1C=CC[CH]C=1'),
E0 = (299.169,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.56291,0.0413666,1.30634e-05,-3.93866e-08,1.57053e-11,36080,18.3901], Tmin=(100,'K'), Tmax=(1056.99,'K')), NASAPolynomial(coeffs=[11.4828,0.0319785,-1.35651e-05,2.60664e-09,-1.8681e-13,32410.3,-37.4565], Tmin=(1056.99,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(299.169,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Allyl_P) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = '[CH2]C1C=C[CH]C=C1(1122)',
structure = SMILES('[CH2]C1[CH]C=CC=C1'),
E0 = (358.591,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.73813,0.0382105,1.75711e-05,-4.30647e-08,1.72123e-11,43220,20.6576], Tmin=(100,'K'), Tmax=(1023.65,'K')), NASAPolynomial(coeffs=[10.2577,0.0318844,-1.26719e-05,2.36496e-09,-1.67453e-13,40063,-27.5443], Tmin=(1023.65,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(358.591,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Isobutyl) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = '[CH2]C1[C]=CCC=C1(1123)',
structure = SMILES('[CH2]C1[C]=CCC=C1'),
E0 = (503.172,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.54866,0.0438673,2.70561e-06,-3.0037e-08,1.35128e-11,60614.7,22.6005], Tmin=(100,'K'), Tmax=(1003.06,'K')), NASAPolynomial(coeffs=[10.6188,0.0305225,-1.14715e-05,2.07158e-09,-1.44066e-13,57646.9,-26.909], Tmin=(1003.06,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(503.172,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cyclohexadiene) + radical(Cds_S) + radical(Isobutyl)"""),
)
species(
label = '[CH2]C1C=[C]CC=C1(1124)',
structure = SMILES('[CH2]C1C=[C]CC=C1'),
E0 = (503.172,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.54866,0.0438673,2.70561e-06,-3.0037e-08,1.35128e-11,60614.7,22.6005], Tmin=(100,'K'), Tmax=(1003.06,'K')), NASAPolynomial(coeffs=[10.6188,0.0305225,-1.14715e-05,2.07158e-09,-1.44066e-13,57646.9,-26.909], Tmin=(1003.06,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(503.172,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cyclohexadiene) + radical(Isobutyl) + radical(Cds_S)"""),
)
species(
label = '[CH]C1C=CCC=C1(1125)',
structure = SMILES('[CH]C1C=CCC=C1'),
E0 = (508.463,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.51969,0.0405311,2.08767e-05,-5.26557e-08,2.19317e-11,61255.6,21.4537], Tmin=(100,'K'), Tmax=(998.229,'K')), NASAPolynomial(coeffs=[12.8619,0.0284074,-1.09829e-05,2.06592e-09,-1.48836e-13,57330.8,-41.5626], Tmin=(998.229,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(508.463,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(1,4-Cyclohexadiene) + radical(CCJ2_triplet)"""),
)
species(
label = 'C1=CC2CC=C2C1(1126)',
structure = SMILES('C1=CC2CC=C2C1'),
E0 = (261.951,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.9311,0.0327551,3.05293e-05,-5.32162e-08,1.95528e-11,31590.7,13.482], Tmin=(100,'K'), Tmax=(1059.9,'K')), NASAPolynomial(coeffs=[10.0137,0.0334006,-1.44665e-05,2.81294e-09,-2.02846e-13,28127.8,-34.2358], Tmin=(1059.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(261.951,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + Estimated bicyclic component: polycyclic(s2_4_5_ane) - ring(Cyclopentane) - ring(Cyclobutane) + ring(Cyclopentene) + ring(Cyclobutene)"""),
)
species(
label = 'C1=CC2CC[C]2C1(1127)',
structure = SMILES('C1=CC2CC[C]2C1'),
E0 = (310.235,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.30646,0.0234885,5.66184e-05,-7.6073e-08,2.65776e-11,37385.5,18.6759], Tmin=(100,'K'), Tmax=(1042.34,'K')), NASAPolynomial(coeffs=[7.51162,0.0388606,-1.63704e-05,3.14398e-09,-2.25516e-13,34380.2,-15.8626], Tmin=(1042.34,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(310.235,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(Tertalkyl)"""),
)
species(
label = 'C1=C[C]2CCC2C1(1128)',
structure = SMILES('C1=C[C]2CCC2C1'),
E0 = (256.794,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.49982,0.0107203,0.000108431,-1.39574e-07,5.14347e-11,30959.1,15.0961], Tmin=(100,'K'), Tmax=(976.008,'K')), NASAPolynomial(coeffs=[11.0641,0.0323778,-1.2081e-05,2.3234e-09,-1.73033e-13,26584,-39.8621], Tmin=(976.008,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(256.794,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(Allyl_T)"""),
)
species(
label = '[C]1=CCC2CCC12(1129)',
structure = SMILES('[C]1=CCC2CCC12'),
E0 = (382.319,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.1887,0.0207131,7.82272e-05,-1.07645e-07,3.98731e-11,46064.5,17.7407], Tmin=(100,'K'), Tmax=(992.856,'K')), NASAPolynomial(coeffs=[11.1624,0.0328033,-1.2924e-05,2.50006e-09,-1.84201e-13,41904.8,-37.4624], Tmin=(992.856,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(382.319,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclopentene-vinyl)"""),
)
species(
label = '[C]1=CC2CCC2C1(1130)',
structure = SMILES('[C]1=CC2CCC2C1'),
E0 = (382.319,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.1887,0.0207131,7.82272e-05,-1.07645e-07,3.98731e-11,46064.5,17.7407], Tmin=(100,'K'), Tmax=(992.856,'K')), NASAPolynomial(coeffs=[11.1624,0.0328033,-1.2924e-05,2.50006e-09,-1.84201e-13,41904.8,-37.4624], Tmin=(992.856,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(382.319,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclopentene-vinyl)"""),
)
species(
label = '[CH]1CC2C=CC[C]12(1131)',
structure = SMILES('[CH]1CC2C=CC[C]12'),
E0 = (498.074,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.48201,0.02385,3.96334e-05,-5.21187e-08,1.69792e-11,59966.9,20.3994], Tmin=(100,'K'), Tmax=(1115.58,'K')), NASAPolynomial(coeffs=[5.47751,0.0396956,-1.74204e-05,3.33938e-09,-2.3628e-13,57644.2,-1.79455], Tmin=(1115.58,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(498.074,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane) + radical(Tertalkyl)"""),
)
species(
label = '[CH]1C[C]2C=CCC12(1132)',
structure = SMILES('[CH]1C[C]2C=CCC12'),
E0 = (444.632,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.66942,0.0112323,9.05168e-05,-1.13789e-07,4.07663e-11,53540.7,16.8358], Tmin=(100,'K'), Tmax=(995.662,'K')), NASAPolynomial(coeffs=[8.52663,0.0340116,-1.35688e-05,2.61833e-09,-1.91815e-13,50078.9,-22.9237], Tmin=(995.662,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(444.632,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane) + radical(Allyl_T)"""),
)
species(
label = '[CH]1[CH]C2CC=CC12(1133)',
structure = SMILES('[CH]1[CH]C2CC=CC12'),
E0 = (500.49,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.56029,0.0171236,6.73464e-05,-8.57393e-08,2.9764e-11,60259.4,20.6314], Tmin=(100,'K'), Tmax=(1035.11,'K')), NASAPolynomial(coeffs=[7.3815,0.0364035,-1.55296e-05,3.01984e-09,-2.18734e-13,57230.3,-12.6049], Tmin=(1035.11,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(500.49,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane) + radical(cyclobutane)"""),
)
species(
label = '[CH]1C=CC2C[CH]C12(1134)',
structure = SMILES('[CH]1C=CC2C[CH]C12'),
E0 = (439.957,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.59609,0.0113301,9.42632e-05,-1.19824e-07,4.32017e-11,52982.4,17.0462], Tmin=(100,'K'), Tmax=(996.256,'K')), NASAPolynomial(coeffs=[9.79898,0.0325479,-1.31722e-05,2.58373e-09,-1.91627e-13,49059.1,-30.1641], Tmin=(996.256,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(439.957,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane) + radical(cyclopentene-allyl)"""),
)
species(
label = '[C]1=CCC2[CH]CC12(1135)',
structure = SMILES('[C]1=CCC2[CH]CC12'),
E0 = (570.158,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.35639,0.021232,6.03711e-05,-8.20716e-08,2.93625e-11,68646.3,19.4882], Tmin=(100,'K'), Tmax=(1023.57,'K')), NASAPolynomial(coeffs=[8.72132,0.0342806,-1.43245e-05,2.77488e-09,-2.01346e-13,65356.8,-21.0711], Tmin=(1023.57,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(570.158,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclobutane) + radical(cyclopentene-vinyl)"""),
)
species(
label = '[C]1=CC2C[CH]C2C1(1136)',
structure = SMILES('[C]1=CC2C[CH]C2C1'),
E0 = (570.158,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.35639,0.021232,6.03711e-05,-8.20716e-08,2.93625e-11,68646.3,19.4882], Tmin=(100,'K'), Tmax=(1023.57,'K')), NASAPolynomial(coeffs=[8.72132,0.0342806,-1.43245e-05,2.77488e-09,-2.01346e-13,65356.8,-21.0711], Tmin=(1023.57,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(570.158,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(cyclopentene-vinyl) + radical(cyclobutane)"""),
)
species(
label = '[CH]1C2CC3C1CC23(1137)',
structure = SMILES('[CH]1C2CC3C1CC23'),
E0 = (425.038,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.61354,0.0194674,5.83434e-05,-6.90103e-08,2.16303e-11,51179.2,9.27091], Tmin=(100,'K'), Tmax=(1137.58,'K')), NASAPolynomial(coeffs=[5.09614,0.0451981,-2.10236e-05,4.13079e-09,-2.95593e-13,48384.6,-12.8265], Tmin=(1137.58,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(425.038,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + polycyclic(s3_4_5_ane) + polycyclic(s2_4_5_ane) + polycyclic(s2_4_4_ane) - ring(Cyclobutane) - ring(Cyclobutane) - ring(Cyclopentane) + radical(bicyclo[2.1.1]hexane-C2)"""),
)
species(
label = '[CH]1CC2C3CC2C13(1138)',
structure = SMILES('[CH]1CC2C3CC2C13'),
E0 = (451.079,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.69917,0.0178323,6.1181e-05,-7.06534e-08,2.19128e-11,54307.8,8.38879], Tmin=(100,'K'), Tmax=(1142.42,'K')), NASAPolynomial(coeffs=[4.58097,0.0460129,-2.14723e-05,4.21981e-09,-3.01782e-13,51608.9,-10.8707], Tmin=(1142.42,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(451.079,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + polycyclic(s2_4_5_ane) + polycyclic(s2_4_5_ane) + polycyclic(s3_4_4_ane) - ring(Cyclobutane) - ring(Cyclobutane) - ring(Cyclopentane) + radical(bicyclo[2.1.1]hexane-C2)"""),
)
species(
label = 'C=CC1[CH]C=CC1(1139)',
structure = SMILES('C=CC1[CH]C=CC1'),
E0 = (221.552,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,3010,987.5,1337.5,450,1655,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.07399,0.0182744,9.77052e-05,-1.37654e-07,5.30915e-11,26737.7,20.4962], Tmin=(100,'K'), Tmax=(962.863,'K')), NASAPolynomial(coeffs=[14.9301,0.0257265,-8.71516e-06,1.67472e-09,-1.28456e-13,21440.8,-55.6906], Tmin=(962.863,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(221.552,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclopentene) + radical(cyclopentene-allyl)"""),
)
species(
label = 'C=C[CH]C1C=CC1(972)',
structure = SMILES('[CH2]C=CC1C=CC1'),
E0 = (331.643,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2995,3025,975,1000,1300,1375,400,500,1630,1680,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.388,0.0419512,2.55895e-05,-6.06455e-08,2.53498e-11,39995.2,22.4016], Tmin=(100,'K'), Tmax=(988.264,'K')), NASAPolynomial(coeffs=[13.8576,0.0287074,-1.08126e-05,2.02716e-09,-1.46698e-13,35712.7,-46.8074], Tmin=(988.264,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(331.643,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(Cyclobutene) + radical(Allyl_P)"""),
)
species(
label = '[CH2]C1C2C=CCC12(1140)',
structure = SMILES('[CH2]C1C2C=CCC12'),
E0 = (317.64,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.00086,0.022135,8.43701e-05,-1.23783e-07,4.85939e-11,38295.1,19.9822], Tmin=(100,'K'), Tmax=(953.955,'K')), NASAPolynomial(coeffs=[14.2375,0.0259375,-8.26671e-06,1.51617e-09,-1.13655e-13,33452.8,-51.6194], Tmin=(953.955,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(317.64,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_5_ene_1) + radical(Isobutyl)"""),
)
species(
label = '[C]1CC2C=CCC12(1141)',
structure = SMILES('[C]1CC2C=CCC12'),
E0 = (573.028,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.17297,0.0191369,8.35515e-05,-1.17208e-07,4.44029e-11,69003.9,17.2282], Tmin=(100,'K'), Tmax=(979.271,'K')), NASAPolynomial(coeffs=[13.1233,0.0274325,-1.03752e-05,2.02806e-09,-1.52842e-13,64316.8,-48.3528], Tmin=(979.271,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(573.028,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_5_ene_1) + radical(CCJ2_triplet)"""),
)
species(
label = 'CC1C=C=CC=C1(1142)',
structure = SMILES('CC1C=C=CC=C1'),
E0 = (354.933,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.51932,0.04084,1.83655e-05,-5.052e-08,2.14499e-11,42790.1,17.1911], Tmin=(100,'K'), Tmax=(991.681,'K')), NASAPolynomial(coeffs=[13.0177,0.0271087,-1.02481e-05,1.91402e-09,-1.37771e-13,38904.1,-46.2801], Tmin=(991.681,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(354.933,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cdd-CdsCds) + ring(124cyclohexatriene)"""),
)
species(
label = 'C[C]1C=CC=CC1(1143)',
structure = SMILES('CC1=CC=C[CH]C1'),
E0 = (146.206,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.54224,0.0440192,4.38828e-06,-2.65191e-08,1.01999e-11,17681.3,19.0387], Tmin=(100,'K'), Tmax=(1131.65,'K')), NASAPolynomial(coeffs=[9.96296,0.0367865,-1.58903e-05,3.02108e-09,-2.1276e-13,14332.6,-29.0024], Tmin=(1131.65,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(146.206,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'CC1C=CC=[C]C1(1144)',
structure = SMILES('CC1C=CC=[C]C1'),
E0 = (294.462,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.77148,0.0301738,5.88366e-05,-9.49817e-08,3.75589e-11,35512.8,19.7421], Tmin=(100,'K'), Tmax=(971.51,'K')), NASAPolynomial(coeffs=[13.7047,0.0278905,-9.97252e-06,1.87345e-09,-1.38121e-13,30983.2,-48.8623], Tmin=(971.51,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(294.462,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Cds_S)"""),
)
species(
label = 'CC1[C]=CC=CC1(1145)',
structure = SMILES('CC1[C]=CC=CC1'),
E0 = (294.462,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.77148,0.0301738,5.88366e-05,-9.49817e-08,3.75589e-11,35512.8,19.7421], Tmin=(100,'K'), Tmax=(971.51,'K')), NASAPolynomial(coeffs=[13.7047,0.0278905,-9.97252e-06,1.87345e-09,-1.38121e-13,30983.2,-48.8623], Tmin=(971.51,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(294.462,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Cds_S)"""),
)
species(
label = 'CC1C=C[C]=CC1(1146)',
structure = SMILES('CC1C=C[C]=CC1'),
E0 = (255.616,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.72009,0.0321972,5.42336e-05,-9.0966e-08,3.65744e-11,30841.8,19.0492], Tmin=(100,'K'), Tmax=(961.203,'K')), NASAPolynomial(coeffs=[13.3575,0.02847,-9.70845e-06,1.76547e-09,-1.28064e-13,26539.6,-47.3759], Tmin=(961.203,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(255.616,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C)"""),
)
species(
label = 'CC1C=[C]C=CC1(1147)',
structure = SMILES('CC1C=[C]C=CC1'),
E0 = (255.616,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.72009,0.0321972,5.42336e-05,-9.0966e-08,3.65744e-11,30841.8,19.0492], Tmin=(100,'K'), Tmax=(961.203,'K')), NASAPolynomial(coeffs=[13.3575,0.02847,-9.70845e-06,1.76547e-09,-1.28064e-13,26539.6,-47.3759], Tmin=(961.203,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(255.616,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C)"""),
)
species(
label = '[CH]1[CH]C=CC=C1(1148)',
structure = SMILES('[CH]1[CH]C=CC=C1'),
E0 = (282.149,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (78.1118,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.65505,0.030509,-1.75379e-06,-8.37309e-09,2.50596e-12,33982.1,11.3122], Tmin=(100,'K'), Tmax=(1572.61,'K')), NASAPolynomial(coeffs=[9.21399,0.0284297,-1.37001e-05,2.5963e-09,-1.76603e-13,30113.3,-29.0418], Tmin=(1572.61,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(282.149,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(282.692,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'CC1[C]=CC=C[CH]1(1149)',
structure = SMILES('CC1[C]=CC=C[CH]1'),
E0 = (391.35,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.67552,0.0411614,5.65849e-06,-2.75448e-08,1.07406e-11,47160.6,19.5808], Tmin=(100,'K'), Tmax=(1108.77,'K')), NASAPolynomial(coeffs=[9.95366,0.0336692,-1.44718e-05,2.75697e-09,-1.94909e-13,43949.7,-27.4126], Tmin=(1108.77,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(391.35,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Cds_S) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'CC1[CH]C=C[C]=C1(1150)',
structure = SMILES('CC1[CH]C=C[C]=C1'),
E0 = (352.504,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.60944,0.0433834,2.33583e-07,-2.22646e-08,9.12346e-12,42490.2,18.939], Tmin=(100,'K'), Tmax=(1103.49,'K')), NASAPolynomial(coeffs=[9.52416,0.0343753,-1.42754e-05,2.66406e-09,-1.86046e-13,39545.1,-25.454], Tmin=(1103.49,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(352.504,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3) + radical(C=CJC=C)"""),
)
species(
label = 'CC1[CH]C=[C]C=C1(1151)',
structure = SMILES('CC1[CH]C=[C]C=C1'),
E0 = (352.504,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.60944,0.0433834,2.33583e-07,-2.22646e-08,9.12346e-12,42490.2,18.2458], Tmin=(100,'K'), Tmax=(1103.49,'K')), NASAPolynomial(coeffs=[9.52416,0.0343753,-1.42754e-05,2.66406e-09,-1.86046e-13,39545.1,-26.1472], Tmin=(1103.49,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(352.504,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'CC1C=CC2[CH]C12(1152)',
structure = SMILES('CC1C=CC2[CH]C12'),
E0 = (339.54,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.14659,0.0207548,8.01627e-05,-1.1223e-07,4.22523e-11,40921.8,20.0603], Tmin=(100,'K'), Tmax=(982.26,'K')), NASAPolynomial(coeffs=[12.1323,0.0304565,-1.15662e-05,2.22852e-09,-1.65329e-13,36530.4,-40.3045], Tmin=(982.26,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(339.54,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_5_ene_1) + radical(cyclopropane)"""),
)
species(
label = 'CC1C2[CH]C=CC12(1153)',
structure = SMILES('CC1C2[CH]C=CC12'),
E0 = (239.863,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.18398,0.0151871,0.00010596,-1.44913e-07,5.52102e-11,28936.6,16.437], Tmin=(100,'K'), Tmax=(966.096,'K')), NASAPolynomial(coeffs=[14.5292,0.0267603,-9.33918e-06,1.81406e-09,-1.39234e-13,23625.8,-57.836], Tmin=(966.096,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(239.863,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_5_ene_1) + radical(cyclopentene-allyl)"""),
)
species(
label = '[CH]=CC=CC=CC(1154)',
structure = SMILES('[CH]=CC=CC=CC'),
E0 = (356.764,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3120,650,792.5,1650,2750,2800,2850,1350,1500,750,1050,1375,1000,2995,3002.5,3010,3017.5,3025,975,981.25,987.5,993.75,1000,1300,1318.75,1337.5,1356.25,1375,400,425,450,475,500,1630,1642.5,1655,1667.5,1680,180],'cm^-1')),
HinderedRotor(inertia=(0.92726,'amu*angstrom^2'), symmetry=1, barrier=(21.3195,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.928214,'amu*angstrom^2'), symmetry=1, barrier=(21.3415,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.924188,'amu*angstrom^2'), symmetry=1, barrier=(21.2489,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.634137,0.0609006,-1.91113e-05,-2.40414e-08,1.51535e-11,43041.8,24.9201], Tmin=(100,'K'), Tmax=(963.844,'K')), NASAPolynomial(coeffs=[17.4008,0.0223664,-7.46131e-06,1.32205e-09,-9.40252e-14,38367.5,-62.8334], Tmin=(963.844,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(356.764,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(Cds_P)"""),
)
species(
label = 'CC1[CH]CC=CC=1(1155)',
structure = SMILES('CC1[CH]CC=CC=1'),
E0 = (146.206,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.54224,0.0440192,4.38828e-06,-2.65191e-08,1.01999e-11,17681.3,19.0387], Tmin=(100,'K'), Tmax=(1131.65,'K')), NASAPolynomial(coeffs=[9.96296,0.0367865,-1.58903e-05,3.02108e-09,-2.1276e-13,14332.6,-29.0024], Tmin=(1131.65,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(146.206,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3)"""),
)
species(
label = 'C[CH]C1C=CC=C1(1156)',
structure = SMILES('C[CH]C1C=CC=C1'),
E0 = (259.422,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.52122,0.0410904,2.002e-05,-4.92637e-08,1.98867e-11,31302.2,21.7404], Tmin=(100,'K'), Tmax=(1021.2,'K')), NASAPolynomial(coeffs=[11.9213,0.031902,-1.28233e-05,2.42907e-09,-1.74101e-13,27533.1,-36.7064], Tmin=(1021.2,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(259.422,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(Cyclopentadiene) + radical(Cs_S)"""),
)
species(
label = 'CC1=CC2C=CC12(1157)',
structure = SMILES('CC1=CC2C=CC12'),
E0 = (350.525,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.42189,0.0517704,-2.85408e-05,7.32027e-09,-7.38392e-13,42255.6,14.1261], Tmin=(100,'K'), Tmax=(2259.39,'K')), NASAPolynomial(coeffs=[18.2081,0.0220524,-8.81129e-06,1.49882e-09,-9.42557e-14,34670.2,-80.5402], Tmin=(2259.39,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(350.525,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + Estimated bicyclic component: polycyclic(s2_4_4_ane) - ring(Cyclobutane) - ring(Cyclobutane) + ring(Cyclobutene) + ring(Cyclobutene)"""),
)
species(
label = 'CC1C=C2C=CC21(1158)',
structure = SMILES('CC1C=C2C=CC21'),
E0 = (336.333,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.45647,0.0413606,1.95625e-05,-5.40421e-08,2.32582e-11,40556.1,14.3074], Tmin=(100,'K'), Tmax=(983.291,'K')), NASAPolynomial(coeffs=[13.9995,0.0253244,-9.34899e-06,1.74745e-09,-1.26834e-13,36398,-54.5951], Tmin=(983.291,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(336.333,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-CsHHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + Estimated bicyclic component: polycyclic(s2_4_4_ane) - ring(Cyclobutane) - ring(Cyclobutane) + ring(Cyclobutene) + ring(Cyclobutene)"""),
)
species(
label = 'C1=CC2C=CC12(1159)',
structure = SMILES('C1=CC2C=CC12'),
E0 = (389.58,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (78.1118,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.24105,0.0324057,-1.67402e-06,-1.3176e-08,5.20455e-12,46924.1,9.01711], Tmin=(100,'K'), Tmax=(1174.09,'K')), NASAPolynomial(coeffs=[7.9921,0.0263739,-1.12936e-05,2.12407e-09,-1.4809e-13,44638.9,-23.632], Tmin=(1174.09,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(389.58,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(282.692,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + Estimated bicyclic component: polycyclic(s2_4_4_ane) - ring(Cyclobutane) - ring(Cyclobutane) + ring(Cyclobutene) + ring(Cyclobutene)"""),
)
species(
label = 'C[C]1CC2C=CC12(1160)',
structure = SMILES('C[C]1CC2C=CC12'),
E0 = (261.759,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.92358,0.0354267,2.17956e-05,-4.10966e-08,1.47546e-11,31565.6,19.5931], Tmin=(100,'K'), Tmax=(1086.48,'K')), NASAPolynomial(coeffs=[7.92062,0.0380624,-1.5964e-05,3.00937e-09,-2.11777e-13,28803.8,-16.5495], Tmin=(1086.48,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(261.759,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Tertalkyl)"""),
)
species(
label = 'CC1C[C]2C=CC21(1161)',
structure = SMILES('CC1C[C]2C=CC21'),
E0 = (208.317,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.13479,0.0224806,7.40625e-05,-1.04917e-07,3.96202e-11,25138.4,15.9472], Tmin=(100,'K'), Tmax=(978.733,'K')), NASAPolynomial(coeffs=[11.1809,0.0320524,-1.19377e-05,2.24929e-09,-1.64211e-13,21138.5,-38.8886], Tmin=(978.733,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(208.317,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Allyl_T)"""),
)
species(
label = 'CC1CC2C=C[C]12(1162)',
structure = SMILES('CC1CC2C=C[C]12'),
E0 = (208.317,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.13479,0.0224806,7.40625e-05,-1.04917e-07,3.96202e-11,25138.4,15.9472], Tmin=(100,'K'), Tmax=(978.733,'K')), NASAPolynomial(coeffs=[11.1809,0.0320524,-1.19377e-05,2.24929e-09,-1.64211e-13,21138.5,-38.8886], Tmin=(978.733,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(208.317,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Allyl_T)"""),
)
species(
label = '[CH2]C1CC2C=CC12(1163)',
structure = SMILES('[CH2]C1CC2C=CC12'),
E0 = (281.418,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.87681,0.0295519,5.60882e-05,-8.95826e-08,3.53019e-11,33938.7,19.7078], Tmin=(100,'K'), Tmax=(967.234,'K')), NASAPolynomial(coeffs=[12.1321,0.0298138,-1.04953e-05,1.92293e-09,-1.38943e-13,29958.7,-39.745], Tmin=(967.234,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(281.418,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Isobutyl)"""),
)
species(
label = 'CC1CC2[C]=CC12(1164)',
structure = SMILES('CC1CC2[C]=CC12'),
E0 = (328.822,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.8192,0.0325238,4.36956e-05,-7.28008e-08,2.7992e-11,39640.2,18.608], Tmin=(100,'K'), Tmax=(1001.9,'K')), NASAPolynomial(coeffs=[11.3144,0.0324193,-1.27474e-05,2.41818e-09,-1.74741e-13,35840.2,-36.688], Tmin=(1001.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(328.822,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutene-vinyl)"""),
)
species(
label = 'CC1CC2C=[C]C12(1165)',
structure = SMILES('CC1CC2C=[C]C12'),
E0 = (328.822,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.8192,0.0325238,4.36956e-05,-7.28008e-08,2.7992e-11,39640.2,18.608], Tmin=(100,'K'), Tmax=(1001.9,'K')), NASAPolynomial(coeffs=[11.3144,0.0324193,-1.27474e-05,2.41818e-09,-1.74741e-13,35840.2,-36.688], Tmin=(1001.9,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(328.822,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutene-vinyl)"""),
)
species(
label = 'CC1[CH]C2C=C[C]12(1166)',
structure = SMILES('CC1[CH]C2C=C[C]12'),
E0 = (396.155,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.2999,0.0230424,5.5992e-05,-7.89616e-08,2.88959e-11,47720.3,17.7032], Tmin=(100,'K'), Tmax=(1006.09,'K')), NASAPolynomial(coeffs=[8.68362,0.0336196,-1.33877e-05,2.53544e-09,-1.82273e-13,44615.9,-22.1774], Tmin=(1006.09,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(396.155,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Allyl_T) + radical(cyclobutane)"""),
)
species(
label = '[CH]1[CH]C2C=CC12(1167)',
structure = SMILES('[CH]1[CH]C2C=CC12'),
E0 = (484.835,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (78.1118,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.87312,0.0160689,3.88556e-05,-4.97889e-08,1.66487e-11,58360,17.0865], Tmin=(100,'K'), Tmax=(1077.19,'K')), NASAPolynomial(coeffs=[5.48658,0.0298224,-1.29623e-05,2.49756e-09,-1.78204e-13,56436,-2.0335], Tmin=(1077.19,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(484.835,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(282.692,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutane) + radical(cyclobutane)"""),
)
species(
label = 'C[C]1[CH]C2C=CC12(1168)',
structure = SMILES('C[C]1[CH]C2C=CC12'),
E0 = (449.597,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.11373,0.0355229,6.15926e-06,-1.94574e-08,6.36522e-12,54146.6,21.2706], Tmin=(100,'K'), Tmax=(1259.19,'K')), NASAPolynomial(coeffs=[6.77783,0.037538,-1.62913e-05,3.04424e-09,-2.09845e-13,51637.7,-7.60437], Tmin=(1259.19,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(449.597,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutane) + radical(Tertalkyl)"""),
)
species(
label = 'CC1[CH][C]2C=CC21(1169)',
structure = SMILES('CC1[CH][C]2C=CC21'),
E0 = (396.155,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.2999,0.0230424,5.5992e-05,-7.89616e-08,2.88959e-11,47720.3,17.7032], Tmin=(100,'K'), Tmax=(1006.09,'K')), NASAPolynomial(coeffs=[8.68362,0.0336196,-1.33877e-05,2.53544e-09,-1.82273e-13,44615.9,-22.1774], Tmin=(1006.09,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(396.155,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutane) + radical(Allyl_T)"""),
)
species(
label = '[CH2]C1[CH]C2C=CC12(1170)',
structure = SMILES('[CH2]C1[CH]C2C=CC12'),
E0 = (469.257,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.03868,0.030161,3.78046e-05,-6.32735e-08,2.43887e-11,56520.6,21.4749], Tmin=(100,'K'), Tmax=(993.955,'K')), NASAPolynomial(coeffs=[9.60448,0.0314292,-1.19719e-05,2.21511e-09,-1.5749e-13,53450,-22.8608], Tmin=(993.955,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(469.257,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Isobutyl) + radical(cyclobutane)"""),
)
species(
label = 'CC1[CH]C2C=[C]C12(1171)',
structure = SMILES('CC1[CH]C2C=[C]C12'),
E0 = (516.66,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.97858,0.0331262,2.56239e-05,-4.70727e-08,1.74752e-11,62222.3,20.3862], Tmin=(100,'K'), Tmax=(1052.29,'K')), NASAPolynomial(coeffs=[9.00098,0.0336891,-1.4032e-05,2.66629e-09,-1.89712e-13,59235.3,-21.0212], Tmin=(1052.29,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(516.66,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutene-vinyl) + radical(cyclobutane)"""),
)
species(
label = 'CC1[CH]C2[C]=CC12(1172)',
structure = SMILES('CC1[CH]C2[C]=CC12'),
E0 = (516.66,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.97858,0.0331262,2.56239e-05,-4.70727e-08,1.74752e-11,62222.3,20.3862], Tmin=(100,'K'), Tmax=(1052.29,'K')), NASAPolynomial(coeffs=[9.00098,0.0336891,-1.4032e-05,2.66629e-09,-1.89712e-13,59235.3,-21.0212], Tmin=(1052.29,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(516.66,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(cyclobutane) + radical(cyclobutene-vinyl)"""),
)
species(
label = 'CC1C2C3[CH]C2C13(1173)',
structure = SMILES('CC1C2C3[CH]C2C13'),
E0 = (549.27,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.43476,0.0229351,5.0885e-05,-6.46996e-08,2.11187e-11,66127.8,12.0191], Tmin=(100,'K'), Tmax=(1107.77,'K')), NASAPolynomial(coeffs=[6.06563,0.0421691,-1.89565e-05,3.68929e-09,-2.63639e-13,63338.8,-14.827], Tmin=(1107.77,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(549.27,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + polycyclic(s2_4_4_ane) + polycyclic(s2_4_4_ane) + polycyclic(s3_4_4_ane) - ring(Cyclobutane) - ring(Cyclobutane) - ring(Cyclobutane) + radical(bicyclo[1.1.1]pentane-C2)"""),
)
species(
label = 'CC1C2[CH]C3C1C23(1174)',
structure = SMILES('CC1C2[CH]C3C1C23'),
E0 = (479.69,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.27365,0.0277547,3.79396e-05,-5.20715e-08,1.69402e-11,57763.9,14.4492], Tmin=(100,'K'), Tmax=(1134.94,'K')), NASAPolynomial(coeffs=[6.42866,0.0419154,-1.88456e-05,3.64636e-09,-2.58898e-13,54965.6,-14.2952], Tmin=(1134.94,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(479.69,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + polycyclic(s2_4_4_ane) + polycyclic(s2_3_4_ane) + polycyclic(s2_3_4_ane) - ring(Cyclobutane) - ring(Cyclopropane) - ring(Cyclobutane) + radical(bicyclo[2.1.1]hexane-C5)"""),
)
species(
label = 'CC=CC1[CH]C=C1(1175)',
structure = SMILES('CC=CC1[CH]C=C1'),
E0 = (344.687,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2883.33,3016.67,3150,900,966.667,1033.33,1100,2995,3025,975,1000,1300,1375,400,500,1630,1680,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.5787,0.0393006,2.57585e-05,-5.5529e-08,2.22309e-11,41555.6,20.331], Tmin=(100,'K'), Tmax=(1009.86,'K')), NASAPolynomial(coeffs=[11.8389,0.0318904,-1.25934e-05,2.37407e-09,-1.70236e-13,37789,-37.6586], Tmin=(1009.86,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(344.687,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + ring(Cyclobutene) + radical(cyclobutene-allyl)"""),
)
species(
label = '[CH]=CC1C=CC1C(1176)',
structure = SMILES('[CH]=CC1C=CC1C'),
E0 = (431.513,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3120,650,792.5,1650,2750,2883.33,3016.67,3150,900,966.667,1033.33,1100,3010,987.5,1337.5,450,1655,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.29825,0.0447319,1.73244e-05,-5.29222e-08,2.29576e-11,52009.4,22.346], Tmin=(100,'K'), Tmax=(984.76,'K')), NASAPolynomial(coeffs=[14.0948,0.0278032,-1.02774e-05,1.90651e-09,-1.37244e-13,47789.6,-47.8221], Tmin=(984.76,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(431.513,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclobutene) + radical(Cds_P)"""),
)
species(
label = 'C[CH]C1C2C=CC12(1177)',
structure = SMILES('C[CH]C1C2C=CC12'),
E0 = (455.918,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3025,407.5,1350,352.5,2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.84413,0.0291642,5.75977e-05,-9.0262e-08,3.47501e-11,54928.1,22.6655], Tmin=(100,'K'), Tmax=(991.652,'K')), NASAPolynomial(coeffs=[12.9334,0.0296643,-1.15762e-05,2.23766e-09,-1.65209e-13,50504.8,-41.9543], Tmin=(991.652,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(455.918,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_3_4_ene_1) + radical(Cs_S)"""),
)
species(
label = 'CC1C2[CH]C1C=C2(1178)',
structure = SMILES('CC1C2[CH]C1C=C2'),
E0 = (426.333,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.31398,0.0138877,0.000102969,-1.37779e-07,5.17055e-11,51357.6,18.8084], Tmin=(100,'K'), Tmax=(973.387,'K')), NASAPolynomial(coeffs=[13.0222,0.0287142,-1.05373e-05,2.05223e-09,-1.55438e-13,46485.9,-46.8801], Tmin=(973.387,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(426.333,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s3_4_5_ene_1) + radical(bicyclo[2.1.1]hex-2-ene-C5)"""),
)
species(
label = 'CC1[C]C2C=CC12(1179)',
structure = SMILES('CC1[C]C2C=CC12'),
E0 = (524.551,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.80719,0.0309059,4.91539e-05,-8.25159e-08,3.25749e-11,63183.3,18.082], Tmin=(100,'K'), Tmax=(983.167,'K')), NASAPolynomial(coeffs=[13.2451,0.0270989,-1.02271e-05,1.95283e-09,-1.43929e-13,58869.2,-47.4072], Tmin=(983.167,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(524.551,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(CCJ2_triplet)"""),
)
species(
label = '[CH2]C1[C]=CC=CC1(1191)',
structure = SMILES('[CH2]C1[C]=CC=CC1'),
E0 = (499.545,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.79573,0.0317879,4.65818e-05,-8.28275e-08,3.41201e-11,60175.8,21.4364], Tmin=(100,'K'), Tmax=(951.957,'K')), NASAPolynomial(coeffs=[13.4436,0.0246032,-7.89595e-06,1.40355e-09,-1.01716e-13,56066.1,-44.1225], Tmin=(951.957,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(499.545,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Isobutyl) + radical(Cds_S)"""),
)
species(
label = '[CH2]C1C=CC=[C]C1(1192)',
structure = SMILES('[CH2]C1C=CC=[C]C1'),
E0 = (499.545,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.79573,0.0317879,4.65818e-05,-8.28275e-08,3.41201e-11,60175.8,21.4364], Tmin=(100,'K'), Tmax=(951.957,'K')), NASAPolynomial(coeffs=[13.4436,0.0246032,-7.89595e-06,1.40355e-09,-1.01716e-13,56066.1,-44.1225], Tmin=(951.957,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(499.545,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Isobutyl) + radical(Cds_S)"""),
)
species(
label = '[CH2]C1C=C[C]=CC1(1193)',
structure = SMILES('[CH2]C1C=C[C]=CC1'),
E0 = (460.698,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.74784,0.033761,4.2203e-05,-7.91839e-08,3.3336e-11,55504.7,20.7316], Tmin=(100,'K'), Tmax=(939.307,'K')), NASAPolynomial(coeffs=[13.1186,0.0251473,-7.61236e-06,1.29111e-09,-9.13001e-14,51612.4,-42.7619], Tmin=(939.307,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(460.698,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Isobutyl) + radical(C=CJC=C)"""),
)
species(
label = '[CH2]C1C=[C]C=CC1(1194)',
structure = SMILES('[CH2]C1C=[C]C=CC1'),
E0 = (460.698,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.74784,0.033761,4.2203e-05,-7.91839e-08,3.3336e-11,55504.7,20.7316], Tmin=(100,'K'), Tmax=(939.307,'K')), NASAPolynomial(coeffs=[13.1186,0.0251473,-7.61236e-06,1.29111e-09,-9.13001e-14,51612.4,-42.7619], Tmin=(939.307,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(460.698,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Isobutyl) + radical(C=CJC=C)"""),
)
species(
label = '[CH]1CC2C=CC1C2(1195)',
structure = SMILES('[CH]1CC2C=CC1C2'),
E0 = (287.679,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.59453,0.00649534,0.000121023,-1.5341e-07,5.6323e-11,34672.3,18.113], Tmin=(100,'K'), Tmax=(977.98,'K')), NASAPolynomial(coeffs=[12.011,0.0304557,-1.1548e-05,2.28035e-09,-1.73286e-13,29842.8,-42.3811], Tmin=(977.98,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(287.679,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s3_5_5_ene_1) + radical(Cs_S)"""),
)
species(
label = '[CH]1C=CC2CC1C2(1196)',
structure = SMILES('[CH]1C=CC2CC1C2'),
E0 = (270.588,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.53186,0.00579415,0.000129804,-1.66586e-07,6.20387e-11,32620.9,13.8558], Tmin=(100,'K'), Tmax=(967.783,'K')), NASAPolynomial(coeffs=[13.3993,0.0284792,-1.01348e-05,1.98992e-09,-1.53308e-13,27351.7,-54.5734], Tmin=(967.783,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(270.588,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s3_4_6_ene_1) + radical(cyclohexene-allyl)"""),
)
species(
label = '[CH]C1C=CC=CC1(1197)',
structure = SMILES('[CH]C1C=CC=CC1'),
E0 = (504.836,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.7563,0.0285823,6.42611e-05,-1.04754e-07,4.2218e-11,60817.3,20.3267], Tmin=(100,'K'), Tmax=(960.122,'K')), NASAPolynomial(coeffs=[15.7067,0.0224528,-7.38637e-06,1.39283e-09,-1.0606e-13,55742.1,-58.8873], Tmin=(960.122,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(504.836,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(CCJ2_triplet)"""),
)
species(
label = 'C=C1C=C=CCC1(1198)',
structure = SMILES('C=C1C=C=CCC1'),
E0 = (213.151,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.9368,0.02027,9.60388e-05,-1.36392e-07,5.21272e-11,25732.9,14.841], Tmin=(100,'K'), Tmax=(977.964,'K')), NASAPolynomial(coeffs=[15.9544,0.026172,-1.00047e-05,2.01432e-09,-1.55799e-13,19967.2,-67.9339], Tmin=(977.964,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(213.151,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cdd-CdsCds) + ring(Cyclohexane)"""),
)
species(
label = 'C=C1C[C]=CCC1(1199)',
structure = SMILES('C=C1C[C]=CCC1'),
E0 = (303.778,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.08485,0.020526,8.84184e-05,-1.19723e-07,4.38137e-11,36624,18.9096], Tmin=(100,'K'), Tmax=(1004.47,'K')), NASAPolynomial(coeffs=[12.2915,0.0346643,-1.45035e-05,2.88281e-09,-2.14809e-13,31809.8,-44.1343], Tmin=(1004.47,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(303.778,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Cds_S)"""),
)
species(
label = 'C=C1[CH]CC=CC1(1200)',
structure = SMILES('C=C1[CH]CC=CC1'),
E0 = (207.049,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.32136,0.0106534,0.00012225,-1.57469e-07,5.76852e-11,24986,17.1726], Tmin=(100,'K'), Tmax=(988.4,'K')), NASAPolynomial(coeffs=[13.4314,0.03283,-1.32945e-05,2.67854e-09,-2.0402e-13,19510.3,-52.8873], Tmin=(988.4,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(207.049,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Allyl_S)"""),
)
species(
label = '[CH]=C1CC=CCC1(1201)',
structure = SMILES('[CH]=C1CC=CCC1'),
E0 = (313.032,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2807.14,2864.29,2921.43,2978.57,3035.71,3092.86,3150,900,928.571,957.143,985.714,1014.29,1042.86,1071.43,1100,3120,650,792.5,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.04205,0.0194849,9.63625e-05,-1.31273e-07,4.86749e-11,37740.5,19.685], Tmin=(100,'K'), Tmax=(994.004,'K')), NASAPolynomial(coeffs=[13.6533,0.032442,-1.32537e-05,2.6493e-09,-1.99775e-13,32483.7,-51.0937], Tmin=(994.004,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(313.032,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Cds_P)"""),
)
species(
label = 'C=C1CC=[C]CC1(1202)',
structure = SMILES('C=C1CC=[C]CC1'),
E0 = (303.778,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.08485,0.020526,8.84184e-05,-1.19723e-07,4.38137e-11,36624,18.9096], Tmin=(100,'K'), Tmax=(1004.47,'K')), NASAPolynomial(coeffs=[12.2915,0.0346643,-1.45035e-05,2.88281e-09,-2.14809e-13,31809.8,-44.1343], Tmin=(1004.47,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(303.778,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Cds_S)"""),
)
species(
label = 'C=C1C[CH]C=CC1(1203)',
structure = SMILES('C=C1C[CH]C=CC1'),
E0 = (204.538,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.32136,0.0106534,0.00012225,-1.57469e-07,5.76852e-11,24684.1,15.7863], Tmin=(100,'K'), Tmax=(988.4,'K')), NASAPolynomial(coeffs=[13.4314,0.03283,-1.32945e-05,2.67854e-09,-2.0402e-13,19208.4,-54.2735], Tmin=(988.4,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(204.538,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(cyclohexene-allyl)"""),
)
species(
label = 'C=C1[CH]C=CC[CH]1(1204)',
structure = SMILES('[CH2]C1[CH]CC=CC=1'),
E0 = (264.261,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.84503,0.0349594,2.76454e-05,-5.16358e-08,1.94794e-11,31871.6,19.5027], Tmin=(100,'K'), Tmax=(1040.47,'K')), NASAPolynomial(coeffs=[9.81315,0.0341423,-1.41611e-05,2.69291e-09,-1.92156e-13,28599.6,-27.0106], Tmin=(1040.47,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(264.261,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Aromatic_pi_S_1_3) + radical(C=CC=CCJ)"""),
)
species(
label = 'C=C1[CH]C=[C]CC1(1205)',
structure = SMILES('C=C1[CH]C=[C]CC1'),
E0 = (405.503,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.41043,0.00810941,0.000125849,-1.63667e-07,6.10414e-11,48852.1,18.1016], Tmin=(100,'K'), Tmax=(972.454,'K')), NASAPolynomial(coeffs=[14.1803,0.0280824,-1.04442e-05,2.08415e-09,-1.61144e-13,43329.4,-54.9782], Tmin=(972.454,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(405.503,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Cds_S) + radical(C=CCJC=C)"""),
)
species(
label = 'C=C1[CH][C]=CCC1(1206)',
structure = SMILES('[CH2]C1=C[C]=CCC1'),
E0 = (366.369,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.84647,0.0306415,5.16383e-05,-8.66328e-08,3.4979e-11,44156.7,19.6042], Tmin=(100,'K'), Tmax=(955.035,'K')), NASAPolynomial(coeffs=[12.5419,0.027617,-9.21837e-06,1.64576e-09,-1.18163e-13,40208.8,-41.4762], Tmin=(955.035,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(366.369,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C) + radical(C=CC=CCJ)"""),
)
species(
label = '[CH]=C1[CH]C=CCC1(1207)',
structure = SMILES('[CH]=C1[CH]C=CCC1'),
E0 = (414.757,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,3120,650,792.5,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.36662,0.00707093,0.000133835,-1.75357e-07,6.60051e-11,49968.6,18.1881], Tmin=(100,'K'), Tmax=(966.697,'K')), NASAPolynomial(coeffs=[15.5976,0.0257695,-9.14361e-06,1.83891e-09,-1.45154e-13,43978.8,-62.9457], Tmin=(966.697,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(414.757,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(357.522,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(C=CCJC=C) + radical(Cds_P)"""),
)
species(
label = 'C=C1CCC2[CH]C12(1208)',
structure = SMILES('C=C1CCC2[CH]C12'),
E0 = (357.854,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.25162,0.0192825,8.08929e-05,-1.09658e-07,4.04178e-11,43119.8,20.2427], Tmin=(100,'K'), Tmax=(993.304,'K')), NASAPolynomial(coeffs=[10.891,0.0329833,-1.30239e-05,2.52214e-09,-1.85895e-13,39011.3,-33.4207], Tmin=(993.304,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(357.854,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsCs) + group(Cds-CdsHH) + polycyclic(s2_3_5_ane) + radical(cyclopropane)"""),
)
species(
label = '[CH2]CC=CC=C=C(879)',
structure = SMILES('[CH2]CC=CC=C=C'),
E0 = (381.075,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,2950,3100,1380,975,1025,1650,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,540,610,2055,3000,3100,440,815,1455,1000,180,180],'cm^-1')),
HinderedRotor(inertia=(0.787279,'amu*angstrom^2'), symmetry=1, barrier=(18.1011,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.785417,'amu*angstrom^2'), symmetry=1, barrier=(18.0583,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.786323,'amu*angstrom^2'), symmetry=1, barrier=(18.0791,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.734248,0.0612921,-2.8976e-05,-6.5409e-09,6.98271e-12,45959.5,27.082], Tmin=(100,'K'), Tmax=(1020.39,'K')), NASAPolynomial(coeffs=[14.9233,0.0271344,-1.03164e-05,1.88298e-09,-1.31933e-13,41946.4,-47.134], Tmin=(1020.39,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(381.075,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cdd-CdsCds) + radical(RCCJ)"""),
)
species(
label = 'C=[C]C1C=CCC1(1209)',
structure = SMILES('C=[C]C1C=CCC1'),
E0 = (331.541,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,1685,370,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.57294,0.0399862,2.23969e-05,-5.14733e-08,2.07032e-11,39974.2,21.4906], Tmin=(100,'K'), Tmax=(1014.05,'K')), NASAPolynomial(coeffs=[11.6313,0.0319946,-1.26503e-05,2.38063e-09,-1.70255e-13,36305.2,-35.2081], Tmin=(1014.05,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(331.541,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclopentene) + radical(Cds_S)"""),
)
species(
label = 'C=CC1[CH]C(=C)C1(1210)',
structure = SMILES('C=CC1[CH]C(=C)C1'),
E0 = (313.716,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,2750,2883.33,3016.67,3150,900,966.667,1033.33,1100,3010,987.5,1337.5,450,1655,849.675,849.675,849.675,849.675,849.675,849.675,849.675,849.675,4000,4000,4000,4000,4000,4000,4000,4000],'cm^-1')),
HinderedRotor(inertia=(0.010536,'amu*angstrom^2'), symmetry=1, barrier=(119.627,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[10.4263,-0.0198107,0.000150274,-1.41109e-07,3.27764e-11,37393.5,-16.9802], Tmin=(100,'K'), Tmax=(1675.73,'K')), NASAPolynomial(coeffs=[67.6739,0.0383492,-7.61689e-05,1.83543e-08,-1.36396e-12,-9144.83,-404.334], Tmin=(1675.73,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(313.716,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-CdsHH) + group(Cds-CdsHH) + ring(methylenecyclobutane) + radical(Allyl_S)"""),
)
species(
label = 'C=C1[C]C=CCC1(1211)',
structure = SMILES('[CH2]C1=[C]C=CCC1'),
E0 = (366.369,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.84647,0.0306415,5.16383e-05,-8.66328e-08,3.4979e-11,44156.7,19.6042], Tmin=(100,'K'), Tmax=(955.035,'K')), NASAPolynomial(coeffs=[12.5419,0.027617,-9.21837e-06,1.64576e-09,-1.18163e-13,40208.8,-41.4762], Tmin=(955.035,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(366.369,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(353.365,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C) + radical(C=CC=CCJ)"""),
)
species(
label = 'CC1=[C]C=CCC1(1212)',
structure = SMILES('CC1=[C]C=CCC1'),
E0 = (248.313,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.55233,0.0397085,2.78051e-05,-5.99049e-08,2.46083e-11,29965.8,19.1023], Tmin=(100,'K'), Tmax=(982.764,'K')), NASAPolynomial(coeffs=[11.9668,0.0314176,-1.15839e-05,2.11906e-09,-1.50486e-13,26272.2,-39.338], Tmin=(982.764,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(248.313,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C)"""),
)
species(
label = 'CC1=C[C]=CCC1(1213)',
structure = SMILES('CC1=C[C]=CCC1'),
E0 = (248.313,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.55233,0.0397085,2.78051e-05,-5.99049e-08,2.46083e-11,29965.8,19.1023], Tmin=(100,'K'), Tmax=(982.764,'K')), NASAPolynomial(coeffs=[11.9668,0.0314176,-1.15839e-05,2.11906e-09,-1.50486e-13,26272.2,-39.338], Tmin=(982.764,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(248.313,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(C=CJC=C)"""),
)
species(
label = 'CC1=CC=[C]CC1(1214)',
structure = SMILES('CC1=CC=[C]CC1'),
E0 = (287.159,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2830,2910,2990,3070,3150,900,940,980,1020,1060,1100,2750,2800,2850,1350,1500,750,1050,1375,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.60409,0.0376735,3.24871e-05,-6.40877e-08,2.56967e-11,34636.8,19.7943], Tmin=(100,'K'), Tmax=(995.29,'K')), NASAPolynomial(coeffs=[12.3477,0.0307833,-1.18173e-05,2.22e-09,-1.59968e-13,30700.9,-41.0157], Tmin=(995.29,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(287.159,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Cds_S)"""),
)
species(
label = '[CH]1C=C2CCC1C2(1215)',
structure = SMILES('C1=CC2CC[C]1C2'),
E0 = (225.118,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2800,2850,2900,2950,3000,3050,3100,3150,900,925,950,975,1000,1025,1050,1075,1100,300,800,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.67648,0.00238572,0.000137602,-1.73969e-07,6.47036e-11,27147.3,14.2083], Tmin=(100,'K'), Tmax=(962.234,'K')), NASAPolynomial(coeffs=[12.7776,0.0286688,-9.79934e-06,1.89359e-09,-1.4558e-13,22042.6,-50.5588], Tmin=(962.234,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(225.118,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s3_5_5_ene_1) + radical(Allyl_T)"""),
)
species(
label = '[CH]1CCC2=CC1C2(1216)',
structure = SMILES('[CH]1CCC2=CC1C2'),
E0 = (513.908,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.37149,0.0148164,9.49047e-05,-1.24647e-07,4.57376e-11,61886.2,18.6748], Tmin=(100,'K'), Tmax=(989.354,'K')), NASAPolynomial(coeffs=[11.2436,0.0323858,-1.27555e-05,2.4945e-09,-1.85793e-13,57515.2,-37.251], Tmin=(989.354,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(513.908,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsCs) + group(Cds-CdsCsH) + polycyclic(s3_4_6_ene_4) + radical(Cs_S)"""),
)
species(
label = '[C]1=CC=CCC1(1217)',
structure = SMILES('[C]1=CC=CCC1'),
E0 = (326.214,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (79.1198,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.45174,0.0176354,6.31059e-05,-9.1148e-08,3.50495e-11,39305,15.9945], Tmin=(100,'K'), Tmax=(970.391,'K')), NASAPolynomial(coeffs=[11.3124,0.0223735,-7.99967e-06,1.52046e-09,-1.13384e-13,35642.6,-36.4967], Tmin=(970.391,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(326.214,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(307.635,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + ring(1,3-Cyclohexadiene) + radical(Cds_S)"""),
)
species(
label = '[CH2]C12C=CC1CC2(1218)',
structure = SMILES('[CH2]C12C=CC1CC2'),
E0 = (281.45,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.69403,0.033375,4.71907e-05,-7.91047e-08,3.04993e-11,33948.9,19.2074], Tmin=(100,'K'), Tmax=(1006.54,'K')), NASAPolynomial(coeffs=[13,0.030963,-1.25771e-05,2.4485e-09,-1.79993e-13,29519.1,-46.1103], Tmin=(1006.54,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(281.45,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsCs) + group(Cs-(Cds-Cds)CsCsH) + group(Cs-CsCsHH) + group(Cs-CsCsHH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + polycyclic(s2_4_4_ene_1) + radical(Neopentyl)"""),
)
species(
label = 'C=C1C=CC[CH]C1(1219)',
structure = SMILES('C=C1C=CC[CH]C1'),
E0 = (243.25,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.10511,0.0208434,8.60737e-05,-1.16878e-07,4.29098e-11,29342.8,18.1865], Tmin=(100,'K'), Tmax=(999.785,'K')), NASAPolynomial(coeffs=[11.7398,0.0350228,-1.43066e-05,2.80592e-09,-2.07686e-13,24781.1,-41.472], Tmin=(999.785,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(243.25,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(RCCJCC)"""),
)
species(
label = 'C=C1C=[C]CCC1(1220)',
structure = SMILES('C=C1C=[C]CCC1'),
E0 = (286.633,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.79989,0.0252137,8.42071e-05,-1.22135e-07,4.64064e-11,34573.9,17.1905], Tmin=(100,'K'), Tmax=(987.786,'K')), NASAPolynomial(coeffs=[14.8337,0.0305297,-1.20868e-05,2.39614e-09,-1.80806e-13,29164.7,-59.8765], Tmin=(987.786,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(286.633,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Cds_S)"""),
)
species(
label = 'C=C1[CH]CCC=C1(1221)',
structure = SMILES('C=C1[CH]CCC=C1'),
E0 = (189.904,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.03562,0.0153425,0.000118076,-1.6e-07,6.03638e-11,22935.9,15.4568], Tmin=(100,'K'), Tmax=(975.927,'K')), NASAPolynomial(coeffs=[16.0202,0.0286194,-1.08353e-05,2.18207e-09,-1.69218e-13,16844.5,-68.8941], Tmin=(975.927,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(189.904,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(Allyl_S)"""),
)
species(
label = 'C=C1[C]=CCCC1(1222)',
structure = SMILES('C=C1[C]=CCCC1'),
E0 = (247.787,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2816.67,2883.33,2950,3016.67,3083.33,3150,900,933.333,966.667,1000,1033.33,1066.67,1100,2950,3100,1380,975,1025,1650,300,800,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.74482,0.0272871,7.9395e-05,-1.17792e-07,4.52548e-11,29903,17.2036], Tmin=(100,'K'), Tmax=(980.31,'K')), NASAPolynomial(coeffs=[14.4731,0.03113,-1.1834e-05,2.29066e-09,-1.70949e-13,24727.3,-57.6202], Tmin=(980.31,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(247.787,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(382.466,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-CsCsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-(Cds-Cds)CsHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(Cyclohexane) + radical(C=CJC=C)"""),
)
species(
label = '[CH2]C(=C)C=CC=C(1223)',
structure = SMILES('[CH2]C(=C)C=CC=C'),
E0 = (259.602,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([350,440,435,1725,2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,3000,3100,440,815,1455,1000,180,180],'cm^-1')),
HinderedRotor(inertia=(1.14088,'amu*angstrom^2'), symmetry=1, barrier=(26.231,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.13823,'amu*angstrom^2'), symmetry=1, barrier=(26.17,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.13833,'amu*angstrom^2'), symmetry=1, barrier=(26.1725,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.554383,0.05878,-1.94984e-06,-4.88247e-08,2.56587e-11,31362.5,23.1857], Tmin=(100,'K'), Tmax=(944.249,'K')), NASAPolynomial(coeffs=[20.0607,0.0184623,-5.12196e-06,8.73652e-10,-6.46581e-14,25792.3,-79.791], Tmin=(944.249,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(259.602,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Allyl_P)"""),
)
species(
label = '[CH2]C1C=CC(=C)C1(1224)',
structure = SMILES('[CH2]C1C=CC(=C)C1'),
E0 = (270.287,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,2950,3050,3150,900,950,1000,1050,1100,2950,3100,1380,975,1025,1650,3000,3100,440,815,1455,1000,300,800,800,800,800,800,800,800,800,800,1600,1600,1600,1600,1600,1600,1600,1600,1600],'cm^-1')),
HinderedRotor(inertia=(0.156089,'amu*angstrom^2'), symmetry=1, barrier=(3.5888,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.67176,0.0313101,6.14177e-05,-1.03287e-07,4.23946e-11,32610,21.9868], Tmin=(100,'K'), Tmax=(945.483,'K')), NASAPolynomial(coeffs=[15.1541,0.0244428,-7.2839e-06,1.27879e-09,-9.42954e-14,27818,-54.1608], Tmin=(945.483,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(270.287,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(378.308,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)CsHH) + group(Cs-CsHHH) + group(Cds-Cds(Cds-Cds)Cs) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + ring(3-Methylenecyclopentene) + radical(Isobutyl)"""),
)
species(
label = 'C=C=CC=CC=C(3634)',
structure = SMILES('C=C=CC=CC=C'),
E0 = (286.272,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,540,610,2055,180,180],'cm^-1')),
HinderedRotor(inertia=(1.18076,'amu*angstrom^2'), symmetry=1, barrier=(27.148,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.18543,'amu*angstrom^2'), symmetry=1, barrier=(27.2555,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.762364,0.0568363,-1.08125e-05,-3.39638e-08,1.92028e-11,34560.2,23.1106], Tmin=(100,'K'), Tmax=(953.296,'K')), NASAPolynomial(coeffs=[18.2283,0.0185707,-5.70656e-06,1.00179e-09,-7.29345e-14,29638.8,-68.6632], Tmin=(953.296,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(286.272,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(349.208,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + group(Cdd-CdsCds)"""),
)
species(
label = 'C=CC=CC=[C]C(7438)',
structure = SMILES('C=CC=CC=[C]C'),
E0 = (347.509,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,1685,370,2750,2800,2850,1350,1500,750,1050,1375,1000,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,180,180],'cm^-1')),
HinderedRotor(inertia=(0.870129,'amu*angstrom^2'), symmetry=1, barrier=(20.006,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.873475,'amu*angstrom^2'), symmetry=1, barrier=(20.0829,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.870553,'amu*angstrom^2'), symmetry=1, barrier=(20.0157,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.666727,0.0620744,-2.75827e-05,-1.17079e-08,9.91062e-12,41925.7,24.8737], Tmin=(100,'K'), Tmax=(981.602,'K')), NASAPolynomial(coeffs=[16.0283,0.0246029,-8.71772e-06,1.55682e-09,-1.09146e-13,37699.4,-55.1178], Tmin=(981.602,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(347.509,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(Cds_S)"""),
)
species(
label = 'C=CC=C[C]=CC(7439)',
structure = SMILES('[CH2]C=CC=C=CC'),
E0 = (280.504,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2750,2800,2850,1350,1500,750,1050,1375,1000,540,610,2055,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,180],'cm^-1')),
HinderedRotor(inertia=(1.14146,'amu*angstrom^2'), symmetry=1, barrier=(26.2444,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.14133,'amu*angstrom^2'), symmetry=1, barrier=(26.2415,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.14158,'amu*angstrom^2'), symmetry=1, barrier=(26.2472,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.985183,0.0553012,-1.32497e-05,-2.08665e-08,1.16785e-11,33855.1,24.8458], Tmin=(100,'K'), Tmax=(998.964,'K')), NASAPolynomial(coeffs=[13.514,0.0294555,-1.09612e-05,1.97846e-09,-1.3804e-13,30138.4,-41.6598], Tmin=(998.964,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(280.504,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cdd-CdsCds) + radical(C=CC=CCJ)"""),
)
species(
label = 'C=CC=[C]C=CC(7440)',
structure = SMILES('C=CC=[C]C=CC'),
E0 = (308.663,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,1685,370,2750,2800,2850,1350,1500,750,1050,1375,1000,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,180,180],'cm^-1')),
HinderedRotor(inertia=(1.06111,'amu*angstrom^2'), symmetry=1, barrier=(24.3971,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.06133,'amu*angstrom^2'), symmetry=1, barrier=(24.402,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.06153,'amu*angstrom^2'), symmetry=1, barrier=(24.4066,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.626221,0.0639671,-3.17197e-05,-8.30427e-09,9.18887e-12,37254.3,24.142], Tmin=(100,'K'), Tmax=(959.702,'K')), NASAPolynomial(coeffs=[15.6348,0.0252614,-8.49925e-06,1.45962e-09,-9.99842e-14,33275.2,-53.3703], Tmin=(959.702,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(308.663,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CJC=C)"""),
)
species(
label = 'C=C[C]=CC=CC(7441)',
structure = SMILES('C=C[C]=CC=CC'),
E0 = (308.663,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,1685,370,2750,2800,2850,1350,1500,750,1050,1375,1000,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,180,180],'cm^-1')),
HinderedRotor(inertia=(1.06111,'amu*angstrom^2'), symmetry=1, barrier=(24.3971,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.06133,'amu*angstrom^2'), symmetry=1, barrier=(24.402,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.06153,'amu*angstrom^2'), symmetry=1, barrier=(24.4066,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.626221,0.0639671,-3.17197e-05,-8.30427e-09,9.18887e-12,37254.3,24.142], Tmin=(100,'K'), Tmax=(959.702,'K')), NASAPolynomial(coeffs=[15.6348,0.0252614,-8.49925e-06,1.45962e-09,-9.99842e-14,33275.2,-53.3703], Tmin=(959.702,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(308.663,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CJC=C)"""),
)
species(
label = 'C=[C]C=CC=CC(7442)',
structure = SMILES('C=C=C[CH]C=CC'),
E0 = (295.028,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,3025,407.5,1350,352.5,2750,2800,2850,1350,1500,750,1050,1375,1000,540,610,2055,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,367.687,367.71],'cm^-1')),
HinderedRotor(inertia=(0.190701,'amu*angstrom^2'), symmetry=1, barrier=(18.2974,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.190802,'amu*angstrom^2'), symmetry=1, barrier=(18.2971,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.417044,'amu*angstrom^2'), symmetry=1, barrier=(39.9911,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.26491,0.0469111,9.70054e-06,-4.39201e-08,1.96121e-11,35593.9,24.9764], Tmin=(100,'K'), Tmax=(986.422,'K')), NASAPolynomial(coeffs=[13.3182,0.0292117,-1.07951e-05,1.97362e-09,-1.39985e-13,31699.1,-40.6977], Tmin=(986.422,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(295.028,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + group(Cdd-CdsCds) + radical(C=CCJC=C)"""),
)
species(
label = 'C2H3(28)(29)',
structure = SMILES('[CH]=C'),
E0 = (286.361,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,677.08,1086.68,3788.01],'cm^-1')),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (27.0452,'amu'),
collisionModel = TransportData(shapeIndex=2, epsilon=(1737.73,'J/mol'), sigma=(4.1,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=1.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.36378,0.000265766,2.79621e-05,-3.72987e-08,1.5159e-11,34475,7.9151], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[4.15027,0.00754021,-2.62998e-06,4.15974e-10,-2.45408e-14,33856.6,1.72812], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(286.361,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(108.088,'J/(mol*K)'), label="""C2H3""", comment="""Thermo library: Klippenstein_Glarborg2016"""),
)
species(
label = '[CH]=CC=C[CH2](880)',
structure = SMILES('[CH]=CC=C[CH2]'),
E0 = (423.048,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,3120,650,792.5,1650],'cm^-1')),
HinderedRotor(inertia=(1.52606,'amu*angstrom^2'), symmetry=1, barrier=(35.0872,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.53677,'amu*angstrom^2'), symmetry=1, barrier=(35.3333,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (66.1011,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.22719,0.0272982,2.2821e-05,-5.20811e-08,2.2993e-11,50955.4,18.1254], Tmin=(100,'K'), Tmax=(937.462,'K')), NASAPolynomial(coeffs=[12.1491,0.0145308,-4.06041e-06,6.79572e-10,-4.92004e-14,47795.9,-36.031], Tmin=(937.462,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(423.048,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(249.434,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CC=CCJ) + radical(Cds_P)"""),
)
species(
label = '[CH2]C=CC=[C]C=C(7443)',
structure = SMILES('[CH2]C=[C]C=CC=C'),
E0 = (426.719,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,1685,370,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,3000,3100,440,815,1455,1000,180,180],'cm^-1')),
HinderedRotor(inertia=(1.61124,'amu*angstrom^2'), symmetry=1, barrier=(37.0455,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.61166,'amu*angstrom^2'), symmetry=1, barrier=(37.0552,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.60267,'amu*angstrom^2'), symmetry=1, barrier=(36.8486,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.92739,0.0547965,-7.41369e-06,-3.58321e-08,1.99964e-11,51444.8,24.6199], Tmin=(100,'K'), Tmax=(929.16,'K')), NASAPolynomial(coeffs=[16.2636,0.0213745,-6.08611e-06,9.75419e-10,-6.678e-14,47187.6,-55.8141], Tmin=(929.16,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(426.719,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(345.051,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CJC=C) + radical(C=CC=CCJ)"""),
)
species(
label = '[CH]=C[CH2](891)',
structure = SMILES('[CH]C=C'),
E0 = (376.654,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,3010,987.5,1337.5,450,1655,229.711,230.18,230.787],'cm^-1')),
HinderedRotor(inertia=(1.33306,'amu*angstrom^2'), symmetry=1, barrier=(50.5153,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (40.0639,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.31912,0.00817959,3.34736e-05,-4.36194e-08,1.58213e-11,45331.5,10.6389], Tmin=(100,'K'), Tmax=(983.754,'K')), NASAPolynomial(coeffs=[5.36755,0.0170743,-6.35108e-06,1.1662e-09,-8.2762e-14,44095,-3.44606], Tmin=(983.754,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(376.654,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(203.705,'J/(mol*K)'), comment="""Thermo library: DFT_QCI_thermo + radical(AllylJ2_triplet)"""),
)
species(
label = 'CH2CHCHCH(913)',
structure = SMILES('[CH]=CC=C'),
E0 = (346.45,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2995,3025,975,1000,1300,1375,400,500,1630,1680,3120,650,792.5,1650],'cm^-1')),
HinderedRotor(inertia=(1.31937,'amu*angstrom^2'), symmetry=1, barrier=(30.3349,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (53.0825,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.64255,0.0163337,3.86225e-05,-6.71377e-08,2.83603e-11,41729.6,13.282], Tmin=(100,'K'), Tmax=(937.724,'K')), NASAPolynomial(coeffs=[12.9705,0.00669127,-1.0007e-06,1.67602e-10,-1.71436e-14,38279.7,-43.9476], Tmin=(937.724,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(346.45,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(203.705,'J/(mol*K)'), label="""CH2CHCHCH""", comment="""Thermo library: DFT_QCI_thermo"""),
)
species(
label = '[CH2]C=C[C]=CC=C(7444)',
structure = SMILES('[CH2]C=C[C]=CC=C'),
E0 = (426.719,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,1685,370,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,2950,3100,1380,975,1025,1650,180,180],'cm^-1')),
HinderedRotor(inertia=(1.60838,'amu*angstrom^2'), symmetry=1, barrier=(36.9799,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.60785,'amu*angstrom^2'), symmetry=1, barrier=(36.9676,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.60784,'amu*angstrom^2'), symmetry=1, barrier=(36.9675,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.92739,0.0547965,-7.41369e-06,-3.58321e-08,1.99964e-11,51444.8,24.6199], Tmin=(100,'K'), Tmax=(929.16,'K')), NASAPolynomial(coeffs=[16.2636,0.0213745,-6.08611e-06,9.75419e-10,-6.678e-14,47187.6,-55.8141], Tmin=(929.16,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(426.719,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(345.051,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CJC=C) + radical(C=CC=CCJ)"""),
)
species(
label = '[CH2]C=CC=C[C]=C(3658)',
structure = SMILES('[CH2]C=CC=C[C]=C'),
E0 = (426.719,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,1685,370,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,2950,3100,1380,975,1025,1650,180,180],'cm^-1')),
HinderedRotor(inertia=(1.60838,'amu*angstrom^2'), symmetry=1, barrier=(36.9799,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.60785,'amu*angstrom^2'), symmetry=1, barrier=(36.9676,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.60784,'amu*angstrom^2'), symmetry=1, barrier=(36.9675,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.92739,0.0547965,-7.41369e-06,-3.58321e-08,1.99964e-11,51444.8,24.6199], Tmin=(100,'K'), Tmax=(929.16,'K')), NASAPolynomial(coeffs=[16.2636,0.0213745,-6.08611e-06,9.75419e-10,-6.678e-14,47187.6,-55.8141], Tmin=(929.16,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(426.719,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(345.051,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CJC=C) + radical(C=CC=CCJ)"""),
)
species(
label = '[CH]=CC=CC=C[CH2](1458)',
structure = SMILES('[CH]=CC=CC=C[CH2]'),
E0 = (474.819,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([3000,3100,440,815,1455,1000,2995,3002.5,3010,3017.5,3025,975,981.25,987.5,993.75,1000,1300,1318.75,1337.5,1356.25,1375,400,425,450,475,500,1630,1642.5,1655,1667.5,1680,3120,650,792.5,1650,180],'cm^-1')),
HinderedRotor(inertia=(1.27297,'amu*angstrom^2'), symmetry=1, barrier=(29.2681,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.27169,'amu*angstrom^2'), symmetry=1, barrier=(29.2388,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.26702,'amu*angstrom^2'), symmetry=1, barrier=(29.1312,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 3,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.929631,0.0518045,4.89414e-06,-5.11141e-08,2.57339e-11,57232.5,25.4179], Tmin=(100,'K'), Tmax=(938.579,'K')), NASAPolynomial(coeffs=[18.0278,0.018481,-5.04836e-06,8.37777e-10,-6.08081e-14,52281.1,-65.2661], Tmin=(938.579,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(474.819,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(345.051,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)HHH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + radical(C=CC=CCJ) + radical(Cds_P)"""),
)
species(
label = '[CH]=CC=CC=C(3632)',
structure = SMILES('[CH]=CC=CC=C'),
E0 = (392.789,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3100,1380,975,1025,1650,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,3120,650,792.5,1650,180],'cm^-1')),
HinderedRotor(inertia=(1.1072,'amu*angstrom^2'), symmetry=1, barrier=(25.4567,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.10579,'amu*angstrom^2'), symmetry=1, barrier=(25.4242,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (79.1198,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.31285,0.0449348,2.88003e-06,-4.44384e-08,2.28046e-11,47351.4,20.6785], Tmin=(100,'K'), Tmax=(937.294,'K')), NASAPolynomial(coeffs=[17.2005,0.0128924,-3.06901e-06,4.97417e-10,-3.78225e-14,42802.3,-63.3206], Tmin=(937.294,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(392.789,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(299.321,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_P)"""),
)
species(
label = '[CH2]C1C=CC1C=C(7445)',
structure = SMILES('[CH2]C1C=CC1C=C'),
E0 = (389.5,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[1.40061,0.0427756,2.18738e-05,-5.83033e-08,2.54796e-11,46952.6,23.3566], Tmin=(100,'K'), Tmax=(959.693,'K')), NASAPolynomial(coeffs=[13.5494,0.0274225,-9.27689e-06,1.64521e-09,-1.16517e-13,42995.9,-43.2197], Tmin=(959.693,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(389.5,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(374.151,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)CsCsH) + group(Cs-(Cds-Cds)(Cds-Cds)CsH) + group(Cs-CsHHH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-CdsHH) + ring(Cyclobutene) + radical(Isobutyl)"""),
)
species(
label = 'C=CC=C=CC=C(7446)',
structure = SMILES('C=CC=C=CC=C'),
E0 = (286.272,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,2995,3005,3015,3025,975,983.333,991.667,1000,1300,1325,1350,1375,400,433.333,466.667,500,1630,1646.67,1663.33,1680,540,610,2055,180,180],'cm^-1')),
HinderedRotor(inertia=(1.18076,'amu*angstrom^2'), symmetry=1, barrier=(27.148,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(1.18543,'amu*angstrom^2'), symmetry=1, barrier=(27.2555,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (92.1384,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.762364,0.0568363,-1.08125e-05,-3.39638e-08,1.92028e-11,34560.2,22.4174], Tmin=(100,'K'), Tmax=(953.296,'K')), NASAPolynomial(coeffs=[18.2283,0.0185707,-5.70656e-06,1.00179e-09,-7.29345e-14,29638.8,-69.3563], Tmin=(953.296,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(286.272,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(349.208,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + group(Cdd-CdsCds)"""),
)
species(
label = 'C=CC=[C]CC=C(914)',
structure = SMILES('C=CC=[C]CC=C'),
E0 = (378.363,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,1685,370,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,180,180,180],'cm^-1')),
HinderedRotor(inertia=(0.719256,'amu*angstrom^2'), symmetry=1, barrier=(16.5371,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.719808,'amu*angstrom^2'), symmetry=1, barrier=(16.5498,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.719701,'amu*angstrom^2'), symmetry=1, barrier=(16.5473,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.882348,0.0574382,-1.97357e-05,-1.54546e-08,1.0032e-11,45628.6,27.1232], Tmin=(100,'K'), Tmax=(1007.51,'K')), NASAPolynomial(coeffs=[14.5906,0.0270138,-1.01707e-05,1.8594e-09,-1.30926e-13,41648.3,-45.1589], Tmin=(1007.51,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(378.363,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_S)"""),
)
species(
label = 'C=[C]CC=CC=C(915)',
structure = SMILES('C=[C]CC=CC=C'),
E0 = (378.363,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,1685,370,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,180,180,180],'cm^-1')),
HinderedRotor(inertia=(0.719256,'amu*angstrom^2'), symmetry=1, barrier=(16.5371,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.719808,'amu*angstrom^2'), symmetry=1, barrier=(16.5498,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.719701,'amu*angstrom^2'), symmetry=1, barrier=(16.5473,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.882348,0.0574382,-1.97357e-05,-1.54546e-08,1.0032e-11,45628.6,27.1232], Tmin=(100,'K'), Tmax=(1007.51,'K')), NASAPolynomial(coeffs=[14.5906,0.0270138,-1.01707e-05,1.8594e-09,-1.30926e-13,41648.3,-45.1589], Tmin=(1007.51,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(378.363,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(Cds_S)"""),
)
species(
label = 'C=C[C]=CCC=C(917)',
structure = SMILES('C=C[C]=CCC=C'),
E0 = (339.517,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,1685,370,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,180,180,180],'cm^-1')),
HinderedRotor(inertia=(0.942684,'amu*angstrom^2'), symmetry=1, barrier=(21.6741,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.943126,'amu*angstrom^2'), symmetry=1, barrier=(21.6843,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.943754,'amu*angstrom^2'), symmetry=1, barrier=(21.6988,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.837331,0.0593947,-2.41508e-05,-1.16023e-08,9.07577e-12,40957.4,26.4071], Tmin=(100,'K'), Tmax=(987.014,'K')), NASAPolynomial(coeffs=[14.17,0.0277146,-9.9751e-06,1.76734e-09,-1.22175e-13,37236.7,-43.2568], Tmin=(987.014,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(339.517,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(C=CJC=C)"""),
)
species(
label = 'C=[C]C=CCC=C(918)',
structure = SMILES('C=[C]C=CCC=C'),
E0 = (339.517,'kJ/mol'),
modes = [
HarmonicOscillator(frequencies=([2750,2850,1437.5,1250,1305,750,350,1685,370,2995,3010,3025,975,987.5,1000,1300,1337.5,1375,400,450,500,1630,1655,1680,2950,3000,3050,3100,1330,1430,900,1050,1000,1050,1600,1700,180,180,180],'cm^-1')),
HinderedRotor(inertia=(0.942684,'amu*angstrom^2'), symmetry=1, barrier=(21.6741,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.943126,'amu*angstrom^2'), symmetry=1, barrier=(21.6843,'kJ/mol'), semiclassical=False),
HinderedRotor(inertia=(0.943754,'amu*angstrom^2'), symmetry=1, barrier=(21.6988,'kJ/mol'), semiclassical=False),
],
spinMultiplicity = 2,
opticalIsomers = 1,
molecularWeight = (93.1464,'amu'),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[0.837331,0.0593947,-2.41508e-05,-1.16023e-08,9.07577e-12,40957.4,26.4071], Tmin=(100,'K'), Tmax=(987.014,'K')), NASAPolynomial(coeffs=[14.17,0.0277146,-9.9751e-06,1.76734e-09,-1.22175e-13,37236.7,-43.2568], Tmin=(987.014,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(339.517,'kJ/mol'), Cp0=(33.2579,'J/(mol*K)'), CpInf=(369.994,'J/(mol*K)'), comment="""Thermo group additivity estimation: group(Cs-(Cds-Cds)(Cds-Cds)HH) + group(Cds-CdsCsH) + group(Cds-CdsCsH) + group(Cds-Cds(Cds-Cds)H) + group(Cds-Cds(Cds-Cds)H) + group(Cds-CdsHH) + group(Cds-CdsHH) + radical(C=CJC=C)"""),
)
species(
label = 'Ne',
structure = SMILES('[Ne]'),
E0 = (-6.19738,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (20.1797,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1235.53,'J/mol'), sigma=(3.758e-10,'m'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0, comment="""Epsilon & sigma estimated with fixed Lennard Jones Parameters. This is the fallback method! Try improving transport databases!"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,3.35532], Tmin=(200,'K'), Tmax=(1000,'K')), NASAPolynomial(coeffs=[2.5,0,0,0,0,-745.375,3.35532], Tmin=(1000,'K'), Tmax=(6000,'K'))], Tmin=(200,'K'), Tmax=(6000,'K'), E0=(-6.19738,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""Ne""", comment="""Thermo library: primaryThermoLibrary"""),
)
species(
label = 'N2',
structure = SMILES('N#N'),
E0 = (-8.69489,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (28.0135,'amu'),
collisionModel = TransportData(shapeIndex=1, epsilon=(810.913,'J/mol'), sigma=(3.621,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(1.76,'angstroms^3'), rotrelaxcollnum=4.0, comment="""PrimaryTransportLibrary"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[3.61263,-0.00100893,2.49898e-06,-1.43376e-09,2.58636e-13,-1051.1,2.6527], Tmin=(100,'K'), Tmax=(1817.04,'K')), NASAPolynomial(coeffs=[2.9759,0.00164141,-7.19722e-07,1.25378e-10,-7.91526e-15,-1025.84,5.53757], Tmin=(1817.04,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-8.69489,'kJ/mol'), Cp0=(29.1007,'J/(mol*K)'), CpInf=(37.4151,'J/(mol*K)'), label="""N2""", comment="""Thermo library: BurkeH2O2"""),
)
species(
label = 'Ar(8)',
structure = SMILES('[Ar]'),
E0 = (-6.19426,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
molecularWeight = (39.348,'amu'),
collisionModel = TransportData(shapeIndex=0, epsilon=(1134.93,'J/mol'), sigma=(3.33,'angstroms'), dipoleMoment=(0,'C*m'), polarizability=(0,'angstroms^3'), rotrelaxcollnum=0.0, comment="""GRI-Mech"""),
energyTransferModel = SingleExponentialDown(alpha0=(3.5886,'kJ/mol'), T0=(300,'K'), n=0.85),
thermo = NASA(polynomials=[NASAPolynomial(coeffs=[2.5,9.24385e-15,-1.3678e-17,6.66185e-21,-1.00107e-24,-745,4.3663], Tmin=(100,'K'), Tmax=(3459.6,'K')), NASAPolynomial(coeffs=[2.5,9.20456e-12,-3.58608e-15,6.15199e-19,-3.92042e-23,-745,4.3663], Tmin=(3459.6,'K'), Tmax=(5000,'K'))], Tmin=(100,'K'), Tmax=(5000,'K'), E0=(-6.19426,'kJ/mol'), Cp0=(20.7862,'J/(mol*K)'), CpInf=(20.7862,'J/(mol*K)'), label="""Ar""", comment="""Thermo library: BurkeH2O2"""),
)
transitionState(
label = 'TS1',
E0 = (409.439,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS2',
E0 = (555.843,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS3',
E0 = (490.934,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS4',
E0 = (641.775,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS5',
E0 = (588.852,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS6',
E0 = (682.265,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS7',
E0 = (595.645,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS8',
E0 = (694.652,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS9',
E0 = (694.194,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS10',
E0 = (695.343,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS11',
E0 = (425.602,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS12',
E0 = (543.996,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS13',
E0 = (418.155,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS14',
E0 = (502.294,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS15',
E0 = (350.415,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS16',
E0 = (373.501,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS17',
E0 = (489.881,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
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label = 'TS191',
E0 = (392.56,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS192',
E0 = (394.371,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS193',
E0 = (457.514,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS194',
E0 = (307.656,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS195',
E0 = (384.116,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS196',
E0 = (566.824,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS197',
E0 = (716.628,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS198',
E0 = (441.738,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS199',
E0 = (460.678,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS200',
E0 = (334.661,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS201',
E0 = (458.217,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS202',
E0 = (453.147,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS203',
E0 = (310.394,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS204',
E0 = (481.817,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS205',
E0 = (481.817,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS206',
E0 = (617.753,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS207',
E0 = (578.161,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS208',
E0 = (627.007,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS209',
E0 = (398.877,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS210',
E0 = (412.982,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS211',
E0 = (456.782,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS212',
E0 = (426.015,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS213',
E0 = (460.766,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS214',
E0 = (578.161,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS215',
E0 = (373.514,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS216',
E0 = (441.391,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS217',
E0 = (273.516,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS218',
E0 = (351.918,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS219',
E0 = (329.836,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS220',
E0 = (318.84,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS221',
E0 = (514.779,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS222',
E0 = (480.73,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS223',
E0 = (707.777,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS224',
E0 = (284.856,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS225',
E0 = (380.939,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS226',
E0 = (367.238,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS227',
E0 = (486.829,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS228',
E0 = (334.252,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS229',
E0 = (395.064,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS230',
E0 = (322.733,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS231',
E0 = (430.222,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS232',
E0 = (423.743,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS233',
E0 = (509.863,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS234',
E0 = (520.308,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS235',
E0 = (473.582,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS236',
E0 = (412.268,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS237',
E0 = (341.703,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS238',
E0 = (437.598,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS239',
E0 = (489.041,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS240',
E0 = (709.408,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS241',
E0 = (642.707,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS242',
E0 = (723.104,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS243',
E0 = (642.707,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS244',
E0 = (642.707,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS245',
E0 = (686.612,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS246',
E0 = (453.659,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS247',
E0 = (367.992,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS248',
E0 = (487.633,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS249',
E0 = (774.352,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS250',
E0 = (389.5,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS251',
E0 = (514.859,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS252',
E0 = (535.263,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS253',
E0 = (490.913,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS254',
E0 = (486.794,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS255',
E0 = (532.803,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS256',
E0 = (383.826,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS257',
E0 = (420.658,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
transitionState(
label = 'TS258',
E0 = (431.693,'kJ/mol'),
spinMultiplicity = 1,
opticalIsomers = 1,
)
reaction(
label = 'reaction152',
reactants = ['H(3)(3)', 'C1C=CC2CC2C=1(1100)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS1',
kinetics = Arrhenius(A=(2.7e+08,'cm^3/(mol*s)'), n=1.64, Ea=(1.58992,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2557 used for Cds-CsH_Cds-CdH;HJ
Exact match found for rate rule [Cds-CsH_Cds-CdH;HJ]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction153',
reactants = ['H(3)(3)', 'C1=CCC2CC2C=1(1180)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS2',
kinetics = Arrhenius(A=(5.46e+08,'cm^3/(mol*s)'), n=1.64, Ea=(15.8155,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2714 used for Ca_Cds-CsH;HJ
Exact match found for rate rule [Ca_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction170',
reactants = ['H(3)(3)', 'C1=CCC2CC2=C1(1188)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS3',
kinetics = Arrhenius(A=(7.17e+07,'cm^3/(mol*s)'), n=1.64, Ea=(4.93712,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2570 used for Cds-CsCs_Cds-CdH;HJ
Exact match found for rate rule [Cds-CsCs_Cds-CdH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction159',
reactants = ['H(3)(3)', '[CH]1C=CC2C[C]2C1(1181)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS4',
kinetics = Arrhenius(A=(6.68468e+06,'m^3/(mol*s)'), n=-0.0135, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [C_rad/Cs3;Y_rad] for rate rule [C_rad/Cs3;H_rad]
Euclidian distance = 1.0
family: R_Recombination
Ea raised from -0.9 to 0 kJ/mol."""),
)
reaction(
label = 'reaction160',
reactants = ['H(3)(3)', '[CH]1C=C[C]2CC2C1(1182)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS5',
kinetics = Arrhenius(A=(2.92e+13,'cm^3/(mol*s)'), n=0.18, Ea=(0.518816,'kJ/mol'), T0=(1,'K'), Tmin=(200,'K'), Tmax=(2000,'K'), comment="""From training reaction 123 used for H_rad;C_rad/OneDeCs2
Exact match found for rate rule [C_rad/OneDeCs2;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction161',
reactants = ['H(3)(3)', '[CH]1C=CC2[CH]C2C1(1183)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS6',
kinetics = Arrhenius(A=(2e+13,'cm^3/(mol*s)','*|/',3.16), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2000,'K'), comment="""From training reaction 59 used for H_rad;C_rad/H/NonDeC
Exact match found for rate rule [C_rad/H/NonDeC;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction162',
reactants = ['H(3)(3)', '[CH]1[CH]C2CC2C=C1(1108)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS7',
kinetics = Arrhenius(A=(8.69202e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction163',
reactants = ['H(3)(3)', '[C]1=C[CH]CC2CC12(1184)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS8',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction164',
reactants = ['H(3)(3)', '[C]1[CH]CC2CC2C=1(1185)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS9',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Y_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction10',
reactants = ['H(3)(3)', '[C]1C=CC2CC2C1(1187)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS10',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction47',
reactants = ['C7H9(452)(451)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS11',
kinetics = Arrhenius(A=(1.682e+10,'s^-1'), n=0.35, Ea=(125.102,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 160 used for R2H_S;C_rad_out_H/NonDeC;Cs_H_out_H/Cd
Exact match found for rate rule [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction154',
reactants = ['[C]1=CC2CC2CC1(1103)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS12',
kinetics = Arrhenius(A=(1.9054e+11,'s^-1'), n=0.853, Ea=(200.196,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction155',
reactants = ['C1=CC2C[C]2CC1(1101)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS13',
kinetics = Arrhenius(A=(1.28e+07,'s^-1'), n=1.56, Ea=(126.775,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_SS_Cs;C_rad_out_Cs2;Cs_H_out_H/Cd] for rate rule [R3H_SS_Cs;C_rad_out_Cs2_cy3;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction156',
reactants = ['C7H9(680)(679)'],
products = ['[C]1=CCCC2CC12(1104)'],
transitionState = 'TS14',
kinetics = Arrhenius(A=(3.96e+10,'s^-1'), n=0.83, Ea=(257.734,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 201 used for R3H_SD;C_rad_out_H/NonDeC;Cd_H_out_singleNd
Exact match found for rate rule [R3H_SD;C_rad_out_H/NonDeC;Cd_H_out_singleNd]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction157',
reactants = ['C7H9(680)(679)'],
products = ['C1=C[C]2CC2CC1(1102)'],
transitionState = 'TS15',
kinetics = Arrhenius(A=(1.78e+06,'s^-1'), n=1.75, Ea=(105.855,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_SSS;C_rad_out_H/Cd;Cs_H_out] for rate rule [R4H_SSS;C_rad_out_H/Cd;Cs_H_out_noH]
Euclidian distance = 1.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction158',
reactants = ['[CH]1C2C=CCCC12(1046)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS16',
kinetics = Arrhenius(A=(0.502,'s^-1'), n=3.86, Ea=(41.6308,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2500,'K'), comment="""From training reaction 332 used for R4H_SSS;C_rad_out_H/NonDeC;Cs_H_out_H/Cd
Exact match found for rate rule [R4H_SSS;C_rad_out_H/NonDeC;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction165',
reactants = ['C7H9(680)(679)'],
products = ['[CH]1C2CC3CC3C12(1186)'],
transitionState = 'TS17',
kinetics = Arrhenius(A=(4.00063e+13,'s^-1'), n=-0.283562, Ea=(245.321,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0c6_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_cs] for rate rule [Rn0c6_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction166',
reactants = ['C=CC=CC1[CH]C1(944)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS18',
kinetics = Arrhenius(A=(1.40768e+07,'s^-1'), n=0.903766, Ea=(44.7452,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R6_SSR;doublebond_intra_pri_2H;radadd_intra_csHCs] + [R6_SSM_D;doublebond_intra_pri_2H;radadd_intra_cs] for rate rule [R6_SSM_D;doublebond_intra_pri_2H;radadd_intra_csHCs]
Euclidian distance = 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction167',
reactants = ['CH2(S)(21)(22)', 'C6H7(464)(463)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS19',
kinetics = Arrhenius(A=(3.08e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [elec_def;mb_db_twocdisub] for rate rule [carbene;mb_db_twocdisub_oneDe]
Euclidian distance = 1.41421356237
Multiplied by reaction path degeneracy 2.0
family: 1+2_Cycloaddition"""),
)
reaction(
label = 'reaction168',
reactants = ['[CH2]C1C=CC2CC12(1115)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS20',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-HH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction21',
reactants = ['C1=CCC2C[C]2C1(1189)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS21',
kinetics = Arrhenius(A=(2.94e+08,'s^-1'), n=1.27, Ea=(125.938,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R2H_S;C_rad_out_Cs2;Cs_H_out_H/Cd] for rate rule [R2H_S;C_rad_out_Cs2_cy3;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction22',
reactants = ['[C]1=CCC2CC2C1(1190)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS22',
kinetics = Arrhenius(A=(1.9054e+11,'s^-1'), n=0.853, Ea=(200.196,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)] for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs/Cs)]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction173',
reactants = ['[CH]1C2CC=CCC12(1098)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS23',
kinetics = Arrhenius(A=(1.364e+10,'s^-1'), n=0.73, Ea=(127.612,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_H/Cd] for rate rule [R3H_SS_12cy3;C_rad_out_H/NonDeC;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 4.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction10',
reactants = ['C7H9(408)(407)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS24',
kinetics = Arrhenius(A=(4.2e+12,'s^-1'), n=0.14, Ea=(1.2552,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0c7_beta_long_SS_D;doublebond_intra_pri_HNd_Cs;radadd_intra] for rate rule [Rn0c7_beta_long_SS_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction174',
reactants = ['C7H9(681)(680)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS25',
kinetics = Arrhenius(A=(1.76771e+11,'s^-1'), n=0.21, Ea=(35.9824,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R7_cyclic;doublebond_intra_pri;radadd_intra_cs2H] + [Rn1c6_alpha_long;doublebond_intra_pri;radadd_intra_cs] for rate rule [Rn1c6_alpha_long;doublebond_intra_pri;radadd_intra_cs2H]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction175',
reactants = ['[CH]1CC2CC=CC12(1038)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS26',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction176',
reactants = ['[CH]1C2C=CCC1C2(1114)'],
products = ['C7H9(680)(679)'],
transitionState = 'TS27',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction1',
reactants = ['H(3)(3)', 'C7H8(415)(414)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS28',
kinetics = Arrhenius(A=(0.0545849,'m^3/(mol*s)'), n=2.81111, Ea=(21.1569,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 26 used for Cds-CdH_Cds-CsH;HJ
Exact match found for rate rule [Cds-CdH_Cds-CsH;HJ]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction29',
reactants = ['C7H9(424)(423)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS29',
kinetics = Arrhenius(A=(5.11e+09,'s^-1'), n=1.34, Ea=(47.7,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 9 C7H9-11 <=> C7H9-12 in intra_H_migration/training
This reaction matched rate rule [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_H/(Cd-Cd-Cd)]
family: intra_H_migration"""),
)
reaction(
label = 'reaction7',
reactants = ['[C]1=CC=CCCC1(845)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS30',
kinetics = Arrhenius(A=(3.32e+09,'s^-1'), n=0.99, Ea=(141.419,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 206 used for R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_H/NonDeC
Exact match found for rate rule [R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction8',
reactants = ['[C]1=CCCCC=C1(846)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS31',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_DSS;Cd_rad_out_single;Cs_H_out_1H] for rate rule [R4H_DSS;Cd_rad_out_singleDe_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction2',
reactants = ['H(3)(3)', '[CH]1[CH]CC=CC=C1(1035)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS32',
kinetics = Arrhenius(A=(8.69202e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction3',
reactants = ['H(3)(3)', '[C]1=CC=CC[CH]C1(1036)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS33',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction4',
reactants = ['H(3)(3)', '[C]1=CC[CH]CC=C1(1037)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS34',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [Cd_rad/Cd;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction9',
reactants = ['[CH]1CC2CC=CC12(1038)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS35',
kinetics = Arrhenius(A=(1.01e+11,'s^-1'), n=0.59, Ea=(21.3,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 116 C7H9-25 <=> C7H9-26 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [Rn0c7_gamma_short_SSDS_D;doublebond_intra_pri_HCd;radadd_intra_csHCs]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction11',
reactants = ['[CH]=CC=CCC=C(920)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS36',
kinetics = Arrhenius(A=(4.625e+11,'s^-1'), n=0.16, Ea=(41.045,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R7;doublebond_intra_pri;radadd_intra_cdsingleH] for rate rule [R7_linear;doublebond_intra_pri_2H;radadd_intra_cdsingleH]
Euclidian distance = 1.41421356237
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction12',
reactants = ['C7H9(408)(407)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS37',
kinetics = Arrhenius(A=(7.06e+06,'s^-1'), n=1.73, Ea=(245.601,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HH)CJ;CsJ;C] for rate rule [cCs(-HH)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction6',
reactants = ['C7H9(408)(407)'],
products = ['[CH]1CC2C=CC2C1(864)'],
transitionState = 'TS38',
kinetics = Arrhenius(A=(4.99998e+11,'s^-1'), n=0.0559095, Ea=(122.413,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [1,3-butadiene_backbone;C=C_1;C=C_2] for rate rule [1,3-butadiene_backbone;CdH(C)_1;CdH(C)_2]
Euclidian distance = 1.41421356237
family: Intra_2+2_cycloaddition_Cd"""),
)
reaction(
label = 'reaction13',
reactants = ['H(3)(3)', '[C]1CC=CC=CC1(1039)'],
products = ['C7H9(408)(407)'],
transitionState = 'TS39',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction1',
reactants = ['H(3)(3)', 'C1=CC=CCCC=1(1040)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS40',
kinetics = Arrhenius(A=(1.149e+09,'cm^3/(mol*s)'), n=1.595, Ea=(16.7946,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Ca_Cds-OneDeH;HJ] for rate rule [Ca_Cds-CdH;HJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction8',
reactants = ['[C]1=CCCC=CC1(1044)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS41',
kinetics = Arrhenius(A=(1.448e+10,'s^-1'), n=0.82, Ea=(156.9,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 154 used for R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd
Exact match found for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction9',
reactants = ['[C]1=CCC=CCC1(1045)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS42',
kinetics = Arrhenius(A=(1.182e+10,'s^-1'), n=0.86, Ea=(149.369,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_Cs;Cs_H_out_1H] for rate rule [R3H_DS;Cd_rad_out_Cs;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction10',
reactants = ['C7H9(449)(448)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS43',
kinetics = Arrhenius(A=(570153,'s^-1'), n=1.88833, Ea=(191.836,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R4H;C_rad_out_H/NonDeC;Cs_H_out_H/Cd] + [R4H_SDS;C_rad_out_single;Cs_H_out_H/Cd] + [R4H_SDS;C_rad_out_H/NonDeC;Cs_H_out_1H] for rate rule [R4H_SDS;C_rad_out_H/NonDeC;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 4.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction3',
reactants = ['H(3)(3)', '[CH]1[CH]CC=CC=C1(1035)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS44',
kinetics = Arrhenius(A=(5.42928e+07,'m^3/(mol*s)'), n=0.107721, Ea=(5.76381,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 36 used for C_rad/H/CdCs;H_rad
Exact match found for rate rule [C_rad/H/CdCs;H_rad]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination"""),
)
reaction(
label = 'reaction4',
reactants = ['H(3)(3)', '[C]1=C[CH]C=CCC1(1041)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS45',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction5',
reactants = ['H(3)(3)', '[C]1[CH]C=CCCC=1(1042)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS46',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction11',
reactants = ['C7H9(424)(423)'],
products = ['[CH]1C2C=CCCC12(1046)'],
transitionState = 'TS47',
kinetics = Arrhenius(A=(8.00127e+13,'s^-1'), n=-0.283562, Ea=(245.321,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0cx_beta;doublebond_intra_pri_HNd_Cs;radadd_intra_csHDe] for rate rule [Rn0c7_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCd]
Euclidian distance = 1.41421356237
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction12',
reactants = ['C=CC1[CH]C1C=C(1047)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS48',
kinetics = Arrhenius(A=(6.21184e+10,'s^-1'), n=0.288169, Ea=(147.049,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [1_5_unsaturated_hexane] for rate rule [1_5_hexadiene]
Euclidian distance = 1.0
family: 6_membered_central_C-C_shift"""),
)
reaction(
label = 'reaction23',
reactants = ['H(3)(3)', '[C]1=C[CH]CCC=C1(1043)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS49',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction2',
reactants = ['C7H9(424)(423)'],
products = ['H(3)(3)', 'C7H8(415)(414)'],
transitionState = 'TS50',
kinetics = Arrhenius(A=(3.58e+10,'s^-1'), n=1.38, Ea=(48.4,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 20 C7H9-12 <=> C7H8-8 + H in R_Addition_MultipleBond/training
This reaction matched rate rule [Cds-CsH_Cds-(Cd-Cd-Cd)H;HJ]
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction51',
reactants = ['[C]1=CC=CCCC1(845)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS51',
kinetics = Arrhenius(A=(1.9054e+11,'s^-1'), n=0.853, Ea=(200.196,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction52',
reactants = ['[C]1=CCCCC=C1(846)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS52',
kinetics = Arrhenius(A=(1.47715e+10,'s^-1'), n=0.8, Ea=(147.277,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_single;Cs_H_out_H/NonDeC] for rate rule [R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction15',
reactants = ['C7H9(424)(423)'],
products = ['[CH]1CCC2C=CC12(863)'],
transitionState = 'TS53',
kinetics = Arrhenius(A=(1.13169e+19,'s^-1'), n=-1.57151, Ea=(227.049,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 131 used for Rn0c7_gamma_long_SDS_D;doublebond_intra_pri_HCd;radadd_intra_csHCd
Exact match found for rate rule [Rn0c7_gamma_long_SDS_D;doublebond_intra_pri_HCd;radadd_intra_csHCd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction16',
reactants = ['[CH]1C=CC2CCC12(1048)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS54',
kinetics = Arrhenius(A=(1.56e+11,'s^-1'), n=0.55, Ea=(26.5,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 112 C7H9-17 <=> C7H9-18 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [Rn0c7_gamma_long_SSS_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction17',
reactants = ['C7H9(424)(423)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS55',
kinetics = Arrhenius(A=(2.03e+10,'s^-1'), n=1.1, Ea=(37,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 1 C7H9-3 <=> C7H9-4 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [R7_SDSD_D;doublebond_intra_pri_2H;radadd_intra_cs2H]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction18',
reactants = ['C7H9(681)(680)'],
products = ['C7H9(424)(423)'],
transitionState = 'TS56',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-HH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction31',
reactants = ['H(3)(3)', 'C7H8(415)(414)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS57',
kinetics = Arrhenius(A=(9.22566,'m^3/(mol*s)'), n=2.04274, Ea=(10.5229,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 101 used for Cds-CdH_Cds-CdH;HJ
Exact match found for rate rule [Cds-CdH_Cds-CdH;HJ]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction32',
reactants = ['H(3)(3)', 'C1=CCC=CCC=1(1094)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS58',
kinetics = Arrhenius(A=(5.46e+08,'cm^3/(mol*s)'), n=1.64, Ea=(15.8155,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2714 used for Ca_Cds-CsH;HJ
Exact match found for rate rule [Ca_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction59',
reactants = ['[C]1=CCC=CCC1(1045)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS59',
kinetics = Arrhenius(A=(2.66329e+10,'s^-1'), n=0.993, Ea=(157.679,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction60',
reactants = ['[C]1=CCCC=CC1(1044)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS60',
kinetics = Arrhenius(A=(1.182e+10,'s^-1'), n=0.86, Ea=(149.369,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 200 used for R3H_DS;Cd_rad_out_Cs;Cs_H_out_H/NonDeC
Exact match found for rate rule [R3H_DS;Cd_rad_out_Cs;Cs_H_out_H/NonDeC]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction61',
reactants = ['H(3)(3)', '[CH]1[CH]CC=CC=C1(1035)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS61',
kinetics = Arrhenius(A=(8.69202e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction36',
reactants = ['H(3)(3)', '[C]1=CCC=C[CH]C1(1095)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS62',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction63',
reactants = ['H(3)(3)', '[C]1=C[CH]CC=CC1(1096)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS63',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction38',
reactants = ['H(3)(3)', '[C]1[CH]CC=CCC=1(1097)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS64',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Y_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction39',
reactants = ['C7H9(449)(448)'],
products = ['[CH]1C2CC=CCC12(1098)'],
transitionState = 'TS65',
kinetics = Arrhenius(A=(4.00063e+13,'s^-1'), n=-0.283562, Ea=(245.321,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0cx_beta;doublebond_intra_pri_HNd_Cs;radadd_intra_cs] for rate rule [Rn0c7_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 2.2360679775
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction40',
reactants = ['C7H9(449)(448)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS66',
kinetics = Arrhenius(A=(1.08454e+19,'s^-1'), n=-0.859165, Ea=(130.857,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0c7_gamma_short;doublebond_intra_pri;radadd_intra_cs] for rate rule [Rn0c7_gamma_short;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCd]
Euclidian distance = 2.82842712475
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction41',
reactants = ['C7H9(449)(448)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS67',
kinetics = Arrhenius(A=(4.2e+12,'s^-1'), n=0.14, Ea=(73.8271,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0c7_beta_long_SS_D;doublebond_intra_pri_HNd_Cs;radadd_intra] for rate rule [Rn0c7_beta_long_SS_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction42',
reactants = ['[CH]=CCC=CC=C(919)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS68',
kinetics = Arrhenius(A=(4.625e+11,'s^-1'), n=0.16, Ea=(41.045,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R7;doublebond_intra_pri;radadd_intra_cdsingleH] for rate rule [R7_linear;doublebond_intra_pri_2H;radadd_intra_cdsingleH]
Euclidian distance = 1.41421356237
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction43',
reactants = ['C7H9(453)(452)'],
products = ['C7H9(449)(448)'],
transitionState = 'TS69',
kinetics = Arrhenius(A=(1.31121e+11,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-HH;C]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction44',
reactants = ['H(3)(3)', 'C1=CC2CC2=CC1(1099)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS70',
kinetics = Arrhenius(A=(7.72e+07,'cm^3/(mol*s)'), n=1.64, Ea=(9.07928,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2568 used for Cds-CsCs_Cds-CsH;HJ
Exact match found for rate rule [Cds-CsCs_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction45',
reactants = ['H(3)(3)', 'C1C=CC2CC2C=1(1100)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS71',
kinetics = Arrhenius(A=(2334.12,'m^3/(mol*s)'), n=1.41424, Ea=(5.49872,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 103 used for Cds-CdH_Cds-(CsH-Cs-Cds)_cyc6;HJ
Exact match found for rate rule [Cds-CdH_Cds-(CsH-Cs-Cds)_cyc6;HJ]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction46',
reactants = ['C7H9(452)(451)'],
products = ['C1=CC2C[C]2CC1(1101)'],
transitionState = 'TS72',
kinetics = Arrhenius(A=(2.74e+09,'s^-1'), n=0.98, Ea=(194.974,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 171 used for R2H_S;C_rad_out_H/NonDeC;Cs_H_out_Cs2_cy3
Exact match found for rate rule [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_Cs2_cy3]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction48',
reactants = ['C7H9(452)(451)'],
products = ['C1=C[C]2CC2CC1(1102)'],
transitionState = 'TS73',
kinetics = Arrhenius(A=(2.76e-23,'s^-1'), n=10.17, Ea=(56.5677,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2500,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_OneDe] for rate rule [R3H_SS_23cy3;C_rad_out_H/NonDeC;Cs_H_out_Cd]
Euclidian distance = 1.41421356237
family: intra_H_migration"""),
)
reaction(
label = 'reaction49',
reactants = ['[CH]1C2C=CCCC12(1046)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS74',
kinetics = Arrhenius(A=(6.15389e+07,'s^-1'), n=1.6, Ea=(161.502,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_H/NonDeC] for rate rule [R3H_SS_12cy3;C_rad_out_H/NonDeC;Cs_H_out_H/NonDeC]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction50',
reactants = ['C7H9(452)(451)'],
products = ['[C]1=CC2CC2CC1(1103)'],
transitionState = 'TS75',
kinetics = Arrhenius(A=(3.37e+07,'s^-1'), n=1.41, Ea=(177.82,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 207 used for R3H_SS_Cs;C_rad_out_H/NonDeC;Cd_H_out_doubleC
Exact match found for rate rule [R3H_SS_Cs;C_rad_out_H/NonDeC;Cd_H_out_doubleC]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction51',
reactants = ['[C]1=CCCC2CC12(1104)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS76',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_RSS;Cd_rad_out;Cs_H_out_1H] for rate rule [R4H_SSS;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 2.44948974278
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction52',
reactants = ['H(3)(3)', '[CH]1CC=CC2C[C]12(1105)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS77',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction53',
reactants = ['H(3)(3)', '[CH]1CC=C[C]2CC12(1106)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS78',
kinetics = Arrhenius(A=(2.92e+13,'cm^3/(mol*s)'), n=0.18, Ea=(0.518816,'kJ/mol'), T0=(1,'K'), Tmin=(200,'K'), Tmax=(2000,'K'), comment="""From training reaction 123 used for H_rad;C_rad/OneDeCs2
Exact match found for rate rule [C_rad/OneDeCs2;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction54',
reactants = ['H(3)(3)', '[CH]1CC=CC2[CH]C12(1107)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS79',
kinetics = Arrhenius(A=(2e+13,'cm^3/(mol*s)','*|/',3.16), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2000,'K'), comment="""From training reaction 59 used for H_rad;C_rad/H/NonDeC
Exact match found for rate rule [C_rad/H/NonDeC;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction55',
reactants = ['H(3)(3)', '[CH]1[CH]C2CC2C=C1(1108)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS80',
kinetics = Arrhenius(A=(8.69202e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction56',
reactants = ['H(3)(3)', '[C]1=CC[CH]C2CC12(1109)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS81',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [H_rad;Cd_rad/NonDe]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction57',
reactants = ['H(3)(3)', '[C]1=CC2CC2[CH]C1(1110)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS82',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction58',
reactants = ['C7H9(452)(451)'],
products = ['[CH]1CC2C1C1CC21(1111)'],
transitionState = 'TS83',
kinetics = Arrhenius(A=(5.4227e+18,'s^-1'), n=-0.859165, Ea=(151.157,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0cx_gamma;doublebond_intra_pri;radadd_intra_csHCs] for rate rule [Rn0c6_gamma;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 2.2360679775
family: Intra_R_Add_Endocyclic
Ea raised from 150.8 to 151.2 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction59',
reactants = ['C7H9(452)(451)'],
products = ['[CH]1C2CC2C2CC12(1112)'],
transitionState = 'TS84',
kinetics = Arrhenius(A=(1.307e+12,'s^-1'), n=0.256, Ea=(153.959,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 83 used for Rn0c6_beta_long_SS_D_HH;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs
Exact match found for rate rule [Rn0c6_beta_long_SS_D_HH;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction60',
reactants = ['[CH]=CC1CC1C=C(1113)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS85',
kinetics = Arrhenius(A=(1.03e+10,'s^-1'), n=0.19, Ea=(16.736,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 149 used for R6_DSS_D;doublebond_intra_pri_2H;radadd_intra_cdsingleH
Exact match found for rate rule [R6_DSS_D;doublebond_intra_pri_2H;radadd_intra_cdsingleH]
Euclidian distance = 0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction61',
reactants = ['C7H9(453)(452)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS86',
kinetics = Arrhenius(A=(3.53542e+11,'s^-1'), n=0.21, Ea=(35.9824,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R7_cyclic;doublebond_intra_pri;radadd_intra_cs2H] + [Rn1c6_alpha_long;doublebond_intra_pri;radadd_intra_cs] for rate rule [Rn1c6_alpha_long;doublebond_intra_pri;radadd_intra_cs2H]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction62',
reactants = ['CH2(S)(21)(22)', 'C6H7(464)(463)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS87',
kinetics = Arrhenius(A=(3.13244e+08,'m^3/(mol*s)'), n=-0.441667, Ea=(7.08269,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [carbene;mb_db]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: 1+2_Cycloaddition"""),
)
reaction(
label = 'reaction63',
reactants = ['C7H9(460)(459)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS88',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction64',
reactants = ['[CH]1C2C=CCC1C2(1114)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS89',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction65',
reactants = ['[CH2]C1C=CC2CC12(1115)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS90',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-HH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction66',
reactants = ['H(3)(3)', '[C]1CC=CC2CC12(1116)'],
products = ['C7H9(452)(451)'],
transitionState = 'TS91',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction67',
reactants = ['[CH]=CCC=CC=C(919)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS92',
kinetics = Arrhenius(A=(17945.7,'s^-1'), n=1.45333, Ea=(16.4571,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R7_RSSR;doublebond_intra_2H_pri;radadd_intra] for rate rule [R7_DSSM_D;doublebond_intra_2H_pri;radadd_intra_cdsingleH]
Euclidian distance = 4.472135955
family: Intra_R_Add_Exocyclic"""),
)
reaction(
label = 'reaction68',
reactants = ['H(3)(3)', 'C=C1C=CCC=C1(1117)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS93',
kinetics = Arrhenius(A=(1.997e+08,'cm^3/(mol*s)'), n=1.629, Ea=(14.7235,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 64 used for Cds-CdCd_Cds-HH;HJ
Exact match found for rate rule [Cds-CdCd_Cds-HH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction69',
reactants = ['C7H9(453)(452)'],
products = ['C[C]1C=CCC=C1(1118)'],
transitionState = 'TS94',
kinetics = Arrhenius(A=(58.4615,'s^-1'), n=3.15787, Ea=(98.4673,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;C_rad_out_2H;Cs_H_out_noH]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction70',
reactants = ['CC1[C]=CCC=C1(1119)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS95',
kinetics = Arrhenius(A=(2.304e+09,'s^-1'), n=1.24, Ea=(151.879,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 204 used for R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_2H
Exact match found for rate rule [R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction71',
reactants = ['CC1C=[C]CC=C1(1120)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS96',
kinetics = Arrhenius(A=(111300,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_DSS;Cd_rad_out_single;Cs_H_out] for rate rule [R4H_DSS;Cd_rad_out_Cs;Cs_H_out_2H]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction72',
reactants = ['C7H9(453)(452)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS97',
kinetics = Arrhenius(A=(62296.1,'s^-1'), n=1.86, Ea=(59.4128,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5H;C_rad_out_2H;Cs_H_out_H/Cd] for rate rule [R5H_SSMS;C_rad_out_2H;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction73',
reactants = ['H(3)(3)', '[CH2][C]1C=CCC=C1(1121)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS98',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction74',
reactants = ['H(3)(3)', '[CH2]C1C=C[CH]C=C1(1122)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS99',
kinetics = Arrhenius(A=(4.14e-13,'cm^3/(molecule*s)'), n=0.611, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 53 used for C_rad/H/CdCd;H_rad
Exact match found for rate rule [C_rad/H/CdCd;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.8 to -1.8 kJ/mol.
Ea raised from -1.8 to 0 kJ/mol."""),
)
reaction(
label = 'reaction75',
reactants = ['H(3)(3)', '[CH2]C1[C]=CCC=C1(1123)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS100',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction76',
reactants = ['H(3)(3)', '[CH2]C1C=[C]CC=C1(1124)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS101',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction77',
reactants = ['C7H9(453)(452)'],
products = ['[CH]1C2C=CCC1C2(1114)'],
transitionState = 'TS102',
kinetics = Arrhenius(A=(5.9095e+13,'s^-1'), n=0.0504177, Ea=(126.933,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R4;doublebond_intra_pri_HNd_Cs;radadd_intra_cs2H] + [R4_cyclic;doublebond_intra_pri;radadd_intra_cs] for rate rule [Rn1c6_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_cs2H]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction78',
reactants = ['C2H2(30)(31)', 'C5H7(211)(210)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS103',
kinetics = Arrhenius(A=(4.88e-07,'m^3/(mol*s)'), n=2.98, Ea=(117.57,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [diene_out;diene_in_2H;yne] for rate rule [diene_out;diene_in_2H;yne_unsub_unsub]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 4.0
family: Diels_alder_addition"""),
)
reaction(
label = 'reaction79',
reactants = ['CH2(17)(18)', 'C6H7(464)(463)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS104',
kinetics = Arrhenius(A=(1.06732e+06,'m^3/(mol*s)'), n=0.472793, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [C_rad/H/TwoDe;Birad]
Euclidian distance = 3.0
family: Birad_R_Recombination
Ea raised from -3.5 to 0 kJ/mol."""),
)
reaction(
label = 'reaction80',
reactants = ['H(3)(3)', '[CH]C1C=CCC=C1(1125)'],
products = ['C7H9(453)(452)'],
transitionState = 'TS105',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction81',
reactants = ['H(3)(3)', 'C1=CC2CC=C2C1(1126)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS106',
kinetics = Arrhenius(A=(7.72e+07,'cm^3/(mol*s)'), n=1.64, Ea=(9.07928,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2568 used for Cds-CsCs_Cds-CsH;HJ
Exact match found for rate rule [Cds-CsCs_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction82',
reactants = ['H(3)(3)', 'C7H8(436)(435)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS107',
kinetics = Arrhenius(A=(1.46e+08,'cm^3/(mol*s)'), n=1.64, Ea=(5.73208,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2555 used for Cds-CsH_Cds-CsH;HJ
Exact match found for rate rule [Cds-CsH_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction83',
reactants = ['C7H9(460)(459)'],
products = ['C1=CC2CC[C]2C1(1127)'],
transitionState = 'TS108',
kinetics = Arrhenius(A=(7.25e+10,'s^-1'), n=0.6, Ea=(154.39,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_Cs2] for rate rule [R2H_S_cy4;C_rad_out_H/NonDeC;Cs_H_out_Cs2]
Euclidian distance = 1.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction84',
reactants = ['[CH]1CC2CC=CC12(1038)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS109',
kinetics = Arrhenius(A=(6.76e+09,'s^-1'), n=0.88, Ea=(158.992,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_H/NonDeC] for rate rule [R2H_S_cy4;C_rad_out_H/NonDeC;Cs_H_out_H/(NonDeC/Cs/Cs)]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction85',
reactants = ['C7H9(460)(459)'],
products = ['C1=C[C]2CCC2C1(1128)'],
transitionState = 'TS110',
kinetics = Arrhenius(A=(2.76e-23,'s^-1'), n=10.17, Ea=(56.5677,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(2500,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_OneDe] for rate rule [R3H_SS_23cy5;C_rad_out_H/NonDeC;Cs_H_out_Cd]
Euclidian distance = 1.41421356237
family: intra_H_migration"""),
)
reaction(
label = 'reaction86',
reactants = ['C7H9(460)(459)'],
products = ['[CH]1C=CC2CCC12(1048)'],
transitionState = 'TS111',
kinetics = Arrhenius(A=(6.82e+09,'s^-1'), n=0.73, Ea=(127.612,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_H/Cd] for rate rule [R3H_SS_12cy4;C_rad_out_H/NonDeC;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction87',
reactants = ['[C]1=CCC2CCC12(1129)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS112',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_RSS;Cd_rad_out;Cs_H_out_1H] for rate rule [R4H_SSS;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 2.44948974278
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction88',
reactants = ['[C]1=CC2CCC2C1(1130)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS113',
kinetics = Arrhenius(A=(236531,'s^-1'), n=1.93, Ea=(59.6011,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R4H_SSS;Y_rad_out;Cs_H_out_H/(NonDeC/Cs)] + [R4H_RSS;Cd_rad_out;Cs_H_out_1H] for rate rule [R4H_SSS;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction89',
reactants = ['H(3)(3)', '[CH]1CC2C=CC[C]12(1131)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS114',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction90',
reactants = ['H(3)(3)', '[CH]1C[C]2C=CCC12(1132)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS115',
kinetics = Arrhenius(A=(2.92e+13,'cm^3/(mol*s)'), n=0.18, Ea=(0.518816,'kJ/mol'), T0=(1,'K'), Tmin=(200,'K'), Tmax=(2000,'K'), comment="""From training reaction 123 used for H_rad;C_rad/OneDeCs2
Exact match found for rate rule [C_rad/OneDeCs2;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction91',
reactants = ['H(3)(3)', '[CH]1[CH]C2CC=CC12(1133)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS116',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction92',
reactants = ['H(3)(3)', '[CH]1C=CC2C[CH]C12(1134)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS117',
kinetics = Arrhenius(A=(2.71464e+07,'m^3/(mol*s)'), n=0.107721, Ea=(5.76381,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 36 used for C_rad/H/CdCs;H_rad
Exact match found for rate rule [C_rad/H/CdCs;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction93',
reactants = ['H(3)(3)', '[C]1=CCC2[CH]CC12(1135)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS118',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [H_rad;Cd_rad/NonDe]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction94',
reactants = ['H(3)(3)', '[C]1=CC2C[CH]C2C1(1136)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS119',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction95',
reactants = ['C7H9(460)(459)'],
products = ['[CH]1C2CC3C1CC23(1137)'],
transitionState = 'TS120',
kinetics = Arrhenius(A=(8.03578e+12,'s^-1'), n=-0.324073, Ea=(180.216,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5_cyclic;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs] for rate rule [Rn1c5_beta_long;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction96',
reactants = ['C7H9(460)(459)'],
products = ['[CH]1CC2C3CC2C13(1138)'],
transitionState = 'TS121',
kinetics = Arrhenius(A=(6.33004e+14,'s^-1'), n=-0.792922, Ea=(179.405,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 112 used for Rn0c7_gamma_long_SSS_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs
Exact match found for rate rule [Rn0c7_gamma_long_SSS_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction97',
reactants = ['C=CC1[CH]C=CC1(1139)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS122',
kinetics = Arrhenius(A=(3.72602e+07,'s^-1'), n=1.21458, Ea=(125.171,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4_Cs_RR_D;doublebond_intra_pri_2H;radadd_intra_cs] for rate rule [R4_Cs_RR_D;doublebond_intra_pri_2H;radadd_intra_csHCd]
Euclidian distance = 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction98',
reactants = ['C=C[CH]C1C=CC1(972)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS123',
kinetics = Arrhenius(A=(1.26e+11,'s^-1'), n=0.16, Ea=(41.84,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R7_cyclic;doublebond_intra_pri;radadd_intra_cs2H] for rate rule [Rn3c4_alpha_long;doublebond_intra_pri;radadd_intra_cs2H]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction99',
reactants = ['[CH]1C2C=CCC1C2(1114)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS124',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction100',
reactants = ['[CH2]C1C2C=CCC12(1140)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS125',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-HH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction101',
reactants = ['H(3)(3)', '[C]1CC2C=CCC12(1141)'],
products = ['C7H9(460)(459)'],
transitionState = 'TS126',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction102',
reactants = ['C7H9(527)(526)'],
products = ['H(3)(3)', 'C7H8(699)(698)'],
transitionState = 'TS127',
kinetics = Arrhenius(A=(1.03e+09,'s^-1'), n=1.36, Ea=(26.5,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 17 C7H9-9 <=> C7H8-5 + H in R_Addition_MultipleBond/training
This reaction matched rate rule [Cds-CdCs_Cds-CdH;HJ]
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction103',
reactants = ['H(3)(3)', 'CC1C=C=CC=C1(1142)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS128',
kinetics = Arrhenius(A=(5.46e+08,'cm^3/(mol*s)'), n=1.64, Ea=(15.8155,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2714 used for Ca_Cds-CsH;HJ
Exact match found for rate rule [Ca_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction104',
reactants = ['C7H9(527)(526)'],
products = ['CH3(15)(16)', 'C6H6(468)(467)'],
transitionState = 'TS129',
kinetics = Arrhenius(A=(2.07e+11,'s^-1'), n=0.83, Ea=(22.8,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 16 C7H9-8 <=> C6H6-2 + CH3 in R_Addition_MultipleBond/training
This reaction matched rate rule [Cds-CdH_Cds-CdH;CsJ-HHH]
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction105',
reactants = ['C7H9(527)(526)'],
products = ['C[C]1C=CC=CC1(1143)'],
transitionState = 'TS130',
kinetics = Arrhenius(A=(1.66458e+08,'s^-1'), n=1.5875, Ea=(125.816,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R2H_S;C_rad_out_H/OneDe;Cs_H_out_(CdCdCd)] for rate rule [R2H_S;C_rad_out_H/(Cd-Cd-Cd);Cs_H_out_(CdCdCd)]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction106',
reactants = ['CC1C=CC=[C]C1(1144)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS131',
kinetics = Arrhenius(A=(1.9054e+11,'s^-1'), n=0.853, Ea=(200.196,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction107',
reactants = ['C7H9(527)(526)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS132',
kinetics = Arrhenius(A=(2.03e+06,'s^-1'), n=1.96, Ea=(50.8,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 10 C7H9-13 <=> C7H9-14 in intra_H_migration/training
This reaction matched rate rule [R3H_SS_Cs;C_rad_out_H/(Cd-Cd-Cd);Cs_H_out_2H]
family: intra_H_migration"""),
)
reaction(
label = 'reaction108',
reactants = ['CC1[C]=CC=CC1(1145)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS133',
kinetics = Arrhenius(A=(3.32e+09,'s^-1'), n=0.99, Ea=(141.419,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_1H] for rate rule [R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_H/(Cd-Cd-Cd)]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction109',
reactants = ['CC1C=C[C]=CC1(1146)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS134',
kinetics = Arrhenius(A=(1.47715e+10,'s^-1'), n=0.8, Ea=(147.277,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_single;Cs_H_out_H/NonDeC] for rate rule [R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction110',
reactants = ['CC1C=[C]C=CC1(1147)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS135',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_DSS;Cd_rad_out_single;Cs_H_out_H/Cd] for rate rule [R4H_DSS;Cd_rad_out_singleDe_Cd;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction111',
reactants = ['CH3(15)(16)', '[CH]1[CH]C=CC=C1(1148)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS136',
kinetics = Arrhenius(A=(8.06808e+07,'m^3/(mol*s)'), n=0.0549843, Ea=(0.0928142,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;C_methyl]
Euclidian distance = 0
Multiplied by reaction path degeneracy 6.0
family: R_Recombination"""),
)
reaction(
label = 'reaction112',
reactants = ['H(3)(3)', 'C7H8(697)(696)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS137',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction113',
reactants = ['H(3)(3)', '[CH2]C1C=C[CH]C=C1(1122)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS138',
kinetics = Arrhenius(A=(3.48677e-12,'cm^3/(molecule*s)'), n=0.6, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 18 used for C_rad/H2/Cs;H_rad
Exact match found for rate rule [C_rad/H2/Cs;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -3.3 to 0 kJ/mol."""),
)
reaction(
label = 'reaction139',
reactants = ['H(3)(3)', 'CC1[C]=CC=C[CH]1(1149)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS139',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction140',
reactants = ['H(3)(3)', 'CC1[CH]C=C[C]=C1(1150)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS140',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [H_rad;Cd_rad/Cd]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction116',
reactants = ['H(3)(3)', 'CC1[CH]C=[C]C=C1(1151)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS141',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction117',
reactants = ['C7H9(527)(526)'],
products = ['CC1C=CC2[CH]C12(1152)'],
transitionState = 'TS142',
kinetics = Arrhenius(A=(4.45714e+12,'s^-1'), n=0, Ea=(186.032,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0cx_beta;doublebond_intra_pri_HCd;radadd_intra_cs] for rate rule [Rn0c6_beta_short;doublebond_intra_pri_HCd;radadd_intra_csHCs]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic
Ea raised from 184.0 to 186.0 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction118',
reactants = ['C7H9(527)(526)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS143',
kinetics = Arrhenius(A=(1.08454e+19,'s^-1'), n=-0.859165, Ea=(130.857,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0cx_gamma;doublebond_intra_pri_HCd;radadd_intra_cs] for rate rule [Rn0c6_gamma;doublebond_intra_pri_HCd;radadd_intra_csHCd]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction119',
reactants = ['CC1C2[CH]C=CC12(1153)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS144',
kinetics = Arrhenius(A=(5.21e+11,'s^-1'), n=0.46, Ea=(16.2,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 110 C7H9-13 <=> C7H9-14 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [Rn0c6_beta_long_SS_D_RH;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction120',
reactants = ['[CH]=CC=CC=CC(1154)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS145',
kinetics = Arrhenius(A=(1.43857e+20,'s^-1'), n=-1.34645, Ea=(10.1028,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""Estimated using template [R6_DSM_D;doublebond_intra_pri_HNd_Cs;radadd_intra_cdsingle] for rate rule [R6_DSM_D;doublebond_intra_pri_HNd_Cs;radadd_intra_cdsingleH]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction121',
reactants = ['CH2(S)(21)(22)', 'C6H7(464)(463)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS146',
kinetics = Arrhenius(A=(143764,'m^3/(mol*s)'), n=0.444, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [carbene;R_H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: 1,2_Insertion_carbene
Ea raised from -5.1 to 0 kJ/mol."""),
)
reaction(
label = 'reaction122',
reactants = ['C7H9(527)(526)'],
products = ['CC1[CH]CC=CC=1(1155)'],
transitionState = 'TS147',
kinetics = Arrhenius(A=(7.85714e+07,'s^-1'), n=1.56, Ea=(259.91,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [1_3_pentadiene;CH_end;CdCJ_2] for rate rule [1_3_pentadiene;CH(CJ)_1;CdCJ_2]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: Intra_ene_reaction"""),
)
reaction(
label = 'reaction123',
reactants = ['C[CH]C1C=CC=C1(1156)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS148',
kinetics = Arrhenius(A=(1.31121e+11,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction124',
reactants = ['CC1C=[C]CC=C1(1120)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS149',
kinetics = Arrhenius(A=(1.448e+10,'s^-1'), n=0.82, Ea=(156.9,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 154 used for R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd
Exact match found for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction125',
reactants = ['CC1[C]=CCC=C1(1119)'],
products = ['C7H9(527)(526)'],
transitionState = 'TS150',
kinetics = Arrhenius(A=(1.182e+10,'s^-1'), n=0.86, Ea=(149.369,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_Cs;Cs_H_out_1H] for rate rule [R3H_DS;Cd_rad_out_Cs;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction126',
reactants = ['C7H9(527)(526)'],
products = ['C[C]1C=CCC=C1(1118)'],
transitionState = 'TS151',
kinetics = Arrhenius(A=(1.33417e+08,'s^-1'), n=0.875, Ea=(262.128,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R4H;C_rad_out_H/Cd;Cs_H_out] + [R4H_SDS;C_rad_out_1H;Cs_H_out] for rate rule [R4H_SDS;C_rad_out_H/Cd;Cs_H_out_noH]
Euclidian distance = 2.2360679775
family: intra_H_migration"""),
)
reaction(
label = 'reaction127',
reactants = ['H(3)(3)', 'CC1=CC2C=CC12(1157)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS152',
kinetics = Arrhenius(A=(7.72e+07,'cm^3/(mol*s)'), n=1.64, Ea=(9.07928,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2568 used for Cds-CsCs_Cds-CsH;HJ
Exact match found for rate rule [Cds-CsCs_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction128',
reactants = ['H(3)(3)', 'CC1C=C2C=CC21(1158)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS153',
kinetics = Arrhenius(A=(1.11e+08,'cm^3/(mol*s)'), n=1.64, Ea=(11.1294,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2594 used for Cds-CdCs_Cds-CsH;HJ
Exact match found for rate rule [Cds-CdCs_Cds-CsH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction129',
reactants = ['CH3(15)(16)', 'C1=CC2C=CC12(1159)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS154',
kinetics = Arrhenius(A=(40400,'cm^3/(mol*s)'), n=2.41, Ea=(28.4512,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [Cds-CsH_Cds-CsH;CsJ-HHH] for rate rule [Cds-CsH_Cds-(CsH-Cds-Cds)_cyc6;CsJ-HHH]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 4.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction130',
reactants = ['C7H9(536)(535)'],
products = ['C[C]1CC2C=CC12(1160)'],
transitionState = 'TS155',
kinetics = Arrhenius(A=(7.25e+10,'s^-1'), n=0.6, Ea=(154.39,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_Cs2] for rate rule [R2H_S_cy4;C_rad_out_H/NonDeC;Cs_H_out_Cs2]
Euclidian distance = 1.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction131',
reactants = ['C7H9(536)(535)'],
products = ['CC1C[C]2C=CC21(1161)'],
transitionState = 'TS156',
kinetics = Arrhenius(A=(2.15233e+09,'s^-1'), n=1.014, Ea=(127.919,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R2H_S;C_rad_out_H/NonDeC;Cs_H_out_noH] for rate rule [R2H_S_cy4;C_rad_out_H/NonDeC;Cs_H_out_noH]
Euclidian distance = 1.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction132',
reactants = ['C7H9(536)(535)'],
products = ['CC1CC2C=C[C]12(1162)'],
transitionState = 'TS157',
kinetics = Arrhenius(A=(7.27e+09,'s^-1'), n=0.66, Ea=(144.766,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using an average for rate rule [R3H_SS_Cs;C_rad_out_H/NonDeC;Cs_H_out_noH]
Euclidian distance = 0
family: intra_H_migration"""),
)
reaction(
label = 'reaction133',
reactants = ['[CH2]C1CC2C=CC12(1163)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS158',
kinetics = Arrhenius(A=(1.064e+06,'s^-1'), n=1.93, Ea=(141.419,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_2H;Cs_H_out_H/(NonDeC/Cs)] for rate rule [R3H_SS_23cy4;C_rad_out_2H;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction134',
reactants = ['CC1CC2[C]=CC12(1164)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS159',
kinetics = Arrhenius(A=(3.32e+09,'s^-1'), n=0.99, Ea=(141.419,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_SS;Cd_rad_out_Cd;Cs_H_out_H/NonDeC] for rate rule [R3H_SS_12cy4;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs/Cs)]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction135',
reactants = ['CC1CC2C=[C]C12(1165)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS160',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_RSS;Cd_rad_out;Cs_H_out_1H] for rate rule [R4H_SSS;Cd_rad_out_Cd;Cs_H_out_H/NonDeC]
Euclidian distance = 2.44948974278
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction136',
reactants = ['H(3)(3)', 'CC1[CH]C2C=C[C]12(1166)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS161',
kinetics = Arrhenius(A=(2.92e+13,'cm^3/(mol*s)'), n=0.18, Ea=(0.518816,'kJ/mol'), T0=(1,'K'), Tmin=(200,'K'), Tmax=(2000,'K'), comment="""From training reaction 123 used for H_rad;C_rad/OneDeCs2
Exact match found for rate rule [C_rad/OneDeCs2;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction137',
reactants = ['CH3(15)(16)', '[CH]1[CH]C2C=CC12(1167)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS162',
kinetics = Arrhenius(A=(2.68936e+07,'m^3/(mol*s)'), n=0.0549843, Ea=(0.0928142,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;C_methyl]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination"""),
)
reaction(
label = 'reaction138',
reactants = ['H(3)(3)', 'C[C]1[CH]C2C=CC12(1168)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS163',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction139',
reactants = ['H(3)(3)', 'CC1[CH][C]2C=CC21(1169)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS164',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction140',
reactants = ['H(3)(3)', '[CH2]C1[CH]C2C=CC12(1170)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS165',
kinetics = Arrhenius(A=(3.48677e-12,'cm^3/(molecule*s)'), n=0.6, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 18 used for C_rad/H2/Cs;H_rad
Exact match found for rate rule [C_rad/H2/Cs;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -3.3 to 0 kJ/mol."""),
)
reaction(
label = 'reaction141',
reactants = ['H(3)(3)', 'CC1[CH]C2C=[C]C12(1171)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS166',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [H_rad;Cd_rad/NonDe]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction142',
reactants = ['H(3)(3)', 'CC1[CH]C2[C]=CC12(1172)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS167',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction143',
reactants = ['C7H9(536)(535)'],
products = ['CC1C2C3[CH]C2C13(1173)'],
transitionState = 'TS168',
kinetics = Arrhenius(A=(5.4227e+18,'s^-1'), n=-0.859165, Ea=(285.096,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0cx_gamma;doublebond_intra_pri;radadd_intra_csHCs] for rate rule [Rn0c6_gamma;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 2.2360679775
family: Intra_R_Add_Endocyclic
Ea raised from 284.1 to 285.1 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction144',
reactants = ['C7H9(536)(535)'],
products = ['CC1C2[CH]C3C1C23(1174)'],
transitionState = 'TS169',
kinetics = Arrhenius(A=(2.26887e+10,'s^-1'), n=0.505957, Ea=(215.516,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Rn0c6_beta_long_SS_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCs]
Euclidian distance = 0
family: Intra_R_Add_Endocyclic
Ea raised from 215.2 to 215.5 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction145',
reactants = ['CC=CC1[CH]C=C1(1175)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS170',
kinetics = Arrhenius(A=(3.22e+08,'s^-1'), n=0.96, Ea=(123.01,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4_Cs_RR_D;doublebond_intra_pri_HNd_Cs;radadd_intra_cs] for rate rule [R4_Cs_RR_D;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction146',
reactants = ['[CH]=CC1C=CC1C(1176)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS171',
kinetics = Arrhenius(A=(2.1e+12,'s^-1'), n=0.14, Ea=(1.2552,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R6_cyclic;doublebond_intra_pri;radadd_intra_cdsingle] for rate rule [Rn2c4_alpha_long;doublebond_intra_pri;radadd_intra_cdsingleH]
Euclidian distance = 1.41421356237
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction147',
reactants = ['CH2(S)(21)(22)', 'C6H7(467)(466)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS172',
kinetics = Arrhenius(A=(143764,'m^3/(mol*s)'), n=0.444, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [carbene;R_H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: 1,2_Insertion_carbene
Ea raised from -5.1 to 0 kJ/mol."""),
)
reaction(
label = 'reaction148',
reactants = ['C[CH]C1C2C=CC12(1177)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS173',
kinetics = Arrhenius(A=(1.31121e+11,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction149',
reactants = ['C7H9(536)(535)'],
products = ['CC1C2[CH]C=CC12(1153)'],
transitionState = 'TS174',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction150',
reactants = ['CC1C2[CH]C1C=C2(1178)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS175',
kinetics = Arrhenius(A=(1.31121e+11,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-CsH;C]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction151',
reactants = ['H(3)(3)', 'CC1[C]C2C=CC12(1179)'],
products = ['C7H9(536)(535)'],
transitionState = 'TS176',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction177',
reactants = ['[CH]=CC=CCC=C(920)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS177',
kinetics = Arrhenius(A=(1e+11,'s^-1'), n=0.21, Ea=(125.52,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Matched reaction 36 C7H9-7 <=> C7H9-8 in Intra_R_Add_Exocyclic/training
This reaction matched rate rule [R7_DSMS_D;doublebond_intra_2H_pri;radadd_intra_cdsingleH]
family: Intra_R_Add_Exocyclic"""),
)
reaction(
label = 'reaction178',
reactants = ['[CH2]C=CC=CC=C(878)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS178',
kinetics = Arrhenius(A=(3.95e+10,'s^-1'), n=0.53, Ea=(31.5,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 27 C7H9-5 <=> C7H9-6 in Intra_R_Add_Exocyclic/training
This reaction matched rate rule [R7_SMSM_D;doublebond_intra_2H_pri;radadd_intra_cs2H]
family: Intra_R_Add_Exocyclic"""),
)
reaction(
label = 'reaction179',
reactants = ['C7H9(681)(680)'],
products = ['H(3)(3)', 'C7H8(690)(689)'],
transitionState = 'TS179',
kinetics = Arrhenius(A=(1.01e+09,'s^-1'), n=1.23, Ea=(28.4982,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 18 C7H9-10 <=> C7H8-6 + H in R_Addition_MultipleBond/training
This reaction matched rate rule [Cds-CdCs_Cds-HH;HJ]
family: R_Addition_MultipleBond
Ea raised from 118.4 to 119.2 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction180',
reactants = ['C[C]1C=CC=CC1(1143)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS180',
kinetics = Arrhenius(A=(2.307e+09,'s^-1'), n=1.31, Ea=(203.342,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 163 used for R2H_S;C_rad_out_OneDe/Cs;Cs_H_out_2H
Exact match found for rate rule [R2H_S;C_rad_out_OneDe/Cs;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction181',
reactants = ['CC1[C]=CC=CC1(1145)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS181',
kinetics = Arrhenius(A=(2.304e+09,'s^-1'), n=1.24, Ea=(151.879,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 204 used for R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_2H
Exact match found for rate rule [R3H_SS_Cs;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction182',
reactants = ['CC1C=CC=[C]C1(1144)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS182',
kinetics = Arrhenius(A=(111300,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_RSS;Cd_rad_out;Cs_H_out] for rate rule [R4H_SSS;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 2.44948974278
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction183',
reactants = ['C7H9(681)(680)'],
products = ['CC1C=[C]C=CC1(1147)'],
transitionState = 'TS183',
kinetics = Arrhenius(A=(1.71035e+12,'s^-1'), n=1.11009, Ea=(419.408,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_RSD;Y_rad_out;Cd_H_out_single] for rate rule [R4H_SSD;C_rad_out_2H;Cd_H_out_singleDe]
Euclidian distance = 2.44948974278
family: intra_H_migration"""),
)
reaction(
label = 'reaction184',
reactants = ['C7H9(681)(680)'],
products = ['CC1C=C[C]=CC1(1146)'],
transitionState = 'TS184',
kinetics = Arrhenius(A=(3.79808e+06,'s^-1'), n=1.3209, Ea=(69.9539,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R5H_SSSR;C_rad_out_2H;XH_out] + [R5H_SSSD;Y_rad_out;Cd_H_out_single] for rate rule [R5H_SSSD;C_rad_out_2H;Cd_H_out_singleDe]
Euclidian distance = 2.2360679775
family: intra_H_migration"""),
)
reaction(
label = 'reaction185',
reactants = ['H(3)(3)', 'C7H8(693)(692)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS185',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction186',
reactants = ['H(3)(3)', '[CH2]C1C=C[CH]C=C1(1122)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS186',
kinetics = Arrhenius(A=(5.42928e+07,'m^3/(mol*s)'), n=0.107721, Ea=(5.76381,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 36 used for C_rad/H/CdCs;H_rad
Exact match found for rate rule [C_rad/H/CdCs;H_rad]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: R_Recombination"""),
)
reaction(
label = 'reaction187',
reactants = ['H(3)(3)', '[CH2]C1[C]=CC=CC1(1191)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS187',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction188',
reactants = ['H(3)(3)', '[CH2]C1C=CC=[C]C1(1192)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS188',
kinetics = Arrhenius(A=(1e+13,'cm^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 40 used for Cd_rad/NonDe;H_rad
Exact match found for rate rule [Cd_rad/NonDe;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction189',
reactants = ['H(3)(3)', '[CH2]C1C=C[C]=CC1(1193)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS189',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [Cd_rad/Cd;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction190',
reactants = ['H(3)(3)', '[CH2]C1C=[C]C=CC1(1194)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS190',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [Cd_rad/Cd;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction191',
reactants = ['C7H9(681)(680)'],
products = ['[CH]1C2C=CCC1C2(1114)'],
transitionState = 'TS191',
kinetics = Arrhenius(A=(5.4227e+18,'s^-1'), n=-0.859165, Ea=(130.857,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4_cyclic;doublebond_intra_pri_HCd;radadd_intra_cs] for rate rule [Rn1c6_beta_short;doublebond_intra_pri_HCd;radadd_intra_cs2H]
Euclidian distance = 2.2360679775
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction192',
reactants = ['[CH]1CC2C=CC1C2(1195)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS192',
kinetics = Arrhenius(A=(1.37e+11,'s^-1'), n=0.73, Ea=(25.5,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 115 C7H9-23 <=> C7H9-24 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [Rn1c6_gamma;doublebond_intra_pri_HCd;radadd_intra_cs2H]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction193',
reactants = ['C7H9(681)(680)'],
products = ['[CH]1C=CC2CC1C2(1196)'],
transitionState = 'TS193',
kinetics = Arrhenius(A=(1.41e+11,'s^-1'), n=0.2, Ea=(195.811,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R6_cyclic;doublebond_intra_pri_HNd_Cs;radadd_intra_cs2H] for rate rule [Rn1c6_beta_long;doublebond_intra_pri_HNd_Cs;radadd_intra_cs2H]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction194',
reactants = ['C7H9(681)(680)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS194',
kinetics = Arrhenius(A=(2.125e+09,'s^-1'), n=0.991, Ea=(45.9529,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [1_3_pentadiene;CH_end;CdHC_2] for rate rule [1_3_pentadiene;CH(CJ)_1;CdHC_2]
Euclidian distance = 1.0
family: Intra_ene_reaction"""),
)
reaction(
label = 'reaction195',
reactants = ['C7H9(681)(680)'],
products = ['[CH2]C1CC2C=CC12(1163)'],
transitionState = 'TS195',
kinetics = Arrhenius(A=(4.99998e+11,'s^-1'), n=0.0559095, Ea=(122.413,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [1,3-butadiene_backbone;C=C_1;C=C_2] for rate rule [1,3-butadiene_backbone;CdH(C)_1;CdH(C)_2]
Euclidian distance = 1.41421356237
family: Intra_2+2_cycloaddition_Cd"""),
)
reaction(
label = 'reaction196',
reactants = ['CH2(17)(18)', 'C6H7(464)(463)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS196',
kinetics = Arrhenius(A=(2.13464e+06,'m^3/(mol*s)'), n=0.472793, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [C_rad/H/OneDeC;Birad]
Euclidian distance = 4.0
Multiplied by reaction path degeneracy 2.0
family: Birad_R_Recombination
Ea raised from -3.5 to 0 kJ/mol."""),
)
reaction(
label = 'reaction197',
reactants = ['H(3)(3)', '[CH]C1C=CC=CC1(1197)'],
products = ['C7H9(681)(680)'],
transitionState = 'TS197',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction198',
reactants = ['H(3)(3)', 'C=C1C=C=CCC1(1198)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS198',
kinetics = Arrhenius(A=(1.149e+09,'cm^3/(mol*s)'), n=1.595, Ea=(16.7946,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Ca_Cds-OneDeH;HJ] for rate rule [Ca_Cds-CdH;HJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction199',
reactants = ['C=C1C[C]=CCC1(1199)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS199',
kinetics = Arrhenius(A=(1.448e+10,'s^-1'), n=0.82, Ea=(156.9,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 154 used for R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd
Exact match found for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction200',
reactants = ['C=C1[CH]CC=CC1(1200)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS200',
kinetics = Arrhenius(A=(6.82e+09,'s^-1'), n=0.73, Ea=(127.612,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/NonDeC;Cs_H_out_H/Cd] for rate rule [R3H_SS_2Cd;C_rad_out_H/NonDeC;Cs_H_out_H/Cd]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction201',
reactants = ['[CH]=C1CC=CCC1(1201)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS201',
kinetics = Arrhenius(A=(1.846e+10,'s^-1'), n=0.74, Ea=(145.185,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_singleH;Cs_H_out_1H] for rate rule [R3H_DS;Cd_rad_out_singleH;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction202',
reactants = ['C=C1CC=[C]CC1(1202)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS202',
kinetics = Arrhenius(A=(1.182e+10,'s^-1'), n=0.86, Ea=(149.369,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_Cs;Cs_H_out_1H] for rate rule [R3H_DS;Cd_rad_out_Cs;Cs_H_out_H/Cd]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction203',
reactants = ['C=C1C[CH]C=CC1(1203)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS203',
kinetics = Arrhenius(A=(7.12e+06,'s^-1'), n=1.75, Ea=(105.855,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R4H_SS(Cd)S;C_rad_out_H/Cd;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 4.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction204',
reactants = ['H(3)(3)', 'C=C1[CH]C=CC[CH]1(1204)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS204',
kinetics = Arrhenius(A=(2.71464e+07,'m^3/(mol*s)'), n=0.107721, Ea=(5.76381,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 36 used for C_rad/H/CdCs;H_rad
Exact match found for rate rule [C_rad/H/CdCs;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction205',
reactants = ['H(3)(3)', 'C7H8(693)(692)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS205',
kinetics = Arrhenius(A=(2.71464e+07,'m^3/(mol*s)'), n=0.107721, Ea=(5.76381,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""From training reaction 36 used for C_rad/H/CdCs;H_rad
Exact match found for rate rule [C_rad/H/CdCs;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction206',
reactants = ['H(3)(3)', 'C=C1[CH]C=[C]CC1(1205)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS206',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction207',
reactants = ['H(3)(3)', 'C=C1[CH][C]=CCC1(1206)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS207',
kinetics = Arrhenius(A=(4.34601e+06,'m^3/(mol*s)'), n=0.278532, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Y_rad;H_rad]
Euclidian distance = 0
family: R_Recombination
Ea raised from -1.4 to 0 kJ/mol."""),
)
reaction(
label = 'reaction208',
reactants = ['H(3)(3)', '[CH]=C1[CH]C=CCC1(1207)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS208',
kinetics = Arrhenius(A=(5.78711e+07,'m^3/(mol*s)'), n=0.0433333, Ea=(0.458029,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [Cd_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction209',
reactants = ['C7H9(682)(681)'],
products = ['C1=CC2C[C]2CC1(1101)'],
transitionState = 'TS209',
kinetics = Arrhenius(A=(3.473e+12,'s^-1'), n=0.247, Ea=(231.216,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3_D;doublebond_intra_secNd_2H;radadd_intra_csHDe] for rate rule [R3_D;doublebond_intra_secNd_2H;radadd_intra_csHCd]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction210',
reactants = ['C7H9(682)(681)'],
products = ['C=C1CCC2[CH]C12(1208)'],
transitionState = 'TS210',
kinetics = Arrhenius(A=(4.00063e+13,'s^-1'), n=-0.283562, Ea=(245.321,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Rn0c6_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_csHDe] for rate rule [Rn0c6_beta_short;doublebond_intra_pri_HNd_Cs;radadd_intra_csHCd]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction211',
reactants = ['[CH2]CC=CC=C=C(879)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS211',
kinetics = Arrhenius(A=(4.00901e+09,'s^-1'), n=0.463766, Ea=(75.7068,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R6_SSM_D;doublebond_intra;radadd_intra_cs] for rate rule [R6_SSM_D;doublebond_intra;radadd_intra_cs2H]
Euclidian distance = 1.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction212',
reactants = ['C=[C]C1C=CCC1(1209)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS212',
kinetics = Arrhenius(A=(8.66e+11,'s^-1'), n=0.438, Ea=(94.4747,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 5 used for cCs(-HC)CJ;CdsJ;C
Exact match found for rate rule [cCs(-HC)CJ;CdsJ;C]
Euclidian distance = 0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction213',
reactants = ['C=CC1[CH]C(=C)C1(1210)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS213',
kinetics = Arrhenius(A=(6.21184e+10,'s^-1'), n=0.288169, Ea=(147.049,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [1_5_unsaturated_hexane] for rate rule [1_5_hexadiene]
Euclidian distance = 1.0
family: 6_membered_central_C-C_shift"""),
)
reaction(
label = 'reaction214',
reactants = ['H(3)(3)', 'C=C1[C]C=CCC1(1211)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS214',
kinetics = Arrhenius(A=(1e+07,'m^3/(mol*s)'), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [H_rad;Birad]
Euclidian distance = 0
family: Birad_R_Recombination"""),
)
reaction(
label = 'reaction215',
reactants = ['C7H9(682)(681)'],
products = ['CC1[CH]CC=CC=1(1155)'],
transitionState = 'TS215',
kinetics = Arrhenius(A=(8.4e+10,'s^-1'), n=0.82, Ea=(205.853,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 210 used for R3H_SS_2Cd;C_rad_out_2H;Cs_H_out_H/NonDeC
Exact match found for rate rule [R3H_SS_2Cd;C_rad_out_2H;Cs_H_out_H/NonDeC]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction216',
reactants = ['CC1=[C]C=CCC1(1212)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS216',
kinetics = Arrhenius(A=(3.85113e+09,'s^-1'), n=1.0541, Ea=(193.078,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""From training reaction 288 used for R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_2H
Exact match found for rate rule [R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction217',
reactants = ['C7H9(682)(681)'],
products = ['C[C]1C=CC=CC1(1143)'],
transitionState = 'TS217',
kinetics = Arrhenius(A=(51600,'s^-1'), n=2.04, Ea=(105.855,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H_S(Cd)SS;C_rad_out_2H;Cs_H_out_H/Cd] for rate rule [R4H_S(Cd)SS;C_rad_out_2H;Cs_H_out_H/(Cd-Cd-Cd)]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction218',
reactants = ['CC1=C[C]=CCC1(1213)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS218',
kinetics = Arrhenius(A=(3.33e+08,'s^-1'), n=1.1915, Ea=(103.605,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""From training reaction 284 used for R4H_SDS;Cd_rad_out_Cd;Cs_H_out_2H
Exact match found for rate rule [R4H_SDS;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction219',
reactants = ['CC1=CC=[C]CC1(1214)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS219',
kinetics = Arrhenius(A=(6900,'s^-1'), n=1.98, Ea=(42.6768,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5H_CCC(Cd);Y_rad_out;Cs_H_out_2H] for rate rule [R5H_CCC(Cd);Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction220',
reactants = ['[CH]1C=C2CCC1C2(1215)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS220',
kinetics = Arrhenius(A=(1.18e+11,'s^-1'), n=0.82, Ea=(22.4,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 123 C7H9-39 <=> C7H9-40 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [Rn1c6_gamma;doublebond_intra_pri_HNd_Cs;radadd_intra_cs2H]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction221',
reactants = ['C7H9(682)(681)'],
products = ['[CH]1CCC2=CC1C2(1216)'],
transitionState = 'TS221',
kinetics = Arrhenius(A=(3.125e+09,'s^-1'), n=0.76, Ea=(347.118,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R6_cyclic;doublebond_intra_pri_HCd;radadd_intra_cs] for rate rule [Rn1c6_beta_long;doublebond_intra_pri_HCd;radadd_intra_cs2H]
Euclidian distance = 1.41421356237
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction222',
reactants = ['[C]1=CC=CCCC1(845)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS222',
kinetics = Arrhenius(A=(1.74842e+09,'s^-1'), n=1.084, Ea=(170.038,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [cCsCJ;CdsJ;C] + [cCs(-HH)CJ;CJ;C] for rate rule [cCs(-HH)CJ;CdsJ;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction223',
reactants = ['CH2(17)(18)', '[C]1=CC=CCC1(1217)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS223',
kinetics = Arrhenius(A=(1.06732e+06,'m^3/(mol*s)'), n=0.472793, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [Cd_rad/NonDe;Birad]
Euclidian distance = 3.0
family: Birad_R_Recombination
Ea raised from -3.5 to 0 kJ/mol."""),
)
reaction(
label = 'reaction224',
reactants = ['C7H9(682)(681)'],
products = ['[CH2]C12C=CC1CC2(1218)'],
transitionState = 'TS224',
kinetics = Arrhenius(A=(1.62826e+06,'s^-1'), n=1.44767, Ea=(117.195,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5_SS_D;doublebond_intra_2H;radadd_intra_cs] for rate rule [R5_SS_D;doublebond_intra_2H_secDe;radadd_intra_csHCd]
Euclidian distance = 2.2360679775
family: Intra_R_Add_Exocyclic"""),
)
reaction(
label = 'reaction225',
reactants = ['C7H9(682)(681)'],
products = ['H(3)(3)', 'C7H8(690)(689)'],
transitionState = 'TS225',
kinetics = Arrhenius(A=(4.47e+10,'s^-1'), n=1.17, Ea=(50.9747,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 23 C7H9-15 <=> C7H8-9 + H in R_Addition_MultipleBond/training
This reaction matched rate rule [Cds-CsH_Cds-(Cd-Cd-Cd)H;HJ]
family: R_Addition_MultipleBond
Ea raised from 172.0 to 213.3 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction226',
reactants = ['C7H9(682)(681)'],
products = ['C=C1C=CC[CH]C1(1219)'],
transitionState = 'TS226',
kinetics = Arrhenius(A=(1.022e+10,'s^-1'), n=1.34, Ea=(199.577,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 9 used for R2H_S;C_rad_out_H/(Cd-Cd-Cd);Cs_H_out_H/NonDeC
Exact match found for rate rule [R2H_S;C_rad_out_H/(Cd-Cd-Cd);Cs_H_out_H/NonDeC]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction227',
reactants = ['C=C1C=[C]CCC1(1220)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS227',
kinetics = Arrhenius(A=(1.9054e+11,'s^-1'), n=0.853, Ea=(200.196,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction228',
reactants = ['C=C1[CH]CCC=C1(1221)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS228',
kinetics = Arrhenius(A=(3.8e+10,'s^-1'), n=0.87, Ea=(144.348,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_SS;C_rad_out_H/Cd;Cs_H_out_H/Cd] for rate rule [R3H_SS_Cs;C_rad_out_H/Cd;Cs_H_out_H/(Cd-Cd-Cd)]
Euclidian distance = 1.41421356237
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction229',
reactants = ['C=C1[C]=CCCC1(1222)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS229',
kinetics = Arrhenius(A=(1.47715e+10,'s^-1'), n=0.8, Ea=(147.277,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_single;Cs_H_out_H/NonDeC] for rate rule [R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_H/(NonDeC/Cs)]
Euclidian distance = 2.2360679775
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction230',
reactants = ['[CH2]C(=C)C=CC=C(1223)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS230',
kinetics = Arrhenius(A=(8.1378e+08,'s^-1'), n=0.637531, Ea=(63.1312,'kJ/mol'), T0=(1,'K'), Tmin=(303.03,'K'), Tmax=(2000,'K'), comment="""Estimated using template [R6_SSM_D;doublebond_intra_pri_2H;radadd_intra_cs] for rate rule [R6_SSM_D;doublebond_intra_pri_2H;radadd_intra_cs2H]
Euclidian distance = 1.0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction231',
reactants = ['[CH2]C1C=CC(=C)C1(1224)'],
products = ['C7H9(682)(681)'],
transitionState = 'TS231',
kinetics = Arrhenius(A=(6.55606e+10,'s^-1'), n=0.64, Ea=(159.935,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [cCs(-HC)CJ;CsJ;C] for rate rule [cCs(-HC)CJ;CsJ-HH;C]
Euclidian distance = 1.0
family: 1,2_shiftC"""),
)
reaction(
label = 'reaction232',
reactants = ['[CH2]C=CC=CC=C(878)'],
products = ['C=C[CH]C1C=CC1(972)'],
transitionState = 'TS232',
kinetics = Arrhenius(A=(2e+10,'s^-1'), n=0, Ea=(196.02,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R5_SD_D;doublebond_intra;radadd_intra_cs] for rate rule [R5_SD_D;doublebond_intra_HCd_pri;radadd_intra_cs2H]
Euclidian distance = 3.16227766017
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Exocyclic"""),
)
reaction(
label = 'reaction233',
reactants = ['H(3)(3)', 'C=C=CC=CC=C(3634)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS233',
kinetics = Arrhenius(A=(4.42e+08,'cm^3/(mol*s)'), n=1.64, Ea=(11.7989,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 2713 used for Ca_Cds-HH;HJ
Exact match found for rate rule [Ca_Cds-HH;HJ]
Euclidian distance = 0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction234',
reactants = ['C=CC=CC=[C]C(7438)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS234',
kinetics = Arrhenius(A=(7.32e+09,'s^-1'), n=1.12, Ea=(172.799,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 152 used for R2H_S;Cd_rad_out_Cd;Cs_H_out_2H
Exact match found for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction235',
reactants = ['C=CC=C[C]=CC(7439)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS235',
kinetics = Arrhenius(A=(3.85113e+09,'s^-1'), n=1.0541, Ea=(193.078,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""From training reaction 288 used for R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_2H
Exact match found for rate rule [R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction236',
reactants = ['C=CC=[C]C=CC(7440)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS236',
kinetics = Arrhenius(A=(3.33e+08,'s^-1'), n=1.1915, Ea=(103.605,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""From training reaction 284 used for R4H_SDS;Cd_rad_out_Cd;Cs_H_out_2H
Exact match found for rate rule [R4H_SDS;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction237',
reactants = ['C=C[C]=CC=CC(7441)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS237',
kinetics = Arrhenius(A=(408000,'s^-1'), n=1.9199, Ea=(33.0402,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""Estimated using template [R5H_DSMS;Cd_rad_out_single;Cs_H_out_2H] for rate rule [R5H_DSMS;Cd_rad_out_singleDe_Cd;Cs_H_out_2H]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction238',
reactants = ['C=[C]C=CC=CC(7442)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS238',
kinetics = Arrhenius(A=(5.59786e+07,'s^-1'), n=1.58088, Ea=(142.57,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [RnH;Cd_rad_out_Cd;Cs_H_out_2H] for rate rule [R6H_SMSMS;Cd_rad_out_Cd;Cs_H_out_2H]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction239',
reactants = ['[CH]=CC=CC=CC(1154)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS239',
kinetics = Arrhenius(A=(22.7193,'s^-1'), n=3.21897, Ea=(132.277,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [RnH;Cd_rad_out_singleH;Cs_H_out_2H] for rate rule [R7H;Cd_rad_out_singleH;Cs_H_out_2H]
Euclidian distance = 2.0
Multiplied by reaction path degeneracy 3.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction240',
reactants = ['C2H3(28)(29)', '[CH]=CC=C[CH2](880)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS240',
kinetics = Arrhenius(A=(7.23e+13,'cm^3/(mol*s)','+|-',1.2e+13), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(298,'K'), comment="""From training reaction 89 used for Cd_pri_rad;Cd_pri_rad
Exact match found for rate rule [Cd_pri_rad;Cd_pri_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction241',
reactants = ['H(3)(3)', '[CH2]C=CC=[C]C=C(7443)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS241',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [Cd_rad/Cd;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction242',
reactants = ['[CH]=C[CH2](891)', 'CH2CHCHCH(913)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS242',
kinetics = Arrhenius(A=(7.23e+13,'cm^3/(mol*s)','+|-',1.2e+13), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(298,'K'), comment="""Estimated using an average for rate rule [Cd_rad;Cd_pri_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction243',
reactants = ['H(3)(3)', '[CH2]C=C[C]=CC=C(7444)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS243',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [Cd_rad/Cd;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction244',
reactants = ['H(3)(3)', '[CH2]C=CC=C[C]=C(3658)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS244',
kinetics = Arrhenius(A=(6.117e+14,'cm^3/(mol*s)'), n=-0.152, Ea=(4.19655,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 49 used for Cd_rad/Cd;H_rad
Exact match found for rate rule [Cd_rad/Cd;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction245',
reactants = ['H(3)(3)', '[CH]=CC=CC=C[CH2](1458)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS245',
kinetics = Arrhenius(A=(1.21e+14,'cm^3/(mol*s)','+|-',4.82e+13), n=0, Ea=(0,'kJ/mol'), T0=(1,'K'), Tmin=(298,'K'), comment="""From training reaction 60 used for H_rad;Cd_pri_rad
Exact match found for rate rule [Cd_pri_rad;H_rad]
Euclidian distance = 0
family: R_Recombination"""),
)
reaction(
label = 'reaction246',
reactants = ['[CH2]C=CC=CC=C(878)'],
products = ['C=CC=CC1[CH]C1(944)'],
transitionState = 'TS246',
kinetics = Arrhenius(A=(2.1e+08,'s^-1'), n=1.192, Ea=(225.936,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""From training reaction 142 used for R3_D;doublebond_intra_pri_HCd;radadd_intra_cs2H
Exact match found for rate rule [R3_D;doublebond_intra_pri_HCd;radadd_intra_cs2H]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction247',
reactants = ['C=CC1[CH]C=CC1(1139)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS247',
kinetics = Arrhenius(A=(2.77e+10,'s^-1'), n=0.87, Ea=(35,'kcal/mol'), T0=(1,'K'), comment="""Matched reaction 113 C7H9-19 <=> C7H9-20 in Intra_R_Add_Endocyclic/training
This reaction matched rate rule [R5_SD_D;doublebond_intra_pri_HCd;radadd_intra_cs2H]
family: Intra_R_Add_Endocyclic"""),
)
reaction(
label = 'reaction248',
reactants = ['[CH2]C=CC=CC=C(878)'],
products = ['[CH2]CC=CC=C=C(879)'],
transitionState = 'TS248',
kinetics = Arrhenius(A=(7.85714e+07,'s^-1'), n=1.56, Ea=(259.91,'kJ/mol'), T0=(1,'K'), comment="""From training reaction 17 used for 1_3_pentadiene;CH=C_1;CdCJ_2
Exact match found for rate rule [1_3_pentadiene;CH=C_1;CdCJ_2]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: Intra_ene_reaction"""),
)
reaction(
label = 'reaction249',
reactants = ['CH2(17)(18)', '[CH]=CC=CC=C(3632)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS249',
kinetics = Arrhenius(A=(1.06732e+06,'m^3/(mol*s)'), n=0.472793, Ea=(0,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Y_rad;Birad] for rate rule [Cd_pri_rad;Birad]
Euclidian distance = 2.0
family: Birad_R_Recombination
Ea raised from -3.5 to 0 kJ/mol."""),
)
reaction(
label = 'reaction250',
reactants = ['[CH2]C=CC=CC=C(878)'],
products = ['[CH2]C1C=CC1C=C(7445)'],
transitionState = 'TS250',
kinetics = Arrhenius(A=(4.34189e+10,'s^-1'), n=0.2405, Ea=(161.776,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R5_SM;doublebond_intra_2H_pri;radadd_intra_cs] + [R5_SD_D;doublebond_intra;radadd_intra_cs] for rate rule [R5_SD_D;doublebond_intra_2H_pri;radadd_intra_csHCd]
Euclidian distance = 2.82842712475
Multiplied by reaction path degeneracy 2.0
family: Intra_R_Add_Exocyclic
Ea raised from 161.4 to 161.8 kJ/mol to match endothermicity of reaction."""),
)
reaction(
label = 'reaction251',
reactants = ['H(3)(3)', 'C=CC=C=CC=C(7446)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS251',
kinetics = Arrhenius(A=(1.149e+09,'cm^3/(mol*s)'), n=1.595, Ea=(16.7946,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [Ca_Cds-OneDeH;HJ] for rate rule [Ca_Cds-CdH;HJ]
Euclidian distance = 1.0
family: R_Addition_MultipleBond"""),
)
reaction(
label = 'reaction252',
reactants = ['C=CC=[C]CC=C(914)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS252',
kinetics = Arrhenius(A=(1.448e+10,'s^-1'), n=0.82, Ea=(156.9,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""From training reaction 154 used for R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd
Exact match found for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/Cd]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction253',
reactants = ['C=[C]CC=CC=C(915)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS253',
kinetics = Arrhenius(A=(2.3e+10,'s^-1'), n=0.98, Ea=(112.55,'kJ/mol'), T0=(1,'K'), comment="""Estimated using an average for rate rule [R2H_S;Cd_rad_out_Cd;Cs_H_out_H/(Cd-Cd-Cd)]
Euclidian distance = 0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction254',
reactants = ['C=C[C]=CCC=C(917)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS254',
kinetics = Arrhenius(A=(1.47715e+10,'s^-1'), n=0.8, Ea=(147.277,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_single;Cs_H_out_1H] for rate rule [R3H_DS;Cd_rad_out_singleDe_Cd;Cs_H_out_H/Cd]
Euclidian distance = 2.82842712475
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction255',
reactants = ['[CH]=CCC=CC=C(919)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS255',
kinetics = Arrhenius(A=(1.846e+10,'s^-1'), n=0.74, Ea=(145.185,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1500,'K'), comment="""Estimated using template [R3H_DS;Cd_rad_out_singleH;Cs_H_out_1H] for rate rule [R3H_DS;Cd_rad_out_singleH;Cs_H_out_H/(Cd-Cd-Cd)]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction256',
reactants = ['C=[C]C=CCC=C(918)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS256',
kinetics = Arrhenius(A=(74200,'s^-1'), n=2.23, Ea=(44.3086,'kJ/mol'), T0=(1,'K'), comment="""Estimated using template [R4H;Cd_rad_out;Cs_H_out_H/Cd] for rate rule [R4H_SDS;Cd_rad_out_Cd;Cs_H_out_H/Cd]
Euclidian distance = 2.82842712475
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction257',
reactants = ['[CH]=CC=CCC=C(920)'],
products = ['[CH2]C=CC=CC=C(878)'],
transitionState = 'TS257',
kinetics = Arrhenius(A=(272000,'s^-1'), n=1.9199, Ea=(33.0402,'kJ/mol'), T0=(1,'K'), Tmin=(300,'K'), Tmax=(1600,'K'), comment="""Estimated using template [R5H_DSMS;Cd_rad_out_singleH;Cs_H_out] for rate rule [R5H_DSMS;Cd_rad_out_singleH;Cs_H_out_H/Cd]
Euclidian distance = 3.0
Multiplied by reaction path degeneracy 2.0
family: intra_H_migration"""),
)
reaction(
label = 'reaction258',
reactants = ['[CH2]C=CC=CC=C(878)'],
products = ['C=CC1[CH]C1C=C(1047)'],
transitionState = 'TS258',
kinetics = Arrhenius(A=(1.52971e+10,'s^-1'), n=0.596, Ea=(203.97,'kJ/mol'), T0=(1,'K'), comment="""Estimated using average of templates [R3;doublebond_intra_pri_HCd;radadd_intra_csHCd] + [R3_D;doublebond_intra_pri_HCd;radadd_intra_cs] for rate rule [R3_D;doublebond_intra_pri_HCd;radadd_intra_csHCd]
Euclidian distance = 2.0
family: Intra_R_Add_Endocyclic"""),
)
network(
label = '52',
isomers = [
'C7H9(408)(407)',
'C7H9(424)(423)',
'C7H9(449)(448)',
'C7H9(452)(451)',
'C7H9(453)(452)',
'C7H9(460)(459)',
'C7H9(527)(526)',
'C7H9(536)(535)',
'C7H9(680)(679)',
'C7H9(681)(680)',
'C7H9(682)(681)',
'[CH2]C=CC=CC=C(878)',
],
reactants = [
('CH2(S)(21)(22)', 'C6H7(464)(463)'),
('H(3)(3)', 'C7H8(415)(414)'),
('C2H2(30)(31)', 'C5H7(211)(210)'),
('CH2(17)(18)', 'C6H7(464)(463)'),
('H(3)(3)', 'C7H8(436)(435)'),
('H(3)(3)', 'C7H8(699)(698)'),
('CH3(15)(16)', 'C6H6(468)(467)'),
('H(3)(3)', 'C7H8(697)(696)'),
('CH2(S)(21)(22)', 'C6H7(467)(466)'),
('H(3)(3)', 'C7H8(690)(689)'),
('H(3)(3)', 'C7H8(693)(692)'),
],
bathGas = {
'Ne': 0.333333,
'N2': 0.333333,
'Ar(8)': 0.333333,
},
)
pressureDependence(
label = '52',
Tmin = (300,'K'),
Tmax = (2000,'K'),
Tcount = 8,
Tlist = ([302.47,323.145,369.86,455.987,609.649,885.262,1353.64,1896.74],'K'),
Pmin = (0.01,'bar'),
Pmax = (100,'bar'),
Pcount = 5,
Plist = ([0.0125282,0.0667467,1,14.982,79.8202],'bar'),
maximumGrainSize = (0.5,'kcal/mol'),
minimumGrainCount = 250,
method = 'modified strong collision',
interpolationModel = ('Chebyshev', 6, 4),
activeKRotor = True,
activeJRotor = True,
rmgmode = True,
)
|
# coding=utf-8
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import smtplib
from email.mime.text import MIMEText
from email.utils import formataddr
from email.header import Header
import time
start_time = time.time()
import re
import requests
import pandas as pd
##############打印昨天####################
import datetime
now_time = datetime.datetime.now()
yes_time =now_time +datetime.timedelta(days=-1)
yes_time_nyr = yes_time.strftime('%Y%m%d')
print yes_time_nyr
url="http://tv.cctv.com/lm/xwlb/day/"+str(yes_time_nyr)+".shtml"
head={
"Host": "tv.cctv.com",
"Connection": "keep-alive",
"Accept": "text/html, */*; q=0.01",
"X-Requested-With": "XMLHttpRequest",
"User-Agent": "Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.101 Safari/537.36",
"Referer": "http://tv.cctv.com/lm/xwlb/",
"Accept-Encoding": "gzip, deflate",
"Accept-Language": "zh-CN,zh;q=0.8"
}
html=requests.get(url).content
# print html
title=[]
title1=re.findall('<div class="text"><div class="title">(.*?)</div><div class="bottom"',str(html),re.S)
for each in title1:
# print each.replace("[视频]","")
title.append(each.replace("[视频]",""))
data=pd.DataFrame({"主要内容":title})
data=data.iloc[1:,]
print data
####################数据框转html#############################
df_html = data.to_html(index=True)
#######################修改html样式##################################
html=str(df_html).replace('<table border="1" class="dataframe">','<table border="0" class="dataframe" style="width:100%" cellspacing="2" cellpadding="2">')
html=str(html).replace('<tr style="text-align: right;">',' <div style="text-align:center;width:100%;padding: 8px; line-height: 1.42857; vertical-align: top; border-top-width: 1px; border-top-color: rgb(221, 221, 221); background-color: #3399CC;color:#fff"><strong><font size="4">新闻联播日报</font></strong></div><tr style="background-color:#FFCC99;text-align:center;">')
html=str(html).replace('<tr>','<tr style="text-align:center">')
html=str(html).replace('<th></th>','<th>num</th>')
# print html
style1="""
<style type="text/css">
table {
border-right: 1px solid #CCCCCC;
border-bottom: 1px solid #CCCCCC;
}
table td {
border-left: 1px solid #CCCCCC;
border-top: 1px solid #CCCCCC;
}
</style>
"""
style2="""
<style type="text/css">
table {
border-right: 1px solid #99CCFF;
border-bottom: 1px solid #99CCFF;
}
table td {
border-left: 1px solid #99CCFF;
border-top: 1px solid #99CCFF;
}
table th {
border-left: 1px solid #99CCFF;
border-top: 1px solid #99CCFF;
}
</style>
"""
###################################发送电子邮件###################################
####################设置发送人#######################
sender = '1973536419@qq.com'
######################设置接收人#######################
receiver1 = 'defa.lai@cgtz.com'
receiver2 = '1973536419@qq.com'
#receiver3='laidefa@dingtalk.com'
#####################设置主题#######################
subject = '【新闻联播】带你看世界'
#################设置发送内容:1：发送html表格数据########################
msg = MIMEText(style2+html,'html','utf-8')
############################设置一些附属表头参数#############################
msg['From']=formataddr(["赖德发",sender])
msg['To']=formataddr(["赖德发",receiver1])
msg['To']=formataddr(["开心果汁",receiver2])
msg['Subject'] = Header(subject, 'utf-8')
###################################登陆邮箱发送##################################
###登录qq邮箱账号
username = '1973536419@qq.com'
####qq授权码(此处需要填写授权码)
password = 'XXXXXXXXXXXXXX'
#################默认传输#################
# smtp = smtplib.SMTP_SSL("smtp.qq.com")
############################加密传送###################
smtp_server = 'smtp.qq.com'
smtp_port = 587
smtp = smtplib.SMTP(smtp_server, smtp_port)
smtp.starttls()
###########################################
smtp.login(username, password)
smtp.sendmail(sender, [receiver1,receiver2], msg.as_string())
# smtp.sendmail(sender, [receiver1,receiver2,receiver3], msg.as_string())
smtp.quit()
print '发送电子邮件完成...'
time2=time.time()
print u'总共耗时：' + str(time2 - start_time) + 's'
|
import pygame
import random
import math
#initializing pygame
pygame.init()
#initializing values
player = 1
level = 1
x = 0
boat_index = 0
lost = None
winners = []
count = 0
player1 = 0
player2 = 0
draw = 0
timer_started = False
timer_started1 = False
clock = pygame.time.Clock()
#initializing fonts
font = pygame.font.Font(None, 40)
font1 = pygame.font.SysFont('Algerian',30)
font2 = pygame.font.SysFont('Algerian',28)
font_level = pygame.font.SysFont('Brush Script MT',66)
font_player = pygame.font.SysFont('Brush Script MT',40)
font_color = pygame.Color('red')
font_color1 = pygame.Color('blue')
font_color2 = pygame.Color('black')
font_color3 = pygame.Color('white')
#intializing screen size
root = pygame.display.set_mode((870,680))
#importing required images
icon = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\icon.png")
background = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\backround.jpg")
win = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\win.jpg")
player1_image = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\player1.png")
player2_image = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\player2.png")
start = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\start.jpg")
arrow = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\arrow.png")
arrowk = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\arrow-key.png")
skull = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\danger.png")
enemy = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\enemy.png")
final = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\back.jpg")
#Initializing player position
player1_x = 450
player1_y = 650
player1_xchange = 0
player1_ychange = 0
player2_x = 450
player2_y = 50
player2_xchange = 0
player2_ychange = 0
pygame.display.set_icon(icon)
#Intializing obstacle position
rock_image = []
stone_image=[]
stone_x = []
rock_x = []
stone1_x = []
rock1_x = []
#Initializing obstacles positions
stone_y = [510, 390, 270, 150]
rock_y = [520, 400, 280, 160]
boat_y = [580, 460, 340, 220, 100, 580, 460, 340, 220, 100]
skull_x=[25, 390, 200, 770, 25, 390, 200, 770]
skull_y=[540, 540, 420, 420, 300, 300, 180, 180]
enemy_y=[540, 420, 300, 180]
enemy_x=[25, 200, 25, 200]
for i in range(0, 4) :
stone_image.append(pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\cave.png"))
rock_image.append(pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\stone.png"))
stone_x.append(random.randint(50, 150))
rock_x.append(random.randint(230, 300))
stone1_x.append(random.randint(600, 630))
rock1_x.append(random.randint(450, 550))
boat_image = []
boat_x = []
for i in range(0, 10) :
boat_image.append(pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\ship.png"))
if boat_index < 5 :
boat_x.append(random.randint(0, 400))
boat_index+=1
if boat_index in range(4, 10) :
boat_x.append(random.randint(450, 800))
boat_index+=1
#This function displays the player 1
def dispplayer1(x, y) :
grass1=pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\grass.jpg")
root.blit(grass1, (0, 650))
root.blit(player1_image, (x, y))
#This function displays the grass
def dispgrass(i, grass_list) :
grass = pygame.image.load(r"C:\Users\DELL\github\pygames\rivergames\grass.jpg")
root.blit(grass, (0, grass_list[i])) |
# _*_ coding:utf-8 _*_
from sklearn.datasets import load_digits
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from twi.com.utility import plot_decision_regions
from sklearn.metrics import accuracy_score
from twi.anns.ann_bp_multi import ANN_Multi
def show_digit():
data = load_digits()
print(data['data'].shape)
print(data['target'].shape)
fig, ax = plt.subplots(2, 5)
ax = ax.flatten()
for i in range(10):
ax[i].imshow(data['images'][i], cmap='Greys', interpolation='nearest')
ax[i].set_xticks([])
ax[i].set_yticks([])
plt.tight_layout()
plt.show()
def test_ann():
data = load_digits()
ohe = OneHotEncoder()
ohe.fit(data['target_names'].reshape(-1,1))
print(ohe.transform([[9]]).toarray())
indice_all = range(data['target'].shape[0])
indice_train, indice_test = train_test_split(indice_all, shuffle=True, test_size=0.3)
X ,y = data['data'][indice_train], data['target'][indice_train]
y = ohe.transform(y.reshape(-1, 1)).toarray()
ann = ANN_Multi(topo_stru=[64, 64, 10], n_iter=1000, epsilon=0.1)
print(X.shape, y.shape)
ann.fit(X, y, ANN_Multi.active_simoid)
image_test = data['images'][indice_test]
X_test, y_test = data['data'][indice_test], data['target'][indice_test]
y_pred = ann.predict(X_test)
s = accuracy_score(y_pred, y_test)
print(s)
l_err = []
l_cmp = y_pred == y_test.reshape(-1, 1)
for j in range(len(l_cmp)):
if not l_cmp[j]:
l_err.append(j)
# plot
fig, ax = plt.subplots(2, 5)
ax = ax.flatten()
for i in range(10):
ax[i].imshow(image_test[l_err[i]], cmap='Greys', interpolation='nearest')
ax[i].set_xticks([])
ax[i].set_yticks([])
ax[i].set_title('%s|%s' % (y_test[l_err[i]], y_pred[l_err[i]]))
plt.tight_layout()
plt.show()
if __name__ == "__main__":
test_ann() |
import math
import os
from collections import Counter
from nltk.tokenize import RegexpTokenizer
from nltk import StanfordNERTagger
import ethnicolr
import pandas as pd
import gender_guesser.detector as gender
BASE_PATH = os.path.dirname(os.path.dirname(__file__))
data_path = os.path.join(BASE_PATH, 'data')
class User:
def __init__(self, name_file):
self.df = pd.read_csv(os.path.join(data_path, name_file))
self.g_detector = gender.Detector(case_sensitive=False)
self.ner = StanfordNERTagger(
os.environ['HOME'] + '/nltk_data/stanford-ner/classifiers/english.all.3class.distsim.crf.ser.gz',
os.environ['HOME'] + '/nltk_data/stanford-ner/stanford-ner.jar',
encoding='utf-8')
self.tokenizer = RegexpTokenizer(r'\w+')
self.initialize()
def initialize(self):
# self.df = self.df.dropna()
names = self.df['user_name'].tolist()
genders = self.df['gender'].tolist()
ethnicities = self.df['ethnicity'].tolist()
names = [str(n).split() for n in names]
is_persons = []
for i in range(len(names)):
if isinstance(genders[i], float) and isinstance(ethnicities[i], float):
is_persons.append(False)
continue
tags = self.ner.tag(names[i])
for t in tags:
if t[1] == 'PERSON':
is_persons.append(True)
break
if len(is_persons) == i:
is_persons.append(False)
if len(is_persons) % 500 == 0:
print('{} records passed.'.format(len(is_persons)))
self.df['is_person'] = is_persons
self.df.to_csv('All_Pulls.csv')
# first_name = [n.split()[0] for n in names]
# last_name = [n.split()[-1] for n in names]
# self.df['first_name'] = first_name
# self.df['last_name'] = last_name
def normalize_ethnicity(self):
self.df['census'] = [e if e != 'api' else 'asian' for e in self.df['census']]
self.df['fl_lname'] = [e if e != 'nh_white' else 'white' for e in self.df['fl_lname']]
self.df['fl_lname'] = [e if e != 'nh_black' else 'black' for e in self.df['fl_lname']]
self.df['fl_flname'] = [e if e != 'nh_white' else 'white' for e in self.df['fl_flname']]
self.df['fl_flname'] = [e if e != 'nh_black' else 'black' for e in self.df['fl_flname']]
def get_ethnicity_agreement(self):
data = list(zip(self.df['census'], self.df['fl_lname'], self.df['fl_flname']))
ethnicities = []
for row in data:
majority = Counter(row).most_common()[0]
if majority[1] == 1:
ethnicities.append(row[2]) # based on full name
else:
ethnicities.append(majority[0])
self.df['ethnicity'] = ethnicities
def collect_gender(self):
genders = []
for fname in self.df['first_name']:
g = self.g_detector.get_gender(fname)
if g.startswith('mostly'):
g = g.split('mostly_')[-1]
elif g == 'andy':
g = 'androgynous'
genders.append(g)
self.df['gender'] = genders
def collect_ethnicity(self):
ethnicolr.pred_census_ln(self.df, 'last_name') # 2000 and 2010 yield same result
self.df = self.df.rename(columns={'race': 'census'})
ethnicolr.pred_fl_reg_ln(self.df, 'last_name')
self.df = self.df.rename(columns={'race': 'fl_lname'})
ethnicolr.pred_fl_reg_name(self.df, 'last_name', 'first_name')
self.df = self.df.rename(columns={'race': 'fl_flname'})
def store_demographic(self):
self.collect_gender()
self.collect_ethnicity()
self.normalize_ethnicity()
self.get_ethnicity_agreement()
self.df.drop(columns=['first_name', 'last_name'])
self.df[['user_login', 'real_name', 'gender', 'ethnicity']] \
.to_csv(os.path.join(data_path, 'demographic.csv'), index=False)
if __name__ == '__main__':
user = User('All_Pulls.csv')
# user.store_demographic()
print('finished.')
# df = pd.read_csv(os.path.join(data_path, 'All_Pulls.csv'))
print()
|
# -*- coding: utf-8 -*-
"""
Created on Tue Jan 29 16:54:18 2019
@author: jbhud
"""
#load list of states from before midterm
import pickle
home="C:/Users/jbhud/Documents/Thesis/Thesis Code/"
#
naturalStates = []
with open(home+"1. Simulation/listNatStates.txt", "r") as f:
for row in f:
naturalStates = naturalStates + [row.rstrip()]
with open(home+"1. Simulation/naturalStates.pkl", "wb") as f:
pickle.dump(naturalStates, f)
stateDict = {naturalStates[i]:i for i in range(len(naturalStates))}
#148 was changed to be the postcancer state, move death states along one
stateDict["1"] = 149
stateDict["2"] = 150
with open(home+"1. Simulation/stateDict.pkl", "wb") as f:
pickle.dump(stateDict, f) |
palabra = input("Ingrese una palabra: ")
while(palabra != "FIN"):
if(palabra[0] == palabra[-1]):
print("Empieza y termina con la misma letra")
palabra = input("Ingrese una palabra: ")
|
#람다 함수
"""
def plus_one(x):
return x+1
print(plus_one(1))
#람다 함수 사용 이유
plus_two=lambda x: x+2
print(plus_two(1))
"""
#map 함수 -> map(함수명,자료형)
def plus_one(x):
return x+1
a=[1,2,3]
print(list(map(plus_one,a)))
print(list(map(lambda x : x+1 ,a)))
#모든 변수가 해당 함수의 영향을 받을 수 있다. !
|
from primitives import ListNode
__author__ = 'V Manikantan'
documentation = '''
DETAILS OF THE METHODS:
1) Push adds new value to queue at rear
2) Pop removes a value from queue at front if it exists, otherwise returns empty queue.
3) Front returns the front value of queue and Rear returns the rear value of quqeue
4) Logically non existing objects are implemented as NoneType objects.
'''
class Queue:
def __init__(self):
self.front = self.rear = None
def __str__(self):
if(self.front):
v1 = self.front.key
else:
v1 = None
if(self.rear):
v2 = self.rear.key
else:
v2 = None
return str([v1,v2])
def Push(self,val):
new_node = ListNode(key=val,next=None)
if(not self.rear):
self.front = self.rear = new_node
else:
self.rear.next = new_node
self.rear = new_node
def Pop(self):
if(self.front):
if(self.front==self.rear):
self.front = self.rear = None
else:
self.front = self.front.next
def Front(self):
if(self.front):
return self.front.key
else:
return None
def Rear(self):
if(self.rear):
return self.rear.key
else:
return None
if __name__ == '__main__':
pass |
import sys
import os
sys.path.insert(0, "./lib")
from pypodio2 import api
import boto3
def handler(event, context):
client_id = os.environ['client_id']
client_secret = os.environ['client_secret']
s_app_id = int(os.environ['s_app_id']) # Staffing app
s_app_token = os.environ['s_app_token'] # Staffing token
emails = get_emails()
if len(emails) < 1:
print "ERROR: Cannot get SDMs emails for alert"
return {}
c = api.OAuthAppClient(
client_id,
client_secret,
s_app_id,
s_app_token,
)
field_week_number = 140525289
field_project = 140523653
field_project_date = 140522955
field_project_type = 140524037
oncall_project = 772605447
oncall_type = 4
a = {
"filters": {
field_project : [oncall_project],
field_project_type: [oncall_type],
field_project_date: {"from": '+4dr', "to": '+4dr' }
},
}
t = c.Item.filter(s_app_id, a)
if int(t['filtered']) < 1:
res = send_email(emails)
else:
res = {}
return res
def get_emails():
client_id = os.environ['client_id']
client_secret = os.environ['client_secret']
app_id = int(os.environ['projects_app_id'])
app_token = os.environ['projects_token']
oncall_project = int(os.environ['oncall_project_id'])
c = api.OAuthAppClient(
client_id,
client_secret,
app_id,
app_token,
)
a = { "filters": { "item_id": oncall_project } }
t = c.Item.filter(app_id, a)
if t['filtered'] != 1:
print "ERROR: Cannot find the project for Managed Services"
return []
sdm_field_id = 140858054
emails = []
for f in t['items'][0]['fields']:
if f['field_id'] == sdm_field_id:
for i in f['values']:
emails += i['value']['mail']
break;
return emails
def send_email(emails):
client = boto3.client('ses')
message = """
Hello, this is a message from Samana\'s Phone System.
You are receiving this message, because next week there is no assignment
for OnCall. This means that in a few days the system will not be able
to accept calls, because no agents will be available.
Please access Podio and assign a resource to OnCall for next week.
If you don't do this whithin the next 24 hours, you'll get this meesage
again.
Thank you
Samana's Phone System
"""
response = client.send_email(
Source='phonesystem@samanagroup.com',
Destination={
'ToAddresses': emails
},
Message={
'Subject': {
'Data': 'ALERT!! OnCall assignment for next week not configured',
'Charset': 'utf8'
},
'Body': {
'Text': {
'Data': message,
'Charset': 'utf8'
}
}
}
)
return response
|
# -*- coding: utf-8 -*-
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from qgis.core import *
from qgis.gui import *
import qgis.utils
import psycopg2
from PyQt4.QtCore import QSettings
import os
import re
import sys
import csv
import resources
import interface_menu
from interface_menu import *
from interface_form import *
try:
_fromUtf8 = QtCore.QString.fromUtf8
except AttributeError:
def _fromUtf8(s):
return s
try:
_encoding = QtGui.QApplication.UnicodeUTF8
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig, _encoding)
except AttributeError:
def _translate(context, text, disambig):
return QtGui.QApplication.translate(context, text, disambig)
class Dialog(object):
def __init__(self, Dialog, face):
self.Dialog=Dialog
self.iface=face
self.Dialog.setObjectName(_fromUtf8("Dialog"))
self.Dialog.setStyleSheet(_fromUtf8("background-color: rgb(205,205,205);"))
self.Dialog.resize(400, 769)
self.verticalLayout = QtGui.QVBoxLayout(self.Dialog)
self.verticalLayout.setObjectName(_fromUtf8("verticalLayout"))
self.formV={}
self.retranslateUi(self.Dialog)
QtCore.QMetaObject.connectSlotsByName(self.Dialog)
def retranslateUi(self, Dialog):
Dialog.setWindowTitle(_translate("Dialog", self.iface.activeLayer().name(), None))
def criarCombo(self, nome, mapaValor, padrao=None):
horizontalLayout_3 = QtGui.QHBoxLayout()
horizontalLayout_3.setObjectName(_fromUtf8("horizontalLayout_3"))
label_2 = QtGui.QLabel(self.Dialog)
label_2.setObjectName(_fromUtf8("label_2"))
label_2.setStyleSheet(_fromUtf8("background-color: rgb(255,255, 255);"))
horizontalLayout_3.addWidget(label_2)
comboBox = QtGui.QComboBox(self.Dialog)
comboBox.setObjectName(_fromUtf8(nome+"CB"))
horizontalLayout_3.addWidget(comboBox)
self.verticalLayout.addLayout(horizontalLayout_3)
label_2.setText(_translate("Dialog", nome, None))
if len(mapaValor)> 0:
for x in mapaValor.keys():
comboBox.addItem(x, x.replace(" ",""))
index=comboBox.findData("Aserpreenchido")
comboBox.setCurrentIndex(index)
if padrao != None:
index=comboBox.findData(padrao[0].replace(" ",""))
comboBox.setCurrentIndex(index)
if len(padrao) == 2:
rgb = padrao[1].split("-")
label_2.setStyleSheet(_fromUtf8("color: rgb("+rgb[0]+","+rgb[1]+","+rgb[2]+");"))
self.valorCombo(comboBox)
def valorCombo(self, combo=None):
def valorC(item):
self.formV[unicode(combo.objectName())]=unicode(item)
self.formV[unicode(combo.objectName())]=""
valorC(unicode(combo.currentText()))
QObject.connect(combo, QtCore.SIGNAL("activated(const QString&)"), valorC)
def criarLine(self, nome, tipo, padrao=None):
horizontalLayout_2 = QtGui.QHBoxLayout()
horizontalLayout_2.setObjectName(_fromUtf8("horizontalLayout_2"))
label = QtGui.QLabel(self.Dialog)
label.setObjectName(_fromUtf8("label"))
label.setStyleSheet(_fromUtf8("background-color: rgb(255,255, 255);"))
#label.setStyleSheet(_fromUtf8("color: rgb(x,x,x);"))
horizontalLayout_2.addWidget(label)
lineEdit = QtGui.QLineEdit(self.Dialog)
lineEdit.setObjectName(_fromUtf8(nome+"LE"))
horizontalLayout_2.addWidget(lineEdit)
self.verticalLayout.addLayout(horizontalLayout_2)
label.setText(_translate("Dialog", nome, None))
if padrao != None:
if (not padrao[0] == "NULL") and (not padrao[0] == None) and (not padrao[0] == 'None' ):
lineEdit.setText(padrao[0])
self.valorLine(lineEdit, tipo)
def valorLine(self, line, tp):
tipagem=tp
def valorL(item):
if tipagem == "float4":
if (item != "") :
try:
self.formV[unicode(line.objectName())]=float(item)
except:
line.clear()
QMessageBox.warning(self.iface.mainWindow(), u"ERRO:", u"<font color=red>Campo só recebe valores 'Float'!</font>", QMessageBox.Close)
else:
self.formV[unicode(line.objectName())]=None
elif tipagem == "varchar":
if item != "":
try:
self.formV[unicode(line.objectName())]=unicode(item)
except:
line.clear()
QMessageBox.warning(self.iface.mainWindow(), u"ERRO:", u"<font color=red>Campo só recebe valores 'Varchar'!</font>", QMessageBox.Close)
else:
self.formV[unicode(line.objectName())]=None
elif tipagem == "int2":
if item != "":
try:
self.formV[unicode(line.objectName())]=int(item)
except:
line.clear()
QMessageBox.warning(self.iface.mainWindow(), u"ERRO:", u"<font color=red>Campo só recebe valores 'Int'!</font>", QMessageBox.Close)
else:
self.formV[unicode(line.objectName())]=None
else:
QMessageBox.warning(self.iface.mainWindow(), u"ERRO:", u"<font color=red>Formato não relacionado!</font>", QMessageBox.Close)
if not line.text() == None:
self.formV[unicode(line.objectName())]=line.text()
line.textEdited.connect(valorL)
def criarBotoes(self):
self.horizontalLayout_3 = QtGui.QHBoxLayout()
self.horizontalLayout_3.setObjectName(_fromUtf8("horizontalLayout_3"))
self.pushButton = QtGui.QPushButton(self.Dialog)
self.pushButton.setObjectName(_fromUtf8("pushButton"))
self.horizontalLayout_3.addWidget(self.pushButton)
self.pushButton_2 = QtGui.QPushButton(self.Dialog)
self.pushButton_2.setObjectName(_fromUtf8("pushButton_2"))
self.horizontalLayout_3.addWidget(self.pushButton_2)
self.verticalLayout.addLayout(self.horizontalLayout_3)
self.pushButton.setText(_translate("Dialog", "Confirmar", None))
self.pushButton_2.setText(_translate("Dialog", "Cancelar", None))
def linha(self):
self.line = QtGui.QFrame(self.Dialog)
self.line.setFrameShape(QtGui.QFrame.HLine)
self.line.setFrameShadow(QtGui.QFrame.Sunken)
self.line.setObjectName(_fromUtf8("line"))
self.verticalLayout.addWidget(self.line)
def obter_FormV(self):
return self.formV
def criarCampos(self, botao, valores):
self.atributagem=valores
grupoType={}
for x in self.iface.activeLayer().pendingAllAttributesList():
grupoType[self.iface.activeLayer().pendingFields()[x].name()]=self.iface.activeLayer().pendingFields()[x].typeName()
for index in self.iface.activeLayer().pendingAllAttributesList():
if not index == 0:
try:
if self.iface.activeLayer().valueMap(index).keys()[0] != "UseHtml":
if self.iface.activeLayer().pendingFields()[index].name() in self.atributagem[botao][self.iface.activeLayer().name()].keys():
self.criarCombo(unicode(self.iface.activeLayer().pendingFields()[index].name()), self.iface.activeLayer().valueMap(index), self.atributagem[botao][self.iface.activeLayer().name()].get(self.iface.activeLayer().pendingFields()[index].name()))
else:
self.criarCombo(unicode(self.iface.activeLayer().pendingFields()[index].name()), self.iface.activeLayer().valueMap(index), None)
else:
padrao = self.atributagem[botao][self.iface.activeLayer().name()].get(self.iface.activeLayer().pendingFields()[index].name())
self.criarLine(unicode(self.iface.activeLayer().pendingFields()[index].name()), grupoType.get(unicode(self.iface.activeLayer().pendingFields()[index].name())), padrao)
except:
padrao = self.atributagem[botao][self.iface.activeLayer().name()].get(self.iface.activeLayer().pendingFields()[index].name())
self.criarLine(unicode(self.iface.activeLayer().pendingFields()[index].name()), grupoType.get(unicode(self.iface.activeLayer().pendingFields()[index].name())), padrao)
def valorCampos(self, i, valores, dialogo):
formV=valores
idt=int(i)
dialogo.accept()
grupoAttr={}
for x in self.iface.activeLayer().pendingAllAttributesList():
grupoAttr[self.iface.activeLayer().pendingFields()[x].name()]=x
for campo in formV.keys():
if (campo[:-2] in grupoAttr) :
if unicode(campo[-2:]) == u"LE":
if formV.get(campo) == u'':
idx = self.iface.activeLayer().fieldNameIndex(unicode(campo[:-2]))
self.iface.activeLayer().changeAttributeValue(int(idt) , idx, None)
else:
idx = self.iface.activeLayer().fieldNameIndex(unicode(campo[:-2]))
self.iface.activeLayer().changeAttributeValue(int(idt) , idx, formV.get(campo))
else:
idx2 = self.iface.activeLayer().fieldNameIndex(unicode(campo[:-2]))
self.iface.activeLayer().changeAttributeValue(int(idt) , idx2, self.iface.activeLayer().valueMap(grupoAttr.get(unicode(campo[:-2]))).setdefault(unicode(formV.get(campo))))
self.removeSelecoes()
def removeSelecoes(self):
for i in range(len(self.iface.mapCanvas().layers())):
try:
self.iface.mapCanvas().layers()[i].removeSelection()
except:
pass
|
#! /usr/bin/python
import argparse
from datetime import datetime
import time
import os
import json
from live_update_server.utils import NonRepeatingLogger
from live_update_server.googlesheet import GoogleSheetReader
# TODO decide better file naming for the following three
from live_update_server.roomdataparser import GoogleRoomDataParser,DummyRoomDataParser
from live_update_server.roomdataformatter import RoomDataFormatter
from live_update_server.nametosvgid import RoomNameToSvgId
from live_update_server.userdataparser import UserDataParser
# from .utils import NonRepeatingLogger
# from .googlesheet import GoogleSheetReader
# from .roomdataparser import RoomDataParser
# from .roomdataformatter import RoomDataFormatter
# from .nametosvgid import RoomNameToSvgId
parser = argparse.ArgumentParser(description='Provide live updates for an instance of the THJCR ballot')
parser.add_argument("--ballot-directory", required=True, help="Path to currently active ballot where this script has write permissions", type=str)
parser.add_argument("--google-API-credentials", required=True, help="Path to JSON file with the Google Drive API secret that authorizes access to the sheet provided", type=str)
parser.add_argument("--google-doc-title", required=True, help="Exact name of the google doc to link the ballot site to.")
parser.add_argument("--google-sheet-name", required=True, help="Name of the single sheet on the Google doc to read updates from", type=str)
parser.add_argument("--google-sheet-format", required=True, help="Path to JSON file that defines the mapping of columns when reading document updates", type=str)
parser.add_argument("--room-svg-id-mapping", required=True, help="Path to CSV file mapping SVG file room id to the name used in the Google Sheet", type=str)
parser.add_argument("--user-sheet-name", required=True, help="Name of the google sheet mapping containing all system users", type=str)
args = parser.parse_args()
# validate given ballot directory is valid
# TODO
# validate can access google sheet with the credentials provided
# TODO
# server logfile for this ballot
logfile = "server_log_{0}.log".format(args.google_sheet_name)
logger = NonRepeatingLogger(logfile, sort_by_most_recent=False)
rooms_sheet_columns_format = json.load(open(args.google_sheet_format))['room_sheet_columns_mapping']
users_sheet_columns_format = json.load(open(args.google_sheet_format))['users_sheet_columns_mapping']
room_name_to_svg_id = RoomNameToSvgId(args.room_svg_id_mapping)
sheet_reader = GoogleSheetReader(args.google_API_credentials)
doc_title = args.google_doc_title
room_sheet_name = args.google_sheet_name
users_sheet_name = args.user_sheet_name
room_sheet = sheet_reader.get_sheet(doc_title, room_sheet_name)
users_sheet = sheet_reader.get_sheet(doc_title, users_sheet_name)
try:
os.mkdir(os.path.join(args.ballot_directory, "data"))
except OSError:
#directory already exists
pass
data_file_path = os.path.join(args.ballot_directory, "data", "data.json")
user_file_path = os.path.join(args.ballot_directory, "data", "users.json")
# fun add on functionality to maintain order of updates bar
def put_updated_rooms_timestamps(new_rooms, old_rooms):
""" Does an in place modification to add timestampe attr. Just uses a local time rather than proper API """
# find all rooms that are different
attr = "lastUpdated"
new_data, old_data = new_rooms.get_parsed_rooms(), old_rooms.get_parsed_rooms()
for room_id in new_data:
new_room = new_data[room_id]
if room_id not in old_data or new_room['surname'].strip() != old_data[room_id]['surname'].strip():
new_rooms.add_attribute(room_id, attr, time.time())
rooms = DummyRoomDataParser()
user_data={}
while True:
print("Polling sheet")
# parse the users sheet into a local data format
users_sheet = sheet_reader.get_sheet(doc_title, users_sheet_name)
new_user_data_object = UserDataParser(users_sheet, users_sheet_columns_format, logger)
new_user_data = new_user_data_object._parsed_users
if new_user_data != user_data:
print("change in users detected")
# if so, save it
user_data = new_user_data
# convert local data format into JSON to send over write to file
with open(user_file_path, 'w') as outfile:
print("Writing new user data file {0}".format(user_file_path))
json.dump(user_data, outfile)
# parse the rooms sheet to into a local data format
room_sheet = sheet_reader.get_sheet(doc_title, room_sheet_name)
new_rooms = GoogleRoomDataParser(room_sheet, rooms_sheet_columns_format, room_name_to_svg_id.names_to_ids(), logger)
# see if it's different from what we parsed before
if new_rooms != rooms:
print("change in ballot detected")
# if so, save it
put_updated_rooms_timestamps(new_rooms, rooms)
rooms = new_rooms
# convert local data format into JSON to send over write to file
json_room_data = RoomDataFormatter.build_rooms_json(rooms)
# overwrite the JSON data file
with open(data_file_path, 'w') as outfile:
print("Writing new data file {0}".format(data_file_path))
json.dump(json_room_data, outfile)
# since this is only 1 connection, we can poll often
time.sleep(3) |
import matplotlib.pyplot as plt
import statistics as stat
import time
import operator
import copy
import sys
def file_to_x_y(filename):
x_y = []
x_cord = []
y_cord = []
try:
file = open(filename, 'r')
no_of_points = int(file.readline())
for i in file:
i = i.split()
i[1] = int(i[1])
i[2] = int(i[2])
x_cord.append(i[1])
y_cord.append(i[2])
x_y.append((i[1],i[2]))
file.close()
except:
print("Invalid file name")
return x_y,x_cord,y_cord
def file_to_list(filename):
""" Returns a list of all points"""
points_list = []
try:
file = open(filename, 'r')
no_of_points = int(file.readline())
for i in file:
i = i.split()
i[0] = int(i[0])
i[1] = int(i[1])
i[2] = int(i[2])
i = tuple(i)
points_list.append(i)
file.close()
except:
print("Invalid file name")
return points_list, no_of_points
def plot(points_list, convex_hull = None, skyline = None):
x=[]
y=[]
for point in points_list:
x.append(point[1])
y.append(point[2])
plt.scatter(x,y)
if convex_hull != None:
for i in convex_hull:
for p1 in convex_hull[i]:
p0 = i
plt.plot((p0[1], p1[1]), (p0[2], p1[2]), 'r')
x1 = []
y1 = []
if skyline != None:
for point in skyline:
x1.append(point[1])
y1.append(point[2])
plt.scatter(x1,y1,marker="X")
plt.title('Convex Hull using Divide N Conquer')
plt.xlabel('x')
plt.ylabel('y')
plt.show()
def median_of_medians(points_list,x_or_y_no):
median_list = []
for i in range(0,len(points_list),5):
temp = points_list[i:i+5]
temp.sort(key = operator.itemgetter(x_or_y_no))
if len(temp) == 5:
median = temp[2]
else:
median = temp[(len(temp)-1)//2]
median_list.append(median)
median_list.sort(key = operator.itemgetter(1))
mom = median_list[(len(median_list)-1)//2]
return mom
def max_of_hull(hull,axis_no):
x_or_y_of_hull = []
for point in hull:
x_or_y_of_hull.append(point[axis_no])
x_or_y_max = max(x_or_y_of_hull)
for point in hull:
if x_or_y_max == point[axis_no]:
return point
def min_of_hull(hull,axis_no):
x_or_y_of_hull = []
for point in hull:
x_or_y_of_hull.append(point[axis_no])
x_or_y_max = min(x_or_y_of_hull)
for point in hull:
if x_or_y_max == point[axis_no]:
return point
def is_on_segment(p1, p2, p3):
"""Needed only for the co-linear condition"""
if (p2[1] <= max(p1[1],p3[1])) and (p2[1] >= min(p1[1],p3[1])) and (p2[2] <= max(p1[2],p3[2])) and (p2[2] >= min(p1[2],p3[2])):
return True
else: return False
def orientation(point1, point2, point3):
"""Return 0 or 1 or 2 Depending on the orientation of points 1 to 3"""
result = (point2[2] - point1[2]) * (point3[1] - point2[1]) - (point3[2] - point2[2]) * (point2[1] - point1[1])
if result == 0:
return 0
elif result > 0:
return 1 # Clockwise Orientation
else: return 2 # CounterClockwise Orientation
def is_intersecting(p1, p2, p3, p4):
"""
:param p1: point of line 1
:param p2: point of line 1
:param p3: point of line 2
:param p4: point of line 2
:return:
"""
o1 = orientation(p1, p2, p3)
o2 = orientation(p1, p2, p4)
o3 = orientation(p3, p4, p1)
o4 = orientation(p3, p4, p2)
# general case
if o1 != o2 and o3 != o4:
return True
# Special Case
elif (o1 == 0 and is_on_segment(p1,p3,p2)) or (o2 == 0 and is_on_segment(p1,p3,p2)) or (o3 == 0 and is_on_segment(p3,p1,p4)) or (o4 == 0 and is_on_segment(p3,p2,p4)):
return True
else: return False
def upper_tangent(left_hull, right_hull, point1, point2):
"""
:param left_hull: left_hull
:param right_hull: right_hull
:param point1: max point of left hull, later changes to form tangents
:param point2: min point of right hull, later changes to form tangents
:return: point1, point2
"""
flag = False
counter = 0
while not flag:
flag = True
o1 = False
while(not o1):
o1 = True
for point in left_hull[point1]:
if orientation(point, point1, point2) != 1:
o1 = False
break
if o1 is False:
point1 = point
o2 = False
while(not o2):
o2 = True
for point in right_hull[point2]:
if orientation(point1, point2, point) != 1:
o2 = False
break
if o2 is False:
flag = False
point2 = point
return point1, point2
def lower_tangent(left_hull, right_hull, point1, point2):
"""
:param left_hull: left_hull
:param right_hull: right_hull
:param point1: max point of left hull, later changes to form tangents
:param point2: min point of right hull, later changes to form tangents
:return: point1, point2
"""
flag = False
counter = 0
while not flag:
flag = True
o1 = False
while (not o1):
o1 = True
for point in left_hull[point1]:
if orientation(point, point1, point2) != 2:
o1 = False
break
if o1 is False:
point1 = point
o2 = False
while (not o2):
o2 = True
for point in right_hull[point2]:
if orientation(point1, point2, point) != 2:
o2 = False
break
if o2 is False:
flag = False
point2 = point
return point1, point2
def delete_hull_points(hull,p1,p2,o_value):
"""Removing all the clockwise and counterclockwise points between upper and lower tangent points from left and right hull respectievely"""
temp = []
for point in hull:
if point != p1 and point != p2:
if orientation(p1, point, p2) != o_value: # if uppertangentpoint, anypoint, lowertangentpoint != ccw
temp.append(point)
for point in temp:
hull.pop(point)
temp = []
for point in hull[p1]:
if point != p2:
if orientation(p1, point, p2) != o_value:
temp.append(point)
for point in temp:
hull[p1].remove(point)
temp = []
for point in hull[p1]:
if point == p2 and len(hull[p1]) == 2:
temp.append(point)
for point in temp:
hull[p1].remove(point)
temp = []
for point in hull[p2]:
if point != p1:
if orientation(p1, point, p2) != o_value:
temp.append(point)
for point in temp:
hull[p2].remove(point)
temp = []
for point in hull[p2]:
if point == p1 and len(hull[p2]) == 2:
temp.append(point)
for point in temp:
hull[p2].remove(point)
return hull
def convex_hull_divide_conquer(points_list):
if len(points_list) <= 3:
convex_dict = {}
for i in points_list:
a = []
for j in points_list:
if i!=j:
a.append(j)
convex_dict[i] = a
return convex_dict
x = []
y = []
l = []
r = []
for point in points_list:
x.append(point[1])
y.append(point[2])
x_median = stat.median_low(x)
# mom = median_of_medians(points_list,1)
# x_median = mom[1]
for i in points_list:
if i[1] > x_median:
r.append(i)
else:
l.append(i)
left_hull = convex_hull_divide_conquer(l)
right_hull = convex_hull_divide_conquer(r)
if left_hull != None:
max_point = max_of_hull(left_hull,1)
if right_hull != None:
min_point = min_of_hull(right_hull,1)
if left_hull != None and right_hull != None:
upper_p1, upper_p2 = upper_tangent(left_hull, right_hull, max_point, min_point)
lower_p1, lower_p2 = lower_tangent(left_hull, right_hull, max_point, min_point)
left_hull = delete_hull_points(left_hull,upper_p1,lower_p1,2)
right_hull = delete_hull_points(right_hull, upper_p2, lower_p2, 1)
left_hull[upper_p1].append(upper_p2)
right_hull[upper_p2].append(upper_p1)
left_hull[lower_p1].append(lower_p2)
right_hull[lower_p2].append(lower_p1)
left_hull.update(right_hull)
convex_hull = left_hull
return convex_hull
def naive_skyline(points_list):
neglect = []
for p1 in points_list:
for p2 in points_list:
if p2 != p1:
if (p2[1] >= p1[1] and p2[2] >= p1[2]):
neglect.append(p1)
break
neglect = set(neglect)
for point in neglect:
points_list.remove(point)
return points_list
def DnQskyline(points_list):
convex_hull = convex_hull_divide_conquer(points_list)
point_with_x_max = max_of_hull(convex_hull,1)
point_with_y_max = max_of_hull(convex_hull,2)
skyline_points = []
for point in convex_hull:
if point[2] >= point_with_x_max[2] and point[1] >= point_with_y_max[1]:
skyline_points.append(point)
return skyline_points
def main(argv):
while(True):
ip = input("\nEnter :"
"\n\t1: Divide and Conquer (Tangent Approach)\n\t2: Naive Skyline using Divide and Conquer\n\t3: Skyline using Convex Hull\nOr Enter any other number to exit\n")
points_list, no_of_points = file_to_list('inputfile.txt')
if int(ip) == 1:
start = time.time()
convex_hull = convex_hull_divide_conquer(points_list)
end = time.time()
finaltime = end - start
print('D n Q convex Hull Time:', finaltime)
plot(points_list,convex_hull)
elif int(ip) == 2:
start = time.time()
skyline1 = naive_skyline(copy.deepcopy(points_list))
end = time.time()
finaltime = end - start
plot(points_list, None, skyline1)
print('\nNaive_skyline Time:', finaltime)
elif int(ip) == 3:
start = time.time()
skyline = DnQskyline(points_list)
end = time.time()
finaltime = end - start
plot(points_list, None, skyline)
print('\nDQ skyline Skyline Time:', finaltime)
else:
break
if __name__ == '__main__':
main(sys.argv) |
from django.db import models
class Usermodel(models.Model):
username = models.CharField(max_length=32, unique=True) #名称
password = models.CharField(max_length=256)#密码
email = models.CharField(max_length=64, unique=True)#邮箱
sex = models.BooleanField(default=False) #性别
icon = models.ImageField(upload_to='icons')#头像
is_delete= models.BooleanField(default=False)#是否删除
class Meta:
db_table = 'axf_users'
class UserTicketModel(models.Model):
user = models.ForeignKey(Usermodel) #关联用户
ticket = models.CharField(max_length=256)#密码
out_time = models.DateTimeField()#过期时间
class Meta:
db_table = 'axf_users_ticket'
|
#Quick n' dirty day 1
file = open('sortedInput.txt', 'r')
content = file.read().split()
lower, upper = list(map(int, content[:9])), list(map(int, content[9:])) # 2020 cant be the sum of two integers where x>1010 OR x<1010 is true for BOTH
li = ui = 0 # so for efficencys sake only compare integers where it's possible for 2020 to be a sum
for l in lower:
for u in upper:
if (l+u==2020):
nums = l,u
print(nums)
print(nums[0]*nums[1])
raw_input("finished")
|
# Copyright (c) 2016 Uber Technologies, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
from __future__ import absolute_import
from __future__ import print_function
import json
import socket
import time
import traceback
import mock
import opentracing
import pytest
import tornado
import tornado.gen
import tornado.testing
import tornado.web
from jaeger_client import Tracer, ConstSampler
from jaeger_client.reporter import InMemoryReporter
from opentracing import Format
from opentracing_instrumentation.client_hooks.tornado_http import (
install_patches,
reset_patchers
)
from opentracing_instrumentation.request_context import (
span_in_stack_context,
get_current_span,
TornadoScopeManager
)
from tchannel import Response, thrift, TChannel, schemes
from tchannel.errors import BadRequestError
from tchannel.event import EventHook
from tornado import netutil
from tornado.httpclient import HTTPRequest
from tchannel import tracing
import six
BAGGAGE_KEY = 'baggage'
# It seems under Travis the 'localhost' is bound to more then one IP address,
# which triggers a bug: https://github.com/tornadoweb/tornado/issues/1573
# Here we monkey-patch tornado/testing.py with the fix from
# https://github.com/tornadoweb/tornado/pull/1574
def patched_bind_unused_port(reuse_port=False):
sock = netutil.bind_sockets(None, '127.0.0.1', family=socket.AF_INET,
reuse_port=reuse_port)[0]
port = sock.getsockname()[1]
return sock, port
tornado.testing.bind_unused_port = patched_bind_unused_port
@pytest.yield_fixture
def tracer():
reporter = InMemoryReporter()
report_func = reporter.report_span
def log_and_report(span):
print(('Reporting span %s' % span))
print(('Span type %s' % type(span)))
print(('SpanContext type %s' % type(span.context)))
report_func(span)
reporter.report_span = log_and_report
tracer = Tracer(
service_name='test-tracer',
sampler=ConstSampler(True),
reporter=reporter,
scope_manager=TornadoScopeManager()
)
opentracing.set_global_tracer(tracer)
try:
yield tracer
finally:
opentracing._reset_global_tracer()
tracer.close()
@pytest.yield_fixture
def http_patchers():
"""Applies tracing monkey-patching to http libs"""
install_patches.__original_func() # install_patches func is a @singleton
try:
yield
finally:
reset_patchers()
@pytest.fixture
def thrift_service(tmpdir):
thrift_file = tmpdir.join('service.thrift')
thrift_file.write('''
service X {
string thrift1(1: string hostport), // calls thrift2()
string thrift2(),
string thrift3(1: string hostport) // calls http
string thrift4(1: string hostport) // calls raw
}
''')
return thrift.load(str(thrift_file), 'test-service')
def register(tchannel, thrift_service, http_client, base_url):
@tchannel.json.register('endpoint1')
@tornado.gen.coroutine
def handler1(request):
host_port = request.body
res = yield tchannel.json(
service='handler2',
hostport=host_port,
endpoint="endpoint2",
)
raise tornado.gen.Return(Response(res.body))
@tchannel.thrift.register(thrift_service.X, method='thrift1')
@tornado.gen.coroutine
def thrift1(request):
host_port = request.body.hostport
res = yield tchannel.thrift(
thrift_service.X.thrift2(),
hostport=host_port,
)
raise tornado.gen.Return(Response(res.body))
@tchannel.json.register('endpoint2')
def handler2(_):
return _extract_span()
@tchannel.thrift.register(thrift_service.X, method='thrift2')
def thrift2(_):
return _extract_span()
def _extract_span():
span = get_current_span()
if span:
return span.get_baggage_item(BAGGAGE_KEY)
return "baggage not found"
@tchannel.json.register('endpoint3')
def handler3(_):
return _call_http()
@tchannel.thrift.register(thrift_service.X, method='thrift3')
def thrift3(_):
return _call_http()
@tornado.gen.coroutine
def _call_http():
response = yield http_client.fetch(base_url)
if response.code != 200:
raise Exception('Downstream http service returned code=%s: %s' % (
response.code, response.body
))
raise tornado.gen.Return(Response(body=response.body))
@tchannel.thrift.register(thrift_service.X, method='thrift4')
@tornado.gen.coroutine
def thrift4(request):
print('thrift4 hit')
host_port = request.body.hostport
res = tchannel.raw(
service='test-service',
endpoint='raw2',
hostport=host_port,
headers='some value that will not be parsed as a dict',
)
res = yield res
print(('result', res))
raise tornado.gen.Return(Response(res.body))
@tchannel.raw.register('raw2')
def raw2(_):
return _extract_span()
@pytest.fixture
def app():
return tornado.web.Application()
# noinspection PyAbstractClass
class HttpHandler(tornado.web.RequestHandler):
def __init__(self, application, request, **kwargs):
super(HttpHandler, self).__init__(application, request, **kwargs)
self.client_channel = kwargs['client_channel']
def initialize(self, **kwargs):
pass
def _get_span(self):
try:
carrier = {}
for k, v in six.iteritems(self.request.headers):
carrier[k] = six.moves.urllib.parse.unquote(v)
span_ctx = opentracing.tracer.extract(Format.TEXT_MAP, carrier)
span = opentracing.tracer.start_span(
operation_name='server',
child_of=span_ctx,
)
except Exception as e:
self.write('ERROR: %s' % e)
self.set_status(200)
return None
if span is None:
self.write('ERROR: Failed to join trace')
self.set_status(200)
return span
def get(self):
span = self._get_span()
if span:
self.write(span.get_baggage_item(BAGGAGE_KEY))
self.set_status(200)
span.finish()
@tornado.gen.coroutine
def post(self):
span = self._get_span()
if span:
try:
with span_in_stack_context(span):
res = self.client_channel.json(
service='handler2',
hostport=self.request.body,
endpoint="endpoint2",
)
res = yield res
body = res.body
except Exception as e:
traceback.print_exc()
self.write('ERROR: %s' % e)
self.set_status(200)
return
else:
self.write(body)
self.set_status(200)
finally:
span.finish()
@pytest.mark.parametrize(
# The call tree in this test is determined by the first three params.
# Strictly speaking, similar permutations can be built with crossdock
# tests and be encoded as a set of instructions to a fewer number of
# endpoints, and the unit test can exercise just the Python subset.
# TODO per above, consider converting this to a crossdock unit test.
'endpoint,transport,encoding,enabled,expect_spans,expect_baggage', [
# tchannel(json)->tchannel(json)
('endpoint1', 'tchannel', 'json', True, 5, True),
('endpoint1', 'tchannel', 'json', False, 0, True),
# tchannel(thrift)->tchannel(thrift)
('thrift1', 'tchannel', 'thrift', True, 5, True),
('thrift1', 'tchannel', 'thrift', False, 0, True),
# tchannel(thrift)->tchannel(raw)
('thrift4', 'tchannel', 'thrift', True, 5, False),
('thrift4', 'tchannel', 'thrift', False, 0, False),
# tchannel(json)->http(json)
('endpoint3', 'tchannel', 'json', True, 5, True),
('endpoint3', 'tchannel', 'json', False, 0, True),
# tchannel(thrift)->http(json)
('thrift3', 'tchannel', 'thrift', True, 5, True),
('thrift3', 'tchannel', 'thrift', False, 0, True),
# http->tchannel
('/', 'http', 'json', True, 5, True),
('/', 'http', 'json', False, 0, True),
])
@pytest.mark.gen_test
def test_trace_propagation(
endpoint, transport, encoding, enabled, expect_spans, expect_baggage,
http_patchers, tracer, mock_server, thrift_service,
app, http_server, base_url, http_client):
"""
Main TChannel-OpenTracing integration test, using basictracer as
implementation of OpenTracing API.
The main logic of this test is as follows:
1. Start a new trace with a root span
2. Store a random value in the baggage
3. Call the first service at the endpoint from `endpoint` parameter.
The first service is either tchannel or http, depending on the value
if `transport` parameter.
4. The first service calls the second service using pre-defined logic
that depends on the endpoint invoked on the first service.
5. The second service accesses the tracing span and returns the value
of the baggage item as the response.
6. The first service responds with the value from the second service.
7. The main test validates that the response is equal to the original
random value of the baggage, proving trace & baggage propagation.
8. The test also validates that all spans have been finished and
recorded, and that they all have the same trace ID.
We expect 5 spans to be created from each test run:
* top-level (root) span started in the test
* client span (calling service-1)
* service-1 server span
* service-1 client span (calling service-2)
* service-2 server span
:param endpoint: name of the endpoint to call on the first service
:param transport: type of the first service: tchannel or http
:param enabled: if False, channels are instructed to disable tracing
:param expect_spans: number of spans we expect to be generated
:param http_patchers: monkey-patching of tornado AsyncHTTPClient
:param tracer: a concrete implementation of OpenTracing Tracer
:param mock_server: tchannel server (from conftest.py)
:param thrift_service: fixture that creates a Thrift service from fake IDL
:param app: tornado.web.Application fixture
:param http_server: http server (provided by pytest-tornado)
:param base_url: address of http server (provided by pytest-tornado)
:param http_client: Tornado's AsyncHTTPClient (provided by pytest-tornado)
"""
# mock_server is created as a fixture, so we need to set tracer on it
mock_server.tchannel._dep_tchannel._tracer = tracer
mock_server.tchannel._dep_tchannel._trace = enabled
register(tchannel=mock_server.tchannel, thrift_service=thrift_service,
http_client=http_client, base_url=base_url)
tchannel = TChannel(name='test', tracer=tracer, trace=enabled)
app.add_handlers(".*$", [
(r"/", HttpHandler, {'client_channel': tchannel})
])
with mock.patch('opentracing.tracer', tracer),\
mock.patch.object(tracing.log, 'exception') as log_exception:
assert opentracing.tracer == tracer # sanity check that patch worked
span = tracer.start_span('root')
baggage = 'from handler3 %d' % time.time()
span.set_baggage_item(BAGGAGE_KEY, baggage)
if not enabled:
span.set_tag('sampling.priority', 0)
with span: # use span as context manager so that it's always finished
response_future = None
with tchannel.context_provider.span_in_context(span):
if transport == 'tchannel':
if encoding == 'json':
response_future = tchannel.json(
service='test-client',
endpoint=endpoint,
hostport=mock_server.hostport,
body=mock_server.hostport,
)
elif encoding == 'thrift':
if endpoint == 'thrift1':
response_future = tchannel.thrift(
thrift_service.X.thrift1(mock_server.hostport),
hostport=mock_server.hostport,
)
elif endpoint == 'thrift3':
response_future = tchannel.thrift(
thrift_service.X.thrift3(mock_server.hostport),
hostport=mock_server.hostport,
)
elif endpoint == 'thrift4':
response_future = tchannel.thrift(
thrift_service.X.thrift4(mock_server.hostport),
hostport=mock_server.hostport,
)
else:
raise ValueError('wrong endpoint %s' % endpoint)
else:
raise ValueError('wrong encoding %s' % encoding)
elif transport == 'http':
response_future = http_client.fetch(
request=HTTPRequest(
url='%s%s' % (base_url, endpoint),
method='POST',
body=mock_server.hostport,
)
)
else:
raise NotImplementedError(
'unknown transport %s' % transport)
response = yield response_future
assert log_exception.call_count == 0
body = response.body
if expect_baggage:
if six.PY3 and isinstance(body, bytes):
body = body.decode('utf8')
assert body == baggage
def get_sampled_spans():
return [s for s in tracer.reporter.get_spans() if s.is_sampled]
# Sometimes the test runs into weird race condition where the
# after_send_response() hook is executed, but the span is not yet
# recorded. To prevent flaky test runs we check and wait until
# all spans are recorded, for up to 1 second.
for i in range(0, 1000):
spans = get_sampled_spans()
if len(spans) >= expect_spans:
break
yield tornado.gen.sleep(0.001) # yield execution and sleep for 1ms
spans = get_sampled_spans()
assert expect_spans == len(spans), 'Unexpected number of spans reported'
# We expect all trace IDs in collected spans to be the same
if expect_spans > 0:
spans = tracer.reporter.get_spans()
assert 1 == len(set([s.trace_id for s in spans])), \
'all spans must have the same trace_id'
@pytest.mark.parametrize('encoding,operation', [
('json', 'foo'),
('thrift', 'X::thrift2'),
])
@pytest.mark.gen_test
def test_span_tags(encoding, operation, tracer, thrift_service):
server = TChannel('server', tracer=tracer)
server.listen()
def get_span_baggage():
sp = server.context_provider.get_current_span()
baggage = sp.get_baggage_item('bender') if sp else None
return {'bender': baggage}
@server.json.register('foo')
def handler(_):
return get_span_baggage()
@server.thrift.register(thrift_service.X, method='thrift2')
def thrift2(_):
return json.dumps(get_span_baggage())
client = TChannel('client', tracer=tracer, trace=True)
opentracing.set_global_tracer(tracer)
span = tracer.start_span('root')
span.set_baggage_item('bender', 'is great')
with span:
res = None
with client.context_provider.span_in_context(span):
if encoding == 'json':
res = client.json(
service='test-service', # match thrift_service name
endpoint='foo',
body={},
hostport=server.hostport,
)
elif encoding == 'thrift':
res = client.thrift(
thrift_service.X.thrift2(),
hostport=server.hostport,
)
else:
raise ValueError('Unknown encoding %s' % encoding)
res = yield res # cannot yield in StackContext
res = res.body
if isinstance(res, six.string_types):
res = json.loads(res)
assert res == {'bender': 'is great'}
for i in range(1000):
spans = tracer.reporter.get_spans()
if len(spans) == 3:
break
yield tornado.gen.sleep(0.001) # yield execution and sleep for 1ms
spans = tracer.reporter.get_spans()
assert len(spans) == 3
trace_ids = set([s.trace_id for s in spans])
assert 1 == len(trace_ids), \
'all spans must have the same trace_id: %s' % trace_ids
@pytest.mark.gen_test
def test_tracing_field_in_error_message():
tchannel = TChannel('test')
class ErrorEventHook(EventHook):
def __init__(self):
self.request_trace = None
self.error_trace = None
def before_receive_request(self, request):
self.request_trace = request.tracing
def after_send_error(self, error):
self.error_trace = error.tracing
hook = ErrorEventHook()
tchannel.hooks.register(hook)
tchannel.listen()
with pytest.raises(BadRequestError):
yield tchannel.call(
scheme=schemes.RAW,
service='test',
arg1='endpoint',
hostport=tchannel.hostport,
timeout=1.0, # used to be 0.02, but was unstable in Travis
)
assert hook.error_trace
assert hook.request_trace
assert hook.error_trace == hook.request_trace
|
#!/usr/bin/python2
import commands as c
import os
while True:
try:
os.system("Xdialog --title 'FTP server Configuration' --cancel-label 'back' --menubox '' 15 50 5 1 configure 2 activate 3 deactivate 4 Status 5 Uninstall 2>.temp")
ch=int(c.getoutput("cat .temp|head -1"))
except:
os.system("rm -f .temp")
exit()
if ch==1:
x=c.getoutput("rpm -q vsftpd")
if x=='package vsftpd is not installed':
ex=os.system("yum install vsftpd -y 1>/dev/null 2>/dev/null")
if ex==0:
pass
else:
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'yum is not configured yet\n\n please configure yum first' 15 50")
exit()
else:
os.system("Xdialog --title 'FTP server configuration' --msgbox 'FTP server allready configured' 15 50")
continue
status=c.getoutput("service vsftpd status|awk -F' ' '{print $3}'")
if status=='stopped':
ex=os.system("Xdialog --title 'FTP server Configuration' --yesno 'service is stopped\n\n want to start service?' 15 50")
if ex==0:
os.system("service vsftpd start >/dev/null")
else:
pass
else:
ex=os.system("Xdialog --title 'FTP server Configuration' --yesno 'service is active\n\nwant to stop service?' 15 50")
if ex==0:
os.system("service vsftpd stop >/dev/null")
else:
pass
ex=os.system("Xdialog --title 'FTP server Configuration' --yesno 'want to start service at boot time' 15 50")
if ex==0:
os.system("chkconfig vsftpd on ")
else:
pass
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'place all the file & folder\nin /var/ftp to be\naccessed by anonymous user' 15 50")
if ch==2:
status=c.getoutput("service vsftpd status|awk -F' ' '{print $3}'")
if status=='stopped':
os.system("service vsftpd start >/dev/null")
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP service successfully Actived' 15 50")
else:
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP service allready Active' 15 50")
elif ch==3:
status=c.getoutput("service vsftpd status|awk -F' ' '{print $5}'")
if status=='running...':
os.system("service vsftpd stop 2>/dev/null")
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP service successfully Dectived' 15 50")
else:
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP service allready Dective' 15 50")
elif ch==4:
x=c.getoutput("rpm -q vsftpd")
if x=='package vsftpd is not installed':
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP server is not configured yet\n first configure FTP server' 15 50")
else:
status=c.getoutput("service vsftpd status|awk -F' ' '{print $5}'")
if status=='running...':
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP server is running...' 15 50")
else:
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP server is stop...' 15 50")
elif ch==5:
x=c.getoutput("rpm -q vsftpd")
if x=='package vsftpd is not installed':
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP server is not configured' 15 50")
else:
ex=os.system("Xdialog --title 'FTP server Configuration' --yesno 'are you sure?' 15 50")
if ex==0:
os.system("rpm -e vsftpd")
os.system("Xdialog --title 'FTP server Configuration' --msgbox 'FTP server successfully Uninstall' 15 50")
else:
pass
|
"""
Class Potential
This class performs calculations on arbitrary potentials.
Public Functions:
minimize : Minimize a generic potential
TODO: Complete this
"""
# Required Modules
import numpy
import sympy as sy
from sympy.matrices import hessian
from sympy.solvers import solve
# Currently Unused
from scipy.optimize import minimize
from scipy import optimize
from sympy.utilities.lambdify import lambdify
# Main Class
class Potential:
# Member Variables
# Constructor
def __init__(self, _potential_parameters, _constants_values=None):
# TODO: Validate _potential_parameters
self.potential = _potential_parameters["potential_form"]
self._fields = _potential_parameters["fields"]
self._constants = _potential_parameters["constants"]
self._masses = _potential_parameters["masses"]
self._vevs = _potential_parameters["vevs"]
self._unknown_constants = _potential_parameters["unknown_constants"]
self._known_constants = _potential_parameters["known_constants"]
self._known_constants_vals = _potential_parameters["known_constants_vals"]
# Create a functional form of the potential
all_parameters = self._fields + self._constants
self.functional_potential = lambdify(all_parameters, self.potential, modules='numpy')
# Define a list of constants as a function of others
self._constant_reduction = self._impose_minima_constraints()
# Constrain the potential
self._constrained_potential = self.potential.subs(self._constant_reduction)
# If we have constants values, set them
if _constants_values is not None:
self.set_constants(_constants_values)
# Other public functions
#the one immediately below is a helper function
def func_to_minimise(self, x):
"""
helper function
:return: functonal potential
"""
freduced_potential = lambdify(self._fields, self._constrained_potential, modules='numpy')
return freduced_potential(*tuple(x))
# TODO: possibly remove initial as argument in the function below
def minimise(self, bounds, initial):
"""
Minimizes a generic potential. Returns the global minimum.
:param Potential : A python function representing the potential
:param Bounds : Limits/Range over which to minimize
:param Temperature : Temperature
:param InitialPoint : Initial guess for minimizes
:return: The global minimum point. Minimize class
"""
# TODO: Implement this properly, currently does tree level potential
print("minimising potential:", self._constrained_potential)
# First use brute force minimisation
min_config=optimize.brute(self.func_to_minimise,bounds)
#print("minima field config:", min_config)
# Use the brute force results to get more accurate results
# Nelder-Mead used because no gradient info required, tolerence specified
min_config_acc = minimize(self.func_to_minimise,min_config, method='Nelder-Mead',tol=1e-12)
# TODO: check if other minimisation methods are better, in general graidient based methods are better
#min_config = minimize(freduced_potential, initial, method='SLSQP', jac=jac_v, bounds=bounds, tol=1e-12)
#print(min_config_acc)
# TODO: probably add field configuration and minimum value as attributes/instance of the class
minima = {}
minima['config'] = min_config_acc.x
minima['val'] = min_config_acc.fun
return minima
def remaining_vars(self):
"""
Returns the remaining variables after imposing the minima constraints
:return: tuple containing remaining variables
"""
impose_constraints = self._impose_minima_constraints()
reduced_potential = self.potential.subs(impose_constraints)
remaining_variables = tuple(reduced_potential.free_symbols - set(self._fields))
return remaining_variables
def set_constants(self, constants):
## TODO: We may want to set a class variable to store this function as opposed to returning it
"""
Sets numeric values for the constants in the constrained potential.
:param constants: Dictionary containing constants
:return: The value of the potential with unkowns specified
"""
return self._constrained_potential.subs(constants)
def numeric_const_values(self,constant_values):
"""
# TODO: Make sure this doesn't fail. I.e Check we have entered enough numbers
Gets the numeric values of constants when fixing the unkown variables
:param constants: Dictionary fixing remaining vars
:return: Numeric values of the constants
"""
numeric_dict = {}
for constant in self._constant_reduction:
# Loops through the constant dependence list, i.e {c1: 3c2 + 4 c4, c3: c2} and subs known values
# for c2, c4 etc
numeric_dict[constant] = self._constant_reduction[constant].subs(constant_values)
return numeric_dict
# Private Variables
# Private Functions
def _calculate_masses(self):
"""
Calculates the hessian of the fields and diagonalizes it to obtain the masses
:return: <Insert Return type and object here>
"""
h = hessian(self.potential, self._fields)
(p, d) = h.diagonalize()
return numpy.diagonal(d)
def _impose_minima_constraints(self):
"""
Imposes the minima constraints on the potential. Optional for 1-loop
:param unknown_constants:
:return:
"""
vevtuple = solve(numpy.subtract(self._fields, self._vevs))
# First Derivative
first_derivative = ([sy.diff(self.potential, i).subs(vevtuple) for i in self._fields])
# TODO: Check this logic - where a field has only mass terms
# Diagaonalized matrices
diagonal_hessian = ([m.subs(vevtuple) for m in self._calculate_masses()]) # subs only work on syMatrices
lhs = tuple(first_derivative) + tuple(diagonal_hessian) + tuple(self._known_constants)
rhs = tuple([0. for i in self._fields]) + tuple([m ** 2 for m in self._masses]) + tuple(self._known_constants_vals)
return solve(list(numpy.subtract(lhs, rhs)), self._constants, check=True) # ,set=True
#return solve(list(numpy.subtract(lhs, rhs)), self._unknown_constants, check=True) # ,set=True
def _quantum_corrections(self,SM=True):
"""
Use on-shell renormalisation with cutoff regularisation so that the
CW contribution does not shift the derivatives of the potential
see e.g. 1409.0005
:return:
"""
# TODO: are we going to let the user decide the field-dependent masses?
# TODO: Need to think about alternate SM structure but no new particles
CW= 0
CW+= 1./64.
if SM==True:
CW+= 1.
def _thermal_correction(self):
"""
Add thermal correction
:return:
"""
""" Old Private Functions. May use in the future...
# Optimization
# Build Jacobian if require to be used in function "minimise:
jac_f = [f.diff(x) for x in xx]
jac_fn = [lambdify(xx, jf, modules='numpy') for jf in jac_f]
# Jacobian Helper for receiving vector parameters
def _jac_v(zz, jac_fn):
return numpy.array([jfn(zz[0], zz[1], zz[2], zz[3], zz[4]) for jfn in jac_fn])
"""
|
import random
import torch
import numpy as np
global RANDOM_SEED
RANDOM_SEED = None
def set_random_seed(seed=None):
global RANDOM_SEED
changed = False
if RANDOM_SEED is None:
RANDOM_SEED = random.randint(1, 10000)
changed = True
if seed is not None:
RANDOM_SEED = seed
changed = True
if changed:
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
torch.backends.cudnn.deterministic = True
torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed_all(RANDOM_SEED)
print('Random seed changed: %i' % RANDOM_SEED)
return RANDOM_SEED
def base_model_name(arch_name, random_seed=None):
seed = random_seed if random_seed else set_random_seed()
return 'seed-%i_%s' % (seed, arch_name)
|
for t in range(int(input())):
l=list(input().split());s=[];c=1
for i in range(len(l)-1):
if l[i].isdigit():s.append(l[i])
else:
try:
a,b=int(s.pop()),int(s.pop())
if l[i]=='+':r=b+a
elif l[i]=='-':r=b-a
elif l[i]=='/':r=b//a
elif l[i]=='*':r=b*a
s.append(str(r))
except:c=0;break
if c and len(s)==1:print(f'#{t+1}',s[0])
elif c==0 or len(s)>1:print(f'#{t+1} error') |
"""
Pabuito Auto Script
3DF History Deleted
This script contains a auto script for the Pabuito Grade Tool. This tool searches for history on a single expected mesh.
!!!
All scripts need to create a progress bar and print status to stdout
!!!
All auto scripts need to return the grade information in the form of a dictionary
return {
'grade_value': int,
'comment_text':string,
'default_comments_text':string,
'example_comments_text':string,
}
the comments can remain '' without effecting the tools functionality.
Written by: Adam Fatka
adam.fatka@gmail.com
"""
import maya.cmds as cmds
import maya.mel
from pabuito_auto import utilities
#import xml.etree.ElementTree as etree
def ThreeDF_IllegalGeo(xmlDefaults, *args):
gradeValue = {'A': int(xmlDefaults.find('gradeValue').find('A').text),
'B': int(xmlDefaults.find('gradeValue').find('B').text),
'C': int(xmlDefaults.find('gradeValue').find('C').text),
'F-': int(xmlDefaults.find('gradeValue').find('F-').text),
}
development = False
if development:
print '3DF Illegal Geo running'
gMainProgressBar = maya.mel.eval('$tmp = $gMainProgressBar')
cmds.progressBar( gMainProgressBar,
edit=True,
beginProgress=True,
isInterruptable=True,
status='Checking for Illegal Geo',
maxValue=100 )
step = 100/7
cmds.progressBar(gMainProgressBar, edit=True, step=step)
utils = utilities.utilities()
cmds.progressBar(gMainProgressBar, edit=True, step=step)
rootNodes = utils.masterGroupTest()
cmds.progressBar(gMainProgressBar, edit=True, step=step)
collectedNodes = utils.nodeCollector(rootNodes)
cmds.progressBar(gMainProgressBar, edit=True, step=step)
sortedNodes = utils.sortNodeList(collectedNodes, 'transform')
cmds.progressBar(gMainProgressBar, edit=True, step=step)
cleanedNodes = []
for node in sortedNodes:
try:
if cmds.nodeType(cmds.listRelatives(node)[0]) != 'imagePlane':
cleanedNodes.append(node)
except RuntimeError:
cmds.warning('RuntimeError Occurred - Adding node anyway...')
cleanedNodes.append(node)
except TypeError:
pass
spottedTriangles = utils.triFinder(cleanedNodes)
# print('triangles: {}'.format(spottedTriangles))
spottedNGons = utils.nGonFinder(cleanedNodes)
# print('nGons: {}'.format(spottedNGons))
spottedLamina = utils.laminaFinder(cleanedNodes)
# print('Lamina: {}'.format(spottedLamina))
troubleGeo = ''
if len(spottedTriangles[0]) != 0:
troubleGeo += 'Triangles'
if len(spottedNGons[0]) != 0:
if troubleGeo != '':
troubleGeo += ', '
troubleGeo += 'NGons'
if len(spottedLamina[0]) != 0:
if troubleGeo != '':
troubleGeo += ', '
troubleGeo += 'Lamina Faces'
total_trouble_geo = len(spottedTriangles[1]) + len(spottedLamina[1]) + len(spottedNGons[1])
cmds.progressBar(gMainProgressBar, edit=True, step=step)
print "Illegal Geo Automation Successful!"
if total_trouble_geo >= 11 and troubleGeo != '':
tempDict = {
'grade_value':gradeValue['F-'],
'comment_text':'',
'default_comments_text':'Illegal Geometry detected. Detected geometry includes {}.'.format(troubleGeo),
'example_comments_text':''}
elif 6 <= total_trouble_geo <= 10 and troubleGeo != '':
tempDict = {
'grade_value':gradeValue['C'],
'comment_text':'',
'default_comments_text':'Illegal Geometry detected. Detected geometry includes {}.'.format(troubleGeo),
'example_comments_text':''}
elif 1 <= total_trouble_geo <= 5 and troubleGeo != '':
tempDict = {
'grade_value':gradeValue['B'],
'comment_text':'',
'default_comments_text':'Illegal Geometry detected. Detected geometry includes {}.'.format(troubleGeo),
'example_comments_text':''}
else:
tempDict = {
'grade_value':gradeValue['A'],
'comment_text':'',
'default_comments_text':'No Illegal Geometry found. Good job!',
'example_comments_text':''}
cmds.progressBar(gMainProgressBar, edit=True, step=step)
cmds.progressBar(gMainProgressBar, edit=True, endProgress=True)
return tempDict
|
#===========================================================================
# iterate through a list using indexing
#===========================================================================
Titans = ['Apple', 'Alphabet', 'Microsoft']
# Iterate through the list using the index
for i in range(len(Titans)):
print(Titans[i]) # Accessing the i-th element of the list Titans |
import collections
def slidingWindow(s: str, t: str):
need = collections.defaultdict(int)
window = collections.defaultdict(int)
for ch in s:
need[ch] += 1
left, right = 0, 0
valid = 0
while(right < len(s)):
# c是将移入窗口的字符
c = s[right]
# 右移窗口
right += 1
# 进行窗口数据的一系列更新
# ... 待进行的操作
'''
debug输出的位置：
'''
print("window:[%d,%d]", left, right)
'''
# 判断左侧窗口是否要收缩
while window needs shrink：
# d是将移除窗口的字符
d = s[left]
# 左移窗口
left += 1
# 进行窗口内数据的一系列更新
# ... 待进行的操作
'''
'''
其中两处 ... 表示的更新窗口数据的地方，到时候你直接往里面填就行了。
而且，这两个 ... 处的操作分别是右移和左移窗口更新操作，等会你会发现它们操作是完全对称的。
'''
|
#!/usr/bin/python
# coding=utf-8
import numpy as np
class BaseLoss(object):
def __init__(self, lamb=0.0):
self.lamb = lamb
def grad(self, preds, labels):
raise NotImplementedError()
def hess(self, preds, labels):
raise NotImplementedError()
class SquareLoss(BaseLoss):
def grad(self, preds, labels):
return preds - labels
def hess(self, preds, labels):
return np.ones_like(labels)
def transform(self, preds):
return preds
class LogisticLoss(BaseLoss):
def grad(self, preds, labels):
preds = self.transform(preds)
return (1 - labels) / (1 - preds) - labels / preds
def hess(self, preds, labels):
preds = self.transform(preds)
return labels / np.square(preds) + (1 - labels) / np.square(1 - preds)
def transform(self, preds):
return 1.0 / (1.0 + np.exp(-preds)) |
import unittest
from mirror.visualisations.core import Visualisation
from mirror.visualisations.web import WebInterface
from functools import partial
class ModuleTracerTest(unittest.TestCase):
def test(self):
class TestVisualisation(Visualisation):
def __call__(self, inputs, layer, something=1, *args, **kwargs):
self.something = something
return None
params = {'something': {
'type': 'slider',
'min': 1,
'max': 100,
'value': 1,
'step': 1,
'params': {}
}
}
TestVisWeb = partial(WebInterface.from_visualisation, TestVisualisation,
params=params,
name='something')
test_vis_web = TestVisWeb(None, None)
test_vis_web(None, None)
self.assertEqual(test_vis_web.callable.something, 1)
state = test_vis_web.to_JSON()
state['params']['something']['value'] = 4 #simulate change in the front end
test_vis_web.from_JSON(state)
self.assertEqual(test_vis_web.callable.something, 1)
|
class Employee:
raise_amount=1.04
def apply_raise(self):
self.pay=self.pay*self.raise_amount
def __init__(self,name,pay):
self.name=name
self.pay=pay
emp_1=Employee('Corey',50000)
emp_2=Employee('John',40000)
emp_1.raise_amount=1.03
Employee.raise_amount=1.02
print(emp_1.raise_amount)
print(emp_2.raise_amount) |
import random
guess=str(random.randint(100,1001))
while True:
inp = input('Enter any three digits: ')
if len(inp) != 3:
print('Number entered is not three digits, Please try again!')
continue
if guess[0] == inp[0] or guess[1] == inp[1] or guess[2] == inp[2]:
print('Match')
else:
print('No Match')
if guess == inp:
print('Right Guess')
break
|
import FWCore.ParameterSet.Config as cms
# Center-of-mass energy 10 TeV
herwigppEnergySettingsBlock = cms.PSet(
hwpp_cm_10TeV = cms.vstring(
'set /Herwig/Generators/LHCGenerator:EventHandler:LuminosityFunction:Energy 10000.0',
'set /Herwig/Shower/Evolver:IntrinsicPtGaussian 2.1*GeV',
),
)
|
import sys
def input():
return sys.stdin.readline()[:-1]
n=int(input())
li=[[0]*10 for i in range(10)]
for i in range(n):
tmp=str(i+1)
li[int(tmp[0])][int(tmp[-1])]+=1
# print(li)
ans=0
for i in range(10):
for j in range(10):
ans+=li[i][j]*li[j][i]
print(ans) |
import random
from Classes.enemy import Enemy
enemy1 = Enemy(200, 60)
print(enemy1.getHp())
'''
playerhp = 400
enemyatkL = 60
enemyatkH = 100
while playerhp > 0:
dmg = random.randrange(enemyatkL,enemyatkH)
playerhp -= dmg
if playerhp <= 30:
playerhp = 30
print('Player is attacked for', dmg,'they now have', playerhp)
if playerhp >30:
continue
print('You have low health. You have been teleported to the nearest checkpoint')
break '''
|
import cv2
import numpy as np
img = cv2.imread("images/shapes oki.jpg", 0)
img = cv2.resize(img, (800, 600))
# cv2.imshow("org", img)
ret, thr = cv2.threshold(img, 120, 255, cv2.THRESH_BINARY_INV)
cv2.imshow('thresh', thr)
cv2.imwrite("thr.jpg", thr)
kernel = np.ones((7, 7), np.uint8)
opening = cv2.morphologyEx(thr, cv2.MORPH_OPEN, kernel) #erode + dilation
cv2.imshow("opening", opening)
contours, ret = cv2.findContours(opening, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
font = cv2.FONT_HERSHEY_COMPLEX
for cnt in contours:
approx = cv2.approxPolyDP(cnt, 0.03*cv2.arcLength(cnt, True), True)
cv2.drawContours(img, [approx], 0, -1, 2, 10)
x = approx.ravel()[0]
y = approx.ravel()[1]
if len(approx) == 3:
cv2.putText(img, "Triangle", (x, y), font, 1, (0))
elif len(approx) == 4:
cv2.putText(img, "Rectangle", (x, y), font, 1, (0))
elif len(approx) == 5:
cv2.putText(img, "Pentagon", (x+50, y+50), font, 1, (0))
elif len(approx) == 6:
cv2.putText(img, "hexagon", (x, y), font, 1, (0))
elif len(approx) == 8:
cv2.putText(img, "octagonal", (x, y), font, 1, (0))
elif 8 < len(approx) < 12:
cv2.putText(img, "Ellipse", (x, y), font, 1, (0))
else:
cv2.putText(img, "Circle", (x, y), font, 1, (0))
cv2.imshow("shapes", img)
cv2.waitKey()
cv2.destroyAllWindows() |
from ray.dag.dag_node import DAGNode
from ray.dag.function_node import FunctionNode
from ray.dag.class_node import ClassNode, ClassMethodNode
from ray.dag.input_node import (
InputNode,
InputAttributeNode,
DAGInputData,
)
from ray.dag.constants import (
PARENT_CLASS_NODE_KEY,
PREV_CLASS_METHOD_CALL_KEY,
DAGNODE_TYPE_KEY,
)
from ray.dag.vis_utils import plot
__all__ = [
"ClassNode",
"ClassMethodNode",
"DAGNode",
"FunctionNode",
"InputNode",
"InputAttributeNode",
"DAGInputData",
"PARENT_CLASS_NODE_KEY",
"PREV_CLASS_METHOD_CALL_KEY",
"DAGNODE_TYPE_KEY",
"plot",
]
|
from quark_runtime import *
import quark.reflect
class pkg_C_event1_Method(quark.reflect.Method):
def _init(self):
quark.reflect.Method._init(self)
def __init__(self):
super(pkg_C_event1_Method, self).__init__(u"quark.void", u"event1", _List([]));
def invoke(self, object, args):
obj = object;
(obj).event1();
return None
def _getClass(self):
return None
def _getField(self, name):
return None
def _setField(self, name, value):
pass
class pkg_C(quark.reflect.Class):
def _init(self):
quark.reflect.Class._init(self)
def __init__(self):
super(pkg_C, self).__init__(u"pkg.C");
(self).name = u"pkg.C"
(self).parameters = _List([])
(self).fields = _List([])
(self).methods = _List([pkg_C_event1_Method()])
def construct(self, args):
return pkg.C()
def _getClass(self):
return None
def _getField(self, name):
return None
def _setField(self, name, value):
pass
pkg_C.singleton = pkg_C()
class quark_List_quark_String_(quark.reflect.Class):
def _init(self):
quark.reflect.Class._init(self)
def __init__(self):
super(quark_List_quark_String_, self).__init__(u"quark.List<quark.String>");
(self).name = u"quark.List"
(self).parameters = _List([u"quark.String"])
(self).fields = _List([])
(self).methods = _List([])
def construct(self, args):
return _List()
def _getClass(self):
return None
def _getField(self, name):
return None
def _setField(self, name, value):
pass
quark_List_quark_String_.singleton = quark_List_quark_String_()
class Root(object):
def _init(self):
pass
def __init__(self): self._init()
def _getClass(self):
return None
def _getField(self, name):
return None
def _setField(self, name, value):
pass
Root.pkg_C_md = pkg_C.singleton
import pkg
|
# -*- coding: utf-8 -*-
"""
/***************************************************************************
-------------------
begin : 06.07.2021
git sha : :%H$
copyright : (C) 2021 by Dave Signer
email : david at opengis ch
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
"""
from qgis.PyQt.QtCore import QSettings
from qgis.PyQt.QtWidgets import QWizardPage
from QgisModelBaker.libili2db.globals import DbActionType, DbIliMode, displayDbIliMode
from QgisModelBaker.utils import db_utils
from ...libqgsprojectgen.db_factory.db_simple_factory import DbSimpleFactory
from ...utils import ui
PAGE_UI = ui.get_ui_class("workflow_wizard/database_selection.ui")
class DatabaseSelectionPage(QWizardPage, PAGE_UI):
def __init__(self, parent, title, db_action_type):
QWizardPage.__init__(self, parent)
self.workflow_wizard = parent
self.db_action_type = db_action_type
self.setupUi(self)
self.setTitle(title)
# in this context we use GENERATE for the project generation and the IMPORT_DATA for the schema import (and the data import)
if db_action_type == DbActionType.GENERATE:
self.description.setText(
self.tr(
"Select an existing database you want to generate a QGIS project from."
)
)
elif db_action_type == DbActionType.EXPORT:
self.description.setText(
self.tr("Select an existing database you want to export from.")
)
else:
self.description.setText(
self.tr("Choose the database you want to create or import to.")
)
self.type_combo_box.clear()
self._lst_panel = dict()
self.db_simple_factory = DbSimpleFactory()
for db_id in self.db_simple_factory.get_db_list(False):
self.type_combo_box.addItem(displayDbIliMode[db_id], db_id)
db_factory = self.db_simple_factory.create_factory(db_id)
item_panel = db_factory.get_config_panel(self, db_action_type)
self._lst_panel[db_id] = item_panel
self.db_layout.addWidget(item_panel)
self.type_combo_box.currentIndexChanged.connect(self._type_changed)
def _type_changed(self):
ili_mode = self.type_combo_box.currentData()
db_id = ili_mode & ~DbIliMode.ili
self.db_wrapper_group_box.setTitle(displayDbIliMode[db_id])
# Refresh panels
for key, value in self._lst_panel.items():
is_current_panel_selected = db_id == key
value.setVisible(is_current_panel_selected)
if is_current_panel_selected:
value._show_panel()
def restore_configuration(self, configuration):
# takes settings from QSettings and provides it to the gui (not the configuration)
# it needs the configuration - this is the same for Schema or Data Config
settings = QSettings()
for db_id in self.db_simple_factory.get_db_list(False):
db_factory = self.db_simple_factory.create_factory(db_id)
config_manager = db_factory.get_db_command_config_manager(configuration)
config_manager.load_config_from_qsettings()
self._lst_panel[db_id].set_fields(configuration)
mode = settings.value("QgisModelBaker/importtype")
mode = DbIliMode[mode] if mode else self.db_simple_factory.default_database
mode = mode & ~DbIliMode.ili
self.type_combo_box.setCurrentIndex(self.type_combo_box.findData(mode))
self._type_changed()
def update_configuration(self, configuration):
# takes settings from the GUI and provides it to the configuration
mode = self.type_combo_box.currentData()
self._lst_panel[mode].get_fields(configuration)
configuration.tool = mode
configuration.db_ili_version = db_utils.db_ili_version(configuration)
def save_configuration(self, updated_configuration):
# puts it to QSettings
settings = QSettings()
settings.setValue(
"QgisModelBaker/importtype", self.type_combo_box.currentData().name
)
mode = self.type_combo_box.currentData()
db_factory = self.db_simple_factory.create_factory(mode)
config_manager = db_factory.get_db_command_config_manager(updated_configuration)
config_manager.save_config_in_qsettings()
def nextId(self):
return self.workflow_wizard.next_id()
|
# Generated by Django 3.0.6 on 2020-05-24 13:55
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import django.utils.timezone
import phonenumber_field.modelfields
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='Application',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('phone_number', phonenumber_field.modelfields.PhoneNumberField(blank=True, max_length=128, region=None)),
('user_address', models.CharField(max_length=100)),
('national_id', models.FileField(upload_to='documents/national_ids')),
('birth_certificate', models.FileField(upload_to='documents/birth_certificates')),
('school_name', models.CharField(max_length=100)),
('school_address', models.CharField(max_length=100)),
('accademic_level', models.CharField(default='year1', max_length=10)),
('expected_completion_date', models.DateField()),
('moltivation', models.TextField(verbose_name='Why do you deserve sponsorship? ')),
('recommendation_letter', models.FileField(upload_to='documents/recommendation_letters')),
('status', models.CharField(default='pending', max_length=30)),
('submission_date', models.DateTimeField(default=django.utils.timezone.now)),
('approval_date', models.DateTimeField(null=True)),
('applicant', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='applicant', to=settings.AUTH_USER_MODEL)),
('approved_by', models.ForeignKey(null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='approved_by', to=settings.AUTH_USER_MODEL)),
],
),
]
|
##############################################
# Project: Microwave Filter Tuner
# File: OpenCV.py
# Date: January 18, 2020
# Programmer(s): Braden Massé and Matthew Rostad
# Sub-Systems: Visual Identification Sub-System
# Version: 1.2
##############################################
# Import Libraries #
import numpy as np
import cv2 as cv2
import math
import statistics
from numpy import array
# Globals #
g_zoomCamPort = 1 # COM port for the zoom camera
g_height1 = 260 # height from the bed to the zoom camera
g_pixelsPerMM = 16.2 # pixels per milimeter at the bed height
##############################################
# Function: zoom_Camera()
# Programmer(s): Matthew Rostad
# Date: January 26,2020
# Purpose: To identify all tuning screws and determine X, Y, and Z position and determine depth and screw angle
# Arguments: sensitivityVal
# Outputs: N/A
##############################################
def zoom_Camera(sensitivityVal):
# values to be outputed
screwDepth = 0
screwLocations = []
minDist = 80 # minimum distance between scerws
upCannyThreshold = 120 # sensitivity for detecting circles
centerThreshold = 55 # sensitivity for detecting circle centers
maxR = 180 # maximum screw radius
screwCount = 0
dp = 1
minDistCenter = 9999 # finds the screw the closest to the center
closestCenter = 0 # the screw that is the closest to the center
measuredDepth = 195 # units are mm
screwDiameter = 10 # units are mm
referenceRadius = 102 # units are pixels
cap = cv2.VideoCapture(g_zoomCamPort)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 2592)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1944)
for i in range(0,20):
ret, img = cap.read()
print(ret)
cap.release()
img = cv2.flip(img, -1)
'''
img = cv2.imread('NewPhotos6/opencv_frame_2.png') # testing, comment out if taking picture
img = cv2.flip(img, -1)
'''
output = img.copy()
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # change to greyscale image
gray = cv2.medianBlur(gray, 5) # apply blur to reduce false positives
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp, minDist, param1=upCannyThreshold, param2=centerThreshold,
minRadius=sensitivityVal, maxRadius=maxR)
if circles is None:
print("No screws Detected")
else:
print(circles.size, "screws detected")
detected_circles = np.uint16(np.around(circles))
for (x, y, r) in detected_circles[0, :]:
# use only the screw in the center of the image
# find the center of the image
height, width = img.shape[:2] # get the height and width of the image
verticalCenter = height/2
horizontalCenter = width/2
#################################
# determine which screw is closest to the center
distCenter = abs(x - horizontalCenter) + abs(y - verticalCenter)
if distCenter < minDistCenter:
minDistCenter = distCenter
closestCenter = screwCount
screwCount = screwCount + 1
x, y, r = detected_circles[0][closestCenter]
'''
xScrewOffset = 37.82
yScrewOffset = 3.4
calculatedDepth = 185
new_pixelsPerMM = g_pixelsPerMM * (g_height1 / calculatedDepth)
print(x)
print(y)
print(verticalCenter)
print(horizontalCenter)
newY = y - verticalCenter
newX = x - horizontalCenter
xOffset = (newX / new_pixelsPerMM) + xScrewOffset
yOffset = (newY / new_pixelsPerMM) + yScrewOffset
'''
#################################
# determine the depth of the screw
focalLength = ((referenceRadius * 2) * measuredDepth) / screwDiameter # determine focal length
calculatedDepth = (screwDiameter * focalLength) / (r * 2)
################################
print('depth', calculatedDepth)
offsetImage = img.copy()
xPixelError = x - horizontalCenter
yPixelError = y - verticalCenter
print('x error', xPixelError)
print('y error', yPixelError)
#new_pixelsPerMM = g_pixelsPerMM * (g_height1 / calculatedDepth)
# calculatedDepth = 205
# bedHeight = 260
# filterHeight = 65
# calculatedDepth = bedHeight - filterHeight
new_pixelsPerMM = -0.1283 * calculatedDepth + 47.762
# new_pixelsPerMM = 21.7
print('px per mm', new_pixelsPerMM)
xMMError = xPixelError / new_pixelsPerMM + 100 # + 137.4
yMMError = yPixelError / new_pixelsPerMM + 100 # + 109.6
print('x error mm', xMMError)
print('y error mm', yMMError)
offsetImage = cv2.line(offsetImage, (0, int(y)), (2592, int(y)), (0, 0, 255), 2)
offsetImage = cv2.line(offsetImage, (int(x), 0), (int(x), 1944), (0, 0, 255), 2)
offsetImage = cv2.line(offsetImage, (0,int(verticalCenter)), (2592, int(verticalCenter)), (0,0,0), 2)
offsetImage = cv2.line(offsetImage, (int(horizontalCenter), 0), (int(horizontalCenter), 1944), (0, 0, 0), 2)
cv2.imshow("offset", offsetImage)
print(closestCenter)
print(screwLocations)
# output a circle on only the screw closest to the center
x,y,r = detected_circles[0, closestCenter]
cv2.circle(output, (x, y), r, (0, 255, 0), 3) # draw circles on detected screws
print("Circle ", screwCount, "at", x, y, " with radius of", r, ) # testing
cv2.circle(output, (x, y), 2, (0, 255, 0), 3) # draw dot on center of detected screws
cv2.imshow('output', output) # display output with screws identified, needs to be integrated into GUI
#########################################
# find screw angle
cropImg = img[(y-r):(y+r), (x-r):(x+r)]
imageSize = r*2
cropCopy = cropImg.copy()
cv2.imshow('crop', cropImg)
crop_gray = cv2.cvtColor(cropImg, cv2.COLOR_BGR2GRAY) # change to greyscale image
cannyEdges = cv2.Canny(crop_gray, 40, 120, None, 3)
linesP = cv2.HoughLinesP(cannyEdges, 1, np.pi / 180, 20, None, 20, 10)
imageCutOff = 20
angleAllowance = 10
similarAngles = []
similarAngle = []
angles = []
if linesP is not None:
for i in range(0, len(linesP)):
if not ((linesP[i][0][0] < imageCutOff or linesP[i][0][2] < imageCutOff) or
(linesP[i][0][0] > imageSize - imageCutOff or linesP[i][0][2] > imageSize - imageCutOff) or
(linesP[i][0][1] > imageSize - imageCutOff or linesP[i][0][3] > imageSize - imageCutOff) or
(linesP[i][0][1] < imageCutOff or linesP[i][0][3] < imageCutOff)):
l = linesP[i][0]
cv2.line(cropCopy, (l[0], l[1]), (l[2], l[3]), (0, 0, 255), 3, cv2.LINE_AA)
angle = math.atan2(linesP[i][0][2] - linesP[i][0][0], linesP[i][0][3] - linesP[i][0][1])
angles.append(math.degrees(angle))
cv2.imshow("Detected Lines (in red) - Probabilistic Line Transform", cropCopy)
# try to average out all angels and find angle of screw
for i in range(len(angles)):
if angles[i] > 90:
angles[i] = angles[i] - 90
for i in range(len(angles)):
for j in range(len(angles)):
if abs(angles[i] - angles[j]) < angleAllowance:
similarAngle.append(angles[j])
similarAngles.append(similarAngle)
similarAngle = []
mostSimilarAngles = max(similarAngles,key=len)
screwAngle = statistics.mean(mostSimilarAngles)
#screwOutputs = [offsetmm, screwAngle, calculatedDepth]
# End of Function Clean-Up *
cv2.waitKey(0) # close image on pressing any key
cv2.destroyAllWindows()
print(screwAngle)
return
# global list
# screw number, X pos, Y pos, Z pos, flag
#screwLocationsGList = []
zoomScrewOutputs = zoom_Camera(30)
'''
screwOffset = zoomScrewOutputs[0]
screwAngle = zoomScrewOutputs[1]
zoomScrewDepth = zoomScrewOutputs[2]
print(screwOffset)
print(screwAngle)
print(zoomScrewDepth)
'''
#wide_Angle_Camera(30) # testing
#print(screwLocationsGList)
|
from board import Board
from snake import Snake
from snakes.codesnake import CodeSnake
from multiprocessing import Pool
from flask import Flask, render_template
from flask_socketio import SocketIO, emit
import time
import pymongo
from pprint import pprint
app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret!'
socketio = SocketIO(app, ping_timeout=100000)
BOARD_HEIGHT = 15
BOARD_WIDTH = 15
NUMBER_OF_SNAKES = 4
NUMBER_OF_FOOD = 5
DELAY = 0.25
db = ''
games_col = ''
@socketio.on('message')
def handleMessage(json):
print('received message: ' + str(json))
@socketio.on('startGame')
def handleStartGame(json):
print ('received game json: ' + str(json))
'''
BOARD_HEIGHT = json['board_height']
BOARD_WIDTH = json['board_width']
NUMBER_OF_SNAKES = json['number_of_snakes']
NUMBER_OF_FOOD = json['number_of_food']
'''
# Create NUMBER_OF_SNAKES snake objects in an array
snakes = [CodeSnake(x) for x in range(NUMBER_OF_SNAKES)]
board = Board(BOARD_HEIGHT, BOARD_WIDTH, NUMBER_OF_FOOD, snakes)
board.print()
turn = 0
# While there is more than one snake on the board, keep stepping forward
while len([snake for snake in board.snakes if snake.health > 0]) > 1:
board.step(turn)
#board.print()
print (turn)
turn += 1
emit('board', board.serialize())
time.sleep(DELAY)
def simulate_game(game_number):
print (game_number)
# Create NUMBER_OF_SNAKES snake objects in an array
snakes = [CodeSnake(x) for x in range(NUMBER_OF_SNAKES)]
board = Board(BOARD_HEIGHT, BOARD_WIDTH, NUMBER_OF_FOOD, snakes)
# Play the game
game = board.play()
# Write game data to database
games_col.insert_one(game)
return game
if __name__ == '__main__':
client = pymongo.MongoClient('mongodb://localhost:27017/')
db = client['snek_db']
games_col = db['games']
#socketio.run(app)
with Pool(3) as p:
print (p.map(simulate_game, [x for x in range(100)]))
#games_col.insert_one(game)
|
from stem import Signal
from stem import CircStatus
from stem.control import Controller
import threading
import os
import urllib2
headers ={'User-Agent' : 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:12.0) Gecko/20100101 Firefox/12.0', 'Accept-Encoding' : 'gzip, deflate', 'Accept' :'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8'}
timeout_time_url = 200
timeout_time_thread= 100
url = "http://example.com/file.jpg"
def new_identity():
with Controller.from_port(port = 9051) as controller:
controller.authenticate()
controller.signal(Signal.NEWNYM)
for circ in controller.get_circuits():
if circ.status != CircStatus.BUILT:continue
entry_fingerprint = circ.path[0][0]
entry_descriptor = controller.get_network_status(entry_fingerprint, None)
if entry_descriptor:
print "Circuit %s starts with %s" % (circ.id, entry_descriptor.address)
def save_file(url):
try:
request = urllib2.Request(url, headers=headers)
raw_data = urllib2.urlopen(request, timeout=timeout_time_url)
with open(os.path.basename(url), "wb") as f:
f.write(raw_data.read())
except Exception,e:
print str(e)
def main(url, timeout_time_thread):
new_identity()
save_file(url)
thread = threading.Thread(target = main,args = (url, timeout_time_thread) )
thread.start()
thread.join(timeout_time_thread)
if thread.is_alive():
main.terminate()
thread.join()
|
# coding=utf-8
import grpc
import os
from fastapi import FastAPI
from pydantic import BaseModel
import _pickle as pickle
import tensorflow as tf
from spacy.tokens import Span
from spacy.lang.fr import French
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
from starlette.middleware.cors import CORSMiddleware
class InputFeatures(object):
def __init__(self, chars, tokens, labels):
self.chars = chars
self.tokens = tokens
self.labels = labels
class InputExample(object):
def __init__(self, text=None, labels=None):
self.text = text
self.labels = labels
class Ad(BaseModel):
text: str
# tf.logging.set_verbosity(tf.logging.INFO)
app = FastAPI()
# CORS
origins = ["*"]
app.add_middleware(CORSMiddleware, allow_origins=origins, allow_methods=["*"], allow_headers=["*"])
METADATA_FILE = os.environ.get("METADATA_FILE")
MODEL_NAME = os.environ.get("MODEL_NAME")
HOST_NAME = os.environ.get("HOST_NAME") if os.environ.get("HOST_NAME") else "localhost"
GRPC_SERVER_PORT = os.environ.get("GRPC_SERVER_PORT") if os.environ.get("GRPC_SERVER_PORT") else 8500
with tf.io.gfile.GFile(METADATA_FILE, "rb") as f:
metadata = pickle.load(f)
label_list = metadata["labels"]
MAX_SENTENCE_SEQ_LENGTH = metadata['max_sentence_seq_length']
MAX_TOKEN_SEQ_LENGTH = metadata['max_token_seq_length']
vocab_tokens = metadata["vocab_tokens"]
vocab_chars = metadata["vocab_chars"]
nlp = French()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
channel = grpc.insecure_channel(HOST_NAME + ":" + str(GRPC_SERVER_PORT))
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
def convert_single_example(ex_index, example):
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens = [w.encode('utf-8') if w in vocab_tokens else b'[UKN]' for w in
example.text.strip().split(u' ')]
labels = [l.encode('utf-8') for l in example.labels.strip().split(u' ')]
chars = [[c.encode('utf-8') if c in vocab_chars else b'[UKN]' for c in w] for w in
example.text.strip().split(u' ')]
if len(tokens) > MAX_SENTENCE_SEQ_LENGTH:
tokens = tokens[0:MAX_SENTENCE_SEQ_LENGTH]
labels = labels[0:MAX_SENTENCE_SEQ_LENGTH]
chars = chars[0:MAX_SENTENCE_SEQ_LENGTH]
for i, _ in enumerate(chars):
if len(chars[i]) > MAX_TOKEN_SEQ_LENGTH:
chars[i] = chars[i][0:MAX_TOKEN_SEQ_LENGTH]
if len(tokens) < MAX_SENTENCE_SEQ_LENGTH:
tokens.extend([b'[PAD]'] * (MAX_SENTENCE_SEQ_LENGTH - len(tokens)))
labels.extend([b'O'] * (MAX_SENTENCE_SEQ_LENGTH - len(labels)))
lengths = [len(c) for c in chars]
chars = [c + [b'[PAD]'] * (MAX_TOKEN_SEQ_LENGTH - l) for c, l in zip(chars, lengths)]
while len(chars) < MAX_SENTENCE_SEQ_LENGTH:
chars.append([b'[PAD]'] * MAX_TOKEN_SEQ_LENGTH)
for tmp_chars in chars:
assert len(tmp_chars) == MAX_TOKEN_SEQ_LENGTH
assert len(tokens) == MAX_SENTENCE_SEQ_LENGTH
assert len(chars) == MAX_SENTENCE_SEQ_LENGTH
assert len(labels) == MAX_SENTENCE_SEQ_LENGTH
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("tokens: %s" % " ".join([str(x) for x in tokens]))
tf.logging.info("chars: %s" % " ".join([str(x) for x in chars]))
tf.logging.info("labels: %s" % " ".join([str(x) for x in labels]))
feature = InputFeatures(
chars=chars,
tokens=tokens,
labels=labels,
)
return feature
@app.get("/api/health")
async def health():
return {"message": "I'm alive"}
@app.post("/api/recognize")
async def get_prediction(ad: Ad):
doc = nlp(ad.text)
txt = " ".join([token.text for token in doc])
input_example = InputExample(text=txt, labels=" ".join(['O'] * len(txt)))
feature = convert_single_example(0, input_example)
model_request = predict_pb2.PredictRequest()
model_request.model_spec.name = MODEL_NAME
model_request.model_spec.signature_name = 'serving_default'
tokens = tf.contrib.util.make_tensor_proto(feature.tokens,
shape=[1, MAX_SENTENCE_SEQ_LENGTH])
chars = tf.contrib.util.make_tensor_proto(feature.chars,
shape=[1, MAX_SENTENCE_SEQ_LENGTH,
MAX_TOKEN_SEQ_LENGTH])
labels = tf.contrib.util.make_tensor_proto(feature.labels, shape=[1, MAX_SENTENCE_SEQ_LENGTH])
size_tokens = tf.contrib.util.make_tensor_proto([MAX_TOKEN_SEQ_LENGTH] *
MAX_SENTENCE_SEQ_LENGTH,
shape=[1, MAX_SENTENCE_SEQ_LENGTH])
size_sentence = tf.contrib.util.make_tensor_proto(MAX_SENTENCE_SEQ_LENGTH, shape=[1])
model_request.inputs['tokens'].CopyFrom(tokens)
model_request.inputs['chars'].CopyFrom(chars)
model_request.inputs['labels'].CopyFrom(labels)
model_request.inputs['size_tokens'].CopyFrom(size_tokens)
model_request.inputs['size_sentence'].CopyFrom(size_sentence)
result = stub.Predict(model_request, 5.0)
result = tf.make_ndarray(result.outputs["output"])
return output_ner(doc, result[0])
@app.post("/api/tokenize")
async def get_tokens(ad: Ad):
doc = nlp(ad.text)
doc = [[token for token in sent] for sent in doc.sents]
sentences = []
for sentence in doc:
tokens = []
for token in sentence:
tokens.append({"value": token.text, "begin": token.idx, "end": token.idx + len(token.text)})
sentences.append({"tokens": tokens})
return {"sentences": sentences}
def output_ner(doc, ids):
res = {"entities": []}
entities = []
tf.logging.info(ids)
tf.logging.info(label_list)
annotations = ids[0:len(doc)]
tf.logging.info(list(doc))
tf.logging.info([label_list[label] for label in annotations])
assert len(doc) == len(annotations)
prev_type = label_list[annotations[0]]
start_span = 0
end_span = 1
for idx in range(1, len(annotations)):
if prev_type != label_list[annotations[idx]] and prev_type != 'O':
entities.append({"type": prev_type, "start": start_span, "end": end_span})
prev_type = label_list[annotations[idx]]
start_span = idx
end_span = idx + 1
elif annotations[idx] != 'O' and prev_type != 'O':
end_span += 1
else:
prev_type = label_list[annotations[idx]]
start_span = idx
end_span = idx + 1
if prev_type != 'O':
entities.append({"type": prev_type, "start": start_span, "end": end_span})
for ent in entities:
span = Span(doc, ent["start"], ent["end"], label=ent["type"])
doc.ents = list(doc.ents) + [span]
for ent in doc.ents:
res["entities"].append({"phrase": ent.text, "type": ent.label_,
"startOffset": ent.start_char, "endOffset": ent.end_char})
return res
|
#!/usr/bin/env python3
import struct
from glob import glob
from functools import partial
from multiprocessing import cpu_count, Pool
import sys
import os
from os.path import dirname, basename, join as p_join
import scipy
import numpy as np
import librosa
def getDtype(inputformat):
if inputformat == 0:
return np.int16
else:
return np.float32
def loadDJSpectrogram(filePath, inputformat):
try:
with open(filePath, "rb") as f:
header = f.read(32)
numOfChannels, lowestFreq, highestFreq, numOfSpectrums = \
struct.unpack('iiii', header[:16])
data = np.fromfile(f, dtype=getDtype(inputformat))
except IOError as e:
print("Couldn't open file (%s.)" % e)
return 0, 0, 0, 0, []
spectrogram = data.reshape(highestFreq - lowestFreq + 1, numOfSpectrums)
return numOfChannels, lowestFreq, highestFreq, numOfSpectrums, spectrogram
def hz2mel(hz):
"""Convert a value in Hertz to Mels
:param hz: a value in Hz. This can also be a numpy array, conversion proceeds element-wise.
:returns: a value in Mels. If an array was passed in, an identical sized array is returned.
"""
return 2595 * np.log10(1+hz/700.)
def mel2hz(mel):
"""Convert a value in Mels to Hertz
:param mel: a value in Mels. This can also be a numpy array, conversion proceeds element-wise.
:returns: a value in Hertz. If an array was passed in, an identical sized array is returned.
"""
return 700*(10**(mel/2595.0)-1)
def get_filterbanks(nfilt=20,samplerate=16000,lowfreq=0,highfreq=None):
"""Compute a Mel-filterbank. The filters are stored in the rows, the columns correspond
to fft bins. The filters are returned as an array of size nfilt * (nfft/2 + 1)
:param nfilt: the number of filters in the filterbank, default 20.
:param nfft: the FFT size. Default is 512.
:param samplerate: the sample rate of the signal we are working with, in Hz. Affects mel spacing.
:param lowfreq: lowest band edge of mel filters, default 0 Hz
:param highfreq: highest band edge of mel filters, default samplerate/2
:returns: A numpy array of size nfilt * (nfft/2 + 1) containing filterbank. Each row holds 1 filter.
"""
highfreq = highfreq or samplerate/2
assert highfreq <= samplerate/2, "highfreq is greater than samplerate/2"
# compute points evenly spaced in mels
lowmel = hz2mel(lowfreq)
highmel = hz2mel(highfreq)
melpoints = np.linspace(lowmel,highmel,nfilt+2)
bin = mel2hz(melpoints).astype(int) - lowfreq
bin[0] = 0
bin[-1] = int(highfreq - lowfreq)
fbank = np.zeros([nfilt, (highfreq - lowfreq + 1)])
for i in range(0, nfilt):
for j in range(bin[i], bin[i + 1]):
fbank[i, j] = (j - bin[i]) / (bin[i + 1] - bin[i])
for j in range(bin[i + 1], bin[i + 2]):
fbank[i, j] = 1 - (j - bin[i + 1])/ (bin[i + 2] - bin[i + 1])
return fbank
def lifter(cepstra, L=22):
"""Apply a cepstral lifter the the matrix of cepstra. This has the effect of increasing the
magnitude of the high frequency DCT coeffs.
:param cepstra: the matrix of mel-cepstra, will be numframes * numcep in size.
:param L: the liftering coefficient to use. Default is 22. L <= 0 disables lifter.
"""
if L > 0:
nframes,ncoeff = np.shape(cepstra)
n = np.arange(ncoeff)
lift = 1 + (L/2.)*np.sin(np.pi*n/L)
return lift*cepstra
else:
# values of L <= 0, do nothing
return cepstra
def fbank(power_spectrogram, n_mels, sample_rate, fmin, fmax):
filterbanks = get_filterbanks(nfilt=n_mels, samplerate=sample_rate,
lowfreq=fmin, highfreq=fmax)
feat = np.dot(power_spectrogram, filterbanks.T) # compute the filterbank energies
feat = np.where(feat == 0,np.finfo(float).eps,feat) # if feat is zero, we get problems with log
energy = np.sum(power_spectrogram, 1)
# if energy is zero, we get problems with log
energy = np.where(energy == 0,np.finfo(float).eps,energy)
return feat, energy
def power_spectrogram(djs, lower_bound_freq, upper_bound_freq):
num_of_channels, lowest_freq, highest_freq, num_of_spectrums, \
spectrogram = loadDJSpectrogram(djs, 1)
assert lower_bound_freq >= lowest_freq
assert highest_freq >= upper_bound_freq
#Transpose to use python_speech_features library
return np.square(spectrogram[(lowest_freq - lower_bound_freq): \
(upper_bound_freq - highest_freq)]).T
def mfcc(power_spectrogram, n_mfcc=20, n_mels=128, dct_type=2, norm='ortho',
lowfreq=50, highfreq=8000, ceplifter=22, appendEnergy=True):
feat, energy = fbank(power_spectrogram, n_mels, 16000, lowfreq, highfreq)
feat = np.log(feat)
feat = scipy.fftpack.dct(feat, type=dct_type, axis=1, norm=norm)[:,:n_mfcc]
feat = lifter(feat,ceplifter)
if appendEnergy: feat[:,0] = np.log(energy) # replace first cepstral coefficient with log of frame energy
return feat
def save_mfcc(djs, output_dir='./'):
#mfcc(djs)
n_mfcc = 30
n_mels = 30
lowest_freq = 50
highest_freq = 7600
# mfcc from DJS
djspec_square = power_spectrogram(djs, lowest_freq, highest_freq)
mfcc_from_djs = mfcc(djspec_square, n_mfcc=n_mfcc, n_mels=n_mels,
lowfreq=lowest_freq, highfreq=highest_freq)
mfcc_djs_path = p_join(output_dir, basename(djs)[:-10] + '.npy')
os.makedirs(dirname(mfcc_djs_path), exist_ok=True)
np.save(mfcc_djs_path, mfcc_from_djs)
print(mfcc_djs_path)
if __name__ == '__main__':
djs_root_dir = sys.argv[1]
output_dir = sys.argv[2]
djses = glob(p_join(djs_root_dir, '**', '*.djs'), recursive=True)
print(djses)
this_save_djs = partial(save_mfcc, output_dir=output_dir)
num_of_cpus = cpu_count()
with Pool(num_of_cpus, maxtasksperchild=100) as p:
p.map(this_save_djs, djses, chunksize=5)
|
import get_db_connection
connection = get_db_connection.getConnection()
print("Connect successful!")
sql_select = "SELECT * FROM test"
sql_insert = "INSERT INTO test (text)" \
+ " VALUES (%s)"
try:
# объект для работы с БД
cursor = connection.cursor()
cursor.execute(sql_select)
print("cursor:description: ", cursor.description)
print()
for row in cursor:
print("---------")
print("Row: ", row)
print("ID: ", row["id"])
print("Text: ", row['text'])
# cursor.execute(sql_insert, 'ten')
#
# cursor.execute(sql_select)
#
# print("cursor:description: ", cursor.description)
# print()
#
# for row in cursor:
# print("---------")
# print("Row: ", row)
# print("ID: ", row["id"])
# print("Text: ", row['text'])
#
# connection.commit()
finally:
connection.close()
|
from datetime import datetime
from django.conf import settings
from django.urls import reverse
from telegram import Update
from bot.common import send_telegram_message, ADMIN_CHAT, remove_action_buttons
from notifications.email.users import send_welcome_drink, send_rejected_email
from notifications.telegram.posts import notify_post_author_approved, notify_post_author_rejected, announce_in_club_chats
from notifications.telegram.users import notify_user_profile_approved, notify_user_profile_rejected
from posts.models import Post
from search.models import SearchIndex
from users.models.user import User
def process_moderator_actions(update):
# find an action processor
action_name, entity_id = update.callback_query.data.split(":", 1)
action = ACTIONS.get(action_name)
moderator = User.objects.filter(telegram_id=update.effective_user.id).first()
if not moderator or not moderator.is_moderator:
send_telegram_message(
chat=ADMIN_CHAT,
text=f"⚠️ '{update.effective_user.full_name}' не модератор или не привязал бота к аккаунту"
)
return
if not action:
send_telegram_message(
chat=ADMIN_CHAT,
text=f"😱 Неизвестная команда '{update.callback_query.data}'"
)
return
# run run run
try:
result, is_final = action(entity_id, update)
except Exception as ex:
send_telegram_message(
chat=ADMIN_CHAT,
text=f"❌ Экшен наебнулся '{update.callback_query.data}': {ex}"
)
return
# send results back to the chat
send_telegram_message(
chat=ADMIN_CHAT,
text=result
)
# hide admin buttons (to not allow people do the same thing twice)
if is_final:
remove_action_buttons(
chat=ADMIN_CHAT,
message_id=update.effective_message.message_id,
)
return result
def approve_post(post_id: str, update: Update) -> (str, bool):
post = Post.objects.get(id=post_id)
if post.is_approved_by_moderator:
return f"Пост «{post.title}» уже одобрен", True
post.is_approved_by_moderator = True
post.last_activity_at = datetime.utcnow()
post.published_at = datetime.utcnow()
post.save()
notify_post_author_approved(post)
announce_in_club_chats(post)
announce_post_url = settings.APP_HOST + reverse("announce_post", kwargs={
"post_slug": post.slug,
})
return f"👍 Пост «{post.title}» одобрен ({update.effective_user.full_name}). " \
f"Можно запостить его на канал вот здесь: {announce_post_url}", True
def forgive_post(post_id: str, update: Update) -> (str, bool):
post = Post.objects.get(id=post_id)
post.is_approved_by_moderator = False
post.published_at = datetime.utcnow()
post.save()
return f"😕 Пост «{post.title}» не одобрен, но оставлен на сайте ({update.effective_user.full_name})", True
def unpublish_post(post_id: str, update: Update) -> (str, bool):
post = Post.objects.get(id=post_id)
if not post.is_visible:
return f"Пост «{post.title}» уже перенесен в черновики", True
post.is_visible = False
post.save()
SearchIndex.update_post_index(post)
notify_post_author_rejected(post)
return f"👎 Пост «{post.title}» перенесен в черновики ({update.effective_user.full_name})", True
def approve_user_profile(user_id: str, update: Update) -> (str, bool):
user = User.objects.get(id=user_id)
if user.moderation_status == User.MODERATION_STATUS_APPROVED:
return f"Пользователь «{user.full_name}» уже одобрен", True
if user.moderation_status == User.MODERATION_STATUS_REJECTED:
return f"Пользователь «{user.full_name}» уже отклонен", True
user.moderation_status = User.MODERATION_STATUS_APPROVED
user.save()
# make intro visible
Post.objects\
.filter(author=user, type=Post.TYPE_INTRO)\
.update(is_visible=True, published_at=datetime.utcnow(), is_approved_by_moderator=True)
SearchIndex.update_user_index(user)
notify_user_profile_approved(user)
send_welcome_drink(user)
return f"✅ Пользователь «{user.full_name}» одобрен ({update.effective_user.full_name})", True
def reject_user_profile(user_id: str, update: Update) -> (str, bool):
user = User.objects.get(id=user_id)
if user.moderation_status == User.MODERATION_STATUS_REJECTED:
return f"Пользователь «{user.full_name}» уже был отклонен и пошел все переделывать", True
user.moderation_status = User.MODERATION_STATUS_REJECTED
user.save()
notify_user_profile_rejected(user)
send_rejected_email(user)
return f"❌ Пользователь «{user.full_name}» отклонен ({update.effective_user.full_name})", True
ACTIONS = {
"approve_post": approve_post,
"forgive_post": forgive_post,
"delete_post": unpublish_post,
"approve_user": approve_user_profile,
"reject_user": reject_user_profile,
}
|
from elasticsearch import Elasticsearch
import os
import json
from wikidata.config_path import WIKIDATA5M_DOCTITLE_ENTITY_DIR
"""
Build elastic search index of the wikipedia titles corresponding to the titles in WIKIPEDIA_CHUNKS_FILE (psgs_100w.tsv) to the wikidata entities.
Some of these wikidata entities may not be in wikidata5m.
"""
es = Elasticsearch([{'host': 'localhost', 'port': 9200}])
#DATA_DIR = '/export/share/amrita/efficientQA/'
#WIKIDATA5M_DIR = DATA_DIR+'/wikidata/wikidata5m'
#WIKIDATA5M_DOCTITLE_ENTITY_DIR = WIKIDATA5M_DIR+'/wikidata5m_doctitle_entity'
def get_wiki_entity_id_from_name(s):
s=s.lower()
ent_index="wikidata5m_entities"
d = es.search(index=ent_index, body={"query":{"match":{"entity_synonyms":s}}})
ent_ids = []
for h in d['hits']['hits']:
h = h['_source']
if s in h['entity_synonyms']:
eid = h['entity_id']
ent_ids.append(eid)
return ent_ids
def build_elastic_search_wikititle_to_entity_id_index():
not_found = 0
not_found_in_wikiapi = 0
count = 0
for f in os.listdir(WIKIDATA5M_DOCTITLE_ENTITY_DIR):
f = WIKIDATA5M_DOCTITLE_ENTITY_DIR +'/'+ f
data = json.load(open(f))
for k,val in data.items():
title = val['title']
wiki_info = []
if 'wikidata_info' not in val:
continue
if 'query' not in val['wikidata_info']:
continue
found_mapping=False
for k,v in val['wikidata_info']['query']['pages'].items():
if 'title' not in v and 'wikibase_item' not in v and 'wikibase-shortdesc' not in v:
print ('cannot find ', title)
if 'title' in v:
wikibase_title = v['title']
else:
wikibase_title = 'NONE'
if 'pageprops' in v and 'wikibase_item' in v['pageprops']:
found_mapping=True
if 'wikibase-shortdesc' in v['pageprops']:
wikibase_shortdesc = v['pageprops']['wikibase-shortdesc']
else:
wikibase_shortdesc = 'NONE'
wikibase_item = v['pageprops']['wikibase_item']
d={'wikibase_title': wikibase_title, 'wikibase_shortdesc': wikibase_shortdesc, 'wikibase_item': wikibase_item}
wiki_info.append(d)
if not found_mapping:
wiki_entities = get_wiki_entity_id_from_name(title)
if len(wiki_entities)>0:
found_mapping = True
wiki_info=[{'wikibase_title':wikibase_title, 'wikibase_item':e} for e in wiki_entities]
else:
not_found +=1
not_found_in_wikiapi+=1
if found_mapping:
d={'title': title, 'wiki_info': wiki_info}
es.index(index='wikipedia_to_entity_id', doc_type='wiki_to_eid', id=count, body=d)
count+=1
if count%1000==0:
print ('added ', count , 'documents, not_found: ', not_found, ' not_found_in_api ', not_found_in_wikiapi)
if __name__=="__main__":
# USAGE: python -m wikidata.build_indices.build_elastic_search_wikititle_to_entity_id_index
build_elastic_search_wikititle_to_entity_id_index() |
import matplotlib as mpl
import numpy as np
import pandas as pd
import scipy.cluster.hierarchy as hac
from matplotlib import pyplot as plt
import vishelper as vh
mpl_update = {'font.size': 14, 'figure.figsize': [12.0, 8.0],
'axes.labelsize': 20, 'axes.labelcolor': '#677385',
'axes.titlesize': 20, 'lines.color': '#0055A7', 'lines.linewidth': 3,
'text.color': '#677385'}
mpl.rcParams.update(mpl_update)
"""
This file provides functions for visualizing principal components from PCA and
visualization of the features of clusters generated. For hierarchical
clustering, specifically, interactive_visualize() allows the visualization of
populations of the clusters that are formed at a number of cophenetic distances
(controlled by a slider) and the distribution of feature values of those
clusters.
The cophenetic distance is the "distance between the largest two clusters that
contain the two objects individually when they are merged into a single cluster
that contains both" (Wikipedia). By decreasing the cophenetic distance,
less points will be able to be merged together and more clusters will form.
Hierarchical clustering creates clusters up until the maximum cophenetic
distance and then that distance has to be decreased to break apart the cluster
until a chosen number of clusters is reached.
The metric used for the distance metric in the cophenetic distance is decided by
the linkage type and distance metric used.
"""
def reset_mpl_format():
"""Reapplies the matplotlib format update."""
mpl.rcParams.update(mpl_update)
def heatmap_pca(V, normalize=True, n_feats=None, n_comps=None, cmap=None,
feature_names=None, transpose=False, **kwargs):
"""
Creates a heatmap of the composition of the principal components given
by V. If normalize is left as default (True), the magnitude of the
components in V will be normalized to give a percent composition of each
feature in V.
:param V: list of list. PCA components. N components x M features.
:param normalize: optional boolean, default True, whether to normalize V
to relative weights.
:param n_feats: optional int - number of features to include in figure.
:param n_comps: optional int - number of components to include in figure.
:param feature_names: optional list of strings to include feature names in
axis labels. Will default to 'Feature 1','Feature 2',
etc if not specified.
Returns nothing, displays a figure.
"""
if n_feats is None:
n_feats = V.shape[1]
if n_comps is None:
n_comps = V.shape[0]
if normalize:
weights = [np.abs(v) / np.sum(np.abs(v)) for v in V]
else:
weights = V
if feature_names is None:
feature_names = ['Feature ' + str(j) for j in range(1, n_feats+1)]
component_names = ['Component ' + str(j) for j in range(1, n_comps+1)]
to_plot = pd.DataFrame(weights[:n_comps], index=component_names, columns=feature_names)
if transpose:
to_plot = to_plot.T
if cmap is None:
cmap = vh.cmaps["blues"]
fig, ax = vh.heatmap(to_plot, cmap=cmap,
xlabel="Principal Component" if transpose else "Feature",
ylabel="Feature" if transpose else "Principal Component",
title="Relative weight of features' contribution to principal components", **kwargs
)
return fig, ax, to_plot
def dendrogram(z, xlabel='Observations', thresh_factor=0.5, remove_ticks=False, **kwargs):
"""
Creates a dendrogram from . Colors clusters below thresh_
factor*max(cophenetic distance).
:param z: The hierarchical clustering encoded with the matrix returned by Scipy's linkage function.
:param xlabel: String for xlabel of figure
:param thresh_factor: Colors clusters according those formed by cutting the dendrogram at
thresh_factor*max(cophenetic distance)
Returns: R (see Scipy dendrogram docs).
Displays: Dendrogram.
"""
color_thresh = thresh_factor * max(z[:, 2])
reset_mpl_format()
if 'figsize' in kwargs:
figsize = kwargs.pop('figsize')
else:
figsize = (12, 12)
plt.figure(figsize=figsize)
R = hac.dendrogram(z, color_threshold=color_thresh, **kwargs)
plt.xlabel(xlabel)
plt.ylabel('Cophenetic distance')
if remove_ticks:
plt.xticks([])
return R
def create_labels(z, features_in, levels, criteria='distance', feature_names=None):
"""
Labels each observation according to what cluster number it would fall into under the specified
criteria and level(s).
:param z: The hierarchical clustering encoded with the matrix returned by Scipy's linkage function.
:param features_in: list of features for each sample or dataframe
:param levels: list of different levels to label samples according to. Will depend on criteria used.
If criteria = 'distance', clusters are formed so that observations in a given cluster
have a cophenetic distance no greater than the level(s) provided. If criteria =
'maxcluster', cuts the dendrogram so that no more than the level number of clusters
is formed. See
http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.fcluster.html
for more options.
:criteria: string referring to which criterion to use for creating clusters. "distance" and "maxclusters"
are two more commonly used. See param levels above. Other options can be found at
http://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.fcluster.html
:feature_names: list of labels corresponding to the features to create pandas dataframe for output if
dataframe is not provided.
Returns: features: Pandas dataframe with feature values for each observation as well as assigned cluster
number for each specified level. Cluster assignment columns are labeled by str(level).
"""
if isinstance(features_in, pd.DataFrame):
features = features_in.copy()
else:
features = pd.DataFrame(features_in, columns=feature_names)
for j in levels:
y = hac.fcluster(z, j, criterion=criteria)
features[str(j)] = y
return features
def separate(features, level, feature_name, minimum_population=10):
"""
Separates features into lists based on their cluster label and separates
out clusters less than the minimum population in size as outliers.
:param features: Pandas dataframe with rows for each observation, columns for each feature value and
cluster label for each level (see create_labels()).
:param level: Level at which you want to separate observations into groups.
:param feature_name: Desired feature for grouping.
:param minimum_population: Minimum population for which a labeled cluster can be considered a full
cluster. Any cluster which has a lower population will be considered
a group of outliers.
Returns: sep_features = list of list of feature values for each labeled group greater
than min_population in size.
outliers = feature values for any observations in cluster which has a size smaller than
minimum population.
"""
sep_features = []
outliers = []
for j in range(1, features[str(level)].max() + 1):
sep_feature = features[features[str(level)] == j][feature_name].tolist()
if len(sep_feature) > minimum_population:
sep_features.append(sep_feature)
else:
outliers += sep_feature
return sep_features, outliers
def visualize_clusters(features, level, feature_names, bins=20, xlim=None, ylim=None, log=False):
""" Plots a histogram of the number of samples assigned to each cluster at
a given cophentic distance and the distribution of the features for each
cluster. This assumes labels exist in the features dataframe in
column str(level). """
reset_mpl_format()
# sns.set_palette('deep')
plt.figure(figsize=(16, 16))
n_rows = int(np.ceil(float(len(feature_names)) / 2)) + 1
plt.subplot(n_rows, 1, 1)
plt.hist(features[str(level)].values, features[str(level)].max())
plt.xlabel('Cluster number')
plt.title('level = ' + str(level))
for j, feat in enumerate(feature_names):
plt.subplot(n_rows, 2, j + 3)
sep_features, outliers = separate(features, level, feat)
labels = ['Cluster ' + str(j) for j in range(1, len(sep_features) + 1)]
if len(outliers) > 1:
sep_features.append(outliers)
labels.append('Outliers')
plt.hist(sep_features, bins, stacked=True,
label=labels);
if log:
plt.xlabel('Log10 of ' + feat)
else:
plt.xlabel(feat)
if xlim is not None:
plt.xlim(xlim)
if ylim is not None:
plt.ylim(ylim)
plt.legend()
plt.tight_layout()
|
def top_words(textsample,lang='english'):
from sklearn.feature_extraction.text import CountVectorizer
if lang == 'english':
vectorizer = CountVectorizer(stop_words=lang,lowercase=True)
if lang == 'spanish':
from nltk.corpus import stopwords
spanish_stopwords = stopwords.words('spanish')
vectorizer = CountVectorizer(encoding=u'utf-8',stop_words=spanish_stopwords,lowercase=True)
words = textsample.split()
matrix = vectorizer.fit_transform(words)
freqs = [(word, matrix.getcol(idx).sum()) for word, idx in vectorizer.vocabulary_.items()]
# Display the most frequent words (those appearing more than 3 times)
freq_words = sorted (freqs, key = lambda x: -x[1])
for w,f in freq_words:
if f >= 3:
print w,f
return
|
import android
import time
import math
global dist
dist=0
droid=android.Android()
def main():
r = droid.dialogGetInput("ATENCAO","Indique o raio de seguranca (m):","50")
raio_seguro = float(r.result)
print raio_seguro
coord = getLocation()
lat1=coord[0]
lon1=coord[1]
#coordenada fatec
#lat1=-22.739584
#lon1=-47.350189
while 1:
coord = getLocation()
print "Longitude: %s - Latitude: %s" %(coord[1],coord[0])
lat2=coord[0]
lon2=coord[1]
#calculando distancia do centro ate a localizacao atual
R = 6371; #Unid.: km - Este e o raio da Terra
dLat = math.radians(lat2-lat1);
dLon = math.radians(lon2-lon1);
lat11 = math.radians(lat1);
lat22 = math.radians(lat2);
a = (math.sin(dLat/2) * math.sin(dLat/2)) + (math.sin(dLon/2) * math.sin(dLon/2) * math.cos(lat11) * math.cos(lat22));
c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a));
dist = R * c;
print "Distance: %s " %(dist)
if (dist*1000)>raio_seguro:
play = droid.mediaPlay('/sdcard/siren.wav')
def getLocation():
achou_coord = True
while achou_coord:
droid.startLocating()
time.sleep(4) # tempo entre as leituras
loc = droid.readLocation().result
if bool(loc)==True:
achou_coord=False
if bool(loc)==False:
print "loc vazio"
try:
lon1 = loc['gps']['longitude']
lat1 = loc['gps']['latitude']
except KeyError:
lat1 = loc['network']['latitude']
lon1 = loc['network']['longitude']
return lat1,lon1
if __name__=="__main__":
main() |
from sqlalchemy import Column, ForeignKey, Integer, String
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import scoped_session, sessionmaker, relationship
from sqlalchemy import create_engine
Base = declarative_base()
engine = create_engine('sqlite:///catalog.db')
# Limited session for changes to objects loaded in the session
# session.commit() will be committed
# Changes can be reverted with session.rollback()
db_session = scoped_session(sessionmaker(autocommit=False, autoflush=False,
bind=engine))
class User(Base):
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
email = Column(String(252), nullable=False)
picture = Column(String(250))
class Categories(Base):
__tablename__ = 'category'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
user_id = Column(Integer, ForeignKey('user.id'))
user = relationship(User)
items = relationship("Items", cascade='delete')
@property
def serialize(self):
"""Return object data in serialized format"""
return {
'name': self.name,
'id': self.id,
}
class Items(Base):
__tablename__ = 'items'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
description = Column(String(1000))
category_id = Column(Integer, ForeignKey('category.id'))
category = relationship(Categories)
user_id = Column(Integer, ForeignKey('user.id'))
user = relationship(User)
@property
def serialize(self):
"""Return object data in serializeable format"""
return {
'id': self.id,
'name': self.name,
'description': self.description,
}
Base.metadata.create_all(engine)
|
"""
Adapters for compatibility between persistables and executors. Adapters are the
pluggable wrappers for persistables that ensure interoperability between
steps in processing.
Adapters must not contain any logic central to the processing. They can only be
interfaces to align formats, signatures, etc for consistent behavior.
"""
__author__ = "Elisha Yadgaran"
|
from numpy import*
falt = array(eval(input("Faltas registradas: ")))
vet = zeros(6)
for i in range(0, size(falt)):
if falt[i] == 2:
vet[0] = vet[0] + 1
elif falt[i] == 3:
vet[1] = vet[1] + 1
elif falt[i] == 4:
vet[2] = vet[2] + 1
elif falt[i] == 5:
vet[3] = vet[3] + 1
elif falt[i] == 6:
vet[4] = vet[4] + 1
else:
vet[5] = vet[5] + 1
for i in range(0, size(vet)):
vet[i] = round(vet[i]/size(falt) * 100, 1)
print(vet)
|
import math
import logging
from cache import Cache
FETCH_INSTRUCTION = 0
READ_MEMORY = 2
WRITE_MEMORY = 3
MES = ('M', 'E', 'S')
class Processor(object):
INTERESTED = 1
NOT_INTERESTED = 0
NOT_STALLED = 0
STALLED = 1
NO_BUS = 0
BUS_READ = 1
BUS_READ_EXCLUSIVE = 2
def __init__(self, identifier, protocol="MESI", associativity=1, block_size=64, cache_size=4096):
self.cache = Cache(associativity=associativity, block_size=block_size, cache_size=cache_size)
self.cycles = 0
self.latency = 0
self.bus_transactions_count = [0, 0] # BUS_READ & BUS_READ_EXCLUSIVE respectively
self.identifier = identifier
self.protocol = protocol
self.log = logging.getLogger("p"+str(identifier))
self.stall_status = self.NOT_STALLED
def check_for_bus_transaction_needed(self, instruction):
instruction_type, address, count = instruction
instruction_type = int(instruction_type)
if self.protocol.upper() == "MESI":
if instruction_type == READ_MEMORY:
ret = self.cache.is_address_present(address, update_hits_misses_count=True)
if ret == self.cache.CACHE_MISS:
self.bus_transactions_count[0] += 1
return (self.BUS_READ, address)
else:
return (self.NO_BUS, address)
elif instruction_type == WRITE_MEMORY:
ret = self.cache.is_address_present(address, update_hits_misses_count=True)
if ret == self.cache.CACHE_MISS:
self.bus_transactions_count[1] += 1
return (self.BUS_READ_EXCLUSIVE, address)
elif ret == self.cache.CACHE_HIT_MODIFIED:
return (self.NO_BUS, address)
elif ret == self.cache.CACHE_HIT_EXCLUSIVE:
return (self.NO_BUS, address)
elif ret == self.cache.CACHE_HIT_SHARED:
self.bus_transactions_count[1] += 1
return (self.BUS_READ_EXCLUSIVE, address)
elif self.protocol.upper() == "DRAGON":
#TODO: DRAGON
pass
def execute(self, instruction, read_type="S"):
# when the execute function is called, it will check if there are
# any latencies. if self.latency != 0, self.latency will decrease by
# 1 cycle and instruction won't be executed. If self.latency == 0,
# the instruction will be executed.
self.cycles += 1
if self.latency > 0:
self.latency -= 1
if self.latency == 0:
self.stall_status = self.NOT_STALLED
else:
self.stall_status = self.STALLED
else:
instruction_type, address, count = instruction
instruction_type = int(instruction_type)
# no need since we are not executing fetch instructions
# if instruction_type == FETCH_INSTRUCTION:
# self.stall_status = self.NOT_STALLED
if instruction_type == READ_MEMORY:
ret = self.cache.is_address_present(address)
if ret == self.cache.CACHE_MISS:
self.cache.read(address, read_type)
self.latency = 10
self.stall_status = self.STALLED
else:
self.cache.read(address, read_type, update_lru_only=True)
self.stall_status = self.NOT_STALLED
elif instruction_type == WRITE_MEMORY:
ret = self.cache.is_address_present(address)
if ret == self.cache.CACHE_MISS:
self.cache.write(address)
self.latency = 10
self.stall_status = self.STALLED
else:
self.cache.write(address, update_lru_only=True)
self.stall_status = self.NOT_STALLED
def snoop(self, bus_transaction_type, address):
if self.protocol.upper() == "MESI":
index = -1
set_index = int((math.floor(address/self.cache.block_size)) % len(self.cache.sets))
set_to_search = self.cache.sets[set_index]
for i, block in enumerate(set_to_search):
if block is not None and address in block.words and block.state in MES:
index = i
break
if index > -1:
# if block is found in set and state is either M, E or S,
# we indicate interest
if bus_transaction_type == self.BUS_READ_EXCLUSIVE:
# by right if state is M, we need to flush to memory also.
# but the doc says theres a write buffer and assume there are
# no latencies for writing to memory, hence don't have to stall.
set_to_search[index].state == 'I'
elif bus_transaction_type == self.BUS_READ:
set_to_search[index].state == 'S'
self.log.debug("snoop result: interested in address "+str(address))
return self.INTERESTED
else:
# if block not in sets or state of block is I,
# just say we are not interested.
self.log.debug("snoop result: NOT interested in address "+str(address))
return self.NOT_INTERESTED
elif self.protocol.upper() == "DRAGON":
# TODO: DRAGON
pass
|
import pymel.core as pm
import maya.mel as mel
import tempfile
def ar_playblast(fileName, width, height, startTime=None, endTime=None):
"""
pcg_playblast using custom setting.
:param fileName: string (file path)
:param width: int
:param height: int
:param startTime: float
:param endTime: float
:return: pcg_playblast
"""
# cam = pm.ls('*:cameraHD', typ='transform')[0]
# noinspection PyTypeChecker
aPlayBackSlider = mel.eval('$tmpVar=$gPlayBackSlider')
soundFile = pm.windows.timeControl(aPlayBackSlider, q=True, s=True)
if startTime and endTime:
pm.playbackOptions(min=startTime)
pm.playbackOptions(ast=startTime)
pm.playbackOptions(max=endTime)
pm.playbackOptions(aet=endTime)
if soundFile:
playblast = pm.playblast(f=fileName, format='qt', s=soundFile[0], sqt=0, fo=True, cc=True, p=100,
compression="H.264",
quality=100, height=height, width=width)
else:
playblast = pm.playblast(f=fileName, format='qt', sqt=0, fo=True, cc=True, p=100,
compression="H.264",
quality=100, height=height, width=width)
return playblast
def ar_convertPerspCamToShotCam(cam, perspCam):
"""
convert perspective cam as shot cam.
cam transform node as constraint
cam shape attributes connect.
:param cam: string
:param perspCam: string
:return: perspCam
"""
cam = pm.PyNode(cam)
perspCam = pm.PyNode(perspCam)
camShape = cam.getShape()
prspShape = perspCam.getShape()
camShapeAttrs = pm.listAttr(camShape)
pm.parentConstraint(cam, perspCam)
for each in camShapeAttrs:
try:
pm.connectAttr(camShape + '.' + each, prspShape + '.' + each, f=True)
except UserWarning:
pass
return perspCam
def ar_getSoundFilePath():
"""
get sound file path and export it in your temp directory.
:param: rawFileName: string (fileNameForExportTxtAsSameNaming)
:return: soundFileTextPath
"""
audio = pm.ls(type='audio')[0]
path = audio.filename.get()
if path:
tempDir = tempfile.gettempdir()
soundFileTextPath = tempDir + '/raw_soundFilePathInText.txt'
with open(soundFileTextPath, 'w') as fi:
fi.write(path)
return soundFileTextPath
else:
return False
|
from src.race_manager import RaceManager
from src.race_inferer import RaceInferer
from src.race_inferer_wrapper import RaceInfererWrapper
from src.race import Race
from multiprocessing import cpu_count, Pool
import gmplot
import numpy as np
if __name__ == '__main__':
# Process & Inference flags
process_all_race = 0
infer_all = 0
# nb_thread = cpu_count()
# print(nb_thread, "CPUs available on this machine...")
###
race_manager = RaceManager()
number_of_races = 0
"""Processing all races take more than 25 minutes !"""
if process_all_race:
print("Process all races")
race_manager.read_all_races()
number_of_races = len(list(race_manager.races.keys()))
i = 0
print("Number of races to process :", number_of_races)
for race_name in race_manager.races:
i += 1
print(str(i) + "/" + str(number_of_races), ":", race_name)
race_manager.compare_all_vs_one(race_name)
race_manager.segment_density(race_name)
race_manager.race_vs_segments(race_name)
race_manager.save()
#####
"""Inferring the best segment of all race take more than ... a lot """
if infer_all:
print("Infer all races")
i = 0
race_inferer = RaceInferer()
for race_name in race_inferer.race_manager.races:
i += 1
print(str(i) + "/" + str(number_of_races), ":", race_name)
race_inferer.find_race_deniv(race_name)
race_inferer.draw_denivelation_segment(race_name)
race_inferer.find_race_density(race_name)
race_inferer.draw_density_segment(race_name)
race_inferer.find_race_length(race_name)
race_inferer.draw_length_segment(race_name)
race_inferer.save()
# ref_race = "2018-12-20-16-42-55"
ref_race = "2019-03-25-17-24-10"
race_inferer_wrapper = RaceInfererWrapper()
# To get statistics of a best segment:
# print(race_inferer.get_statistics_from_deniv_seg(ref_race))
# Check the "inference"
deniv, length, density = race_inferer_wrapper.get_best_segment(ref_race)
# print("Found", len(deniv), "races matching the best deniv segment of ref race :", ref_race)
# print("These matching reaces are :", [race_name for (race_name, seg) in deniv])
# print("Segment.type for best devi. seg. :", [seg.segment_type for (race_name, seg) in deniv])
# print("")
# print("Found", len(density), "races matching the best density segment of ref race :", ref_race)
# print("These matching reaces are :", [race_name for (race_name, seg) in density])
# print("Segment.type for best devi. seg. :", [seg.segment_type for (race_name, seg) in density])
# print("")
# print("Found", len(length), "races matching the best length segment of ref race :", ref_race)
# print("These matching reaces are :", [race_name for (race_name, seg) in length])
# print("Segment.type for best devi. seg. :", [seg.segment_type for (race_name, seg) in length])
# print("")
|
"""
Ragdoll initialisation.
"""
from .db import *
from .composite import *
from .nutrient import *
from .flyweight import *
from .dictionary import *
from .human import *
from .req import *
from .plots import *
__version__=0.1 |
# -*- coding: utf-8 -*-
"""
This example demonstrates the use of GLSurfacePlotItem.
"""
from pyqtgraph.Qt import QtCore, QtGui
import pyqtgraph as pg
import pyqtgraph.opengl as gl
import numpy as np
import pyqtgraph_visualizer as pqvis
class Solid:
def __init__(self, timeres=100, cols=90, rows=100):
self.timeRes = timeres
## Animated example
## compute surface vertex data
self.cols = cols
self.rows = rows
self.x = np.linspace(-8, 8, self.cols+1)
self.y = np.linspace(-8, 8, self.rows+1)
xx, yy = np.meshgrid(self.y, self.x)
self.z = np.sin(xx**2 + yy**2) *20 / (xx**2 + yy**2 + 0.1)# - abs(xx) -abs(yy)
# self.z = 8-abs(xx+yy)-abs(yy-xx)
self.pTime = []
for i in range(self.timeRes):
self.pTime.append(self.z * i/self.timeRes)
## create a surface plot, tell it to use the 'heightColor' shader
## since this does not require normal vectors to render (thus we
## can set computeNormals=False to save time when the mesh updates)
self.p = gl.GLSurfacePlotItem(x=self.x, y = self.y, shader='heightColor', computeNormals=False, smooth=False)
self.p.shader()['colorMap'] = np.array([0.2, 3, 0.5, 0.2, 1, 1, 0.2, 0, 2])
class Grapher:
def __init__(self, **kwargs):
if "datastream" in kwargs.keys():
self.datastream = kwargs.get("datastream")
elif "file" in kwargs.keys():
self.datastream = pqvis.Datastream(kwargs.get("file"), sections=4)
else:
raise InvalidKWargs('kwargs must contain "datastream" object or "file" string')
self.app = QtGui.QApplication([])
self.w = gl.GLViewWidget()
self.w.resize(2000,2000)
self.w.show()
self.w.setWindowTitle('GLSurfacePlot')
self.w.setCameraPosition()
# Add a grid to the view
self.g = gl.GLGridItem()
self.g.scale(2,2,1)
self.g.setDepthValue(10) # draw grid after surfaces since they may be translucent
self.w.addItem(self.g)
self.solid = Solid()
self.p = self.solid.p
self.p.translate(0, -20, 0)
self.w.addItem(self.p)
self.solid2 = Solid()
self.p2 = self.solid2.p
self.p2.translate(0,20,0)
self.w.addItem(self.p2)
self.solid3 = Solid()
self.p3 = self.solid3.p
self.p3.translate(20, 0, 0)
self.w.addItem(self.p3)
self.solid4 = Solid()
self.p4 = self.solid4.p
self.p4.translate(-20,0,0)
self.w.addItem(self.p4)
# n = 51
# y = np.linspace(-10,10,n)
# x = np.linspace(-10,10,100)
# for i in range(n):
# yi = np.array([y[i]]*100)
# d = (x**2 + yi**2)**0.5
# z = 10 * np.cos(d) / (d+1)
# pts = np.vstack([x,yi,z]).transpose()
# plt = gl.GLLinePlotItem(pos=pts, color=pg.glColor((i,n*1.3)), width=(i+1)/10., antialias=True)
# self.w.addItem(plt)
# self.index = 0
self.timer = QtCore.QTimer()
self.timer.timeout.connect(self.update)
self.timer.start(0.00001)
def update(self):
try:
# Must call nextChunk/multiChunk to update chunks
self.datastream.nextChunk()
self.datastream.multiChunk()
areas = self.datastream.getAreas()
# Max(bass) range is ~2.5e6? so now we map the range
index = [int(min(a * self.solid.timeRes / 5000000, self.solid.timeRes-1)) for a in areas]
self.p.setData(z=self.solid.pTime[index[0]])
self.p.rotate(1/4, 0, 10, 0, local=True)
self.p.rotate(3/4, 0, 0, 10, local=False)
self.p2.setData(z=self.solid2.pTime[index[1]])
self.p2.rotate(1/4, 0, 10, 0, local=True)
self.p2.rotate(3/4, 0, 0, 10, local=False)
self.p3.setData(z=self.solid2.pTime[index[2]])
self.p3.rotate(1/4, 0, 10, 0, local=True)
self.p3.rotate(3/4, 0, 0, 10, local=False)
self.p4.setData(z=self.solid2.pTime[index[3]])
self.p4.rotate(1/4, 0, 10, 0, local=True)
self.p4.rotate(3/4, 0, 0, 10, local=False)
except BufferError:
self.datastream.close()
sys.exit("end of WAV")
## Start Qt event loop unless running in interactive mode.
if __name__ == '__main__':
import sys
grapher = Grapher(file="03 Apocalypse Dreams.wav")
if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'):
grapher.app.instance().exec_()
|
from django.db import models
class Group(models.Model):
group_text= models.CharField(max_length=150)
created = models.DateTimeField(auto_now_add=True)
updated = models.DateTimeField(auto_now=True)
def __str__(self):
return self.group_text
class Question(models.Model):
group = models.ForeignKey(Group)
question_text = models.CharField(max_length=200)
def __str__(self):
return self.question_text
class Choice(models.Model):
question = models.ForeignKey(Question)
choice_text = models.CharField(max_length=200)
corrected = models.BooleanField(default=False)
def __str__(self):
return self.choice_text |
#!/usr/bin/env python
# Copyright (c) 2015, Intel Corporation
#
# Redistribution and use in source and binary forms, with or without modification,
# are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of Intel Corporation nor the names of its contributors
# may be used to endorse or promote products derived from this software
# without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
# ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
# ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""@package Data
Methods for IoT Analytics component type catalog
"""
from globals import *
from utils import check, get_auth_headers
import globals
import requests
import json
class ComponentCatalog(object):
account = None
def __init__(self, account=None):
self.account = account
self.client = account.client
def add_comp_type(self, component_info=None):
if component_info:
url = "{0}/accounts/{1}/cmpcatalog".format(self.client.base_url,
self.account.id)
data = json.dumps(component_info)
resp = requests.post(url, data=data, headers=get_auth_headers(
self.client.user_token), proxies=self.client.proxies, verify=globals.g_verify)
check(resp, 201)
js = resp.json()
return js
else:
raise ValueError("No component info given.")
def get_comp_types(self, full=False):
url = "{0}/accounts/{1}/cmpcatalog".format(self.client.base_url,
self.account.id)
if full == True:
url += "?full=true"
resp = requests.get(url, headers=get_auth_headers(
self.client.user_token), proxies=self.client.proxies, verify=globals.g_verify)
check(resp, 200)
js = resp.json()
return js
def get_comp_type(self, component_id):
if component_id:
url = "{0}/accounts/{1}/cmpcatalog/{2}".format(self.client.base_url,
self.account.id,
component_id)
resp = requests.get(url, headers=get_auth_headers(
self.client.user_token), proxies=self.client.proxies, verify=globals.g_verify)
#check(resp, 200)
if resp.status_code == 404:
return None
js = resp.json()
return js
else:
raise ValueError("No component ID given.")
def update_comp_type(self, component_id=None, component_info=None):
if component_id:
if component_info:
url = "{0}/accounts/{1}/cmpcatalog/{2}".format(self.client.base_url,
self.account.id,
component_id)
data = json.dumps(component_info)
resp = requests.put(url, data=data, headers=get_auth_headers(
self.client.user_token), proxies=self.client.proxies, verify=globals.g_verify)
check(resp, 201)
js = resp.json()
return js
else:
raise ValueError("No component info given.")
else:
raise ValueError("No component_id given.")
|
from django.conf.urls import patterns, include, url
urlpatterns = patterns('accounts.views',
url(r'^(?P<user_id>\d+)/queues/', include('queues.urls')),
url(r'^(?P<user_id>\d+)/music/', include('music.urls')),
url(r'^register/$', 'register'),
url(r'^(?P<user_id>\d+)/$', 'view')
)
|
import socket
import sys
port = 10000
size = 1024
s = None
try:
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
host = socket.gethostname()
s.connect(('127.0.0.1', port))
except socket.error as message:
if s:
s.close()
print ("Could not open socket: " + message)
sys.exit(1)
while True:
data = input('> ')
s.sendall(data.encode())
data = s.recv(size)
print("Server sent: %s " % data.decode())
s.close()
|
n = int(input())
list1 = list(map(int, input().split()))
a = 0
b = 0
for i in list1:
if i % 5 == 0:
a += i
else:
b += i
print(a)
|
# -*- coding: utf8 -*-
"""`veetou.model.entities_`
Provides the Set class
"""
from . import functions_
import collections.abc
import abc
__all__ = ('Set',)
class Set(collections.abc.MutableSet):
"""A set of unique items stored as references (but key-compared by value).
This set also counts how many times given value was added."""
__slots__ = ('_data', '_counts')
def __init__(self, items = ()):
self._data = dict()
self._counts = dict()
for item in items: self.add(item)
def __contains__(self, item):
return item in self._data
def __iter__(self):
return iter(self._data)
def __len__(self):
return len(self._data)
def add(self, item):
try:
item = self._data[item]
except KeyError:
self._data[item] = item
self._counts[item] = 1
else:
self._counts[item] += 1
return item
def inc(self, item):
try:
n = self._counts[item] + 1
except KeyError:
n = 1
self._data[item] = item
self._counts[item] = n
return n
def dec(self, item):
self._counts[item] -= 1
n = self._counts[item]
if n < 1:
del self._data[item]
return n
def discard(self, item):
if item in self:
del self._data[item]
del self._counts[item]
def __getitem__(self, item):
return self._data[item]
def get(self, item, default = None):
return self[item] if item in self else default
def count(self, item):
return self._counts.get(item, 0)
def clear(self):
self._data.clear()
self._counts.clear()
def __repr__(self):
return "%s([%s])" % (functions_.modelname(self), ','.join(repr(x) for x in self))
# Local Variables:
# # tab-width:4
# # indent-tabs-mode:nil
# # End:
# vim: set syntax=python expandtab tabstop=4 shiftwidth=4:
|
# Copyright (c) 2021 Qualcomm Technologies, Inc.
# All Rights Reserved.
from quantization.adaround.adaround import apply_adaround_to_layer
from quantization.adaround.utils import (
AdaRoundInitMode,
AdaRoundMode,
AdaRoundActQuantMode,
AdaRoundLossType,
AdaRoundTempDecayType,
)
|
# -*- coding: utf-8 -*-
#
# Blocksmurfer - Blockchain Explorer
#
# © 2020-2021 March - 1200 Web Development
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
from flask import abort, current_app, session
from bitcoinlib.encoding import *
from bitcoinlib.keys import Address
from bitcoinlib.services.services import Service, ServiceError
network_code_translation = {
'btc': 'bitcoin',
'ltc': 'litecoin',
'xlt': 'litecoin_testnet',
'tbtc': 'testnet',
}
class SmurferService(Service):
def __init__(self, network_code='btc', timeout=5, *args, **kwargs):
nw_enabled = network_code_translation.keys() if not current_app else current_app.config['NETWORKS_ENABLED']
# TODO: Enable other networks
# if not network_code and current_app:
# network_code = session.get('network_code', current_app.config['NETWORK_DEFAULT'])
# session['network_code'] = network_code
if network_code in network_code_translation and network_code in nw_enabled:
network = network_code_translation[network_code]
Service.__init__(self, network=network, timeout=timeout, *args, **kwargs)
else:
abort(422, "Error opening network with specified code")
def check_txid(txid=None, error_on_empty=False):
if not txid:
if error_on_empty:
return False
return True
try:
assert (len(txid) == 64)
assert (to_hexstring(txid) == txid)
except (AssertionError, EncodingError):
return False
return True
def check_address(address=None, error_on_empty=False):
if not address:
if error_on_empty:
return False
return True
try:
assert (Address.import_address(address).address == address)
except (AssertionError, EncodingError):
return False
return True
|
#!/usr/bin/python3
# -*- coding: utf-8 -*-
Allowed = [ 'a', 'b', 'c' ] #список допустимых припереборе элементов
def select( n ) :
'''
generator passwords выдает элементы по одному
'''
if n == 0 :
yield ''
else :
for prev in select(n-1) :
for s in Allowed :
yield prev + s #берем послед-ть в 4 и добавляем еще один элемент
for k, x in enumerate( select (5) ) : #вывод последовательности нумерованной |
from lxml import html
from colorama import Fore, Back, Style
from crawler_api.crawler import Crawler
from crawler_api.forum_item import ForumItem
from crawler_api.booter_url import BooterURL
class Crawler_Hackforums(Crawler):
'Crawler for web services of www.hackforums.net'
def __init__(self, sleep_level=1):
domain = 'http://www.hackforums.net/'
Crawler.__init__(self, domain, sleep_level)
self.PrintNote('CRAWLING HACKFORUMS')
self.PrintDivider()
self.Initialize()
# Login to hackforums.net
# NOTE: somtimes their login procedures block automated attempts; in that case a semi-brute
# force attempt is executed in the Crawler superclass.
def Login(self):
username = 'your_username_here'
password = 'your_password_here'
url = self.Target + 'member.php'
post_data = {
'username': username,
'password': password,
'action': 'do_login',
'url': 'http://www.hackforums.net/index.php',
'my_post_key': '60aae6001602ef2e0bd45033d53f53dd'
}
return super(Crawler_Hackforums, self).Login(
url,
self.Target + 'index.php',
post_data
)
# overrides Crawler's crawl function
def Crawl(self, max_date):
# crawl of hackforums.net is executed in three steps:
# 1. we retrieve all interesting forum posts
# 2. we extract potential Booter URLs from these posts
# 3. collect all related evidence and calculate scores
# for later evaluation
target_url = self.Target + 'forumdisplay.php?fid=232&page='
### step 1. retrieve all relevant forum posts
forum_items = []
current_page = 1
max_pages = 1
max_date_reached = False
self.PrintUpdate('initiating crawling procedures: HackForums')
self.PrintDivider()
# crawl first forum page and parse into XML tree
response = self.Session.post(target_url + str(current_page), headers=self.Header)
tree = html.fromstring(response.text)
# analyze structure and get relevant properties (via XPATH)
self.PrintUpdate('analyzing structure and retrieving Booter candidates')
self.PrintDivider()
max_pages = int(tree.xpath('//a[@class="pagination_last"]/text()')[0])
# max_pages = 1 # for debug
# now start crawling
while current_page <= max_pages and not max_date_reached:
self.PrintUpdate('crawling page ' + str(current_page) + '/' + str(max_pages))
self.PrintDivider()
# get forum items
forum_titles = tree.xpath('//td[contains(@class,"forumdisplay_")]/div/span[1]//a[contains(@class, " subject_")]/text()')
forum_urls = tree.xpath('//td[contains(@class,"forumdisplay_")]/div/span[1]//a[contains(@class, " subject_")]/@href')
forum_dates = tree.xpath('//td[contains(@class,"forumdisplay_")]/span/text()[1]')
# get data of each forum item
for i in range(len(forum_titles)):
item = ForumItem(forum_titles[i], self.Target + forum_urls[i], forum_dates[i])
if item.IsPotentialBooter():
forum_items.append(item)
print(item)
# check if max date is reached
if item.Date < max_date:
max_date_reached = True
self.PrintDivider()
self.PrintUpdate('date limit reached; aborting...')
self.PrintDivider()
break
# print a divider after each forum page
self.PrintDivider()
# get url of next page and re-iterate
current_page = current_page + 1
next_url = target_url + str(current_page)
response = self.Session.post(next_url, headers=self.Header)
tree = html.fromstring(response.text)
if current_page <= max_pages:
self.Sleep()
# forum crawling is complete, print (sub)results
self.PrintUpdate('items found: ' + str(len(forum_items)))
self.PrintDivider()
### step 2. extract potential Booters from target forum posts
self.PrintUpdate('attempting to obtain Booter URLs')
self.PrintDivider()
# start crawilng for each forum item
counter = 0
for item in forum_items:
# parse html
response = self.Session.post(item.URL, headers=self.Header)
tree = html.fromstring(response.text)
url = ''
# check for URLs inside image tags
tree_image = tree.xpath('(//tbody)[1]//div[contains(@class,"post_body")]//a[.//img and not(contains(@href, "hackforums.net")) and not(contains(@href, ".jpg") or contains(@href, ".png") or contains(@href, ".jpeg") or contains(@href, "gif"))]/@href')
if tree_image:
url = tree_image[0]
else:
# otherwise check for URL in the post's content
tree_links = tree.xpath('(//tbody)[1]//div[contains(@class,"post_body")]//a[not(@onclick) and not(contains(@href, "hackforums.net")) and not(contains(@href, ".jpg") or contains(@href, ".png") or contains(@href, ".jpeg") or contains(@href, ".gif"))]/@href')
if tree_links:
url = tree_links[0]
# add found url to list
if url != '':
self.AddToList(BooterURL(url), item.URL)
# print a divider line every 10 results (to keep things organized)
counter = counter + 1
if counter % 10 == 0:
self.PrintDivider()
# finished, print results
self.PrintDivider()
self.PrintUpdate('DONE; Resolved: ' + str(len(self.URLs)) + ' Booter URLs')
self.PrintDivider()
|
import numpy as np
import cv2
import os
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.layers import Dropout,Flatten
from keras.layers.convolutional import Conv2D,MaxPooling2D
import pickle
import random
import pandas as pd
path = 'Train'
labelFile = 'signnames.csv'
testRatio = 0.2
valRatio = 0.2
imageDimensions= (32,32,3)
batchSizeVal= 50
epochsVal = 20
stepsPerEpochVal = 2000
#IMPORTING DATA/IMAGES FROM FOLDERS
count = 0
images = [] # LIST CONTAINING ALL THE IMAGES
classNo = [] # LIST CONTAINING ALL THE CORRESPONDING CLASS ID OF IMAGES
myList = os.listdir(path)
print("Total Classes Detected:",len(myList))
noOfClasses = len(myList)
print("Importing Classes .......")
for x in range (0,noOfClasses):
myPicList = os.listdir(path+"/"+str(x))
for y in myPicList:
curImg = cv2.imread(path+"/"+str(x)+"/"+y)
curImg = cv2.resize(curImg,(32,32))
images.append(curImg)
classNo.append(x)
print(x,end= " ")
print(" ")
print("Total Images in Images List = ",len(images))
print("Total IDS in classNo List= ",len(classNo))
# CONVERT TO NUMPY ARRAY
images = np.array(images)
classNo = np.array(classNo)
print(images.shape)
# SPLITTING THE DATA
X_train,X_test,y_train,y_test = train_test_split(images,classNo,test_size=testRatio)
X_train,X_validation,y_train,y_validation = train_test_split(X_train,y_train,test_size=valRatio)
print(X_train.shape)
print(X_test.shape)
print(X_validation.shape)
print("Data Shapes")
print("Train",end = "");print(X_train.shape,y_train.shape)
print("Validation",end = "");print(X_validation.shape,y_validation.shape)
print("Test",end = "");print(X_test.shape,y_test.shape)
assert(X_train.shape[0]==y_train.shape[0]), "The number of images in not equal to the number of lables in training set"
assert(X_validation.shape[0]==y_validation.shape[0]), "The number of images in not equal to the number of lables in validation set"
assert(X_test.shape[0]==y_test.shape[0]), "The number of images in not equal to the number of lables in test set"
assert(X_train.shape[1:]==(imageDimensions))," The dimesions of the Training images are wrong "
assert(X_validation.shape[1:]==(imageDimensions))," The dimesionas of the Validation images are wrong "
assert(X_test.shape[1:]==(imageDimensions))," The dimesionas of the Test images are wrong"
#READING THE CSV FILE FOR ADDING THE LABEL
data=pd.read_csv(labelFile)
print("data shape ",data.shape,type(data))
# PLOT BAR CHART FOR DISTRIBUTION OF IMAGES
numOfSamples= []
cols=5
num_classes = noOfClasses
fig, axs = plt.subplots(nrows=num_classes, ncols=cols, figsize=(5, 300))
fig.tight_layout()
for x in range(0,noOfClasses):
#print(len(np.where(y_train==x)[0]))
numOfSamples.append(len(np.where(y_train==x)[0]))
print(numOfSamples)
print(numOfSamples)
plt.figure(figsize=(12, 4))
plt.bar(range(0, num_classes), numOfSamples)
plt.title("Distribution of the training dataset")
plt.xlabel("Class number")
plt.ylabel("Number of images")
plt.show()
# PREPOSSESSING FUNCTION FOR IMAGES FOR TRAINING
def preProcessing(img):
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = cv2.equalizeHist(img)
img = img/255
return img
# img = preProcessing(X_train[30])
# img = cv2.resize(img,(300,300))
# cv2.imshow("PreProcesssed",img)
# cv2.waitKey(0)
X_train= np.array(list(map(preProcessing,X_train)))
X_test= np.array(list(map(preProcessing,X_test)))
X_validation= np.array(list(map(preProcessing,X_validation)))
cv2.imshow("GrayScale Images",X_train[random.randint(0,len(X_train)-1)])
# RESHAPE IMAGES
X_train = X_train.reshape(X_train.shape[0],X_train.shape[1],X_train.shape[2],1)
X_test = X_test.reshape(X_test.shape[0],X_test.shape[1],X_test.shape[2],1)
X_validation = X_validation.reshape(X_validation.shape[0],X_validation.shape[1],X_validation.shape[2],1)
# IMAGE AUGMENTATION
dataGen = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.2,
shear_range=0.1,
rotation_range=10)
dataGen.fit(X_train)
batches= dataGen.flow(X_train,y_train,batch_size=20) # REQUESTING DATA GENRATOR TO GENERATE IMAGES BATCH SIZE = NO. OF IMAGES CREAED EACH TIME ITS CALLED
X_batch,y_batch = next(batches)
fig,axs=plt.subplots(1,15,figsize=(20,5))
fig.tight_layout()
for i in range(15):
axs[i].imshow(X_batch[i].reshape(imageDimensions[0],imageDimensions[1]))
axs[i].axis('off')
plt.show()
# ONE HOT ENCODING OF MATRICES
y_train = to_categorical(y_train,noOfClasses)
y_test = to_categorical(y_test,noOfClasses)
y_validation = to_categorical(y_validation,noOfClasses)
# CREATING THE MODEL
def myModel():
noOfFilters = 60
sizeOfFilter1 = (5,5)
sizeOfFilter2 = (3, 3)
sizeOfPool = (2,2)
noOfNodes= 500
model = Sequential()
model.add((Conv2D(noOfFilters,sizeOfFilter1,input_shape=(imageDimensions[0],
imageDimensions[1],1),activation='relu')))
model.add((Conv2D(noOfFilters, sizeOfFilter1, activation='relu')))
model.add(MaxPooling2D(pool_size=sizeOfPool))
model.add((Conv2D(noOfFilters//2, sizeOfFilter2, activation='relu')))
model.add((Conv2D(noOfFilters//2, sizeOfFilter2, activation='relu')))
model.add(MaxPooling2D(pool_size=sizeOfPool))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(noOfNodes,activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(noOfClasses, activation='softmax'))
model.compile(Adam(lr=0.001),loss='categorical_crossentropy',metrics=['accuracy'])
return model
model = myModel()
print(model.summary())
history = model.fit_generator(dataGen.flow(X_train,y_train,
batch_size=batchSizeVal),
steps_per_epoch=stepsPerEpochVal,
epochs=epochsVal,
validation_data=(X_validation,y_validation),
shuffle=1)
# PLOT THE RESULTS
plt.figure(1)
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['training','validation'])
plt.title('Loss')
plt.xlabel('epoch')
plt.figure(2)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['training','validation'])
plt.title('Accuracy')
plt.xlabel('epoch')
plt.show()
# EVALUATE USING TEST IMAGES
score = model.evaluate(X_test,y_test,verbose=0)
print('Test Score = ',score[0])
print('Test Accuracy =', score[1])
# SAVE THE TRAINED MODEL
pickle_out= open("model_trained43.p", "wb") #MODEL_TRAINED43 WILL BE UNPICKLED IN TRAIN.PY SO THAT WE CAN USE THE TRAINED MODEL FOR PREDICTING :)
pickle.dump(model,pickle_out)
pickle_out.close()
#MAKE SURE YOU CLOSE THE PICKLE FILE ILLANA SO SAVING!
|
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 04 19:11:07 2017
@author: Alexander
"""
import random
import math
import matplotlib.pyplot
from matplotlib.pyplot import *
def f(x):
# A function to generate example y values
r = random.random()
y = x-10*r-x**r
return y
random.seed(352) # Ensures that we all generate the same random numbers
x_values = range(1,101) # Numbers from 1 to 100
y_values = [f(x) for x in x_values] # Get y value for each x value
n = len(x_values) # the number of observations
#my code
#define variables for best fit line and correlation coefficient
sigXY = sum([x*y for x,y in zip(x_values, y_values)])
sigX = sum(x_values)
sigY = sum(y_values)
sigX2 = sum([x*x for x in x_values])
sigY2 = sum([y*y for y in y_values])
#define slope
slopeNumer = n * sigXY - sigX*sigY
slopeDenom = n * sigX2 - sigX **2.0
#define best fit line
m = slopeNumer/slopeDenom
b = (sigY - m*sigX)/n
linReg_yValues = [m*x + b for x in x_values]
rNumer = (n * sigXY) - (sigX * sigY)
rDenom = (math.sqrt(n * sigX2 - sigX**2)) * (math.sqrt(n* sigY2 - sigY**2))
r = rNumer/rDenom
figure()
plot(x_values, y_values, 'b')
plot(x_values, linReg_yValues, 'r')
legend(('Random Seed','Line of Best Fit'),loc=4)
title('Python Discussion 2')
show()
print "Line of Best Fit equals %f*x + %f" %(m,b)
print "r = %f" %r
|
from sqlalchemy import Column, ForeignKey, Integer, String, Date, DateTime
from sqlalchemy import Float, Text, Boolean, Table
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship, sessionmaker
from sqlalchemy import create_engine
from passlib.apps import custom_app_context as pwd_context
from itsdangerous import (TimedJSONWebSignatureSerializer
as Serializer, BadSignature, SignatureExpired)
import random, string, datetime
# database config
from config import DB_USER, DB_PASSWORD, DB_END, DB_PORT, DB_DATABASE
Base = declarative_base()
secret_key = ''.join(random.choice(string.ascii_uppercase +
string.digits) for x in range(32))
''' About User'''
class User(Base):
# store user info
__tablename__ = 'user'
id = Column(Integer, primary_key=True)
picture = Column(String(250))
title = Column(String(50))
email = Column(String(250), index=True)
password_hash = Column(String(250), nullable=False)
note = relationship('Note', backref='user')
def hash_password(self, password):
self.password_hash = pwd_context.encrypt(password)
def verify_password(self, password):
return pwd_context.verify(password, self.password_hash)
def generate_auth_token(self, expiration=600):
s = Serializer(secret_key, expires_in=expiration)
return s.dumps({'id':self.id})
def is_active(self):
return True
def get_id(self):
return self.email
def is_authenticated(self):
return self.authenticated
@staticmethod
def verify_auth_token(token):
s = Serializer(secret_key)
try:
data=s.loads(token)
except SignatureExpired:
# Valid token but expired
return None
except BadSignature:
return None
user_id = data['id']
return user_id
@property
def serialize(self):
return{
'id':self.id,
'email':self.email,
'title':self.title
}
'''About Employee '''
class Department(Base):
# store all the department
__tablename__ = 'department'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
description = Column(Text)
employee = relationship('Employee', backref='department')
@property
def serialize(self):
return{
'id':self.id,
'name': self.name,
'description': self.description
}
class Employee(Base):
# manage and store employee info
__tablename__ = 'employee'
id = Column(Integer, primary_key=True)
picture = Column(String(250))
firstName = Column(String(250), nullable = False)
lastName = Column(String(250), nullable=False)
middleName = Column(String(250))
birthdate = Column(Date)
email = Column(String(250))
ssn = Column(String(9))
gender = Column(String(10))
homePhone = Column(String(15))
cellPhone = Column(String(15))
address = Column(String(250))
city = Column(String(50))
zipCode = Column(String(5))
State = Column(String(20))
hiringDate = Column(Date)
title = Column(String(250))
payRate = Column(Float)
status = Column(String(50), nullable=False, default='Active')
rating = Column(Integer)
department_id = Column(Integer, ForeignKey('department.id'))
education = relationship('Education', backref='employee')
note = relationship('Note', backref='employee')
training = relationship('Training', backref='employee')
documents = relationship('Documents', backref='employee')
emergency = relationship('Emergency', backref='employee')
boarding = relationship('Onboarding', backref='employee')
@property
def serialize(self):
return{
'id':self.id,
'picture':self.picture,
'firstName':self.firstName,
'lastName':self.lastName,
'middleName':self.middleName,
'birthdate':self.birthdate,
'email':self.email,
'ssn':self.ssn,
'gender':self.gender,
'homePhone':self.homePhone,
'cellPhone':self.cellPhone,
'address':self.address,
'city':self.city,
'zipCode':self.zipCode,
'State':self.State,
'hiringDate':self.hiringDate,
'title':self.title,
'payRate':self.payRate,
'status':self.status,
'rating':self.rating,
'departmentId':self.department_id
}
class Education(Base):
# inside education
__tablename__ = 'education'
id = Column(Integer, primary_key=True)
institution = Column(String(250), nullable=False)
major = Column(String(250))
start = Column(Date)
end = Column(Date)
employee_id = Column(Integer, ForeignKey('employee.id'))
@property
def serialize(self):
return{
'id':self.id,
'institution':self.institution,
'major':self.major,
'start':self.start,
'end':self.end,
'employeeId':self.employee_id
}
class Note(Base):
# inside note
__tablename__ = 'note'
id = Column(Integer, primary_key=True)
body = Column(Text, nullable=False)
user_id = Column(Integer, ForeignKey('user.id'))
employee_id = Column(Integer, ForeignKey('employee.id'))
@property
def serialize(self):
return{
'id':self.id,
'body':self.body,
'user_id':self.user_id,
'employeeId':self.employee_id
}
class Traininglist(Base):
# list of training
__tablename__ = 'traininglist'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
description = Column(Text)
training = relationship('Training', backref='traininglist')
@property
def serialize(self):
return{
'id':self.id,
'name':self.name,
'description':self.description
}
class Training(Base):
# inside training
__tablename__ = 'training'
id = Column(Integer, primary_key=True)
traininglist_id = Column(Integer, ForeignKey('traininglist.id'))
provided = Column(Date, default=datetime.datetime.utcnow)
due = Column(Date)
employee_id = Column(Integer, ForeignKey('employee.id'))
@property
def serialize(self):
return{
'id':self.id,
'provided':self.provided,
'due':self.due,
'trainingListID':self.traininglist_id,
'employeeId':self.employee_id
}
class Documents(Base):
# list of document for each employee
__tablename__ = 'documents'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
category = Column(String(250), nullable=False)
employee_id = Column(Integer, ForeignKey('employee.id'))
@property
def serialize(self):
return{
'id':self.id,
'name':self.name,
'category':self.category,
'employeeId':self.employee_id
}
class Emergency(Base):
# employee emergency contact
__tablename__ = 'emergency'
id = Column(Integer, primary_key=True)
firstName = Column(String(250), nullable=False)
lastName = Column(String(250), nullable=False)
homePhone = Column(String(15))
cellPhone = Column(String(15))
employee_id = Column(Integer, ForeignKey('employee.id'))
@property
def serialize(self):
return{
'id':self.id,
'firstName':self.firstName,
'lastName':self.lastName,
'homePhone':self.homePhone,
'cellPhone':self.cellPhone,
'employeeId':self.employee_id
}
class Onboardinglist(Base):
# contain list of onboarding task create by company
__tablename__ = 'onboardinglist'
id = Column(Integer, primary_key=True)
name = Column(String(250))
description = Column(Text)
onboarding = relationship('Onboarding', backref='onboardinglist')
@property
def serialize(self):
return{
'id':self.id,
'name':self.name,
'description':self.description
}
class Onboarding(Base):
# employee requirement needed
__tablename__ = 'onboarding'
id = Column(Integer, primary_key=True)
provided = Column(Date, nullable=False)
expired = Column(Date, nullable=True)
employee_id = Column(Integer, ForeignKey('employee.id'))
onboardinglist_id = Column(Integer, ForeignKey('onboardinglist.id'))
@property
def serialize(self):
return{
'id':self.id,
'provided':self.provided,
'expired':self.expired,
'onboardingListId':self.onboardinglist_id,
'employeeId':self.employee_id
}
linking_members = Table('linking', Base.metadata,
Column('patientId', Integer, ForeignKey('patient.id')),
Column('employeeId', Integer, ForeignKey('employee.id'))
)
'''About Patient '''
class Patient(Base):
# inside patient class
__tablename__ = 'patient'
id = Column(Integer, primary_key=True)
patientId = Column(String(15), nullable=False)
firstName = Column(String(250), nullable=False)
lastName = Column(String(250), nullable=False)
middleName = Column(String(250))
birthdate = Column(Date)
gender = Column(String(10))
homePhone = Column(String(15))
cellPhone = Column(String(15))
address = Column(String(250))
city = Column(String(50))
zipCode = Column(String(5))
State = Column(String(20))
status = Column(String(50), nullable=False)
care = relationship('Employee', secondary=linking_members, backref="Patient")
@property
def serialize(self):
return{
'id':self.id,
'patientId':self.provided,
'firstName':self.firstName,
'lastName':self.lastName,
'middleName':self.middleName,
'birthdate':self.birthdate,
'gender':self.gender,
'homePhone':self.homePhone,
'cellPhone':self.cellPhone,
'address':self.address,
'city':self.city,
'zipCode':self.zipCode,
'State':self.State,
'status':self.status
}
'''About Company '''
class Company(Base):
# detail about company
__tablename__ = 'company'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
@property
def serialize(self):
return{
'id':self.id,
'name':self.name
}
class CompanyLinks(Base):
# store usefull links used by the company
__tablename__ = 'companylinks'
id = Column(Integer, primary_key=True)
name = Column(String(250), nullable=False)
link = Column(Text, nullable=False)
@property
def serialize(self):
return{
'id':self.id,
'name':self.name,
'link':self.link
}
engine = create_engine('mysql+pymysql://'+DB_USER+':'+DB_PASSWORD+'@'+DB_END+':'+DB_PORT+'/'
+DB_DATABASE)
Base.metadata.create_all(engine)
|
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/6/12 12:25
# @Author : cui
# @File : runner.py
#定义一个test组件
from jiaoben.ATM.flow.test_liow import Flow
Flow().main_test()
|
from django.conf.urls import url,include
from django.contrib import admin
from appointmentscheduler.views.Options.Invoice import Invoice
from .Options import Options,Payments,CheckoutForm,terms
urlpatterns = [
# url(r'^admin/', admin.site.urls),
url(r'^CountryTemplate/', include('shoppingcart.options.countries.urls') ),
url(r'^backup/', include('shoppingcart.options.Backup.urls'), name="backup"),
url(r'^Company/$', Invoice.Company, name="Company"),
url(r'^SmsEmailSettings/', include('shoppingcart.options.SmsEmailSettings.urls'), name="SmsEmailSettings"),
url(r'^SmsEmailTemplates/', include('shoppingcart.options.SmsEmailTemplates.urls'), name="SmsEmailTemplates"),
url(r'^ShippingAndTax/', include('shoppingcart.options.ShippingAndTax.urls'), name="ShippingAndTax"),
url(r'^BookingOptions/$', Options.BookingOptions, name="BookingOptions"),
url(r'^PaymentOptions/$', Payments.PaymentOptions, name="PaymentOptions"),
url(r'^CheckoutFormOptions/$', CheckoutForm.CheckoutFormOptions, name="CheckoutFormOptions"),
url(r'^terms/$', terms.terms, name="terms"),
] |
import sys
sys.stdin = open("Line_test2_input.txt", "r")
def permute(arr):
global mat
ans = [arr[:]]
c = [0] * len(arr)
i = 0
while i < len(arr):
if c[i] < i:
if i % 2 == 0:
arr[0], arr[i] = arr[i], arr[0]
else:
arr[c[i]], arr[i] = arr[i], arr[c[i]]
ans.append(arr[:])
c[i] += 1
i = 0
else:
c[i] = 0
i += 1
data = list(map(int, input().split()))
N = int(input())
mat, answer, total, j = [], [], 0, 1
permute(data)
for i in mat[N]:
total += i * 10 ** (len(data) - j)
j += 1
print(total) |
"""
Logging facilities
These are thin wrappers on logging facilities; logs
are all directed either to stdout or to $GAME_DIR/server/logs.
"""
import sys
from traceback import format_exc
import os
import threading
import logging
from logging.handlers import TimedRotatingFileHandler
class Logger(object):
"""
The logger object.
"""
def __init__(self, log_name, log_level, log_file=None, log_to_console=False):
"""
Init the logger.
"""
self.logger = self.setup_log(log_name, log_level, log_file, log_to_console)
def setup_log(self, log_name, log_level, log_file=None, log_to_console=False):
"""
Create a logger.
"""
# Create the logging object.
logger = logging.getLogger(log_name)
# Set log level.
logger.setLevel(log_level)
logging.getLogger('apscheduler.executors.default').setLevel(log_level)
# Divide logs by date.
if log_file:
file_handler = TimedRotatingFileHandler(filename=log_file, when="MIDNIGHT", interval=1)
file_handler.suffix = "%Y-%m-%d.log"
# Set output format.
file_handler.setFormatter(logging.Formatter("[%(asctime)s] - %(message)s"))
logger.addHandler(file_handler)
if log_to_console:
console_handler = logging.StreamHandler(sys.stdout)
console_handler.setFormatter(logging.Formatter("[%(asctime)s] - %(message)s"))
logger.addHandler(console_handler)
return logger
def log_trace(self, errmsg=None):
"""
Log a traceback to the log. This should be called from within an
exception.
Args:
errmsg (str, optional): Adds an extra line with added info
at the end of the traceback in the log.
"""
trace_string = format_exc()
try:
if trace_string:
for line in trace_string.splitlines():
self.logger.error("[::] %s" % line)
if errmsg:
try:
errmsg = str(errmsg)
except Exception as e:
errmsg = str(e)
for line in errmsg.splitlines():
self.logger.error("[EE] %s" % line)
except Exception as e:
self.logger.error("[EE] %s" % errmsg)
def log_critical(self, msg):
"""
Prints/logs a critical message to the server log.
Args:
msg (str): The message to be logged.
"""
try:
msg = str(msg)
except Exception as e:
msg = str(e)
for line in msg.splitlines():
self.logger.critical("[CC] %s" % line)
def log_err(self, errmsg):
"""
Prints/logs an error message to the server log.
Args:
errmsg (str): The message to be logged.
"""
try:
errmsg = str(errmsg)
except Exception as e:
errmsg = str(e)
for line in errmsg.splitlines():
self.logger.error("[EE] %s" % line)
def log_warn(self, warnmsg):
"""
Prints/logs any warnings that aren't critical but should be noted.
Args:
warnmsg (str): The message to be logged.
"""
try:
warnmsg = str(warnmsg)
except Exception as e:
warnmsg = str(e)
for line in warnmsg.splitlines():
self.logger.warning("[WW] %s" % line)
def log_info(self, infomsg):
"""
Prints any generic informative info that should appear in the log.
infomsg: (string) The message to be logged.
"""
try:
infomsg = str(infomsg)
except Exception as e:
infomsg = str(e)
for line in infomsg.splitlines():
self.logger.info("[..] %s" % line)
def log_debug(self, msg):
"""
Prints any generic debugging info that should appear in the log.
infomsg: (string) The message to be logged.
"""
try:
msg = str(msg)
except Exception as e:
msg = str(e)
for line in msg.splitlines():
self.logger.debug("[DD] %s" % line)
def log_dep(self, depmsg):
"""
Prints a deprecation message.
Args:
depmsg (str): The deprecation message to log.
"""
try:
depmsg = str(depmsg)
except Exception as e:
depmsg = str(e)
for line in depmsg.splitlines():
self.logger.warning("[DP] %s" % line)
def log_sec(self, secmsg):
"""
Prints a security-related message.
Args:
secmsg (str): The security message to log.
"""
try:
secmsg = str(secmsg)
except Exception as e:
secmsg = str(e)
for line in secmsg.splitlines():
self.logger.info("[SS] %s" % line)
|
from unittest import TestCase
try:
from unittest.mock import MagicMock
except ImportError:
from mock import MagicMock
from artificial.utils import PriorityQueue
class PriorityQueueTest(TestCase):
def setUp(self):
self.sample_sequence = (('first', 1), ('second', 10),
('third', 5), ('forth', 1))
self.sample_expected_pop_sequence = ('first', 'forth',
'third', 'second')
def test_sanity(self):
p = PriorityQueue()
self.assertIsNotNone(p)
def test_add(self):
queue = PriorityQueue()
[queue.add(entry, p) for entry, p in self.sample_sequence]
self.assertEqual(len(queue), len(self.sample_sequence))
# Test inserting repetitions.
[queue.add(entry, p) for entry, p in self.sample_sequence]
self.assertEqual(len(queue), len(self.sample_sequence))
def test_pop(self):
queue = PriorityQueue()
[queue.add(e, p) for e, p in self.sample_sequence]
self.assertEqual(len(queue), len(self.sample_expected_pop_sequence))
for expected in self.sample_expected_pop_sequence:
actual = queue.pop()
self.assertEqual(actual, expected)
self.assertEqual(len(queue), 0)
with self.assertRaises(KeyError):
queue.pop()
[queue.add(e, p) for e, p in self.sample_sequence]
self.assertEqual(len(queue), len(self.sample_expected_pop_sequence))
queue.remove('first')
actual = queue.pop()
self.assertEqual(actual, 'forth')
def test_remove(self):
queue = PriorityQueue()
entries = ('first', 'forth', 'third', 'second')
[queue.add(entry) for entry in entries]
self.assertEqual(len(queue), 4)
queue.remove('first')
self.assertEqual(len(queue), 3)
self.assertNotIn('first', queue)
[self.assertIn(e, queue) for e in ('second', 'third', 'forth')]
queue.remove('third')
self.assertEqual(len(queue), 2)
self.assertNotIn('third', queue)
[self.assertIn(e, queue) for e in ('second', 'forth')]
def test___contains__(self):
queue = PriorityQueue()
expected = (('first', 1), ('second', 10), ('third', 5))
[queue.add(entry, priority) for entry, priority in expected]
self.assertTrue('first' in queue)
def test___getitem__(self):
queue = PriorityQueue()
[queue.add(e, p) for e, p in self.sample_sequence]
node = queue['third']
self.assertListEqual(node, [5, 2, 'third'])
def test___len__(self):
q = PriorityQueue()
[q.add(e, p) for e, p in self.sample_sequence]
self.assertEqual(len(q), len(self.sample_sequence))
def test__bool__(self):
q = PriorityQueue()
self.assertFalse(q)
q.add('one')
self.assertTrue(q)
|
# -*- coding:utf-8 -*-
from numpy.core import overrides
from models import User
import pymssql
import pyodbc
import engine_conf
from flask import Flask, render_template, flash, request, redirect, url_for
from flask_sqlalchemy import SQLAlchemy
from sqlalchemy import create_engine
from sqlalchemy.orm import scoped_session, sessionmaker
from sqlalchemy.ext.declarative import declarative_base
import urllib
from flask_wtf import FlaskForm
from wtforms import StringField, SubmitField,SelectField
from wtforms.validators import DataRequired
import pandas as pd
import config
from sqlalchemy import text
import os
import sys
import imp
imp.reload(sys)
app = Flask(__name__)
# 数据库配置: 数据库地址/关闭自动跟踪修改
app.config['SQLALCHEMY_DATABASE_URI'] = 'mssql+pyodbc://lancer:Lrd19970323@test1-server.database.windows.net/test1database?driver=ODBC+Driver+17+for+SQL+Server&charset=utf8'
# app.config['SQLALCHEMY_DATABASE_URI'] = config.DB_URI
app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False
app.secret_key = 'lancer'
#--------------------------
#-----------------
# 创建数据库对象
# db = SQLAlchemy(app,session_options=sessionmaker(autocommit=False,autoflush=False,bind=engine))
db = SQLAlchemy(app)
# engine = create_engine(config.DB_URI)
# db.session = scoped_session(sessionmaker(autocommit=False,
# autoflush=False,
# bind=engine))
print('----------------engine initialized..--------------------------------------------------')
print(os.getcwd())
df = pd.read_csv('./static/people.csv')
'''
1. 配置数据库
a. 导入SQLAlchemy扩展
b. 创建db对象, 并配置参数
c. 终端创建数据库
2. 添加书和作者模型
a. 模型继承db.Model
b. __tablename__:表名
c. db.Column:字段
d. db.relationship: 关系引用
3. 添加数据
4. 使用模板显示数据库查询的数据
a. 查询所有的作者信息, 让信息传递给模板
b. 模板中按照格式, 依次for循环作者和书籍即可 (作者获取书籍, 用的是关系引用)
5. 使用WTF显示表单
a. 自定义表单类
b. 模板中显示
c. secret_key / 编码 / csrf_token
6. 实现相关的增删逻辑
a. 增加数据
b. 删除书籍 url_for的使用 / for else的使用 / redirect的使用
c. 删除作者
'''
# 定义书和作者模型
# 作者模型
class peopleForm(FlaskForm):
Attribute = SelectField(label='Attribute',
validators=[DataRequired('please select an attribute')],
choices=['salary','grade'])
cpr = SelectField(label='comparator',
validators=[DataRequired('please select an attribute')],
choices=['>','<'])
attr_value = StringField(label='value',validators=[DataRequired('please enter a value')])
submit = SubmitField('search')
class UpForm(FlaskForm):
name = SelectField(label='Name',
validators=[DataRequired('please select an attribute')],
choices=df['Name'])
attr = SelectField(label='Attribute',
validators=[DataRequired('please select an attribute')],
choices=df.columns)
value = StringField(label='value',validators=[DataRequired('please enter a value')])
submit = SubmitField('Update')
class User(db.Model):
# 表名
__tablename__ = 'users'
# 字段
id = db.Column(db.Integer, primary_key=True)
name = db.Column(db.String(32), unique=True)
state = db.Column(db.String(32))
salary = db.Column(db.String(32))
grade = db.Column(db.String(32))
room = db.Column(db.String(32))
telnum = db.Column(db.String(32))
picture = db.Column(db.String(32))
keywords = db.Column(db.String(300))
def save(self):
db.session.add(self)
db.session.commit()
@app.route('/search', methods=['post'])
def search():
seach_form = peopleForm(request.form)
form_dict = request.form.to_dict()
attr = form_dict.get("Attribute")
cpr = form_dict.get("cpr")
value = form_dict.get("attr_value")
query = str(attr)+str(cpr)+str(value)
people = User.query.filter(text(query))
return render_template('result.html', people=people,df=df)
@app.route('/delete_person/<person_id>')
def delete_person(person_id):
# 1. 查询数据库, 是否有该ID的书, 如果有就删除, 没有提示错误
person = User.query.get(person_id)
# 2. 如果有就删除
if person:
try:
db.session.delete(person)
db.session.commit()
flash('Delete success')
except Exception as e:
print(e)
flash('Delete failed')
db.session.rollback()
# redirect: 重定向, 需要传入网络/路由地址
# url_for('index'): 需要传入视图函数名, 返回改视图函数对应的路由地址
return redirect(url_for('index'))
@app.route('/update',methods=['post'])
def update():
# 1. 查询数据库, 是否有该ID的书, 如果有就删除, 没有提示错误
from_dict = request.form.to_dict()
name = from_dict.get("name")
attr = from_dict.get("attr").lower()
value = from_dict.get("value")
# 3. 判断作者是否存在
person = User.query.filter_by(name=name).first()
print(type(person))
print(person)
if person:
try:
res = User.query.filter_by(name=name).update({attr:value})
db.session.commit()
flash('update success')
except Exception as e:
print(e)
flash('update failed')
db.session.rollback()
# redirect: 重定向, 需要传入网络/路由地址
# url_for('index'): 需要传入视图函数名, 返回改视图函数对应的路由地址
return redirect(url_for('index'))
@app.route('/', methods=['GET', 'POST'])
def index():
# 创建自定义的表单类
# author_form = AuthorForm()
'''
验证逻辑:
1. 调用WTF的函数实现验证
2. 验证通过获取数据
3. 判断作者是否存在
4. 如果作者存在, 判断书籍是否存在, 没有重复书籍就添加数据, 如果重复就提示错误
5. 如果作者不存在, 添加作者和书籍
6. 验证不通过就提示错误
'''
# 查询所有的作者信息, 让信息传递给模板
people = User.query.all()
form = peopleForm()
form2 = UpForm()
print(form.data)
return render_template('book.html', people=people,df=df,form=form,form2=form2)
if __name__ == '__main__':
# 删除表
db.drop_all()
# 创建表
db.create_all()
people_list = []
df.fillna(' ', inplace=True)
for idx in range(len(df)):
person_name = df.iloc[idx]['Name']
person_state = df.iloc[idx]['State']
person_salary = df.iloc[idx]['Salary']
person_grade = df.iloc[idx]['Grade']
person_room = df.iloc[idx]['Room']
person_telnum = df.iloc[idx]['Telnum']
person_picture = df.iloc[idx]['Picture']
person_keywords = df.iloc[idx]['Keywords']
person = User(name=person_name,salary=person_salary,state=person_state,
grade=person_grade,room=person_room,telnum=person_telnum,picture=person_picture,
keywords=person_keywords)
people_list.append(person)
db.session.add_all(people_list)
db.session.commit()
app.run(debug=True) |
#!/usr/bin/env python2
#-*-encoding:utf-8-*-
### <-----------------------------------------------------------------------------------> ###
# welcome to use this script
# This script is used to generate and execute CBA Netconf xml, and then check result #
# ./generate_NetconfXml_CBA.py snmpSet [-u|-d|] Moname attribute1 value1 ... ... #
# ./generate_NetconfXml_CBA.py operation Moname attribute1 value1 ... ... #
# author: eyuyany (Jenny Yu, CBC/XYD/D) #
# Version: v1 #
# Date:16th Apr, 2014 #
### <------------------------------------------------------------------------------------> ###
## remove attributes which contain DataStatus sting such as ss7IsupItuNodeConnectionDataStatus in date 17th Mar.
## determine the delete,update,create for option -d, -u ,none in SnmpSet command line in date 17th Mar
## Get attributes of Mo table from MGC mim file , 19th Mar
## usage is /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet Moname MoId create attrib1 value1 attrib2 value2
## analyze the codecs attributes of MoSipprofile and MoMediaGateways in date 25th Mar
## '''sipProfileCodecs g711mLaw+g711aLaw+g729a+g7231+gsmEfr+amr+dtmfToneRelay+g72632+g72624+comfortNoise+clearMode+t38fax'''
## abstract from SipProfile, create SipProfileGroup1 and SipProfileGroup2 in dependence of input attribute in date 25th Mar
### extra the netconf xml three parts : rpc_mf, mf_mo, mo_attrib
## get the ecim key from get mgc all configuration result, the ecim key is used for merge and delete operation. on 8th Apr.
### remove restricted attribute from input arguments on date 8th Apr.
### swith the type from integer to enum for some special attribute on date 8th Apr.
### change before process to adapt MoSipProfile on date 9th Apr.
### MoH248 cannot be created, just can be merged. swith operation type from create to merge for this kind of mo. on date 10th Apr
### create xml for Action such as activateBNumberAction,configSs7Action on date 15th Apr
## snmpSet -u MoRadiusAccountingServer acctRadiusServerName 'radius' acctRadiusServerAction eUnlocking, executed fail.
### using get mo instead of geting all
import xml.etree.ElementTree as ET
import re,os,getopt,sys
Element_Dict = {'rpc':{'message-id':"1",'xmlns':"urn:ietf:params:xml:ns:netconf:base:1.0"},
'edit-config':{},
'target':{},
'running':{},
'config':{'xmlns:xc':"urn:ietf:params:xml:ns:netconf:base:1.0"},
'ManagedElement':{'xmlns':"urn:com:ericsson:ecim:ComTop"},
'ManagedFunction':{'xmlns':"urn:com:ericsson:ecim:ManagedFunction"},
'action':{'xmlns':"urn:com:ericsson:ecim:1.0"},
'filter':{'type':"subtree"}
}
attribute_Restricted = {'MoRouteAnalysis':['routeAnalysisRoutingCase','routeAnalysisTimeCategoryForRouting'],
'MoRouteAnalysisRoute':['routeAnalysisRoutingCase','routeAnalysisTimeCategoryForRouting','routeAnalysisRoutePriority','routeAnalysisResourceGroupIdentity'],
'MoSipProfile':['sipProfileName'],
'MoBNumberPreAnalysis':['bNumberPreAnalysisOrigin','bNumberPreAnalysisNumberingPlan','bNumberPreAnalysisType','bNumberPreAnalysisAdditionalInfo'],
'MoBNumberAnalysis':['bNumberAnalysisOriginForAnalysis','bNumberAnalysisDialedNumber']
}
attribute_int2enum = {'sipProfileDiversionType':{'0':'eDiversionHeader', '1':'eHistoryInfoHeader'}}
JustOneInstanceMo = ['MoH248','MoAccountingConfiguration','MoSip','MoMgcf']
ActionMo = {'MoMgcf':['activateANumberAction', 'activateANumberPreAction', 'activateBNumberAction', 'activateBNumberPreAction',
'activateRouteAction', 'activateRouteAnalysisRouteAction', 'configSs7Action', 'restartSs7ConfigAction'],
'MoMgDevice':['forcedLockingAction','gracefulLockingAction','unlockingAction','blockTimeSlotsAction',
'unblockTimeSlotsAction','resetTimeSlotsAction','auditDeviceAction'],
'MoMediaGateway':['forcedLockingAction','gracefulLockingAction','unlockingAction']
}
xml_head = '''
<?xml version="1.0" encoding="UTF-8"?>
<hello xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<capabilities>
<capability>urn:ietf:params:netconf:base:1.0</capability>
</capabilities>
</hello>
]]>]]>
<?xml version="1.0" encoding="UTF-8"?>
<rpc message-id="19" xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
'''
xml_tail = '''
</rpc>
]]>]]>
<?xml version="1.0" encoding="UTF-8"?>
<rpc message-id="19" xmlns="urn:ietf:params:xml:ns:netconf:base:1.0">
<close-session/>
</rpc>
]]>]]>
'''
xml_getall = '''
<get-config>
<source>
<running/>
</source>
<filter type="subtree">
<ManagedElement xmlns="urn:com:ericsson:ecim:ComTop">
<managedElementId>1</managedElementId>
<ManagedFunction xmlns="urn:com:ericsson:ecim:ManagedFunction">
<managedFunctionId>1</managedFunctionId>
</ManagedFunction>
</ManagedElement>
</filter>
</get-config>
'''
def read_file(filename):
f = open(filename,"r")
content = f.read()
f.close()
return content
def execCmd(cmd):
r = os.popen(cmd)
text = r.read()
r.close()
return text
def search_elem(elem,list):
if elem in list:
return list.index(elem)
else:
return -1
def remove_staus(attri_list):
length = len(attri_list)
search_status = "[^\s]*(?:TableRowStatus|DataStatus)"
pattern_status = re.compile(search_status)
i = 0
while i < length -1:
if pattern_status.findall(attri_list[i]) :
attri_list.remove(attri_list[i])
attri_list.remove(attri_list[i])
length = length - 2
else:
i = i + 2
return attri_list
def split_codecs(attri_list):
length = len(attri_list)
search_codecs = "[^\s]*Codecs"
pattern_codecs = re.compile(search_codecs)
i = 0
first_match = True
while i < length -1:
if pattern_codecs.findall(attri_list[i]) and first_match == True:
attrib_name = attri_list[i]
attrib_val = attri_list[i+1]
attri_list.remove(attri_list[i])
attri_list.remove(attri_list[i])
first_match = False
lcodecs = attrib_val.split('+')
for codec in lcodecs :
attri_list.append(attrib_name)
attri_list.append(codec)
else:
i = i + 2
return attri_list
def Transform_Tag(tag):
if (tag.find("Mo",0)==0):
new_name = tag[2].lower()+tag[3:]+'Id'
else:
new_name = tag[0].lower()+tag[1:]+'Id'
return new_name
def Remove_RestrictedAttrib(mo_name, attrs_list):
length = len(attrs_list)
i = 0
if mo_name in attribute_Restricted:
while i < length-1 :
if attrs_list[i] in attribute_Restricted[mo_name]:
attrs_list.remove(attrs_list[i])
attrs_list.remove(attrs_list[i])
length = length -2
else:
i = i + 2
return attrs_list
def SwitchType_int2enum( attrs_list ):
length = len(attrs_list)
i = 0
while i < length:
if attrs_list[i] in attribute_int2enum:
enum_value = attribute_int2enum[attrs_list[i]]
if attrs_list[i+1] in enum_value:
attrs_list[i+1] = enum_value[attrs_list[i+1]]
break
else:
i = i +2
return attrs_list
def GetTableAttrib(root,table_name):
attrib_list = []
for child in root:
if child.tag == 'mim' :
for node in child :
if node.tag == "class" and node.attrib == {'name': table_name}:
for attribute in node :
if attribute.tag == "attribute":
attrib_list.append(attribute.attrib['name'])
return attrib_list
def construct_edit_config_me():
root_config = ET.Element('edit-config')
target = ET.SubElement(root_config, "target")
running = ET.SubElement(target, "running")
config = ET.SubElement(edit_config, "config",Element_Dict['config'])
return (root_config,config)
def construct_action_me():
root_action = ET.Element('action',Element_Dict['action'])
data = ET.SubElement(root_action, "data")
return (root_action,data)
def construct_get_config_me():
get_config = ET.Element('get-config')
source = ET.SubElement(get_config, "source")
running = ET.SubElement(source, "running")
filter = ET.SubElement(get_config, "filter",Element_Dict['filter'])
return (get_config,filter)
def construct_me_mf():
ManagedElement = ET.Element('ManagedElement',Element_Dict['ManagedElement'])
ManagedElementId = ET.SubElement(ManagedElement, Transform_Tag("ManagedElement"))
ManagedElementId.text = '1'
ManagedFunction = ET.SubElement(ManagedElement, "ManagedFunction",Element_Dict['ManagedFunction'])
ManagedFunctionId = ET.SubElement(ManagedFunction, Transform_Tag("ManagedFunction"))
ManagedFunctionId.text = '1'
return (ManagedElement,ManagedFunction)
def extra_mf_mo_layers( moname , mim_content ):
# search the relationship parent and child , and save the search result
search_keys = ''' <containment>\s*<parent>\s*<hasClass name="(\w*)">\s*<mimName>\w*</mimName>\s*</hasClass>\s*</parent>\s*<child>\s*<hasClass name="(\w*)">\s*<mimName>\w*</mimName>\s*</hasClass>\s*<cardinality>\s*<min>\d*</min>\s*<max>\d*</max>\s*</cardinality>\s*</child>\s*</containment>'''
pattern = re.compile(search_keys)
lists = pattern.findall(mim_content)
# find the layers from mo name to ManagedFunction and return the layer list
parents = []
children = []
length = len(lists)
for i in range(0,length):
parents.append( lists[i][0] )
children.append( lists[i][1])
mo_mf_layers =[moname]
pindex = search_elem(moname,children)
parent = parents[pindex]
while (pindex != -1) and (parent != "ManagedFunction") :
mo_mf_layers.append(parent)
pindex = search_elem(parent,children)
parent = parents[pindex]
if parent == "ManagedFunction":
mo_mf_layers.append(parent)
return mo_mf_layers
def construct_mf_mo(mo_mf_layers):
#construct xml format for mo_mf_layers
length = len(mo_mf_layers)
mf_mo_root=ET.Element(mo_mf_layers[length-2])
mo_Id = ET.SubElement(mf_mo_root, Transform_Tag( mo_mf_layers[length-2]))
mo_Id.text = '1'
parent = mf_mo_root
for i in range(length-3,0,-1):
child = ET.SubElement(parent, mo_mf_layers[i])
childId = ET.SubElement(child, Transform_Tag( mo_mf_layers[i]))
childId.text = '1'
parent = child
return (mf_mo_root,parent)
def construct_mo_attrib(mo_name,moId,operation_type,attribs_list):
mo_root=ET.Element(mo_name)
mo_Id = ET.SubElement(mo_root, Transform_Tag(mo_name))
if moId:
mo_Id.text = moId
else:
mo_Id.text = '1'
if operation_type == 'action' :
ET.SubElement(mo_root,attribs_list[0])
else :
mo_root.attrib['operation'] = operation_type
i = 0
while i < len( attribs_list )-1:
subelem = ET.SubElement(mo_root,attribs_list[i])
subelem.text = attribs_list[i+1]
i = i + 2
return mo_root
def SplitMoSipProfileToGroups(fname, moName, attrib_list):
# this function is for MoSipProfile
tree_mimfile = ET.parse(fname)
root_mimfile = tree_mimfile.getroot()
attribOfMoSipProfileGroup1 = GetTableAttrib(root_mimfile,'MoSipProfileGroup1')
attribOfMoSipProfileGroup2 = GetTableAttrib(root_mimfile,'MoSipProfileGroup2')
length = len(attrib_list)
i = 0
lattri_group1 = []
lattri_group2 = []
groups = []
while i < length-1 :
if attrib_list[i] in attribOfMoSipProfileGroup1 :
lattri_group1.append(attrib_list[i])
lattri_group1.append(attrib_list[i+1])
if attrib_list[i] in attribOfMoSipProfileGroup2 :
lattri_group2.append(attrib_list[i])
lattri_group2.append(attrib_list[i+1])
i = i + 2
if len(lattri_group1) >= 2 and lattri_group1[len(lattri_group1)-1] != 'sipProfileName' : # lattri_group1 is not empty
groups = ['MoSipProfileGroup1', lattri_group1]
if len(lattri_group2) >=2 and lattri_group2[len(lattri_group2)-1 ] != 'sipProfileName' :
groups.append('MoSipProfileGroup2')
groups.append(lattri_group2)
return groups
def handle_argvs(first_index ):
''' output is :operation_type,MOname,MoId,attribs_list
snmpSet usage is /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet [-u|-d] Moname [MoId] attrib1 value1 attrib2 value2 ... ...
/NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet Moname action_type
netconf usage is /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath Moname [create|merge|delete] attrib1 value1 attrib2 value2 ... ...
/NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath Moname [MoId] action_type
Get all:
/NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath getall
'''
attri_list = sys.argv
operation_type = MOname = MoId = attribs_list =None
attribs_list = attri_list[start_index+3:]
if re.findall(r'^snmp\s*',attri_list[start_index]): #snmpSet cmd
if attri_list[start_index+1] == '-d':
operation_type = 'delete'
MOname = attri_list[start_index+2]
elif attri_list[start_index+1] == '-u':
operation_type = 'merge'
MOname = attri_list[start_index+2]
else:
operation_type = 'create'
MOname = attri_list[start_index+1]
if re.findall(r'^\d',attri_list[start_index+2]) : #input arguments contain MoId.
MoId = attri_list[start_index+2]
else :
attribs_list = attri_list[start_index+2:]
if (MOname in JustOneInstanceMo) and ( len(attribs_list) > 1 ):
operation_type = 'merge'
if attribs_list[0] in ActionMo[MOname] :
operation_type = 'action'
elif re.findall(r'^Mo\s*',attri_list[start_index]): # for netconf cmd
MOname = attri_list[start_index]
operation_type = attri_list[start_index+1] #create or merge or delete
attribs_list = attri_list[start_index+2:]
if re.findall(r'^\d',operation_type): #contain MoId for action
MoId = operation_type
operation_type = 'action'
elif operation_type in ActionMo[MOname] : #no MoId
operation_type = 'action'
MoId = '1'
attribs_list = attri_list[start_index+1]
elif re.findall(r'^getall',attri_list[start_index]):
operation_type = 'getall'
attribs_list = split_codecs(attribs_list) #split codecs.
attribs_list = SwitchType_int2enum(attribs_list) #switch type from int to enum
return (operation_type,MOname,MoId,attribs_list)
def construct_fxml(moName,moId,operation,attrs_list,content,Netconfxmldir):
#construct the xml from rpc to attributes
mo_mf_layers = extra_mf_mo_layers( moName , content )
if operation == 'action':
(root,first_child)= construct_action_me()
else:
(root,first_child)=construct_edit_config_me()
(ManagedElement,ManagedFunction)=construct_me_mf()
(mf_mo_root,mo_parent) = construct_mf_mo(mo_mf_layers)
if not moId: # in condition MoId is null
(MaxMatchId, MaxId) = GetEcimKey( moName,operation,attrs_list)
if operation_type == 'create':
moId = str(MaxId + 1)
elif operation_type == 'merge' or operation_type == 'delete':
moId = str(MaxMatchId)
mo_root = construct_mo_attrib(moName,moId,operation,attrs_list)
print 'mo_root********',ET.tostring(mo_root)
mo_parent.append(mo_root)
ManagedFunction.append(mf_mo_root)
first_child.append(ManagedElement)
rpc_attib_string = ET.tostring(root)
return rpc_attib_string
def read_mimfile():
fmim = execCmd('find /cluster/storage/system/software/coremw/repository/ -name MGC_mp.xml')
content = read_file(fmim[:-1])
tree_mimfile = ET.parse(fmim[:-1])
root_mimfile = tree_mimfile.getroot()
return (fmim[:-1], content)
def tupleswitchtolist(MOinGetAll_tuple):
length = len(MOinGetAll_tuple)
MOinGetAll_list = []
for i in range( 0, length):
MOinGetAll_list.append(MOinGetAll_tuple[i][0])
MOinGetAll_list.append(MOinGetAll_tuple[i][1])
return MOinGetAll_list
def writexml( xml_body,Netconfxmldir,Moname='netconfXml' ,method = 'w',resultfile='netconfXml.result' ):
xml_str = xml_head + xml_body + xml_tail
xml_fname = Netconfxmldir + '/' + Moname+'.xml'
print xml_str
f = open ( xml_fname ,method)
f.write(xml_str)
f.close()
result = execCmd('/opt/com/bin/netconf < '+ Moname+'.xml' )
if Moname=='netconfXml':
ok_num = re.findall(r'<ok/>',result)
print result
if len(ok_num) == 2 :
print 'configured OK'
else :
print 'configured fail!!!!'
f = open(Netconfxmldir + '/' + resultfile,method)
f.write(result)
f.close()
def GetMoConfig(moname):
''' Get all instance in the assigned mo before determin the ECIM Key'''
(edit_config, filter) = construct_edit_config_me()
(me,mf) = construct_me_mf()
(mf_mo_root,mo_parent) = construct_mf_mo(mo_mf_layers)
filter.append(me)
mf.append(mf_mo_root)
ET.SubElement(mo_parent, moName)
edit_config_moname_string = ET.tostring(edit_config)
MoConfigResult = 'GetMoConfig.result'
writexml( edit_config_moname_string, Moname='GetMoConfig',resultfile=MoConfigResult)
MoConfiguration = read_file(MoConfigResult)
return MoConfiguration
def GetEcimKey( moName ,operation_type, attrs_input ):
''' get ecim key for replace operation.
'''
content = GetMoConfig(moName)
search_mo = '<'+moName+'>([\d\w\s\D]*?)</'+moName+'>'
pattern = re.compile(search_mo)
MOs = pattern.findall(content) ## find instance of MO
attr_pattern = '<(\w*)>([^\s]*)</\w*>'
pattern = re.compile(attr_pattern)
MaxId = 0
MaxMatch = 0
MaxCount = 0
mo_num = len(MOs)
if mo_num == 1:
attrs_mo = pattern.findall(MOs[0])
attrs_mo_list = tupleswitchtolist(attrs_mo)
ecim_attrib = Transform_Tag(moName)
if ecim_attrib in attrs_mo_list:
ecim_id_in_list = attrs_mo_list.index(ecim_attrib) + 1
MaxMatch = MaxId = attrs_mo_list[ecim_id_in_list]
elif mo_num > 1:
for mo_index in range(0,mo_num):
attrs_mo = pattern.findall(MOs[mo_index])
attrs_mo_list = tupleswitchtolist(attrs_mo)
count = i = current_id = 0
while i < len(attrs_input) -1:
attrib_index = search_elem(attrs_input[i],attrs_mo_list)
value_index = search_elem( attrs_input[i+1],attrs_mo_list )
current_id = attrs_mo_list[1]
if (attrib_index != -1) and (value_index != -1) and (attrib_index == value_index -1 ):
count = count +1
i = i+2
if MaxCount < count:
MaxMatch = attrs_mo_list[1]
MaxCount = count
if MaxId < current_id :
MaxId = current_id
attribs_list = remove_staus(attrs_input) # remove DataStatus and RowStatus
if operation_type != 'create':
attribs_list = Remove_RestrictedAttrib(MOname, attribs_list)
print MaxMatch,MaxId
return (int(MaxMatch),int(MaxId))
def handleSipProfileGroups(ProfileGroups,operation_type,MoId,content, Netconfxmldir):
i = 0
while i < len(ProfileGroups)-1:
MOname = ProfileGroups[i]
attribs_list = ProfileGroups[i+1]
if not MoId: # in condition MoId is null
(MaxMatchId, MaxId) = GetEcimKey( moName,operation_type,attribs_list)
if operation_type == 'create':
MoId = str(MaxId + 1)
elif operation_type == 'merge' or operation_type == 'delete':
MoId = str(MaxMatchId)
xml = construct_fxml(MOname,MoId,operation_type,attribs_list,content, Netconfxmldir)
writexml(xml,Netconfxmldir)
i = i + 2
if __name__ == '__main__':
'''snmpSet usage is /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet [-u|-d] Moname [MoId] attrib1 value1 attrib2 value2 ... ...
initial create operation is : /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet Moname MoId attrib1 value1 attrib2 value2 ... ...
create during FT or RT opearion is : /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet -d Moname attrib1 value1 attrib2 value2 ... ...
merge opearion is : /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet -u Moname attrib1 value1 attrib2 value2 ... ...
delete opearion is : /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet -d Moname attrib1 value1 attrib2 value2 ... ...
Action operation is : /NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath snmpSet Moname Action_Type
/NetconfScriptPath/AutoRunNetconf.py /NetconfScriptPath Moname Action_Type
'''
length_argv = len(sys.argv)
start_index = 2 # the first index to read the input useful arguments.
Netconfxmldir = sys.argv[start_index - 1] # the directory to store generated netconf xml files
(fmim, content) = read_mimfile() # find file MGC_mp.xml, and read the content of it.
(operation_type,MOname,MoId,attribs_list) = handle_argvs( start_index ) # reomve status, and restricted attributes, switch type from int to enum for some especial attribute
print 'operation_type,MOname,MoId,attribs_list',operation_type,MOname,MoId,attribs_list
if operation_type == 'create' or operation_type == 'merge' or operation_type == 'delete' or operation_type == 'action' :
if MOname == 'MoSipProfile' :
profilegroups = SplitMoSipProfileToGroups(fmim, MOname, attribs_list)
handleSipProfileGroups(profilegroups,operation_type,MoId,content, Netconfxmldir)
else : # other MOs
xml = construct_fxml(MOname,MoId,operation_type,attribs_list,content, Netconfxmldir)
writexml(xml,Netconfxmldir)
elif operation_type == 'getall' :
writexml(xml_getall,Netconfxmldir,'GetMGCAllConfig')
|
'''
Exercício 2 - Substring
Dada uma string , ache o tamanho da maior substring , que não tenha caracteres
repetidos. Complexidade de tempo limite aceitável: O(n^2) .
'''
def most_substring(string):
letter_not_repeat = set()
most_value = 0
for letter in string:
if letter not in letter_not_repeat:
letter_not_repeat.add(letter)
length_string = len(letter_not_repeat)
if length_string > most_value:
most_value = length_string
else:
letter_not_repeat.clear()
letter_not_repeat.add(letter)
return most_value
string = "serdevemuitolegalmasehprecisoestudarbastante"
# string = "abcabcajklmnopqrstabcdefgh"
print(most_substring(string))
|
class Scene(object):
def __init__(self):
self.backgroundColor = [0.0, 0.0, 0.0]
self.cameraPos = [0.0, 0.0, 0.0]
self.lookAt = [0.0, 0.0, 1.0]
self.lightPos = [0.0, 0.0, 0.0]
self.objs = [] # objects in the scene, class Obj
self.spheres = [] # legacy
class Obj(object):
# /param type_ name of type of object, "s" for sphere, "b" for box
# /param extend can be single value for radius or 3d vector for box
def __init__(self, type_, center, extend):
self.type_ = type_
self.center = center
self.extend = extend
self.isTextured = False # is a standard texture applied?
# generate povray scene, write it to file, render file
# write png to local file
def renderScene(scene, displaySize):
sceneTemplate = """
#include "colors.inc"
#include "textures.inc"
background { color rgb BGCOLOR }
camera {
location <CAMERAPOS>
look_at <LOOKAT>
}
OBJS
light_source { <LIGHTPOS> color White}
""".replace("BGCOLOR", f'<{scene.backgroundColor[0]},{scene.backgroundColor[1]},{scene.backgroundColor[2]}>')
sceneContent = sceneTemplate[:]
sceneContent = sceneContent.replace("CAMERAPOS", str(scene.cameraPos[0])+","+str(scene.cameraPos[1])+","+str(scene.cameraPos[2]))
sceneContent = sceneContent.replace("LIGHTPOS", str(scene.lightPos[0])+","+str(scene.lightPos[1])+","+str(scene.lightPos[2]))
sceneContent = sceneContent.replace("LOOKAT", str(scene.lookAt[0])+","+str(scene.lookAt[1])+","+str(scene.lookAt[2]))
objsText = ""
for iSphereCenter, iSphereR in scene.spheres:
spherePosAsStr = f'<{iSphereCenter[0]},{iSphereCenter[1]},{iSphereCenter[2]}>, {iSphereR}'
objsText += """
sphere {
POSS
texture {
pigment { color Yellow }
}
}""".replace("POSS", spherePosAsStr) # replace position with src-code of position
#default boxcenter = [-0.1, 0.0, -2.8]
#default boxextend = [0.08, 0.08, 0.08]
sceneContent = sceneContent.replace("OBJS", objsText)
# append objects
for iObj in scene.objs:
povType = None
povPos = None
if iObj.type_ == "s": # is sphere?
povType = "sphere"
povPos = f'<{iObj.center[0]},{iObj.center[1]},{iObj.center[2]}>, {iObj.extend}'
elif iObj.type_ == "b": # is box?
povType = "box"
povPos = f"<{iObj.center[0]-iObj.extend[0]/2.0},{iObj.center[1]-iObj.extend[1]/2.0},{iObj.center[2]-iObj.extend[2]/2.0}>, <{iObj.center[0]+iObj.extend[0]/2.0},{iObj.center[1]+iObj.extend[1]/2.0},{iObj.center[2]+iObj.extend[2]/2.0}>"
else:
raise Exception(f'Invalid type "{iObj.type_}"')
povTexture = "texture { pigment { color Yellow } }"
if iObj.isTextured:
povTexture = "texture { Blue_Sky2 }" # UNTESTED
sceneContent += f'{povType} {{ {povPos} {povTexture} }} \n'
f = open("TEMPScene.pov", 'w')
f.write(sceneContent)
f.close()
import subprocess
subprocess.call(["povray", "TEMPScene.pov", "+W"+str(displaySize[0]), "+H"+str(displaySize[1])], stderr=subprocess.PIPE)
|
import wx
class LoginDialog(wx.Frame):
def __init__(self, parent=None, title='Enter username password', size=(300, 300)):
#super(LoginDialog, self).__init__(parent, title=title, size=(250, 150))
#wx.Dialog.__init__(self, None, title='abcd', size=(300,300))
wx.Frame.__init__(self, None, -1, 'Run ATE Manual mode', size=(400, 400))
wx.Panel = wx.Panel(self)
self.lbUserName = wx.StaticText(self.panel, label="UserId")
self.tbUserName = wx.TextCtrl(self.panel, size=(400, -1))
self.lbPassword = wx.StaticText(self.panel, label="Password")
self.tbPassword = wx.TextCtrl(self.panel, style=wx.TE_PASSWORD, size=(400, -1))
self.btnLogin = wx.Button(self.panel, label="Login")
self.Bind(wx.EVT_BUTTON, self.OnClickLogin, self.btnLogin)
self.btnCancel = wx.Button(self.panel, label="Cancel")
self.Bind(wx.EVT_BUTTON, self.OnClickCancel, self.btnCancel)
# UI alignment
gridXMax = 3
gridYMax = 2
gridIndex = 1
# window sizer
self.windowSizer = wx.BoxSizer()
self.windowSizer.Add(self.panel, 1, wx.ALL | wx.EXPAND)
self.sizer = wx.GridBagSizer(gridXMax, gridYMax)
self.border = wx.BoxSizer()
self.border.Add(self.sizer, 1, wx.ALL | wx.EXPAND, 5)
# Use the sizers
self.panel.SetSizerAndFit(self.border)
self.sizer.Add(self.lbUserName, (gridIndex, 0))
self.sizer.Add(self.tbUserName, (gridIndex, 1))
gridIndex = gridIndex + 1
self.sizer.Add(self.lbPassword, (gridIndex, 0))
self.sizer.Add(self.tbPassword, (gridIndex, 1))
gridIndex = gridIndex + 1
self.sizer.Add(self.btnLogin, (gridIndex, 0))
self.panel.SetSizerAndFit(self.border)
self.SetSizerAndFit(self.windowSizer)
self.panel.SetBackgroundColour(wx.Colour(221, 227, 242))
# sizer code end221
self.Maximize(True)
# class varibles
self.UserId = None
self.Password = None
self.Iscancelled = False
def OnClickLogin(self, event):
try:
self.UserId = self.tbUserName.GetValue()
self.Password = self.tbPassword.GetValue()
if not self.UserId:
wx.MessageBox('invalid Userid')
return
if not self.Password:
wx.MessageBox('invalid password')
return
self.close()
except Exception as Error:
print(str(Error))
def OnClickCancel(self, event):
self.Iscancelled = False
self.Close()
|
import ExternalCreditRatings
p1 = ExternalCreditRatings.probability_default(4, 'AAA')
p2 = ExternalCreditRatings.probability_default(3, 'B', 'during')
p3 = ExternalCreditRatings.probability_default(5, 'B', 'during')
p4 = ExternalCreditRatings.probability_default(3, 'BBB', 'during', 'no')
p5 = ExternalCreditRatings.probability_default(4, 'AAA', 'during', 'no')
print(p1)
print(p2)
print(p3)
print(p4)
print(p5) |
# import the necessary packages
"""
For P3 cameras:
python3 real_time_object_detection.py -n 1 \
-w 3840 -t 1080 -r "1200x300" \
-p P3_mobilenetSSD_deploy.prototxt \
-m P3_mobilenetSSD_deploy.caffemodel -c 0.3
"""
import numpy as np
import argparse
import imutils
import time
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", default="MobileNetSSD_deploy.prototxt.txt",
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model", default="MobileNetSSD_deploy.caffemodel",
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.2,
help="minimum probability to filter weak detections")
ap.add_argument("-n", "--camera", type=str, default="0",
help="camera number")
ap.add_argument("-w", "--width", type=int, default=3840,
help="camera capture width")
ap.add_argument("-t", "--height", type=int, default=1080,
help="camera capture height")
ap.add_argument("-r", "--cnn_resolution", type=str, default="1066x300",
help="resolution at which the cnn was trained on")
args = vars(ap.parse_args())
# initialize the list of class labels MobileNet SSD was trained to
# detect, then generate a set of bounding box colors for each class
class PeopleCounter:
def __init__(self, args):
self.net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
self.cam = args["camera"]
def getPeopleCount(self):
self.cap = cv2.VideoCapture(self.cam)
CLASSES = ["background", "head", "body", "wb"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
HeatMap = []
try:
(cnnInputWidth, cnnInputHeight) = tuple(map(int, args["cnn_resolution"].split('x')))
print("Parsed CNN input resolution as (%d, %d)" % (cnnInputWidth, cnnInputHeight))
except:
print('Caught an exception parsing %s' % args["cnn_resolution"])
sys.exit(1)
for i in range(30):
# flushing out bad frames
ret, frame = self.cap.read()
if ret:
(h, w, c) = frame.shape
adjustAspect = True
if adjustAspect:
resized = np.zeros((cnnInputHeight, cnnInputWidth, c), dtype=np.uint8)
resized[0:cnnInputHeight, 0:int(cnnInputWidth/2)] = cv2.resize(frame, (int(cnnInputWidth/2), cnnInputHeight))
else:
resized = cv2.resize(frame, (cnnInputWidth, cnnInputHeight))
blob = cv2.dnn.blobFromImage(resized, 0.007843, (cnnInputWidth, cnnInputHeight), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
self.net.setInput(blob)
detections = self.net.forward()
# loop over the detections
detected = 0
peopleCount = 0
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the prediction
idx = int(detections[0, 0, i, 1])
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > args["confidence"] and idx < len(CLASSES) and idx >= 0:
# extract the index of the class label from the
# `detections`, then compute the (x, y)-coordinates of
# the bounding box for the object
scale_box = np.array([w, h, w, h])
if adjustAspect: scale_box = np.array([w*2, h, w*2, h])
box = detections[0, 0, i, 3:7] * scale_box
(startX, startY, endX, endY) = box.astype("int")
faces = (startX, startY, endX, endY)
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
detected = 1
if CLASSES[idx] == "head":
HeatMap.append(faces)
peopleCount += 1
print("RETURNING PEOPLECOUNT")
self.cap.release()
return peopleCount, HeatMap
else:
return 0
def PeopleCount(args):
CLASSES = ["background", "head", "body", "wb"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
img = None
cam = args["camera"]
width = args["width"]
height = args["height"]
try:
cam = int(cam)
print('Using camera %d' % cam)
except:
img = cv2.imread(cam)
if img is not None:
width = img.shape[1]
height = img.shape[0]
print('input %s is of shape %s' % (cam, repr(img.shape[:2])))
else:
print('input could be a video')
# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
cap = None
if img is None:
cap = cv2.VideoCapture(cam)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)
print('Capturing with resolution %d,%d' % (height, width))
fcount = 0
fcount_det = 0
start = time.time()
try:
(cnnInputWidth, cnnInputHeight) = tuple(map(int, args["cnn_resolution"].split('x')))
print("Parsed CNN input resolution as (%d, %d)" % (cnnInputWidth, cnnInputHeight))
except:
print('Caught an exception parsing %s' % args["cnn_resolution"])
sys.exit(1)
P3 = False
if args["model"].startswith("P3"):
P3 = True
CLASSES = ["unknown", "head", "person", "whiteboard"]
adjustAspect = False
aspect = width / height
if P3:
if aspect != (3840/1080) and aspect != (3840/1088):
adjustAspect = True
# loop over the frames from the video stream
while True:
try:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
if img is None:
ret, frame = cap.read()
if ret < 0 or frame is None:
print('Unable to read from camera')
break
if frame.shape[:2] != (height, width):
print('Unable to read %d,%d from the camera; read %s' % (height, width, repr(frame.shape[:2])))
break
else:
frame = img.copy()
# grab the frame dimensions and convert it to a blob
(h, w, c) = frame.shape
if adjustAspect:
resized = np.zeros((cnnInputHeight, cnnInputWidth, c), dtype=np.uint8)
resized[0:cnnInputHeight, 0:int(cnnInputWidth/2)] = cv2.resize(frame, (int(cnnInputWidth/2), cnnInputHeight))
else:
resized = cv2.resize(frame, (cnnInputWidth, cnnInputHeight))
blob = cv2.dnn.blobFromImage(resized, 0.007843, (cnnInputWidth, cnnInputHeight), 127.5)
# pass the blob through the network and obtain the detections and
# predictions
net.setInput(blob)
detections = net.forward()
# loop over the detections
detected = 0
peopleCount = 0
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with
# the prediction
idx = int(detections[0, 0, i, 1])
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > args["confidence"] and idx < len(CLASSES) and idx >= 0:
# extract the index of the class label from the
# `detections`, then compute the (x, y)-coordinates of
# the bounding box for the object
scale_box = np.array([w, h, w, h])
if adjustAspect: scale_box = np.array([w*2, h, w*2, h])
box = detections[0, 0, i, 3:7] * scale_box
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx],
confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
detected = 1
if CLASSES[idx] == "head":
peopleCount += 1
if img is not None:
if CLASSES[idx] == "whiteboard":
wb = frame[startY:endY, startX:endX]
cv2.imshow('wb', wb)
fcount_det += detected;
print(peopleCount)
# show the output frame
#cv2.imshow("Frame", frame)
key = cv2.waitKey(2) & 0xFF
# if the `q` key was pressed, break from the loop
if key == ord('q') or key == 27:
break
# update the FPS counter
#fps.update()
fcount += 1
if time.time() - start > 3.0:
#print('elapsed time = %f, fcount = %d' % (time.time() - start, fcount))
#print('fcount = %d, fcount_det = %d' % (fcount, fcount_det))
print('fps = ', fcount / (time.time() - start))
start = time.time()
fcount = 0
fcount_det = 0
except KeyboardInterrupt:
break
cv2.destroyAllWindows()
if cap is not None: cap.release()
if __name__ == "__main__":
PeopleCount(args)
|
#!/usr/bin/env python3
import os
import shutil
import ntpath
from basic.clean import *
for p in os.walk('..'):
if '__pycache__'==ntpath.basename(p[0]):
shutil.rmtree(p[0],ignore_errors=True)
|
import unittest
from mesa.space import ContinuousSpace
from test_grid import MockAgent
TEST_AGENTS = [(-20, -20), (-20, -20.05), (65, 18)]
class TestSpaceToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, True, -30, -30, 100, 100)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_1 = (-30, -30)
pos_2 = (70, 20)
assert self.space.get_distance(pos_1, pos_2) == 0
pos_3 = (-30, -20)
assert self.space.get_distance(pos_1, pos_3) == 10
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 1
class TestSpaceNonToroidal(unittest.TestCase):
'''
Testing a toroidal continuous space.
'''
def setUp(self):
'''
Create a test space and populate with Mock Agents.
'''
self.space = ContinuousSpace(70, 20, False, -30, -30, 100, 100)
self.agents = []
for i, pos in enumerate(TEST_AGENTS):
a = MockAgent(i, None)
self.agents.append(a)
self.space.place_agent(a, pos)
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for i, pos in enumerate(TEST_AGENTS):
a = self.agents[i]
assert a.pos == pos
def test_distance_calculations(self):
'''
Test toroidal distance calculations.
'''
pos_2 = (70, 20)
pos_3 = (-30, -20)
assert self.space.get_distance(pos_2, pos_3) == 107.70329614269008
def test_neighborhood_retrieval(self):
'''
Test neighborhood retrieval
'''
neighbors_1 = self.space.get_neighbors((-20, -20), 1)
assert len(neighbors_1) == 2
neighbors_2 = self.space.get_neighbors((40, -10), 10)
assert len(neighbors_2) == 0
neighbors_3 = self.space.get_neighbors((-30, -30), 10)
assert len(neighbors_3) == 0
|
import arcade
import pathlib as p
import numpy as np
from numpy.random import choice, uniform
import pyglet.clock as clock
from enemy_spawns import *
from modifier import *
class level(metaclass=ABCMeta):
enemy_spawns=[]
number_of_enemies=-1
name=""
modifier_field=[]
def begin(self):
for spawn in self.enemy_spawns:
spawn.enable()
for spawn in self.modifier_field:
spawn.enable()
def __del__(self):
for spawn in self.enemy_spawns:
spawn.__del__()
for spawn in self.modifier_field:
spawn.clear()
class zombie_level(level):
number_of_enemies = 10
name="Zombies only"
def setup(self, game_controller):
self.enemy_spawns=[
enemy_spawn(
width=BOX_WIDTH//2,
height=game_controller.screen_dimensions[1],
pos_x=game_controller.screen_dimensions[0]+BOX_WIDTH/2,
pos_y=game_controller.screen_dimensions[1]/2,
available_enemies=[zombie],
spawn_ratios=[1],
game_controller=game_controller
)
]
self.modifier_field=[
modifier_spawn_field(
available_modifiers = [one_hp_for_enemies],
spawn_ratios = [1],
game_controller = game_controller,
)
]
class crossbowman_level(level):
number_of_enemies = 10
name="Crossbows"
def setup(self, game_controller):
self.enemy_spawns=[
enemy_spawn(
width=BOX_WIDTH//2,
height=game_controller.screen_dimensions[1]//4,
pos_x=game_controller.screen_dimensions[0]+BOX_WIDTH//2,
pos_y=game_controller.screen_dimensions[1]//8,
available_enemies=[crossbowman],
spawn_ratios=[1],
game_controller=game_controller,
spawn_interval=2.5,
spawn_offset=0.5
),
enemy_spawn(
width=BOX_WIDTH//2,
height=game_controller.screen_dimensions[1]//4,
pos_x=-BOX_WIDTH//2,
pos_y=game_controller.screen_dimensions[1]*7//8,
available_enemies=[crossbowman],
spawn_ratios=[1],
game_controller=game_controller,
spawn_interval=2.5,
spawn_offset=1
),
enemy_spawn(
width=game_controller.screen_dimensions[0]//4,
height=BOX_WIDTH//2,
pos_x=game_controller.screen_dimensions[0]//8,
pos_y=game_controller.screen_dimensions[1]+BOX_WIDTH//2,
available_enemies=[crossbowman],
spawn_ratios=[1],
game_controller=game_controller,
spawn_interval=2.5,
spawn_offset=1.5
),
enemy_spawn(
width=game_controller.screen_dimensions[0]//4,
height=BOX_WIDTH//2,
pos_x=game_controller.screen_dimensions[0]*7//8,
pos_y=-BOX_WIDTH//2,
available_enemies=[crossbowman],
spawn_ratios=[1],
game_controller=game_controller,
spawn_interval=2.5,
spawn_offset=2
),
]
self.modifier_field=[
modifier_spawn_field(
available_modifiers = [railgun_weapons],
spawn_ratios = [1],
game_controller = game_controller,
)
]
class final_level(level):
number_of_enemies = 50
name="All elements"
def setup(self, game_controller):
self.enemy_spawns=[
enemy_spawn(
width=BOX_WIDTH//2,
height=game_controller.screen_dimensions[1]//4,
pos_x=game_controller.screen_dimensions[0]+BOX_WIDTH//2,
pos_y=game_controller.screen_dimensions[1]//2,
available_enemies=[zombie,crossbowman],
spawn_ratios=[2,1],
game_controller=game_controller,
spawn_interval=3,
spawn_offset=0.5
),
enemy_spawn(
width=BOX_WIDTH//2,
height=game_controller.screen_dimensions[1]//4,
pos_x=-BOX_WIDTH//2,
pos_y=game_controller.screen_dimensions[1]//2,
available_enemies=[zombie,crossbowman],
spawn_ratios=[2,1],
game_controller=game_controller,
spawn_interval=3,
spawn_offset=1
),
enemy_spawn(
width=game_controller.screen_dimensions[0]//4,
height=BOX_WIDTH//2,
pos_x=game_controller.screen_dimensions[0]//2,
pos_y=game_controller.screen_dimensions[1]+BOX_WIDTH//2,
available_enemies=[zombie, hound],
spawn_ratios=[1.25,1],
game_controller=game_controller,
spawn_interval=3,
spawn_offset=1.5
),
enemy_spawn(
width=game_controller.screen_dimensions[0]//4,
height=BOX_WIDTH//2,
pos_x=game_controller.screen_dimensions[0]//2,
pos_y=-BOX_WIDTH//2,
available_enemies=[zombie, hound],
spawn_ratios=[1.5,1],
game_controller=game_controller,
spawn_interval=3,
spawn_offset=2
),
]
self.modifier_field=[
modifier_spawn_field(
available_modifiers = [cherry, one_hp_for_enemies,railgun_weapons],
spawn_ratios = [5,1,1],
game_controller = game_controller,
spawn_interval=5
)
]
class mod_level(level):
number_of_enemies = 1
name="TEST"
def setup(self, game_controller):
self.modifier_field=[
modifier_spawn_field(
available_modifiers = [cherry, turning_screen_red],
spawn_ratios = [1,1],
game_controller = game_controller,
)
]
level_list=[
zombie_level,
crossbowman_level,
final_level,
] |
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